Geospatial Assessment of Land Use and Land Cover Change in Alappuzha District, Western Kerala, India

Geospatial Assessment of Land Use and Land Cover Change in Alappuzha District, Western Kerala, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Geospatial Assessment of Land Use and Land Cover Change in Alappuzha District, Western Kerala, India Vijayakumar Anitha, Duraisamy Prabha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8412738/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 Rapid urbanization has altered land use and land cover, leading to a decline in environmental quality. Monitoring these changes with LULC analysis is, therefore, indispensable. This study looks at LULC changes in Alappuzha district, Kerala, for the years 2000, 2015, and 2025 using Remote Sensing and GIS. LULC maps were generated from Landsat images using Maximum Likelihood Classification for five categories: agriculture, built-up land, mixed vegetation, uncultivable land, and water bodies. The results indicate substantial reductions in mixed vegetation (35.63%), agricultural land (23.88%), water bodies (20.25%), and uncultivable land (14.32%) between 2000 and 2025. Conversely, built-up land expanded by 344.61%, indicating rapid urban growth. Socioeconomic changes, population growth, climate change, and shifts in employment patterns have contributed to the decline in agricultural areas. The observed decreases in mixed vegetation and water bodies highlight ecological stress and underscore the urgent need for restoration initiatives. The study highlights the need for well-planned land-use strategies that prioritise resource sustainability and ecological protection. Further research into the effects of LULC Changes on surface temperatures, hydrology and biodiversity is recommended to inform environmental planning in the ecologically sensitive Alappuzha district. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Alappuzha Ecological stress Land Use Land Cover Changes Landsat Remote Sensing and GIS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Land Use and Land Cover (LULC) transitions have become one of the most critical environmental concerns of the twenty-first century, driven by rapid urbanization, changing agricultural practices, infrastructure expansion, and climate variability. Land Cover (LC) refers to the biophysical features, such as soil, water, flora and anthropogenic activities of the Earth's surfaces, and Land Use (LU) refers to how humans manage or modify land (Lambin and Geist, 2008 ). Together, LULC patterns fundamentally shape ecological processes, resource distribution, and human environment interactions across spatial and temporal scales. Consequently, monitoring LULC dynamics is vital for understanding landscape transformations, informing land governance, and advancing global sustainability agendas, particularly the targets of SDG 15 (Life on Land). These Land Use and Land Cover Changes (LULCCs), are driven by the complex interplay of socioeconomic, political, and natural factors (Prasad & Ramesh, 2019 ), which play a crucial role in shaping landscape dynamics and determining environmental health and sustainability. These drivers often lead to rapid urbanization and infrastructure development, resulting in a shift from pervious to impervious surfaces, which significantly alters hydrological cycles, enhances surface runoff, degrades water quality and reduces groundwater recharge (Lerner & Harris, 2009 ; Tahiru et al. 2020 ). These transformations alter the surface energy balance and increase Land Surface Temperature (LST), thereby contributing to the Urban Heat Island (UHI) effect in rapidly urbanising regions (Imran et al. 2019 , 2021 ). In addition to thermal and hydrological impacts, LULC transitions influence biodiversity, soil health, ecological productivity, food security, increase CO 2 and the stability of ecosystem services. These changes have direct implications for Sustainable Development Goals (SDG) 2 (Zero Hunger), 6 (Clean Water and Sanitation), 11 (Sustainable Cities and Communities), and 13 (Climate Action). In contrast, sustainable land-use strategies such as conservation zoning, wetland protection, and green infrastructure enhance ecological resilience, mitigate climate extremes, and protect livelihoods that depend on natural resources. Remote sensing (RS) combined with Geographic Information System (GIS) techniques has substantially improved the ability to detect, classify, and analyse LULC changes across various spatial and temporal scales. Satellite-based observations offer temporally consistent and spatially explicit data essential for long-term environmental monitoring. These technologies enable the identification of subtle or gradual landscape changes that are often undetectable by traditional ground-based methods. Landsat imagery, in particular, is frequently utilised due to its extensive multi-decadal archive, appropriate spatial resolution, and broad coverage, establishing it as a dependable resource for spatiotemporal LULC analysis (Kumar & Acharya, 2016 ). Numerous studies have investigated LULC changes using RS and GIS techniques, consistently demonstrating strong linkages between anthropogenic activities, ecological transformations, and thermal responses. At the global scale, urbanization driven LULCCs have been linked to rising LST (Falcucci et al. 2007 ; Hashim et al. 2022 ; Vasanthawada et al. 2023 ), ecosystem degradation (Gong et al. 2022 ), carbon emissions (Zhu et al. 2022 ), landscape fragmentation (Ning et al. 2023 ), biodiversity loss (Amini et al. 2016), and altered flood regimes (Dezso et al. 2005 ). Similar patterns have been observed across Asia and Africa, where agricultural expansion, aquaculture intensification, and urban sprawl have transformed landscapes and affected soil properties, hydrology, and UHI formation (Muttitanon & Tripathi, 2005 ; Biro et al. 2013 ; Arowolo & Deng, 2018 ; Addae & Oppelt, 2019 ). In India, extensive LULCC has been documented in regions such as Madhya Pradesh (Jaiswal et al. 1999 ), Tirupati (Mallupattu & Sreenivasula, 2013), Patna (Mishra et al. 2018 ), Mumbai (Vinayak et al. 2021 ), Durgapur Municipal Corporation (Haldar et al. 2023 ) the Himalayan Region (Yadav et al. 2024 ), and Dhanbad (Ranjan et al. 2025 ), revealing consistent declines in vegetation, agricultural land, and ecosystem stability due to urban growth. Studies in Kerala, further show land-cover transformations linked to urbanization, resulting in declining greenness, flooding, and UHI intensification (Sonu et al. 2022; Skariah & Suriyakala, 2022 ; Anitha et al. 2023 ; Tesfamariam et al. 2023 ). While research in districts such as Wayanad and Kottayam demonstrates the value of RS–GIS in monitoring vegetation and surface temperatures (John et al. 2020 ; Anitha & Prabha, 2024 ), investigations remain fragmented and district-specific. Notably, despite rapid land transformation in Alappuzha, systematic multi-temporal assessments of LULCC and associated environmental impacts are scarce, indicating a clear need for comprehensive geospatial analyses in the region. Despite global progress, Alappuzha District in Kerala remains understudied, even though it represents one of India’s most environmentally sensitive and socioeconomically important coastal landscapes. Characterised by an intricate network of canals, the Vembanad backwaters, wetlands, paddy fields, and coastal belts, Alappuzha is exceptionally vulnerable to landscape changes. The district’s economy, which relies on agriculture, fishing, coir production, and tourism, depends directly on stable land and water systems. LULCCs can disrupt the balance of water systems, increase flood risks, harm wetlands, raise LST, and affect ecosystem services that are vital for local livelihoods. Rapid population growth, increasing human activity in fragile ecosystems, and the demands of urban development further increase the district’s vulnerability to environmental stress. In this situation, evaluating LULCCs in Alappuzha is essential for understanding how human activities are altering productive landscapes and ecological systems. Therefore, this study aims to map, quantify, and analyse LULC changes in Alappuzha District over a period of 25 years (2000, 2015, and 2025) using multi-temporal Landsat datasets and geospatial techniques. Through the documentation of long-term patterns and the identification of key drivers of landscape transformation, this research offers critical insights for sustainable land management, climate adaptation, and ecosystem protection in accordance with SDG 15 (Life on Land). These findings are intended to assist policymakers, planners, and local stakeholders in advancing development strategies that reconcile economic objectives with environmental integrity and enduring ecological sustainability. RESULTS Land Cover Changes Analysis Five major land cover categories representing the foremost land cover in Alappuzha District have been analyzed based on the proportion of the area. Accuracy Assessment The accuracy assessment results for the classified LULC maps are presented in Table 1 . The overall classification accuracy exceeded 90% for all three years, with Kappa coefficients greater than 90% (0.90), indicating a high level of agreement between classified outputs and reference data. These results confirm the reliability of the LULC classifications used for change detection and transition analysis in this study. Table 1 Accuracy assessment of LULC of 2000, 2015 and 2025. LULC Class 2000 2015 2025 Producer’s Accuracy (%) User’s Accuracy (%) Producer’s Accuracy (%) User’s Accuracy (%) Producer’s Accuracy (%) User’s Accuracy (%) Agriculture 87 94 88 91 84 88 Built-up 91 100 87 100 83 100 Mixed vegetation 98 95 93 92 92 92 Uncultivable land 95 82 93 81 93 81 Water body 100 100 100 100 100 100 Overall Accuracy 93 90 91 Kappa Coefficient 93 91 90 [Table 1 Here] Land Use and Land Cover Change Dynamics The spatial distribution of LULC classes in Alappuzha district for the years 2000, 2015, and 2025 is presented in Table 2 and Figs. 2 and 3 . Five major LULC categories were identified: built-up land, agricultural land, mixed vegetation, water bodies, and uncultivable land. Significant changes were observed across all classes over the 25-year study period. Additionally, the annual rates of change for all LULC classes are summarised in Table 3 . Table 2 LULC distribution of Alappuzha District for the years 2000, 2015 and 2025. (‘-’ sign indicates decrease) LULC Class name Year Area in change in km 2 (% Change) 2000 2015 2025 Area (km 2 ) % of the total area Area (km 2 ) % of the total area Area (km 2 ) % of the total area 2000 and 2015 2015 and 2025 2000 and 2025 Agriculture 513.79 41.32 471.90 37.95 391.10 31.45 -41.89 (-8.15) -80.80 (-17.12) -122.69 (-23.88) Built-up land 79.47 6.39 122.93 9.89 353.33 28.41 43.46 (54.69) 230.40 (187.42) 273.86 (344.61) Mixed vegetation 252.92 20.34 239.73 19.28 162.81 13.09 -13.19 (-5.22) -76.92 (-32.09) -90.11 (-35.63) Uncultivable land 327.27 26.32 347.91 27.98 280.40 22.55 20.65 (6.31) -67.51 (-19.40) -46.87 (-14.32) Water body 70.06 5.63 61.04 4.91 55.87 4.49 -9.02 (-12.87) -5.17 (-8.47) -14.19 (-20.25) Total area 1243.51 100 1243.51 100 1243.51 100 Table 3 Percentage of annual rate change (‘-’ sign indicates decrease) LULC Class Annual Rate Change (2000–2015) % Annual Rate Change (2015–2025) % Annual Rate Change (2000–2025) % Agriculture -0.57 -1.88 -1.09 Built-up land 2.91 10.56 5.97 Mixed vegetation -0.36 -3.87 -1.76 Uncultivable land 0.41 -2.16 -0.62 Water body -0.92 -0.89 -0.91 Agricultural land showed a consistent decline throughout the study period. The area under agriculture decreased from 513.79 km² (41.32%) in 2000 to 471.90 km² (37.95%) in 2015 and further to 391.10 km² (31.45%) in 2025. This corresponds to a net loss of 122.69 km² (23.88%) over the entire period, with a higher rate of decline (80.80 km² with 17.12%) observed during 2015–2025. Agricultural land exhibited a negative annual growth rate, which intensified from − 0.57% per year (2000–2015) to − 1.88% per year (2015–2025). In contrast, built-up land exhibited the most pronounced expansion, increasing steadily across all periods. The built-up area increased from 79.45 km² (6.39%) in 2000 to 122.93 km² (9.89%) in 2015, and further to 353.33 km² (28.41%) in 2025. Overall, the built-up land increased by 344.61% (273.86 km²) between 2000 and 2025. The most rapid expansion occurred during 2015–2026, accounting for 187.42% (230.40 km 2 ). Built-up land recorded the highest annual growth rate, increasing from 2.91% per year during 2000–2015 to 10.56% per year during 2015–2025. Mixed vegetation also declined substantially over time. The area decreased from 252.92 km² (20.34%) in 2000 to 239.73 km² (19.28%) in 2015, followed by a sharper reduction to 162.81 km² (13.09%) in 2025. The overall reduction in mixed vegetation amounted to 90.11 km² (35.63%) between 2000 and 2025. Mixed vegetation also showed an accelerated rate of decline during the later period, with the annual rate of loss increasing from − 0.36% to − 3.87%. Uncultivable land showed comparatively minor changes during the study period. The area increased from 327.27 km² (26.32%) in 2000 to 347.91 km² (27.98%) in 2015, representing a gain of 20.65 km² (6.31%). This was followed by a decline to 280.40 km² (22.55%) in 2025, corresponding to a loss of 67.51 km² (19.40%) during 2015–2025. Overall, uncultivable land declined by 46.87 km 2 (14.32%) over the entire study period. Water bodies steadily declined over the study period, from 70.06 km² (5.63%) in 2000 to 61.04 km² (4.91%) in 2015, representing a loss of 9.02 km² (12.87%) and further to 55.87 km² (4.49%) in 2025, resulting in a total loss of 14.19 km² (20.25%). This corresponds to an average annual decline of approximately 0.9%. [Table 2 Here] [Table 3 Here] Land Use and Land Cover Change Transition Matrix Figure 4 and Tables 4 , 5 , and 6 show the cross-tabulation change matrix for the transformed regions, indicating the proportional shifts between different LULC classes relative to the total area of each class from 2000 to 2015, 2015 to 2025, and 2015 to 2025. Even though all LULC classes transformed in the study region, the extent of these changes varied significantly, with conversions observed throughout the entire study area. The diagonal values indicate the quantity of unchanged land use types. Between 2000 and 2015, agricultural land moderately declined, with 439.55 km² remaining stable, while 51.22 km² shifted to uncultivable land, 14.03 km 2 into waterbodies and 8.84 km² to built-up areas. Built-up land expanded from 79.47 km² to 122.93 km², mainly at the expense of water bodies (19.45 km²), mixed vegetation (12.17 km²), agriculture (8.84 km²), and uncultivable land (3 km²). Mixed vegetation remained largely stable (239.37 km²), with minor conversions into uncultivable land (0.21 km²) and agricultural land (0.15km 2 ). Uncultivable land remained mostly unchanged (295.14 km²), while water bodies were relatively stable (46.29 km² retained), despite small conversions (Table 4 ). From 2015 to 2025, rapid urban growth was observed, with built-up land increasing from 122.93 km² to 353.33 km², primarily converted from agriculture (83.63 km²), mixed vegetation (75.68 km²), and uncultivable land (70.82 km²). Agricultural and mixed vegetation areas declined considerably, while water bodies showed only a slight reduction, with 53.09 km² remaining stable (Table 5 ). Over the long term (2000 to 2025), a major landscape transformation occurred. Agricultural land decreased, with 358.01 km² remaining, while significant portions were converted to built-up (80.42 km²), uncultivable land (60.20 km²) and water bodies (14.60 km 2 ). Built-up areas increased most sharply, gaining land mainly from uncultivable land (105.57 km²), mixed vegetation (87.66 km²), and agriculture (80.42 km 2 ). Mixed vegetation and water bodies showed consistent declines, with only 161.67 km² and 40.45 km² remaining, respectively (Table 6 ). Table 4 LULC change matrix from 2000 to 2015 2000 (Area in km 2 ) LULC Class 2015 (Area in km 2 ) Agriculture Built-up land Mixed vegetation Uncultivable land Water body Total 2000 Agriculture 439.55 8.84 0.15 51.22 14.03 513.79 Built-up land 0.00 79.47 0.00 0.00 0.00 79.47 Mixed vegetation 0.36 12.17 239.37 0.90 0.12 252.92 Uncultivable land 28.32 3.00 0.21 295.14 0.60 327.27 Water body 3.67 19.45 0.00 0.65 46.29 70.06 Total 2015 471.90 122.93 239.73 347.91 61.04 1243.51 Table 5 LULC change matrix from 2015 to 2025 2015 (Area in km 2 ) LULC Class 2025 (Area in km 2 ) Agriculture Built-up land Mixed Vegetation Uncultivable land Water body Total 2015 Agriculture 355.35 83.63 0.50 30.46 1.96 471.90 Built-up land 0.00 122.93 0.00 0.00 0.00 122.93 Mixed vegetation 0.42 75.68 161.74 1.89 0.00 239.73 Uncultivable land 30.56 70.82 0.57 245.14 0.82 347.91 Water body 4.77 0.27 0.00 2.91 53.09 61.04 Total 2025 391.10 353.33 162.81 280.40 55.87 1243.51 Table 6 LULC change matrix from 2000 to 2025 2000 (Area in km 2 ) LULC Class 2025 (Area in km 2 ) Agriculture Built-up land Mixed vegetation Uncultivable land Water body Total 2000 Agriculture 358.01 80.42 0.56 60.20 14.60 513.79 Built-up land 0.00 79.47 0.00 0.00 0.00 79.47 Mixed vegetation 1.07 87.66 161.67 2.37 0.15 252.92 Uncultivable land 25.78 105.57 0.58 194.67 0.67 327.27 Water body 6.24 0.21 0.00 23.16 40.45 70.06 2025 391.10 353.33 162.81 280.40 55.87 1243.51 Table 7 Description of LULC classes in the study area. LU/LC Classes Description Agriculture The components of cropland include mechanized farms and smallholder-operated farms, as well as paddy fields. Built-up area All kinds of infrastructure, such as housing, commercial, service, industrial, socioeconomic infrastructure regions, villages, cities, and transportation infrastructure. Mixed vegetation Trees and small grasses, including both natural and planted varieties. Uncultivable land Land without vegetation cover or very little greenness, primarily encompassing areas with exposed soil and bare rock surfaces, and excavation sites. Water body All types of water bodies within the study area were considered, excluding the Vembanad Lake Table 8 Characteristics of Landsat images used for the study. Satellite Sensor Path/ Row Date of Image Acquisition Spatial Resolution Cloud Cover % Landsat-7 Enhanced Thematic Mapper Plus (ETM+) 144/53 28/01/2000 30 8 144/54 1 Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) 144/53 13/01/2015 30 3.34 144/54 1.23 Landsat 9 Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) 144/53 00/01/2025 30 23.62 144/54 32.51 Discussion The land use and land cover (LULC) dynamics of Alappuzha district over the 25 years from 2000 to 2025 reveal substantial transformations, reflecting rapid socio-economic development and increasing anthropogenic pressure on natural landscapes. The overall pattern is characterised by pronounced urban expansion accompanied by a decline in natural and semi-natural land cover types, indicating growing ecological stress in the district. The most prominent transformation was the increase in built-up land during the study period, evolving from a relatively minor component of the landscape in 2000 to the second most dominant land cover class by 2025. The overall increase of 344.61% (273.86 km 2 ) highlights the rapid pace of urbanization in Alappuzha. While built-up expansion during 2000–2015 was moderate (54.69%, 43.46 km 2 ), growth accelerated sharply after 2015 (187.42%, 230.40 km 2 during 2015–2025), indicating a shift in the district’s development trajectory (Table 2 ). This expansion occurred largely at the expense of uncultivable land (105.57 km 2 ), mixed vegetation (87.66 km 2 ), and agricultural land (80.42 km 2 ), as reflected in the conversion of substantial areas of these classes to built-up land (Table 6 ). Similar long-term (1973 to 2017) urban growth trends in Alappuzha have been reported by Prasad and Ramesh ( 2019 ). The increase in built-up land in Alappuzha is driven by population growth in the district. According to census data, the population in our study area was 2,001,083 in 1991, which increased to 2,127,789 in 2011, reflecting a growth of 7.26%. By 2021, the population further increased to an estimated 2.20 million, projected growth for 2021 in the absence of official census data due to delays associated with the COVID-19 pandemic. This substantial rise in population has led to a higher demand for residential, commercial, and infrastructural development, contributing significantly to the expansion of built-up land. In addition to population pressure, the flourishing tourism industry has substantially influenced land transformation in the Alappuzha District. Tourism demanded the construction of resorts, hotels, restaurants, and associated facilities, particularly in ecologically sensitive coastal and backwater regions, resulting in changes in land use. Although tourism provides economic benefits, it has also accelerated habitat fragmentation and environmental degradation, placing considerable pressure on natural resources and ecosystems. Road infrastructure development has further amplified urban expansion. Major road projects such as the National Highway 66 (NH-66) Alappuzha Bypass, six-laning works, and the Aroor–Thuravoor elevated highway have improved accessibility and connectivity, making the surrounding areas more attractive for urban and commercial development. These transport corridors have served as focal points, especially after 2015, for land-use conversion, reinforcing the dominance of built-up land across the Alappuzha district. Agricultural land experienced a sustained decline of 23.88 km 2 (122.69%) between 2000–2025, with an average annual decline of 1.09% per year (Tables 2 and 3 ), reflecting increasing pressure from urban and semi-urban activities and structural changes in the local economy. The reduction in agricultural area is closely linked to shifts in livelihood preferences, with a growing proportion of the population moving toward education-based and non-agricultural employment. This transition has reduced the availability of agricultural labour, leading to land abandonment and its subsequent conversion for residential, commercial, and infrastructural purposes. Such a shift has also altered paddy cultivation in Alappuzha, which become increasingly concentrated in the Kuttanad region, while paddy fields in the northern and southern parts of the district have largely diminished. This spatial contraction of rice cultivation challenges Alappuzha’s long-standing identity as the ‘rice bowl of Kerala’. Similar trends have been reported elsewhere in the state, where higher literacy rates and the appeal of secondary and tertiary sector employment have reduced the attractiveness of farming (Firoz et al., 2014 ). Another factor is urban migration and improved infrastructure, which have shifted focus away from agriculture, further increasing the decline. In the Kuttanad region, agricultural decline has been further exacerbated by frequent and prolonged inundation of low-lying paddy fields. Anthropogenic alterations to hydrological systems have intensified flooding, rendering traditional cultivation increasingly unviable (Chandran and Purkayastha, 2022 ). The results indicate that a notable proportion of agricultural land (14.60 km 2 ) was converted into water bodies between 2000 and 2025, largely associated with the expansion of aquaculture, particularly prawn and shrimp farming ponds in areas such as Chemmen Padam. Previous studies have reported that aquaculture offers higher economic returns than conventional paddy cultivation, encouraging farmers to shift land use in response to market demand and export opportunities (Nair et al., 2002 ; Morshed et al., 2020 ). Although agricultural land has declined substantially, approximately 358.01 km² remained under cultivation during 2000–2025 (Table 6 ), supported by legal safeguards, cultural practices, and physical constraints. The Kerala Paddy Land and Wetland Conservation Act (2008) has played a critical role in restricting the conversion of paddy fields and wetlands, particularly in ecologically sensitive low-lying areas. Community-based initiatives such as padasekharams and Kudumbashree joint farming schemes have fostered strong cultural and social commitment to paddy cultivation, Kuttanad, preserving its identity as the ‘rice bowl of Kerala’. The unique below-sea-level farming system of Kuttanad further limits alternative land uses, as frequent flooding and waterlogged conditions make urban development technically challenging and economically unviable. Together, these legal, cultural, and environmental factors have contributed to the continued persistence of paddy fields in the region, even as agricultural land elsewhere in the district has declined. Uncultivable land exhibited a fluctuating trend during the study period, reflecting the dynamic interaction between socio-economic change, policy interventions, and regulatory limitations. The area under uncultivable land increased from 327.27 km² in 2000 to 347.91 km² in 2015 (6.31%), indicating temporary land degradation and the abandonment of productive agricultural land (Table 2 ). This initial increase suggests a transitional phase, during which agricultural land was rendered non-viable due to labour shortages, financial constraints, recurrent flooding, and declining profitability, leading to its reclassification as fallow or uncultivable land. Between 2015 and 2025, uncultivable land declined sharply to 280.40 km² (19.40%), resulting in a net decrease of 46.87 km² (14.32%) over the entire study period (Table 2 ). A significant portion of this land was converted into built-up areas (105.57 km²) and agricultural land (25.78 km²) (Table 6 ). While some degraded lands were reclaimed for cultivation through collective farming initiatives, a larger share was absorbed by expanding urban and infrastructural development, highlighting increasing anthropogenic pressure on land resources. At the same time, approximately 60.20 km² of agricultural land was converted into uncultivable land, largely due to the shift of agricultural labourers toward non-farm employment, financial limitations, and land abandonment (George et al. 2016 ). The transition from joint family systems to nuclear households further weakened traditional farming structures, reducing the capacity for labour-intensive agricultural practices. Policy interventions also played a mixed role in shaping these changes. While the Kudumbashree Collective Farming initiative facilitated the reclamation of fallow and waste lands for cultivation, contributing to the conversion of 25.78 km² of uncultivable land back into agricultural land, the Kerala Land Reforms Act (1963) and its 1969 amendment fragmented large landholdings into smaller, economically less viable plots, indirectly encouraging land conversion for residential and commercial purposes (Nirmal, 2016). Despite the presence of legal frameworks aimed at protecting agricultural land, land-use conversion continues through a combination of regulatory loopholes and socio-economic pressures. In many cases, agricultural land is first abandoned and reclassified as uncultivable or fallow land, after which it becomes eligible for conversion into built-up land. In other instances, agricultural land is directly converted into built-up land, particularly for housing, through approvals granted under the Kerala Land Utilisation (KLU) Order, 1967, or through corrections in local paddy land data banks under the Kerala Paddy Land and Wetland Conservation Act, 2008. Administrative challenges, delays in updating land records, fragmented landholdings, and local-level decision-making further weaken effective enforcement of land-protection regulations. The pronounced decline in uncultivable land after 2015 is also closely associated with large-scale infrastructure projects such as the NH-66 Alappuzha Bypass and six-laning works, which accelerated land acquisition and land-use conversion along major transport corridors. Decline in uncultivated land as a result of rapid urbanization has also been reported by Prasad and Ramesh ( 2019 ). Collectively, these processes explain the observed land-use transitions and highlight the limitations of existing regulatory mechanisms in preventing agricultural land loss despite statutory protection. Mixed vegetation experienced a net decrease of 35.63% (90.11 km 2 ) over the span of 25 years, with an annual decline of 1.76%. During 2000 to 2015, 13.19 km 2 was lost, recording a 5.22% decrease with an annual decline of 0.36%. During 2015 to 2025, mixed vegetation further lost 76.92 km 2 (32.09%), representing an annual decline of 3.87% (Tables 2 and 3 ). Changes in the socioeconomic status of the people led to accelerated development of infrastructure and real estate in Kerala (Dixit et al. 2018), which eventually required the removal of vegetative cover, resulting in LULCC. Perusal of literature has shown decreased vegetative cover in various parts of Kerala resulting from escalated urbanization (Shaji et al. 2017 ), which is evident from our result, where 87.66 km 2 of mixed vegetation was converted into Built-up land (Table 6 ). The reduction of mixed vegetation in the study area has been most pronounced after 2015, largely due to rapid infrastructure expansion and intensified human-driven land-use changes. The widening of NH 66 in Alappuzha district led to the removal of a large number of trees, with estimates ranging from approximately 12,000 to 70,000 trees along the Thuravoor–Oachira stretch, including ecologically important species such as banyan and jackfruit (The Hindu, 2022). This also resulted in habitat loss, fragmentation of vegetation patches, and disruption of local microclimatic conditions and increased the Land surface temperature of the area. Although compensatory plantation programmes were initiated under the Green Highway Policy, the slow pace of implementation and low short-term survival of transplanted trees meant that lost ecological functions were not adequately replaced. Additionally, before land sale and acquisition, some landowners felled mature trees and sold the timber for higher financial returns, while others cut old trees to meet emergency economic needs. All these factors significantly contributed to the decline of mixed vegetation in the region and weakened its ecological stability. The water bodies experienced a net decrease of 20.25% (14.19 km 2 ) over the span of 25 years, with an annual decline of 0.91%. During the period from 2000 to 2015, 9.02 km 2 was lost, recording a 12.87% decrease with an annual decline of 0.92%. Mixed vegetation further declined during 2015–2025, 5.17 km 2 (8.47%), with an annual decline of 0.89% (Tables 4 and 5 ). Over the entire study period, 23.16 km 2 of water bodies were converted into uncultivable land (Table 6 ). From 2000 to 2015, 19.45 km 2 of water bodies were converted into built-up land (Table 6 ). This transition can be attributed to both natural factors, such as groundwater depletion, and anthropogenic activities, including sand filling for construction and agricultural expansion. The encroachment of infrastructure, such as concreting natural channels, has further disrupted hydrological systems, leading to desiccation of water bodies (Gadgil et al. 2011 ). A similar trend of water body decline in the district has been noted by Varunprasath et al. ( 2025 ). While the present study observed a decline in water bodies between 2000 and 2025, a previous study by Prasad and Ramesh ( 2019 ) in the same region reported an increase of 5.56 km 2 in water bodies between 1973 and 2017. This discrepancy may be linked to temporal differences and the expansion of aquaculture ponds, which are often spectrally similar to natural water bodies and may influence classification outcomes. The study investigated the LULCCs in Alappuzha for the years 2000, 2015, and 2025. The findings indicate severe ecological stress on the district’s land use and land cover. A positive change was observed for built-up areas, whereas negative change was noted for all other classes, with the highest decline recorded in mixed vegetation. The steady rise in built-up areas and reduction in natural land reflect a complex interaction of socio-economic growth, NRI remittances, population growth, and urban expansion. Rise in population density can be justified by the concentration of the municipalities of Alappuzha (Alappuzha, Cherthala, Kayamkulam, Mavelikkara, Chengannur, and Haripad), as reported by Prasad and Ramesh ( 2019 ) for the period 1973–2017, which highlights the district’s long-term demographic intensification. In addition, infrastructure development and the district’s shift toward a more diversified economy have further contributed to land conversion. While traditional sectors like coir and fisheries continue to shape local livelihoods, new investments in IT, tourism, and small-scale industries have intensified land demand and accelerated the transformation of rural landscapes. Major projects such as the NH-66 expansion, improved connectivity, and the growth of tourism infrastructure have pushed development into ecologically sensitive zones, especially low-lying paddy fields. As land availability shrinks and environmental pressures increase, the findings underscore the need for balanced planning and sustainable management of LULCC to preserve ecological stability and support Alappuzha’s evolving economic trajectory. METHODS Study Area Alappuzha is a small district in western Kerala, located between 9° 06' 36" and 9° 53' 30" N latitudes and 76° 19' 03" and 76° 41' 33" E longitudes. The district is bordered by Ernakulam District in the north, Kottayam and Pathanamthitta Districts in the east, the Lakshadweep Sea in the west, and Kollam District in the south. It has a total land area of 1414 square kilometres. According to the 2011 census, it has a total population of 2,127,789 people, with a population density of 1504 people per square kilometre. Alappuzha features a diverse landscape, comprising a sandy stretch of land interrupted by lagoons, rivers, and canals. The district lacks significant elevations, such as hills or mountains, except for scattered hillocks in the eastern part between Bharanikkavu and Chengannur blocks. Ambalappuzha, Cherthala, Karthikappally, and Kuttanad Taluks are entirely situated within the lowland regions. About 80% of the district lies within the coastal area, while the remaining 20% is in the midland province. It has an 82-kilometre-long contiguous coastline. Alappuzha is Kerala's only district without highlands or forested areas. Water bodies constitute 13% of the district, and the terrain is below sea level in parts. The climate along the coast is moist and hot, while it is slightly cooler and drier inland. The average monthly temperature is 25°C, and the average annual rainfall is 2763 mm. [ Figure 1 Here ] Data Acquisition and Pre-processing. In this study, three Landsat images (path/row: 144/53 and 144/54) were obtained from the United States Geological Survey (USGS) website ( https://earthexplorer.usgs.gov/ ) for three different years. Landsat 7 ETM+ (2000), Landsat 8 OLI/TIRS (2015) and Landsat 9 OLI-2/TIRS (2025) were based on the requirement of minimum cloud cover and of the same month (January) to achieve marginal atmospheric and seasonal effects (Emran et al. 2018 ), with 30m resolution, and were utilized in the preparation of thematic maps i.e., LULC. Table 1 presents a description of the three Landsat datasets utilized in the study. Data from the Google Earth platform were utilized for the validation of the Landsat data. The three images were registered to a common Universal Transverse Mercator (UTM) coordinate system, Zone 43N, with World geocoded system (UTM WGS) 1984 Projection parameters. The ERDAS Imagine Software were utilized to execute standard image-processing techniques, encompassed extraction, geometric correction or georeferencing, atmospheric correction, topographic correction, layer stacking (band selection and combination), Mosaicking, image enhancement, and subsetting (clipping). [Table 1 Here] Image Mosaicking Subsequent to the fundamental image pre-processing, data were further processed to facilitate the operations ahead. As the investigative region falls within the images of two separate rows 53 and 54, it was essential to mosaic the images to create a single raster image. Throughout mosaicking, a certain strip of overlapping area common to both images was generated, which generates uncertainty since the Digital Number (DN) values vary across different images. Also, the rotated raster image includes '0' values to represent areas with no data (Firl, 2010 ). To address this ambiguity, the ‘Maximum’ option was selected as the ‘Mosaic Method’. This choice ensures that the output cell value for overlapping areas becomes the maximum value among the overlapped cells, effectively eliminating the ‘0’ value. Additionally, ‘Match’ was designated as the ‘Mosaic Colour Mode’. This option meticulously considers all colour maps during the mosaicking process, and if all feasible values are already allocated (based on the bit depth), it endeavours to match the value with the closest available colour. Detection and Analysis of Land Use and Land Cover Changes. In the ERDAS Imagine Software, the supervised classification method with the Maximum Likelihood Algorithm (MLC), the most widely used supervised classification method in remote sensing, was used to determine the LULC classification. It relies on determining the likelihood that a pixel corresponds to a specific class. In the study region, five LU/LC types have been identified, namely, (i) Agriculture zone (ii) Built-up land (iii) Mixed vegetation (iv) Uncultivable land (v) Water body. The characteristics of the five primary land-cover classes are listed in Table 2 . RGB colour composites of 4–3–2 were selected for Landsat-7 and Landsat-8/9, respectively. The change matrix was created using ERDAS Imagine 14 software. The quantitative area data of overall LULCCs, as well as gains and losses in each category, were analysed for the years 2000, 2015 and 2025. [Figure 5 Here] Assessment of the Accuracy of the Land Cover Map. The LULC classified images were subjected to accuracy assessment, a necessary procedure for extracting features from classified images. The "error or confusion matrix" was employed for determining the correctness of a classified image (Foody 2002 ; Mujabar and Chandraseker, 2013). This method compares pixels in a classified image to pixels in a referenced image, where the class of each pixel is known (Smith et al., 2003 ; Szuster et al., 2011 ). Based on the error of omission and commission, three different scales, user’s accuracy, producer’s accuracy and overall accuracy were used to calculate the accuracy in an error matrix (Coppin and Bauer, 1996 ; Carlotto 2009 ). $$\:Overall\:Accuracy\:=\:\frac{Total\:EquationNumber\:of\:correctly\:classified\:pixels}{Total\:EquationNumber\:of\:reference\:pixels}\:X\:100\%$$ 1 \(\:Comission\:error\:=\:\frac{\sum\:Off\:diagonal\:row\:elements}{Total\:of\:row}\:\) X100% (2) \(\:Omission\:error\:=\:\frac{\sum\:Off\:diagonal\:column\:elements}{Total\:of\:column}\:\) X100% (3) Producer’s accuracy (%) = 100% - Error of omission (%) (4) User’s accuracy = 100% - Error of commission (%) (5) The Kappa coefficient was employed to assess classification accuracy, including the diagonal elements (Coppin and Bauer, 1996 ; Foody, 2010 ). % of \(\:k\:=\:\frac{The\:Total\:sum\:of\:correct\:-\:Sum\:of\:all\:the\:\left(row\:total\:x\:column\:total\right)}{Total\:squared\:-\:Sum\:of\:all\:the\:\left(row\:total\:x\:column\:total\right)}\:x\:100\) (6) In this study, a total of 300 samples were randomly selected for the purpose of accuracy assessment for the years 2000, 2015 and 2025. A stratified sampling method was adopted to obtain a minimum of ten ground truth data points from Google Earth Services. Results of the LULC classification accuracy assessment are shown in Table 3 . Change Detection through Post-Classification Comparison and LULC Dynamics. Spatial change was identified through the application of the Post Classification Comparison (PCC) method (Yagoub and Kolan, 2006 ), which generated a cross-tabulation (transition) matrix. This matrix depicted the transitions in LULC over time by overlaying and comparing two successive classified land cover layers. Detection of LULC changes in reclaimed areas was achieved using Symmetrical Difference Overlay Analysis (Kamini et al. 2006 ). Subsequently, maps were compiled, and the conversion of areas from one LULC Class to another during the study period was quantified. Additionally, the annual rate of change was calculated according to Puyravaud ( 2003 ) and Teferi et al. ( 2013 ). Eq. ( 7 ) provided a standardized measure for LULCCs across different time periods within the study. $$\:r=\left(\frac{1}{{t}_{1}-{t}_{2}}\right)xIn\left(\frac{{S}_{1}}{{S}_{2}}\right)$$ 7 Here, ‘r’ represents the annual rate of change for each class, and S 1 and S 2 are the LULC class areas at t 1 and t 2 , respectively. Use of Large Language Models (LLMs) An AI-based large language model was used exclusively for language editing, grammatical correction, and improvement of academic clarity. The model was not used for data generation, data analysis, interpretation of results, or formulation of scientific conclusions. All scientific content, interpretations, and conclusions are the sole responsibility of the authors. Declarations Competing Interest The authors declare that they have no competing interests. Ethics This study did not involve any human participants or animal experiments. Hence, ethical approval was not required. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Dr. Anitha V carried out the data analysis, interpretation, and manuscript writing, while Dr. D. Prabha contributed through critical review, proofreading, and editorial corrections. Acknowledgements We would like to thank the Managing Editor for providing this opportunity, and the Chief Editor and the reviewers for their valuable comments and constructive suggestions. Data Availability All data generated or analysed during this study are included in this published article. References Addae, B. & Oppelt, N. Land-use/land-cover change analysis and urban growth modelling in the Greater Accra Metropolitan Area (GAMA), Ghana. Urban Sci. 3 (1), 26. https://doi.org/10.3390/urbansci3010026 (2019). Amini Parsa, V., Yavari, A. & Nejadi, A. Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran. 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Analysis of carbon emissions from land cover change during 2000 to 2020 in Shandong Province, China. Sci. Rep. 12 (1), 8021. https://doi.org/10.1038/s41598-022-12080-0 (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8412738","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594215113,"identity":"e86e5cec-a5ea-469a-a4a8-86b193f45748","order_by":0,"name":"Vijayakumar 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University","correspondingAuthor":false,"prefix":"","firstName":"Duraisamy","middleName":"","lastName":"Prabha","suffix":""}],"badges":[],"createdAt":"2025-12-20 14:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8412738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8412738/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103397915,"identity":"b7baf6ae-74f3-42b7-80b0-2ca90018e6d1","added_by":"auto","created_at":"2026-02-25 08:58:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141263,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8412738/v1/34a11502f343526e2e31f2af.png"},{"id":103397931,"identity":"eb040de5-8a4a-4ff2-945d-9505f4b09762","added_by":"auto","created_at":"2026-02-25 08:58:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160845,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover classes in the study area for the years 2000, 2015 and 2025.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8412738/v1/9ad1884f6b4de43a8dcbf30f.png"},{"id":103398135,"identity":"d497b47e-419d-44e0-99b4-708bedffb3ad","added_by":"auto","created_at":"2026-02-25 08:58:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70166,"visible":true,"origin":"","legend":"\u003cp\u003eDoughnut Chart showing the land utilization for each land use classes in Alappuzha District for the years 2000, 2015 and 2025.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8412738/v1/c2c4e6b361b4ebbced89db08.png"},{"id":103397984,"identity":"15a3b6b5-6a95-48c5-b113-5b42a4f777bd","added_by":"auto","created_at":"2026-02-25 08:58:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":142967,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of LULCC during 2000 – 2015 – 2025.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8412738/v1/45a3a6fa8cc8b9a0d2219407.png"},{"id":103397985,"identity":"8c16f9de-e6fb-4f5d-aa21-904289f8ae9a","added_by":"auto","created_at":"2026-02-25 08:58:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11230,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of methodology for LULC and LULCC detection.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8412738/v1/d17527a6782d9a5b043af14b.png"},{"id":106806094,"identity":"6059944a-b111-4ce4-86a0-ddce5fb1fa6f","added_by":"auto","created_at":"2026-04-13 15:27:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2258395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8412738/v1/7af0ac0e-a722-49a8-9c42-c8bbab2f67b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eGeospatial Assessment of Land Use and Land Cover Change in Alappuzha District, Western Kerala, India\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLand Use and Land Cover (LULC) transitions have become one of the most critical environmental concerns of the twenty-first century, driven by rapid urbanization, changing agricultural practices, infrastructure expansion, and climate variability. Land Cover (LC) refers to the biophysical features, such as soil, water, flora and anthropogenic activities of the Earth's surfaces, and Land Use (LU) refers to how humans manage or modify land (Lambin and Geist, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Together, LULC patterns fundamentally shape ecological processes, resource distribution, and human environment interactions across spatial and temporal scales. Consequently, monitoring LULC dynamics is vital for understanding landscape transformations, informing land governance, and advancing global sustainability agendas, particularly the targets of SDG 15 (Life on Land).\u003c/p\u003e \u003cp\u003eThese Land Use and Land Cover Changes (LULCCs), are driven by the complex interplay of socioeconomic, political, and natural factors (Prasad \u0026amp; Ramesh, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which play a crucial role in shaping landscape dynamics and determining environmental health and sustainability. These drivers often lead to rapid urbanization and infrastructure development, resulting in a shift from pervious to impervious surfaces, which significantly alters hydrological cycles, enhances surface runoff, degrades water quality and reduces groundwater recharge (Lerner \u0026amp; Harris, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tahiru et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These transformations alter the surface energy balance and increase Land Surface Temperature (LST), thereby contributing to the Urban Heat Island (UHI) effect in rapidly urbanising regions (Imran et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition to thermal and hydrological impacts, LULC transitions influence biodiversity, soil health, ecological productivity, food security, increase CO\u003csub\u003e2\u003c/sub\u003e and the stability of ecosystem services. These changes have direct implications for Sustainable Development Goals (SDG) 2 (Zero Hunger), 6 (Clean Water and Sanitation), 11 (Sustainable Cities and Communities), and 13 (Climate Action). In contrast, sustainable land-use strategies such as conservation zoning, wetland protection, and green infrastructure enhance ecological resilience, mitigate climate extremes, and protect livelihoods that depend on natural resources. Remote sensing (RS) combined with Geographic Information System (GIS) techniques has substantially improved the ability to detect, classify, and analyse LULC changes across various spatial and temporal scales. Satellite-based observations offer temporally consistent and spatially explicit data essential for long-term environmental monitoring. These technologies enable the identification of subtle or gradual landscape changes that are often undetectable by traditional ground-based methods. Landsat imagery, in particular, is frequently utilised due to its extensive multi-decadal archive, appropriate spatial resolution, and broad coverage, establishing it as a dependable resource for spatiotemporal LULC analysis (Kumar \u0026amp; Acharya, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies have investigated LULC changes using RS and GIS techniques, consistently demonstrating strong linkages between anthropogenic activities, ecological transformations, and thermal responses. At the global scale, urbanization driven LULCCs have been linked to rising LST (Falcucci et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hashim et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vasanthawada et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), ecosystem degradation (Gong et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), carbon emissions (Zhu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), landscape fragmentation (Ning et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), biodiversity loss (Amini et al. 2016), and altered flood regimes (Dezso et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Similar patterns have been observed across Asia and Africa, where agricultural expansion, aquaculture intensification, and urban sprawl have transformed landscapes and affected soil properties, hydrology, and UHI formation (Muttitanon \u0026amp; Tripathi, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Biro et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Arowolo \u0026amp; Deng, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Addae \u0026amp; Oppelt, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In India, extensive LULCC has been documented in regions such as Madhya Pradesh (Jaiswal et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), Tirupati (Mallupattu \u0026amp; Sreenivasula, 2013), Patna (Mishra et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Mumbai (Vinayak et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Durgapur Municipal Corporation (Haldar et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) the Himalayan Region (Yadav et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and Dhanbad (Ranjan et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), revealing consistent declines in vegetation, agricultural land, and ecosystem stability due to urban growth. Studies in Kerala, further show land-cover transformations linked to urbanization, resulting in declining greenness, flooding, and UHI intensification (Sonu et al. 2022; Skariah \u0026amp; Suriyakala, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Anitha et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tesfamariam et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While research in districts such as Wayanad and Kottayam demonstrates the value of RS\u0026ndash;GIS in monitoring vegetation and surface temperatures (John et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Anitha \u0026amp; Prabha, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), investigations remain fragmented and district-specific. Notably, despite rapid land transformation in Alappuzha, systematic multi-temporal assessments of LULCC and associated environmental impacts are scarce, indicating a clear need for comprehensive geospatial analyses in the region.\u003c/p\u003e \u003cp\u003eDespite global progress, Alappuzha District in Kerala remains understudied, even though it represents one of India\u0026rsquo;s most environmentally sensitive and socioeconomically important coastal landscapes. Characterised by an intricate network of canals, the Vembanad backwaters, wetlands, paddy fields, and coastal belts, Alappuzha is exceptionally vulnerable to landscape changes. The district\u0026rsquo;s economy, which relies on agriculture, fishing, coir production, and tourism, depends directly on stable land and water systems. LULCCs can disrupt the balance of water systems, increase flood risks, harm wetlands, raise LST, and affect ecosystem services that are vital for local livelihoods. Rapid population growth, increasing human activity in fragile ecosystems, and the demands of urban development further increase the district\u0026rsquo;s vulnerability to environmental stress.\u003c/p\u003e \u003cp\u003eIn this situation, evaluating LULCCs in Alappuzha is essential for understanding how human activities are altering productive landscapes and ecological systems. Therefore, this study aims to map, quantify, and analyse LULC changes in Alappuzha District over a period of 25 years (2000, 2015, and 2025) using multi-temporal Landsat datasets and geospatial techniques. Through the documentation of long-term patterns and the identification of key drivers of landscape transformation, this research offers critical insights for sustainable land management, climate adaptation, and ecosystem protection in accordance with SDG 15 (Life on Land). These findings are intended to assist policymakers, planners, and local stakeholders in advancing development strategies that reconcile economic objectives with environmental integrity and enduring ecological sustainability.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLand Cover Changes Analysis\u003c/h2\u003e \u003cp\u003eFive major land cover categories representing the foremost land cover in Alappuzha District have been analyzed based on the proportion of the area.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAccuracy Assessment\u003c/h3\u003e\n\u003cp\u003eThe accuracy assessment results for the classified LULC maps are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The overall classification accuracy exceeded 90% for all three years, with Kappa coefficients greater than 90% (0.90), indicating a high level of agreement between classified outputs and reference data. These results confirm the reliability of the LULC classifications used for change detection and transition analysis in this study.\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\u003eAccuracy assessment of LULC of 2000, 2015 and 2025.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eProducer\u0026rsquo;s Accuracy (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eUser\u0026rsquo;s Accuracy (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eProducer\u0026rsquo;s Accuracy (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUser\u0026rsquo;s Accuracy (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eProducer\u0026rsquo;s Accuracy (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eUser\u0026rsquo;s Accuracy (%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91\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\u003e87\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\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncultivable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKappa Coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e90\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\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Here]\u003c/p\u003e\n\u003ch3\u003eLand Use and Land Cover Change Dynamics\u003c/h3\u003e\n\u003cp\u003eThe spatial distribution of LULC classes in Alappuzha district for the years 2000, 2015, and 2025 is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Five major LULC categories were identified: built-up land, agricultural land, mixed vegetation, water bodies, and uncultivable land. Significant changes were observed across all classes over the 25-year study period. Additionally, the annual rates of change for all LULC classes are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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 distribution of Alappuzha District for the years 2000, 2015 and 2025. (\u0026lsquo;-\u0026rsquo; sign indicates decrease)\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLULC Class name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c10\" namest=\"c8\" rowspan=\"2\"\u003e \u003cp\u003eArea in change in km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(% Change)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2000\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2015\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2025\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eArea (km\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e% of the total area\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eArea (km\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e% of the total area\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eArea (km\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e% of the total area\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2000 and 2015\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2015 and 2025\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2000 and 2025\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e513.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e471.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e391.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-41.89\u003c/p\u003e \u003cp\u003e(-8.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-80.80\u003c/p\u003e \u003cp\u003e(-17.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-122.69\u003c/p\u003e \u003cp\u003e(-23.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003cp\u003eland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e353.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.46\u003c/p\u003e \u003cp\u003e(54.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e230.40\u003c/p\u003e \u003cp\u003e(187.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e273.86\u003c/p\u003e \u003cp\u003e(344.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003evegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e162.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-13.19\u003c/p\u003e \u003cp\u003e(-5.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-76.92\u003c/p\u003e \u003cp\u003e(-32.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-90.11\u003c/p\u003e \u003cp\u003e(-35.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncultivable\u003c/p\u003e \u003cp\u003eland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e280.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.65\u003c/p\u003e \u003cp\u003e(6.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-67.51\u003c/p\u003e \u003cp\u003e(-19.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-46.87\u003c/p\u003e \u003cp\u003e(-14.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-9.02\u003c/p\u003e \u003cp\u003e(-12.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-5.17\u003c/p\u003e \u003cp\u003e(-8.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-14.19\u003c/p\u003e \u003cp\u003e(-20.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1243.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1243.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1243.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage of annual rate change (\u0026lsquo;-\u0026rsquo; sign indicates decrease)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual Rate Change\u003c/p\u003e \u003cp\u003e(2000\u0026ndash;2015) %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual Rate Change\u003c/p\u003e \u003cp\u003e(2015\u0026ndash;2025) %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual Rate Change (2000\u0026ndash;2025) %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncultivable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.91\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\u003e \u003cb\u003eAgricultural land\u003c/b\u003e showed a consistent decline throughout the study period. The area under agriculture decreased from 513.79 km\u0026sup2; (41.32%) in 2000 to 471.90 km\u0026sup2; (37.95%) in 2015 and further to 391.10 km\u0026sup2; (31.45%) in 2025. This corresponds to a net loss of 122.69 km\u0026sup2; (23.88%) over the entire period, with a higher rate of decline (80.80 km\u0026sup2; with 17.12%) observed during 2015\u0026ndash;2025. Agricultural land exhibited a negative annual growth rate, which intensified from \u0026minus;\u0026thinsp;0.57% per year (2000\u0026ndash;2015) to \u0026minus;\u0026thinsp;1.88% per year (2015\u0026ndash;2025).\u003c/p\u003e \u003cp\u003eIn contrast, \u003cb\u003ebuilt-up land\u003c/b\u003e exhibited the most pronounced expansion, increasing steadily across all periods. The built-up area increased from 79.45 km\u0026sup2; (6.39%) in 2000 to 122.93 km\u0026sup2; (9.89%) in 2015, and further to 353.33 km\u0026sup2; (28.41%) in 2025. Overall, the built-up land increased by 344.61% (273.86 km\u0026sup2;) between 2000 and 2025. The most rapid expansion occurred during 2015\u0026ndash;2026, accounting for 187.42% (230.40 km\u003csup\u003e2\u003c/sup\u003e). Built-up land recorded the highest annual growth rate, increasing from 2.91% per year during 2000\u0026ndash;2015 to 10.56% per year during 2015\u0026ndash;2025.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMixed vegetation\u003c/b\u003e also declined substantially over time. The area decreased from 252.92 km\u0026sup2; (20.34%) in 2000 to 239.73 km\u0026sup2; (19.28%) in 2015, followed by a sharper reduction to 162.81 km\u0026sup2; (13.09%) in 2025. The overall reduction in mixed vegetation amounted to 90.11 km\u0026sup2; (35.63%) between 2000 and 2025. Mixed vegetation also showed an accelerated rate of decline during the later period, with the annual rate of loss increasing from \u0026minus;\u0026thinsp;0.36% to \u0026minus;\u0026thinsp;3.87%.\u003c/p\u003e \u003cp\u003e \u003cb\u003eUncultivable land\u003c/b\u003e showed comparatively minor changes during the study period. The area increased from 327.27 km\u0026sup2; (26.32%) in 2000 to 347.91 km\u0026sup2; (27.98%) in 2015, representing a gain of 20.65 km\u0026sup2; (6.31%). This was followed by a decline to 280.40 km\u0026sup2; (22.55%) in 2025, corresponding to a loss of 67.51 km\u0026sup2; (19.40%) during 2015\u0026ndash;2025. Overall, uncultivable land declined by 46.87 km\u003csup\u003e2\u003c/sup\u003e (14.32%) over the entire study period.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWater bodies\u003c/b\u003e steadily declined over the study period, from 70.06 km\u0026sup2; (5.63%) in 2000 to 61.04 km\u0026sup2; (4.91%) in 2015, representing a loss of 9.02 km\u0026sup2; (12.87%) and further to 55.87 km\u0026sup2; (4.49%) in 2025, resulting in a total loss of 14.19 km\u0026sup2; (20.25%). This corresponds to an average annual decline of approximately 0.9%.\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Here]\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Here]\u003c/p\u003e\n\u003ch3\u003eLand Use and Land Cover Change Transition Matrix\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show the cross-tabulation change matrix for the transformed regions, indicating the proportional shifts between different LULC classes relative to the total area of each class from 2000 to 2015, 2015 to 2025, and 2015 to 2025. Even though all LULC classes transformed in the study region, the extent of these changes varied significantly, with conversions observed throughout the entire study area. The diagonal values indicate the quantity of unchanged land use types. Between 2000 and 2015, agricultural land moderately declined, with 439.55 km\u0026sup2; remaining stable, while 51.22 km\u0026sup2; shifted to uncultivable land, 14.03 km\u003csup\u003e2\u003c/sup\u003e into waterbodies and 8.84 km\u0026sup2; to built-up areas. Built-up land expanded from 79.47 km\u0026sup2; to 122.93 km\u0026sup2;, mainly at the expense of water bodies (19.45 km\u0026sup2;), mixed vegetation (12.17 km\u0026sup2;), agriculture (8.84 km\u0026sup2;), and uncultivable land (3 km\u0026sup2;). Mixed vegetation remained largely stable (239.37 km\u0026sup2;), with minor conversions into uncultivable land (0.21 km\u0026sup2;) and agricultural land (0.15km\u003csup\u003e2\u003c/sup\u003e). Uncultivable land remained mostly unchanged (295.14 km\u0026sup2;), while water bodies were relatively stable (46.29 km\u0026sup2; retained), despite small conversions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). From 2015 to 2025, rapid urban growth was observed, with built-up land increasing from 122.93 km\u0026sup2; to 353.33 km\u0026sup2;, primarily converted from agriculture (83.63 km\u0026sup2;), mixed vegetation (75.68 km\u0026sup2;), and uncultivable land (70.82 km\u0026sup2;). Agricultural and mixed vegetation areas declined considerably, while water bodies showed only a slight reduction, with 53.09 km\u0026sup2; remaining stable (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Over the long term (2000 to 2025), a major landscape transformation occurred. Agricultural land decreased, with 358.01 km\u0026sup2; remaining, while significant portions were converted to built-up (80.42 km\u0026sup2;), uncultivable land (60.20 km\u0026sup2;) and water bodies (14.60 km\u003csup\u003e2\u003c/sup\u003e). Built-up areas increased most sharply, gaining land mainly from uncultivable land (105.57 km\u0026sup2;), mixed vegetation (87.66 km\u0026sup2;), and agriculture (80.42 km\u003csup\u003e2\u003c/sup\u003e). Mixed vegetation and water bodies showed consistent declines, with only 161.67 km\u0026sup2; and 40.45 km\u0026sup2; remaining, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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\u003eLULC change matrix from 2000 to 2015\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e2000 (Area in km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003e2015 (Area in km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAgriculture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBuilt-up land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMixed vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUncultivable land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eWater body\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eTotal 2000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAgriculture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e439.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e513.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBuilt-up land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e79.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e79.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMixed vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e239.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e252.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUncultivable land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e295.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e327.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWater body\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e46.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e70.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal 2015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e471.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e122.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e239.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e347.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e61.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1243.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\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\u003eLULC change matrix from 2015 to 2025\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e2015 (Area in km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003e2025 (Area in km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAgriculture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBuilt-up land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMixed Vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUncultivable land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eWater body\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eTotal 2015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAgriculture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e355.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e471.90\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBuilt-up land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e122.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e122.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMixed vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e161.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e239.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUncultivable land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e245.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e347.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWater body\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e53.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e61.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal 2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e391.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e353.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e162.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e280.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e55.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1243.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLULC change matrix from 2000 to 2025\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e2000 (Area in km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLULC Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003e2025 (Area in km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAgriculture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eBuilt-up land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMixed vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eUncultivable land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eWater body\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eTotal 2000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAgriculture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e358.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e513.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBuilt-up land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e79.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e79.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMixed vegetation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e161.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e252.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUncultivable land\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e194.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e327.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWater body\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e40.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e70.06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e391.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e353.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e162.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e280.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e55.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1243.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of LULC classes in the study area.\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\u003eLU/LC Classes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe components of cropland include mechanized farms and smallholder-operated farms, as well as paddy fields.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll kinds of infrastructure, such as housing, commercial, service, industrial, socioeconomic infrastructure regions, villages, cities, and transportation infrastructure.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrees and small grasses, including both natural and planted varieties.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncultivable land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand without vegetation cover or very little greenness, primarily encompassing areas with exposed soil and bare rock surfaces, and excavation sites.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll types of water bodies within the study area were considered, excluding the Vembanad Lake\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Landsat images used for the study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatellite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePath/\u003c/p\u003e \u003cp\u003eRow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDate of Image Acquisition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCloud Cover\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLandsat-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnhanced Thematic Mapper Plus (ETM+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144/53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e28/01/2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144/54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLandsat-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOperational Land Imager (OLI) and Thermal Infrared Sensor\u003c/p\u003e \u003cp\u003e(TIRS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144/53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13/01/2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144/54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLandsat 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOperational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144/53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e00/01/2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144/54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe land use and land cover (LULC) dynamics of Alappuzha district over the 25 years from 2000 to 2025 reveal substantial transformations, reflecting rapid socio-economic development and increasing anthropogenic pressure on natural landscapes. The overall pattern is characterised by pronounced urban expansion accompanied by a decline in natural and semi-natural land cover types, indicating growing ecological stress in the district.\u003c/p\u003e \u003cp\u003eThe most prominent transformation was the increase in built-up land during the study period, evolving from a relatively minor component of the landscape in 2000 to the second most dominant land cover class by 2025. The overall increase of 344.61% (273.86 km\u003csup\u003e2\u003c/sup\u003e) highlights the rapid pace of urbanization in Alappuzha. While built-up expansion during 2000\u0026ndash;2015 was moderate (54.69%, 43.46 km\u003csup\u003e2\u003c/sup\u003e), growth accelerated sharply after 2015 (187.42%, 230.40 km\u003csup\u003e2\u003c/sup\u003e during 2015\u0026ndash;2025), indicating a shift in the district\u0026rsquo;s development trajectory (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This expansion occurred largely at the expense of uncultivable land (105.57 km\u003csup\u003e2\u003c/sup\u003e), mixed vegetation (87.66 km\u003csup\u003e2\u003c/sup\u003e), and agricultural land (80.42 km\u003csup\u003e2\u003c/sup\u003e), as reflected in the conversion of substantial areas of these classes to built-up land (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similar long-term (1973 to 2017) urban growth trends in Alappuzha have been reported by Prasad and Ramesh (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The increase in built-up land in Alappuzha is driven by population growth in the district. According to census data, the population in our study area was 2,001,083 in 1991, which increased to 2,127,789 in 2011, reflecting a growth of 7.26%. By 2021, the population further increased to an estimated 2.20\u0026nbsp;million, projected growth for 2021 in the absence of official census data due to delays associated with the COVID-19 pandemic. This substantial rise in population has led to a higher demand for residential, commercial, and infrastructural development, contributing significantly to the expansion of built-up land. In addition to population pressure, the flourishing tourism industry has substantially influenced land transformation in the Alappuzha District. Tourism demanded the construction of resorts, hotels, restaurants, and associated facilities, particularly in ecologically sensitive coastal and backwater regions, resulting in changes in land use. Although tourism provides economic benefits, it has also accelerated habitat fragmentation and environmental degradation, placing considerable pressure on natural resources and ecosystems. Road infrastructure development has further amplified urban expansion. Major road projects such as the National Highway 66 (NH-66) Alappuzha Bypass, six-laning works, and the Aroor\u0026ndash;Thuravoor elevated highway have improved accessibility and connectivity, making the surrounding areas more attractive for urban and commercial development. These transport corridors have served as focal points, especially after 2015, for land-use conversion, reinforcing the dominance of built-up land across the Alappuzha district.\u003c/p\u003e \u003cp\u003eAgricultural land experienced a sustained decline of 23.88 km\u003csup\u003e2\u003c/sup\u003e (122.69%) between 2000\u0026ndash;2025, with an average annual decline of 1.09% per year (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), reflecting increasing pressure from urban and semi-urban activities and structural changes in the local economy. The reduction in agricultural area is closely linked to shifts in livelihood preferences, with a growing proportion of the population moving toward education-based and non-agricultural employment. This transition has reduced the availability of agricultural labour, leading to land abandonment and its subsequent conversion for residential, commercial, and infrastructural purposes. Such a shift has also altered paddy cultivation in Alappuzha, which become increasingly concentrated in the Kuttanad region, while paddy fields in the northern and southern parts of the district have largely diminished. This spatial contraction of rice cultivation challenges Alappuzha\u0026rsquo;s long-standing identity as the \u0026lsquo;rice bowl of Kerala\u0026rsquo;. Similar trends have been reported elsewhere in the state, where higher literacy rates and the appeal of secondary and tertiary sector employment have reduced the attractiveness of farming (Firoz et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother factor is urban migration and improved infrastructure, which have shifted focus away from agriculture, further increasing the decline. In the Kuttanad region, agricultural decline has been further exacerbated by frequent and prolonged inundation of low-lying paddy fields. Anthropogenic alterations to hydrological systems have intensified flooding, rendering traditional cultivation increasingly unviable (Chandran and Purkayastha, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The results indicate that a notable proportion of agricultural land (14.60 km\u003csup\u003e2\u003c/sup\u003e) was converted into water bodies between 2000 and 2025, largely associated with the expansion of aquaculture, particularly prawn and shrimp farming ponds in areas such as Chemmen Padam. Previous studies have reported that aquaculture offers higher economic returns than conventional paddy cultivation, encouraging farmers to shift land use in response to market demand and export opportunities (Nair et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Morshed et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough agricultural land has declined substantially, approximately 358.01 km\u0026sup2; remained under cultivation during 2000\u0026ndash;2025 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), supported by legal safeguards, cultural practices, and physical constraints. The Kerala Paddy Land and Wetland Conservation Act (2008) has played a critical role in restricting the conversion of paddy fields and wetlands, particularly in ecologically sensitive low-lying areas. Community-based initiatives such as padasekharams and Kudumbashree joint farming schemes have fostered strong cultural and social commitment to paddy cultivation, Kuttanad, preserving its identity as the \u0026lsquo;rice bowl of Kerala\u0026rsquo;. The unique below-sea-level farming system of Kuttanad further limits alternative land uses, as frequent flooding and waterlogged conditions make urban development technically challenging and economically unviable. Together, these legal, cultural, and environmental factors have contributed to the continued persistence of paddy fields in the region, even as agricultural land elsewhere in the district has declined.\u003c/p\u003e \u003cp\u003eUncultivable land exhibited a fluctuating trend during the study period, reflecting the dynamic interaction between socio-economic change, policy interventions, and regulatory limitations. The area under uncultivable land increased from 327.27 km\u0026sup2; in 2000 to 347.91 km\u0026sup2; in 2015 (6.31%), indicating temporary land degradation and the abandonment of productive agricultural land (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This initial increase suggests a transitional phase, during which agricultural land was rendered non-viable due to labour shortages, financial constraints, recurrent flooding, and declining profitability, leading to its reclassification as fallow or uncultivable land. Between 2015 and 2025, uncultivable land declined sharply to 280.40 km\u0026sup2; (19.40%), resulting in a net decrease of 46.87 km\u0026sup2; (14.32%) over the entire study period (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A significant portion of this land was converted into built-up areas (105.57 km\u0026sup2;) and agricultural land (25.78 km\u0026sup2;) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). While some degraded lands were reclaimed for cultivation through collective farming initiatives, a larger share was absorbed by expanding urban and infrastructural development, highlighting increasing anthropogenic pressure on land resources.\u003c/p\u003e \u003cp\u003eAt the same time, approximately 60.20 km\u0026sup2; of agricultural land was converted into uncultivable land, largely due to the shift of agricultural labourers toward non-farm employment, financial limitations, and land abandonment (George et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The transition from joint family systems to nuclear households further weakened traditional farming structures, reducing the capacity for labour-intensive agricultural practices. Policy interventions also played a mixed role in shaping these changes. While the Kudumbashree Collective Farming initiative facilitated the reclamation of fallow and waste lands for cultivation, contributing to the conversion of 25.78 km\u0026sup2; of uncultivable land back into agricultural land, the Kerala Land Reforms Act (1963) and its 1969 amendment fragmented large landholdings into smaller, economically less viable plots, indirectly encouraging land conversion for residential and commercial purposes (Nirmal, 2016).\u003c/p\u003e \u003cp\u003eDespite the presence of legal frameworks aimed at protecting agricultural land, land-use conversion continues through a combination of regulatory loopholes and socio-economic pressures. In many cases, agricultural land is first abandoned and reclassified as uncultivable or fallow land, after which it becomes eligible for conversion into built-up land. In other instances, agricultural land is directly converted into built-up land, particularly for housing, through approvals granted under the Kerala Land Utilisation (KLU) Order, 1967, or through corrections in local paddy land data banks under the Kerala Paddy Land and Wetland Conservation Act, 2008. Administrative challenges, delays in updating land records, fragmented landholdings, and local-level decision-making further weaken effective enforcement of land-protection regulations.\u003c/p\u003e \u003cp\u003eThe pronounced decline in uncultivable land after 2015 is also closely associated with large-scale infrastructure projects such as the NH-66 Alappuzha Bypass and six-laning works, which accelerated land acquisition and land-use conversion along major transport corridors. Decline in uncultivated land as a result of rapid urbanization has also been reported by Prasad and Ramesh (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Collectively, these processes explain the observed land-use transitions and highlight the limitations of existing regulatory mechanisms in preventing agricultural land loss despite statutory protection.\u003c/p\u003e \u003cp\u003eMixed vegetation experienced a net decrease of 35.63% (90.11 km\u003csup\u003e2\u003c/sup\u003e) over the span of 25 years, with an annual decline of 1.76%. During 2000 to 2015, 13.19 km\u003csup\u003e2\u003c/sup\u003e was lost, recording a 5.22% decrease with an annual decline of 0.36%. During 2015 to 2025, mixed vegetation further lost 76.92 km\u003csup\u003e2\u003c/sup\u003e (32.09%), representing an annual decline of 3.87% (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Changes in the socioeconomic status of the people led to accelerated development of infrastructure and real estate in Kerala (Dixit et al. 2018), which eventually required the removal of vegetative cover, resulting in LULCC. Perusal of literature has shown decreased vegetative cover in various parts of Kerala resulting from escalated urbanization (Shaji et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which is evident from our result, where 87.66 km\u003csup\u003e2\u003c/sup\u003e of mixed vegetation was converted into Built-up land (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The reduction of mixed vegetation in the study area has been most pronounced after 2015, largely due to rapid infrastructure expansion and intensified human-driven land-use changes. The widening of NH 66 in Alappuzha district led to the removal of a large number of trees, with estimates ranging from approximately 12,000 to 70,000 trees along the Thuravoor\u0026ndash;Oachira stretch, including ecologically important species such as banyan and jackfruit (The Hindu, 2022). This also resulted in habitat loss, fragmentation of vegetation patches, and disruption of local microclimatic conditions and increased the Land surface temperature of the area. Although compensatory plantation programmes were initiated under the Green Highway Policy, the slow pace of implementation and low short-term survival of transplanted trees meant that lost ecological functions were not adequately replaced. Additionally, before land sale and acquisition, some landowners felled mature trees and sold the timber for higher financial returns, while others cut old trees to meet emergency economic needs. All these factors significantly contributed to the decline of mixed vegetation in the region and weakened its ecological stability.\u003c/p\u003e \u003cp\u003eThe water bodies experienced a net decrease of 20.25% (14.19 km\u003csup\u003e2\u003c/sup\u003e) over the span of 25 years, with an annual decline of 0.91%. During the period from 2000 to 2015, 9.02 km\u003csup\u003e2\u003c/sup\u003e was lost, recording a 12.87% decrease with an annual decline of 0.92%. Mixed vegetation further declined during 2015\u0026ndash;2025, 5.17 km\u003csup\u003e2\u003c/sup\u003e (8.47%), with an annual decline of 0.89% (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Over the entire study period, 23.16 km\u003csup\u003e2\u003c/sup\u003e of water bodies were converted into uncultivable land (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). From 2000 to 2015, 19.45 km\u003csup\u003e2\u003c/sup\u003e of water bodies were converted into built-up land (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This transition can be attributed to both natural factors, such as groundwater depletion, and anthropogenic activities, including sand filling for construction and agricultural expansion. The encroachment of infrastructure, such as concreting natural channels, has further disrupted hydrological systems, leading to desiccation of water bodies (Gadgil et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). A similar trend of water body decline in the district has been noted by Varunprasath et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While the present study observed a decline in water bodies between 2000 and 2025, a previous study by Prasad and Ramesh (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in the same region reported an increase of 5.56 km\u003csup\u003e2\u003c/sup\u003e in water bodies between 1973 and 2017. This discrepancy may be linked to temporal differences and the expansion of aquaculture ponds, which are often spectrally similar to natural water bodies and may influence classification outcomes.\u003c/p\u003e \u003cp\u003eThe study investigated the LULCCs in Alappuzha for the years 2000, 2015, and 2025. The findings indicate severe ecological stress on the district\u0026rsquo;s land use and land cover. A positive change was observed for built-up areas, whereas negative change was noted for all other classes, with the highest decline recorded in mixed vegetation. The steady rise in built-up areas and reduction in natural land reflect a complex interaction of socio-economic growth, NRI remittances, population growth, and urban expansion. Rise in population density can be justified by the concentration of the municipalities of Alappuzha (Alappuzha, Cherthala, Kayamkulam, Mavelikkara, Chengannur, and Haripad), as reported by Prasad and Ramesh (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for the period 1973\u0026ndash;2017, which highlights the district\u0026rsquo;s long-term demographic intensification. In addition, infrastructure development and the district\u0026rsquo;s shift toward a more diversified economy have further contributed to land conversion. While traditional sectors like coir and fisheries continue to shape local livelihoods, new investments in IT, tourism, and small-scale industries have intensified land demand and accelerated the transformation of rural landscapes. Major projects such as the NH-66 expansion, improved connectivity, and the growth of tourism infrastructure have pushed development into ecologically sensitive zones, especially low-lying paddy fields. As land availability shrinks and environmental pressures increase, the findings underscore the need for balanced planning and sustainable management of LULCC to preserve ecological stability and support Alappuzha\u0026rsquo;s evolving economic trajectory.\u003c/p\u003e "},{"header":"METHODS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eAlappuzha is a small district in western Kerala, located between 9\u0026deg; 06' 36\" and 9\u0026deg; 53' 30\" N latitudes and 76\u0026deg; 19' 03\" and 76\u0026deg; 41' 33\" E longitudes. The district is bordered by Ernakulam District in the north, Kottayam and Pathanamthitta Districts in the east, the Lakshadweep Sea in the west, and Kollam District in the south. It has a total land area of 1414 square kilometres. According to the 2011 census, it has a total population of 2,127,789 people, with a population density of 1504 people per square kilometre. Alappuzha features a diverse landscape, comprising a sandy stretch of land interrupted by lagoons, rivers, and canals. The district lacks significant elevations, such as hills or mountains, except for scattered hillocks in the eastern part between Bharanikkavu and Chengannur blocks. Ambalappuzha, Cherthala, Karthikappally, and Kuttanad Taluks are entirely situated within the lowland regions. About 80% of the district lies within the coastal area, while the remaining 20% is in the midland province. It has an 82-kilometre-long contiguous coastline. Alappuzha is Kerala's only district without highlands or forested areas. Water bodies constitute 13% of the district, and the terrain is below sea level in parts. The climate along the coast is moist and hot, while it is slightly cooler and drier inland. The average monthly temperature is 25\u0026deg;C, and the average annual rainfall is 2763 mm.\u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e Here\u003cb\u003e]\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData Acquisition and Pre-processing.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, three Landsat images (path/row: 144/53 and 144/54) were obtained 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) for three different years. Landsat 7 ETM+ (2000), Landsat 8 OLI/TIRS (2015) and Landsat 9 OLI-2/TIRS (2025) were based on the requirement of minimum cloud cover and of the same month (January) to achieve marginal atmospheric and seasonal effects (Emran et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), with 30m resolution, and were utilized in the preparation of thematic maps i.e., LULC. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a description of the three Landsat datasets utilized in the study. Data from the Google Earth platform were utilized for the validation of the Landsat data. The three images were registered to a common Universal Transverse Mercator (UTM) coordinate system, Zone 43N, with World geocoded system (UTM WGS) 1984 Projection parameters. The ERDAS Imagine Software were utilized to execute standard image-processing techniques, encompassed extraction, geometric correction or georeferencing, atmospheric correction, topographic correction, layer stacking (band selection and combination), Mosaicking, image enhancement, and subsetting (clipping).\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Here]\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eImage Mosaicking\u003c/h3\u003e\n\u003cp\u003eSubsequent to the fundamental image pre-processing, data were further processed to facilitate the operations ahead. As the investigative region falls within the images of two separate rows 53 and 54, it was essential to mosaic the images to create a single raster image. Throughout mosaicking, a certain strip of overlapping area common to both images was generated, which generates uncertainty since the Digital Number (DN) values vary across different images. Also, the rotated raster image includes '0' values to represent areas with no data (Firl, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To address this ambiguity, the \u0026lsquo;Maximum\u0026rsquo; option was selected as the \u0026lsquo;Mosaic Method\u0026rsquo;. This choice ensures that the output cell value for overlapping areas becomes the maximum value among the overlapped cells, effectively eliminating the \u0026lsquo;0\u0026rsquo; value. Additionally, \u0026lsquo;Match\u0026rsquo; was designated as the \u0026lsquo;Mosaic Colour Mode\u0026rsquo;. This option meticulously considers all colour maps during the mosaicking process, and if all feasible values are already allocated (based on the bit depth), it endeavours to match the value with the closest available colour.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetection and Analysis of Land Use and Land Cover Changes.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the ERDAS Imagine Software, the supervised classification method with the Maximum Likelihood Algorithm (MLC), the most widely used supervised classification method in remote sensing, was used to determine the LULC classification. It relies on determining the likelihood that a pixel corresponds to a specific class. In the study region, five LU/LC types have been identified, namely, (i) Agriculture zone (ii) Built-up land (iii) Mixed vegetation (iv) Uncultivable land (v) Water body. The characteristics of the five primary land-cover classes are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. RGB colour composites of 4\u0026ndash;3\u0026ndash;2 were selected for Landsat-7 and Landsat-8/9, respectively. The change matrix was created using ERDAS Imagine 14 software. The quantitative area data of overall LULCCs, as well as gains and losses in each category, were analysed for the years 2000, 2015 and 2025.\u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e Here]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessment of the Accuracy of the Land Cover Map.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe LULC classified images were subjected to accuracy assessment, a necessary procedure for extracting features from classified images. The \"error or confusion matrix\" was employed for determining the correctness of a classified image (Foody \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mujabar and Chandraseker, 2013). This method compares pixels in a classified image to pixels in a referenced image, where the class of each pixel is known (Smith et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Szuster et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Based on the error of omission and commission, three different scales, user\u0026rsquo;s accuracy, producer\u0026rsquo;s accuracy and overall accuracy were used to calculate the accuracy in an error matrix (Coppin and Bauer, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Carlotto \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Overall\\:Accuracy\\:=\\:\\frac{Total\\:EquationNumber\\:of\\:correctly\\:classified\\:pixels}{Total\\:EquationNumber\\:of\\:reference\\:pixels}\\:X\\:100\\%$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Comission\\:error\\:=\\:\\frac{\\sum\\:Off\\:diagonal\\:row\\:elements}{Total\\:of\\:row}\\:\\)\u003c/span\u003e \u003c/span\u003eX100% \u003cb\u003e(2)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Omission\\:error\\:=\\:\\frac{\\sum\\:Off\\:diagonal\\:column\\:elements}{Total\\:of\\:column}\\:\\)\u003c/span\u003e \u003c/span\u003eX100% \u003cb\u003e(3)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eProducer\u0026rsquo;s accuracy (%)\u0026thinsp;=\u0026thinsp;100% - Error of omission (%) \u003cb\u003e(4)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUser\u0026rsquo;s accuracy\u0026thinsp;=\u0026thinsp;100% - Error of commission (%) \u003cb\u003e(5)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe Kappa coefficient was employed to assess classification accuracy, including the diagonal elements (Coppin and Bauer, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Foody, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e% of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\:=\\:\\frac{The\\:Total\\:sum\\:of\\:correct\\:-\\:Sum\\:of\\:all\\:the\\:\\left(row\\:total\\:x\\:column\\:total\\right)}{Total\\:squared\\:-\\:Sum\\:of\\:all\\:the\\:\\left(row\\:total\\:x\\:column\\:total\\right)}\\:x\\:100\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e(6)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, a total of 300 samples were randomly selected for the purpose of accuracy assessment for the years 2000, 2015 and 2025. A stratified sampling method was adopted to obtain a minimum of ten ground truth data points from Google Earth Services. Results of the LULC classification accuracy assessment are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eChange Detection through Post-Classification Comparison and LULC Dynamics.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSpatial change was identified through the application of the Post Classification Comparison (PCC) method (Yagoub and Kolan, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), which generated a cross-tabulation (transition) matrix. This matrix depicted the transitions in LULC over time by overlaying and comparing two successive classified land cover layers. Detection of LULC changes in reclaimed areas was achieved using Symmetrical Difference Overlay Analysis (Kamini et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Subsequently, maps were compiled, and the conversion of areas from one LULC Class to another during the study period was quantified. Additionally, the annual rate of change was calculated according to Puyravaud (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Teferi et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e7\u003c/span\u003e) provided a standardized measure for LULCCs across different time periods within the study.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:r=\\left(\\frac{1}{{t}_{1}-{t}_{2}}\\right)xIn\\left(\\frac{{S}_{1}}{{S}_{2}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u0026lsquo;r\u0026rsquo; represents the annual rate of change for each class, and S\u003csub\u003e1\u003c/sub\u003e and S\u003csub\u003e2\u003c/sub\u003e are the LULC class areas at t\u003csub\u003e1\u003c/sub\u003e and t\u003csub\u003e2\u003c/sub\u003e, respectively.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUse of Large Language Models (LLMs)\u003c/h2\u003e \u003cp\u003eAn AI-based large language model was used exclusively for language editing, grammatical correction, and improvement of academic clarity. The model was not used for data generation, data analysis, interpretation of results, or formulation of scientific conclusions. All scientific content, interpretations, and conclusions are the sole responsibility of the authors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003eThis study did not involve any human participants or animal experiments. Hence, ethical approval was not required.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDr. Anitha V carried out the data analysis, interpretation, and manuscript writing, while Dr. D. Prabha contributed through critical review, proofreading, and editorial corrections.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to thank the Managing Editor for providing this opportunity, and the Chief Editor and the reviewers for their valuable comments and constructive suggestions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAddae, B. \u0026amp; Oppelt, N. 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Analysis of carbon emissions from land cover change during 2000 to 2020 in Shandong Province, China. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 8021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-022-12080-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-12080-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alappuzha, Ecological stress, Land Use Land Cover Changes, Landsat, Remote Sensing and GIS","lastPublishedDoi":"10.21203/rs.3.rs-8412738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8412738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRapid urbanization has altered land use and land cover, leading to a decline in environmental quality. Monitoring these changes with LULC analysis is, therefore, indispensable. This study looks at LULC changes in Alappuzha district, Kerala, for the years 2000, 2015, and 2025 using Remote Sensing and GIS. LULC maps were generated from Landsat images using Maximum Likelihood Classification for five categories: agriculture, built-up land, mixed vegetation, uncultivable land, and water bodies. The results indicate substantial reductions in mixed vegetation (35.63%), agricultural land (23.88%), water bodies (20.25%), and uncultivable land (14.32%) between 2000 and 2025. Conversely, built-up land expanded by 344.61%, indicating rapid urban growth. Socioeconomic changes, population growth, climate change, and shifts in employment patterns have contributed to the decline in agricultural areas. The observed decreases in mixed vegetation and water bodies highlight ecological stress and underscore the urgent need for restoration initiatives. The study highlights the need for well-planned land-use strategies that prioritise resource sustainability and ecological protection. Further research into the effects of LULC Changes on surface temperatures, hydrology and biodiversity is recommended to inform environmental planning in the ecologically sensitive Alappuzha district.\u003c/p\u003e","manuscriptTitle":"Geospatial Assessment of Land Use and Land Cover Change in Alappuzha District, Western Kerala, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 08:55:40","doi":"10.21203/rs.3.rs-8412738/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":"8ba480a5-1b43-493e-a4a0-7f6cfb8fa3c4","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63472981,"name":"Biological sciences/Ecology"},{"id":63472982,"name":"Earth and environmental sciences/Ecology"},{"id":63472983,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-04-13T15:26:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 08:55:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8412738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8412738","identity":"rs-8412738","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}