Monitoring Wetland Variations Surrounding Addis Ababa: Unveiling Changes through Landsat Data Analysis

Monitoring Wetland Variations Surrounding Addis Ababa: Unveiling Changes through Landsat Data Analysis | 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 Monitoring Wetland Variations Surrounding Addis Ababa: Unveiling Changes through Landsat Data Analysis Tesfasilassie Girma Mugoro*, Legesse Begashaw This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8449459/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cities and towns in Ethiopia are expanding, often around water bodies and wetlands. A wetland is not only a water source but also offers many economic, social, and environmental benefits. These wetlands near urban areas face negative impacts from the growing sectors. The amount of solid and liquid waste produced by various sources is increasing in both volume and complexity. This study highlights the use of remote sensing and GIS methods to detect changes in wetlands around Addis Ababa. The area was classified into six categories, and accuracy was assessed by comparing classification results with ground truth using a confusion matrix. The overall accuracy was 95.58% for 1986 and 86% for 2019, with an average of 90.5%. The confusion matrix also reports the Kappa Coefficient, measuring classification accuracy, which was 0.8645 in 1986 and 0.82 in 2019. In 1986, about 52.66 km2 of land was classified as wetlands, decreasing to 17.03 km2 in 2019. The proportions of land cover types in both years, including built-up areas and other factors, have contributed to the loss of wetlands in and around the city. This calls for immediate and medium- to long-term restoration and management plans to help develop a smart city and promote a sustainable, climate-resilient green economy. Addis Ababa GIS Land use-Land cover Remote sensing Wetlands Figures Figure 1 Figure 2 1. Introduction Urbanization has brought about several undesirable environmental changes. In the process of urbanization, land cover changes and natural surfaces are replaced by the urban fabric, which is characterized by higher temperatures than the surrounding rural environment, a pattern described as urban warming (Feyisa et al. 2014), and even the undesirable expansion of the illegal action, which disturbs proper planning and implementation of our natural resources in a proactive manner, was the main gap in many African countries. Cities were established in place of fertile agricultural areas, and at the same time, cultivation was prohibited in improper slope areas, while agriculture was allowed in other areas, and vice versa. This was our main problem, which requires pragmatic action from all actors in a holistic manner. Cities and towns in Ethiopia are expanding, and it is not uncommon for most of these to be established around water bodies and wetlands. Similarly, a wetland is not only a source of water but also provides many economic, social, and environmental services. These wetlands near urban areas are suffering negative consequences from the expanding sources of these sectors. For instance, the amount of solid and liquid waste generated by different sources is increasing in size and composition. This is more severe, as most waste from developing societies is organic, although toxic inorganic and pathogenic wastes are also present (Lardinois and Klundert 1993). Organic waste loading in such systems affects different ecosystem elements, including biological resources (Cunningham and Saigo 1995 ; Miller 1995 ). Industrial centers in Ethiopia, such as Addis Ababa, Mojo, Akaki, Hawassa, and Bahir Dar, are good examples of sourcing different solid and liquid effluents from their respective nearby wetlands (EWNRA 2008 ). The illegal settlements in and around wetlands also affect the health and size of the wetlands significantly. Most of the problems from urban to wet lands in Ethiopia are related to the absence of systems that collect and manage solid and liquid wastes (Abebe, Y. D., & Geheb 2003). Humans usually interact with the natural and working landscapes to fulfil their physical health and well-being (Cox and Gaston, 2015 ), psychological comfort (Curtin and Kragh 2014 ), as well as social and spiritual comfort (Keniger et al. 2013 ), but also tend to alter the landscapes (Kareiva et al. 2007 ). The degradation of natural resources and derived environmental problems are greatly related to the increase and expansion of built-up spaces (Desta et al. 2012; Fetene and Worku 2013 ; Haddad 2015; Lian et al. 2016 ; Wu 2014 ; Zhang et al. 2017 ). Over the last couple of decades, urban expansion in the global south has increased rapidly (Cohen, 2006 ; Larsen et al., 2019 ), inducing changes such as the decrease of agricultural and arable lands (Fenta et al. 2017 ; Meskerem et al. 2017; Mohamed and Worku 2019 ) and pollution of the water bodies (Beyene 2009 ; Yohannes and Elias 2017). In Ethiopia, this occurred at the expense of productive and natural areas. In this regard, Addis Ababa city has been growing in all directions, encroaching on farmlands and other open spaces (Meskerem et al. 2017). That situation brought along major environmental problems, such as deforestation, biodiversity loss, environmental pollution, land degradation, and a and a reduction in the existence of surface and ground water (Beyene 2009 ), as well as changes in the profile of neighboring towns formerly dedicated to farming (Abo-El-Wafa et al. 2017 ). For instance, from 1986 to 2011, the built-up area of Addis Ababa city increased by 121.88 km2 (Arsiso et al. 2018; Kasa et al. 2011 ). Accordingly, most previous research on wetland was conducted in relation with other land use land cover with a focus on land use land cover changes with relation to climate change and temperature nexus (Balew & Semaw 2021), identifying challenges and prospects to sustain natural and working landscape (Shiflett et al. 2017), influence of urbanization-driven land use/cover change on climate (Arsiso et al. 2018), impact of landscape dynamics and intensities on the ecological land of major cities in Ethiopia(Degefu et al. 2021), efficiency of parks in mitigating urban heat island effect: by taking Addis Ababa city as example (Feyisa et al. 2014), contamination of Rivers and Water Reservoirs in and Around Addis Ababa City and actions to combat it (Yohannes & Elias 2017) and wetland management and policy implications (Hailu et al. 2003 ; Wood, 2000 ), hydrological impacts of wetland cultivation (Dixon 2002 ; Dixon and Wood 2003 ), socio-economic determinants (Mulugeta 1999 ), and gender dimension of wetland use (Wood 2001 ). With respect to using remote sensing (RS) technology to dictate the change on wet land in Ethiopia (Benti et al. 2021), the specific wet land dictation around Addis Ababa City in detail has not been studied so far. Effective and efficient management of wetlands requires an exhaustive survey (mapping) of their distribution and the determination of whether or not they have changed over time and to what extent (Jensen et al. 1993 ; Baker et al. 2006 ). Ground-based surveying of wetlands of large or even small wetlands is very time-consuming. The use of remote sensing techniques offers a cost-effective and time-saving alternative for delineating wetlands over a large area compared to conventional field mapping methods (Ozesmi and Bauer 2002 ; Toyra and Pietroniro 2005). Landsat, Satellite Pour observation de la Terre (SPOT), Advanced Very High-Resolution Radiometer (AVHRR), Indian Remote Sensing Satellites (IRS), radar systems (Ozesmi and Bauer 2002 ), Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) (Wei et al. 2008 ; Pantaleoni et al. 2009 ), and Moderate-resolution Imaging Spectroradiometer (MODIS) are the most frequently used satellite sensors for wetland detection. However, there is no standard method for computer-based wetland classification (Frazier and Page 2000 ). The aim of this study is to monitoring wetland variations surrounding in Addis Ababa. It is common that small headwater wetlands are ignored in wetland studies, and often the importance of large wetlands is recognized and appreciated. But these seemingly insignificant individual wetlands can collectively play important roles in moderating flows and improving water quality (McKergow et al. 2007 ). A large number of such small wetlands, ranging from sedge swamps to seasonally flooded grasslands, exist around and in the Addis Ababa City areas. However, very little is known about the spatial distribution, the variability in space and time, and their hydrological and ecological functioning. Hence, this paper deals with analyzing the situations of wet land in and around Addis Ababa using the RS and GIS software applications since Addis Ababa and its surroundings are among the most well-known Ethiopian urbanization areas, and the wet land around the city is not well studied except in relation to pollution from industries and other rear by dweller settlements in solid and liquid waste and climate change impacts. Observing the situation of wet land in and around Addis Ababa from 1986 up to 2019 is crucial to indicating the trends of land use change and its impacts on the wet lands around the area. The current work provides vital information about how wetlands in and around the city has been changing from time to time. It is also significant in transferring information about rapid urban LULC changes and their impact on wet land. Therefore, this study is very important for city planners to formulate policies and strategies for urban LULC management and set mitigation measures for sustainable wetland restoration and management. To achieve this objective, a hybrid supervised/unsupervised classification of Landsat imagery acquired in 1986 and 2019 was considered. Before classification, it was necessary to create images of surface reflectance that are radiometrically consistent and to ensure inter-image comparability between TM and ETM + images. This was done by applying a combination of cross-calibration and atmospheric correction (Vogelman-DOS3) methods (Paolini et al., 2006 ). 2. Method and materials 2.1. Study Area Addis Ababa is the capital city of Ethiopia, and it has a total surface area of 528 km2. It is located between 80 50′ 18" to 90 6′ 5"N and 380 39′ 00" to 380 54′ 15" E (Fig. 1 ). According to the population estimation of the Central Statistical Agency (CSA) of Ethiopia, the city had a population of 3.4 million in 2017. Addis Ababa city is found in the Woina Dega agro-climate zone and obtains high rainfall during the summer season from June to September and high temperatures during the winter season from January to March. The study was carried out in Addis Ababa city and its surroundings, which covered an area of 2577.5 km2. To analyze urban landscape change and its effect on the wetland in and around the city, a distance of 5 km in all directions surrounding the city was considered in the study. Therefore, the study area extends from 8 0 48′ 26" to 9 0 8′ 29"N and 38 0 36′ 5" to 39 0 7′ 0" E. 2.2. Data Types and Sources In order to carry out the work, the Landsat time-series images covering Addis Ababa city with paths 168 and 54 are downloaded free of charge from the United States Geological Survey (USGS) website ( https://earthexplorer.usgs.gov ). The Landsat multi-temporal imageries of 1986 and 2019 and the Digital Elevation Model (DEM) were used for the study area, and the data for the Landsat multi-temporal imageries were acquired on 9/10/2021. Table 1 Description of Landsat images used Sensor Landsat 5 Landsat 8 OLI/TIRS Acquisition date 26 March 1986 16 January 2019; Spatial resolution 30m 30m Path / Row 168/54 168/54 2.3. Analysis methods 2.3.1. Pre-processing of satellite images It involves operations that are required before the main data analysis and extraction of information, which include image sub-setting, layer stacking, mosaicking, gap filling, geometric correction, and radiometric correction. For this study, the pre-processing of the acquired image was carried out using radiometric calibration for each selected band of the image. 2.3.2. Radiometric calibration The absolute radiometric correction method used in this paper involved a combination of the cross-calibration method developed by Vogelman et al. ( 2001 ) with an atmospheric correction algorithm based on the COST model (Chavez 1996 ), denoted DOS3 by Song et al ( 2001 ). Paolini et al. ( 2006 ) called this combination of methods "Vogelman-DOS3." Cross-calibration: When using both Landsat TM and ETM + images in studies that require radiometric consistency between images, special attention has to be paid to the differences in sensor responses. Previous studies have demonstrated significant differences in the radiometric response between the Landsat 5 TM and Landsat 8 OLI/TIRS spectral bands (Teillet et al. 2001 ; Vogelmann et al. 2001). To reduce these differences and ensure image intercompatibility, a cross-calibration was needed (Paolini et al. 2006 ; Vicente-Serrano et al. 2008). 2.3.3. Atmospheric correction The TOA reflectance value does not take into account the signal attenuation by the atmosphere, which strongly affects the intercomparison of the satellite images taken on different dates. But atmospheric correction methods account for one or more of the distorting effects of the atmosphere and thereby convert the brightness values of each pixel to their actual reflectances as they would have been measured on the ground. For this work, Quick Atmospheric Correction (QAC) was used to convert the sensor spectral radiance to reflectance at the surface of the earth. 2.3.4. Image classification Both supervised and unsupervised classification approach was used to classify the images. This approach involved: (1) unsupervised classification using ISODATA (Iterative Self-Organizing Data Analysis) to determine the spectral classes into which the image resolved; (2) using the maximum resolution of Google Earth pro image (reference data) were collected to associate the spectral classes with the cover types observed at the ground for the 1986, and 2019 image reference data were collected; and (3) classification of the entire image using the maximum likelihood algorithm. A hierarchical class grouping was adopted to label and identify the classes (Thenkabail et al. 2005 ) in both the wet and dry season images. First, the unsupervised ISODATA clustering was used. 7 initial classes were found and then after a rigorous identification and labeling process the original 7 classes were progressively reduced to 6 classes by using ground truth data from Google Earth. The six classes were: Vegetation, Crop Land, Built-up Area, Fallow land, Wet Land, and Grass land (Table 2 ). These Six-LULC classification systems were chosen considering the standard classes explained by the National Aeronautics and Space Administration (NASA) and the US Geological Survey (USGS). Table 2 LULC class’s description of Addis Ababa city and its surrounding (1986 and 2019) LULC Descriptions of LULC classes Wet Land water bodies (static or flowing water), seasonal wetlands with high moisture, and seasonal wetlands with low moisture Impervious land Built-up, asphalt and concrete roads, parking lots and industrial zones Vegetation Forest, shrub land, woodland and plantation Fallow Land An area of agricultural land but not covered with crop, and open land such as playground and bare soil Grass land Area covered with dry grass and pastureland Crop Land Area covered with crop and agricultural land Finally, a post-classification comparison changes detection approach that compares two independently produced classified land use and cover maps from images of two different dates was used. Other studies (Mas 1999 ; Civco et al. 2002 ) illustrated that it was the most precise measure of change. The principal advantage of post-classification is that the two dates of imagery are separately classified; hence, it does not require data normalization between two dates (e.g., Singh 1989 ). The other advantage of post-classification is that it provides information about the nature of change (including trajectories of change) (Song et al. 2001 ; Coppin et al. 2004 ). The accuracy of the classification results was assessed by computing the confusion matrix (error matrix), which compares the classification result with ground truth information. 3. Results and Discussion The study area was classified into six classes. The description for each class is given in Table 3 . A confusion matrix was computed to assess the accuracy of the classification by comparing the classification results with ground-truth information. From the Confusion Matrix report, 95.58% and 86% overall accuracy for the 1986 and 2019 images were attained, respectively. The overall accuracy was calculated by summing the number of pixels classified correctly and dividing by the total number of pixels. An overall accuracy level of 90.5% was adopted. The Confusion Matrix also reports the Kappa Coefficient (k), which is another measure of the accuracy of the classification (Cohen, 1960 ). The k values for the 1986 and 2019 image classifications were 0.8645 and 0.82, respectively. It ranges from − 1 to + 1. However, it should be positive. Landis and Koch ( 1977 ) characterised the possible ranges for k into three groupings: a value greater than 0.80 (i.e., 80%) represents strong agreement; a value between 0.40 and 0.80 (i.e., 40–80%) represents moderate agreement; and a value below 0.40 (i.e., 40%) represents poor agreement. The accuracies for both the 1986 and 2019 image classifications were considered good enough to apply post classification change detection analysis. Basically, this analysis focuses on the initial state classification changes (1986 image). For each initial state class (1986), the post classification comparison technique identifies the classes into which those pixels changed in the final state image (2019). The result of the study shows that the aerial extent of wet land in 1986 was 52.66 km2, and it decreased to 17.03 km2 in 2019, which shows how much built-up and other land use change factors affect the coverage of wet land areas. To show the other land use change, one is cropland, with an aerial coverage of 998.22 km2 in 1986 and a reduction to 704.4849 km2 in 2019. On the other hand, the spatial extent of fallow land increased from 235.5 km2 in 1986 to 834.6 km2 in 2019 (Table 4 ). The change in agricultural land use is mostly related to seasonal variation. The study also indicates that vegetation cover was found in the northern parts of Addis Ababa. In terms of aerial coverage, vegetation cover was reduced from 988.8 km2 in 1986 to 186.11 km2 in 2019, but in some studies, it shows incremental growth because of the afforestation program in the city and the expansion of green spaces (Balew & Semaw 2021), which needs further investigation on the impacts and afforestation initiation. Incremental impacts should need follow-up and documentation. Table 3 LULC of the two years after the detection of changes Final stage Image(2019)in KM2 First stage Image(1986)in KM2 Built-up area Vegetation Wet land Crop land Grass land Fallow land Row Total Class Total Unclassified 0 0 0 0 0 0 0 0 Built up Area 138.6018 274.7871 12.6981 270.5346 17.4096 27.1728 741.204 741.204 Vegetation 1.4949 174.6747 0.5265 4.3623 0.8955 4.1814 186.1353 186.1353 Wet Land 0.0819 0.6399 15.7122 0.3717 0.0558 0.1719 17.0334 17.0334 Crop Land 30.2598 68.7807 8.5644 594.0054 1.1448 1.7298 704.4849 704.4849 Fallow Land 44.6454 420.6834 7.9443 105.5682 58.077 197.6796 834.5979 834.5979 Grass Land 5.607 49.2678 7.2216 23.3802 4.9941 3.5586 94.0293 94.0293 Class Total 220.6908 988.8336 52.6671 998.2224 82.5768 234.4941 0 0 Class Changes 82.089 814.1589 36.9549 404.217 77.5827 36.8145 0 0 Image Difference 520.5132 -802.6983 -35.6337 -293.7375 11.4525 600.1038 0 0 The built-up areas and associated infrastructure, like roads and other paved surfaces, were about 220.6908 and 741.204 km2 in 1986 and 2019, respectively. Therefore, the study revealed that the built-up area was extremely expanded at the expense of vegetation cover, agricultural land, and dry grass, which have direct and indirect impacts on the existence and distribution of wet land. The aerial extent of grassland indicated an incremental from 82.57 km2 in 1986 to 94.02 km2 in 2019. During 1986–2019, 35.63 km2 of water bodies were changed to other LULCs, mainly due to urban expansion (Table 4 ). In most of the area, if the current initiation of afforestation and implementation of a proper land use plan with pragmatic action are realized, the current dramatic land use and land cover change with the impact of climate change and the current demand from the multi-sector for proper water supply will become a headache in the near future. Table 4 lists the initial state classes in the column and the final state classes in the raw Pixel Counts OF Final stage Image(12019) Pixel Counts OF First stage Image(1986) Built up area Vegetation Wet land Crop land Grass land Fallow land Row Total Class Total Built up Area 154002 305319 14109 300594 19344 30192 823560 823560 Vegetation 1661 194083 585 4847 995 4646 206817 206817 Wet Land 91 711 17458 413 62 191 18926 18926 Crop Land 33622 76423 9516 660006 1272 1922 782761 782761 Fallow Land 49606 467426 8827 117298 64530 219644 927331 927331 Grass Land 6230 54742 8024 25978 5549 3954 104477 104477 Class Total 245212 1098704 58519 1109136 91752 260549 0 0 Class Changes 91210 904621 41061 449130 86203 40905 0 0 Image Difference 578348 -891887 -39593 -326375 12725 666782 0 0 For each initial state class (i.e., each column), the table indicates how these pixels were classified in the final state image. Table 5 Change of the initial state classes and the final state classes in % Pixel Counts OF Final stage Image(12019)in % First stage Image(1986)in % Built up area Vegetation Wet land Crop land Grass land Fallow land Row Total Class Total Built up Area 62.804 27.789 24.11 27.102 21.083 11.588 100 100 Vegetation 0.677 17.665 1 0.437 1.084 1.783 100 100 Wet Land 0.037 0.065 29.833 0.037 0.068 0.073 100 100 Crop Land 13.711 6.956 16.261 59.506 1.386 0.738 100 100 Fallow Land 20.23 42.543 15.084 10.576 70.331 84.3 100 100 Grass Land 2.541 4.982 13.712 2.342 6.048 1.518 100 100 Class Total 100 100 100 100 100 100 0 0 Class Changes 37.196 82.335 70.167 40.494 93.952 15.7 0 0 Image Difference 235.856 -81.176 -67.658 -29.426 13.869 255.914 0 0 From the table, one can observe that urban development trends are occurring at an alarming rate (235.856), and this trend in land-use and land cover change is altering the soil (Bewket and Stroosnijder 2003 ) and hydrologic (Bewket and Sterk 2005 ) characteristics of upland watersheds in Addis Ababa City, which affects runoff and infiltration as well as wet land quality and quantity in and around the area. This may also influence the livelihoods of the population living in downstream areas by changing critical watershed functions (e.g., the availability of water during the dry season). In the initial state (1986) image, 52.66 km2 of land, which decreased to 17.03 km2 in 2019, was classified as wetlands. Proportions of land cover types in 1986 and 2019 for built-up and other issues impacted the degradation of the wetland in and around the city, which needs an immediate and medium-long-term plan and restoration management plan for the wet land in the area to attain a smart city and a sustainable climate climate-resilient green economy. 4. Conclusions The study demonstrated the use of remote sensing techniques to delineate headwater wetlands from non-wetlands and determine the dynamics over large areas of Addis Ababa with an overall accuracy of 95.58% and 86% and a Kappa coefficient of 0.8645 and 0.82 for the 1986 and 2019 imageries, respectively. Therefore, ground-based wetland surveying for large areas, especially for small wetlands, is a very time-consuming and ineffective process. The use of remote sensing and GIS techniques in wetland mapping reduces cost and enhances accuracy. Creating radiometrically consistent image data through Vogelman-DOS3 methods before applying hybrid supervised and unsupervised classification was found to be effective in wetland mapping. However, the application of this technique to Landsat images was not effective in mapping riparian vegetation as a wetland. To undertake a more focused wetland management plan implementation, the use of mandatory environmental characteristics (vegetation, soil, and hydrology) for wetland detection is still important. In general, from the work one can understand, there is land use and land cover change at an alarming rate, and to mitigate land use and land cover change that impacts wet land and other ecosystems, proper integrated urban and rural holistic action must be actualized. Furthermore, proper soil and water conservation, as well as water resource management, should be implemented sustainably. If this does not come to an end soon, wetlands in and around the city will suffer from the inability to provide ecosystem services in safe and sustainable ways, and the impact of climate change will exacerbate the situation. Reserved, the city planner should also implement a proper long-term restoration plan with respect to vital wetland and apply water resource management. Declarations Conflict of interest No funding was received to assist with the preparation of this manuscript The authors have no competing interests to declare that are relevant to the content of this article. 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Remote Sens Environ 75(2):230–244 Töyrä J, Pietroniro A (2005) Towards operational monitoring of a northern wetland using geomatics-based techniques. Remote Sens Environ 97(2):174–191 Teillet PM, Barker JL, Markham BL, Irish RR, Fedosejevs G, Storey JC (2001) Radiometric cross-calibration of the Landsat-7 ETM + and Landsat-5 TM sensors based on tandem data sets. Remote Sens Environ 78(1–2):39–54 Thenkabail PS, Schull M, Turral H (2005) Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sens Environ 95(3):317–341 Vicente-Serrano SM, López‐Moreno JI (2008) Nonstationary influence of the North Atlantic Oscillation on European precipitation. J Geophys Research: Atmos 113:D20 Vogelman L, Holman JR, Marshall RC, Jordan B (2001) Technical competency in flexible sigmoidoscopy. J Am Board Family Pract 14(6):424–429 Wei H, Li B, Li J, Dong S, Wang E (2008) DNAzyme-based colorimetric sensing of lead (Pb2+) using unmodified gold nanoparticle probes. Nanotechnology 19(9):095501 Wood D (2001) What is Eco phenomenology? Res phenomenology 31(1):78–95 Wood W (2000) Attitude change: Persuasion and social influence. Ann Rev Psychol 51(1):539–570 Wu J (2014) Urban ecology and sustainability: The state-of-the-science and future directions. Landsc urban Plann 125:209–221 Zhang Y, Pezeshki M, Brakel P, Zhang S, Bengio CLY, Courville A (2017) Towards end-to-end speech recognition with deep convolutional neural networks. arXiv preprint arXiv :170102720 Additional Declarations The authors declare no competing interests. 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Mugoro*","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYJCCDwwMEkCK+cABIIuBjZ2Ach4GBsYZEC1siQ9ngChm4rSAmcbGPCCakBZ79vaHDT9+WeTrth8wk7b5tU2ej5mB8cPHHDy28JwxbOztk7DcdiYhTTq377ZhGzMDs+TMbXi0SOSwP+DtkTAwO5BwTDq35zYjUAsbMy8+LfLPHzb+BWk5/7BN2rLntj1hLRIMhs08P4BabiQzGzP8uJ1IWMuZHMNm2QaQlmeMD3sbbie3MTM24/ULe/vxh41v/tQBHZb/4cCPP7dt57c3H/zwEY8WMGBsQ2EwNhBQDwJ/MBijYBSMglEwChAAAFHiUzCQsA83AAAAAElFTkSuQmCC","orcid":"","institution":"African Center of Excellence for Climate Smart Agriculture and Biodiversity Conservation (ACE-Climate SABC), Haramaya University, Dire Dawa , Ethiopia","correspondingAuthor":true,"prefix":"","firstName":"Tesfasilassie","middleName":"Girma","lastName":"Mugoro*","suffix":""},{"id":565503903,"identity":"b70dbdbb-8019-4564-9eae-1f65325078e2","order_by":1,"name":"Legesse Begashaw","email":"","orcid":"","institution":"2.\tAddis Ababa University, School of Civil and 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16:33:27","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119996,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8449459/v1/e4edaa73f4d69bafa74baa37.html"},{"id":99319946,"identity":"52e4a367-d179-4e7a-b3b7-4dead33d5857","added_by":"auto","created_at":"2025-12-31 16:38:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282385,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8449459/v1/128e7b89243c2ee188e6d368.png"},{"id":99320090,"identity":"b2d7d38c-6a41-4be1-b1d2-cb4a7d1cc370","added_by":"auto","created_at":"2025-12-31 16:38:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1109316,"visible":true,"origin":"","legend":"\u003cp\u003eIndicates LULC OF 1986 AND 2019\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8449459/v1/d639fcc8b3d83da168f54144.png"},{"id":99788064,"identity":"942482e7-6617-406e-b7ba-d2a69e5e7639","added_by":"auto","created_at":"2026-01-08 12:44:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1886083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8449459/v1/1e819b1e-e549-4ffd-a2a7-71a2a4a9d8fe.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMonitoring Wetland Variations Surrounding Addis Ababa: Unveiling Changes through Landsat Data Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUrbanization has brought about several undesirable environmental changes. In the process of urbanization, land cover changes and natural surfaces are replaced by the urban fabric, which is characterized by higher temperatures than the surrounding rural environment, a pattern described as urban warming (Feyisa et al. 2014), and even the undesirable expansion of the illegal action, which disturbs proper planning and implementation of our natural resources in a proactive manner, was the main gap in many African countries. Cities were established in place of fertile agricultural areas, and at the same time, cultivation was prohibited in improper slope areas, while agriculture was allowed in other areas, and vice versa. This was our main problem, which requires pragmatic action from all actors in a holistic manner.\u003c/p\u003e \u003cp\u003eCities and towns in Ethiopia are expanding, and it is not uncommon for most of these to be established around water bodies and wetlands. Similarly, a wetland is not only a source of water but also provides many economic, social, and environmental services. These wetlands near urban areas are suffering negative consequences from the expanding sources of these sectors. For instance, the amount of solid and liquid waste generated by different sources is increasing in size and composition. This is more severe, as most waste from developing societies is organic, although toxic inorganic and pathogenic wastes are also present (Lardinois and Klundert 1993). Organic waste loading in such systems affects different ecosystem elements, including biological resources (Cunningham and Saigo \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Miller \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Industrial centers in Ethiopia, such as Addis Ababa, Mojo, Akaki, Hawassa, and Bahir Dar, are good examples of sourcing different solid and liquid effluents from their respective nearby wetlands (EWNRA \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The illegal settlements in and around wetlands also affect the health and size of the wetlands significantly. Most of the problems from urban to wet lands in Ethiopia are related to the absence of systems that collect and manage solid and liquid wastes (Abebe, Y. D., \u0026amp; Geheb 2003).\u003c/p\u003e \u003cp\u003eHumans usually interact with the natural and working landscapes to fulfil their physical health and well-being (Cox and Gaston, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), psychological comfort (Curtin and Kragh \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), as well as social and spiritual comfort (Keniger et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), but also tend to alter the landscapes (Kareiva et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe degradation of natural resources and derived environmental problems are greatly related to the increase and expansion of built-up spaces (Desta \u003cem\u003eet al.\u003c/em\u003e 2012; Fetene and Worku \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Haddad 2015; Lian et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wu \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Over the last couple of decades, urban expansion in the global south has increased rapidly (Cohen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Larsen et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), inducing changes such as the decrease of agricultural and arable lands (Fenta et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Meskerem \u003cem\u003eet al.\u003c/em\u003e 2017; Mohamed and Worku \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and pollution of the water bodies (Beyene \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yohannes and Elias 2017).\u003c/p\u003e \u003cp\u003eIn Ethiopia, this occurred at the expense of productive and natural areas. In this regard, Addis Ababa city has been growing in all directions, encroaching on farmlands and other open spaces (Meskerem \u003cem\u003eet al.\u003c/em\u003e 2017). That situation brought along major environmental problems, such as deforestation, biodiversity loss, environmental pollution, land degradation, and a and a reduction in the existence of surface and ground water (Beyene \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), as well as changes in the profile of neighboring towns formerly dedicated to farming (Abo-El-Wafa et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, from 1986 to 2011, the built-up area of Addis Ababa city increased by 121.88 km2 (Arsiso \u003cem\u003eet al.\u003c/em\u003e 2018; Kasa et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccordingly, most previous research on wetland was conducted in relation with other land use land cover with a focus on land use land cover changes with relation to climate change and temperature nexus (Balew \u0026amp; Semaw 2021), identifying challenges and prospects to sustain natural and working landscape (Shiflett \u003cem\u003eet al.\u003c/em\u003e 2017), influence of urbanization-driven land use/cover change on climate (Arsiso \u003cem\u003eet al.\u003c/em\u003e 2018), impact of landscape dynamics and intensities on the ecological land of major cities in Ethiopia(Degefu \u003cem\u003eet al.\u003c/em\u003e 2021), efficiency of parks in mitigating urban heat island effect: by taking Addis Ababa city as example (Feyisa \u003cem\u003eet al.\u003c/em\u003e 2014), contamination of Rivers and Water Reservoirs in and Around Addis Ababa City and actions to combat it (Yohannes \u0026amp; Elias 2017) and wetland management and policy implications (Hailu et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Wood, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), hydrological impacts of wetland cultivation (Dixon \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Dixon and Wood \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), socio-economic determinants (Mulugeta \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and gender dimension of wetland use (Wood \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). With respect to using remote sensing (RS) technology to dictate the change on wet land in Ethiopia (Benti \u003cem\u003eet al.\u003c/em\u003e 2021), the specific wet land dictation around Addis Ababa City in detail has not been studied so far.\u003c/p\u003e \u003cp\u003eEffective and efficient management of wetlands requires an exhaustive survey (mapping) of their distribution and the determination of whether or not they have changed over time and to what extent (Jensen et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Baker et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Ground-based surveying of wetlands of large or even small wetlands is very time-consuming. The use of remote sensing techniques offers a cost-effective and time-saving alternative for delineating wetlands over a large area compared to conventional field mapping methods (Ozesmi and Bauer \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Toyra and Pietroniro 2005). Landsat, Satellite Pour observation de la Terre (SPOT), Advanced Very High-Resolution Radiometer (AVHRR), Indian Remote Sensing Satellites (IRS), radar systems (Ozesmi and Bauer \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) (Wei et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pantaleoni et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and Moderate-resolution Imaging Spectroradiometer (MODIS) are the most frequently used satellite sensors for wetland detection. However, there is no standard method for computer-based wetland classification (Frazier and Page \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The aim of this study is to monitoring wetland variations surrounding in Addis Ababa.\u003c/p\u003e \u003cp\u003eIt is common that small headwater wetlands are ignored in wetland studies, and often the importance of large wetlands is recognized and appreciated. But these seemingly insignificant individual wetlands can collectively play important roles in moderating flows and improving water quality (McKergow et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A large number of such small wetlands, ranging from sedge swamps to seasonally flooded grasslands, exist around and in the Addis Ababa City areas. However, very little is known about the spatial distribution, the variability in space and time, and their hydrological and ecological functioning.\u003c/p\u003e \u003cp\u003eHence, this paper deals with analyzing the situations of wet land in and around Addis Ababa using the RS and GIS software applications since Addis Ababa and its surroundings are among the most well-known Ethiopian urbanization areas, and the wet land around the city is not well studied except in relation to pollution from industries and other rear by dweller settlements in solid and liquid waste and climate change impacts. Observing the situation of wet land in and around Addis Ababa from 1986 up to 2019 is crucial to indicating the trends of land use change and its impacts on the wet lands around the area.\u003c/p\u003e \u003cp\u003eThe current work provides vital information about how wetlands in and around the city has been changing from time to time. It is also significant in transferring information about rapid urban LULC changes and their impact on wet land. Therefore, this study is very important for city planners to formulate policies and strategies for urban LULC management and set mitigation measures for sustainable wetland restoration and management.\u003c/p\u003e \u003cp\u003eTo achieve this objective, a hybrid supervised/unsupervised classification of Landsat imagery acquired in 1986 and 2019 was considered. Before classification, it was necessary to create images of surface reflectance that are radiometrically consistent and to ensure inter-image comparability between TM and ETM\u0026thinsp;+\u0026thinsp;images. This was done by applying a combination of cross-calibration and atmospheric correction (Vogelman-DOS3) methods (Paolini et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Method and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eAddis Ababa is the capital city of Ethiopia, and it has a total surface area of 528 km2. It is located between 80 50\u0026prime; 18\" to 90 6\u0026prime; 5\"N and 380 39\u0026prime; 00\" to 380 54\u0026prime; 15\" E (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to the population estimation of the Central Statistical Agency (CSA) of Ethiopia, the city had a population of 3.4\u0026nbsp;million in 2017. Addis Ababa city is found in the Woina Dega agro-climate zone and obtains high rainfall during the summer season from June to September and high temperatures during the winter season from January to March. The study was carried out in Addis Ababa city and its surroundings, which covered an area of 2577.5 km2. To analyze urban landscape change and its effect on the wetland in and around the city, a distance of 5 km in all directions surrounding the city was considered in the study. Therefore, the study area extends from 8\u003csup\u003e0\u003c/sup\u003e 48\u0026prime; 26\" to 9\u003csup\u003e0\u003c/sup\u003e 8\u0026prime; 29\"N and 38\u003csup\u003e0\u003c/sup\u003e 36\u0026prime; 5\" to 39\u003csup\u003e0\u003c/sup\u003e 7\u0026prime; 0\" E.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Types and Sources\u003c/h2\u003e \u003cp\u003eIn order to carry out the work, the Landsat time-series images covering Addis Ababa city with paths 168 and 54 are downloaded free of charge from the United States Geological Survey (USGS) website (\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). The Landsat multi-temporal imageries of 1986 and 2019 and the Digital Elevation Model (DEM) were used for the study area, and the data for the Landsat multi-temporal imageries were acquired on 9/10/2021.\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 Landsat images used\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\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandsat 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLandsat 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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 March 1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 January 2019;\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\" colname=\"c2\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath / Row\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168/54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168/54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Analysis methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Pre-processing of satellite images\u003c/h2\u003e \u003cp\u003eIt involves operations that are required before the main data analysis and extraction of information, which include image sub-setting, layer stacking, mosaicking, gap filling, geometric correction, and radiometric correction. For this study, the pre-processing of the acquired image was carried out using radiometric calibration for each selected band of the image.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Radiometric calibration\u003c/h2\u003e \u003cp\u003eThe absolute radiometric correction method used in this paper involved a combination of the cross-calibration method developed by Vogelman et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) with an atmospheric correction algorithm based on the COST model (Chavez \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), denoted DOS3 by Song et al (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Paolini et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) called this combination of methods \"Vogelman-DOS3.\" Cross-calibration: When using both Landsat TM and ETM\u0026thinsp;+\u0026thinsp;images in studies that require radiometric consistency between images, special attention has to be paid to the differences in sensor responses. Previous studies have demonstrated significant differences in the radiometric response between the Landsat 5 TM and Landsat 8 OLI/TIRS spectral bands (Teillet et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Vogelmann \u003cem\u003eet al.\u003c/em\u003e 2001). To reduce these differences and ensure image intercompatibility, a cross-calibration was needed (Paolini et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Vicente-Serrano \u003cem\u003eet al.\u003c/em\u003e 2008).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Atmospheric correction\u003c/h2\u003e \u003cp\u003eThe TOA reflectance value does not take into account the signal attenuation by the atmosphere, which strongly affects the intercomparison of the satellite images taken on different dates. But atmospheric correction methods account for one or more of the distorting effects of the atmosphere and thereby convert the brightness values of each pixel to their actual reflectances as they would have been measured on the ground. For this work, Quick Atmospheric Correction (QAC) was used to convert the sensor spectral radiance to reflectance at the surface of the earth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4. Image classification\u003c/h2\u003e \u003cp\u003eBoth supervised and unsupervised classification approach was used to classify the images. This approach involved: (1) unsupervised classification using ISODATA (Iterative Self-Organizing Data Analysis) to determine the spectral classes into which the image resolved; (2) using the maximum resolution of Google Earth pro image (reference data) were collected to associate the spectral classes with the cover types observed at the ground for the 1986, and 2019 image reference data were collected; and (3) classification of the entire image using the maximum likelihood algorithm. A hierarchical class grouping was adopted to label and identify the classes (Thenkabail et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) in both the wet and dry season images. First, the unsupervised ISODATA clustering was used. 7 initial classes were found and then after a rigorous identification and labeling process the original 7 classes were progressively reduced to 6 classes by using ground truth data from Google Earth.\u003c/p\u003e \u003cp\u003eThe six classes were: Vegetation, Crop Land, Built-up Area, Fallow land, Wet Land, and Grass land (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These Six-LULC classification systems were chosen considering the standard classes explained by the National Aeronautics and Space Administration (NASA) and the US Geological Survey (USGS).\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 class\u0026rsquo;s description of Addis Ababa city and its surrounding (1986 and 2019)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescriptions of LULC classes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWet Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewater bodies (static or flowing water), seasonal wetlands with high moisture, and seasonal wetlands with low moisture\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpervious land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt-up, asphalt and concrete roads, parking lots and industrial zones\u003c/p\u003e \u003c/td\u003e \u003c/tr\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, shrub land, woodland and plantation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFallow Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn area of agricultural land but not covered with crop, and open land such as playground and bare soil\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrass land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea covered with dry grass and pastureland\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea covered with crop and agricultural land\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\u003eFinally, a post-classification comparison changes detection approach that compares two independently produced classified land use and cover maps from images of two different dates was used. Other studies (Mas \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Civco et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) illustrated that it was the most precise measure of change. The principal advantage of post-classification is that the two dates of imagery are separately classified; hence, it does not require data normalization between two dates (e.g., Singh \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). The other advantage of post-classification is that it provides information about the nature of change (including trajectories of change) (Song et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Coppin et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The accuracy of the classification results was assessed by computing the confusion matrix (error matrix), which compares the classification result with ground truth information.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eThe study area was classified into six classes. The description for each class is given in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A confusion matrix was computed to assess the accuracy of the classification by comparing the classification results with ground-truth information. From the Confusion Matrix report, 95.58% and 86% overall accuracy for the 1986 and 2019 images were attained, respectively. The overall accuracy was calculated by summing the number of pixels classified correctly and dividing by the total number of pixels. An overall accuracy level of 90.5% was adopted.\u003c/p\u003e \u003cp\u003eThe Confusion Matrix also reports the Kappa Coefficient (k), which is another measure of the accuracy of the classification (Cohen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1960\u003c/span\u003e). The k values for the 1986 and 2019 image classifications were 0.8645 and 0.82, respectively. It ranges from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1. However, it should be positive. Landis and Koch (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) characterised the possible ranges for k into three groupings: a value greater than 0.80 (i.e., 80%) represents strong agreement; a value between 0.40 and 0.80 (i.e., 40\u0026ndash;80%) represents moderate agreement; and a value below 0.40 (i.e., 40%) represents poor agreement. The accuracies for both the 1986 and 2019 image classifications were considered good enough to apply post classification change detection analysis. Basically, this analysis focuses on the initial state classification changes (1986 image). For each initial state class (1986), the post classification comparison technique identifies the classes into which those pixels changed in the final state image (2019).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe result of the study shows that the aerial extent of wet land in 1986 was 52.66 km2, and it decreased to 17.03 km2 in 2019, which shows how much built-up and other land use change factors affect the coverage of wet land areas. To show the other land use change, one is cropland, with an aerial coverage of 998.22 km2 in 1986 and a reduction to 704.4849 km2 in 2019. On the other hand, the spatial extent of fallow land increased from 235.5 km2 in 1986 to 834.6 km2 in 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The change in agricultural land use is mostly related to seasonal variation. The study also indicates that vegetation cover was found in the northern parts of Addis Ababa. In terms of aerial coverage, vegetation cover was reduced from 988.8 km2 in 1986 to 186.11 km2 in 2019, but in some studies, it shows incremental growth because of the afforestation program in the city and the expansion of green spaces (Balew \u0026amp; Semaw 2021), which needs further investigation on the impacts and afforestation initiation. Incremental impacts should need follow-up and documentation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC of the two years after the detection of changes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eFinal stage Image(2019)in KM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eFirst stage Image(1986)in KM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBuilt-up area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWet land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCrop land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGrass land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFallow land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRow Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClass Total\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnclassified\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 \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.6018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274.7871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.6981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e270.5346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.4096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.1728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e741.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e741.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.6747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.3623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.1814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e186.1353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e186.1353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.7122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.0334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17.0334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.2598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68.7807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e594.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.1448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.7298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e704.4849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e704.4849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFallow Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.6454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420.6834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.9443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e105.5682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e197.6796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e834.5979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e834.5979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrass Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.2678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.3802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.9941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.5586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94.0293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94.0293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220.6908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e988.8336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.6671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e998.2224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.5768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e234.4941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass Changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e814.1589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.9549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e404.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.5827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.8145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImage Difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520.5132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-802.6983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-35.6337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-293.7375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.4525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e600.1038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\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\u003eThe built-up areas and associated infrastructure, like roads and other paved surfaces, were about 220.6908 and 741.204 km2 in 1986 and 2019, respectively. Therefore, the study revealed that the built-up area was extremely expanded at the expense of vegetation cover, agricultural land, and dry grass, which have direct and indirect impacts on the existence and distribution of wet land. The aerial extent of grassland indicated an incremental from 82.57 km2 in 1986 to 94.02 km2 in 2019. During 1986\u0026ndash;2019, 35.63 km2 of water bodies were changed to other LULCs, mainly due to urban expansion (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In most of the area, if the current initiation of afforestation and implementation of a proper land use plan with pragmatic action are realized, the current dramatic land use and land cover change with the impact of climate change and the current demand from the multi-sector for proper water supply will become a headache in the near future.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003elists the initial state classes in the column and the final state classes in the raw\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003ePixel Counts OF Final stage Image(12019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003ePixel Counts OF First stage Image(1986)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBuilt up area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWet land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCrop land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGrass land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFallow land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRow Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClass Total\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e823560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e823560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e206817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e206817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e660006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e782761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e782761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFallow Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e219644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e927331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e927331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrass Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e104477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e104477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1098704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1109136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e260549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass Changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e904621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e449130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImage Difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-891887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-39593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-326375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e666782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\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\u003eFor each initial state class (i.e., each column), the table indicates how these pixels were classified in the final state image.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChange of the initial state classes and the final state classes in %\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003ePixel Counts OF Final stage Image(12019)in %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003eFirst stage Image(1986)in %\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBuilt up area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWet land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCrop land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGrass land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFallow land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRow Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eClass Total\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuilt up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWet Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFallow Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrass Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass Changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImage Difference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-81.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-67.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-29.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e255.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\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\u003eFrom the table, one can observe that urban development trends are occurring at an alarming rate (235.856), and this trend in land-use and land cover change is altering the soil (Bewket and Stroosnijder \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and hydrologic (Bewket and Sterk \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) characteristics of upland watersheds in Addis Ababa City, which affects runoff and infiltration as well as wet land quality and quantity in and around the area. This may also influence the livelihoods of the population living in downstream areas by changing critical watershed functions (e.g., the availability of water during the dry season).\u003c/p\u003e \u003cp\u003eIn the initial state (1986) image, 52.66 km2 of land, which decreased to 17.03 km2 in 2019, was classified as wetlands. Proportions of land cover types in 1986 and 2019 for built-up and other issues impacted the degradation of the wetland in and around the city, which needs an immediate and medium-long-term plan and restoration management plan for the wet land in the area to attain a smart city and a sustainable climate climate-resilient green economy.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe study demonstrated the use of remote sensing techniques to delineate headwater wetlands from non-wetlands and determine the dynamics over large areas of Addis Ababa with an overall accuracy of 95.58% and 86% and a Kappa coefficient of 0.8645 and 0.82 for the 1986 and 2019 imageries, respectively. Therefore, ground-based wetland surveying for large areas, especially for small wetlands, is a very time-consuming and ineffective process. The use of remote sensing and GIS techniques in wetland mapping reduces cost and enhances accuracy. Creating radiometrically consistent image data through Vogelman-DOS3 methods before applying hybrid supervised and unsupervised classification was found to be effective in wetland mapping. However, the application of this technique to Landsat images was not effective in mapping riparian vegetation as a wetland. To undertake a more focused wetland management plan implementation, the use of mandatory environmental characteristics (vegetation, soil, and hydrology) for wetland detection is still important.\u003c/p\u003e \u003cp\u003eIn general, from the work one can understand, there is land use and land cover change at an alarming rate, and to mitigate land use and land cover change that impacts wet land and other ecosystems, proper integrated urban and rural holistic action must be actualized. Furthermore, proper soil and water conservation, as well as water resource management, should be implemented sustainably. If this does not come to an end soon, wetlands in and around the city will suffer from the inability to provide ecosystem services in safe and sustainable ways, and the impact of climate change will exacerbate the situation. Reserved, the city planner should also implement a proper long-term restoration plan with respect to vital wetland and apply water resource management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are included in the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbebe YD, Geheb K (2003) Wetlands of Ethiopia. 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Landsc urban Plann 125:209\u0026ndash;221\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Pezeshki M, Brakel P, Zhang S, Bengio CLY, Courville A (2017) Towards end-to-end speech recognition with deep convolutional neural networks. arXiv preprint arXiv :170102720\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Addis Ababa, GIS, Land use-Land cover, Remote sensing, Wetlands","lastPublishedDoi":"10.21203/rs.3.rs-8449459/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8449459/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCities and towns in Ethiopia are expanding, often around water bodies and wetlands. A wetland is not only a water source but also offers many economic, social, and environmental benefits. These wetlands near urban areas face negative impacts from the growing sectors. The amount of solid and liquid waste produced by various sources is increasing in both volume and complexity. This study highlights the use of remote sensing and GIS methods to detect changes in wetlands around Addis Ababa. The area was classified into six categories, and accuracy was assessed by comparing classification results with ground truth using a confusion matrix. The overall accuracy was 95.58% for 1986 and 86% for 2019, with an average of 90.5%. The confusion matrix also reports the Kappa Coefficient, measuring classification accuracy, which was 0.8645 in 1986 and 0.82 in 2019. In 1986, about 52.66 km2 of land was classified as wetlands, decreasing to 17.03 km2 in 2019. The proportions of land cover types in both years, including built-up areas and other factors, have contributed to the loss of wetlands in and around the city. This calls for immediate and medium- to long-term restoration and management plans to help develop a smart city and promote a sustainable, climate-resilient green economy.\u003c/p\u003e","manuscriptTitle":"Monitoring Wetland Variations Surrounding Addis Ababa: Unveiling Changes through Landsat Data Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 06:19:26","doi":"10.21203/rs.3.rs-8449459/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b34ae37-9fba-491a-8d75-c59d59c6dccd","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-31T06:19:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 06:19:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8449459","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8449459","identity":"rs-8449459","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}