Coastal wetlands of Indus River Delta are under risk due to reclamation: A spatiotemporal analysis during the past 50 years from 1972 to 2022

Coastal wetlands of Indus River Delta are under risk due to reclamation: A spatiotemporal analysis during the past 50 years from 1972 to 2022 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Coastal wetlands of Indus River Delta are under risk due to reclamation: A spatiotemporal analysis during the past 50 years from 1972 to 2022 Yaseen Laghari, Shibiao Bai, Shah Jahan Leghari, Wenjing Wei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3301912/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coastal wetlands are the most productive and biologically diverse ecosystems, benefiting both human populations and the total environment. However, they are continuously threatened by anthropogenic activities. The Indus River Delta, the 6th largest in the world, has been adversely affected due to reclamation. We examined the spatiotemporal dynamics of coastal wetlands and reclamation in the Indus River Delta from 1972 to 2022. Wetlands conversion to reclamation was extracted from 6-Landsat images. Land cover transfer matrix was used to analyze land use land cover (LULC) changes in different time intervals. Area-weight centroid was constructed to determine the migration trend of reclamation and coastal wetlands. Spatial accurateness was assessed using Producer's Accuracy (PA), User Accuracy (UA), and KAPPA coefficient (KC). Our results reveled that from the 1972 to 2022, the net area of natural wetlands declined by 1.9% (26.1 km 2) , while reclamation (settlement and cultivated land) increased by 14.7% (200.1 km 2 ), and 27.5% (373.5 km 2 ), respectively. The fastest areal change rate for coastal wetlands was − 1.1 km 2 /yr from 2012 to 2022, whereas the fastest areal change rate for settlement and cultivated land were 7.6 km 2 /yr from 1992 to 2002 and 28.6 km 2 /yr from 2012 to 2022. Centroids of wetlands moved slowly eastwards from Kharo Chan taluka to Keti Bandar in the first and third decades, then southwards in the second decade, later on, westwards in the fourth decade, and finally back southwards from Keti Bandar taluka to the Kharo Chan in the fifth decade with fastest movement. Centroids of settlement expanded slowly in all directions over five decades. Centroids of cultivated land migrated westwards in the first, third, and fourth decades, northwards in the second decade, and southwards in the fifth decade from Keti Bandar to Kharo Chan. The findings of this study would provide a scientific basis for sustainable land development. coastal wetlands reclamation maximum likelihood classification land cover transfer matrix area-weight centroid the Indus River Delta Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Coastal ecosystems are the distinct habitats established by plants and other species that may exist at the ocean-land interface, where they must contend with saltwater and fluctuating tides (Donato et al., 2011 ). The coral reefs, islands, lagoon floors, sea grass, woodlands, estuaries, forested floodplains, sedge lands, shrub lands, mangroves forests, rainforests, and coastal wetlands are the most important coastal habitats (Ecosystem, 2023 ). Many human coastal uses, such as offshore development, fishing, and nutrient inputs, can have a negative impact on the status of coastal systems (Lillebø et al., 2019 ). Extreme natural calamities such as hurricanes, coastal storms, tsunamis, landslides, and the longer-term concerns of coastal erosion and sea level rise are all threats to coastal communities (Davis, 2007 ). Coastal wetlands provide a wide range of ecosystem services as the habitat between terrestrial and marine ecosystems (Jiang et al., 2015 ). They are essential to preserving the biological diversification of the coast. Wetlands are one of the most vulnerable ecosystems. In many regions throughout the world in the last century, it is believed that 50–80% of the natural coastal wetlands have been lost (Gibson et al., 2007). Wetland losses have been caused by sea and land threats, including reclamation, sediments, starvation, and sea level rise (Ma et al., 2014 ). Land reclamation is a major subject among other anthropogenic effects on coastal wetlands that contributes to their loss and degradation (Tian et al., 2016 ). Coastal areas make up about 4% of the planet's total land area, which sustains more than one-third of the world's population and are heavily reclaimed to accommodate an agricultural activity, building of ports, industries, residences, and construction of dams etc. (Shi et al., 2015 ). The ambiguity of scientific management and conservation of coastal wetlands is further exacerbated by these direct threats from human activities in coastal areas (Wang et al., 2014 ). Coastal reclamation may result in land degradation, biodiversity loss, offshore eutrophication, soil heavy metal, and organic pollution concentration (Balmford et al., 2005 ). To prevent the deterioration of coastal wetlands, it is therefore necessary to give basic data support by historically monitoring the relative dynamics of reclamation and natural changes in coastal wetlands. In addition, the reclamation rate of coastal wetlands should be managed so that it does not surpass their accretion rate to ensure the recovery and sustainable use of coastal wetlands resources (Hodoki and Murakami, 2006 ). Therefore, monitoring the coastal wetlands is important. Remote sensing has shown to be the most effective method for monitoring the current status of coastal wetlands and their temporal dynamics due to its ability to obtain a synoptic view of targets and historical images (Wu et al., 2018 ). The previous wetlands studies have used remote sensing to examine the function of ecosystem services, ecosystem structure, landscape pattern, fragmentation, and transition of coastal wetlands due to climate change, reclamation, and human activity in different parts of the world. Stein et al. ( 2020 ) studied wetlands change and classified them into archetypes that represent diverse settings and processes and estimated future distributions in the USA; Sousa et al. ( 2020 ) used different datasets to assess land use changes caused by coastal wetlands reclamation during decadal intervals in Portugal; Chen et al. ( 2018 ) determined coastal wetlands and reclamation historically in China; Gaglio et al. ( 2016 ) analyzed the changes in a protected wetlands and related ecosystem services in Italy; and Rogers et al. ( 2013 ) estimated the amount of sediment and carbon buried across the estuary under various scenarios, taking into account the accretion and vertical elevation responses of mangroves and saltmarsh to rising sea level, and projected the distribution of saline coastal wetlands in Australia. There is very less research work on wetlands in Pakistan. Wetlands cover about 9.7% of Pakistan's total land area (Ahmad et al., 2019 ). Wetlands types are classified according to the Indus River's course, which ranges from glaciers and high alpine lakes through riverine and freshwater lakes and lastly to the coastal wetlands of the Indus Delta (Khan and Arshad, 2014 ). Coastal wetlands are essential for mangroves, estuaries, beaches, and coral reefs. In watershed areas, land use changes result in deforestation for the supply of fuel, wood and timber, and the conversion of land for agricultural and settlement, which increases run-off and soil erosion, and sediment loads in rivers and lakes (Chaudhry, 2010 ). Siltation, extensive deforestation, agricultural activity, and construction of human settlements have resulted in major losses of wetlands habitat (Leghari et al., 2003 ). However, there is a large research gap related to the spatiotemporal dynamics of coastal wetlands and reclamation in the Indus River deltaic region. Therefore, monitoring coastal wetlands changes is crucial for creating proactive conservation plans that emphasize protecting ecosystem structure and functions and the ecological security of the surrounding areas. We hypothesized that the Indus River Delta's coastal wetlands had been adversely affected due to rapid population growth and development infrastructure during the past 5 decades. This study aimed to comprehensive identify changes in coastal wetlands and land reclamation during the last 50 years in the Indus River Delta and identify dynamic drivers. 2. Materials and Methods 2.1 Study Site The study area was selected according to research targets, only coastal wetlands which are situated in the deltaic region. The study area is comprised of two talukas; Keti Bander in Thatta district and Kharo Chan in Sijawal district (Fig. 1 ) in Indus deltaic region. The Indus delta is situated in the southeastern part of country in the coastal area of Sindh Province of Pakistan with coordinates 24.15°N and 67.63°E. The delta forms where the Indus River enters the Arabian Sea (Shabir et al., 2019). The Indus River delta is the sixth largest delta in the world and was listed under the Ramsar treaty on marshy wetlands in 1971, which contains seventeen important creeks, three major lakes, six brackish lakes, and wide mud flats, makes up 563 km of Sindh's whole coastline region (Munir et al., 2017). It belongs to the category of arid tropical zone, with temperatures ranging from 24°C to 37°C, and receives an average annual rainfall of about 220 mm, mostly from July to September in the monsoon season (Solangi et al., 2019 ). The 263046 ha of mangrove forests in the Indus delta ranks fifth-largest mangrove forest in the world (Khan and Arshad, 2014 ). The delta receives water from the Indus River, which flows about 180 billion cubic meters each year, 400 million tons of sediment, and large amounts of contaminants from diverse sources (Giosan et al., 2006 ). The delta's location has changed southward over time (Syvitski et al., 2013 ). It is currently located in the southern Pakistani districts of Thatta and Sijawal, covers 41,440 km 2 , and is 210 kilometers from the Arabian Sea (Siyal, 2019 ). The Indus delta's coastal portions are home to almost 20 million people, and about 100,000 people rely on the Indus deltaic fishing sector for their livelihood. The remaining major portion of the population uses fertile deltaic land for agricultural activity (Chandio et al., 2011 ). 2.2. Data Collection and Processing In this study, remotely sensed Landsat images from the sensors Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), and Operational Land Imager (OLI) of the different time intervals, i.e., 1972, 1986, 1992, 2002, 2012 and 2022 were downloaded from the USGS Centre for Earth Resources Observation and Science ( http://glovis.usgs.gov/ ). Comprehensive explanations of several Landsat image types, acquisition times, and corresponding resolutions are shown in Table 1 . Images from the following years, 1972, 1986, 1992, 2002, 2012, and 2022 were obtained (as data for 1982 was not accessible, 1986 was selected instead of this year). These images accurately depict the spatial land-use patterns during the corresponding time. The acquisition time was mid-spring and fall when crops were normally at their best. This is useful and helps differentiate wetlands and reclamation zones from other forms of land cover. The Landsat sensors normally repeat their cycle every 16 days on earth, which is long enough to examine long-term land-use changes within a given geographic area. To avoid being affected by cloud situations, all images captured from sensors onboard Landsat 3, 5, 7, and 8 have less than 10% cloud coverage for changes in Landsat roughly correspond to actual environmental conditions and shifts in land-use patterns (EROS USGS, 2023). The Fast Line-of-sight Atmospheric Analysis of Hypercube (FLAASH) approach, established by Exelis Visual Information Solutions Inc., Boulder, CO, USA, was applied to perform radiometric calibrations and atmospheric correction, which enhanced data quality (Visual Information Solutions, 2009 ). Table 1 General characteristic of the employed Landsat images, including the year, path/row, acquisition data, sensor, spatial resolution and temporal resolution Year Path/Row Acquisition date Sensor Spatial resolution (m) Temporal resolution 1972 163/43 15 October, 1972 MSS 60 14-orbits per day 1986 152/43 05 November, 1986 MSS 60 14-orbits per day 1992 152/43 27 April, 1992 TM 30 16-days repeat cycle 2002 152/43 18 February, 2002 TM 30 16-days repeat cycle 2012 152/43 25March, 2012 ETM 30 16-days repeat cycle 2022 152/43 13 March, 2022 OLI 30 16-days repeat cycle Note : MSS, TM, ETM and OLI represents Multi-spectral Scanner, Thematic Mapper, Enhanced Thematic Mapper and Operational Land Imager, respectively Landsat datasets were widely employed due to their long-term and thorough digital record, medium spatial resolution, and usage in wetlands dynamics due to reclamation and LULC change research (Wang et al., 2018 ), and relative uniformity of spectral and radiometric resolutions (Almazroui et al., 2017 ). By projecting all accessible Landsat datasets of the research region onto a single mesh created and pre-set inside the ArcGIS platform. The geographical homogeneity was achieved so all datasets had equal spatial resolutions for an objective comparison (Mundia et al., 2007). Overall, using Landsat datasets to detect changes in reclamation is quite advantageous and feasible. Furthermore, the settlement environment is typically characterized by extremely varied surface covers as well as important inter- and intra-pixel fluctuations. The capabilities of change detection may be fundamentally described and tracked from satellite images taken in reclamation zones that have a fine enough spatial resolution (Lu et al., 2004 ). The land cover classification system for this study was established by referring to the land cover classification system and considering the land cover types of the study area, which were provided the following major land cover types: natural vegetation, barren land, wetlands, settlement, and cultivated land are shown in Table 2 . To accurately determine the land reclamation area, the boundary of the 1972 land use was used as the baseline data, and the areal change of the Indus delta in each interval was compared with the basic region which was divided into reclamation areas and wetlands. The Origin Pro 2020b, Sigma Plot 10.0 and Graph Pad Prism 8.0.1, and BioRender software were used to graphically visualize data. Table 2 The classified scheme employed in the study Land use / Land cover Description Reclamation Cultivated Land This class consists area of arable land with crops and pastures. Settlement In this class build up area includes where population is settled. Other classes Wetlands It includes all types of surface wetlands found in drains, reservoirs/ponds, lakes, creeks, salt marsh, freshwater marsh, mud flat, beach, aquaculture pond and salt field. Natural Vegetation Natural vegetation cover consisting of herbs, shrubs, grassland, mangroves trees, and other plants. Barren Land It includes the remaining land, which has no vegetation and a rough terrain with very little moisture. 2.2.1. Algorithms for Image and Land Use Classification Historically, Level 1 of the Anderson classification system was typically used for analyzing Landsat-based data (Mallinis et al., 2011 ) since it effectively reduces the potential for misclassification mistakes and makes the classification of distinct land-use groups more trustworthy (Kantakumar et al., 2015). Remote sensing (RS) based image classification algorithms are the most practical and affordable approach for producing and evaluating LULC information because attributes can be collected without touching the ground surface (Hadeel et al., 2009). There is no single sensor to be used. In particular, Landsat 8 OLI RS-based datasets have been utilized in Cambodia, Laos, Myanmar, Thailand, Vietnam, and other Southeast Asian countries to retrieve a total of 8 land cover classes with excellent precision (Boori et al., 2020). Land-cover maps for South and Southeast Asia were also produced using regional SPOT-VEGETATION satellite data for three years (Dhodhi et al., 1999 ). The classification technique can often be either supervised or unsupervised, or both. For instance, Landsat images can be processed beforehand using ISODATA unsupervised classification (Bakr et al., 2010), Iterative hybrid-classification techniques, and maximum likelihood supervised classification (Dhodhi et al., 1999 ). Land-use features in this study were divided into five major categories, including settlement, cultivated land, natural vegetation, wetlands, and barren area. In principle, additional classification into more specific analogues, such as various agricultural features, may be done, although it is not necessary in this case. First, there is not much agriculture in most Pakistani urban areas; therefore, 5 land-use categories are sufficient to determine LULC changes in our research area in the Indus deltaic region of Sindh Province. Second, settlement or impervious surfaces are typically associated with man-made structures like concrete, stone, and rooftops, whereas arid or barren land is typically associated with areas with thin soil, sand, or rocks, such as deserts and beaches. This distinction is particularly crucial when quantifying land-use changes in developing deltas like the Indus River delta. Recent LULC change studies have frequently utilized the words "settlement" and "barren land" (Hua et al., 2020 ; Yan et al., 2015; Lu et al., 2006). To categorize land usage, one may either utilize the peaks created by the histograms of different acquired Landsat images, which are shown in Table 1 that accurately categorize pixels as either wetlands or land areas (Chen et al., 2018 ). Alternately, we may infer this information from the pixel's spectral curve's spectrum reflectance, which shows that built-up areas have higher reflectance values in each band than pixels from agricultural land, flora, marshes, and barren ground, which have comparably lower reflectance. An index called the "Enhanced Normalized Difference Impervious Surfaces index" has been developed to distinguish the various land-use types (Chen et al., 2019 ). Apart from comparing reflectance data, the supervised statistical learning technique, the Maximum Likelihood Classification (MLC) (Liang et al., 2022 ), was used in this work to accurately classify each land-use category (Asad and Bais, 2020 ). The capabilities of MLC and related statistical studies have recently been shown to be useful and practical in a range of contexts and applications (Mondal et al., 2012 ). For each land-use category, a sufficient number of training samples were originally gathered by visual interpretation. An acceptable spectral signature is one that reduces misunderstanding between mapped land covers (Ul Din et al., 2021 ). Following collection, the Landsat datasets were classified using MLC with the appropriate statistical kernel based on mathematical properties. 2.2.2. Maximum Likelihood Classification (MLC) The MLC is a procedure for determining the maximum known class distribution for a given statistic (Mondal et al., 2012 ). This method has been most widely and frequently employed in remote sensing, where a pixel is assigned to the class with the highest likelihood (Scott et al., 1971). If there are m pre-set classes, the class posterior probability is written as $$P\left(k\right|x)=\frac{P\left(k\right)P\left(k\right|x)}{\sum _{i=1}^{m}P\left(i\right)P\left(k\right|i) }$$ 1 P(k) represents the prior probability of class k , and P(x | k) is the conditional probability of witnessing x from class k . (probability density function). P(x | k) is the likelihood function for normal distributions. $${L}_{k}\left(x\right)=\frac{1}{{\left(2\pi \right)}^{\frac{n}{2}}|{\sum }_{k}{|}^{\frac{1}{2}} }\text{e}\text{x}\text{p}\left({-\frac{1}{2}(x-\mu }_{k}{)}^{T}{\sum }_{k}^{-1}\left(x-{\pi }_{k}\right)\right)$$ 2 Where x = (x1, x2. .. xn) T represents the vector of a pixel with n bands, L k (x) represents the likelihood membership function of x belonging to class k , and µ k = (µ k 1 µ k 2. .. µ k n) T represents the mean of the kth class. 2.2.3. LULC change and accuracy assessment Many polygons were selected in ArcGIS using the processed remotely sensed Landsat datasets during each of the six acquisition times. Then, using a combination of supervised classification and the MLC technique, the land cover classification maps of 1972, 1986, 1992, 2002, 2012, and 2022 were obtained. To undertake an objective accuracy assessment in LULC classification (Fang et al., 2020 ), The Ground Truth Points (GTP) were chosen inside the region of interest from all Landsat images, which were stratified by using random selection (Li et al., 2010 ). Numerous of GTP produced by each type of land usage is shown in Table 3 . Google Earth was employed for conducting ground verification within the deltaic region to ensure the accuracy of all these GTP. The GTP was chosen within image samples by using a confusion matrix created by the study of the land cover transfer matrix of all classified images in ArcMap 10.5. Numerous metrics, such as (1) Producer Accuracy (PA), (2) User Accuracy (UA), (3) Overall Accuracy (OA), and (4) KAPPA coefficient (KC), have been used throughout this process to evaluate overall accuracy in land-use classification. The different metrics were used to deeply examine the accuracy between classified images and ground data of all Landsat images (Ul Din et al., 2021 ). In prior investigations, the following measurements were universally accepted and relevant to Benchoufi et al. ( 2020 ); Stehman and Foody ( 2019 ). Table 3 The many Ground Truth Points (GTP) were taken from each Landsat images to represent each land use category. The number values inside the bracket indicate the year that the datasets were attained. Land Use Type Image 1972 Image 1986 Image 1992 Image 2002 Image 2012 Image 2022 Wetlands 217 204 208 210 206 199 Natural vegetation 228 218 219 210 203 202 Settlement 156 190 202 210 199 199 Cultivated land 164 193 201 210 197 202 Barren land 266 228 224 210 202 199 Next, in order to further illuminate PA, UA, and OA concepts, the representations and computations of these metrics are shown using the three mathematical formulations, namely Eq. ( 3 ). The probability that a reference pixel could be correctly classified, as determined by the referenced dataset, can be expressed as the total number of correct pixels of a particular land-use type divided by the total number of pixels within that land-use type. This is because a producer is interested in how well the desired land-use characteristics are classified (Grybas and Congalton, 2021 ). Following that, UA is a trustworthy metric that can be expressed as the proportion of all accurately categorized pixels for a given land use type to all pixels within that land use type. (Rahaman et al., 2020 ). OA is easily calculated by dividing the total number of pixels correctly identified by the total number of pixels. $$\left(\text{a}\right){\text{P}\text{r}\text{o}\text{d}\text{u}\text{c}\text{e}\text{r}}^{{\prime }}\text{s} \text{A}\text{c}\text{c}\text{u}\text{r}\text{a}\text{c}\text{y} \left(\text{P}\text{A}\right) \text{o}\text{f} \text{t}\text{h}\text{e} \text{k}\text{t}\text{h} \text{l}\text{a}\text{n}\text{d}\text{u}\text{s}\text{e} \text{t}\text{y}\text{p}\text{e} \frac{{x}_{kk}}{{\sum }_{i=1}^{5} {x}_{ik} }$$ $$\left(\text{b}\right){ \text{U}\text{s}\text{e}\text{r}}^{{\prime }}\text{s} \text{A}\text{c}\text{c}\text{u}\text{r}\text{a}\text{c}\text{y} \left(\text{U}\text{A}\right) \text{o}\text{f} \text{t}\text{h}\text{e} \text{k}\text{t}\text{h} \text{l}\text{a}\text{n}\text{d}\text{u}\text{s}\text{e} \text{t}\text{y}\text{p}\text{e} \frac{{x}_{kk}}{{\sum }_{j=1}^{5} {x}_{jk} }$$ 3 $$\left(\text{c}\right) \text{O}\text{v}\text{e}\text{r}\text{a}\text{l}\text{l} \text{A}\text{c}\text{c}\text{u}\text{r}\text{a}\text{c}\text{y} \left(\text{O}\text{A}\right) \text{o}\text{f} \text{c}\text{l}\text{a}\text{s}\text{s}\text{i}\text{f}\text{i}\text{c}\text{a}\text{t}\text{i}\text{o}\text{n} \frac{{x}_{11}+{x}_{22}+{x}_{33}+{x}_{44}+{x}_{55}}{{\sum }_{j=1}^{5} {\sum }_{i=1}^{5} {x}_{ij} }$$ For instance, \({x}_{11}/{\sum }_{i=1}^{5} {x}_{i1 }\) and \({x}_{11}/{\sum }_{j=1}^{5} {x}_{j1 }\) are the PA and UA, respectively, for categorizing cultivated land in the Pakistani research region. $$\text{K}\text{C}=\frac{{\sum }_{K=1}^{5}xkk-{\sum }_{K=1}^{5}\left({x}_{k+} {·x}_{+k}\right)}{{N}^{2}- {\sum }_{K=1}^{5}\left({x}_{k+} {·x}_{+k}\right)} = \frac{{\text{P}}_{\text{c}\text{h}\text{a}\text{n}\text{c}\text{e}}-{\text{P}}_{\text{c}\text{h}\text{a}\text{n}\text{c}\text{e}}}{1- {\text{P}}_{\text{c}\text{h}\text{a}\text{n}\text{c}\text{e}}} \left(4\right)$$ KC is a discrete multivariate mathematical approach to assessing accuracy (Bakeman, 2022 ). Eq. (4)'s first equal sign is used to express KC, where N denotes the total number of observations and x k+ and x + k denote the total number of data in the kth row and kth column, respectively (Carriquiry et al., 2019 ). If KC is understood probabilistically, it may be written down as the second part of Eq. (4), where P agree and P chance are the proportions (or probabilities) of properly classified pixels, and the classification agreement is of anticipated value. Because 64–100% of the data is valid, a KC value greater than 0.8 might be read as "nearly perfect" classification accuracy, whilst a value between 0.61–0.79 denotes "considerable" accuracy. Nonetheless, as only 4% of the data are regarded as credible, the categorization strategy for KC values less than 0.2 can be called "poor"(Bakeman, 2022 ). 2.2.4. Land Cover Transfer Matrix The structure and direction of the dynamic change in land cover are often described using the land cover transfer matrix (Zhao et al., 2020 ). The LULC change matrix reflects dynamic change, which is information on the mutual alteration of a certain region at the beginning and end of a specified time (Liping et al., 2018 ). It provides details than only the region's historical static area of a specific type of land (Li et al., 2018 ) but also provides information on the types of land that are changed to other LULC types or that are transformed from one LULC type to another (Yumin et al., 2016 ). The land cover transfer matrix was generated using the ArcGIS software to assess conversions of coastal wetlands in the Indus Delta: S ij = \(\left[\begin{array}{ccc}\begin{array}{cc}{S}_{11}& {S}_{12}\\ {S}_{21}& {S}_{22}\end{array}& \begin{array}{c}\cdots \\ \cdots \end{array}& \begin{array}{c}{S}_{1n}\\ {S}_{2n}\end{array}\\ ⋮ ⋮& \ddots & ⋮\\ \begin{array}{cc}{S}_{n1}& {S}_{2n}\end{array}& \cdots & {S}_{nn}\end{array}\right]\) (5) Where S ij is the area of land cover type i transferred to land cover type j ; n denotes the number of land cover types; i and j (1, 2, ..., n ) denote land cover types before and after a specific transfer operation; Each entry on the matrix's main diagonal indicates the area of each land cover category that remains constant. 2.2.5. Area-Weight Centroid The centroid of an area is the location of a specific land cover type that is determined by the coordinates of the geometric center of a polygon or multiple polygons (Parsons, 2021 ). It has been effectively applied in dynamic landscape analysis (Grandi et al., 2004 ), including the transition to desertification, the evolution of wetlands type, the pattern of the landscape's thermal environment, and changes to the coastline's topography (Chen et al., 2018 ). The centroids of wetlands and reclamation in 1972, 1986, 1992, 2002, 2012, and 2022 were computed and mapped to describe the spatial pattern of land cover change. The centroid of area-weight is defined as: $${X}_{t}=\sum _{i=1}^{N}({C}_{ti} . {X}_{i})/\sum _{i=1}^{N}{C}_{ti}, { Y}_{t}=\sum _{i=1}^{N}({C}_{ti} . {Y}_{i})/\sum _{i=1}^{N}{C}_{ti} \left(6\right)$$ Where the centroids of natural wetlands or reclamation areas in year t are represented by X t and Y t , respectively; The area of patch i in year t known as C ti ; The longitude and latitude of patch i , which can be natural wetlands, man-made wetlands, or reclamation areas, are represented by X i and Y i , respectively. The total number of natural wetlands or reclamation areas in a patch is called N . 2.2.6. Correlation Coefficient The Pearson correlation coefficient is a statistical indicator that assesses the strength and direction of a two-variable linear connection. It is represented by the symbol " r " and has a value between − 1 and 1, with a positive value indicating a positive correlation, a negative value indicating a negative correlation, and a value of zero indicating no correlation between the variables (Asuero et al., 2006). In this study correlation coefficient was used to show the relationship strength between different land use types. The mathematical written as: Where Σxy is the sum of the product of the deviations of each value from their respective means; Σx and Σy are the sums of the x and y values, respectively; n is the sample size. 3. Results 3.1. Land Cover Classification Maps Accuracy Assessment from 1972 to 2022 A number of training samples were chosen from the Landsat datasets for each of all years to assess the applicability of maximum likelihood classification approaches for obtaining relatively accurate LULC change maps. The parameters introduced in Section 2.2.3 (i.e., PA, UA, OA, and KC) were adopted for accuracy evaluations after being compared with a few GTP. Each of the five land-use categories' PA and UA, as well as the matching OA and KC, for each of the six research years are shown in the Table 4 . The PA and UA of all land-use categories in 1972, 1986, 1992, 2002, 2012 and 2022 were over 90%. The PA of wetlands was highest (100%) in 1992, 2002, 2012 and 2022 years and lowest (98%) in 1972 year compared to other years. Whereas the PA of natural vegetation was highest (98.7%) in 1972 year and lowest (95.9%) in 2002 year. The PA of settlement was highest (99.5%) in 1986 and 1992 years and lowest (98.5%) in 2002 and 2022 years. The PA of cultivated land was highest (100%) in 2022 year and lowest (96.3%) in 1972 year. The PA of barren land was highest (99.6%) in 1986 year and lowest (96.6%) in 2022 year. The UA of wetlands, natural vegetation, and barren land was 100% in all years. The UA of settlement was highest (96.5%) in 1992 year and lowest (94.2%) in 1972 year. The UA of cultivated land was highest (98.4%) in 1986 year and lowest (94.8%) in 2002 (Table 4 ). The OA was highest (98.8%) in 1986 and 1992 years and lowest (98.4%) in 1972 compared to other years. The KC was highest (0.98) in 1986 and 1992 years and lowest (0.97) in 2002 year. To summarize, the average OA and KC for all years were 96.10% and 0.94, respectively, providing further confidence in using this remotely sensed and statistical framework to detect future changes in land-use patterns and morphologies. Table 4 Based on chosen Ground Truth Points (GTP) from Landsat images accuracy assessments of recovered LULC change maps for the years 1972, 1986, 1992, 2002, 2012, and 2022, respectively, were made. The five main land-use kinds in this study are used to classify the numerical Producer Accuracy (PA) and User Accuracy (UA) values. Year Metrics Wetlands Natural vegetation Settlement Cultivated land Barren land 1972 Producer Accuracy (PA) 98.60 98.70 99.30 96.30 98.50 User Accuracy (UA) 100.00 100.00 94.20 95.10 100.00 Overall Accuracy (OA) 98.40 Kappa Coefficient (KC) 0.97 1986 Producer Accuracy (PA) 99.50 98.20 99.50 97.40 99.60 User Accuracy (UA) 100.00 100.00 95.30 98.40 100.00 Overall Accuracy (OA) 98.80 Kappa Coefficient (KC) 0.98 1992 Producer Accuracy (PA) 100.00 97.30 99.50 97.90 99.10 User Accuracy (UA) 100.00 100.00 96.50 97.00 100.00 Overall Accuracy (OA) 98.80 Kappa Coefficient (KC) 0.98 2002 Producer Accuracy (PA) 100.00 95.90 98.50 97.50 98.60 User Accuracy (UA) 100.00 100.00 95.70 94.80 100.00 Overall Accuracy (OA) 98.50 Kappa Coefficient (KC) 0.97 2012 Producer Accuracy (PA) 100.00 97.60 98.90 96.90 99.00 User Accuracy (UA) 100.00 100.00 94.90 97.90 100.00 Overall Accuracy (OA) 98.60 Kappa Coefficient (KC) 0.98 2022 Producer Accuracy (PA) 100.00 97.60 98.50 100.00 96.60 User Accuracy (UA) 100.00 100.00 95.50 97.00 100.00 Overall Accuracy (OA) 98.50 Kappa Coefficient (KC) 0.98 Note : All numerical values are corrected to 2 decimal places. 3.2. Areal changes of wetlands, reclamation, and other land use types from 1972 to 2022 Figure 2 and 3 show the land-use classes in the Indus River Delta from 1972 to 2022 at different intervals. In the first land-use class, wetlands saw a gradual decline in the area from 175.80 km 2 in 1972 to 149.71 km 2 in 2022. Thus, the proportion of wetlands decreased from 12.93% in 1972 to 11.01% in 2022. In the second land-use class, natural vegetation showed a more drastic change in the area, declining from 225.92 km 2 in 1972 to only 43.26 km 2 in 2022. This decline is reflected in the proportion of natural vegetation, which decreased from 16.61% in 1972 to 3.18% in 2022. In the third land-use class, settlement showed an increasing trend in both area and proportion from 24.76 km 2 and 1.82% in 1972 to 224.97 km 2 and 16.54%, respectively in 2022. In the fourth land-use class, cultivated land increased in area from 54.86 km 2 in 1972 to 428.41 km 2 in 2022. The proportion of cultivated land also increased largely from 4.03% in 1972 to 31.50% in 2022. The fifth land-use class was barren land, which had the largest area in 1972 (878.48 km 2 ), and saw a gradual decline to 513.55 km 2 in 2022. The proportion of Barren land decreased from 64.60% in 1972 to 37.76% in 2022. The total area of the region remained constant at 1359.82 km 2 from 1972 to 1992. The combined proportion of wetlands and natural vegetation decreased from 29.54% in 1972 to 14.19% in 2022, indicating a significant loss of natural habitats in the region; settlements have grown rapidly over the last three decades, increasing by 83.16% and 907.14% from 1992 to 2022. Cultivated land has steadily increased throughout the years, reaching more than four times its 1972 size by 2022. The fraction of barren land decreased by more than half, from 64.60% in 1972 to 31.50% in 2022, indicating a change towards more sustainable land-use practices or the consequences of human activities on the ecosystem. It can be seen from Figs. 2 and 3 that there was a huge change in land-use patterns in the region during the previous five decades. The settlements and cultivated land have extended and converted into wetlands and natural vegetation. The decline in barren land may reflect a change in land management practices or increased human activities. The data highlights the need for sustainable land-use practices to preserve natural resources and maintain a healthy environment. The large increase in the share of settlements from 3.08% in 1986 to 16.54% in 2022 implies that the region is rapidly urbanizing. This tendency might have an impact on social and economic progress, as well as environmental sustainability. The growth in cultivated land from 5.84% in 1986 to 31.50% in 2022 might be attributed to agricultural intensification, which entails increasing productivity via the use of modern farming equipment and methods. Yet, this tendency may have unintended repercussions such as soil deterioration, water pollution, and biodiversity loss. The decrease in natural vegetation from 16.61% in 1972 to 3.18% in 2022 is a reason for concern since natural vegetation supports ecosystems, regulates the water cycle, and sequesters carbon. Wetlands have decreased from 12.93% in 1972 to 11.01% in 2022, indicating that they are under threat from human activities such as land-use change, pollution, and drainage. Barren land decreased from 64.60% in 1972 to 37.76% in 2022, which might be attributed to human actions such as reclamation. From Table 5 , it can be seen that the changes in the area of wetlands and reclamation in a study area from 1972 to 2022, as well as the average speed of area change on different time intervals. The "wetlands" class has decreased in area from 175.8 km 2 in 1972 to 149.7 km 2 in 2022, with a net change of -6.5 km 2 between 1972 and 1986 and an average speed of area change ranging from − 0.5 km 2 /yr to -1.1 km 2 /yr in the different time interval. Wetlands have experienced a net loss of 26.1 km 2 from 1972 to 2022, with the highest rate of loss occurring in the most recent decade (2012–2022) at -3.6 km 2 /yr. The "reclamation settlement" class has increased in area from 24.8 km 2 in 1972 to 224.9 km 2 in 2022, with a net change of 17 km 2 between 1972 and 1986 and an average speed of area change ranging from 1.2 km 2 /yr to 3.4 km 2 /yr in the different time interval. Reclamation Settlement has experienced a net gain of 200.1 km 2 from 1972 to 2022, with the highest rate of gain occurring in the decade from 1992–2002 at 75.9 km 2 /yr. The "reclamation cultivated land" class has increased in area from 54.9 km 2 in 1972 to 428.4 km 2 in 2022, with a net change of 24.5 km 2 between 1972 and 1986 and an average speed of area change ranging from 1.8 km 2 /yr to 28.6 km 2 /yr in the different time interval. Cultivated land has experienced a net gain of 373.5 km 2 from 1972 to 2022, with the highest rate of gain occurring in the decade from 2012–2022 at 28.6 km 2 /yr. The net gain in settlement and cultivated areas from 1972 to 2022 was 225.0 km 2 and 373.5 km 2 , respectively. This represents a combined net gain of 598.5 km 2 or 44.0% of the total study area. The wetlands account for the largest percentage of area loss at 14.4% from 1972 to 2022. The average pace of area changes for each class varied between time intervals. The rate of change for wetlands, for example, was highest in the most recent decade (2012–2022), whereas the rate of change for settlement reclamation was highest in the 1992–2002 decade. From 1972 to 2002, the rate of change for cultivated land reclamation climbed continuously, with the largest rates of gain happening in the most recent decade (2012–2022), shown in Fig. 4 . It can be seen from Table 5 that throughout the years, the study area's land cover has changed dramatically, with wetlands shrinking and reclamation settlement, and cultivated land expanding. The average rate of area change differed by class and historical time points, with some displaying faster rates of development than others. These changes can potentially have important ecological, social, and economic consequences for the study area and its adjacent regions. Table 5 Areal change of coastal wetlands and reclamation in the Indus River Delta from 1972 to 2022 Classes Area in 1972 (km 2 ) Area in 2022 (km 2 ) Area change (km 2 ) Average speed of area change (km 2 /yr) 1972– 1986 1986– 1992 1992– 2002 2002– 2012 2012– 2022 1972– 1986 1986– 1992 1992– 2002 2002– 2012 2012– 2022 Wetlands 175.8 149.7 –6.5 –1.5 –3.3 –3.6 –11.2 –0.5 –0.3 –0.3 –0.4 –1.1 Reclamation Settlement 24.8 224.9 17 29.5 75.9 43.6 34.1 1.2 4.9 7.6 4.4 3.4 Cultivated land 54.9 428.4 24.5 1 1.7 56.2 286.2 1.8 0.2 0.2 5.6 28.6 Total study area 1359.8 1359.8 1359.8 1359.8 1359.8 1359.8 1359.8 1359.8 1359.8 1359.8 1359.8 1359.8 Note : All numerical values are corrected to 1 decimal places. 3.3. Conversions of wetlands and reclamation in the Indus River Delta Figure 5 and 6 shows the area changes from 1972 to 2022 for different land cover types in the study area, categorized based on their conversion to or from wetlands. Specifically explained below: Wetlands to Settlement: There was a net conversion of 3 km 2 of wetlands to settlement areas between 1972 and 1986, followed by a slight recovery of 0.7 km 2 between 1986 and 1992. However, there was a further net loss of 3.8 km 2 between 1992 and 2002 and another 1 km 2 between 2002 and 2012. The time interval between 2012 and 2022 saw a major net loss of 2.6 km 2 of wetlands to settlement areas. Wetlands to Cultivated land: There was a net conversion of 9.2 km 2 of wetlands to cultivated land between 1972 and 1986, followed by a slight recovery of 2.5 km 2 between 1986 and 1992. The time interval between 1992 and 2002 saw a net loss of 2.9 km 2 of wetlands to cultivated land, followed by a further loss of 1.4 km 2 between 2002 and 2012. The time interval between 2012 and 2022 saw a major net gain of 12.5 km 2 of wetlands from cultivated land. Wetlands to Natural vegetation: There was a net conversion of 8.5 km 2 of wetlands to natural vegetation between 1972 and 1986, followed by a net loss of 9.5 km 2 between 1986 and 1992. The time interval between 1992 and 2002 saw a further net loss of 3.3 km 2 of wetlands to natural vegetation, followed by a net recovery of 7.1 km 2 between 2002 and 2012. The time interval between 2012 and 2022 saw a slight net loss of 0.8 km 2 of wetlands to natural vegetation. Wetlands to Barren land: There was a net loss of 14.2 km 2 of wetlands to barren land between 1972 and 1986, followed by a net gain of 14.3 km 2 between 1986 and 1992. The time interval between 1992 and 2002 saw a net loss of 0.1 km 2 of wetlands to barren land, followed by a further loss of 5.9 km 2 between 2002 and 2012. The time interval between 2012 and 2022 saw a slight net recovery of 2.2 km 2 of wetlands from barren land. Settlement to Wetlands: There was a net recovery of 3 km 2 of wetlands from settlement areas between 1972 and 1986, followed by a slight loss of 0.7 km 2 between 1986 and 1992. The time interval between 1992 and 2002 saw a further net loss of 3.8 km 2 of wetlands from settlement areas, followed by a loss of 1 km 2 between 2002 and 2012. The time interval between 2012 and 2022 saw a net recovery of 2.6 km 2 of wetlands from settlement areas. Cultivated land to Wetlands: There was a net loss of 9.2 km 2 of wetlands to cultivated land between 1972 and 1986, followed by a net recovery of 2.5 km 2 between 1986 and 1992. The time interval between 1992 and 2002 saw a net loss of 2.9 km 2 of wetlands from cultivated land. The time interval between 2002 and 2012 saw a further net loss of 1.4 km 2 of wetlands from cultivated land, followed by a loss of 12.5 km 2 between 2012 and 2022. There has been an extensive conversion of wetlands into settlement, with a net gain of 2.5 km 2 of settlement area at the cost of wetlands throughout the course of the research. Also, there has been a net conversion of wetlands to cultivated land, with a total gain in cultivated land area at the cost of wetlands by 24.5 km 2 . There was also a massive conversion of natural vegetation to cultivated land, with a total increase of 286.2 km 2 of cultivated land area at the cost of natural vegetation. Nonetheless, the area of natural vegetation has grown by 10.1 km 2 throughout the research time. Finally, during the research time, there was a net conversion of barren land to settlement, with a total gain of 58.9 km 2 of settlement area at the outflow of barren land. However, there was a net increase of 4.3 km 2 in the amount of barren land, shown in Figs. 5 and 6 . According to the findings, the conversion of wetlands and natural vegetation to other land uses is a big issue in the stud area. Converting these vital ecosystems to other land uses can have detrimental consequences for the environment and human well-being. Table 6 shows the areas of settlement, cultivated land, and reclamation area in various intervals from 1972 to 2022. The settlement area increased from 6.7 km 2 in 1972–1986 to 81.3 km 2 in 2012–2022, showing a major increase over the years. The proportion of settlement area increased from 26% in 1972–1986 to 36.1% in 2012–2022, indicating that the rate of settlement growth has slowed up over time. The area of cultivated land increased from 19.1 km 2 in 1972–1986 to 143.8 km2 in 2012–2022, indicating major growth in agricultural activities. The proportion of cultivated land increased from 74% in 1972–1986 to 63.9% in 2012–2022, indicating that cultivated land has become the dominant land use over the years. The time interval from 1992–2002 saw the largest increase in the settlement area, with the settlement area increasing from 9.2 km 2 to 50.0 km 2 . This was also the time interval where the proportion of cultivated land increased the most, from 71–42%. The area of reclamation land increased from 25.8 km 2 in 1972–1986 to 225.1 km 2 in 2012–2022, showing a major increase over the years. The proportion of reclamation area increased from 0% in 1972–1986 to 29.2% in 2002–2012, indicating that efforts to reclaim land from the sea have been successful in recent years. The total reclamation study area increased from 25.8 km2 in 1972–1986 to 225.1 km 2 in 2012–2022, indicating a major expansion of the land area time by time. The time interval from 2002–2012 saw the largest increase in reclamation area, with the reclamation area increasing from 43.9 km 2 to 150.2 km 2, indicating that efforts to reclaim land from the wetlands have been successful in recent years. There was a major expansion in settlement area, cultivated land, and reclamation areas over time. The rate of settlement expansion has increased throughout time, and cultivated land has surpassed all other land uses. Land reclamation efforts have been effective, with a large increase in reclamation area over the years, as shown in Table 6 . Table 6 Change area between land use types of reclamation areas in the Indus River Delta Intervals Settlement Cultivated land Reclamation area Area (km 2 ) Proportion (%) Area (km 2 ) Proportion (%) Area (km 2 ) 1972–1986 6.7 26.0 19.1 74.0 25.8 1986–1992 9.2 26.2 24.9 71.0 35.1 1992–2002 50.0 58.1 36.1 42.0 86.1 2002–2012 106.3 70.8 43.9 29.2 150.2 2012–2022 81.3 36.1 143.8 63.9 225.1 Note : All numerical values are corrected to 1 decimal places. 3.4. Centroids movement of coastal wetlands and reclamation in the Indus River Delta Figure 7 shows the centroids' movement of wetlands and reclamation. The centroids of wetlands moved slowly from Kharo Chan taluka northern side of the Khar creek to the eastwards in Keti Bandar taluka in the northern side of Turshian creek of Arabian Sea during the time interval of 1972 to 1986; then slowly moved towards the south direction northern side of the Gaghiar and Khobar creeks during the time interval of 1986 to 1992; after that again slowly moved to eastwards between Turshian and Hajambro creeks from 1992 to 2002, later on slowly moved to westwards northern part of Keti Bandar taluka from 2002 to 2012, again fastest movement were from Keti Bandar to Kharo Chan taluka northern side of the Khar creek during the time interval of 2012 to 2022. In terms of reclamation, the centroids of settlement migrated from the eastern part of Kharo Chan to the northern part of Keti Bandar taluka with the fastest movement during the time interval of 1972 to 1986; then shifted southwards from the eastern part of Keti Bandar taluka to eastern part of Kharo Chan with fastest movement from 1986 to 1992; after that again slowly moved eastwards from eastern part of Kharo Chan to western part of Keti Bandar taluka from 1992 to 2002; later on slowly moved to westwards from 2002 to 2012, then slowly shifted from western part of Keti Bandar to eastern part of Kharo Chan during the time interval of 2012 to 2022. The centroids of cultivated land shifted slowly westwards from the northern part of Keti Bandar to the western part of the taluka from 1972 to 1986; then migrated northwards in Keti Bandar taluka from 1986 to 1992, later on slowly moved westwards from 1992 to 2012 in the Keti Bandar taluka; after that shifted to southwards from the northern side of Keti Bandar taluka to the southern part of Kharo Chan taluka western side of the Khar creek with fastest movement during the time interval of 2012 to 2022. 3.5. Relationship strength between different land use types in the Indus River Delta Figure 8 shows the relationship strength between different land use types. According to the results, the negative correlation means that one land use type is increasing and the other is decreasing. If both land use types are increasing or decreasing, that indicates a positive correlation. The correlation degrees between wetlands and cultivated land and wetlands and settlement were both strongest negative (-0.91 and − 0.93), indicating that the wetlands are decreasing and cultivated land and settlements are increasing in the same area. The correlation between wetlands and natural vegetation and wetlands and barren land were both strongest positive (0.88 and 0.89), meaning that these land use types are decreasing. The correlation between cultivated land and settlement was also strongly positive (0.75), indicating that these land use types are increasing. The correlation between barren land and cultivated land and barren land and settlement were both strongest negative (-0.96 and − 0.82), meaning that the barren land is declining and cultivated land and settlements are increasing. The correlation between natural vegetation and cultivated land and natural vegetation and settlement were strongly negative (-0.71 and − 0.82), indicating that the natural vegetation is decreasing and cultivated land and settlements are increasing. The correlation between natural vegetation and barren land was moderately positive (0.62), meaning these land use types are somewhat likely to coexist in the same area. 4. Discussion 4.1. Importance of coastal wetlands and impact of other land use types dynamics on coastal wetlands in the Indus River delta during the past 50 years Coastal wetlands are important habitats that sustain a variety of plants and animals as well as offer a wide range of ecological functions. Nevertheless, they are also among the most endangered ecosystems on the planet as a result of the fast industrialization and urbanization that is causing these vital ecosystems to degrade and disappear. The Indus river delta has a distinct environment that supports a diverse range of flora and fauna, including mangroves, sea grasses, and several fish species. The delta is threatened by various anthropogenic activities, including land-use change, pollution, overfishing, and coastal erosion (Chaudhry, 2010 ). Reclamation is a key diver that converts wetlands or other aquatic ecosystems into land for human use, is one of the most serious dangers to the Indus River Delta. For several decades, the deltic region has been undergoing reclamation, mostly for agricultural and settlement purposes (Solangi et al., 2022 ). Large ecological harm to the delta has been caused by the conversion of wetlands into agricultural land and urban area, including the loss of fish and wildlife habitats and an increase in soil and water salinization (Bot, 2018 ). The results of this study indicate that the proportion of wetlands has decreased gradually from 12.93% in 1972 to 11.0087% in 2022. The decrease in the proportion of wetlands over time is a concerning trend, as wetlands provide a variety of important ecosystem services. They also provide habitats for a variety of plant and animal species, some of which are unique and rare (Nayak and Bhushan, 2022 ). Wetlands loss may lead to a drop in water quality, an increased danger of floods, and a loss of biodiversity. This can have serious environmental and economic consequences, such as diminished fishing and tourism prospects (Bhowmik, 2022 ). Natural vegetation has declined dramatically from 16.61% in 1972 to 3.18% in 2022. The loss of natural vegetation is also a concerning trend, as it can have major ecological and social impacts. Natural vegetation plays a vital role in maintaining soil health, preventing erosion, and regulating water cycles. Moreover, it offers habitats for wildlife and, by storing carbon, aids in reducing climate change. Degradation of the soil, a decline in biodiversity, and an increase in greenhouse gas emissions can all be brought about by the loss of natural vegetation. Reduced food and water security are just a couple of the negative effects this can have on people's well-being (Kusch et al., 2022 ). The proportion of settlement has increased largely from 1.82% in 1972 to 16.5435% in 2022. The increase in the proportion of settlement over time reflects a trend towards urbanization and increased human population. Urbanization can have major impacts on the environment, including increased energy consumption, air pollution, and land use change (Almulhim et al., 2022 ). Urban areas are frequently more susceptible to the effects of climate change and extreme weather occurrences. Conflicts over land use and competition for natural resources may arise as a result of increased settlement (Liang et al., 2019 ). The proportion of cultivated land has increased gradually from 4.03% in 1972 to 31.503% in 2022. A trend towards increased agricultural production to meet the demands of a growing world population is reflected in the increase in the proportion of cultivated land (Barati et al., 2023 ). Deforestation, soil erosion, and water pollution are just a few of the negative effects that agriculture can have on the environment. The intensification of agriculture can also result in increased use of fertilizers and pesticides, which can have negative impacts on human health and the environment (Tudi et al., 2021 ). It is crucial to ensure that agricultural methods are environmentally friendly and have as little detrimental impact as possible (Mandal et al., 2020 ). Between 1972 and 2022, barren land gradually decreased from 64.60–37.7637% in the study area. Since it reflects the transformation of formerly unusable land into productive land uses, the decrease in the proportion of barren land over time can be seen as a positive trend (Nguyen et al., 2021 ). However, it is crucial to remember that barren land may serve crucial ecological purposes, including serving as a habitat for unique plant and animal species. The conversion of barren land into other land uses can also result in the loss of these ecological functions. Several similar studies have been conducted in different regions of the world and reported concerns are consistent with the results of our research work. For instance, a study by Sun et al. ( 2018 ) analyzed the land use changes in the Yangtze River Basin in China during 35 years using remote sensing data. The study found that agricultural land increased, while forest, wetlands, and grasslands decreased dramatically, leading to soil erosion, water pollution, and biodiversity loss. Lambin and Meyfroidt ( 2011 ) reviewed the global trends in land use changes during four decades and reported that agricultural expansion, urbanization, and infrastructure development were the main drivers of land use changes, leading to deforestation, habitat loss, and climate change. Reis ( 2008 ) analyzed land use and land cover changes in Rize, North-East Turkey during 24 years and showed that urban area and agricultural land increased, pasture and forestry area decreased. These studies, emphasize the significance of tracking and controlling the wetlands changes to ensure sustainable development and resource preservation. 4.2. Consequences of reclamation on coastal wetlands of Indus River Delta from 1972 to 2022 In our study results, it can be clearly seen the enormous changes in the area of wetlands and reclamation (settlement and cultivated land) in a study area between 1972 and 2022. The wetlands area decreased by 26.1 km 2 , while the settlement area and cultivated area increased by 200.1 km 2 and 373.5 km 2 , respectively. Wetlands are important ecosystems that provide numerous benefits, such as water purification, flood control, and habitat for wildlife (H. Wu et al., 2023 ). In addition, wetlands play an important role in reducing the consequences of climate change by storing plentiful amounts of carbon in their soils (Let and Pal, 2023 ). The decrease in wetlands area observed in this study is in-line with global trends. It is estimated that the world has lost approximately 64–71% of its wetlands since the 1900s (Nie et al., 2023 ). The decline in wetlands area determined in this study is concerning as it may lead to a loss of these benefits. Wetlands loss are caused by a number of factors, including shifting land use patterns, climate change, and human activities like dredging, filling, and draining, which can have negative impacts on human communities (Davidson, 2014 ). Wetlands protection and restoration efforts are so critical for sustaining these precious ecosystems. Population growth and urbanization, which have resulted in the extension of built-up areas, can be ascribed to the rise in reclamation settlement areas seen in this study (Chen et al., 2023 ). The expansion of built-up regions can have severe environmental consequences, such as increased air and water pollution, habitat loss, and changes in microclimate (Zhan et al., 2023 ). Furthermore, urbanization can result in a loss of biodiversity as well as the loss of important ecosystem services such as flood control and water filtration (Chen et al., 2022 ). Urbanization may also lead to increased air and water pollution, which can be harmful to human health. It is estimated that urban air pollution kills around 1.8 million people worldwide each year (Cohen et al., 2017 ). To mitigate these adverse effects, urban planning and management measures that take into account the environmental implications of built-up regions are required. The increase in reclamation cultivated area observed in this study is consistent with global trends, as agriculture has expanded to meet rising food demand (Ahmed and Ambinakudige, 2023 ). However, the expansion of agricultural land can also have negative impacts on the environment (Barkah et al., 2022 ). It is estimated that agriculture is responsible for approximately 25% of global greenhouse gas emissions (Subedi et al., 2021 ). Therefore, in order to ensure that agricultural expansion is environmentally sustainable, best farming practices that minimize environmental impacts are required, such as integrated pest management, precision agriculture, and conservation agriculture. The changes observed in the wetlands and reclamation (settlement and cultivated land) in the study area reflect broader global trends of environmental change due to human activities. The efforts to maintain and restore wetlands, reduce the negative consequences of urbanization, and promote sustainable farming methods are essential to ensure a sustainable future for both people and the environment. Our study exhibited that from 1972 to 1986, there was a net loss of 3 km 2 of wetlands to settlement and a net increase of 9.2 km 2 of wetlands to cultivated land. This trend continued in the following two decades, with wetlands continuing to be lost to agriculture and urbanization. However, due to some restoration efforts, there was a modest trend reversal from 2002 to 2012, with a net increase of wetlands replacing barren land (Pupins et al., 2023 ). The research also highlights the importance of measures to protect wetlands and other natural habitats. Ramsar Agreement on Wetlands, a 1971 international agreement, aims to advance the global preservation and responsible use of wetlands (Ramsar, 2023). Similar to this, the United States Clean Water Act, passed in 1972, offers a framework for controlling the flow of pollutants into wetlands and other bodies of water (US, 2023). From 1972 to 1986, wetlands were lost primarily to settlement and gained to cultivated land. With estimates indicating that more than 40% of the world's wetlands have been converted to agricultural land, the conversion of wetlands to agriculture and settlement poses a serious threat to wetlands conservation globally (Moisa et al., 2023 ). From 2002 to 2012, there was a net gain of wetlands from barren land, suggesting successful restoration efforts. Wetlands restoration is a critical strategy for conserving and restoring wetland ecosystems globally (Moisa et al., 2023 ). Our results are consistent with many other wetlands studies in the world. For example, Saha et al. ( 2022 ) studied in India and found that 28.11% and 30.14% of wetland areas declined and converted into settlement and cultivated land over three decades. Chuma et al. ( 2022 ) conducted a study in Congo and investigated that 35% of wetlands decreased and rapidly shifted into cultivated land. Li et al. ( 2022 ) studied in China and reported that wetlands declined by 11% and converted into barren land, forest, and grassland, and Hine et al. ( 2017 ) reported that 10% of wetlands shifted into vegetation in the USA. 4.3. Reclamation status of five different intervals in the Indus River Delta from 1972 to 2022 compared to other studies Regarding the changing area of reclamation, including settlement and cultivated land during five different time intervals: 1972–1986, 1986–1992, 1992–2002, 2002–2012, and 2012–2022 shown in Table 6 , our study indicates that: From 1972 to 1986, the settlement area was 26% of the total area, while the cultivated land was 74%, and the reclamation area was 25.8 km 2 . From 1986 to 1992, the settlement area increased to 26.2% of the total area, while the cultivated land increased to 71%, and the reclamation area increased to 35.1 km 2 . From 1992 to 2002, there was a major increase in the settlement area to 58.1% of the total area, while the cultivated land increased to 42%, and the reclamation area increased to 86.1 km 2 . From 2002 to 2012, the settlement area continued to increase to 70.8% of the total area, while the cultivated land further increased by 29.2%, and the reclamation area increased to 150.2 km 2 . From 2012 to 2022, the settlement area increased to 36.1% of the total area, while the cultivated land largely increased to 63.9%, and the reclamation area increased to 225.1 km 2 . Global land use changes are driven by population growth and economic development, leading to increased demand for land for settlements and agriculture. These findings align with other studies that have investigated land use changes in other countries over time. For example, Maru et al. ( 2023 ) reported that the built-up area has increased by 5.3% over 23 years. Gohain et al. ( 2023 ) investigated that settlement land has increased by 28.7% during 5 years. Shekar and Mathew (2023) found that agricultural land and built-up have increased by 5.17% and 0.84% during 23 years. Daba and You ( 2022 ) studied that cultivated land increased by 62% during 35 years. Abdullah et al. ( 2019 ) reported that cultivated area has increased by 5.44% over 27 years. 5. Conclusion Coastal wetlands are one most important ecosystems of our planet. Wetlands have been lost all across the world, including those in Pakistan's Indus River Delta. The spatiotemporal dynamics of coastal wetlands and reclamation in the Indus River Delta during the past 50 years revealed changes in the extent of wetlands and their conversion to cultivated land and urban areas due to human activities, including reclamation and land-use changes. Our findings showed that from 1972 to 2022, the net area of natural wetlands decreased by 26.1 km 2 , whereas the reclamation (settlement and cultivated land) rose by 200.1 and 373.5 km 2, respectively. The fastest conversion rate from wetlands to settlement was 3.8 km 2 between 1992 and 2002, and the fastest conversion rate from wetlands to cultivated land was 12.5 km 2 between 2012 and 2022 over the five study time points. Wetlands centroids moved progressively eastwards from Kharo Chan taluka to Keti Bandar taluka in the first and third decades, then southwards in the second decade, then westwards in the fourth decade, and ultimately southwards from Keti Bandar taluka to Kharo Chan taluka in the fifth decade. During the five decades, settlement centroids slowly expanded in all directions. From Keti Bandar to Kharo Chan Taluka, the centroids of cultivated land shifted westward in the first, third, and fourth decades, northward in the second, and southward in the fifth decade. The average accuracy assessment of all metrics was 98%, and KC was 0.98. The study emphasizes the importance of sustainable land-use practices to protect natural resources and maintain a healthy ecosystem. Protection and restoration of wetlands and natural vegetation, promotion of sustainable agricultural methods, and careful land-use planning and management are all critical solutions for addressing the difficulties confronting the Indus River Delta region. Generally, the data suggest that the region is undergoing major land-use changes, indicating the need for ongoing monitoring and timely action to guarantee sustainable development and natural resource protection. 6. Recommendations and future research direction During the last 50 years, the Indus River Delta's coastal wetlands and reclamation efforts have seen major modifications. Understanding these spatiotemporal changes is critical for designing successful conservation and management measures for the region. Future recommendations and proposals based on the examination of the spatiotemporal dynamics of coastal wetlands and reclamation in the Indus River Delta might include: Creating and executing appropriate conservation and management methods for the region's surviving coastal wetlands in order to protect its biological functions and biodiversity. Promoting sustainable land use practices and reducing the environmental and community consequences of reclamation works. Raising public awareness and education about the value of coastal environments. The study of the Indus River Delta's coastal wetlands and their deterioration due to reclamation is a significant and complicated issue that requires a multidisciplinary approach. Future studies should look at the ecological, economic, and social consequences of wetlands reclamation, prospective policy and management solutions, prediction models, and comparative assessments of wetlands conservation and restoration activities. Declarations Authorship contribution statement Yaseen Laghari: Data collection, Software, Validation & writing-original draft. Shibiao Bai: Supervision, Writing-reviewing & editing. Shah Jahan Leghari: Co-Supervision, Conceptualization, Writing-reviewing & editing. Wenjing Wei: Conceptualization & Methodology. Abdul Hafeez Laghari: Methodology. Acknowledgments This study was supported by the National Key Research and Development Program of China (Grant No. 2021YFE0116800). We are grateful to the editorial team and anonymous reviewers for their efforts to improve this manuscript. Data availability Data will be made available on request. Funding The authors have not disclosed any funding. Conflict of interest The authors declare no conflict of interest. References Abdullah, A. Y. M., Masrur, A., Gani Adnan, M. S., Al Baky, M. A., Hassan, Q. K., & Dewan, A., 2019. Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017. Remote Sensing. 11, 790. https://doi.org/10.3390/RS11070790. Ahmad, Z., Ashtar, H., & Shakeel, A., 2019. Economics Importance of Wetlands, their Benefits and Values Case of Pakistan. MPRA. Paper No. 95260, 1–13. Ahmed, Z., & Ambinakudige, S., 2023. Does land use change, waterlogging, and salinity impact on sustainability of agriculture and food security? Evidence from southwestern coastal region of Bangladesh. Environmental Monitoring and Assessment. 195, 1–28. https://doi.org/10.1007/S10661-022-10673-W/METRICS. Almazroui, M., Mashat, A., Assiri, M. E., & Butt, M. J., 2017. Application of Landsat Data for Urban Growth Monitoring in Jeddah. Earth Systems and Environment. 1, 1-11. https://doi.org/10.1007/S41748-017-0028-4. Almulhim, A. I., Bibri, S. E., Sharifi, A., Ahmad, S., & Almatar, K. M., 2022. Emerging Trends and Knowledge Structures of Urbanization and Environmental Sustainability: A Regional Perspective. Sustainability. 14, 13195. https://doi.org/10.3390/SU142013195. Asad, M. H., & Bais, A., 2020. Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture. 7, 535–545. https://doi.org/10.1016/J.INPA.2019.12.002. Asuero, A., Sayago, A., 2006. The correlation coefficient: An overview. Critical reviews in analytical chemistry.. 36, 41–59. https://doi.org/10.1080/10408340500526766. Bakeman, R., 2022. KappaAcc: A program for assessing the adequacy of kappa. Behavior Research Methods. 5, 1–6. https://doi.org/10.3758/S13428-022-01836-1/METRICS. Bakr, N., Weindorf, D., 2010. Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography. 30, 592-605. https://doi.org/10.1016/j.apgeog.2009.10.008. Balmford, A., Bennun, L., Ten Brink, B., Cooper, D., Côté, I. M., Crane, P., Dobson, A., Dudley, N., Dutton, I., Green, R. E., Gregory, R. D., Harrison, J., Kennedy, E. T., Kremen, C., Leader-Williams, N., Lovejoy, T. E., Mace, G., May, R., Mayaux, P., … Walther, B. A., 2005. The convention on biological diversity's 2010 target. Science. 307, 212–213. https://doi.org/10.1126/SCIENCE.1106281/SUPPL_FILE/BALMFORD.SOM.PDF. Barati, A. A., Azadi, H., Movahhed Moghaddam, S., Scheffran, J., & Dehghani Pour, M., 2023. Agricultural expansion and its impacts on climate change: evidence from Iran. Environment, Development and Sustainability. 20, 1–27. https://doi.org/10.1007/S10668-023-02926-6/METRICS. Barkah, T. T., Rusgianto, S., & Wardhana, A. K., 2022. Impact of Agricultural Land and the Output of Agricultural Products Moderated with Internet Users toward the Total export of Agricultural Product in Three Islamic South East Asian Countries. Media Agribisnis. 6, 11–22. https://doi.org/10.35326/AGRIBISNIS.V6I1.2261. Benchoufi, M., Matzner-Lober, E., Molinari, N., Jannot, A. S., & Soyer, P., 2020. Interobserver agreement issues in radiology. Diagnostic and Interventional Imaging. 101, 639–641. https://doi.org/10.1016/J.DIII.2020.09.001. Bhowmik, S., 2022. Ecological and Economic Importance of Wetlands and Their Vulnerability: A Review. Research Anthology on Ecosystem Conservation and Preserving Biodiversity. 22, 11-27. https://doi.org/10.4018/978-1-6684-5678-1.CH002 Bot, M. R.-P. J., 2018. A review on the status, ecological importance, vulnerabilities, and conservation strategies for the Mangrove ecosystems of Pakistan. Pakistan Journal of Botany. 50, 1645–1659. https://www.pakbs.org/pjbot/papers/1524563266.pdf. Carriquiry, A. L., Reid, N., & Slavković, A. B., 2019. Stephen Elliott Fienberg 1942–2016, Founding Editor of the Annual Review of Statistics and Its Application. Annual review of statistics and its application. 6, 1–18. https://doi.org/10.1146/ANNUREV-STATISTICS-030718-105334. Chandio, N. H., M. M. Anwar, and A. A. Chandio., 2011. Degradation of Indus delta, removal mangroves forestland its causes: A case study of Indus River delta. Sindh University Research Journal. 1, 43. https://sujo-old.usindh.edu.pk/index.php/SURJ/article/view/1306. Chaudhry, A. A., 2010. Wetlands in Pakistan : What Is Happening To Them? World Environment Day. 6, 49–70. Chen, J., Yang, K., Chen, S., 2019. Enhanced normalized difference index for impervious surface area estimation at the plateau basin scale. Journal of Applied Remote Sensing. 13, 016502-016502. https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-13/issue-1/016502/Enhanced-normalized-difference-index-for-impervious-surface-area-estimation-at/10.1117/1.JRS.13.016502.short. Chen, L., Ren, C., Zhang, B., Li, L., Wang, Z., & Song, K., 2018. Spatiotemporal Dynamics of Coastal Wetlands and Reclamation in the Yangtze Estuary During Past 50 Years (1960s–2015). Chinese Geographical Science. 28, 386–399. https://doi.org/10.1007/S11769-017-0925-3. Chen, W., Wang, G., & Zeng, J., 2023. Impact of urbanization on ecosystem health in Chinese urban agglomerations. Environmental Impact Assessment Review. 98, 106964. https://doi.org/10.1016/J.EIAR.2022.106964. Chen, X., Shang, J., & Ji, X., 2022. Impact of urbanization on agricultural ecological efficiency: evidence from China. Ciência Rural. 53, 20210650. https://doi.org/10.1590/0103-8478CR20210650. Chuma, G. B., Mondo, J. M., Sonwa, D. J., Karume, K., Mushagalusa, G. N., & Schmitz, S., 2022. Socio-economic determinants of land use and land cover change in South-Kivu wetlands, eastern D.R. Congo: Case study of Hogola and Chisheke wetlands. Environmental Development. 43, 100711. https://doi.org/10.1016/J.ENVDEV.2022.100711. Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., … Forouzanfar, M. H., 2017. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet. 389, 1907–1918. https://doi.org/10.1016/S0140-6736(17)30505-6. Daba, M. H., & You, S., 2022. Quantitatively Assessing the Future Land-Use/Land-Cover Changes and Their Driving Factors in the Upper Stream of the Awash River Based on the CA-Markov Model and Their Implications for Water Resources Management. Sustainability. 14, 1538. https://doi.org/10.3390/SU14031538. Davis, G. K., 2007. History of the NOAA satellite program. Journal of Applied Remote Sensing 1, 012504. https://doi.org/10.1117/1.2642347. Davidson, N. C., 2014. How much wetland has the world lost? Long-term and recent trends in global wetland area. Marine and Freshwater Research. 65, 934-941. https://doi.org/10.1071/MF14173. De Grandi, G., Spirolazzi, V., Rauste, Y. A., Curto, L., Rosenqvist, A., & Shimada, M., 2004. The GBFM radar mosaic of the eurasian taiga: Selected topics on geo-location and preliminary thematic products. International Geoscience and Remote Sensing Symposium. 1, 507–510. https://doi.org/10.1109/IGARSS.2004.1369075. Dhodhi, M. K., Saghri, J. A., Ahmad, I., & Ul-Mustafa, R., 1999. D-ISODATA: A distributed algorithm for unsupervised classification of remotely sensed data on network of workstations. Journal of Parallel and Distributed Computing, 59, 280-301. https://www.sciencedirect.com/science/article/pii/S0743731599915733. Donato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., & Kanninen, M., 2011. Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience. 4, 293–297. https://doi.org/10.1038/NGEO1123. Ecosystem, C., 2023. Coastal ecosystems | gbrmpa (accessed 20.1.23) https://www2.gbrmpa.gov.au/learn/coastal-ecosystems. EROS USGS., 2023. USGS EROS Archive - Landsat Archives - Landsat 4-5 Thematic Mapper (TM) Level-1 Data Products | U.S. Geological Survey (accessed 2.2.23) https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-tm-level-1-data. Fang, X., Zhao, W., Zhang, C., Zhang, D., Wei, X., Qiu, W., & Ye, Y., 2020. Methodology for credibility assessment of historical global LUCC datasets. Science China Earth Sciences. 63, 1013–1025. https://doi.org/10.1007/S11430-019-9555-3. Gaglio, M., Aschonitis, V. G., Gissi, E., Castaldelli, G., & Fano, E. A., 2016. Land use change effects on ecosystem services of river deltas and coastal wetlands: case study in Volano–Mesola–Goro in Po river delta (Italy). Wetlands Ecology and Management. 25, 67–86. https://doi.org/10.1007/S11273-016-9503-1. Gibson, R. N., Atkinson, R. J. A., & Gordon, J. D. M., 2011. Oceanography and marine biology: An annual review. In Oceanography and Marine Biology: An Annual Review. 49, 1–297. https://doi.org/10.1016/0025-326x(94)90189-9. Giosan, L., Constantinescu, S., Clift, P. D., Tabrez, A. R., Danish, M., & Inam, A., 2006. Recent morphodynamics of the Indus delta shore and shelf. Continental Shelf Research. 26, 1668–1684. https://doi.org/10.1016/J.CSR.2006.05.009. Gohain, K. J., Goswami, A., Mohammad, P., & Kumar, S., 2023. Modelling relationship between land use land cover changes, land surface temperature and urban heat island in Indore city of central India. Theoretical and Applied Climatology. 151, 1981–2000. https://doi.org/10.1007/S00704-023-04371-X/METRICS. Grybas, H., & Congalton, R. G., 2021. A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests. Remote Sensing. 13, 2631. https://doi.org/10.3390/RS13132631. Hadeel, A., Jabbar, M., 2009. Application of remote sensing and GIS to the study of land use/cover change and urbanization expansion in Basrah province, southern Iraq. Taylor & Francis. 12, 135–141. https://doi.org/10.1007/s11806-009-0244-7. Hine, C. S., Hagy, H. M., Horath, M. M., Yetter, A. P., Smith, R. V., & Stafford, J. D., 2017. Response of aquatic vegetation communities and other wetland cover types to floodplain restoration at Emiquon Preserve. Hydrobiologia. 804, 59–71. https://doi.org/10.1007/S10750-016-2893-5/METRICS. Hodoki, Y., & Murakami, T., 2006. Effects of tidal flat reclamation on sediment quality and hypoxia in Isahaya Bay. Aquatic Conservation: Marine and Freshwater Ecosystems. 16, 555–567. https://doi.org/10.1002/AQC.723. Hua, L., Zhang, X., Nie, Q., Sun, F., 2020. The impacts of the expansion of urban impervious surfaces on urban heat islands in a coastal city in China. Sustainability. 12, 475. Mdpi.Com. https://doi.org/10.3390/su12020475. Jiang, T. ting, Pan, J. fen, Pu, X. M., Wang, B., & Pan, J. J., 2015. Current status of coastal wetlands in China: Degradation, restoration, and future management. Estuarine, Coastal and Shelf Science. 164, 265–275. https://doi.org/10.1016/J.ECSS.2015.07.046. Kantakumar, L. N., & Neelamsetti, P., 2015. Multi-temporal land use classification using hybrid approach. The Egyptian Journal of Remote Sensing and Space Science. 18, 289-295. https://www.sciencedirect.com/science/article/pii/S1110982315000381. Khan, A. A., & Arshad, S., 2014. Wetlands of Pakistan : Distribution, Degradation and Management. Pakistan Geographical Review. 69, 28–45. Kusch, E., Davy, R., & Seddon, A. W. R., 2022. Vegetation-memory effects and their association with vegetation resilience in global drylands. Journal of Ecology, 110, 1561–1574. https://doi.org/10.1111/1365-2745.13891. Lambin, E. F., & Meyfroidt, P., 2011. Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences of the United States of America. 108, 3465–3472. https://doi.org/10.1073/PNAS.1100480108. Leghari, S. M., Jahangir, T. M., Khuhawar, M. Y., & Leghari, A., 2003. Assessment of water quality and biological indicators in the feeding and spinal drains of the left bank outfall drain (LBOD), Sindh, Pakistan. Proceedings of Pakistan Congress of Zoology. 23, 223–231. Let, M., & Pal, S., 2023. Socio-ecological well-being perspectives of wetland loss scenario: A review. Journal of Environmental Management. 326, 116692. https://doi.org/10.1016/J.JENVMAN.2022.116692. Li, B. B., Fang, X. Q., Ye, Y., & Zhang, X. Z., 2010. Accuracy assessment of global historical cropland datasets based on regional reconstructed historical data—A case study in Northeast China. Science China Earth Sciences. 53, 1689–1699. https://doi.org/10.1007/S11430-010-4053-5. Li, J., Zheng, X., Zhang, C., & Chen, Y., 2018. Impact of Land-Use and Land-Cover Change on Meteorology in the Beijing–Tianjin–Hebei Region from 1990 to 2010. Sustainability. 10, 176. https://doi.org/10.3390/SU10010176. Li, M., Ti, P., Zhu, X., Xiong, T., Mei, Y., & Li, Z., 2022. Analysis of Spatial and Temporal Variability of Global Wetlands during the Last 20 Years Using GlobeLand30 Data. Remote Sensing. 14, 5553. https://doi.org/10.3390/RS14215553. Liang, F., Zhang, X., Li, H., Yu, H., Lin, Q., Jiang, M., & Zhang, J., 2022. Land Use Classification Based on Maximum Likelihood Method. Smart Innovation, Systems and Technologies. 253, 133–139. https://doi.org/10.1007/978-981-16-5036-9_15/COVER. Liang, L., Wang, Z., & Li, J., 2019. The effect of urbanization on environmental pollution in rapidly developing urban agglomerations. Journal of Cleaner Production. 237, 117649. https://doi.org/10.1016/J.JCLEPRO.2019.117649. Lillebø, A. I., Willaert, T., Garcia-Alegre, A. G., Queiroga, H., & Cunha e Sá, M. A., 2019. Measuring vulnerability of marine and coastal habitats' potential to deliver ecosystem services: Complex Atlantic region as case study. Frontiers in Marine Science. 6, 199. https://doi.org/10.3389/FMARS.2019.00199/BIBTEX. Liping, C., Yujun, S., & Saeed, S., 2018. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PLoS ONE, 13, 0200493. https://doi.org/10.1371/JOURNAL.PONE.0200493. Lu, D., & Weng, Q., 2006. Use of impervious surface in urban land-use classification. Remote sensing of environment. 102, 146-160. https://www.sciencedirect.com/science/article/pii/S0034425706000721. Lu, D., Mausel, P., Brondízio, E., & Moran, E., 2004. Change detection techniques. International Journal of Remote Sensing. 25, 2365–2401. https://doi.org/10.1080/0143116031000139863. Ma, Z., Melville, D. S., Liu, J., Chen, Y., Yang, H., Ren, W., Zhang, Z., Piersma, T., & Li, B., 2014. Rethinking China's new great wall. Science. 346, 912–914. https://doi.org/10.1126/SCIENCE.1257258/SUPPL_FILE/1257258.MA.SM.PDF. Mallinis, G., Emmanoloudis, D., Giannakopoulos, V., Maris, F., & Koutsias, N., 2011. Mapping and interpreting historical land cover/land use changes in a Natura 2000 site using earth observational data: The case of Nestos delta, Greece. Applied Geography. 31, 312-320. https://doi.org/10.1016/j.apgeog.2010.07.002. Mandal, A., Majumder, A., Dhaliwal, S. S., Toor, A. S., Mani, P. K., Naresh, R. K., Gupta, R. K., & Mitran, T., 2020. Impact of agricultural management practices on soil carbon sequestration and its monitoring through simulation models and remote sensing techniques: A review. Critical Reviews in Environmental Science and Technology. 52, 1-49. https://doi.org/10.1080/10643389.2020.1811590. Maru, H., Haileslassie, A., Zeleke, T., & Teferi, E., 2023. Analysis of the impacts of land use land cover change on streamflow and surface water availability in Awash Basin, Ethiopia. Geomatics, Natural Hazards and Risk. 14, 1-25. https://doi.org/10.1080/19475705.2022.2163193. Moisa, M. B., Bulto, T. W., Werku, B. C., Berkessa, Y. W., Chebo, A. K., Negash, D. A., & Gemeda, D. O., 2023. Analyzing Wetland Dynamics Using Geospatial Techniques: A Case of Abay Choman and Jimma Geneti Watershed, Horo Guduru Wollega Zone, Western Ethiopia. Air, Soil and Water Research. 16, 11786221221150183. https://doi.org/10.1177/11786221221150183/ASSET/IMAGES/LARGE/10.1177_11786221221150183-FIG2.JPEG. Mondal, A., Kundu, S., Chandniha, D. K., Chandniha, S. K., Shukla, R., & Mishra, P. K., 2012. Comparison of support vector machine and maximum likelihood classification technique using satellite imagery.. International Journal of Remote Sensing and GIS. 1, 116-123. Mundia, C., sensing, M. A.-I., 2005. Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS. Taylor & Francis. 26, 2831–2849. https://doi.org/10.1080/01431160500117865. Munir, S. N. U., & Kidwai, A. A., 2017. Signature based geospatial analysis to find out the suspended sediment concentration in indus river delta, pakistan. International Journal of Applied Sciences and Management. 2, 233-245. https://scholar.archive.org/work/7a425avtsfa4zj5tpfmlderwqi/access/wayback/https://www.waljatcollege.edu.om/journal/images/IJASM_020201.pdf. Nayak, A., & Bhushan, B., 2022. Wetland Ecosystems and Their Relevance to the Environment: Importance of Wetlands. Handbook of Research on Monitoring and Evaluating the Ecological Health of Wetlands. 22, 1-16. https://doi.org/10.4018/978-1-7998-9498-8.CH001. Nguyen, C. T., Chidthaisong, A., Diem, P. K., & Huo, L. Z., 2021. A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8. Land. 10, 231. https://doi.org/10.3390/LAND10030231. Nie, X., Jin, X., Wu, J., Li, W., Wang, H., & Yao, Y., 2023. Evaluation of coastal wetland ecosystem services based on modified choice experimental model: A case study of mangrove wetland in Beibu Gulf, Guangxi. Habitat International. 131, 102735. https://doi.org/10.1016/J.HABITATINT.2022.102735. Parsons, T., 2021. The Weight of Cities: Urbanization Effects on Earth's Subsurface. AGU Advances. 2, e2020AV000277. https://doi.org/10.1029/2020AV000277. Pupins, M., Nekrasova, O., Marushchak, O., Tytar, V., Theissinger, K., Čeirāns, A., Skute, A., & Georges, J. Y., 2023. Potential Threat of an Invasive Fish Species for Two Native Newts Inhabiting Wetlands of Europe Vulnerable to Climate Change. Diversity. 15, 201. https://doi.org/10.3390/D15020201/S1. Rahaman, S., Kumar, P., Chen, R., Meadows, M. E., & Singh, R. B., 2020. Remote Sensing Assessment of the Impact of Land Use and Land Cover Change on the Environment of Barddhaman District, West Bengal, India. Frontiers in Environmental Science. 8, 127. https://doi.org/10.3389/FENVS.2020.00127/BIBTEX. Raja Shekar, P., & Mathew, A., 2023. Detection of land use/land cover changes in a watershed: A case study of the Murredu watershed in Telangana state, India. Watershed Ecology and the Environment. 5, 46–55. https://doi.org/10.1016/J.WSEE.2022.12.003. Ramsar., 2023. Homepage | Convention on Wetlands (accessed 4.1.23) https://www.ramsar.org/ Reis, S., 2008. Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey. Sensors, 8, 6188–6202. https://doi.org/10.3390/S8106188. Rogers, K., Saintilan, N., & Copeland, C., 2013. Managed Retreat of Saline Coastal Wetlands: Challenges and Opportunities Identified from the Hunter River Estuary, Australia. Estuaries and Coasts. 37, 67–78. https://doi.org/10.1007/S12237-013-9664-6. Saha, U. D., Mitra, S., Mondal, M., & Biswas, N., 2022. Assessment of the impact of anthropogenic stress on wetlands of the upper Ganga Delta, India: a multi criteria decision making approach. Geocarto International. 37, 12580–12601. https://doi.org/10.1080/10106049.2022.2068678. Scott, A. J., & Symons, M. J., 1971. Clustering methods based on likelihood ratio criteria. Biometrics. 71, 387-397. https://www.jstor.org/stable/2529003. Shi, L., Liu, F., Zhang, Z., Zhao, X., Liu, B., Xu, J., Wen, Q., Yi, L., & Hu, S., 2015. Spatial differences of coastal urban expansion in China from 1970s to 2013. Chinese Geographical Science. 25, 389–403. https://doi.org/10.1007/S11769-015-0765-Y. Singh Boori, M., Choudhary, K., & Kupriyanov, A., 2020. 2019 Land Cover Map of Southeast Asia at 30 m Spatial Resolution with Changes Since 2010. Optical Memory and Neural Networks. 29, 257–262. https://doi.org/10.3103/S1060992X20030091. Siyal, D., 2019. Impact of Climate Change in the Indus River Delta and Coastal Region of Pakistan. Sindh University Research Journal. 19, 53. http://www.gcisc.org.pk/UnivCon/Pakistan-MUET.pdf. Solangi, G. S., Siyal, A. A., Babar, M. M., & Siyal, P., 2019. Groundwater quality evaluation using the water quality index (WQI), the synthetic pollution index (SPI), and geospatial tools: a case study of Sujawal district, Pakistan. Human and Ecological Risk Assessment: An International Journal. 26, 1529–1549. https://doi.org/10.1080/10807039.2019.1588099. Solangi, G. S., Siyal, A. A., Siyal, Z. U. A., Siyal, P., Panhwar, S., Keerio, H. A., & Bhatti, N. B., 2022. Social and ecological climate change vulnerability assessment in the Indus delta, Pakistan. Water Practice and Technology. 17, 1666–1678. https://doi.org/10.2166/WPT.2022.087. Sousa, C. A. M., Cunha, M. E., & Ribeiro, L., 2020. Tracking 130 years of coastal wetland reclamation in Ria Formosa, Portugal: Opportunities for conservation and aquaculture. Land Use Policy. 94, 104544. https://doi.org/10.1016/J.LANDUSEPOL.2020.104544. Stehman, S. V., & Foody, G. M., 2019. Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment. 231, 111199. https://doi.org/10.1016/J.RSE.2019.05.018. Stein, E. D., Doughty, C. L., Lowe, J., Cooper, M., Sloane, E. B., & Bram, D. L., 2020. Establishing Targets for Regional Coastal Wetland Restoration Planning Using Historical Ecology and Future Scenario Analysis: The Past, Present, Future Approach. Estuaries and Coasts. 43, 207–222. https://doi.org/10.1007/S12237-019-00681-4. Subedi, Y. R., Kristiansen, P., Cacho, O., & Ojha, R. B., 2021. Agricultural Land Abandonment in the Hill Agro-ecological Region of Nepal: Analysis of Extent, Drivers and Impact of Change. Environmental Management. 67, 1100–1118. https://doi.org/10.1007/S00267-021-01461-2/METRICS. Sun, J., Huang, J., Wang, Q., -, A., Zhao, S., Wang, G., Liu, Y., Xu, J., Yao, L., Guo, P., & Yuan, Z., 2018. The land use pattern and its changes in the upper reaches of the Yangtze River from 1980 to 2015. Earth and Environmental Science. 676, 012097. https://doi.org/10.1088/1755-1315/676/1/012097. Syvitski, J. P. M., Kettner, A. J., Overeem, I., Giosan, L., Brakenridge, G. R., Hannon, M., & Bilham, R., 2013. Anthropocene metamorphosis of the Indus Delta and lower floodplain. Anthropocene. 3, 24–35. https://doi.org/10.1016/J.ANCENE.2014.02.003. Tian, B., Wu, W., Yang, Z., & Zhou, Y., 2016. Drivers, trends, and potential impacts of long-term coastal reclamation in China from 1985 to 2010. Estuarine, Coastal and Shelf Science. 170, 83–90. https://doi.org/10.1016/J.ECSS.2016.01.006. Tudi, M., Ruan, H. D., Wang, L., Lyu, J., Sadler, R., Connell, D., Chu, C., & Phung, D. T., 2021. Agriculture Development, Pesticide Application and Its Impact on the Environment. International Journal of Environmental Research and Public Health. 18 1112. https://doi.org/10.3390/IJERPH18031112. Ul Din, S., Wai Leung Mak, H., Sarathi Roy, P., & Alexakis, D. D., 2021. Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework. Remote Sensing. 13, 3337. https://doi.org/10.3390/RS13163337. US., 2023. Summary of the Clean Water Act | US EPA (accessed 22.3.23) https://www.epa.gov/laws-regulations/summary-clean-water-act Visual Information Solutions, I., 2009. Atmospheric Correction Module Atmospheric Correction Module: QUAC and FLAASH User's Guide 20AC47DOC (accessed 15.3.23) www.apache.org/ Wang, S., Ma, Q., Ding, H., 2018. Application of Landsat Data for Urban Growth Monitoring in Jeddah. Earth Systems and Environment. 1, 1-11. https://www.sciencedirect.com/science/article/pii/S0921344916301215. Wang, W., Liu, H., Li, Y., & Su, J., 2014. Development and management of land reclamation in China. Ocean & Coastal Management. 102, 415–425. https://doi.org/10.1016/J.OCECOAMAN.2014.03.009. Wu, H., Wang, R., Yan, P., Wu, S., Chen, Z., Zhao, Y., Cheng, C., Hu, Z., Zhuang, L., Guo, Z., Xie, H., & Zhang, J., 2023. Constructed wetlands for pollution control. Nature Reviews Earth & Environment. 23, 1–17. https://doi.org/10.1038/s43017-023-00395-z. Wu, W., Yang, Z., Tian, B., Huang, Y., Zhou, Y., & Zhang, T., 2018. Impacts of coastal reclamation on wetlands: Loss, resilience, and sustainable management. Estuarine, Coastal and Shelf Science. 210, 153–161. https://doi.org/10.1016/J.ECSS.2018.06.013. Yan, Y., Kuang, W., Zhang, C., 2012. Impacts of impervious surface expansion on soil organic carbon–a spatially explicit study. Scientific reports, 5, 17905. https://www.nature.com/articles/srep17905. Yumin, T., Bingxin, B., & Mohammad, M. S., 2016. Time series remote sensing based dynamic monitoring of land use and land cover change. Earth Observation and Remote Sensing Applications. 16, 202–206. https://doi.org/10.1109/EORSA.2016.7552797. Zhan, C., Xie, M., Lu, H., Liu, B., Wu, Z., Wang, T., Zhuang, B., Li, M., & Li, S., 2023. Impacts of urbanization on air quality and the related health risks in a city with complex terrain. Atmospheric Chemistry and Physics. 23, 771–788. https://doi.org/10.5194/ACP-23-771-2023. Zhao, J., Dong, Y., Zhang, M., & Huang, L., 2020. Comparison of identifying land cover tempo-spatial changes using GlobCover and MCD12Q1 global land cover products. Arabian Journal of Geosciences. 13, 1–12. https://doi.org/10.1007/S12517-020-05780-2. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3301912","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":229992631,"identity":"9e69db35-de64-4959-87b0-8f6c35e6a1ca","order_by":0,"name":"Yaseen Laghari","email":"","orcid":"","institution":"Nanjing Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yaseen","middleName":"","lastName":"Laghari","suffix":""},{"id":229992632,"identity":"ea434605-8d0f-4fc9-a11e-919a178ff2e2","order_by":1,"name":"Shibiao Bai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCcY2hgQGIGJmPsAgARZKIFoLWwKxWhjYoMp4DKBCBLTIz25ue/CgxiaPv53n2wPLHYcZ+NlzDBh+7sCtxeDOwXaDhGNpxRKHebcbSJ45zCDZ88aAsfcMHi0SiW0SCWyHExsO826TkGw7zGBwI8eAGehB3A6bAdLy73Di/MM8z8Ba7AlpYbgB1JLYdjhxw2EeNogtEgS0GNxIbDdI7EsrNjzMZgbUks4jceZZwcFevA5Lf/bwxzebPLnzh59JS7ZZy/G3J2988BOfw5ABMzAqeUCMA0RqYGBg/EC00lEwCkbBKBhJAADYhVCtjmdvZQAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Normal University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shibiao","middleName":"","lastName":"Bai","suffix":""},{"id":229992633,"identity":"fc891708-bd48-496d-8e02-31a33aab1202","order_by":2,"name":"Shah Jahan Leghari","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shah","middleName":"Jahan","lastName":"Leghari","suffix":""},{"id":229992634,"identity":"ede12db2-7cec-4873-804a-39f6fcc5075b","order_by":3,"name":"Wenjing Wei","email":"","orcid":"","institution":"Nanjing Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Wei","suffix":""},{"id":229992635,"identity":"a1dcc2f0-af8e-4d6e-8401-c5c331b7b797","order_by":4,"name":"Abdul Hafeez Laghari","email":"","orcid":"","institution":"Sindh Agriculture University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"Hafeez","lastName":"Laghari","suffix":""}],"badges":[],"createdAt":"2023-08-28 05:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3301912/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3301912/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42650463,"identity":"758fc21d-18d6-4568-be9e-24f9dfeccde5","added_by":"auto","created_at":"2023-09-05 15:05:48","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":333226,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the research area—Indus deltaic region on coast of Sindh province of Pakistan\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3301912/v1/8e80230555a4d82a3d6bba03.jpeg"},{"id":42651434,"identity":"eca54919-32c4-466a-9443-07938ad7860d","added_by":"auto","created_at":"2023-09-05 15:13:48","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":645067,"visible":true,"origin":"","legend":"\u003cp\u003eCoastal wetlands in the Indus River Delta\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3301912/v1/555edc5eeeede6797f9c975a.jpeg"},{"id":42651432,"identity":"14b861c4-16a2-403b-b720-cd4e8136493e","added_by":"auto","created_at":"2023-09-05 15:13:48","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":143402,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial areas allocated for coastal wetlands, reclamation and other land use types of Indus River Delta during 1972, 1986, 1992, 2002, 2012 and 2022\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3301912/v1/4bcfdf69e894f5a25bc6b683.jpeg"},{"id":42650467,"identity":"8c4a6d55-50ff-4b0a-a647-d4cb0171deec","added_by":"auto","created_at":"2023-09-05 15:05:48","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":402000,"visible":true,"origin":"","legend":"\u003cp\u003eReclamation area in the Indus River Delta\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3301912/v1/ef5a0c57652899ee7ebac1ea.jpeg"},{"id":42651433,"identity":"c9b77548-aad4-4eb0-8c3c-ac8f61584367","added_by":"auto","created_at":"2023-09-05 15:13:48","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":460054,"visible":true,"origin":"","legend":"\u003cp\u003eTypical spatial distribution of coastal wetlands changes in the Indus River delta\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3301912/v1/82ea01f71ac93195d7650da4.jpeg"},{"id":42650466,"identity":"b0749b74-9971-4557-bd10-381f4b428e40","added_by":"auto","created_at":"2023-09-05 15:05:48","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":118812,"visible":true,"origin":"","legend":"\u003cp\u003eAreal change between coastal wetlands and other land use types in the Indus River Delta\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3301912/v1/4802308a91bb17dda100bf77.jpeg"},{"id":42650461,"identity":"d866a713-3714-4ff9-9526-964f96a599a8","added_by":"auto","created_at":"2023-09-05 15:05:48","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":113407,"visible":true,"origin":"","legend":"\u003cp\u003eArea-weight centroids movement for coastal wetlands and reclamation activities in the Indus River Delta from the 1972 to 2022\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3301912/v1/2a77978f35a427e147b4d3bb.jpeg"},{"id":42650468,"identity":"f59cf87d-8992-475b-a01f-66ff3bda1127","added_by":"auto","created_at":"2023-09-05 15:05:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":142042,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation network graph showing relationship strength between different land use types in the Indus River Delta from the 1972 to 2022.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-3301912/v1/dd9aa7bc46f74147df15f11e.png"},{"id":45398917,"identity":"954f9bb0-4bd9-4cf4-92d8-09a71f3332c1","added_by":"auto","created_at":"2023-10-29 10:22:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1793466,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3301912/v1/0276ff77-355e-4e63-920f-92da99788d63.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Coastal wetlands of Indus River Delta are under risk due to reclamation: A spatiotemporal analysis during the past 50 years from 1972 to 2022","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoastal ecosystems are the distinct habitats established by plants and other species that may exist at the ocean-land interface, where they must contend with saltwater and fluctuating tides (Donato et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The coral reefs, islands, lagoon floors, sea grass, woodlands, estuaries, forested floodplains, sedge lands, shrub lands, mangroves forests, rainforests, and coastal wetlands are the most important coastal habitats (Ecosystem, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Many human coastal uses, such as offshore development, fishing, and nutrient inputs, can have a negative impact on the status of coastal systems (Lilleb\u0026oslash; et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Extreme natural calamities such as hurricanes, coastal storms, tsunamis, landslides, and the longer-term concerns of coastal erosion and sea level rise are all threats to coastal communities (Davis, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoastal wetlands provide a wide range of ecosystem services as the habitat between terrestrial and marine ecosystems (Jiang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). They are essential to preserving the biological diversification of the coast. Wetlands are one of the most vulnerable ecosystems. In many regions throughout the world in the last century, it is believed that 50\u0026ndash;80% of the natural coastal wetlands have been lost (Gibson et al., 2007). Wetland losses have been caused by sea and land threats, including reclamation, sediments, starvation, and sea level rise (Ma et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Land reclamation is a major subject among other anthropogenic effects on coastal wetlands that contributes to their loss and degradation (Tian et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Coastal areas make up about 4% of the planet's total land area, which sustains more than one-third of the world's population and are heavily reclaimed to accommodate an agricultural activity, building of ports, industries, residences, and construction of dams etc. (Shi et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The ambiguity of scientific management and conservation of coastal wetlands is further exacerbated by these direct threats from human activities in coastal areas (Wang et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Coastal reclamation may result in land degradation, biodiversity loss, offshore eutrophication, soil heavy metal, and organic pollution concentration (Balmford et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). To prevent the deterioration of coastal wetlands, it is therefore necessary to give basic data support by historically monitoring the relative dynamics of reclamation and natural changes in coastal wetlands. In addition, the reclamation rate of coastal wetlands should be managed so that it does not surpass their accretion rate to ensure the recovery and sustainable use of coastal wetlands resources (Hodoki and Murakami, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, monitoring the coastal wetlands is important.\u003c/p\u003e \u003cp\u003eRemote sensing has shown to be the most effective method for monitoring the current status of coastal wetlands and their temporal dynamics due to its ability to obtain a synoptic view of targets and historical images (Wu et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The previous wetlands studies have used remote sensing to examine the function of ecosystem services, ecosystem structure, landscape pattern, fragmentation, and transition of coastal wetlands due to climate change, reclamation, and human activity in different parts of the world. Stein et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) studied wetlands change and classified them into archetypes that represent diverse settings and processes and estimated future distributions in the USA; Sousa et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used different datasets to assess land use changes caused by coastal wetlands reclamation during decadal intervals in Portugal; Chen et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) determined coastal wetlands and reclamation historically in China; Gaglio et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) analyzed the changes in a protected wetlands and related ecosystem services in Italy; and Rogers et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) estimated the amount of sediment and carbon buried across the estuary under various scenarios, taking into account the accretion and vertical elevation responses of mangroves and saltmarsh to rising sea level, and projected the distribution of saline coastal wetlands in Australia. There is very less research work on wetlands in Pakistan.\u003c/p\u003e \u003cp\u003eWetlands cover about 9.7% of Pakistan's total land area (Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Wetlands types are classified according to the Indus River's course, which ranges from glaciers and high alpine lakes through riverine and freshwater lakes and lastly to the coastal wetlands of the Indus Delta (Khan and Arshad, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Coastal wetlands are essential for mangroves, estuaries, beaches, and coral reefs. In watershed areas, land use changes result in deforestation for the supply of fuel, wood and timber, and the conversion of land for agricultural and settlement, which increases run-off and soil erosion, and sediment loads in rivers and lakes (Chaudhry, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Siltation, extensive deforestation, agricultural activity, and construction of human settlements have resulted in major losses of wetlands habitat (Leghari et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, there is a large research gap related to the spatiotemporal dynamics of coastal wetlands and reclamation in the Indus River deltaic region. Therefore, monitoring coastal wetlands changes is crucial for creating proactive conservation plans that emphasize protecting ecosystem structure and functions and the ecological security of the surrounding areas. We hypothesized that the Indus River Delta's coastal wetlands had been adversely affected due to rapid population growth and development infrastructure during the past 5 decades. This study aimed to comprehensive identify changes in coastal wetlands and land reclamation during the last 50 years in the Indus River Delta and identify dynamic drivers.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Site\u003c/h2\u003e \u003cp\u003eThe study area was selected according to research targets, only coastal wetlands which are situated in the deltaic region. The study area is comprised of two talukas; Keti Bander in Thatta district and Kharo Chan in Sijawal district (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in Indus deltaic region. The Indus delta is situated in the southeastern part of country in the coastal area of Sindh Province of Pakistan with coordinates 24.15\u0026deg;N and 67.63\u0026deg;E. The delta forms where the Indus River enters the Arabian Sea (Shabir et al., 2019). The Indus River delta is the sixth largest delta in the world and was listed under the Ramsar treaty on marshy wetlands in 1971, which contains seventeen important creeks, three major lakes, six brackish lakes, and wide mud flats, makes up 563 km of Sindh's whole coastline region (Munir et al., 2017). It belongs to the category of arid tropical zone, with temperatures ranging from 24\u0026deg;C to 37\u0026deg;C, and receives an average annual rainfall of about 220 mm, mostly from July to September in the monsoon season (Solangi et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The 263046 ha of mangrove forests in the Indus delta ranks fifth-largest mangrove forest in the world (Khan and Arshad, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The delta receives water from the Indus River, which flows about 180\u0026nbsp;billion cubic meters each year, 400\u0026nbsp;million tons of sediment, and large amounts of contaminants from diverse sources (Giosan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The delta's location has changed southward over time (Syvitski et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It is currently located in the southern Pakistani districts of Thatta and Sijawal, covers 41,440 km\u003csup\u003e2\u003c/sup\u003e, and is 210 kilometers from the Arabian Sea (Siyal, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Indus delta's coastal portions are home to almost 20\u0026nbsp;million people, and about 100,000 people rely on the Indus deltaic fishing sector for their livelihood. The remaining major portion of the population uses fertile deltaic land for agricultural activity (Chandio et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Collection and Processing\u003c/h2\u003e \u003cp\u003eIn this study, remotely sensed Landsat images from the sensors Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM), and Operational Land Imager (OLI) of the different time intervals, i.e., 1972, 1986, 1992, 2002, 2012 and 2022 were downloaded from the USGS Centre for Earth Resources Observation and Science (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://glovis.usgs.gov/\u003c/span\u003e\u003cspan address=\"http://glovis.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Comprehensive explanations of several Landsat image types, acquisition times, and corresponding resolutions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Images from the following years, 1972, 1986, 1992, 2002, 2012, and 2022 were obtained (as data for 1982 was not accessible, 1986 was selected instead of this year). These images accurately depict the spatial land-use patterns during the corresponding time. The acquisition time was mid-spring and fall when crops were normally at their best. This is useful and helps differentiate wetlands and reclamation zones from other forms of land cover. The Landsat sensors normally repeat their cycle every 16 days on earth, which is long enough to examine long-term land-use changes within a given geographic area. To avoid being affected by cloud situations, all images captured from sensors onboard Landsat 3, 5, 7, and 8 have less than 10% cloud coverage for changes in Landsat roughly correspond to actual environmental conditions and shifts in land-use patterns (EROS USGS, 2023). The Fast Line-of-sight Atmospheric Analysis of Hypercube (FLAASH) approach, established by Exelis Visual Information Solutions Inc., Boulder, CO, USA, was applied to perform radiometric calibrations and atmospheric correction, which enhanced data quality (Visual Information Solutions, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristic of the employed Landsat images, including the year, path/row, acquisition data, sensor, spatial resolution and temporal resolution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath/Row\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcquisition date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpatial resolution (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemporal resolution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163/43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 October, 1972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14-orbits per day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152/43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e05 November, 1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14-orbits per day\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152/43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 April, 1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16-days repeat cycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152/43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 February, 2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16-days repeat cycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152/43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25March, 2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eETM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16-days repeat cycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152/43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 March, 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16-days repeat cycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote\u003c/b\u003e: MSS, TM, ETM and OLI represents Multi-spectral Scanner, Thematic Mapper, Enhanced Thematic Mapper and Operational Land Imager, respectively\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLandsat datasets were widely employed due to their long-term and thorough digital record, medium spatial resolution, and usage in wetlands dynamics due to reclamation and LULC change research (Wang et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and relative uniformity of spectral and radiometric resolutions (Almazroui et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By projecting all accessible Landsat datasets of the research region onto a single mesh created and pre-set inside the ArcGIS platform. The geographical homogeneity was achieved so all datasets had equal spatial resolutions for an objective comparison (Mundia et al., 2007). Overall, using Landsat datasets to detect changes in reclamation is quite advantageous and feasible. Furthermore, the settlement environment is typically characterized by extremely varied surface covers as well as important inter- and intra-pixel fluctuations. The capabilities of change detection may be fundamentally described and tracked from satellite images taken in reclamation zones that have a fine enough spatial resolution (Lu et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe land cover classification system for this study was established by referring to the land cover classification system and considering the land cover types of the study area, which were provided the following major land cover types: natural vegetation, barren land, wetlands, settlement, and cultivated land are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. To accurately determine the land reclamation area, the boundary of the 1972 land use was used as the baseline data, and the areal change of the Indus delta in each interval was compared with the basic region which was divided into reclamation areas and wetlands. The Origin Pro 2020b, Sigma Plot 10.0 and Graph Pad Prism 8.0.1, and BioRender software were used to graphically visualize data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe classified scheme employed in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLand use / Land cover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReclamation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCultivated Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThis class consists area of arable land with crops and pastures.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn this class build up area includes where population is settled.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOther classes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIt includes all types of surface wetlands found in drains, reservoirs/ponds, lakes, creeks, salt marsh, freshwater marsh, mud flat, beach, aquaculture pond and salt field.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNatural vegetation cover consisting of herbs, shrubs, grassland, mangroves trees, and other plants.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarren Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIt includes the remaining land, which has no vegetation and a rough terrain with very little moisture.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Algorithms for Image and Land Use Classification\u003c/h2\u003e \u003cp\u003eHistorically, Level 1 of the Anderson classification system was typically used for analyzing Landsat-based data (Mallinis et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) since it effectively reduces the potential for misclassification mistakes and makes the classification of distinct land-use groups more trustworthy (Kantakumar et al., 2015). Remote sensing (RS) based image classification algorithms are the most practical and affordable approach for producing and evaluating LULC information because attributes can be collected without touching the ground surface (Hadeel et al., 2009). There is no single sensor to be used. In particular, Landsat 8 OLI RS-based datasets have been utilized in Cambodia, Laos, Myanmar, Thailand, Vietnam, and other Southeast Asian countries to retrieve a total of 8 land cover classes with excellent precision (Boori et al., 2020). Land-cover maps for South and Southeast Asia were also produced using regional SPOT-VEGETATION satellite data for three years (Dhodhi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The classification technique can often be either supervised or unsupervised, or both. For instance, Landsat images can be processed beforehand using ISODATA unsupervised classification (Bakr et al., 2010), Iterative hybrid-classification techniques, and maximum likelihood supervised classification (Dhodhi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLand-use features in this study were divided into five major categories, including settlement, cultivated land, natural vegetation, wetlands, and barren area. In principle, additional classification into more specific analogues, such as various agricultural features, may be done, although it is not necessary in this case. First, there is not much agriculture in most Pakistani urban areas; therefore, 5 land-use categories are sufficient to determine LULC changes in our research area in the Indus deltaic region of Sindh Province. Second, settlement or impervious surfaces are typically associated with man-made structures like concrete, stone, and rooftops, whereas arid or barren land is typically associated with areas with thin soil, sand, or rocks, such as deserts and beaches. This distinction is particularly crucial when quantifying land-use changes in developing deltas like the Indus River delta. Recent LULC change studies have frequently utilized the words \"settlement\" and \"barren land\" (Hua et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yan et al., 2015; Lu et al., 2006). To categorize land usage, one may either utilize the peaks created by the histograms of different acquired Landsat images, which are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e that accurately categorize pixels as either wetlands or land areas (Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Alternately, we may infer this information from the pixel's spectral curve's spectrum reflectance, which shows that built-up areas have higher reflectance values in each band than pixels from agricultural land, flora, marshes, and barren ground, which have comparably lower reflectance. An index called the \"Enhanced Normalized Difference Impervious Surfaces index\" has been developed to distinguish the various land-use types (Chen et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApart from comparing reflectance data, the supervised statistical learning technique, the Maximum Likelihood Classification (MLC) (Liang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), was used in this work to accurately classify each land-use category (Asad and Bais, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The capabilities of MLC and related statistical studies have recently been shown to be useful and practical in a range of contexts and applications (Mondal et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For each land-use category, a sufficient number of training samples were originally gathered by visual interpretation. An acceptable spectral signature is one that reduces misunderstanding between mapped land covers (Ul Din et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Following collection, the Landsat datasets were classified using MLC with the appropriate statistical kernel based on mathematical properties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Maximum Likelihood Classification (MLC)\u003c/h2\u003e \u003cp\u003eThe MLC is a procedure for determining the maximum known class distribution for a given statistic (Mondal et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This method has been most widely and frequently employed in remote sensing, where a pixel is assigned to the class with the highest likelihood (Scott et al., 1971). If there are m pre-set classes, the class posterior probability is written as\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$P\\left(k\\right|x)=\\frac{P\\left(k\\right)P\\left(k\\right|x)}{\\sum _{i=1}^{m}P\\left(i\\right)P\\left(k\\right|i) }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eP(k)\u003c/em\u003e represents the prior probability of class \u003cem\u003ek\u003c/em\u003e, and \u003cem\u003eP(x | k)\u003c/em\u003e is the conditional probability of witnessing \u003cem\u003ex\u003c/em\u003e from class \u003cem\u003ek\u003c/em\u003e. (probability density function). \u003cem\u003eP(x | k)\u003c/em\u003e is the likelihood function for normal distributions.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${L}_{k}\\left(x\\right)=\\frac{1}{{\\left(2\\pi \\right)}^{\\frac{n}{2}}|{\\sum }_{k}{|}^{\\frac{1}{2}} }\\text{e}\\text{x}\\text{p}\\left({-\\frac{1}{2}(x-\\mu }_{k}{)}^{T}{\\sum }_{k}^{-1}\\left(x-{\\pi }_{k}\\right)\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ex = (x1, x2. .. xn)\u003c/em\u003e\u003csup\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sup\u003e represents the vector of a pixel with n bands, \u003cem\u003eL\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(x)\u003c/em\u003e represents the likelihood membership function of \u003cem\u003ex\u003c/em\u003e belonging to class \u003cem\u003ek\u003c/em\u003e, and \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e= (\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e1 \u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e2. .. \u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ek\u003c/em\u003e\u003c/sub\u003e\u003cem\u003en)\u003c/em\u003e\u003csup\u003e\u003cem\u003eT\u003c/em\u003e\u003c/sup\u003e represents the mean of the \u003cem\u003ekth\u003c/em\u003e class.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. LULC change and accuracy assessment\u003c/h2\u003e \u003cp\u003eMany polygons were selected in ArcGIS using the processed remotely sensed Landsat datasets during each of the six acquisition times. Then, using a combination of supervised classification and the MLC technique, the land cover classification maps of 1972, 1986, 1992, 2002, 2012, and 2022 were obtained. To undertake an objective accuracy assessment in LULC classification (Fang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), The Ground Truth Points (GTP) were chosen inside the region of interest from all Landsat images, which were stratified by using random selection (Li et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Numerous of GTP produced by each type of land usage is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Google Earth was employed for conducting ground verification within the deltaic region to ensure the accuracy of all these GTP.\u003c/p\u003e \u003cp\u003eThe GTP was chosen within image samples by using a confusion matrix created by the study of the land cover transfer matrix of all classified images in ArcMap 10.5. Numerous metrics, such as (1) Producer Accuracy (PA), (2) User Accuracy (UA), (3) Overall Accuracy (OA), and (4) KAPPA coefficient (KC), have been used throughout this process to evaluate overall accuracy in land-use classification. The different metrics were used to deeply examine the accuracy between classified images and ground data of all Landsat images (Ul Din et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In prior investigations, the following measurements were universally accepted and relevant to Benchoufi et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Stehman and Foody (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe many Ground Truth Points (GTP) were taken from each Landsat images to represent each land use category. The number values inside the bracket indicate the year that the datasets were attained.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Use Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImage 1972\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImage 1986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImage 1992\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImage 2002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImage 2012\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eImage 2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivated land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarren land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, in order to further illuminate PA, UA, and OA concepts, the representations and computations of these metrics are shown using the three mathematical formulations, namely Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The probability that a reference pixel could be correctly classified, as determined by the referenced dataset, can be expressed as the total number of correct pixels of a particular land-use type divided by the total number of pixels within that land-use type. This is because a producer is interested in how well the desired land-use characteristics are classified (Grybas and Congalton, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Following that, UA is a trustworthy metric that can be expressed as the proportion of all accurately categorized pixels for a given land use type to all pixels within that land use type. (Rahaman et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). OA is easily calculated by dividing the total number of pixels correctly identified by the total number of pixels.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\left(\\text{a}\\right){\\text{P}\\text{r}\\text{o}\\text{d}\\text{u}\\text{c}\\text{e}\\text{r}}^{{\\prime }}\\text{s} \\text{A}\\text{c}\\text{c}\\text{u}\\text{r}\\text{a}\\text{c}\\text{y} \\left(\\text{P}\\text{A}\\right) \\text{o}\\text{f} \\text{t}\\text{h}\\text{e} \\text{k}\\text{t}\\text{h} \\text{l}\\text{a}\\text{n}\\text{d}\\text{u}\\text{s}\\text{e} \\text{t}\\text{y}\\text{p}\\text{e} \\frac{{x}_{kk}}{{\\sum }_{i=1}^{5} {x}_{ik} }$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\left(\\text{b}\\right){ \\text{U}\\text{s}\\text{e}\\text{r}}^{{\\prime }}\\text{s} \\text{A}\\text{c}\\text{c}\\text{u}\\text{r}\\text{a}\\text{c}\\text{y} \\left(\\text{U}\\text{A}\\right) \\text{o}\\text{f} \\text{t}\\text{h}\\text{e} \\text{k}\\text{t}\\text{h} \\text{l}\\text{a}\\text{n}\\text{d}\\text{u}\\text{s}\\text{e} \\text{t}\\text{y}\\text{p}\\text{e} \\frac{{x}_{kk}}{{\\sum }_{j=1}^{5} {x}_{jk} }$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\left(\\text{c}\\right) \\text{O}\\text{v}\\text{e}\\text{r}\\text{a}\\text{l}\\text{l} \\text{A}\\text{c}\\text{c}\\text{u}\\text{r}\\text{a}\\text{c}\\text{y} \\left(\\text{O}\\text{A}\\right) \\text{o}\\text{f} \\text{c}\\text{l}\\text{a}\\text{s}\\text{s}\\text{i}\\text{f}\\text{i}\\text{c}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n} \\frac{{x}_{11}+{x}_{22}+{x}_{33}+{x}_{44}+{x}_{55}}{{\\sum }_{j=1}^{5} {\\sum }_{i=1}^{5} {x}_{ij} }$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFor instance, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{11}/{\\sum }_{i=1}^{5} {x}_{i1 }\\)\u003c/span\u003e\u003c/span\u003eand\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{11}/{\\sum }_{j=1}^{5} {x}_{j1 }\\)\u003c/span\u003e\u003c/span\u003e are the PA and UA, respectively, for categorizing cultivated land in the Pakistani research region.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\text{K}\\text{C}=\\frac{{\\sum }_{K=1}^{5}xkk-{\\sum }_{K=1}^{5}\\left({x}_{k+} {\u0026middot;x}_{+k}\\right)}{{N}^{2}- {\\sum }_{K=1}^{5}\\left({x}_{k+} {\u0026middot;x}_{+k}\\right)} = \\frac{{\\text{P}}_{\\text{c}\\text{h}\\text{a}\\text{n}\\text{c}\\text{e}}-{\\text{P}}_{\\text{c}\\text{h}\\text{a}\\text{n}\\text{c}\\text{e}}}{1- {\\text{P}}_{\\text{c}\\text{h}\\text{a}\\text{n}\\text{c}\\text{e}}} \\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eKC is a discrete multivariate mathematical approach to assessing accuracy (Bakeman, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Eq.\u0026nbsp;(4)'s first equal sign is used to express KC, where \u003cem\u003eN\u003c/em\u003e denotes the total number of observations and \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ek+\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003e+\u0026thinsp;k\u003c/em\u003e\u003c/sub\u003e denote the total number of data in the \u003cem\u003ekth\u003c/em\u003e row and \u003cem\u003ekth\u003c/em\u003e column, respectively (Carriquiry et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). If KC is understood probabilistically, it may be written down as the second part of Eq.\u0026nbsp;(4), where \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eagree\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003echance\u003c/em\u003e\u003c/sub\u003e are the proportions (or probabilities) of properly classified pixels, and the classification agreement is of anticipated value. Because 64\u0026ndash;100% of the data is valid, a KC value greater than 0.8 might be read as \"nearly perfect\" classification accuracy, whilst a value between 0.61\u0026ndash;0.79 denotes \"considerable\" accuracy. Nonetheless, as only 4% of the data are regarded as credible, the categorization strategy for KC values less than 0.2 can be called \"poor\"(Bakeman, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Land Cover Transfer Matrix\u003c/h2\u003e \u003cp\u003eThe structure and direction of the dynamic change in land cover are often described using the land cover transfer matrix (Zhao et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The LULC change matrix reflects dynamic change, which is information on the mutual alteration of a certain region at the beginning and end of a specified time (Liping et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It provides details than only the region's historical static area of a specific type of land (Li et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) but also provides information on the types of land that are changed to other LULC types or that are transformed from one LULC type to another (Yumin et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The land cover transfer matrix was generated using the ArcGIS software to assess conversions of coastal wetlands in the Indus Delta:\u003c/p\u003e \u003cp\u003eS\u003csub\u003eij\u003c/sub\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left[\\begin{array}{ccc}\\begin{array}{cc}{S}_{11}\u0026amp; {S}_{12}\\\\ {S}_{21}\u0026amp; {S}_{22}\\end{array}\u0026amp; \\begin{array}{c}\\cdots \\\\ \\cdots \\end{array}\u0026amp; \\begin{array}{c}{S}_{1n}\\\\ {S}_{2n}\\end{array}\\\\ ⋮ ⋮\u0026amp; \\ddots \u0026amp; ⋮\\\\ \\begin{array}{cc}{S}_{n1}\u0026amp; {S}_{2n}\\end{array}\u0026amp; \\cdots \u0026amp; {S}_{nn}\\end{array}\\right]\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is the area of land cover type \u003cem\u003ei\u003c/em\u003e transferred to land cover type \u003cem\u003ej\u003c/em\u003e; n denotes the number of land cover types; \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e (1, 2, ..., \u003cem\u003en\u003c/em\u003e) denote land cover types before and after a specific transfer operation; Each entry on the matrix's main diagonal indicates the area of each land cover category that remains constant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5. Area-Weight Centroid\u003c/h2\u003e \u003cp\u003eThe centroid of an area is the location of a specific land cover type that is determined by the coordinates of the geometric center of a polygon or multiple polygons (Parsons, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It has been effectively applied in dynamic landscape analysis (Grandi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), including the transition to desertification, the evolution of wetlands type, the pattern of the landscape's thermal environment, and changes to the coastline's topography (Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The centroids of wetlands and reclamation in 1972, 1986, 1992, 2002, 2012, and 2022 were computed and mapped to describe the spatial pattern of land cover change. The centroid of area-weight is defined as:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${X}_{t}=\\sum _{i=1}^{N}({C}_{ti} . {X}_{i})/\\sum _{i=1}^{N}{C}_{ti}, { Y}_{t}=\\sum _{i=1}^{N}({C}_{ti} . {Y}_{i})/\\sum _{i=1}^{N}{C}_{ti} \\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere the centroids of natural wetlands or reclamation areas in year \u003cem\u003et\u003c/em\u003e are represented by \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e, respectively; The area of patch \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e known as \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eti\u003c/em\u003e\u003c/sub\u003e; The longitude and latitude of patch \u003cem\u003ei\u003c/em\u003e, which can be natural wetlands, man-made wetlands, or reclamation areas, are represented by \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, respectively. The total number of natural wetlands or reclamation areas in a patch is called \u003cem\u003eN\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6. Correlation Coefficient\u003c/h2\u003e \u003cp\u003eThe Pearson correlation coefficient is a statistical indicator that assesses the strength and direction of a two-variable linear connection. It is represented by the symbol \"\u003cem\u003er\u003c/em\u003e\" and has a value between \u0026minus;\u0026thinsp;1 and 1, with a positive value indicating a positive correlation, a negative value indicating a negative correlation, and a value of zero indicating no correlation between the variables (Asuero et al., 2006). In this study correlation coefficient was used to show the relationship strength between different land use types. The mathematical written as:\u003c/p\u003e \n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"30\" width=\"762\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere \u003cem\u003eΣxy\u003c/em\u003e is the sum of the product of the deviations of each value from their respective means; \u003cem\u003eΣx\u003c/em\u003e and \u003cem\u003eΣy\u003c/em\u003e are the sums of the \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e values, respectively; \u003cem\u003en\u003c/em\u003e is the sample size.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Land Cover Classification Maps Accuracy Assessment from 1972 to 2022\u003c/h2\u003e \u003cp\u003eA number of training samples were chosen from the Landsat datasets for each of all years to assess the applicability of maximum likelihood classification approaches for obtaining relatively accurate LULC change maps. The parameters introduced in Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.2.3\u003c/span\u003e (i.e., PA, UA, OA, and KC) were adopted for accuracy evaluations after being compared with a few GTP. Each of the five land-use categories' PA and UA, as well as the matching OA and KC, for each of the six research years are shown in the Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The PA and UA of all land-use categories in 1972, 1986, 1992, 2002, 2012 and 2022 were over 90%. The PA of wetlands was highest (100%) in 1992, 2002, 2012 and 2022 years and lowest (98%) in 1972 year compared to other years. Whereas the PA of natural vegetation was highest (98.7%) in 1972 year and lowest (95.9%) in 2002 year. The PA of settlement was highest (99.5%) in 1986 and 1992 years and lowest (98.5%) in 2002 and 2022 years. The PA of cultivated land was highest (100%) in 2022 year and lowest (96.3%) in 1972 year. The PA of barren land was highest (99.6%) in 1986 year and lowest (96.6%) in 2022 year. The UA of wetlands, natural vegetation, and barren land was 100% in all years. The UA of settlement was highest (96.5%) in 1992 year and lowest (94.2%) in 1972 year. The UA of cultivated land was highest (98.4%) in 1986 year and lowest (94.8%) in 2002 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The OA was highest (98.8%) in 1986 and 1992 years and lowest (98.4%) in 1972 compared to other years. The KC was highest (0.98) in 1986 and 1992 years and lowest (0.97) in 2002 year. To summarize, the average OA and KC for all years were 96.10% and 0.94, respectively, providing further confidence in using this remotely sensed and statistical framework to detect future changes in land-use patterns and morphologies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBased on chosen Ground Truth Points (GTP) from Landsat images accuracy assessments of recovered LULC change maps for the years 1972, 1986, 1992, 2002, 2012, and 2022, respectively, were made. The five main land-use kinds in this study are used to classify the numerical Producer Accuracy (PA) and User Accuracy (UA) values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWetlands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural vegetation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCultivated land\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBarren land\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer Accuracy (PA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Accuracy (UA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Accuracy (OA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e98.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa Coefficient (KC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer Accuracy (PA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Accuracy (UA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Accuracy (OA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e98.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa Coefficient (KC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer Accuracy (PA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Accuracy (UA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Accuracy (OA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e98.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa Coefficient (KC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer Accuracy (PA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Accuracy (UA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Accuracy (OA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e98.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa Coefficient (KC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer Accuracy (PA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Accuracy (UA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Accuracy (OA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e98.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa Coefficient (KC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProducer Accuracy (PA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Accuracy (UA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall Accuracy (OA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e98.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa Coefficient (KC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: All numerical values are corrected to 2 decimal places.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Areal changes of wetlands, reclamation, and other land use types from 1972 to 2022\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the land-use classes in the Indus River Delta from 1972 to 2022 at different intervals. In the first land-use class, wetlands saw a gradual decline in the area from 175.80 km\u003csup\u003e2\u003c/sup\u003e in 1972 to 149.71 km\u003csup\u003e2\u003c/sup\u003e in 2022. Thus, the proportion of wetlands decreased from 12.93% in 1972 to 11.01% in 2022. In the second land-use class, natural vegetation showed a more drastic change in the area, declining from 225.92 km\u003csup\u003e2\u003c/sup\u003e in 1972 to only 43.26 km\u003csup\u003e2\u003c/sup\u003e in 2022. This decline is reflected in the proportion of natural vegetation, which decreased from 16.61% in 1972 to 3.18% in 2022. In the third land-use class, settlement showed an increasing trend in both area and proportion from 24.76 km\u003csup\u003e2\u003c/sup\u003e and 1.82% in 1972 to 224.97 km\u003csup\u003e2\u003c/sup\u003e and 16.54%, respectively in 2022. In the fourth land-use class, cultivated land increased in area from 54.86 km\u003csup\u003e2\u003c/sup\u003e in 1972 to 428.41 km\u003csup\u003e2\u003c/sup\u003e in 2022. The proportion of cultivated land also increased largely from 4.03% in 1972 to 31.50% in 2022. The fifth land-use class was barren land, which had the largest area in 1972 (878.48 km\u003csup\u003e2\u003c/sup\u003e), and saw a gradual decline to 513.55 km\u003csup\u003e2\u003c/sup\u003e in 2022. The proportion of Barren land decreased from 64.60% in 1972 to 37.76% in 2022.\u003c/p\u003e \u003cp\u003eThe total area of the region remained constant at 1359.82 km\u003csup\u003e2\u003c/sup\u003e from 1972 to 1992. The combined proportion of wetlands and natural vegetation decreased from 29.54% in 1972 to 14.19% in 2022, indicating a significant loss of natural habitats in the region; settlements have grown rapidly over the last three decades, increasing by 83.16% and 907.14% from 1992 to 2022. Cultivated land has steadily increased throughout the years, reaching more than four times its 1972 size by 2022. The fraction of barren land decreased by more than half, from 64.60% in 1972 to 31.50% in 2022, indicating a change towards more sustainable land-use practices or the consequences of human activities on the ecosystem.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt can be seen from Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e that there was a huge change in land-use patterns in the region during the previous five decades. The settlements and cultivated land have extended and converted into wetlands and natural vegetation. The decline in barren land may reflect a change in land management practices or increased human activities. The data highlights the need for sustainable land-use practices to preserve natural resources and maintain a healthy environment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe large increase in the share of settlements from 3.08% in 1986 to 16.54% in 2022 implies that the region is rapidly urbanizing. This tendency might have an impact on social and economic progress, as well as environmental sustainability. The growth in cultivated land from 5.84% in 1986 to 31.50% in 2022 might be attributed to agricultural intensification, which entails increasing productivity via the use of modern farming equipment and methods. Yet, this tendency may have unintended repercussions such as soil deterioration, water pollution, and biodiversity loss. The decrease in natural vegetation from 16.61% in 1972 to 3.18% in 2022 is a reason for concern since natural vegetation supports ecosystems, regulates the water cycle, and sequesters carbon. Wetlands have decreased from 12.93% in 1972 to 11.01% in 2022, indicating that they are under threat from human activities such as land-use change, pollution, and drainage. Barren land decreased from 64.60% in 1972 to 37.76% in 2022, which might be attributed to human actions such as reclamation.\u003c/p\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it can be seen that the changes in the area of wetlands and reclamation in a study area from 1972 to 2022, as well as the average speed of area change on different time intervals. The \"wetlands\" class has decreased in area from 175.8 km\u003csup\u003e2\u003c/sup\u003e in 1972 to 149.7 km\u003csup\u003e2\u003c/sup\u003e in 2022, with a net change of -6.5 km\u003csup\u003e2\u003c/sup\u003e between 1972 and 1986 and an average speed of area change ranging from \u0026minus;\u0026thinsp;0.5 km\u003csup\u003e2\u003c/sup\u003e/yr to -1.1 km\u003csup\u003e2\u003c/sup\u003e/yr in the different time interval. Wetlands have experienced a net loss of 26.1 km\u003csup\u003e2\u003c/sup\u003e from 1972 to 2022, with the highest rate of loss occurring in the most recent decade (2012\u0026ndash;2022) at -3.6 km\u003csup\u003e2\u003c/sup\u003e/yr. The \"reclamation settlement\" class has increased in area from 24.8 km\u003csup\u003e2\u003c/sup\u003e in 1972 to 224.9 km\u003csup\u003e2\u003c/sup\u003e in 2022, with a net change of 17 km\u003csup\u003e2\u003c/sup\u003e between 1972 and 1986 and an average speed of area change ranging from 1.2 km\u003csup\u003e2\u003c/sup\u003e/yr to 3.4 km\u003csup\u003e2\u003c/sup\u003e/yr in the different time interval. Reclamation Settlement has experienced a net gain of 200.1 km\u003csup\u003e2\u003c/sup\u003e from 1972 to 2022, with the highest rate of gain occurring in the decade from 1992\u0026ndash;2002 at 75.9 km\u003csup\u003e2\u003c/sup\u003e/yr. The \"reclamation cultivated land\" class has increased in area from 54.9 km\u003csup\u003e2\u003c/sup\u003e in 1972 to 428.4 km\u003csup\u003e2\u003c/sup\u003e in 2022, with a net change of 24.5 km\u003csup\u003e2\u003c/sup\u003e between 1972 and 1986 and an average speed of area change ranging from 1.8 km\u003csup\u003e2\u003c/sup\u003e/yr to 28.6 km\u003csup\u003e2\u003c/sup\u003e/yr in the different time interval. Cultivated land has experienced a net gain of 373.5 km\u003csup\u003e2\u003c/sup\u003e from 1972 to 2022, with the highest rate of gain occurring in the decade from 2012\u0026ndash;2022 at 28.6 km\u003csup\u003e2\u003c/sup\u003e/yr. The net gain in settlement and cultivated areas from 1972 to 2022 was 225.0 km\u003csup\u003e2\u003c/sup\u003e and 373.5 km\u003csup\u003e2\u003c/sup\u003e, respectively. This represents a combined net gain of 598.5 km\u003csup\u003e2\u003c/sup\u003e or 44.0% of the total study area. The wetlands account for the largest percentage of area loss at 14.4% from 1972 to 2022.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe average pace of area changes for each class varied between time intervals. The rate of change for wetlands, for example, was highest in the most recent decade (2012\u0026ndash;2022), whereas the rate of change for settlement reclamation was highest in the 1992\u0026ndash;2002 decade. From 1972 to 2002, the rate of change for cultivated land reclamation climbed continuously, with the largest rates of gain happening in the most recent decade (2012\u0026ndash;2022), shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e that throughout the years, the study area's land cover has changed dramatically, with wetlands shrinking and reclamation settlement, and cultivated land expanding. The average rate of area change differed by class and historical time points, with some displaying faster rates of development than others. These changes can potentially have important ecological, social, and economic consequences for the study area and its adjacent regions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAreal change of coastal wetlands and reclamation in the Indus River Delta from 1972 to 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eClasses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea in 1972 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea in 2022 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003eArea change (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e \u003cp\u003eAverage speed of area change (km\u003csup\u003e2\u003c/sup\u003e/yr)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1972\u0026ndash; 1986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1986\u0026ndash; 1992\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1992\u0026ndash; 2002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2002\u0026ndash; 2012\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2012\u0026ndash; 2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1972\u0026ndash; 1986\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1986\u0026ndash; 1992\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1992\u0026ndash; 2002\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2002\u0026ndash; 2012\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2012\u0026ndash; 2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWetlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026ndash;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026ndash;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026ndash;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReclamation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e224.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCultivated land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e428.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e286.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal study area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1359.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003cb\u003eNote\u003c/b\u003e: All numerical values are corrected to 1 decimal places.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Conversions of wetlands and reclamation in the Indus River Delta\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the area changes from 1972 to 2022 for different land cover types in the study area, categorized based on their conversion to or from wetlands. Specifically explained below:\u003c/p\u003e \u003cp\u003eWetlands to Settlement: There was a net conversion of 3 km\u003csup\u003e2\u003c/sup\u003e of wetlands to settlement areas between 1972 and 1986, followed by a slight recovery of 0.7 km\u003csup\u003e2\u003c/sup\u003e between 1986 and 1992. However, there was a further net loss of 3.8 km\u003csup\u003e2\u003c/sup\u003e between 1992 and 2002 and another 1 km\u003csup\u003e2\u003c/sup\u003e between 2002 and 2012. The time interval between 2012 and 2022 saw a major net loss of 2.6 km\u003csup\u003e2\u003c/sup\u003e of wetlands to settlement areas.\u003c/p\u003e \u003cp\u003eWetlands to Cultivated land: There was a net conversion of 9.2 km\u003csup\u003e2\u003c/sup\u003e of wetlands to cultivated land between 1972 and 1986, followed by a slight recovery of 2.5 km\u003csup\u003e2\u003c/sup\u003e between 1986 and 1992. The time interval between 1992 and 2002 saw a net loss of 2.9 km\u003csup\u003e2\u003c/sup\u003e of wetlands to cultivated land, followed by a further loss of 1.4 km\u003csup\u003e2\u003c/sup\u003e between 2002 and 2012. The time interval between 2012 and 2022 saw a major net gain of 12.5 km\u003csup\u003e2\u003c/sup\u003e of wetlands from cultivated land.\u003c/p\u003e \u003cp\u003eWetlands to Natural vegetation: There was a net conversion of 8.5 km\u003csup\u003e2\u003c/sup\u003e of wetlands to natural vegetation between 1972 and 1986, followed by a net loss of 9.5 km\u003csup\u003e2\u003c/sup\u003e between 1986 and 1992. The time interval between 1992 and 2002 saw a further net loss of 3.3 km\u003csup\u003e2\u003c/sup\u003e of wetlands to natural vegetation, followed by a net recovery of 7.1 km\u003csup\u003e2\u003c/sup\u003e between 2002 and 2012. The time interval between 2012 and 2022 saw a slight net loss of 0.8 km\u003csup\u003e2\u003c/sup\u003e of wetlands to natural vegetation.\u003c/p\u003e \u003cp\u003eWetlands to Barren land: There was a net loss of 14.2 km\u003csup\u003e2\u003c/sup\u003e of wetlands to barren land between 1972 and 1986, followed by a net gain of 14.3 km\u003csup\u003e2\u003c/sup\u003e between 1986 and 1992. The time interval between 1992 and 2002 saw a net loss of 0.1 km\u003csup\u003e2\u003c/sup\u003e of wetlands to barren land, followed by a further loss of 5.9 km\u003csup\u003e2\u003c/sup\u003e between 2002 and 2012. The time interval between 2012 and 2022 saw a slight net recovery of 2.2 km\u003csup\u003e2\u003c/sup\u003e of wetlands from barren land.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSettlement to Wetlands: There was a net recovery of 3 km\u003csup\u003e2\u003c/sup\u003e of wetlands from settlement areas between 1972 and 1986, followed by a slight loss of 0.7 km\u003csup\u003e2\u003c/sup\u003e between 1986 and 1992. The time interval between 1992 and 2002 saw a further net loss of 3.8 km\u003csup\u003e2\u003c/sup\u003e of wetlands from settlement areas, followed by a loss of 1 km\u003csup\u003e2\u003c/sup\u003e between 2002 and 2012. The time interval between 2012 and 2022 saw a net recovery of 2.6 km\u003csup\u003e2\u003c/sup\u003e of wetlands from settlement areas.\u003c/p\u003e \u003cp\u003eCultivated land to Wetlands: There was a net loss of 9.2 km\u003csup\u003e2\u003c/sup\u003e of wetlands to cultivated land between 1972 and 1986, followed by a net recovery of 2.5 km\u003csup\u003e2\u003c/sup\u003e between 1986 and 1992. The time interval between 1992 and 2002 saw a net loss of 2.9 km\u003csup\u003e2\u003c/sup\u003e of wetlands from cultivated land. The time interval between 2002 and 2012 saw a further net loss of 1.4 km\u003csup\u003e2\u003c/sup\u003e of wetlands from cultivated land, followed by a loss of 12.5 km\u003csup\u003e2\u003c/sup\u003e between 2012 and 2022.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere has been an extensive conversion of wetlands into settlement, with a net gain of 2.5 km\u003csup\u003e2\u003c/sup\u003e of settlement area at the cost of wetlands throughout the course of the research. Also, there has been a net conversion of wetlands to cultivated land, with a total gain in cultivated land area at the cost of wetlands by 24.5 km\u003csup\u003e2\u003c/sup\u003e. There was also a massive conversion of natural vegetation to cultivated land, with a total increase of 286.2 km\u003csup\u003e2\u003c/sup\u003e of cultivated land area at the cost of natural vegetation. Nonetheless, the area of natural vegetation has grown by 10.1 km\u003csup\u003e2\u003c/sup\u003e throughout the research time. Finally, during the research time, there was a net conversion of barren land to settlement, with a total gain of 58.9 km\u003csup\u003e2\u003c/sup\u003e of settlement area at the outflow of barren land. However, there was a net increase of 4.3 km\u003csup\u003e2\u003c/sup\u003e in the amount of barren land, shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAccording to the findings, the conversion of wetlands and natural vegetation to other land uses is a big issue in the stud area. Converting these vital ecosystems to other land uses can have detrimental consequences for the environment and human well-being.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the areas of settlement, cultivated land, and reclamation area in various intervals from 1972 to 2022. The settlement area increased from 6.7 km\u003csup\u003e2\u003c/sup\u003e in 1972\u0026ndash;1986 to 81.3 km\u003csup\u003e2\u003c/sup\u003e in 2012\u0026ndash;2022, showing a major increase over the years. The proportion of settlement area increased from 26% in 1972\u0026ndash;1986 to 36.1% in 2012\u0026ndash;2022, indicating that the rate of settlement growth has slowed up over time. The area of cultivated land increased from 19.1 km\u003csup\u003e2\u003c/sup\u003e in 1972\u0026ndash;1986 to 143.8 km2 in 2012\u0026ndash;2022, indicating major growth in agricultural activities. The proportion of cultivated land increased from 74% in 1972\u0026ndash;1986 to 63.9% in 2012\u0026ndash;2022, indicating that cultivated land has become the dominant land use over the years. The time interval from 1992\u0026ndash;2002 saw the largest increase in the settlement area, with the settlement area increasing from 9.2 km\u003csup\u003e2\u003c/sup\u003e to 50.0 km\u003csup\u003e2\u003c/sup\u003e. This was also the time interval where the proportion of cultivated land increased the most, from 71\u0026ndash;42%. The area of reclamation land increased from 25.8 km\u003csup\u003e2\u003c/sup\u003e in 1972\u0026ndash;1986 to 225.1 km\u003csup\u003e2\u003c/sup\u003e in 2012\u0026ndash;2022, showing a major increase over the years. The proportion of reclamation area increased from 0% in 1972\u0026ndash;1986 to 29.2% in 2002\u0026ndash;2012, indicating that efforts to reclaim land from the sea have been successful in recent years. The total reclamation study area increased from 25.8 km2 in 1972\u0026ndash;1986 to 225.1 km\u003csup\u003e2\u003c/sup\u003e in 2012\u0026ndash;2022, indicating a major expansion of the land area time by time. The time interval from 2002\u0026ndash;2012 saw the largest increase in reclamation area, with the reclamation area increasing from 43.9 km\u003csup\u003e2\u003c/sup\u003e to 150.2 km\u003csup\u003e2,\u003c/sup\u003e indicating that efforts to reclaim land from the wetlands have been successful in recent years.\u003c/p\u003e \u003cp\u003eThere was a major expansion in settlement area, cultivated land, and reclamation areas over time. The rate of settlement expansion has increased throughout time, and cultivated land has surpassed all other land uses. Land reclamation efforts have been effective, with a large increase in reclamation area over the years, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChange area between land use types of reclamation areas in the Indus River Delta\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIntervals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSettlement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCultivated land\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReclamation area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1972\u0026ndash;1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1986\u0026ndash;1992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1992\u0026ndash;2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u0026ndash;2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e150.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e225.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: All numerical values are corrected to 1 decimal places.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Centroids movement of coastal wetlands and reclamation in the Indus River Delta\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the centroids' movement of wetlands and reclamation. The centroids of wetlands moved slowly from Kharo Chan taluka northern side of the Khar creek to the eastwards in Keti Bandar taluka in the northern side of Turshian creek of Arabian Sea during the time interval of 1972 to 1986; then slowly moved towards the south direction northern side of the Gaghiar and Khobar creeks during the time interval of 1986 to 1992; after that again slowly moved to eastwards between Turshian and Hajambro creeks from 1992 to 2002, later on slowly moved to westwards northern part of Keti Bandar taluka from 2002 to 2012, again fastest movement were from Keti Bandar to Kharo Chan taluka northern side of the Khar creek during the time interval of 2012 to 2022. In terms of reclamation, the centroids of settlement migrated from the eastern part of Kharo Chan to the northern part of Keti Bandar taluka with the fastest movement during the time interval of 1972 to 1986; then shifted southwards from the eastern part of Keti Bandar taluka to eastern part of Kharo Chan with fastest movement from 1986 to 1992; after that again slowly moved eastwards from eastern part of Kharo Chan to western part of Keti Bandar taluka from 1992 to 2002; later on slowly moved to westwards from 2002 to 2012, then slowly shifted from western part of Keti Bandar to eastern part of Kharo Chan during the time interval of 2012 to 2022. The centroids of cultivated land shifted slowly westwards from the northern part of Keti Bandar to the western part of the taluka from 1972 to 1986; then migrated northwards in Keti Bandar taluka from 1986 to 1992, later on slowly moved westwards from 1992 to 2012 in the Keti Bandar taluka; after that shifted to southwards from the northern side of Keti Bandar taluka to the southern part of Kharo Chan taluka western side of the Khar creek with fastest movement during the time interval of 2012 to 2022.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Relationship strength between different land use types in the Indus River Delta\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the relationship strength between different land use types. According to the results, the negative correlation means that one land use type is increasing and the other is decreasing. If both land use types are increasing or decreasing, that indicates a positive correlation. The correlation degrees between wetlands and cultivated land and wetlands and settlement were both strongest negative (-0.91 and \u0026minus;\u0026thinsp;0.93), indicating that the wetlands are decreasing and cultivated land and settlements are increasing in the same area. The correlation between wetlands and natural vegetation and wetlands and barren land were both strongest positive (0.88 and 0.89), meaning that these land use types are decreasing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe correlation between cultivated land and settlement was also strongly positive (0.75), indicating that these land use types are increasing. The correlation between barren land and cultivated land and barren land and settlement were both strongest negative (-0.96 and \u0026minus;\u0026thinsp;0.82), meaning that the barren land is declining and cultivated land and settlements are increasing. The correlation between natural vegetation and cultivated land and natural vegetation and settlement were strongly negative (-0.71 and \u0026minus;\u0026thinsp;0.82), indicating that the natural vegetation is decreasing and cultivated land and settlements are increasing. The correlation between natural vegetation and barren land was moderately positive (0.62), meaning these land use types are somewhat likely to coexist in the same area.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cb\u003e4.1. Importance of coastal wetlands and impact of other land use types dynamics on coastal wetlands in the Indus River delta during the past 50 years\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCoastal wetlands are important habitats that sustain a variety of plants and animals as well as offer a wide range of ecological functions. Nevertheless, they are also among the most endangered ecosystems on the planet as a result of the fast industrialization and urbanization that is causing these vital ecosystems to degrade and disappear. The Indus river delta has a distinct environment that supports a diverse range of flora and fauna, including mangroves, sea grasses, and several fish species. The delta is threatened by various anthropogenic activities, including land-use change, pollution, overfishing, and coastal erosion (Chaudhry, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Reclamation is a key diver that converts wetlands or other aquatic ecosystems into land for human use, is one of the most serious dangers to the Indus River Delta. For several decades, the deltic region has been undergoing reclamation, mostly for agricultural and settlement purposes (Solangi et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Large ecological harm to the delta has been caused by the conversion of wetlands into agricultural land and urban area, including the loss of fish and wildlife habitats and an increase in soil and water salinization (Bot, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of this study indicate that the proportion of wetlands has decreased gradually from 12.93% in 1972 to 11.0087% in 2022. The decrease in the proportion of wetlands over time is a concerning trend, as wetlands provide a variety of important ecosystem services. They also provide habitats for a variety of plant and animal species, some of which are unique and rare (Nayak and Bhushan, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Wetlands loss may lead to a drop in water quality, an increased danger of floods, and a loss of biodiversity. This can have serious environmental and economic consequences, such as diminished fishing and tourism prospects (Bhowmik, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Natural vegetation has declined dramatically from 16.61% in 1972 to 3.18% in 2022. The loss of natural vegetation is also a concerning trend, as it can have major ecological and social impacts. Natural vegetation plays a vital role in maintaining soil health, preventing erosion, and regulating water cycles. Moreover, it offers habitats for wildlife and, by storing carbon, aids in reducing climate change. Degradation of the soil, a decline in biodiversity, and an increase in greenhouse gas emissions can all be brought about by the loss of natural vegetation. Reduced food and water security are just a couple of the negative effects this can have on people's well-being (Kusch et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe proportion of settlement has increased largely from 1.82% in 1972 to 16.5435% in 2022. The increase in the proportion of settlement over time reflects a trend towards urbanization and increased human population. Urbanization can have major impacts on the environment, including increased energy consumption, air pollution, and land use change (Almulhim et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Urban areas are frequently more susceptible to the effects of climate change and extreme weather occurrences. Conflicts over land use and competition for natural resources may arise as a result of increased settlement (Liang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The proportion of cultivated land has increased gradually from 4.03% in 1972 to 31.503% in 2022. A trend towards increased agricultural production to meet the demands of a growing world population is reflected in the increase in the proportion of cultivated land (Barati et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Deforestation, soil erosion, and water pollution are just a few of the negative effects that agriculture can have on the environment. The intensification of agriculture can also result in increased use of fertilizers and pesticides, which can have negative impacts on human health and the environment (Tudi et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is crucial to ensure that agricultural methods are environmentally friendly and have as little detrimental impact as possible (Mandal et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Between 1972 and 2022, barren land gradually decreased from 64.60\u0026ndash;37.7637% in the study area. Since it reflects the transformation of formerly unusable land into productive land uses, the decrease in the proportion of barren land over time can be seen as a positive trend (Nguyen et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, it is crucial to remember that barren land may serve crucial ecological purposes, including serving as a habitat for unique plant and animal species. The conversion of barren land into other land uses can also result in the loss of these ecological functions. Several similar studies have been conducted in different regions of the world and reported concerns are consistent with the results of our research work. For instance, a study by Sun et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) analyzed the land use changes in the Yangtze River Basin in China during 35 years using remote sensing data. The study found that agricultural land increased, while forest, wetlands, and grasslands decreased dramatically, leading to soil erosion, water pollution, and biodiversity loss. Lambin and Meyfroidt (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reviewed the global trends in land use changes during four decades and reported that agricultural expansion, urbanization, and infrastructure development were the main drivers of land use changes, leading to deforestation, habitat loss, and climate change. Reis (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) analyzed land use and land cover changes in Rize, North-East Turkey during 24 years and showed that urban area and agricultural land increased, pasture and forestry area decreased. These studies, emphasize the significance of tracking and controlling the wetlands changes to ensure sustainable development and resource preservation.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Consequences of reclamation on coastal wetlands of Indus River Delta from 1972 to 2022\u003c/h2\u003e \u003cp\u003eIn our study results, it can be clearly seen the enormous changes in the area of wetlands and reclamation (settlement and cultivated land) in a study area between 1972 and 2022. The wetlands area decreased by 26.1 km\u003csup\u003e2\u003c/sup\u003e, while the settlement area and cultivated area increased by 200.1 km\u003csup\u003e2\u003c/sup\u003e and 373.5 km\u003csup\u003e2\u003c/sup\u003e, respectively. Wetlands are important ecosystems that provide numerous benefits, such as water purification, flood control, and habitat for wildlife (H. Wu et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, wetlands play an important role in reducing the consequences of climate change by storing plentiful amounts of carbon in their soils (Let and Pal, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The decrease in wetlands area observed in this study is in-line with global trends. It is estimated that the world has lost approximately 64\u0026ndash;71% of its wetlands since the 1900s (Nie et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The decline in wetlands area determined in this study is concerning as it may lead to a loss of these benefits. Wetlands loss are caused by a number of factors, including shifting land use patterns, climate change, and human activities like dredging, filling, and draining, which can have negative impacts on human communities (Davidson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Wetlands protection and restoration efforts are so critical for sustaining these precious ecosystems.\u003c/p\u003e \u003cp\u003ePopulation growth and urbanization, which have resulted in the extension of built-up areas, can be ascribed to the rise in reclamation settlement areas seen in this study (Chen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The expansion of built-up regions can have severe environmental consequences, such as increased air and water pollution, habitat loss, and changes in microclimate (Zhan et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, urbanization can result in a loss of biodiversity as well as the loss of important ecosystem services such as flood control and water filtration (Chen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Urbanization may also lead to increased air and water pollution, which can be harmful to human health. It is estimated that urban air pollution kills around 1.8\u0026nbsp;million people worldwide each year (Cohen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To mitigate these adverse effects, urban planning and management measures that take into account the environmental implications of built-up regions are required.\u003c/p\u003e \u003cp\u003eThe increase in reclamation cultivated area observed in this study is consistent with global trends, as agriculture has expanded to meet rising food demand (Ahmed and Ambinakudige, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the expansion of agricultural land can also have negative impacts on the environment (Barkah et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is estimated that agriculture is responsible for approximately 25% of global greenhouse gas emissions (Subedi et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, in order to ensure that agricultural expansion is environmentally sustainable, best farming practices that minimize environmental impacts are required, such as integrated pest management, precision agriculture, and conservation agriculture. The changes observed in the wetlands and reclamation (settlement and cultivated land) in the study area reflect broader global trends of environmental change due to human activities. The efforts to maintain and restore wetlands, reduce the negative consequences of urbanization, and promote sustainable farming methods are essential to ensure a sustainable future for both people and the environment.\u003c/p\u003e \u003cp\u003eOur study exhibited that from 1972 to 1986, there was a net loss of 3 km\u003csup\u003e2\u003c/sup\u003e of wetlands to settlement and a net increase of 9.2 km\u003csup\u003e2\u003c/sup\u003e of wetlands to cultivated land. This trend continued in the following two decades, with wetlands continuing to be lost to agriculture and urbanization. However, due to some restoration efforts, there was a modest trend reversal from 2002 to 2012, with a net increase of wetlands replacing barren land (Pupins et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The research also highlights the importance of measures to protect wetlands and other natural habitats. Ramsar Agreement on Wetlands, a 1971 international agreement, aims to advance the global preservation and responsible use of wetlands (Ramsar, 2023). Similar to this, the United States Clean Water Act, passed in 1972, offers a framework for controlling the flow of pollutants into wetlands and other bodies of water (US, 2023). From 1972 to 1986, wetlands were lost primarily to settlement and gained to cultivated land. With estimates indicating that more than 40% of the world's wetlands have been converted to agricultural land, the conversion of wetlands to agriculture and settlement poses a serious threat to wetlands conservation globally (Moisa et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). From 2002 to 2012, there was a net gain of wetlands from barren land, suggesting successful restoration efforts. Wetlands restoration is a critical strategy for conserving and restoring wetland ecosystems globally (Moisa et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our results are consistent with many other wetlands studies in the world. For example, Saha et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) studied in India and found that 28.11% and 30.14% of wetland areas declined and converted into settlement and cultivated land over three decades. Chuma et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted a study in Congo and investigated that 35% of wetlands decreased and rapidly shifted into cultivated land. Li et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) studied in China and reported that wetlands declined by 11% and converted into barren land, forest, and grassland, and Hine et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that 10% of wetlands shifted into vegetation in the USA.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.3. Reclamation status of five different intervals in the Indus River Delta from 1972 to 2022 compared to other studies\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRegarding the changing area of reclamation, including settlement and cultivated land during five different time intervals: 1972\u0026ndash;1986, 1986\u0026ndash;1992, 1992\u0026ndash;2002, 2002\u0026ndash;2012, and 2012\u0026ndash;2022 shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, our study indicates that: From 1972 to 1986, the settlement area was 26% of the total area, while the cultivated land was 74%, and the reclamation area was 25.8 km\u003csup\u003e2\u003c/sup\u003e. From 1986 to 1992, the settlement area increased to 26.2% of the total area, while the cultivated land increased to 71%, and the reclamation area increased to 35.1 km\u003csup\u003e2\u003c/sup\u003e. From 1992 to 2002, there was a major increase in the settlement area to 58.1% of the total area, while the cultivated land increased to 42%, and the reclamation area increased to 86.1 km\u003csup\u003e2\u003c/sup\u003e. From 2002 to 2012, the settlement area continued to increase to 70.8% of the total area, while the cultivated land further increased by 29.2%, and the reclamation area increased to 150.2 km\u003csup\u003e2\u003c/sup\u003e. From 2012 to 2022, the settlement area increased to 36.1% of the total area, while the cultivated land largely increased to 63.9%, and the reclamation area increased to 225.1 km\u003csup\u003e2\u003c/sup\u003e. Global land use changes are driven by population growth and economic development, leading to increased demand for land for settlements and agriculture. These findings align with other studies that have investigated land use changes in other countries over time. For example, Maru et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that the built-up area has increased by 5.3% over 23 years. Gohain et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) investigated that settlement land has increased by 28.7% during 5 years. Shekar and Mathew (2023) found that agricultural land and built-up have increased by 5.17% and 0.84% during 23 years. Daba and You (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) studied that cultivated land increased by 62% during 35 years. Abdullah et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that cultivated area has increased by 5.44% over 27 years.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eCoastal wetlands are one most important ecosystems of our planet. Wetlands have been lost all across the world, including those in Pakistan's Indus River Delta. The spatiotemporal dynamics of coastal wetlands and reclamation in the Indus River Delta during the past 50 years revealed changes in the extent of wetlands and their conversion to cultivated land and urban areas due to human activities, including reclamation and land-use changes. Our findings showed that from 1972 to 2022, the net area of natural wetlands decreased by 26.1 km\u003csup\u003e2\u003c/sup\u003e, whereas the reclamation (settlement and cultivated land) rose by 200.1 and 373.5 km\u003csup\u003e2,\u003c/sup\u003e respectively. The fastest conversion rate from wetlands to settlement was 3.8 km\u003csup\u003e2\u003c/sup\u003e between 1992 and 2002, and the fastest conversion rate from wetlands to cultivated land was 12.5 km\u003csup\u003e2\u003c/sup\u003e between 2012 and 2022 over the five study time points. Wetlands centroids moved progressively eastwards from Kharo Chan taluka to Keti Bandar taluka in the first and third decades, then southwards in the second decade, then westwards in the fourth decade, and ultimately southwards from Keti Bandar taluka to Kharo Chan taluka in the fifth decade. During the five decades, settlement centroids slowly expanded in all directions. From Keti Bandar to Kharo Chan Taluka, the centroids of cultivated land shifted westward in the first, third, and fourth decades, northward in the second, and southward in the fifth decade. The average accuracy assessment of all metrics was 98%, and KC was 0.98. The study emphasizes the importance of sustainable land-use practices to protect natural resources and maintain a healthy ecosystem. Protection and restoration of wetlands and natural vegetation, promotion of sustainable agricultural methods, and careful land-use planning and management are all critical solutions for addressing the difficulties confronting the Indus River Delta region. Generally, the data suggest that the region is undergoing major land-use changes, indicating the need for ongoing monitoring and timely action to guarantee sustainable development and natural resource protection.\u003c/p\u003e"},{"header":"6. Recommendations and future research direction","content":"\u003cp\u003eDuring the last 50 years, the Indus River Delta's coastal wetlands and reclamation efforts have seen major modifications. Understanding these spatiotemporal changes is critical for designing successful conservation and management measures for the region. Future recommendations and proposals based on the examination of the spatiotemporal dynamics of coastal wetlands and reclamation in the Indus River Delta might include:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCreating and executing appropriate conservation and management methods for the region's surviving coastal wetlands in order to protect its biological functions and biodiversity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePromoting sustainable land use practices and reducing the environmental and community consequences of reclamation works.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRaising public awareness and education about the value of coastal environments.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe study of the Indus River Delta's coastal wetlands and their deterioration due to reclamation is a significant and complicated issue that requires a multidisciplinary approach. Future studies should look at the ecological, economic, and social consequences of wetlands reclamation, prospective policy and management solutions, prediction models, and comparative assessments of wetlands conservation and restoration activities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement\u0026nbsp;\u003c/strong\u003eYaseen Laghari: Data collection, Software, Validation \u0026amp; writing-original draft. Shibiao Bai: Supervision, Writing-reviewing \u0026amp; editing. Shah Jahan Leghari: Co-Supervision, Conceptualization, Writing-reviewing \u0026amp; editing. Wenjing Wei: Conceptualization \u0026amp; Methodology. Abdul Hafeez Laghari: Methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eThis study was supported by the National Key Research and Development Program of China (Grant No. 2021YFE0116800). We are grateful to the editorial team and anonymous reviewers for their efforts to improve this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authors have not disclosed any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdullah, A. Y. M., Masrur, A., Gani Adnan, M. S., Al Baky, M. A., Hassan, Q. K., \u0026amp; Dewan, A., 2019. Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017. Remote Sensing. 11, 790. https://doi.org/10.3390/RS11070790.\u003c/li\u003e\n\u003cli\u003eAhmad, Z., Ashtar, H., \u0026amp; Shakeel, A., 2019. Economics Importance of Wetlands, their Benefits and Values Case of Pakistan. MPRA. Paper No. 95260, 1\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eAhmed, Z., \u0026amp; Ambinakudige, S., 2023. Does land use change, waterlogging, and salinity impact on sustainability of agriculture and food security? Evidence from southwestern coastal region of Bangladesh. Environmental Monitoring and Assessment. 195, 1\u0026ndash;28. https://doi.org/10.1007/S10661-022-10673-W/METRICS.\u003c/li\u003e\n\u003cli\u003eAlmazroui, M., Mashat, A., Assiri, M. E., \u0026amp; Butt, M. J., 2017. Application of Landsat Data for Urban Growth Monitoring in Jeddah. Earth Systems and Environment. 1, 1-11. https://doi.org/10.1007/S41748-017-0028-4.\u003c/li\u003e\n\u003cli\u003eAlmulhim, A. I., Bibri, S. E., Sharifi, A., Ahmad, S., \u0026amp; Almatar, K. M., 2022. Emerging Trends and Knowledge Structures of Urbanization and Environmental Sustainability: A Regional Perspective. Sustainability. 14, 13195. https://doi.org/10.3390/SU142013195.\u003c/li\u003e\n\u003cli\u003eAsad, M. H., \u0026amp; Bais, A., 2020. Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture. 7, 535\u0026ndash;545. https://doi.org/10.1016/J.INPA.2019.12.002.\u003c/li\u003e\n\u003cli\u003eAsuero, A., Sayago, A., 2006. The correlation coefficient: An overview. Critical reviews in analytical chemistry.. 36, 41\u0026ndash;59. https://doi.org/10.1080/10408340500526766.\u003c/li\u003e\n\u003cli\u003eBakeman, R., 2022. KappaAcc: A program for assessing the adequacy of kappa. Behavior Research Methods. 5, 1\u0026ndash;6. https://doi.org/10.3758/S13428-022-01836-1/METRICS.\u003c/li\u003e\n\u003cli\u003eBakr, N., Weindorf, D., 2010. Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Applied Geography. 30, 592-605. https://doi.org/10.1016/j.apgeog.2009.10.008.\u003c/li\u003e\n\u003cli\u003eBalmford, A., Bennun, L., Ten Brink, B., Cooper, D., C\u0026ocirc;t\u0026eacute;, I. M., Crane, P., Dobson, A., Dudley, N., Dutton, I., Green, R. E., Gregory, R. D., Harrison, J., Kennedy, E. T., Kremen, C., Leader-Williams, N., Lovejoy, T. E., Mace, G., May, R., Mayaux, P., \u0026hellip; Walther, B. A., 2005. The convention on biological diversity\u0026apos;s 2010 target. Science. 307, 212\u0026ndash;213. https://doi.org/10.1126/SCIENCE.1106281/SUPPL_FILE/BALMFORD.SOM.PDF.\u003c/li\u003e\n\u003cli\u003eBarati, A. A., Azadi, H., Movahhed Moghaddam, S., Scheffran, J., \u0026amp; Dehghani Pour, M., 2023. Agricultural expansion and its impacts on climate change: evidence from Iran. Environment, Development and Sustainability. 20, 1\u0026ndash;27. https://doi.org/10.1007/S10668-023-02926-6/METRICS.\u003c/li\u003e\n\u003cli\u003eBarkah, T. T., Rusgianto, S., \u0026amp; Wardhana, A. K., 2022. Impact of Agricultural Land and the Output of Agricultural Products Moderated with Internet Users toward the Total export of Agricultural Product in Three Islamic South East Asian Countries. Media Agribisnis. 6, 11\u0026ndash;22. https://doi.org/10.35326/AGRIBISNIS.V6I1.2261.\u003c/li\u003e\n\u003cli\u003eBenchoufi, M., Matzner-Lober, E., Molinari, N., Jannot, A. S., \u0026amp; Soyer, P., 2020. Interobserver agreement issues in radiology. Diagnostic and Interventional Imaging. 101, 639\u0026ndash;641. https://doi.org/10.1016/J.DIII.2020.09.001.\u003c/li\u003e\n\u003cli\u003eBhowmik, S., 2022. Ecological and Economic Importance of Wetlands and Their Vulnerability: A Review. Research Anthology on Ecosystem Conservation and Preserving Biodiversity. 22, 11-27. https://doi.org/10.4018/978-1-6684-5678-1.CH002\u003c/li\u003e\n\u003cli\u003eBot, M. R.-P. J., 2018. A review on the status, ecological importance, vulnerabilities, and conservation strategies for the Mangrove ecosystems of Pakistan. Pakistan Journal of Botany. 50, 1645\u0026ndash;1659. https://www.pakbs.org/pjbot/papers/1524563266.pdf.\u003c/li\u003e\n\u003cli\u003eCarriquiry, A. L., Reid, N., \u0026amp; Slavković, A. B., 2019. Stephen Elliott Fienberg 1942\u0026ndash;2016, Founding Editor of the Annual Review of Statistics and Its Application. Annual review of statistics and its application. 6, 1\u0026ndash;18. https://doi.org/10.1146/ANNUREV-STATISTICS-030718-105334.\u003c/li\u003e\n\u003cli\u003eChandio, N. H., M. M. Anwar, and A. A. Chandio., 2011. Degradation of Indus delta, removal mangroves forestland its causes: A case study of Indus River delta. Sindh University Research Journal. 1, 43. https://sujo-old.usindh.edu.pk/index.php/SURJ/article/view/1306.\u003c/li\u003e\n\u003cli\u003eChaudhry, A. A., 2010. Wetlands in Pakistan : What Is Happening To Them? World Environment Day. 6, 49\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eChen, J., Yang, K., Chen, S., 2019. Enhanced normalized difference index for impervious surface area estimation at the plateau basin scale. Journal of Applied Remote Sensing. 13, 016502-016502. https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-13/issue-1/016502/Enhanced-normalized-difference-index-for-impervious-surface-area-estimation-at/10.1117/1.JRS.13.016502.short.\u003c/li\u003e\n\u003cli\u003eChen, L., Ren, C., Zhang, B., Li, L., Wang, Z., \u0026amp; Song, K., 2018. Spatiotemporal Dynamics of Coastal Wetlands and Reclamation in the Yangtze Estuary During Past 50 Years (1960s\u0026ndash;2015). Chinese Geographical Science. 28, 386\u0026ndash;399. https://doi.org/10.1007/S11769-017-0925-3.\u003c/li\u003e\n\u003cli\u003eChen, W., Wang, G., \u0026amp; Zeng, J., 2023. Impact of urbanization on ecosystem health in Chinese urban agglomerations. Environmental Impact Assessment Review. 98, 106964. https://doi.org/10.1016/J.EIAR.2022.106964.\u003c/li\u003e\n\u003cli\u003eChen, X., Shang, J., \u0026amp; Ji, X., 2022. Impact of urbanization on agricultural ecological efficiency: evidence from China. Ci\u0026ecirc;ncia Rural. 53, 20210650. https://doi.org/10.1590/0103-8478CR20210650.\u003c/li\u003e\n\u003cli\u003eChuma, G. B., Mondo, J. M., Sonwa, D. J., Karume, K., Mushagalusa, G. N., \u0026amp; Schmitz, S., 2022. Socio-economic determinants of land use and land cover change in South-Kivu wetlands, eastern D.R. Congo: Case study of Hogola and Chisheke wetlands. Environmental Development. 43, 100711. https://doi.org/10.1016/J.ENVDEV.2022.100711.\u003c/li\u003e\n\u003cli\u003eCohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., \u0026hellip; Forouzanfar, M. H., 2017. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet. 389, 1907\u0026ndash;1918. https://doi.org/10.1016/S0140-6736(17)30505-6.\u003c/li\u003e\n\u003cli\u003eDaba, M. H., \u0026amp; You, S., 2022. Quantitatively Assessing the Future Land-Use/Land-Cover Changes and Their Driving Factors in the Upper Stream of the Awash River Based on the CA-Markov Model and Their Implications for Water Resources Management. Sustainability. 14, 1538. https://doi.org/10.3390/SU14031538.\u003c/li\u003e\n\u003cli\u003eDavis, G. K., 2007. History of the NOAA satellite program. Journal of Applied Remote Sensing 1, 012504. https://doi.org/10.1117/1.2642347.\u003c/li\u003e\n\u003cli\u003eDavidson, N. C., 2014. How much wetland has the world lost? Long-term and recent trends in global wetland area. Marine and Freshwater Research. 65, 934-941. https://doi.org/10.1071/MF14173.\u003c/li\u003e\n\u003cli\u003eDe Grandi, G., Spirolazzi, V., Rauste, Y. A., Curto, L., Rosenqvist, A., \u0026amp; Shimada, M., 2004. The GBFM radar mosaic of the eurasian taiga: Selected topics on geo-location and preliminary thematic products. International Geoscience and Remote Sensing Symposium. 1, 507\u0026ndash;510. https://doi.org/10.1109/IGARSS.2004.1369075.\u003c/li\u003e\n\u003cli\u003eDhodhi, M. K., Saghri, J. A., Ahmad, I., \u0026amp; Ul-Mustafa, R., 1999. D-ISODATA: A distributed algorithm for unsupervised classification of remotely sensed data on network of workstations. Journal of Parallel and Distributed Computing, 59, 280-301. https://www.sciencedirect.com/science/article/pii/S0743731599915733.\u003c/li\u003e\n\u003cli\u003eDonato, D. C., Kauffman, J. B., Murdiyarso, D., Kurnianto, S., Stidham, M., \u0026amp; Kanninen, M., 2011. Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience. 4, 293\u0026ndash;297. https://doi.org/10.1038/NGEO1123.\u003c/li\u003e\n\u003cli\u003eEcosystem, C., 2023. Coastal ecosystems | gbrmpa (accessed 20.1.23) https://www2.gbrmpa.gov.au/learn/coastal-ecosystems.\u003c/li\u003e\n\u003cli\u003eEROS USGS., 2023. USGS EROS Archive - Landsat Archives - Landsat 4-5 Thematic Mapper (TM) Level-1 Data Products | U.S. Geological Survey (accessed 2.2.23) https://www.usgs.gov/centers/eros/science/usgs-eros-archive-landsat-archives-landsat-4-5-thematic-mapper-tm-level-1-data.\u003c/li\u003e\n\u003cli\u003eFang, X., Zhao, W., Zhang, C., Zhang, D., Wei, X., Qiu, W., \u0026amp; Ye, Y., 2020. Methodology for credibility assessment of historical global LUCC datasets. Science China Earth Sciences. 63, 1013\u0026ndash;1025. https://doi.org/10.1007/S11430-019-9555-3.\u003c/li\u003e\n\u003cli\u003eGaglio, M., Aschonitis, V. G., Gissi, E., Castaldelli, G., \u0026amp; Fano, E. A., 2016. Land use change effects on ecosystem services of river deltas and coastal wetlands: case study in Volano\u0026ndash;Mesola\u0026ndash;Goro in Po river delta (Italy). Wetlands Ecology and Management. 25, 67\u0026ndash;86. https://doi.org/10.1007/S11273-016-9503-1.\u003c/li\u003e\n\u003cli\u003eGibson, R. N., Atkinson, R. J. A., \u0026amp; Gordon, J. D. M., 2011. Oceanography and marine biology: An annual review. In Oceanography and Marine Biology: An Annual Review. 49, 1\u0026ndash;297. https://doi.org/10.1016/0025-326x(94)90189-9.\u003c/li\u003e\n\u003cli\u003eGiosan, L., Constantinescu, S., Clift, P. D., Tabrez, A. R., Danish, M., \u0026amp; Inam, A., 2006. Recent morphodynamics of the Indus delta shore and shelf. Continental Shelf Research. 26, 1668\u0026ndash;1684. https://doi.org/10.1016/J.CSR.2006.05.009.\u003c/li\u003e\n\u003cli\u003eGohain, K. J., Goswami, A., Mohammad, P., \u0026amp; Kumar, S., 2023. Modelling relationship between land use land cover changes, land surface temperature and urban heat island in Indore city of central India. Theoretical and Applied Climatology. 151, 1981\u0026ndash;2000. https://doi.org/10.1007/S00704-023-04371-X/METRICS.\u003c/li\u003e\n\u003cli\u003eGrybas, H., \u0026amp; Congalton, R. G., 2021. A Comparison of Multi-Temporal RGB and Multispectral UAS Imagery for Tree Species Classification in Heterogeneous New Hampshire Forests. Remote Sensing. 13, 2631. https://doi.org/10.3390/RS13132631.\u003c/li\u003e\n\u003cli\u003eHadeel, A., Jabbar, M., 2009. Application of remote sensing and GIS to the study of land use/cover change and urbanization expansion in Basrah province, southern Iraq. Taylor \u0026amp; Francis. 12, 135\u0026ndash;141. https://doi.org/10.1007/s11806-009-0244-7.\u003c/li\u003e\n\u003cli\u003eHine, C. S., Hagy, H. M., Horath, M. M., Yetter, A. P., Smith, R. V., \u0026amp; Stafford, J. D., 2017. Response of aquatic vegetation communities and other wetland cover types to floodplain restoration at Emiquon Preserve. Hydrobiologia. 804, 59\u0026ndash;71. https://doi.org/10.1007/S10750-016-2893-5/METRICS.\u003c/li\u003e\n\u003cli\u003eHodoki, Y., \u0026amp; Murakami, T., 2006. Effects of tidal flat reclamation on sediment quality and hypoxia in Isahaya Bay. Aquatic Conservation: Marine and Freshwater Ecosystems. 16, 555\u0026ndash;567. https://doi.org/10.1002/AQC.723.\u003c/li\u003e\n\u003cli\u003eHua, L., Zhang, X., Nie, Q., Sun, F., 2020. The impacts of the expansion of urban impervious surfaces on urban heat islands in a coastal city in China. Sustainability. 12, 475. Mdpi.Com. https://doi.org/10.3390/su12020475.\u003c/li\u003e\n\u003cli\u003eJiang, T. ting, Pan, J. fen, Pu, X. M., Wang, B., \u0026amp; Pan, J. J., 2015. Current status of coastal wetlands in China: Degradation, restoration, and future management. Estuarine, Coastal and Shelf Science. 164, 265\u0026ndash;275. https://doi.org/10.1016/J.ECSS.2015.07.046.\u003c/li\u003e\n\u003cli\u003eKantakumar, L. N., \u0026amp; Neelamsetti, P., 2015. Multi-temporal land use classification using hybrid approach. The Egyptian Journal of Remote Sensing and Space Science. 18, 289-295. https://www.sciencedirect.com/science/article/pii/S1110982315000381.\u003c/li\u003e\n\u003cli\u003eKhan, A. A., \u0026amp; Arshad, S., 2014. Wetlands of Pakistan : Distribution, Degradation and Management. Pakistan Geographical Review. 69, 28\u0026ndash;45.\u003c/li\u003e\n\u003cli\u003eKusch, E., Davy, R., \u0026amp; Seddon, A. W. R., 2022. Vegetation-memory effects and their association with vegetation resilience in global drylands. Journal of Ecology, 110, 1561\u0026ndash;1574. https://doi.org/10.1111/1365-2745.13891.\u003c/li\u003e\n\u003cli\u003eLambin, E. F., \u0026amp; Meyfroidt, P., 2011. Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences of the United States of America. 108, 3465\u0026ndash;3472. https://doi.org/10.1073/PNAS.1100480108.\u003c/li\u003e\n\u003cli\u003eLeghari, S. M., Jahangir, T. M., Khuhawar, M. Y., \u0026amp; Leghari, A., 2003. Assessment of water quality and biological indicators in the feeding and spinal drains of the left bank outfall drain (LBOD), Sindh, Pakistan. Proceedings of Pakistan Congress of Zoology. 23, 223\u0026ndash;231.\u003c/li\u003e\n\u003cli\u003eLet, M., \u0026amp; Pal, S., 2023. Socio-ecological well-being perspectives of wetland loss scenario: A review. Journal of Environmental Management. 326, 116692. https://doi.org/10.1016/J.JENVMAN.2022.116692.\u003c/li\u003e\n\u003cli\u003eLi, B. B., Fang, X. Q., Ye, Y., \u0026amp; Zhang, X. Z., 2010. Accuracy assessment of global historical cropland datasets based on regional reconstructed historical data\u0026mdash;A case study in Northeast China. Science China Earth Sciences. 53, 1689\u0026ndash;1699. https://doi.org/10.1007/S11430-010-4053-5.\u003c/li\u003e\n\u003cli\u003eLi, J., Zheng, X., Zhang, C., \u0026amp; Chen, Y., 2018. Impact of Land-Use and Land-Cover Change on Meteorology in the Beijing\u0026ndash;Tianjin\u0026ndash;Hebei Region from 1990 to 2010. Sustainability. 10, 176. https://doi.org/10.3390/SU10010176.\u003c/li\u003e\n\u003cli\u003eLi, M., Ti, P., Zhu, X., Xiong, T., Mei, Y., \u0026amp; Li, Z., 2022. Analysis of Spatial and Temporal Variability of Global Wetlands during the Last 20 Years Using GlobeLand30 Data. Remote Sensing. 14, 5553. https://doi.org/10.3390/RS14215553.\u003c/li\u003e\n\u003cli\u003eLiang, F., Zhang, X., Li, H., Yu, H., Lin, Q., Jiang, M., \u0026amp; Zhang, J., 2022. Land Use Classification Based on Maximum Likelihood Method. Smart Innovation, Systems and Technologies. 253, 133\u0026ndash;139. https://doi.org/10.1007/978-981-16-5036-9_15/COVER.\u003c/li\u003e\n\u003cli\u003eLiang, L., Wang, Z., \u0026amp; Li, J., 2019. The effect of urbanization on environmental pollution in rapidly developing urban agglomerations. Journal of Cleaner Production. 237, 117649. https://doi.org/10.1016/J.JCLEPRO.2019.117649.\u003c/li\u003e\n\u003cli\u003eLilleb\u0026oslash;, A. I., Willaert, T., Garcia-Alegre, A. G., Queiroga, H., \u0026amp; Cunha e S\u0026aacute;, M. A., 2019. Measuring vulnerability of marine and coastal habitats\u0026apos; potential to deliver ecosystem services: Complex Atlantic region as case study. Frontiers in Marine Science. 6, 199. https://doi.org/10.3389/FMARS.2019.00199/BIBTEX.\u003c/li\u003e\n\u003cli\u003eLiping, C., Yujun, S., \u0026amp; Saeed, S., 2018. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques\u0026mdash;A case study of a hilly area, Jiangle, China. PLoS ONE, 13, 0200493. https://doi.org/10.1371/JOURNAL.PONE.0200493.\u003c/li\u003e\n\u003cli\u003eLu, D., \u0026amp; Weng, Q., 2006. Use of impervious surface in urban land-use classification. Remote sensing of environment. 102, 146-160. https://www.sciencedirect.com/science/article/pii/S0034425706000721.\u003c/li\u003e\n\u003cli\u003eLu, D., Mausel, P., Brond\u0026iacute;zio, E., \u0026amp; Moran, E., 2004. Change detection techniques. International Journal of Remote Sensing. 25, 2365\u0026ndash;2401. https://doi.org/10.1080/0143116031000139863.\u003c/li\u003e\n\u003cli\u003eMa, Z., Melville, D. S., Liu, J., Chen, Y., Yang, H., Ren, W., Zhang, Z., Piersma, T., \u0026amp; Li, B., 2014. Rethinking China\u0026apos;s new great wall. Science. 346, 912\u0026ndash;914. https://doi.org/10.1126/SCIENCE.1257258/SUPPL_FILE/1257258.MA.SM.PDF.\u003c/li\u003e\n\u003cli\u003eMallinis, G., Emmanoloudis, D., Giannakopoulos, V., Maris, F., \u0026amp; Koutsias, N., 2011. Mapping and interpreting historical land cover/land use changes in a Natura 2000 site using earth observational data: The case of Nestos delta, Greece. Applied Geography. 31, 312-320. https://doi.org/10.1016/j.apgeog.2010.07.002.\u003c/li\u003e\n\u003cli\u003eMandal, A., Majumder, A., Dhaliwal, S. S., Toor, A. S., Mani, P. K., Naresh, R. K., Gupta, R. K., \u0026amp; Mitran, T., 2020. Impact of agricultural management practices on soil carbon sequestration and its monitoring through simulation models and remote sensing techniques: A review. Critical Reviews in Environmental Science and Technology. 52, 1-49. https://doi.org/10.1080/10643389.2020.1811590.\u003c/li\u003e\n\u003cli\u003eMaru, H., Haileslassie, A., Zeleke, T., \u0026amp; Teferi, E., 2023. Analysis of the impacts of land use land cover change on streamflow and surface water availability in Awash Basin, Ethiopia. Geomatics, Natural Hazards and Risk. 14, 1-25. https://doi.org/10.1080/19475705.2022.2163193.\u003c/li\u003e\n\u003cli\u003eMoisa, M. B., Bulto, T. W., Werku, B. C., Berkessa, Y. W., Chebo, A. K., Negash, D. A., \u0026amp; Gemeda, D. O., 2023. Analyzing Wetland Dynamics Using Geospatial Techniques: A Case of Abay Choman and Jimma Geneti Watershed, Horo Guduru Wollega Zone, Western Ethiopia. Air, Soil and Water Research. 16, 11786221221150183. https://doi.org/10.1177/11786221221150183/ASSET/IMAGES/LARGE/10.1177_11786221221150183-FIG2.JPEG.\u003c/li\u003e\n\u003cli\u003eMondal, A., Kundu, S., Chandniha, D. K., Chandniha, S. K., Shukla, R., \u0026amp; Mishra, P. K., 2012. Comparison of support vector machine and maximum likelihood classification technique using satellite imagery.. International Journal of Remote Sensing and GIS. 1, 116-123.\u003c/li\u003e\n\u003cli\u003eMundia, C., sensing, M. A.-I., 2005. Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS. Taylor \u0026amp; Francis. 26, 2831\u0026ndash;2849. https://doi.org/10.1080/01431160500117865.\u003c/li\u003e\n\u003cli\u003eMunir, S. N. U., \u0026amp; Kidwai, A. A., 2017. Signature based geospatial analysis to find out the suspended sediment concentration in indus river delta, pakistan. International Journal of Applied Sciences and Management. 2, 233-245. https://scholar.archive.org/work/7a425avtsfa4zj5tpfmlderwqi/access/wayback/https://www.waljatcollege.edu.om/journal/images/IJASM_020201.pdf.\u003c/li\u003e\n\u003cli\u003eNayak, A., \u0026amp; Bhushan, B., 2022. Wetland Ecosystems and Their Relevance to the Environment: Importance of Wetlands. Handbook of Research on Monitoring and Evaluating the Ecological Health of Wetlands. 22, 1-16. https://doi.org/10.4018/978-1-7998-9498-8.CH001.\u003c/li\u003e\n\u003cli\u003eNguyen, C. T., Chidthaisong, A., Diem, P. K., \u0026amp; Huo, L. Z., 2021. A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8. Land. 10, 231. https://doi.org/10.3390/LAND10030231.\u003c/li\u003e\n\u003cli\u003eNie, X., Jin, X., Wu, J., Li, W., Wang, H., \u0026amp; Yao, Y., 2023. Evaluation of coastal wetland ecosystem services based on modified choice experimental model: A case study of mangrove wetland in Beibu Gulf, Guangxi. Habitat International. 131, 102735. https://doi.org/10.1016/J.HABITATINT.2022.102735.\u003c/li\u003e\n\u003cli\u003eParsons, T., 2021. The Weight of Cities: Urbanization Effects on Earth\u0026apos;s Subsurface. AGU Advances. 2, e2020AV000277. https://doi.org/10.1029/2020AV000277.\u003c/li\u003e\n\u003cli\u003ePupins, M., Nekrasova, O., Marushchak, O., Tytar, V., Theissinger, K., Čeirāns, A., Skute, A., \u0026amp; Georges, J. Y., 2023. Potential Threat of an Invasive Fish Species for Two Native Newts Inhabiting Wetlands of Europe Vulnerable to Climate Change. Diversity. 15, 201. https://doi.org/10.3390/D15020201/S1.\u003c/li\u003e\n\u003cli\u003eRahaman, S., Kumar, P., Chen, R., Meadows, M. E., \u0026amp; Singh, R. B., 2020. Remote Sensing Assessment of the Impact of Land Use and Land Cover Change on the Environment of Barddhaman District, West Bengal, India. Frontiers in Environmental Science. 8, 127. https://doi.org/10.3389/FENVS.2020.00127/BIBTEX.\u003c/li\u003e\n\u003cli\u003eRaja Shekar, P., \u0026amp; Mathew, A., 2023. Detection of land use/land cover changes in a watershed: A case study of the Murredu watershed in Telangana state, India. Watershed Ecology and the Environment. 5, 46\u0026ndash;55. https://doi.org/10.1016/J.WSEE.2022.12.003.\u003c/li\u003e\n\u003cli\u003eRamsar., 2023. Homepage | Convention on Wetlands (accessed 4.1.23) https://www.ramsar.org/\u003c/li\u003e\n\u003cli\u003eReis, S., 2008. Analyzing Land Use/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey. Sensors, 8, 6188\u0026ndash;6202. https://doi.org/10.3390/S8106188.\u003c/li\u003e\n\u003cli\u003eRogers, K., Saintilan, N., \u0026amp; Copeland, C., 2013. Managed Retreat of Saline Coastal Wetlands: Challenges and Opportunities Identified from the Hunter River Estuary, Australia. Estuaries and Coasts. 37, 67\u0026ndash;78. https://doi.org/10.1007/S12237-013-9664-6.\u003c/li\u003e\n\u003cli\u003eSaha, U. D., Mitra, S., Mondal, M., \u0026amp; Biswas, N., 2022. Assessment of the impact of anthropogenic stress on wetlands of the upper Ganga Delta, India: a multi criteria decision making approach. Geocarto International. 37, 12580\u0026ndash;12601. https://doi.org/10.1080/10106049.2022.2068678.\u003c/li\u003e\n\u003cli\u003eScott, A. J., \u0026amp; Symons, M. J., 1971. Clustering methods based on likelihood ratio criteria. Biometrics. 71, 387-397. https://www.jstor.org/stable/2529003.\u003c/li\u003e\n\u003cli\u003eShi, L., Liu, F., Zhang, Z., Zhao, X., Liu, B., Xu, J., Wen, Q., Yi, L., \u0026amp; Hu, S., 2015. Spatial differences of coastal urban expansion in China from 1970s to 2013. Chinese Geographical Science. 25, 389\u0026ndash;403. https://doi.org/10.1007/S11769-015-0765-Y.\u003c/li\u003e\n\u003cli\u003eSingh Boori, M., Choudhary, K., \u0026amp; Kupriyanov, A., 2020. 2019 Land Cover Map of Southeast Asia at 30 m Spatial Resolution with Changes Since 2010. Optical Memory and Neural Networks. 29, 257\u0026ndash;262. https://doi.org/10.3103/S1060992X20030091.\u003c/li\u003e\n\u003cli\u003eSiyal, D., 2019. Impact of Climate Change in the Indus River Delta and Coastal Region of Pakistan. Sindh University Research Journal. 19, 53. http://www.gcisc.org.pk/UnivCon/Pakistan-MUET.pdf.\u003c/li\u003e\n\u003cli\u003eSolangi, G. S., Siyal, A. A., Babar, M. M., \u0026amp; Siyal, P., 2019. Groundwater quality evaluation using the water quality index (WQI), the synthetic pollution index (SPI), and geospatial tools: a case study of Sujawal district, Pakistan. Human and Ecological Risk Assessment: An International Journal. 26, 1529\u0026ndash;1549. https://doi.org/10.1080/10807039.2019.1588099.\u003c/li\u003e\n\u003cli\u003eSolangi, G. S., Siyal, A. A., Siyal, Z. U. A., Siyal, P., Panhwar, S., Keerio, H. A., \u0026amp; Bhatti, N. B., 2022. Social and ecological climate change vulnerability assessment in the Indus delta, Pakistan. Water Practice and Technology. 17, 1666\u0026ndash;1678. https://doi.org/10.2166/WPT.2022.087.\u003c/li\u003e\n\u003cli\u003eSousa, C. A. M., Cunha, M. E., \u0026amp; Ribeiro, L., 2020. Tracking 130 years of coastal wetland reclamation in Ria Formosa, Portugal: Opportunities for conservation and aquaculture. Land Use Policy. 94, 104544. https://doi.org/10.1016/J.LANDUSEPOL.2020.104544.\u003c/li\u003e\n\u003cli\u003eStehman, S. V., \u0026amp; Foody, G. M., 2019. Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment. 231, 111199. https://doi.org/10.1016/J.RSE.2019.05.018.\u003c/li\u003e\n\u003cli\u003eStein, E. D., Doughty, C. L., Lowe, J., Cooper, M., Sloane, E. B., \u0026amp; Bram, D. L., 2020. Establishing Targets for Regional Coastal Wetland Restoration Planning Using Historical Ecology and Future Scenario Analysis: The Past, Present, Future Approach. Estuaries and Coasts. 43, 207\u0026ndash;222. https://doi.org/10.1007/S12237-019-00681-4.\u003c/li\u003e\n\u003cli\u003eSubedi, Y. R., Kristiansen, P., Cacho, O., \u0026amp; Ojha, R. B., 2021. Agricultural Land Abandonment in the Hill Agro-ecological Region of Nepal: Analysis of Extent, Drivers and Impact of Change. Environmental Management. 67, 1100\u0026ndash;1118. https://doi.org/10.1007/S00267-021-01461-2/METRICS.\u003c/li\u003e\n\u003cli\u003eSun, J., Huang, J., Wang, Q., -, A., Zhao, S., Wang, G., Liu, Y., Xu, J., Yao, L., Guo, P., \u0026amp; Yuan, Z., 2018. The land use pattern and its changes in the upper reaches of the Yangtze River from 1980 to 2015. Earth and Environmental Science. 676, 012097. https://doi.org/10.1088/1755-1315/676/1/012097.\u003c/li\u003e\n\u003cli\u003eSyvitski, J. P. M., Kettner, A. J., Overeem, I., Giosan, L., Brakenridge, G. R., Hannon, M., \u0026amp; Bilham, R., 2013. Anthropocene metamorphosis of the Indus Delta and lower floodplain. Anthropocene. 3, 24\u0026ndash;35. https://doi.org/10.1016/J.ANCENE.2014.02.003.\u003c/li\u003e\n\u003cli\u003eTian, B., Wu, W., Yang, Z., \u0026amp; Zhou, Y., 2016. Drivers, trends, and potential impacts of long-term coastal reclamation in China from 1985 to 2010. Estuarine, Coastal and Shelf Science. 170, 83\u0026ndash;90. https://doi.org/10.1016/J.ECSS.2016.01.006.\u003c/li\u003e\n\u003cli\u003eTudi, M., Ruan, H. D., Wang, L., Lyu, J., Sadler, R., Connell, D., Chu, C., \u0026amp; Phung, D. T., 2021. Agriculture Development, Pesticide Application and Its Impact on the Environment. International Journal of Environmental Research and Public Health. 18 1112. https://doi.org/10.3390/IJERPH18031112.\u003c/li\u003e\n\u003cli\u003eUl Din, S., Wai Leung Mak, H., Sarathi Roy, P., \u0026amp; Alexakis, D. D., 2021. Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework. Remote Sensing. 13, 3337. https://doi.org/10.3390/RS13163337.\u003c/li\u003e\n\u003cli\u003eUS., 2023. Summary of the Clean Water Act | US EPA (accessed 22.3.23) https://www.epa.gov/laws-regulations/summary-clean-water-act\u003c/li\u003e\n\u003cli\u003eVisual Information Solutions, I., 2009. Atmospheric Correction Module Atmospheric Correction Module: QUAC and FLAASH User\u0026apos;s Guide 20AC47DOC (accessed 15.3.23) www.apache.org/\u003c/li\u003e\n\u003cli\u003eWang, S., Ma, Q., Ding, H., 2018. Application of Landsat Data for Urban Growth Monitoring in Jeddah. Earth Systems and Environment. 1, 1-11. https://www.sciencedirect.com/science/article/pii/S0921344916301215.\u003c/li\u003e\n\u003cli\u003eWang, W., Liu, H., Li, Y., \u0026amp; Su, J., 2014. Development and management of land reclamation in China. Ocean \u0026amp; Coastal Management. 102, 415\u0026ndash;425. https://doi.org/10.1016/J.OCECOAMAN.2014.03.009.\u003c/li\u003e\n\u003cli\u003eWu, H., Wang, R., Yan, P., Wu, S., Chen, Z., Zhao, Y., Cheng, C., Hu, Z., Zhuang, L., Guo, Z., Xie, H., \u0026amp; Zhang, J., 2023. Constructed wetlands for pollution control. Nature Reviews Earth \u0026amp; Environment. 23, 1\u0026ndash;17. https://doi.org/10.1038/s43017-023-00395-z.\u003c/li\u003e\n\u003cli\u003eWu, W., Yang, Z., Tian, B., Huang, Y., Zhou, Y., \u0026amp; Zhang, T., 2018. Impacts of coastal reclamation on wetlands: Loss, resilience, and sustainable management. Estuarine, Coastal and Shelf Science. 210, 153\u0026ndash;161. https://doi.org/10.1016/J.ECSS.2018.06.013.\u003c/li\u003e\n\u003cli\u003eYan, Y., Kuang, W., Zhang, C., 2012. Impacts of impervious surface expansion on soil organic carbon\u0026ndash;a spatially explicit study. Scientific reports, 5, 17905. https://www.nature.com/articles/srep17905.\u003c/li\u003e\n\u003cli\u003eYumin, T., Bingxin, B., \u0026amp; Mohammad, M. S., 2016. Time series remote sensing based dynamic monitoring of land use and land cover change. Earth Observation and Remote Sensing Applications. 16, 202\u0026ndash;206. https://doi.org/10.1109/EORSA.2016.7552797.\u003c/li\u003e\n\u003cli\u003eZhan, C., Xie, M., Lu, H., Liu, B., Wu, Z., Wang, T., Zhuang, B., Li, M., \u0026amp; Li, S., 2023. Impacts of urbanization on air quality and the related health risks in a city with complex terrain. Atmospheric Chemistry and Physics. 23, 771\u0026ndash;788. https://doi.org/10.5194/ACP-23-771-2023.\u003c/li\u003e\n\u003cli\u003eZhao, J., Dong, Y., Zhang, M., \u0026amp; Huang, L., 2020. Comparison of identifying land cover tempo-spatial changes using GlobCover and MCD12Q1 global land cover products. Arabian Journal of Geosciences. 13, 1\u0026ndash;12. https://doi.org/10.1007/S12517-020-05780-2.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"coastal wetlands, reclamation, maximum likelihood classification, land cover transfer matrix, area-weight centroid, the Indus River Delta","lastPublishedDoi":"10.21203/rs.3.rs-3301912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3301912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal wetlands are the most productive and biologically diverse ecosystems, benefiting both human populations and the total environment. However, they are continuously threatened by anthropogenic activities. The Indus River Delta, the 6th largest in the world, has been adversely affected due to reclamation. We examined the spatiotemporal dynamics of coastal wetlands and reclamation in the Indus River Delta from 1972 to 2022. Wetlands conversion to reclamation was extracted from 6-Landsat images. Land cover transfer matrix was used to analyze land use land cover (LULC) changes in different time intervals. Area-weight centroid was constructed to determine the migration trend of reclamation and coastal wetlands. Spatial accurateness was assessed using Producer's Accuracy (PA), User Accuracy (UA), and KAPPA coefficient (KC). Our results reveled that from the 1972 to 2022, the net area of natural wetlands declined by 1.9% (26.1 km\u003csup\u003e2)\u003c/sup\u003e, while reclamation (settlement and cultivated land) increased by 14.7% (200.1 km\u003csup\u003e2\u003c/sup\u003e), and 27.5% (373.5 km\u003csup\u003e2\u003c/sup\u003e), respectively. The fastest areal change rate for coastal wetlands was \u0026minus;\u0026thinsp;1.1 km\u003csup\u003e2\u003c/sup\u003e/yr from 2012 to 2022, whereas the fastest areal change rate for settlement and cultivated land were 7.6 km\u003csup\u003e2\u003c/sup\u003e/yr from 1992 to 2002 and 28.6 km\u003csup\u003e2\u003c/sup\u003e/yr from 2012 to 2022. Centroids of wetlands moved slowly eastwards from Kharo Chan taluka to Keti Bandar in the first and third decades, then southwards in the second decade, later on, westwards in the fourth decade, and finally back southwards from Keti Bandar taluka to the Kharo Chan in the fifth decade with fastest movement. Centroids of settlement expanded slowly in all directions over five decades. Centroids of cultivated land migrated westwards in the first, third, and fourth decades, northwards in the second decade, and southwards in the fifth decade from Keti Bandar to Kharo Chan. The findings of this study would provide a scientific basis for sustainable land development.\u003c/p\u003e","manuscriptTitle":"Coastal wetlands of Indus River Delta are under risk due to reclamation: A spatiotemporal analysis during the past 50 years from 1972 to 2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-05 15:05:43","doi":"10.21203/rs.3.rs-3301912/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64cdcb0b-ac23-4b1b-9e54-085fad5fa3cb","owner":[],"postedDate":"September 5th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-29T10:14:13+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-05 15:05:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3301912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3301912","identity":"rs-3301912","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}