Dynamicity of Ecosystem Service Value Driven by Land Use/Land Cover Alteration in Vadodara City, India

Dynamicity of Ecosystem Service Value Driven by Land Use/Land Cover Alteration in Vadodara City, India | 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 Dynamicity of Ecosystem Service Value Driven by Land Use/Land Cover Alteration in Vadodara City, India Anindita Pal, Atul K Tiwari, Shyamal Dutta, Ami Rawal, Rolee Kanchan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4694960/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The ecosystem is considered the fundamental unit of ecology which plays a crucial role in providing a range of essential services to individuals. These services include the provision of food and shelter, as well as the regulation of climate and environment, either directly or indirectly. However, the emergence of urban industrial cities has witnessed the uncontrolled exploitation of resources, the decline of biodiversity, unprecedented population growth and climate change. Consequently, these factors have led to ecological crises as the ecosystem services experience a gradual increase in the built-up areas. Considering this, the current study examines the relationship between land use change and the status of ecosystem services in Vadodara City, India. The ecosystem service value was calculated using the methodology proposed by Costanza in 1997 and 2014, while the contribution of Land Use/Land Cover (LULC) was determined using the Maximum Likelihood Classification. The findings highlighted that LULCs are critical drivers for the loss of ecosystem services. During 2001–2021, it was found that built-up and water bodies increased by 24.48% and 0.21%, respectively, while vegetation, agricultural and barren land decreased by 1.11%, 25.61% and 0.19% respectively. The valuation of ecosystem services in 2001 observed $ 3,517,118 and $ 125,607,186 using the corresponding coefficients of Costanza, 1997 and 2014, while in 2021, their cumulative value slightly increased to $ 3,629,024 based on the 1997 coefficient and $ 131,537,398 based on the 2014 coefficient. Furthermore, the study analyses the response of ecosystem service values to land use alterations and the elasticity value at the zonal level to gain insight into spatial variation. Ecosystem Service Value Geospatial Techniques Land Use Urban Ecosystem Vadodara City Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Ecosystem Services (ESs) refer to the benefits that humans gain from the functions performed by ecosystems, as well as the direct and indirect contributions of ecosystems to human well-being (Wang et al., 2024). In addition to the provision of various tangible resources such as food, wood and other raw materials, ecosystems also offer intangible services such as carbon sequestration, water purification and aesthetic benefits, all of which play a crucial role in human survival, health and well-being (Chowdhury & Behera, 2021). The economic assessment of these services enables the quantification of the benefits derived from ecosystems to enhance the well-organized management of natural resources in a sustainable manner (Tolessa et al., 2017). The Expansion of the urban area constitutes one of the primary catalysts accountable for the extensive alterations in ecological conditions and the depletion of natural resources. The rapid surge in population and the subsequent surge in infrastructural development manifest as highly rampant phenomena within urban areas, both contributing to the transformation of Land Use and Land Cover (LULC) patterns (Song & Deng, 2017). The gradual transformation of natural ecosystems into a combination of human and natural elements is a result of this process, ultimately leading to the degradation of the structural and functional coherence of these ecosystems (Sannigrahi et al., 2019). Furthermore, the incessant loss of water bodies and the depletion of vegetation cover within urban spaces poses a formidable threat to the long-term environmental sustainability and regenerative capacity of these areas (Everard et al., 2021). Such degradation can potentially cause a rise in temperature within urban environments, exacerbating the urban heat island effect, urban food effect, and the subsequent diminishment or alteration of ESs (Haque et al., 2023). Consequently, the multifaceted impacts of urban expansion on the ecological landscape necessitates immediate attention and concerted efforts towards mitigating these adverse effects. The relationship between Ecosystem Services and changes in land use has established alterations in land use as a primary concern in the field of global environmental sustainability. The rapid transition of LULC is propelled by various factors, including the rapid growth of population, the swift trends of urbanization, and the unregulated pursuit of economic expansion (Datta & Deb, 2012). The unregulated changes in LULC have specifically given rise to environmental predicaments in developing nations that are undergoing rapid urbanization (Dutta & Guchhait, 2022; Navara & Vedamuthu, 2022). These alterations in land use and cover have posed threats to both the natural world and humanity (Somvanshi et al., 2024). The changes in LULC are a crucial catalyst for environmental deterioration on a global scale and modifications in ESs (Chatterjee et al., 2022). Therefore, it is imperative to investigate the consequences of changes in land use patterns, practices and their implications for ESs. The impacts of LULC extend to the natural environment, leading to alterations in ES (Yang et al., 2022; Haldar et al., 2023). The processes associated with LULC play a vital role in sustaining and regulating the Ecosystem Services Value (ESV) due to their direct effects on the provision of such services (Sharma et al., 2023). United Nations introduced the Ecosystem Assessment in 2005, while Germany established the Economics of Ecosystems and Biodiversity (TEEB) Foundation. European countries also aimed to develop a comprehensive system of monetary accounting methods through the TEEB Foundation in 2010. However, this methodology proved to be intricate and uncertain. ESs refer to the advantages that individuals derive either directly or indirectly from ecosystems. The Millennium Ecosystem Assessment in 2005 categorized Ecosystem Service into four groups: supporting, provisioning, regulating, and cultural services. Subsequently, the European Environment Agency (EEA) released the Common International Classification of Ecosystem Services (CICES, V5.1), which reclassified ES into three categories: provisioning, regulating and maintenance, and cultural services (Evaluación del Ecosistema del Milenio (MEA), 2005). This classification has been utilized in the current study. Ecosystem valuation is an economic process that assigns a value to an ecosystem and its services, known as the ESV (He et al., 2021). The topic of ESV, being a significant global concern, has been explored in different countries, prompting numerous scholars to dedicate their efforts to assessing and evaluating the value of ecosystem services on a worldwide level (M. Das & Das, 2019; Ankur et al., 2022; Zhou et al., 2022; Shrestha & Acharya, 2021; Das et al., 2023; Sarwate et al., 2023; Sharma et al., 2021). In the light of the rapid advancement of industrialization and urban expansion, numerous studies have also been conducted in various regions of India and the consequences of LULC changes on ESs in a general manner have been examined (Sharma et al., 2020). However, it is imperative to conduct a specific study on urban industrial centers to comprehensively evaluate the extent of ESs depletion and develop potential management strategies. Vadodara City is currently undergoing a swift and profound transformation in terms of its LULC due to the unprecedented expansion of its urban areas (Singh et al., 2016). The monitoring of spatial and temporal variations in ESV within the Vadodara is crucial for the awareness of planners and policymakers in devising suitable strategies to enhance the quality of urban life for its residents. The future alterations in ESVs will have direct consequences on LULC changes, which in turn will indirectly impact the well-being of the general populace (Ding et al., 2024). Regarding these factors, this study is an effort to 1) unveil the spatio-temporal patterns of LULC from 2001 to 2021, 2) assess the variations in ESV that arise from changes in land use and ( 3 ) ultimately evaluate the responsiveness of ESV to variations in LULC. 2. Materials and Methods 2.1 Study Area The present study was conducted in Vadodara City, which is part of the Vadodara district of Gujarat State (India). The city is situated on both sides of the river Vishwamitri and lies between 22°22`53``N to 22°12`7``N and 73°5`6``E to 73°17`22``E (Fig. 1 ). It covers an area of 217.21 km2. Geomorphologically, 94% of the area is covered by the pediment pediplain complex, and older alluvial plains cover 6% of the total area (Pal et al., 2023). The city is situated in the transition zone between the Champaner Series and the Great Gujarat Plain. The mean elevation is 34.72 m, but it varies in different parts of the city. The core of city is the highest elevated (50–56 m) area, and the southern parts are low elevated (16–20 m). According to Koppen’s classification scheme of climate, the city comes under the Tropical Savannah (Aw) type climate. The annual count of days with rainfall amounts to an average of 37, while the average annual precipitation is recorded at 806 mm. According to Indian Meteorological Department (2021), the average annual maximum and minimum temperatures stand at 34.4°C and 21.3°C, respectively. Administratively, city is divided into 19 municipal wards and 4 administrative zones. The city turned out to be a million-person city in 1991 (1.03 million) when LPG reforms were introduced in India, and it reached 1.75 million in 2011 with a 6485 persons per km2 population density (Census, 2011). The literacy rate was 89.74% in 2011. The city is also known as the educational hub of western India, has 13 engineering, 136 science and commerce colleges, 5 management institutes, 12 medical colleges, and 8 universities situated in and around the city. In terms of network and connectivity, Vadodara city is well connected by road, railway, and air. The Golden Quadrantal Route, Delhi-Mumbai Railway and Motorway, Ahmedabad-Mumbai Semi-High-Speed Rail, and other highways pass through Vadodara city. Various industrial complexes are established in the city and its outer areas, which make it an important industrial city and growth centre in western India and Gujarat as well. The city is a tourist destination because of various historical sites like Laxmi Vilas Palace, Lal Baug Palace, Sayaji Zoo, museums, Sardar Sarovar Dam and Statue of Unity etc. As per the report of the McKinsey Global Institute, the population of the city is projected to reach 4.2 million with a GDP of 35 billion dollars by 2030. These facts and driving factors accelerate the faster pace of urbanisation and tend to become metropolises. Hence, the study of LULC change and Ecosystem Service Evaluation are essential to manage the urban environment and sustainability of Vadodara city and other tier-2 cities of India. 2.2 Datasets and Methods Two sets of Landsat data from different sensors, specifically the Thematic Mapper (TM) and Operational Land Imager (OLI), were utilized in the current investigation. The datasets were sourced from the USGS Earth Explorer ( https://earthexplorer.usgs.gov/ ) and description of data has been given in Table 1 . Additionally, location-specific data from Google Earth and a zone-wise administrative boundary map from the Diary of Vadodara Municipal Corporation (2023) were employed in this study. These data were applied to validate the image classification and aid in the creation of various thematic layers within a GIS environment. Table 1 Description of Satellite Data Period/Satellite 2001/Landsat-5 TM 2021/Landsat-8 OLI/TIRS Acquisition Date & Time 20/04/2001–11:28:53 24/04/2021-11:42:25 Path & Row 148/045 Spatial Resolution 30 meter Purpose LULC Classification Source: https://earthexplorer.usgs.gov/ 2.2.1 Land Use and Land Cover Classification Satellite-based remote sensing using imagery has gained widespread acceptance as a valuable approach for identifying LULC, thanks to its accessibility across diverse terrains, availability of real-time data, and broad applicability in scientific research. In this particular investigation, a range of band-composites were utilized to identify distinct characteristics of LULC for the years 2001 and 2021 respectively. Firstly, the utilization of the image enhancement and geoprocessing tools were employed to categorize and select the study area with the intention of generating a land cover map. Subsequently, the Landsat satellite images underwent radiometric and atmospheric corrections. The most prevalent techniques for improving the quality of Landsat images encompass contrast enhancement, saturation, colour adjustment, intensity manipulation, and density slicing, among other methods. Furthermore, the satellite imagery was then overlaid into a single layer utilizing the Band Composite tool within the ArcMap software, resulting in the creation of a False Colour Composite (FCC) image. To identify the land use types within the research area, various composite band combinations such as Natural Colour Composite (NCC), True Colour Composite (TCC), and FCC were employed. Moreover, both Landsat 5 TM and Landsat 8 OLI images were processed using a total of 7 bands to enhance visual identification. As multiple studies have recommended incorporating a greater number of bands with different compositions, all these bands were utilized to accurately define each class. Using ArcGIS 10.8, the land cover categories were classified into five distinct groups: built-up area, agricultural land, water bodies, vegetation cover and barren land. For the purpose of this study, the maximum likelihood classification technique was used. Following the LULC scheme proposed by Costanza et al., 1997, a supervised classification algorithm was employed to generate a map of LULC. The selection of training sets is a crucial step in supervised classification, whereby pixels with similar digital values (DN values) are assigned as training sets. On average, 150 training sets were established for each class, scattered across different sections of the study area. Based on these sites, the computer was directed to classify the entire image. Any misclassifications resulting from user error or other factors in the final image were rectified through the application of masking techniques involving different layers. 2.2.2 Accuracy Assessment To achieve a higher level of precision in the results, the assessment of accuracy in relation to the validation of ground truth is an essential component of image classification for users, which should not be lower than 85% to ensure a more accurate explanation and recognition. For this study, a total of 750 sample locations for the year 2001 and 2021 were randomly selected as training sets from the overall study area. These locations were then cross validated using data provided by Google Earth and ESRI land cover maps. User and producer accuracy were assessed by employing error metrics. To ascertain the correlation between producer and user accuracy, the Kappa coefficient (with a value ranging from 0 to 1) was scrutinized. The overall accuracy of the classified images was found to be 89% and 91% with Kappa coefficients of 0.87 and 0.92, in 2001 and 2021 respectively. These values exceed the accepted threshold and indicate a strong agreement between the corresponding pixels in the classified image and the Google map. The Kappa coefficient, which is a measure of accuracy, was calculated through the following Eq. 1 (Tiwari et al., 2024): $$\:\text{k}=\frac{\text{N}{\sum\:}_{\text{i}-1}^{\text{r}}{\text{X}}_{\text{i}\text{i}}-{\sum\:}_{\text{i}=1}^{\text{r}}({\text{X}}_{\text{i}+}\:\:\times\:{\text{X}}_{+\text{i}})}{{\text{N}}^{2}-{\sum\:}_{\text{i}=1}^{\text{r}}({\text{X}}_{\text{i}+}\:\:\times\:{\text{X}}_{+\text{i}})}$$ 1 Where, variable 'k' denotes the kappa coefficient, while 'N' signifies the total number of observations. Additionally, 'r' represents the rows present in the error matrix. Notably, X ii stands for the number of observations in a specific row and column 'i', while X i+ denotes the total number of observations in a given row 'i'. Moreover, 'X i+ ' refers to the total observations in a particular column 'i'. 2.2.3 Change Detection of Land Use and Land Cover The assessment of changes in land use involves the evaluation of LULC data, which have been collected over various time periods, with the aim of identifying modifications. The utilization of a change detection method contributes to a more comprehensive understanding of the consequences of land exploitation over this time span. By detecting, identifying, and estimating changes in land use, valuable insights are gained that can ultimately inform decision-making and the development of futuristic plans. The monitoring of land use change through the application of Remote Sensing (RS) and Geographic Information System (GIS) techniques allows for the examination of spatial and temporal patterns, which are crucial for ensuring sustainable management of land resources and addressing environmental concerns. Initially, the prepared classified raster dataset is transformed into a vector file. Through the utilization of the dissolve geoprocessing tool, the dataset is consolidated into five distinct classes, as previously outlined. Furthermore, the intersection method is applied to detect changes during the two periods of classification. 2.2.4 Estimation of Ecosystem Services Value The estimated Ecosystem Services Values (ESVs) have been determined for five LULC classes for both the years 2001 and 2021 across the four spatial units within the study area. Subsequently, a comparison has been made between the LULC classes and the sixteen biomes identified in the ESV model proposed by Costanza et al., (1997). However, it is worth noting that the ESV model formulated by Costanza et al., (1997) has faced criticism due to its strong association with the developed regions of the Western world (Song & Deng, 2017). As a result, several scholars have made certain modifications, such as grouping ecosystem services into four major types with nine subtypes, as done by (Xue & Luo, 2015). By incorporating the framework of both Costanza et al., (1997) and the data from the MEA (2005), Song & Deng (2017) estimated the ESV in specific regions of China. Later, Costanza et al., (2014) introduced an enhanced scheme for estimating global ESVs, which improved the ESVs of certain classes. In the present study, the ESV for each individual LULC class has been estimated using both the model of Costanza et al., 1997, 2014 (Table 2 ). This study identifies five LULC classes (Vegetation cover, barren land, built-up land, agricultural land, and water bodies) that are like the LULC classes proposed by Costanza et al., (1997). Table 2 LULC and ESV as per (Costanza et al., 1997, 2014) Land use Type Equivalent Biome (Costanza et al. 1997) Ecosystem service Coefficient (US $ ha − 1 year − 1 ) Costanza et al. 1997 Costanza et al. 2014 Vegetation Forest 969 3800 Bare Land Barren land 0 0 Built up Urban 0 6661 Agricultural Land Cropland 92 5568 Water body Wetland and rivers 8498 12512 Source: Based on (Costanza et al., 1997, 2014) ESV has been calculated for the entire area and at the Zone level using Eqs. 2 and 3 derived from the framework proposed by Costanza et al., (1997) and Song & Deng (2017). $$\:ES{V}_{t}=\sum\:Am\times\:Vm$$ 2 $$\:{V}_{m}=\sum\:_{i=1}^{n}ES{V}_{{m}_{i}}$$ 3 Where, the total ESV can be represented as \(\:ES{V}_{t}\) , where Am represents the area of LULC type m, Vm refers to the ESV of LULC type m, and \(\:ES{V}_{mi}\) represents a specific ESV for LULC type m; to estimate the changes in ESV in the study area, Eq. 4 formulated by Song & Deng (2017) was followed. $$\:{D}_{b}=\frac{{E}_{{en}d}-{E}_{Start}}{{E}_{start}}\times\:100\%$$ 4 Where, \(\:{D}_{b}\) represents the dynamics in the ESV in block b, while \(\:{E}_{Start}\) indicates the ESV at the beginning of the study time for block b, and Eend signifies the ESV at the end of the study period. 2.2.5 Elasticity of ESV Changes in Response to LULC Elasticity pertains to the quantification of the sensitivity of a specific variable to alterations in another variable within a unified framework. The present study employed elasticity measurement from the techniques proposed by Song & Deng (2017) to assess the percentage change in ESV resulting from the percentage change in LULC for each zone. This measurement serves as an indicator of the overall alteration in ecosystem services in relation to changes in land usage. $$\:EEL={mode}value\:of\frac{\left({E}_{end}-{E}_{start}\right)∕{E}_{start}x100\%}{LCP}$$ 5 $$\:LCP=\frac{{\sum\:}_{m=1}^{5}\varDelta\:{LUT}_{m}}{{\sum\:}_{m=1}^{5}{LUT}_{m}}\times\:\frac{1}{T}\times\:100\%$$ 6 Where, EEL denoted elasticity of end-systolic volume changes with respect to alterations in LULC, which can be determined by comparing \(\:{E}_{end}\) , the ESV at the conclusion of the investigation period (2021), to \(\:{E}_{start}\) , the ESV at the initiation of the study phase (2001). Additionally, the conversion percentage of land (LCP), which indicates the magnitude and momentum of LULC conversion, can be calculated. The converted area of the m-type of LULC, represented as \(\:\varDelta\:{LUT}_{m}\) , as well as the area of m type of LULC, are also factors to consider. The time gap (T) between the commencement and conclusion of the study period, which in this present study spans 20 years, must be considered. 3. Results 3.1 Spatio-temporal Outlook of LULC and its Change Detection The diverse interregional and intraregional differences in ES provision may not be adequately encapsulated when the entire study area is considered as one cohesive entity. This is because the distribution of LULC types within any given subregion is not uniform, resulting in the formation of varied ecosystems. Consequently, it is imperative to integrate an understanding of the manifold LULC types within each subregion. In this study, five categories of LULC have been effectively identified and categorized. These categories comprised of the built-up land, encompassing structures built by humans such as residential and commercial buildings, infrastructure and roads. The second category was agricultural land, which includes areas utilized for cultivation, farming and other agricultural practices. The third category was bodies of water, referring to both natural and man-made bodies of water such as rivers, lakes, ponds and reservoirs. The fourth category was vegetation, encompassing all forms of plant life, including forests, grasslands and shrubs. Lastly, the fifth category was barren land, which pertains to areas lacking significant vegetation or human-made structures. This classification system offers a comprehensive comprehension of the various types of land cover found within the study area, enabling a more intricate analysis of the composition and dynamics of the landscape. According to the data presented in Table 3 a, the predominant land use in the study region in 2001 was built-up land, covering 42.83% of the total area. Following this, there was a proportion of 44.43% of agricultural land, with vegetation accounting for 11.73%, barren land encompassing 0.92% and water bodies constituting only 0.09%. Moreover, Zone IV demonstrated the greatest concentration of urban development, plant cover and cultivated land. The prevalence of water bodies was recorded in Zone I. Table 3 b revealed the distribution of diverse LULC types in 2021. Built-up areas had notably encompassed the largest expanse of land, specifically measuring 67.31%, whereas water bodies have been confined to the smallest area (0.3%). However, by 2021, agricultural land (18.82%) has assumed the role of the second most prevalent LULC type, followed by vegetation (12.84%) and barren land (0.73%). Likewise, in the year 2021, Zone IV showcased the greatest expanse comprising built-up land, vegetation and agricultural land. On the other hand, Zone II was characterized by the prevalence of water bodies and barren land. The Fig. 2 present an analysis of the spatial distribution and temporal evolution of LULC changes in Vadodara during the past two decades. In Fig. 2 , the distribution of built-up areas in 2001 was illustrated with a significant concentration in the central area of the city and subsequent expansion towards the periphery over the past two decades. Initially, the northeastern part of Vadodara was predominantly characterized by agricultural land, but as time progressed, a portion of this agricultural land and vegetation in the southwestern and southeastern regions underwent transformation into built-up areas. This transformation can be attributed to the rapid pace of urbanization experienced in Vadodara. Table 3 a: Zone wise LULC Area 2001 Zone Total Area (hectares) Built-up Area Water Bodies Vegetation Agricultural Land Barren Land I 4562.82 2340.9 6.57 358.02 1800.36 56.97 II 3670.83 1300.32 6.3 18.9 2304.99 40.32 III 5345.37 2038.5 5.94 711 2544.12 45.81 IV 8138.25 3622.23 0.09 1459.8 3000.15 55.98 Source: Computed from LULC Classification Table 3 b: Zone wise LULC Area 2021 Zone Total Area (hectares) Built-up Area Water Bodies Vegetation Agricultural Land Barren Land I 4562.82 3347.55 11.16 637.47 536.22 30.42 II 3670.83 2445.03 24.12 46.08 1099.35 56.25 III 5345.37 3572.46 12.06 717.75 1006.74 36.36 IV 8138.25 5252.04 17.46 1387.35 1446.3 35.1 Source: Computed from LULC Classification 3.2 Change Detection Figure.3 illustrates the changes in land use that occurred between the years 2001 and 2021. The results of the change detection analysis reveal that there were minimal modifications in the areas designated as barren land and water bodies respectively. However, the most notable transformation took place in agricultural land, which experienced conversions into barren land (103.89 ha), built-up area (5857.13 ha), water bodies (20.62 ha), and vegetation cover (1211.12 ha). Similarly, barren land was transformed into agricultural land by an extent of 16.02 ha and into built-up area by 198.144 ha. Conversely, the built-up area underwent conversion into barren land (15.54 ha) and vegetation cover (12.34 ha). Furthermore, a significant conversion was identified in vegetation, where 628.92 ha was altered into agricultural land, 15.78 ha into barren land, 2.91 ha into water bodies and 1958.38 ha into the built-up area. Lastly, water bodies experienced a trivial conversion of 1.76 ha into vegetation and 17.08 ha into the built-up area. 3.3 Estimation of Ecosystem Service Value (ESV) Each LULC category provided an assessment of the cumulative value of ESs based on the coefficient values used by Costanza et al. (1997 & 2014) on a global scale (Fig. 4 ). Tables 4 a and 4 b present the spatial and temporal allocation and fluctuations of ESVs over a span of two decades (2001 and 2011). The assessment of ES in 2001 amounted to $3517118 and $125607186 based on the respective coefficient values of 1997 and 2014. Agricultural land and built-up areas had the highest value, contributing $61,960,289 and $53,729,084 respectively, based on the 2014 coefficient. In contrast, agricultural land accounted for $887,765 with the 1997 coefficient, while built-up areas were considered negligible. The 1997 coefficient assigned a value of $160,612.2 and $2,468,741 to other classes like water bodies and vegetation, whereas for the 2014 coefficient, the value of water bodies and vegetation was $236,476.8 and $9,681,336. The coefficient value of the bare land class remains zero for both the coefficient values of 1997 and 2014 (Table 4 a). In the year 2021, the collective worth of ESVs witnessed a slightly incline, amounting to $3629024 based on the 1997 coefficient and $131537398 based on the 2014 coefficient. The 1997 coefficient revealed that the highest contribution was made by the vegetation, amounting to $2702202, whereas the built-up land valued at $0. Interestingly, no value was found for land use under built-up areas according to the 1997 coefficient, while it became the most significant contributor according to the 2014 coefficient ($97364370). The impact of agricultural land ($22765380), vegetation ($10596870) and water bodies ($810777.5) on the overall value was reported to be minimal according to 2014 coefficient. The coefficient value for the classification of bare land remains unchanged, with a value of zero for both the coefficient 1997 and 2014. According to Costanza et al. (1997), the maximum contribution of ESVs at the zone level has been observed in Zone IV, while the minimum contribution has been observed in Zone II in the western most part of the study area for 2001 and 2021. However, according to the coefficient obtained in 2014, it is outward that the highest magnitudes of ESVs and the minimum extent remain unaltered. (Table 4 b). Table 4 a: Zone level ESV (in USD/yr) in respect to LU/LC classes of 2001 based on the coefficient of Costanza et al. (1997 and 2014) Zone 1997 2014 1997 2014 1997 2014 1997 2014 1997 2014 Built up Water bodies Vegetation Agricultural land Barren land I 0.00 15592734.90 55831.86 82203.84 346921.38 1360476.00 165633.12 10024404.48 0 0 II 0.00 8661431.52 53537.40 78825.60 18314.10 71820.00 212059.08 12834184.32 0 0 III 0.00 13578448.50 50478.12 74321.28 688959.00 2701800.00 234059.04 14165660.16 0 0 IV 0.00 24127674.03 764.82 1126.08 1414546.20 5547240.00 276013.80 16704835.20 0 0 Source: Computed based on ESV calculation Table 4 b: Zone level ESV (in USD/yr) in respect to LU/LC classes of 2021 based on the coefficient of Costanza et al. (1997 and 2014) Zone 1997 2014 1997 2014 1997 2014 1997 2014 1997 2014 Built up Water bodies Vegetation Agricultural land Barren land I 0 22298031 94837.68 139633.9 617708.4 2422386 49332.24 2985673 0 0 II 0 16286345 204971.8 301789.4 44651.52 175104 101140.2 6121181 0 0 III 0 23796156 102485.9 150894.7 695499.8 2727450 92620.08 5605528 0 0 IV 0 34983838 148375.1 218459.5 1344342 5271930 133059.6 8052998 0 0 Source: Computed based on ESV calculation 3.4 Dynamics in Net Ecosystem Service Value (ESV) The current section focuses on the net change in ESVs between 2001 and 2021 in the study area. This analysis is based on two coefficients, one from 1997 and the other from 2014 (Fig. 5 ). Over the designated timeframe, the vegetation's ESV exhibited a combination of both positive and negative alterations, spanning a broad range from − 4.96–78.05% across all zones according to 1997 coefficient (Fig. 7 ). However, in Vadodara, the vegetation ESV experienced a reduction of 7.8%. The negative change in agricultural land ranged from − 51.79% to -70.22%. In terms of water bodies, the entire city had a negative change ranging from 69.86–193%. The coefficient value of 2014 for built-up areas indicated a positive rate of around in four zones, ranging from 43–88% (Fig. 8 ). Similarly, water bodies had a positive change (63.82 to 193), agricultural land has a negative change (-49.79 to -70.22) and vegetation had a medley change (-4.96 to 71.05) In general, there were similar trends of favourable transformation observed for both vegetation and water bodies across most zones within the study area, apart from Zone IV where only vegetation showed a divergent pattern. Conversely, a negative alteration was noted for agricultural land during the period from 2001 to 2021. Considering the combined ESV values of the four zones within the study area, it can be observed that the overall Ecosystem Services Values experienced a net rate of change ranging from -$65547.99 to $193491.99, according to the findings of Costanza et al. (1997). Similarly, Costanza et al. (2014) reported a range of $785904.21 to $5930212.1 for the same metric over a span of 20 years, from 2001 to 2021. According to the coefficient of 1997, the rate of net change of ESV ranged between − 3.88% and 34.04% while it is ranged between 2.90% and 5.76% according to the coefficient of 2014 (Table 5 ). Table 5 ESV Change (%) in respect to LULC classes between 2001–2021 based on the coefficient of Costanza et al. (1997 and 2014) Zone Costanza et al. 1997 Costanza et al. 2014 Agricultural Land Vegetation Water bodies Agricultural Land Vegetation Built up Land Water bodies I -70.22 78.05 69.86 -70.22 71.05 43.00 63.82 II -52.31 123.81 282.86 -48.31 143.81 88.03 282.86 III -60.43 0.95 103.03 -60.43 0.75 75.25 83.03 IV -51.79 -4.96 193 -49.79 -4.96 44.99 193 Source: Computed based on ESV calculation 3.5 Ecosystem Service Changes in Response to LULC Changes The elasticity technique was employed to identify the changes in ESV in relation to LULC dynamics based on the coefficient of 1997 and 2014 within the time phase of 2001 to 2021 (Fig. 6 ). It is widely acknowledged that higher elasticity values indicate greater responses in terms of LULC dynamics. The range of elasticity value was − 0.98 to 0.79 based on the coefficient of 1997, whereas it increased 1.30 to 2.04 for the coefficient of 2014. This indicated that the ESV values calculated based on the coefficient of 2014 exhibit stronger responses to LULC changes compared to the ESV estimation based on the coefficient of 1997 in Vadodara City. The elasticity of the ESVs, determined by the coefficient of 1997 for the period of 2001 to 2021, revealed that the Zone II had the highest and positive elasticity, whereas the remaining three zones had the negative elasticity. LCP values ranging from 0.02 to 0.03 in all the zones of the study area displayed very low EEL values. However, when the EEL value was estimated based on the coefficient of 2014, Zone I (1.30), III (1.87) and IV (1.98), demonstrated poor elasticity rates while Zone II (2.04) had moderate elasticity, indicating that the impact of LULC on the ESV blueprint was not properly reflected (Table 6 ). Table 6 EEL and LCP based on the Coefficient of Costanza et al. (1997 and 2014) Zone EEL LCP 1997 2014 Zone I -0.97753 1.301079 0.0313 Zone II 0.794063 2.043094 0.0323 Zone III -5.98863 1.870797 0.0344 Zone IV -4.45284 1.978988 0.0266 Source: Computed based on EEL calculation 4. Discussion LULC alterations in this particular region have become increasingly conspicuous in recent decades due to the significant rise in population pressure, resulting in the conversion of various land uses into built-up areas within Vadodara city. The transformation of LULC categories within the study area has exhibited remarkable dynamism over a span of 20 years. In 2021, the current land utilization in the study region is dominated by built-up land, which accounts for 42.83% followed by agricultural land at 44.43%, Vegetation accounted for 11.73%, while agricultural land covered 0.92%, water bodies constituted 0.09% of the area. Across all zones, the built-up area increased by 24.48% between 2001 and 2021. Simultaneously, agricultural land experienced a reduction due to its conversion into built-up areas. Consequently, there has been a considerable fluctuation in the ESVs, due to being influenced by the substantial increase in the built-up areas. On the other hand, the ESV obtained from built-up showed a considerable increase, which can be explained by the clearance of forests (Das et al., 2022). The alteration of ecosystems and their associated services is largely influenced by LULC change (Tiwari & Kanchan, 2024). The overall ES in the study area has experienced a significant decrease over time due to the depletion of important landscape elements, particularly forests, shrub/bush land and grassland. The study area featured vegetation as a prominent land-use class, which experienced a continual decline throughout the study period, subsequently converting into built-up areas and agricultural land even agricultural land also experienced downfall. This progressive reduction in vegetation can be ascribed to the substantial influx of individuals migrating from the surrounding rural and semi-urban areas (Kaul & Sopan, 2012). As a result, some areas that were once covered with vegetation have been cleared and transformed into agricultural land. This transformation has enabled a continuous increase in agricultural practices to meet the demands of the local population. The ESV play a crucial role in assessing regional sustainable development and it is important to comprehend the underlying mechanisms driving it (Ocloo et al., 2024). This comprehension is essential for effective scientific management, optimization and decision-making related to ecosystem function (Luan & Liu, 2022). Despite extensive research on the relationship between land use and socio-economic factors with ecosystem services, accurately assessing the impact of changes in land use types on ESV has remained a challenge (Haase et al., 2014; M. Das & Das, 2019; Halmy et al., 2015; Harrison et al., 2018; Nguyen & Chidthaisong, 2023; Vatitsi et al., 2023; Zaman-Ul-haq et al., 2022). Furthermore, many studies have failed to account for the gains and losses of ESV resulting from the conversion of land for construction purposes (Bera et al., 2022; Caro et al., 2020; Chowdhury & Behera, 2021; Haque et al., 2023; Hu et al., 2019; Nguyen & Chidthaisong, 2023; Ziaul Hoque et al., 2022). However, this study explicitly addressed the ESV of construction land (classified under built-up areas), considering its potential adverse impacts on ecosystem functions like Climate Regulation and Environmental Purification. The results showed that converting land to construction use can diminish the ESV of the area and result in a loss of value. When it comes to spatial arrangement, the ESV was observed to be lower in central part of the city compared to the surrounding regions. This finding indicates that the expansion of cities significantly contributes to the exacerbation of imbalances between the availability and requirement of Ecosystem services. On the other hand, spatio-temporal variation of ESV is influenced by a wide range of variables, resulting from their intricate interactions (Luan & Liu, 2022; Xue & Luo, 2015). Both natural and human disturbance factors play a role in shaping ecosystem functioning, with human activities having a more immediate and evident impact in the short-to-medium term (M. Das et al., 2023). As a result, the variability in ESV across different regions is not only influenced by human activities but also by the interplay of natural factors that define the unique ecological characteristics of each area. Alterations in land use patterns directly affect the spatial distribution of biological habitats and resources, thereby impacting the processes and functions of ecosystems. 5. Conclusion The current investigation examined the dynamics of the ESVs in relation to changes in LULC. Landsat satellite data from two different time periods were utilized to classify land use and land cover and its change detection for 2001 and 2021. Additionally, the coefficient of ecosystem services value, as outlined by Costanza et al., 1997, 2014, was calculated for the same year. Due to the inevitable consequences of population growth, industrialization and urbanization, various land use and land cover categories underwent significant modifications over a span of 20 years (2001–2021). Changes in land usage were primarily focused on developed and agricultural areas, with vegetation being predominantly affected. However, the developed land had the greatest impact on the ecosystem, despite being negative. The ESV of the Vadodara City in the year 2021, which amounted to $ 135166421.8, exhibited an upward trend when compared to the value of $ 129124303.8 recorded in 2001, thereby signifying an overall decrement in the ecological condition. Furthermore, it is essential to implement sustainable and effective environmental legislation to ensure a balance between environmental integrity and economic growth. Based on this research and the future plan of Vadodara Urban Development Authority (VUDA-2041), further policy measures for the ecosystem restoration might be recommended as follows: It is crucial and need to be given top priority to protect water bodies, flora and fauna. Promoting the installation of green roofs more successfully would contribute to the sustainability of the urban future by incorporating nature into cities. It should be a priority to restore the ability of damaged terrestrial environments to enable them to get back to their original functions. To encourage ecosystem management and conservation, it is necessary to raise community understanding of the value of ecosystem services and include them in the restoration procedure. It is crucial for the preservation of ecosystems to precisely pinpoint flood flow zones, canals, rivers, ponds and locations where flood is retained. This will support proper drainage, promote groundwater replenishment and shield surface water from contamination. In summary, this study has established a comprehensive research framework that has applied the academic research findings of ES supply and demand to the formulation of specific policies at the local level. Similarly, further exploration of similar applications at either a national or regional level could be advantageous for informing policy related issues to land use and ES. This framework has played a significant role in delineating distinct areas for managing ES and in suggesting specific recommendations for adapting planning practices. To be specific, effective land use strategies that can offset ES deficits include curbing the uncontrolled expansion of urban areas, enhancing the efficiency of land use, reducing carbon emissions from industries, increasing the presence of green spaces and open spaces in urban areas. By promoting the sustainable development of ES, these strategies also make a significant contribution to the social and economic progress of urban areas. Declarations Author Contribution AP collected, analyzed the data and drafted the first manuscript. AKT undertook the cartographic groundwork, outlined the methodology and contributed to some portion of the manuscript writing. SD assisted in the formulation of the methodology and prepared the maps. AR and RK were involved in literature review, shaping the conceptual framework and providing detailed feedback on the manuscript. Acknowledgement We would like to express our sincere thanks to the USGS for providing Landsat satellite data. 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04:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4694960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4694960/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62183302,"identity":"4c91acd2-af49-4917-ab68-7efc20bf4b22","added_by":"auto","created_at":"2024-08-10 11:35:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":757254,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4694960/v1/39f1c46203396ff05b11fe9a.png"},{"id":62185149,"identity":"b57f3e6a-f253-40bd-9ee6-f14d3ef41751","added_by":"auto","created_at":"2024-08-10 11:51:03","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":773683,"visible":true,"origin":"","legend":"\u003cp\u003eZone-wise LULC of 2001 and 2021\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4694960/v1/5910ea484ee0a388e058ff07.jpeg"},{"id":62184425,"identity":"918c8a3c-edec-437d-aa5f-41eef970493e","added_by":"auto","created_at":"2024-08-10 11:43:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":536339,"visible":true,"origin":"","legend":"\u003cp\u003eLULC Change Detection\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4694960/v1/162f38fc033150b7c94b05ff.png"},{"id":62183307,"identity":"9860d23b-a1ff-45bf-8ce4-e7385edc0a73","added_by":"auto","created_at":"2024-08-10 11:35:03","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":262800,"visible":true,"origin":"","legend":"\u003cp\u003eZone Wise Ecosystem Services of 2001 and 2021\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4694960/v1/2ccb07f6fad6006d9ddbd524.jpeg"},{"id":62184424,"identity":"5c259ed3-ded7-4e6d-92fd-8e795215e815","added_by":"auto","created_at":"2024-08-10 11:43:03","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":188798,"visible":true,"origin":"","legend":"\u003cp\u003eNet Change Rates (2001-2021) of Ecosystem Services for Each LULC Classes Based on (A) Costanza et al. (1997) and (B) Costanza et al. (2014).\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4694960/v1/00f45e77549ee95f632676b5.jpeg"},{"id":62185150,"identity":"3c0c0bd5-e28c-4ca0-b1cb-d730ccb49842","added_by":"auto","created_at":"2024-08-10 11:51:03","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":192743,"visible":true,"origin":"","legend":"\u003cp\u003eElasticity of Ecosystem Service Values between 2001 and 2021\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4694960/v1/f8b83f9e2604b09ed490ea67.jpeg"},{"id":62183305,"identity":"955c20b9-2a74-462d-9a82-eb04402039bb","added_by":"auto","created_at":"2024-08-10 11:35:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":52139,"visible":true,"origin":"","legend":"\u003cp\u003eLULC Class-wise ESV Change at Zone Level\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4694960/v1/08a564b3f6f2da001d6a31b1.png"},{"id":62183303,"identity":"ad890106-cf6c-430b-87e8-7618dfb90b0b","added_by":"auto","created_at":"2024-08-10 11:35:03","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":72276,"visible":true,"origin":"","legend":"\u003cp\u003eLULC Class-wise ESV Change in Zonal Level\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4694960/v1/42f7bd7bfd9832e6ce202c4f.png"},{"id":62185829,"identity":"ddbed72f-9482-4b78-b3e8-d9a5fdd8fd36","added_by":"auto","created_at":"2024-08-10 11:59:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":841307,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4694960/v1/ccf48b81-ecfb-46cc-b3f6-80b6a154c516.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamicity of Ecosystem Service Value Driven by Land Use/Land Cover Alteration in Vadodara City, India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEcosystem Services (ESs) refer to the benefits that humans gain from the functions performed by ecosystems, as well as the direct and indirect contributions of ecosystems to human well-being (Wang et al., 2024). In addition to the provision of various tangible resources such as food, wood and other raw materials, ecosystems also offer intangible services such as carbon sequestration, water purification and aesthetic benefits, all of which play a crucial role in human survival, health and well-being (Chowdhury \u0026amp; Behera, 2021). The economic assessment of these services enables the quantification of the benefits derived from ecosystems to enhance the well-organized management of natural resources in a sustainable manner (Tolessa et al., 2017).\u003c/p\u003e \u003cp\u003eThe Expansion of the urban area constitutes one of the primary catalysts accountable for the extensive alterations in ecological conditions and the depletion of natural resources. The rapid surge in population and the subsequent surge in infrastructural development manifest as highly rampant phenomena within urban areas, both contributing to the transformation of Land Use and Land Cover (LULC) patterns (Song \u0026amp; Deng, 2017). The gradual transformation of natural ecosystems into a combination of human and natural elements is a result of this process, ultimately leading to the degradation of the structural and functional coherence of these ecosystems (Sannigrahi et al., 2019). Furthermore, the incessant loss of water bodies and the depletion of vegetation cover within urban spaces poses a formidable threat to the long-term environmental sustainability and regenerative capacity of these areas (Everard et al., 2021). Such degradation can potentially cause a rise in temperature within urban environments, exacerbating the urban heat island effect, urban food effect, and the subsequent diminishment or alteration of ESs (Haque et al., 2023). Consequently, the multifaceted impacts of urban expansion on the ecological landscape necessitates immediate attention and concerted efforts towards mitigating these adverse effects.\u003c/p\u003e \u003cp\u003eThe relationship between Ecosystem Services and changes in land use has established alterations in land use as a primary concern in the field of global environmental sustainability. The rapid transition of LULC is propelled by various factors, including the rapid growth of population, the swift trends of urbanization, and the unregulated pursuit of economic expansion (Datta \u0026amp; Deb, 2012). The unregulated changes in LULC have specifically given rise to environmental predicaments in developing nations that are undergoing rapid urbanization (Dutta \u0026amp; Guchhait, 2022; Navara \u0026amp; Vedamuthu, 2022). These alterations in land use and cover have posed threats to both the natural world and humanity (Somvanshi et al., 2024). The changes in LULC are a crucial catalyst for environmental deterioration on a global scale and modifications in ESs (Chatterjee et al., 2022). Therefore, it is imperative to investigate the consequences of changes in land use patterns, practices and their implications for ESs. The impacts of LULC extend to the natural environment, leading to alterations in ES (Yang et al., 2022; Haldar et al., 2023). The processes associated with LULC play a vital role in sustaining and regulating the Ecosystem Services Value (ESV) due to their direct effects on the provision of such services (Sharma et al., 2023).\u003c/p\u003e \u003cp\u003eUnited Nations introduced the Ecosystem Assessment in 2005, while Germany established the Economics of Ecosystems and Biodiversity (TEEB) Foundation. European countries also aimed to develop a comprehensive system of monetary accounting methods through the TEEB Foundation in 2010. However, this methodology proved to be intricate and uncertain. ESs refer to the advantages that individuals derive either directly or indirectly from ecosystems. The Millennium Ecosystem Assessment in 2005 categorized Ecosystem Service into four groups: supporting, provisioning, regulating, and cultural services. Subsequently, the European Environment Agency (EEA) released the Common International Classification of Ecosystem Services (CICES, V5.1), which reclassified ES into three categories: provisioning, regulating and maintenance, and cultural services (Evaluaci\u0026oacute;n del Ecosistema del Milenio (MEA), 2005). This classification has been utilized in the current study. Ecosystem valuation is an economic process that assigns a value to an ecosystem and its services, known as the ESV (He et al., 2021).\u003c/p\u003e \u003cp\u003eThe topic of ESV, being a significant global concern, has been explored in different countries, prompting numerous scholars to dedicate their efforts to assessing and evaluating the value of ecosystem services on a worldwide level (M. Das \u0026amp; Das, 2019; Ankur et al., 2022; Zhou et al., 2022; Shrestha \u0026amp; Acharya, 2021; Das et al., 2023; Sarwate et al., 2023; Sharma et al., 2021). In the light of the rapid advancement of industrialization and urban expansion, numerous studies have also been conducted in various regions of India and the consequences of LULC changes on ESs in a general manner have been examined (Sharma et al., 2020). However, it is imperative to conduct a specific study on urban industrial centers to comprehensively evaluate the extent of ESs depletion and develop potential management strategies.\u003c/p\u003e \u003cp\u003eVadodara City is currently undergoing a swift and profound transformation in terms of its LULC due to the unprecedented expansion of its urban areas (Singh et al., 2016). The monitoring of spatial and temporal variations in ESV within the Vadodara is crucial for the awareness of planners and policymakers in devising suitable strategies to enhance the quality of urban life for its residents. The future alterations in ESVs will have direct consequences on LULC changes, which in turn will indirectly impact the well-being of the general populace (Ding et al., 2024). Regarding these factors, this study is an effort to 1) unveil the spatio-temporal patterns of LULC from 2001 to 2021, 2) assess the variations in ESV that arise from changes in land use and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) ultimately evaluate the responsiveness of ESV to variations in LULC.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe present study was conducted in Vadodara City, which is part of the Vadodara district of Gujarat State (India). The city is situated on both sides of the river Vishwamitri and lies between 22\u0026deg;22`53``N to 22\u0026deg;12`7``N and 73\u0026deg;5`6``E to 73\u0026deg;17`22``E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It covers an area of 217.21 km2. Geomorphologically, 94% of the area is covered by the pediment pediplain complex, and older alluvial plains cover 6% of the total area (Pal et al., 2023). The city is situated in the transition zone between the Champaner Series and the Great Gujarat Plain. The mean elevation is 34.72 m, but it varies in different parts of the city. The core of city is the highest elevated (50\u0026ndash;56 m) area, and the southern parts are low elevated (16\u0026ndash;20 m). According to Koppen\u0026rsquo;s classification scheme of climate, the city comes under the Tropical Savannah (Aw) type climate. The annual count of days with rainfall amounts to an average of 37, while the average annual precipitation is recorded at 806 mm. According to Indian Meteorological Department (2021), the average annual maximum and minimum temperatures stand at 34.4\u0026deg;C and 21.3\u0026deg;C, respectively. Administratively, city is divided into 19 municipal wards and 4 administrative zones. The city turned out to be a million-person city in 1991 (1.03\u0026nbsp;million) when LPG reforms were introduced in India, and it reached 1.75\u0026nbsp;million in 2011 with a 6485 persons per km2 population density (Census, 2011). The literacy rate was 89.74% in 2011. The city is also known as the educational hub of western India, has 13 engineering, 136 science and commerce colleges, 5 management institutes, 12 medical colleges, and 8 universities situated in and around the city. In terms of network and connectivity, Vadodara city is well connected by road, railway, and air. The Golden Quadrantal Route, Delhi-Mumbai Railway and Motorway, Ahmedabad-Mumbai Semi-High-Speed Rail, and other highways pass through Vadodara city. Various industrial complexes are established in the city and its outer areas, which make it an important industrial city and growth centre in western India and Gujarat as well. The city is a tourist destination because of various historical sites like Laxmi Vilas Palace, Lal Baug Palace, Sayaji Zoo, museums, Sardar Sarovar Dam and Statue of Unity etc. As per the report of the McKinsey Global Institute, the population of the city is projected to reach 4.2\u0026nbsp;million with a GDP of 35\u0026nbsp;billion dollars by 2030. These facts and driving factors accelerate the faster pace of urbanisation and tend to become metropolises. Hence, the study of LULC change and Ecosystem Service Evaluation are essential to manage the urban environment and sustainability of Vadodara city and other tier-2 cities of India.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Datasets and Methods\u003c/h2\u003e \u003cp\u003eTwo sets of Landsat data from different sensors, specifically the Thematic Mapper (TM) and Operational Land Imager (OLI), were utilized in the current investigation. The datasets were sourced from the USGS Earth Explorer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and description of data has been given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additionally, location-specific data from Google Earth and a zone-wise administrative boundary map from the Diary of Vadodara Municipal Corporation (2023) were employed in this study. These data were applied to validate the image classification and aid in the creation of various thematic layers within a GIS environment.\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\u003eDescription of Satellite Data\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\" colname=\"c1\"\u003e \u003cp\u003ePeriod/Satellite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2001/Landsat-5 TM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021/Landsat-8 OLI/TIRS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcquisition Date \u0026amp; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20/04/2001\u0026ndash;11:28:53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24/04/2021-11:42:25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath \u0026amp; Row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e148/045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e30 meter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLULC Classification\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSource: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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 Land Use and Land Cover Classification\u003c/h2\u003e \u003cp\u003eSatellite-based remote sensing using imagery has gained widespread acceptance as a valuable approach for identifying LULC, thanks to its accessibility across diverse terrains, availability of real-time data, and broad applicability in scientific research. In this particular investigation, a range of band-composites were utilized to identify distinct characteristics of LULC for the years 2001 and 2021 respectively.\u003c/p\u003e \u003cp\u003eFirstly, the utilization of the image enhancement and geoprocessing tools were employed to categorize and select the study area with the intention of generating a land cover map. Subsequently, the Landsat satellite images underwent radiometric and atmospheric corrections. The most prevalent techniques for improving the quality of Landsat images encompass contrast enhancement, saturation, colour adjustment, intensity manipulation, and density slicing, among other methods. Furthermore, the satellite imagery was then overlaid into a single layer utilizing the Band Composite tool within the ArcMap software, resulting in the creation of a False Colour Composite (FCC) image. To identify the land use types within the research area, various composite band combinations such as Natural Colour Composite (NCC), True Colour Composite (TCC), and FCC were employed. Moreover, both Landsat 5 TM and Landsat 8 OLI images were processed using a total of 7 bands to enhance visual identification. As multiple studies have recommended incorporating a greater number of bands with different compositions, all these bands were utilized to accurately define each class. Using ArcGIS 10.8, the land cover categories were classified into five distinct groups: built-up area, agricultural land, water bodies, vegetation cover and barren land. For the purpose of this study, the maximum likelihood classification technique was used.\u003c/p\u003e \u003cp\u003eFollowing the LULC scheme proposed by Costanza et al., 1997, a supervised classification algorithm was employed to generate a map of LULC. The selection of training sets is a crucial step in supervised classification, whereby pixels with similar digital values (DN values) are assigned as training sets. On average, 150 training sets were established for each class, scattered across different sections of the study area. Based on these sites, the computer was directed to classify the entire image. Any misclassifications resulting from user error or other factors in the final image were rectified through the application of masking techniques involving different layers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Accuracy Assessment\u003c/h2\u003e \u003cp\u003eTo achieve a higher level of precision in the results, the assessment of accuracy in relation to the validation of ground truth is an essential component of image classification for users, which should not be lower than 85% to ensure a more accurate explanation and recognition. For this study, a total of 750 sample locations for the year 2001 and 2021 were randomly selected as training sets from the overall study area. These locations were then cross validated using data provided by Google Earth and ESRI land cover maps. User and producer accuracy were assessed by employing error metrics. To ascertain the correlation between producer and user accuracy, the Kappa coefficient (with a value ranging from 0 to 1) was scrutinized. The overall accuracy of the classified images was found to be 89% and 91% with Kappa coefficients of 0.87 and 0.92, in 2001 and 2021 respectively. These values exceed the accepted threshold and indicate a strong agreement between the corresponding pixels in the classified image and the Google map. The Kappa coefficient, which is a measure of accuracy, was calculated through the following Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Tiwari et al., 2024):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{k}=\\frac{\\text{N}{\\sum\\:}_{\\text{i}-1}^{\\text{r}}{\\text{X}}_{\\text{i}\\text{i}}-{\\sum\\:}_{\\text{i}=1}^{\\text{r}}({\\text{X}}_{\\text{i}+}\\:\\:\\times\\:{\\text{X}}_{+\\text{i}})}{{\\text{N}}^{2}-{\\sum\\:}_{\\text{i}=1}^{\\text{r}}({\\text{X}}_{\\text{i}+}\\:\\:\\times\\:{\\text{X}}_{+\\text{i}})}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, variable 'k' denotes the kappa coefficient, while 'N' signifies the total number of observations. Additionally, 'r' represents the rows present in the error matrix. Notably, X\u003csub\u003eii\u003c/sub\u003e stands for the number of observations in a specific row and column 'i', while X\u003csub\u003ei+\u003c/sub\u003e denotes the total number of observations in a given row 'i'. Moreover, 'X\u003csub\u003ei+\u003c/sub\u003e' refers to the total observations in a particular column 'i'.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Change Detection of Land Use and Land Cover\u003c/h2\u003e \u003cp\u003eThe assessment of changes in land use involves the evaluation of LULC data, which have been collected over various time periods, with the aim of identifying modifications. The utilization of a change detection method contributes to a more comprehensive understanding of the consequences of land exploitation over this time span. By detecting, identifying, and estimating changes in land use, valuable insights are gained that can ultimately inform decision-making and the development of futuristic plans. The monitoring of land use change through the application of Remote Sensing (RS) and Geographic Information System (GIS) techniques allows for the examination of spatial and temporal patterns, which are crucial for ensuring sustainable management of land resources and addressing environmental concerns. Initially, the prepared classified raster dataset is transformed into a vector file. Through the utilization of the dissolve geoprocessing tool, the dataset is consolidated into five distinct classes, as previously outlined. Furthermore, the intersection method is applied to detect changes during the two periods of classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Estimation of Ecosystem Services Value\u003c/h2\u003e \u003cp\u003eThe estimated Ecosystem Services Values (ESVs) have been determined for five LULC classes for both the years 2001 and 2021 across the four spatial units within the study area. Subsequently, a comparison has been made between the LULC classes and the sixteen biomes identified in the ESV model proposed by Costanza et al., (1997). However, it is worth noting that the ESV model formulated by Costanza et al., (1997) has faced criticism due to its strong association with the developed regions of the Western world (Song \u0026amp; Deng, 2017). As a result, several scholars have made certain modifications, such as grouping ecosystem services into four major types with nine subtypes, as done by (Xue \u0026amp; Luo, 2015). By incorporating the framework of both Costanza et al., (1997) and the data from the MEA (2005), Song \u0026amp; Deng (2017) estimated the ESV in specific regions of China. Later, Costanza et al., (2014) introduced an enhanced scheme for estimating global ESVs, which improved the ESVs of certain classes. In the present study, the ESV for each individual LULC class has been estimated using both the model of Costanza et al., 1997, 2014 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This study identifies five LULC classes (Vegetation cover, barren land, built-up land, agricultural land, and water bodies) that are like the LULC classes proposed by Costanza et al., (1997).\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\u003eLULC and ESV as per (Costanza et al., 1997, 2014)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLand use Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEquivalent Biome (Costanza et al. 1997)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEcosystem service Coefficient (US \u003cspan\u003e$\u003c/span\u003e ha \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e year \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCostanza et al. 1997\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCostanza et al. 2014\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBarren land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland and rivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSource: Based on (Costanza et al., 1997, 2014)\u003c/em\u003e\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\u003eESV has been calculated for the entire area and at the Zone level using Eqs.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e derived from the framework proposed by Costanza et al., (1997) and Song \u0026amp; Deng (2017).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:ES{V}_{t}=\\sum\\:Am\\times\\:Vm$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{V}_{m}=\\sum\\:_{i=1}^{n}ES{V}_{{m}_{i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, the total ESV can be represented as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ES{V}_{t}\\)\u003c/span\u003e\u003c/span\u003e, where Am represents the area of LULC type m, Vm refers to the ESV of LULC type m, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:ES{V}_{mi}\\)\u003c/span\u003e\u003c/span\u003e represents a specific ESV for LULC type m; to estimate the changes in ESV in the study area, Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e formulated by Song \u0026amp; Deng (2017) was followed.\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{D}_{b}=\\frac{{E}_{{en}d}-{E}_{Start}}{{E}_{start}}\\times\\:100\\%$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{b}\\)\u003c/span\u003e\u003c/span\u003e represents the dynamics in the ESV in block b, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{Start}\\)\u003c/span\u003e\u003c/span\u003e indicates the ESV at the beginning of the study time for block b, and Eend signifies the ESV at the end of the study period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Elasticity of ESV Changes in Response to LULC\u003c/h2\u003e \u003cp\u003eElasticity pertains to the quantification of the sensitivity of a specific variable to alterations in another variable within a unified framework. The present study employed elasticity measurement from the techniques proposed by Song \u0026amp; Deng (2017) to assess the percentage change in ESV resulting from the percentage change in LULC for each zone. This measurement serves as an indicator of the overall alteration in ecosystem services in relation to changes in land usage.\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:EEL={mode}value\\:of\\frac{\\left({E}_{end}-{E}_{start}\\right)∕{E}_{start}x100\\%}{LCP}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:LCP=\\frac{{\\sum\\:}_{m=1}^{5}\\varDelta\\:{LUT}_{m}}{{\\sum\\:}_{m=1}^{5}{LUT}_{m}}\\times\\:\\frac{1}{T}\\times\\:100\\%$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, EEL denoted elasticity of end-systolic volume changes with respect to alterations in LULC, which can be determined by comparing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{end}\\)\u003c/span\u003e\u003c/span\u003e, the ESV at the conclusion of the investigation period (2021), to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{start}\\)\u003c/span\u003e\u003c/span\u003e, the ESV at the initiation of the study phase (2001). Additionally, the conversion percentage of land (LCP), which indicates the magnitude and momentum of LULC conversion, can be calculated. The converted area of the m-type of LULC, represented as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{LUT}_{m}\\)\u003c/span\u003e\u003c/span\u003e, as well as the area of m type of LULC, are also factors to consider. The time gap (T) between the commencement and conclusion of the study period, which in this present study spans 20 years, must be considered.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Spatio-temporal Outlook of LULC and its Change Detection\u003c/h2\u003e\n \u003cp\u003eThe diverse interregional and intraregional differences in ES provision may not be adequately encapsulated when the entire study area is considered as one cohesive entity. This is because the distribution of LULC types within any given subregion is not uniform, resulting in the formation of varied ecosystems. Consequently, it is imperative to integrate an understanding of the manifold LULC types within each subregion. In this study, five categories of LULC have been effectively identified and categorized. These categories comprised of the built-up land, encompassing structures built by humans such as residential and commercial buildings, infrastructure and roads. The second category was agricultural land, which includes areas utilized for cultivation, farming and other agricultural practices. The third category was bodies of water, referring to both natural and man-made bodies of water such as rivers, lakes, ponds and reservoirs. The fourth category was vegetation, encompassing all forms of plant life, including forests, grasslands and shrubs. Lastly, the fifth category was barren land, which pertains to areas lacking significant vegetation or human-made structures. This classification system offers a comprehensive comprehension of the various types of land cover found within the study area, enabling a more intricate analysis of the composition and dynamics of the landscape.\u003c/p\u003e\n \u003cp\u003eAccording to the data presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, the predominant land use in the study region in 2001 was built-up land, covering 42.83% of the total area. Following this, there was a proportion of 44.43% of agricultural land, with vegetation accounting for 11.73%, barren land encompassing 0.92% and water bodies constituting only 0.09%. Moreover, Zone IV demonstrated the greatest concentration of urban development, plant cover and cultivated land. The prevalence of water bodies was recorded in Zone I. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb revealed the distribution of diverse LULC types in 2021. Built-up areas had notably encompassed the largest expanse of land, specifically measuring 67.31%, whereas water bodies have been confined to the smallest area (0.3%). However, by 2021, agricultural land (18.82%) has assumed the role of the second most prevalent LULC type, followed by vegetation (12.84%) and barren land (0.73%). Likewise, in the year 2021, Zone IV showcased the greatest expanse comprising built-up land, vegetation and agricultural land. On the other hand, Zone II was characterized by the prevalence of water bodies and barren land.\u003c/p\u003e\n \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e present an analysis of the spatial distribution and temporal evolution of LULC changes in Vadodara during the past two decades. In Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the distribution of built-up areas in 2001 was illustrated with a significant concentration in the central area of the city and subsequent expansion towards the periphery over the past two decades. Initially, the northeastern part of Vadodara was predominantly characterized by agricultural land, but as time progressed, a portion of this agricultural land and vegetation in the southwestern and southeastern regions underwent transformation into built-up areas. This transformation can be attributed to the rapid pace of urbanization experienced in Vadodara.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ea: Zone wise LULC Area 2001\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Area (hectares)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBuilt-up Area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater Bodies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgricultural Land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBarren Land\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4562.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2340.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e358.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1800.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3670.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1300.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2304.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5345.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2038.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2544.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8138.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3622.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1459.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3000.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSource: Computed from LULC Classification\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eb: Zone wise LULC Area 2021\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"73\"\u003e\n \u003cp\u003e\u003cstrong\u003eZone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"101\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Area (hectares)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"95\"\u003e\n \u003cp\u003e\u003cstrong\u003eBuilt-up Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Bodies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"104\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgricultural Land\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"89\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarren Land\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"101\"\u003e\n \u003cp\u003e4562.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"95\"\u003e\n \u003cp\u003e3347.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71\"\u003e\n \u003cp\u003e11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"104\"\u003e\n \u003cp\u003e637.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\"\u003e\n \u003cp\u003e536.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"89\"\u003e\n \u003cp\u003e30.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73\"\u003e\n \u003cp\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"101\"\u003e\n \u003cp\u003e3670.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"95\"\u003e\n \u003cp\u003e2445.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71\"\u003e\n \u003cp\u003e24.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"104\"\u003e\n \u003cp\u003e46.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\"\u003e\n \u003cp\u003e1099.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"89\"\u003e\n \u003cp\u003e56.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"101\"\u003e\n \u003cp\u003e5345.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"95\"\u003e\n \u003cp\u003e3572.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71\"\u003e\n \u003cp\u003e12.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"104\"\u003e\n \u003cp\u003e717.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\"\u003e\n \u003cp\u003e1006.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"89\"\u003e\n \u003cp\u003e36.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"73\"\u003e\n \u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"101\"\u003e\n \u003cp\u003e8138.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"95\"\u003e\n \u003cp\u003e5252.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71\"\u003e\n \u003cp\u003e17.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"104\"\u003e\n \u003cp\u003e1387.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\"\u003e\n \u003cp\u003e1446.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"89\"\u003e\n \u003cp\u003e35.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" width=\"633\"\u003e\n \u003cp\u003e\u003cem\u003eSource: Computed from LULC Classification\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Change Detection\u003c/h2\u003e\n \u003cp\u003eFigure.3 illustrates the changes in land use that occurred between the years 2001 and 2021. The results of the change detection analysis reveal that there were minimal modifications in the areas designated as barren land and water bodies respectively. However, the most notable transformation took place in agricultural land, which experienced conversions into barren land (103.89 ha), built-up area (5857.13 ha), water bodies (20.62 ha), and vegetation cover (1211.12 ha). Similarly, barren land was transformed into agricultural land by an extent of 16.02 ha and into built-up area by 198.144 ha. Conversely, the built-up area underwent conversion into barren land (15.54 ha) and vegetation cover (12.34 ha). Furthermore, a significant conversion was identified in vegetation, where 628.92 ha was altered into agricultural land, 15.78 ha into barren land, 2.91 ha into water bodies and 1958.38 ha into the built-up area. Lastly, water bodies experienced a trivial conversion of 1.76 ha into vegetation and 17.08 ha into the built-up area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Estimation of Ecosystem Service Value (ESV)\u003c/h2\u003e\n \u003cp\u003eEach LULC category provided an assessment of the cumulative value of ESs based on the coefficient values used by Costanza et al. (1997 \u0026amp; 2014) on a global scale (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Tables\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb present the spatial and temporal allocation and fluctuations of ESVs over a span of two decades (2001 and 2011). The assessment of ES in 2001 amounted to $3517118 and $125607186 based on the respective coefficient values of 1997 and 2014. Agricultural land and built-up areas had the highest value, contributing $61,960,289 and $53,729,084 respectively, based on the 2014 coefficient. In contrast, agricultural land accounted for $887,765 with the 1997 coefficient, while built-up areas were considered negligible. The 1997 coefficient assigned a value of $160,612.2 and $2,468,741 to other classes like water bodies and vegetation, whereas for the 2014 coefficient, the value of water bodies and vegetation was $236,476.8 and $9,681,336. The coefficient value of the bare land class remains zero for both the coefficient values of 1997 and 2014 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e\n \u003cp\u003eIn the year 2021, the collective worth of ESVs witnessed a slightly incline, amounting to $3629024 based on the 1997 coefficient and $131537398 based on the 2014 coefficient. The 1997 coefficient revealed that the highest contribution was made by the vegetation, amounting to $2702202, whereas the built-up land valued at $0. Interestingly, no value was found for land use under built-up areas according to the 1997 coefficient, while it became the most significant contributor according to the 2014 coefficient ($97364370). The impact of agricultural land ($22765380), vegetation ($10596870) and water bodies ($810777.5) on the overall value was reported to be minimal according to 2014 coefficient. The coefficient value for the classification of bare land remains unchanged, with a value of zero for both the coefficient 1997 and 2014. According to Costanza et al. (1997), the maximum contribution of ESVs at the zone level has been observed in Zone IV, while the minimum contribution has been observed in Zone II in the western most part of the study area for 2001 and 2021. However, according to the coefficient obtained in 2014, it is outward that the highest magnitudes of ESVs and the minimum extent remain unaltered. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ea: Zone level ESV (in USD/yr) in respect to LU/LC classes of 2001 based on the coefficient of Costanza et al. (1997 and 2014)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eZone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBuilt up\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAgricultural land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBarren land\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15592734.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55831.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82203.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346921.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1360476.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165633.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10024404.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8661431.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53537.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78825.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18314.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71820.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212059.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12834184.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13578448.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50478.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74321.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e688959.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2701800.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234059.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14165660.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24127674.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e764.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1126.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1414546.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5547240.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e276013.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16704835.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSource: Computed based on ESV calculation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eb: Zone level ESV (in USD/yr) in respect to LU/LC classes of 2021 based on the coefficient of Costanza et al. (1997 and 2014)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eZone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBuilt up\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAgricultural land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBarren land\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22298031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94837.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139633.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e617708.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2422386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49332.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2985673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16286345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204971.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301789.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44651.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101140.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6121181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23796156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102485.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150894.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e695499.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2727450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92620.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5605528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34983838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148375.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218459.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1344342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5271930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133059.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8052998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSource: Computed based on ESV calculation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Dynamics in Net Ecosystem Service Value (ESV)\u003c/h2\u003e\n \u003cp\u003eThe current section focuses on the net change in ESVs between 2001 and 2021 in the study area. This analysis is based on two coefficients, one from 1997 and the other from 2014 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Over the designated timeframe, the vegetation\u0026apos;s ESV exhibited a combination of both positive and negative alterations, spanning a broad range from \u0026minus;\u0026thinsp;4.96\u0026ndash;78.05% across all zones according to 1997 coefficient (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). However, in Vadodara, the vegetation ESV experienced a reduction of 7.8%. The negative change in agricultural land ranged from \u0026minus;\u0026thinsp;51.79% to -70.22%. In terms of water bodies, the entire city had a negative change ranging from 69.86\u0026ndash;193%. The coefficient value of 2014 for built-up areas indicated a positive rate of around in four zones, ranging from 43\u0026ndash;88% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). Similarly, water bodies had a positive change (63.82 to 193), agricultural land has a negative change (-49.79 to -70.22) and vegetation had a medley change (-4.96 to 71.05) In general, there were similar trends of favourable transformation observed for both vegetation and water bodies across most zones within the study area, apart from Zone IV where only vegetation showed a divergent pattern. Conversely, a negative alteration was noted for agricultural land during the period from 2001 to 2021. Considering the combined ESV values of the four zones within the study area, it can be observed that the overall Ecosystem Services Values experienced a net rate of change ranging from -$65547.99 to $193491.99, according to the findings of Costanza et al. (1997). Similarly, Costanza et al. (2014) reported a range of $785904.21 to $5930212.1 for the same metric over a span of 20 years, from 2001 to 2021. According to the coefficient of 1997, the rate of net change of ESV ranged between \u0026minus;\u0026thinsp;3.88% and 34.04% while it is ranged between 2.90% and 5.76% according to the coefficient of 2014 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eESV Change (%) in respect to LULC classes between 2001\u0026ndash;2021 based on the coefficient of Costanza et al. (1997 and 2014)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eZone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eCostanza et al. 1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eCostanza et al. 2014\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgricultural Land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgricultural Land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBuilt up Land\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWater bodies\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-70.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-70.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-52.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-48.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-60.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-60.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-51.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-49.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSource: Computed based on ESV calculation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Ecosystem Service Changes in Response to LULC Changes\u003c/h2\u003e\n \u003cp\u003eThe elasticity technique was employed to identify the changes in ESV in relation to LULC dynamics based on the coefficient of 1997 and 2014 within the time phase of 2001 to 2021 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). It is widely acknowledged that higher elasticity values indicate greater responses in terms of LULC dynamics. The range of elasticity value was \u0026minus;\u0026thinsp;0.98 to 0.79 based on the coefficient of 1997, whereas it increased 1.30 to 2.04 for the coefficient of 2014. This indicated that the ESV values calculated based on the coefficient of 2014 exhibit stronger responses to LULC changes compared to the ESV estimation based on the coefficient of 1997 in Vadodara City. The elasticity of the ESVs, determined by the coefficient of 1997 for the period of 2001 to 2021, revealed that the Zone II had the highest and positive elasticity, whereas the remaining three zones had the negative elasticity. LCP values ranging from 0.02 to 0.03 in all the zones of the study area displayed very low EEL values. However, when the EEL value was estimated based on the coefficient of 2014, Zone I (1.30), III (1.87) and IV (1.98), demonstrated poor elasticity rates while Zone II (2.04) had moderate elasticity, indicating that the impact of LULC on the ESV blueprint was not properly reflected (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEEL and LCP based on the Coefficient of Costanza et al. (1997 and 2014)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eZone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEEL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLCP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1997\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZone I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.97753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.301079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZone II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.794063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.043094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZone III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.98863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.870797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZone IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.45284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.978988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0266\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSource: Computed based on EEL calculation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLULC alterations in this particular region have become increasingly conspicuous in recent decades due to the significant rise in population pressure, resulting in the conversion of various land uses into built-up areas within Vadodara city. The transformation of LULC categories within the study area has exhibited remarkable dynamism over a span of 20 years. In 2021, the current land utilization in the study region is dominated by built-up land, which accounts for 42.83% followed by agricultural land at 44.43%, Vegetation accounted for 11.73%, while agricultural land covered 0.92%, water bodies constituted 0.09% of the area. Across all zones, the built-up area increased by 24.48% between 2001 and 2021. Simultaneously, agricultural land experienced a reduction due to its conversion into built-up areas. Consequently, there has been a considerable fluctuation in the ESVs, due to being influenced by the substantial increase in the built-up areas. On the other hand, the ESV obtained from built-up showed a considerable increase, which can be explained by the clearance of forests (Das et al., 2022).\u003c/p\u003e \u003cp\u003eThe alteration of ecosystems and their associated services is largely influenced by LULC change (Tiwari \u0026amp; Kanchan, 2024). The overall ES in the study area has experienced a significant decrease over time due to the depletion of important landscape elements, particularly forests, shrub/bush land and grassland. The study area featured vegetation as a prominent land-use class, which experienced a continual decline throughout the study period, subsequently converting into built-up areas and agricultural land even agricultural land also experienced downfall. This progressive reduction in vegetation can be ascribed to the substantial influx of individuals migrating from the surrounding rural and semi-urban areas (Kaul \u0026amp; Sopan, 2012). As a result, some areas that were once covered with vegetation have been cleared and transformed into agricultural land. This transformation has enabled a continuous increase in agricultural practices to meet the demands of the local population.\u003c/p\u003e \u003cp\u003eThe ESV play a crucial role in assessing regional sustainable development and it is important to comprehend the underlying mechanisms driving it (Ocloo et al., 2024). This comprehension is essential for effective scientific management, optimization and decision-making related to ecosystem function (Luan \u0026amp; Liu, 2022). Despite extensive research on the relationship between land use and socio-economic factors with ecosystem services, accurately assessing the impact of changes in land use types on ESV has remained a challenge (Haase et al., 2014; M. Das \u0026amp; Das, 2019; Halmy et al., 2015; Harrison et al., 2018; Nguyen \u0026amp; Chidthaisong, 2023; Vatitsi et al., 2023; Zaman-Ul-haq et al., 2022). Furthermore, many studies have failed to account for the gains and losses of ESV resulting from the conversion of land for construction purposes (Bera et al., 2022; Caro et al., 2020; Chowdhury \u0026amp; Behera, 2021; Haque et al., 2023; Hu et al., 2019; Nguyen \u0026amp; Chidthaisong, 2023; Ziaul Hoque et al., 2022). However, this study explicitly addressed the ESV of construction land (classified under built-up areas), considering its potential adverse impacts on ecosystem functions like Climate Regulation and Environmental Purification. The results showed that converting land to construction use can diminish the ESV of the area and result in a loss of value. When it comes to spatial arrangement, the ESV was observed to be lower in central part of the city compared to the surrounding regions. This finding indicates that the expansion of cities significantly contributes to the exacerbation of imbalances between the availability and requirement of Ecosystem services.\u003c/p\u003e \u003cp\u003eOn the other hand, spatio-temporal variation of ESV is influenced by a wide range of variables, resulting from their intricate interactions (Luan \u0026amp; Liu, 2022; Xue \u0026amp; Luo, 2015). Both natural and human disturbance factors play a role in shaping ecosystem functioning, with human activities having a more immediate and evident impact in the short-to-medium term (M. Das et al., 2023). As a result, the variability in ESV across different regions is not only influenced by human activities but also by the interplay of natural factors that define the unique ecological characteristics of each area. Alterations in land use patterns directly affect the spatial distribution of biological habitats and resources, thereby impacting the processes and functions of ecosystems.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe current investigation examined the dynamics of the ESVs in relation to changes in LULC. Landsat satellite data from two different time periods were utilized to classify land use and land cover and its change detection for 2001 and 2021. Additionally, the coefficient of ecosystem services value, as outlined by Costanza et al., 1997, 2014, was calculated for the same year. Due to the inevitable consequences of population growth, industrialization and urbanization, various land use and land cover categories underwent significant modifications over a span of 20 years (2001\u0026ndash;2021). Changes in land usage were primarily focused on developed and agricultural areas, with vegetation being predominantly affected. However, the developed land had the greatest impact on the ecosystem, despite being negative. The ESV of the Vadodara City in the year 2021, which amounted to \u003cspan\u003e$\u003c/span\u003e135166421.8, exhibited an upward trend when compared to the value of \u003cspan\u003e$\u003c/span\u003e129124303.8 recorded in 2001, thereby signifying an overall decrement in the ecological condition.\u003c/p\u003e \u003cp\u003eFurthermore, it is essential to implement sustainable and effective environmental legislation to ensure a balance between environmental integrity and economic growth. Based on this research and the future plan of Vadodara Urban Development Authority (VUDA-2041), further policy measures for the ecosystem restoration might be recommended as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIt is crucial and need to be given top priority to protect water bodies, flora and fauna.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromoting the installation of green roofs more successfully would contribute to the sustainability of the urban future by incorporating nature into cities.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIt should be a priority to restore the ability of damaged terrestrial environments to enable them to get back to their original functions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo encourage ecosystem management and conservation, it is necessary to raise community understanding of the value of ecosystem services and include them in the restoration procedure.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIt is crucial for the preservation of ecosystems to precisely pinpoint flood flow zones, canals, rivers, ponds and locations where flood is retained. This will support proper drainage, promote groundwater replenishment and shield surface water from contamination.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn summary, this study has established a comprehensive research framework that has applied the academic research findings of ES supply and demand to the formulation of specific policies at the local level. Similarly, further exploration of similar applications at either a national or regional level could be advantageous for informing policy related issues to land use and ES. This framework has played a significant role in delineating distinct areas for managing ES and in suggesting specific recommendations for adapting planning practices. To be specific, effective land use strategies that can offset ES deficits include curbing the uncontrolled expansion of urban areas, enhancing the efficiency of land use, reducing carbon emissions from industries, increasing the presence of green spaces and open spaces in urban areas. By promoting the sustainable development of ES, these strategies also make a significant contribution to the social and economic progress of urban areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAP collected, analyzed the data and drafted the first manuscript. AKT undertook the cartographic groundwork, outlined the methodology and contributed to some portion of the manuscript writing. SD assisted in the formulation of the methodology and prepared the maps. AR and RK were involved in literature review, shaping the conceptual framework and providing detailed feedback on the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our sincere thanks to the USGS for providing Landsat satellite data. We would also like to thank Mr. Soumen Chatterjee (Department of Geography, University of Burdwan, India) for his support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnkur, P., Chatterjee, S., \u0026amp; Gupta, K. (2022). Evaluation on the change characteristics of ecosystem service in Dhanbad district of Jharkhand, India based on land use change. GeoJournal, \u003cem\u003e87\u003c/em\u003e(s4), 413\u0026ndash;437. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10708-022-10588-6\u003c/span\u003e\u003cspan address=\"10.1007/s10708-022-10588-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBera, B., Bhattacharjee, S., Sengupta, N., Shit, P. K., Adhikary, P. P., Sengupta, D., \u0026amp; Saha, S. (2022). Significant reduction of carbon stocks and changes of ecosystem service valuation of Indian Sundarban. 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Egyptian Journal of Remote Sensing and Space Science, \u003cem\u003e25\u003c/em\u003e(1), 173\u0026ndash;180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrs.2022.01.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrs.2022.01.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-cities","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Cities](https://www.springer.com/journal/44327)","snPcode":"44327","submissionUrl":"https://submission.springernature.com/new-submission/44327/3","title":"Discover Cities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ecosystem Service Value, Geospatial Techniques, Land Use, Urban Ecosystem, Vadodara City","lastPublishedDoi":"10.21203/rs.3.rs-4694960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4694960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe ecosystem is considered the fundamental unit of ecology which plays a crucial role in providing a range of essential services to individuals. These services include the provision of food and shelter, as well as the regulation of climate and environment, either directly or indirectly. However, the emergence of urban industrial cities has witnessed the uncontrolled exploitation of resources, the decline of biodiversity, unprecedented population growth and climate change. Consequently, these factors have led to ecological crises as the ecosystem services experience a gradual increase in the built-up areas. Considering this, the current study examines the relationship between land use change and the status of ecosystem services in Vadodara City, India. The ecosystem service value was calculated using the methodology proposed by Costanza in 1997 and 2014, while the contribution of Land Use/Land Cover (LULC) was determined using the Maximum Likelihood Classification. The findings highlighted that LULCs are critical drivers for the loss of ecosystem services. During 2001\u0026ndash;2021, it was found that built-up and water bodies increased by 24.48% and 0.21%, respectively, while vegetation, agricultural and barren land decreased by 1.11%, 25.61% and 0.19% respectively. The valuation of ecosystem services in 2001 observed \u003cspan\u003e$\u003c/span\u003e3,517,118 and \u003cspan\u003e$\u003c/span\u003e125,607,186 using the corresponding coefficients of Costanza, 1997 and 2014, while in 2021, their cumulative value slightly increased to \u003cspan\u003e$\u003c/span\u003e3,629,024 based on the 1997 coefficient and \u003cspan\u003e$\u003c/span\u003e131,537,398 based on the 2014 coefficient. Furthermore, the study analyses the response of ecosystem service values to land use alterations and the elasticity value at the zonal level to gain insight into spatial variation.\u003c/p\u003e","manuscriptTitle":"Dynamicity of Ecosystem Service Value Driven by Land Use/Land Cover Alteration in Vadodara City, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 11:34:58","doi":"10.21203/rs.3.rs-4694960/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-28T16:28:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-27T11:41:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T04:54:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204767984722497864520133173645020159065","date":"2024-08-12T08:00:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104592566431061815724633569471607878753","date":"2024-08-06T05:24:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53650540704574844090891579365275820210","date":"2024-08-06T01:03:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-05T22:56:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-23T20:35:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-10T11:11:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Cities","date":"2024-07-06T04:31:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-cities","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Cities](https://www.springer.com/journal/44327)","snPcode":"44327","submissionUrl":"https://submission.springernature.com/new-submission/44327/3","title":"Discover Cities","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d933253b-22e7-49ff-8624-0f4b10808619","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-18T07:24:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-10 11:34:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4694960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4694960","identity":"rs-4694960","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}