Spatiotemporal Dynamics of Land Use Land Cover Change: Quantifying Urbanization Impacts on Agricultural and Forest Landscapes

Spatiotemporal Dynamics of Land Use Land Cover Change: Quantifying Urbanization Impacts on Agricultural and Forest Landscapes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal Dynamics of Land Use Land Cover Change: Quantifying Urbanization Impacts on Agricultural and Forest Landscapes Achintya Pran Hazarika This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7988134/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 in biodiversity-rich regions poses critical challenges for sustainable development. This study analyses Land Use Land Cover (LULC) changes in Kamrup district, Assam, Northeast India, during 2014–2024 using Landsat 8 OLI multispectral satellite imagery and Maximum Likelihood Classification in ArcGIS. Six LULC classes were mapped: Agricultural Land, Barren Land, Built-up Area, Forest Cover, Sparse Vegetation, and Waterbodies. Results reveal landscape transformations with built-up areas experiencing unprecedented expansion of 504.32 km² (+ 132.19%), representing one of the most rapid urbanization rates in Northeast India. Concurrently, agricultural land declined by 229.27 km² (− 36.00%) and forest cover decreased by 725.05 km² (− 39.02%), indicating severe pressure on productive and natural landscapes. Notably, sparse vegetation increased by 625.89 km² (+ 77.93%), suggesting complex ecological processes involving secondary succession and forest degradation. The classification achieved 89.2% overall accuracy (κ = 0.847), validating result reliability. The documented changes underscore fundamental landscape reorganization driven by metropolitan expansion, threatening food security, biodiversity conservation, and ecosystem service provision. These findings establish essential baseline information for integrated land management strategies balancing urbanization, agricultural sustainability, and environmental conservation in biodiversity hotspots. The research demonstrates that evidence-based policy interventions addressing interconnections between urban development, agricultural protection, and forest conservation can guide sustainable development trajectories in rapidly changing regions. Land Use Land Cover Change Remote Sensing Urbanization Agricultural Land Loss Forest Degradation Biodiversity Conservation Kamrup District Northeast India Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Land Use and Land Cover Change (LULCC) represent one of the most profound anthropogenic transformations of Earth's terrestrial systems in the 21st century, fundamentally altering ecosystem functions, biodiversity patterns, climate regulation, and socioeconomic development trajectories (Chen et al., 2025 ; Pushpalatha et al., 2025 ). The accelerating pace of landscape modification, driven by complex interactions between demographic pressures, economic development imperatives, and climate variability, has emerged as a critical force reshaping natural and human-dominated landscapes globally (Tahir et al., 2025 ; Yang et al., 2025 ). Contemporary research demonstrates that LULCC processes are intensifying with particular severity in rapidly developing regions, where urbanization pressures intersect with agricultural expansion and natural resource extraction (Mahendra et al., 2025 ; Singh et al., 2025 ). The integration of advanced remote sensing technologies with sophisticated Geographic Information Systems (GIS) has revolutionized our capacity to monitor, analyze, and predict these complex spatiotemporal dynamics with unprecedented precision (Khan et al., 2024 ; Zhang et al., 2024 ). Recent methodological advances, including the deployment of high-resolution satellite constellations such as Landsat 8/9 and Sentinel-1/2, coupled with artificial intelligence-driven classification algorithms and cloud-computing platforms like Google Earth Engine, have enhanced our understanding of LULCC processes while providing essential tools for sustainable land management and environmental conservation (Bairwa et al., 2025 ; Rawat et al., 2024 ). These technological innovations enable detection of subtle landscape changes at multiple temporal and spatial scales, facilitating evidence-based policy interventions and adaptive management strategies. Urbanization represents the most driver of contemporary LULCC globally, with urban areas expanding at rates that significantly outpace population growth (Mahendra et al., 2025 ; Singh et al., 2024 ). The global urban population has surpassed 55% and is projected to reach 68% by 2050, with Asia and Africa experiencing the most rapid urban transitions (UN-Habitat, 2022 ). This unprecedented urban expansion exerts profound pressures on agricultural and forest landscapes, manifesting through direct conversion of productive lands to built-up areas, infrastructure development, and indirect effects including habitat fragmentation, altered hydrological regimes, and increased resource extraction (Sethi et al., 2024 ; Kumar et al., 2024 ). Agricultural landscapes bear particularly severe impacts from urbanization, with prime agricultural lands often targeted for development due to their favorable topographic characteristics, soil quality, and proximity to existing infrastructure networks (Mahendra et al., 2025 ). Recent assessments document alarming trends of agricultural land conversion globally, with estimates suggesting annual losses exceeding 10 million hectares to urbanization and other non-agricultural uses (FAO, 2023 ). In rapidly developing regions, this conversion threatens food security, disrupts traditional livelihood systems, and diminishes ecosystem services provided by agricultural landscapes including pollination, nutrient cycling, and carbon sequestration (Bairwa et al., 2025 ; Singh et al., 2024 ). Forest ecosystems face equally severe pressures from urban expansion and associated development activities. Urban-driven deforestation occurs through multiple pathways: direct clearing for residential and commercial development, infrastructure corridor establishment, and intensified resource extraction to meet urban demands for timber, fuel, and non-timber forest products (Marchang, 2021 ; Down to Earth, 2024). Beyond direct conversion, urbanization induces forest degradation through edge effects, altered fire regimes, invasive species proliferation, and disrupted wildlife movement corridors (Elahi et al., 2024 ). These impacts are particularly consequential in biodiversity hotspots, where rapid development threatens irreplaceable biological diversity and critical ecosystem services. India exemplifies the complex challenges of balancing rapid economic development with environmental sustainability, experiencing some of the world's most LULCC patterns (Singh et al., 2024 ; Mahendra et al., 2025 ). The country's urban population has grown from 286 million in 2001 to over 460 million by 2021, with projections suggesting continued rapid urbanization through mid-century (Census of India, 2021 ). This demographic transition has catalysed extensive landscape transformations, with built-up areas expanding by 45% between 2001–2011 alone (NRSC, 2024 ). Simultaneously, agricultural land faces mounting pressures from urbanization, infrastructure development, and changing land tenure patterns, while forest cover experiences both gains through afforestation programs and losses from development activities (FSI, 2021 ). Northeast India, encompassing eight states including Assam, represents a region of exceptional ecological and socioeconomic significance within this national context (Bhattacharya et al., 2025; UN Global Compact Network India, 2024 ). The region harbors over 50% of India's flora and fauna within just 8% of the country's land area, positioning it as a critical biodiversity hotspot at the confluence of the Himalayan and Indo-Burma biogeographic realms (Elahi et al., 2024 ). This extraordinary biological richness occurs alongside complex social dynamics, including diverse indigenous communities maintaining traditional resource management systems and cultural landscapes of global significance. However, this ecological wealth faces unprecedented pressures from development initiatives positioning Northeast India as the nation's gateway to Southeast Asia (Bhattacharya et al., 2025). Recent assessments reveal alarming LULCC trends across the region, with forest cover reductions ranging from 5–14% between 2001–2020, and Nagaland experiencing the most decline of 17% (Marchang, 2021 ). Assam, the region's largest and most populous state, contributed to a 14.1% decrease in forest cover during this period while simultaneously experiencing rapid urban expansion concentrated in metropolitan centers like Guwahati (Down to Earth, 2024). These transformations reflect broader patterns of development-driven landscape change but occur within ecosystems of exceptional conservation value, creating particularly acute sustainability challenges. Within Northeast India's complex LULCC landscape, Kamrup district emerges as a particularly compelling and representative case study for understanding contemporary urbanization impacts on agricultural and forest landscapes. The district encompasses Guwahati, Northeast India's largest metropolitan centre and primary economic hub, making it a focal point for regional development pressures and associated landscape transformations (Baruah et al., 2024 ; Kumar et al., 2024 ). Kamrup's strategic position along major transportation corridors, combined with its role as the region's educational, commercial, and administrative center, has catalyzed unprecedented growth dynamics that exemplify broader urban expansion patterns across rapidly developing biodiversity hotspots globally. Recent assessments document the district's demographic and spatial transformations. The Kamrup Metropolitan District has experienced over 40% population growth between 2001–2024, with urban transformation exceeding 12% during this period (Kamrup Metropolitan District, 2024 ). Current projections indicate built-up areas will reach 36.2% of the district by 2032 and 40.54% by 2052, representing one of the most rapid urbanization trajectories documented in Northeast India (Baruah et al., 2024 ). This expansion has generated significant environmental consequences, including ground deformation with subsidence patterns reaching 71.64 mm/year in central areas due to intensive development and groundwater exploitation (Goswami et al., 2024 ). The district's ecological significance extends well beyond its urban centers, encompassing critical biodiversity resources and ecosystem services. Recent biological inventories document 126 bryophyte species from 76 genera, including 50 new records for Assam and four species reported for the first time in the Northeastern Himalayas (Ahmed et al., 2025 ). The district contains multiple protected areas including Deepor Beel Ramsar wetland, supporting 219 bird species and serving as a critical stopover for migratory waterfowl, and Amchang Wildlife Sanctuary, harbouring diverse mammalian fauna including threatened species like leopards and elephants (Das et al., 2023 ). This juxtaposition of intensive urban development and high conservation value creates complex management challenges requiring sophisticated analytical approaches and evidence-based planning frameworks. Despite significant technological and methodological advances in LULCC analysis, critical knowledge gaps persist in understanding landscape change dynamics in biodiversity hotspots undergoing rapid development. Current literature lacks comprehensive decadal assessments integrating high-resolution remote sensing with detailed socioeconomic and ecological analysis for Northeast India's critical landscapes (Khan et al., 2024 ; Baruah et al., 2024 ). The complex interactions between urbanization pressures, traditional land use systems, agricultural transformation, forest degradation, and biodiversity conservation require integrated analytical frameworks that move beyond simple change detection to explore causal mechanisms, spatial patterns, and future scenarios (Yang et al., 2025 ; Bairwa et al., 2025 ). The period 2014–2024 represents a particularly critical decade for Northeast India, characterized by major policy initiatives including Smart Cities Mission, infrastructure investments under the Act East Policy, and climate change impacts that have fundamentally altered regional development trajectories (Bhattacharya et al., 2025). This temporal window coincides with availability of high-quality satellite data from multiple sensors and advanced analytical tools including cloud-computing platforms and machine learning algorithms, creating unprecedented opportunities for comprehensive landscape analysis (Khan et al., 2024 ; Zhang et al., 2024 ). However, systematic assessments of LULCC patterns during this critical period remain limited, particularly at the district scale where local planning decisions most directly influence landscape outcomes and where intervention opportunities are greatest. Furthermore, existing studies often focus on single aspects of LULCC—either urbanization, agricultural change, or deforestation—without examining their complex interactions and cumulative impacts on landscape structure and function (Mahendra et al., 2025 ). Integrated analyses quantifying urbanization's simultaneous impacts on both agricultural and forest landscapes, while considering spatial heterogeneity and temporal dynamics, remain scarce. Such integrated approaches are essential for developing holistic understanding of landscape transformation processes and for designing effective interventions that address multiple sustainability dimensions simultaneously. To address these the following objectives are framed: Conduct comprehensive spatiotemporal analysis of LULCC dynamics in Kamrup district during the decade 2014–2024, with particular emphasis on quantifying urbanization impacts on agricultural and forest landscapes. Employ state-of-the-art remote sensing and GIS techniques to analyse transformations across six major LULC categories: Agricultural Land, Barren Land, Built-up Area, Forest Cover, Sparse Vegetation, and Waterbodies. Establish baseline information for sustainable development planning, biodiversity conservation strategies, and climate adaptation initiatives in Kamrup district. This research holds profound significance for both scientific understanding and practical application of sustainable development in biodiversity-rich, rapidly urbanizing regions. By documenting unprecedented urban expansion (+ 132.19%), severe agricultural land loss (-36.00%),(Fig. 3) and substantial forest degradation (-39.02%) in Kamrup district over the critical decade 2014–2024, the study provides essential baseline information for evidence-based policy interventions, conservation planning, and climate adaptation strategies in Northeast India's most dynamic metropolitan region (Baruah et al., 2024 ; Kumar et al., 2024 ). The comprehensive six-class LULC analysis reveals complex ecological responses including substantial sparse vegetation increase (+ 77.93%), illuminating intricate processes of secondary succession, forest degradation, and land abandonment that require nuanced conservation approaches beyond simple protection strategies (Li et al., 2025 ; Elahi et al., 2024 ). Beyond local importance, this research contributes to global understanding of landscape transformation dynamics in biodiversity hotspots where economic development pressures intersect with critical conservation imperatives, providing a replicable methodological framework applicable to similar regions worldwide (Yang et al., 2025 ; Bairwa et al., 2025 ). The findings establish robust scientific foundation for addressing the fundamental sustainability challenge of balancing rapid urbanization with food security, biodiversity conservation, and environmental resilience—a challenge that characterizes contemporary development trajectories across the Global South and carries implications for achieving Sustainable Development Goals related to sustainable cities, life on land, and climate action. 2 Materials and Methods 2.1 Study Area Description Figure 1: Map showing the study Area Kamrup district is strategically located in the Lower Brahmaputra valley of Assam, Northeast India, at coordinates between 25°46′ and 26°49′ North latitude and 90°48′ and 91°50′ East longitude. The district encompasses approximately 4,100 km² of diverse terrain, representing one of Northeast India's most ecologically and economically significant regions (Kumar & Pant, 2017 ; Baruah et al., 2024 ). The mighty Brahmaputra River, Asia's second-largest river by water discharge, flows through the district from east to west, dividing it into northern and southern portions and profoundly influencing landscape ecology, settlement patterns, and livelihood systems. The district's physiography reflects its position at the convergence of multiple geomorphological systems. Northern areas comprise the alluvial plains of the Brahmaputra Valley, characterized by low-lying, fertile plains frequently subjected to seasonal inundation. In contrast, southern portions encompass the foothills of the Khasi-Jaintia and Garo hills, rising to elevations of 800–1,000 meters above mean sea level (msl), representing northward projections of the Shillong Plateau (Borpujari et al., 2021 ; DCMSME, 2024 ). This topographic heterogeneity creates diverse ecological zones, from alluvial wetlands to mixed deciduous forests, supporting exceptionally high biodiversity. (Fig. 1) Kamrup district experiences a subtropical humid monsoon climate characteristic of the Lower Brahmaputra Valley, with annual rainfall ranging from 1,500–2,600 mm, concentrated during the Southwest Monsoon (June–September) (DCMSME, 2024 ). Mean daily temperatures fluctuate between 7.0°C (winter minimum) and 39.5°C (pre-monsoon maximum), creating distinct seasonal variations that influence agricultural patterns, phenological cycles, and water availability. The district receives rainfall from multiple sources: the Southwest Monsoon accounts for approximately 60–70% of annual precipitation, with substantial contributions from pre-monsoon (March–May) thunderstorms and winter precipitation events. The Brahmaputra River and its tributaries form the district's primary hydrological network, with major tributaries including the Manas, Chaul Khoya, and Barnadi rivers. Seasonal flooding represents a critical environmental feature, with extensive wetland systems including the internationally important Deepor Beel (42.6 km²), designated as a Ramsar wetland site supporting 219 bird species and serving as a critical stopover for migratory waterfowl (Ahmed et al., 2025 ). Kamrup district falls within Critical Biogeographic Zones (9A and 9B) of the Northeast Brahmaputra valley, representing a convergence of Himalayan, Indo-Burma, and biogeographic elements of exceptional conservation importance. Recent biodiversity assessments document 126 bryophyte species from 76 genera, including 50 new records for Assam and four species never previously recorded in the Northeastern Himalayas (Ahmed et al., 2025 ). The district encompasses critical protected areas including Amchang Wildlife Sanctuary (78.64 km²) harboring diverse mammalian fauna including threatened species like Asian elephants, Bengal tigers, and clouded leopards; Deepor Beel Ramsar site; and multiple Forest Reserves protecting remnant natural ecosystems (Elahi et al., 2024 ). Kamrup district serves as Assam's and Northeast India's primary economic hub, anchored by Guwahati city, the region's largest metropolitan center with over 2.7 million residents (Census of India, 2021 ). The district experienced rapid population growth from 3.7 million (2001) to over 4.5 million (2021), with urban population concentration in Guwahati Metropolitan area creating intense development pressures on surrounding agricultural and forest landscapes (Kamrup Metropolitan, 2024). This demographic expansion has catalyzed economic diversification beyond agriculture, including substantial growth in services, education, healthcare, and manufacturing sectors, driving extensive land use transformations documented in this study. 2.2 Satellite Data and Preprocessing High-resolution Landsat 8 OLI (Operational Land Imager) multispectral satellite imagery served as the primary data source for LULC classification. Two cloud-free scenes were acquired for 2014 (Path/Row 135/042) and 2024 (same Path/Row), ensuring consistent spatial coverage and temporal comparability. Landsat 8 OLI provides 11 spectral bands with spatial resolutions of 30 meters for optical bands (visible, near-infrared, shortwave-infrared), 100 meters for thermal bands, and 15 meters for panchromatic band (USGS, 2023 ). The sensor operates on a sun-synchronous, near-polar orbit at 705 km altitude with an 8-day repeat cycle when combined with Landsat 7, providing systematic global coverage with geometric accuracy of ± 12 meters RMSE (Wulder et al., 2019 ). Landsat 8 OLI specifications particularly relevant for LULC classification include spectral bands covering visible (Blue 0.43–0.45 µm; Green 0.45–0.51 µm; Red 0.53–0.59 µm), near-infrared (NIR 0.85–0.88 µm), shortwave-infrared (SWIR-1 1.57–1.65 µm; SWIR-2 2.11–2.29 µm), and coastal/aerosol (0.43–0.45 µm) regions. The data achieved geometric accuracy of 12-meter radial root mean square error (RMSE), meeting Tier 1 accuracy standards, and radiometric accuracy within 5% absolute spectral radiance and 3% top-of-atmosphere reflectance. The data is freely available through the USGS Earth Explorer platform with 16-day single-mission repeat and 8-day combined constellation temporal resolution, enabling systematic monitoring capabilities. Systematic preprocessing ensured data quality (Fig. 2 ), geometric consistency, and atmospheric correction. Both 2014 and 2024 Landsat scenes underwent geometric correction to minimize positional errors. Ground Control Points (GCPs) were selected from permanent features (highway intersections, bridges, building complexes) with known coordinates obtained from Survey of India topographic maps and differential GPS surveys. Second-order polynomial transformation with nearest-neighbor resampling was applied, maintaining root mean square error (RMSE) below one pixel (30 meters) to ensure acceptable geometric accuracy for change detection analysis. Radiometric normalization addressed differences in atmospheric conditions, solar angles, and sensor calibration between acquisition dates. The Dark Object Subtraction (DOS) method was applied to correct for atmospheric effects by identifying dark objects (water bodies, shadows) with zero or near-zero spectral reflectance and subtracting their recorded values from all pixels as atmospheric offset (Chander et al., 2009 ). Top-of-atmosphere reflectance calculation converted raw digital numbers to surface reflectance values, enabling direct spectral comparison between temporal datasets. Automated cloud and shadow detection using Landsat's Quality Assessment (QA) band and spectral indices (Normalized Difference Moisture Index) identified and masked cloud-contaminated pixels. Manual visual interpretation verified automated results and corrected misclassified pixels, ensuring that only high-quality data pixels contributed to classification analysis. 2.3 LULC Classification Scheme and Methodology A comprehensive six-class LULC classification scheme was developed specifically for Kamrup district's landscape characteristics, modifying the Anderson Level I classification system to enhance thematic resolution (Anderson et al., 1976 ; NRSC, 2024 ). The first class, Agricultural Land, encompasses cultivated areas including crop fields (rice paddies, wheat fields, pulses, oilseeds), irrigated and rain-fed agriculture, agricultural settlements, and farm boundaries, characterized by moderate-to-high NDVI values during growing season, typically ranging from 0.4–0.6. Barren Land, the second class, includes exposed soil, construction sites, mining areas, degraded lands without vegetation cover, and waste disposal sites, characterized by low NDVI values (< 0.2), high visible band reflectance, and distinctive spectral patterns in visible and shortwave-infrared bands. Built-up Area, the third class, comprises urban settlements, residential complexes, commercial zones, industrial facilities, and infrastructure (roads, railways, utilities, airports), characterized by high reflectance in visible bands, distinctive spectral patterns from concrete and asphalt, and moderate thermal signature. Forest Cover, the fourth class, represents dense forests with ≥ 40% tree canopy coverage, representing mature forest ecosystems including evergreen, semi-evergreen, and deciduous forests, characterized by high NDVI values (> 0.6), distinctive spectral signatures across near-infrared and shortwave-infrared bands, and low visible band reflectance. Sparse Vegetation, the fifth class, includes open forests, grasslands, scrublands, secondary vegetation with 10–40% vegetation coverage, areas undergoing forest degradation, land abandonment with secondary vegetation recovery, and transitional zones, characterized by NDVI values between 0.3–0.5 and intermediate spectral signatures between agricultural land and dense forest. Finally, Waterbodies, the sixth class, encompasses rivers, ponds, lakes, reservoirs, wetland systems, and inundated areas, characterized by low reflectance across visible and near-infrared bands, high absorptivity in shortwave-infrared, and distinctive spectral signature with Modified Normalized Difference Water Index (MNDWI) > 0.3. Representative training samples were collected through integrated approach combining visual interpretation of high-resolution satellite imagery, expert field knowledge, and ground-truthing information. Training polygon development followed strict protocols with 450 training polygons (approximately 75 polygons per class) manually delineated representing diverse environmental conditions, topographic settings, and spectral variations within each class. Training samples were stratified across the study area to represent geographic variability and ensure representative coverage of spectral characteristics, with training polygons maintaining minimum 0.5-hectare size (approximately 555 pixels at 30-meter resolution) to minimize mixed-pixel effects and representativeness issues. Spectral signature analysis employed multivariate statistical approaches. For each class, spectral signatures were computed calculating mean values and covariance matrices across all spectral bands for each training sample. Jeffries-Matusita (J-M) distance was calculated to assess spectral separability between class pairs, confirming adequate class discrimination (J-M distance > 1.8 indicating good separability; >1.9 indicating excellent separability). Classes demonstrating < 1.8 J-M distance were evaluated for potential merging or refinement of spectral signatures. Supervised Maximum Likelihood Classifier (MLC) was employed for LULC classification within ArcGIS 10.8 Spatial Analyst environment. MLC assigns each pixel to the class with highest probability of belonging based on Bayesian decision theory, assuming multivariate normal probability distribution within each class (ESRI, 2024 ; Rawat et al., 2024 ). The classifier calculates likelihood functions for each class and assigns pixels to the class with maximum probability value, producing discrete classification map. Prior probabilities were established based on relative training sample sizes for each class, incorporating class frequency information into decision rule. While contemporary machine learning algorithms (Random Forest, Support Vector Machine, Deep Learning) have achieved marginally higher classification accuracies in recent studies, the Maximum Likelihood Classifier was selected for this study based on several methodologically sound justifications. MLC provides robust statistical foundation based on established Bayesian decision theory and multivariate normal distribution assumptions, which are commonly satisfied by multispectral satellite data (Rawat et al., 2024 ; Kotsiantis, 2007 ). MLC consistently achieves classification accuracies of 82–89% for similar multi-temporal LULC analysis in comparable biogeographic regions, making it highly suitable for operational change detection applications. Recent comparative studies confirm MLC's reliability for regional-scale mapping despite marginal accuracy differences with machine learning algorithms. The ArcGIS MLC implementation offers superior integration with comprehensive GIS analytical capabilities, enabling seamless post-classification processing, change detection analysis, and spatial statistics within unified platform. Computational efficiency and reduced parameter tuning requirements make it optimal for operational monitoring in resource-limited environments. Extensively validated confusion matrix protocols and well-established accuracy metrics enable rigorous, comparable accuracy assessment aligned with international standards. MLC's extensive validation in similar Northeast Indian biogeographic contexts provides confidence in methodology transferability and results comparability with existing literature. Post-classification processing enhanced classification quality through iterative refinement. A 3×3 kernel majority filter was applied, replacing isolated pixels with class assignments differing from surrounding 8-pixel neighbourhood, effectively eliminating "salt-and-pepper" noise while preserving legitimate small features and improving overall classification coherence. Raster-to-vector conversion with minimum mapping unit (MMU) of 0.5 hectares eliminated spurious small patches. Any connected class patches smaller than MMU were merged with largest adjacent class, preventing false fragmentation and improving interpretability. Experienced analysts conducted careful visual quality control comparing classified maps with original multispectral imagery and high-resolution Google Earth imagery. Obvious misclassifications (e.g., water classified as built-up, forest as agricultural) were manually corrected based on visual interpretation and contextual analysis. 2.4 Accuracy Assessment and Validation Independent ground truth reference dataset was systematically generated for objective accuracy assessment. Stratified random sampling with proportional allocation across all six LULC classes generated 600 validation points (100 points per class). Reference labels were assigned through detailed visual interpretation of multi-temporal Google Earth imagery at sub-meter resolution, which provided primary reference information, personal field investigations conducted during pre-monsoon (April–May 2024) that verified ground conditions for representative sample locations, and trained remote sensing analysts with 10 + years’ experience who provided professional judgment on class assignments for ambiguous cases. Multiple complementary accuracy metrics provided comprehensive assessment. Overall Accuracy (OA) calculated the percentage of correctly classified pixels by summing diagonal elements of the confusion matrix and dividing by total number of reference pixels, multiplied by 100. Producer's Accuracy (PA) indicated class-specific omission error, calculated as correctly classified pixels of each class divided by total reference pixels of that class, multiplied by 100. User's Accuracy (UA) indicated class-specific commission error, calculated as correctly classified pixels of each class divided by total classified pixels of that class, multiplied by 100. Kappa Coefficient (κ) measured agreement accounting for chance agreement using the formula: κ = (PO - PE) / (1 - PE), where PO equals observed proportion of agreement and PE equals expected proportion of agreement by chance. The classification achieved Overall Accuracy of 89.2% with Kappa coefficient of 0.847, confirming acceptable classification quality for operational LULC analysis. 2.5 Change Detection Analysis Post-classification comparison methodology enabled detailed change detection analysis through pixel-by-pixel comparison of 2014 and 2024 classified maps, generating comprehensive transition matrices documenting area converted from each 2014 LULC class to each 2024 class. Rows represented 2014 classifications, columns represented 2024 classifications, and matrix cells contained area values for each transition. Total area of each LULC class for both dates was computed by multiplying pixel count by pixel area (900 m²). Area changes were calculated as difference between 2024 and 2024 values, providing absolute change metrics. Percentage changes were calculated as [(2024 Area − 2014 Area) / 2014 Area] × 100, providing scale-independent change metrics suitable for comparison across classes of different sizes. Decadal changes were converted to annual rates using compound interest formula for temporal standardization and comparability with other studies. Advanced spatial analysis techniques revealed change patterns and hotspots through multiple approaches. The Getis-Ord Gi* statistic identified statistically significant change clusters, distinguishing high-change hotspots from background change patterns. Fragmentation indices (patch density, mean patch size, edge density) quantified landscape structure changes using FRAGSTATS software, revealing implications for habitat connectivity and biodiversity conservation. Distance-based analysis examined relationships between change patterns and proximity to urban centres, transportation networks, protected areas, and natural features, identifying drivers of spatial variation. Classification and analysis were conducted using ArcGIS 10.8 Spatial Analyst for image for descriptive statistics and data organization, ArcGIS 10.8 for change detection, distance analysis, and map generation, and confusion matrix analysis using standard remote sensing accuracy protocols for accuracy assessment. 3 Results 3.1 Overview of Land Use Land Cover Changes The spatiotemporal analysis reveals landscape transformations across Kamrup district during the 2014–2024 study decade, characterized by unprecedented urban expansion coupled with simultaneous loss of agricultural and forest resources. Quantitative change statistics for all six LULC classes are presented in Table 1 , which demonstrates the magnitude and direction of transformations across the 4,100.67 km² study area. The results unveil a landscape in profound transition, driven primarily by socioeconomic development cantered on Guwahati's metropolitan expansion and associated infrastructure development. The cumulative effect of individual class changes reflects broader regional development trajectories and highlights the critical nexus between urban growth, agricultural sustainability, and environmental conservation in rapidly developing biodiversity hotspots (Table 1 ). Table 1 LULC area distribution and changes in Kamrup district (2014–2024) using six-class classification scheme LULC Category 2014 Area (km²) 2014 (%) 2024 Area (km²) 2024 (%) Area Change (km²) Percentage Change (%) Agricultural Land 638.78 15.53 407.51 9.94 -229.27 36 Barren Land 300.29 7.32 140.78 3.43 -159.51 -53 Built up Area 381.51 9.3 885.84 21.6 504.32 132 Forest Cover 1857.96 45.31 1132.9 27.63 -725.05 -39 Sparse Vegetation 803.19 19.59 1429.07 34.85 625.89 77.93 Waterbodies 120.94 2.95 104.08 2.54 -16.86 -13.94 4102.67 100 4100.18 99.99 -0.48 139.99 3.2 Urban Expansion: Built-Up Area Dynamics The most striking and spatially extensive transformation documented in this study is the unprecedented expansion of built-up areas. Built-up area increased by 504.32 km² over the decade, representing a remarkable 132.19% change rate, more than doubling from 381.51 km² (9.30% of district area) in 2014 to 885.84 km² (21.60%) in 2024. This expansion rate positions Kamrup district among the most rapidly urbanizing regions documented in Northeast India during this period, far exceeding regional average urbanization rates. The dominant driver of this transformation is Guwahati's emergence as Northeast India's primary metropolitan center and economic hub, with its population concentration creating intense developmental pressure on surrounding agricultural and natural landscapes. Urban expansion has manifested through multiple spatial patterns including radial development from the metropolitan core, linear growth along major transportation corridors (particularly NH-27 and NH-37), and cluster development around nodes of economic activity including the airport, industrial parks, and commercial zones. The spatial concentration of urban growth in peri-urban zones has created particularly intense pressures on erstwhile agricultural lands, fragmenting remaining productive farmland and disrupting traditional agricultural systems. This urbanization trajectory, if sustained, will substantially reshape the district's landscape structure and functional characteristics within the coming decades. 3.3 Agricultural Land Contraction Agricultural lands experienced severe contraction (Fig. 3) during the study period, declining by 229.27 km² (− 36.00%), from 636.78 km² in 2014 to 407.51 km² in 2024. This represents a reduction from 15.53% to 9.94% of the total district area, indicating significant conversion pressure concentrated on the most productive farmlands. The spatial pattern of agricultural loss reveals preferential conversion in peri-urban areas, valley bottoms with optimal accessibility and fertility, and along transportation corridors where development pressures are most intense. This agricultural contraction carries profound socioeconomic implications, threatening food security in a district where agriculture remains an important sector for rural livelihoods despite declining relative economic importance. The conversion of productive farmland to non-agricultural uses represents an essentially irreversible loss of agricultural capital, particularly concerning given global pressures on food production and the region's agricultural heritage. The majority of lost agricultural land has been converted to built-up areas, though some portions have transitioned to sparse vegetation through natural succession or abandonment. The loss of agricultural land also has cascading effects on agricultural communities, local food systems, rural employment, and ecosystem services including pollination and nutrient cycling that agricultural landscapes provide. 3.4 Forest Cover Degradation Forest ecosystems in Kamrup district suffered substantial losses during the study decade, with forest cover decreasing by 725.05 km² (− 39.02%), from 1,857.96 km² (45.31%) in 2014 to 1,132.90 km² (27.63%) in 2024. This deforestation rate substantially exceeds regional average forest loss patterns and raises critical concerns for biodiversity conservation in this biogeographic zone of exceptional ecological significance. The spatial distribution of forest loss reveals concentration in areas adjacent to urban expansion zones, particularly in southern hill areas undergoing development pressure, and along infrastructure corridors including roads and utility lines. The mechanisms of forest loss include both direct clearing for development purposes and indirect degradation from increased edge effects, altered fire regimes, illegal harvesting, and hunting pressures associated with proximity to expanding settlements. This magnitude of forest loss represents a fundamental transformation of the district's ecological character, with implications extending beyond local biodiversity to include impacts on watershed functions, carbon sequestration, microclimate regulation, and cultural ecosystem services. The fragmentation of remaining forest patches into isolated fragments reduces habitat connectivity, increases extinction risks for area-sensitive species, and degrades ecosystem integrity. The loss of forest cover in a region harbouring exceptional biodiversity, including threatened species of global conservation significance, represents a particularly acute conservation challenge requiring immediate policy and management interventions. 3.5 Sparse Vegetation Expansion Notably, sparse vegetation increased significantly during the study period by 625.89 km² (+77.93%), expanding from 803.19 km² (19.59%) in 2014 to 1,429.07 km² (34.85%) in 2024. This substantial increase represents the highest relative growth rate among all LULC classes and reflects complex ecological processes and land use transitions. The expansion of sparse vegetation reflects multiple underlying mechanisms, including secondary vegetation colonization on abandoned or transitioned agricultural lands following farm abandonment and land use shifts; forest degradation processes where dense forests transition to open, degraded forest conditions due to harvesting, edge effects, or disturbance; and managed grassland or open vegetation systems emerging on formerly cultivated or cleared areas. In many locations, particularly on steep slopes and marginal lands, the expansion of sparse vegetation may represent natural ecological recovery and secondary succession following agricultural abandonment. However, in other contexts, particularly in areas of intensive forest pressure, the transition from dense forest to sparse vegetation represents degradation rather than recovery. The net balance between recovery and degradation processes varies spatially across the district, requiring differentiated management responses. The expansion of sparse vegetation has important implications for landscape heterogeneity, species habitat provision, and functional ecosystem characteristics, with consequences depending on the specific ecological and socioeconomic drivers in different locations (Figure 4). 3.6 Barren Land and Waterbodies Changes Barren land decreased by 159.51 km² (− 53.12%), from 300.29 km² in 2014 to 140.78 km² in 2024. This reduction reflects land use transitions away from bare, unvegetated conditions, with conversion proceeding through multiple pathways including development to built-up areas through urban expansion in zones with existing barren conditions, and vegetation colonization through natural succession or active restoration in degraded areas. The decline of barren land, while representing a modest absolute area, indicates ecosystem recovery processes or intensification of land use through development. When combined with agricultural land loss, the barren land reduction suggests significant conversion to either developed areas (contributing to built-up area expansion) or to vegetated categories through ecological succession. Waterbodies marginally contracted by 16.86 km² (− 13.94%), from 120.94 km² to 104.08 km². While this represents a relatively modest change in percentage terms, the persistence and integrity of wetland and riverine systems remain vital for regional hydrological function, groundwater recharge, biodiversity provision, and ecosystem services including water purification and flood mitigation. Even small reductions in waterbody extent can disproportionately impact ecosystem function and species dependent on aquatic habitats, particularly migratory waterfowl and other aquatic fauna utilizing these systems as critical stopover points or breeding grounds (Figure 5 ). 3.7 Summary of Decadal Transformations Over the decade from 2014 to 2024, Kamrup district experienced profound land use and land cover (LULC) changes marked by rapid urban expansion coupled with transformation of natural and agricultural landscapes. The comprehensive spatial reorganization is characterized by built-up areas more than doubling (+ 132.19%), agricultural land contracting severely (− 36.00%), and forest cover declining substantially (− 39.02%). In contrast, sparse vegetation showed remarkable expansion (+ 77.93%), suggesting complex secondary vegetation dynamics. Simultaneously, barren land reduced significantly (− 53.12%) and waterbodies slightly contracted (− 13.94%). These changes collectively illustrate fundamental shifts in land use dynamics driven by intensive socioeconomic development concentrated on metropolitan expansion. The pattern of change reveals a district in transition from traditionally agricultural and forest-dominated landscape to increasingly urbanized system, with cascading implications for food security, biodiversity conservation, ecosystem services, and livelihood sustainability. The magnitude, pace, and spatial pattern of these changes underscore the urgent need for integrated land management strategies and sustainable development planning that consciously addresses the interconnected challenges of urbanization, agricultural sustainability, and environmental conservation (Fig. 6 ). 3.8 Change detection analysis The analysis of land use and land cover change in Kamrup district over the decade revealed significant transformations. Agricultural land decreased notably, with approximately 86.96 km² of agricultural fields changing to other classes, including 93.78 km² converting to built-up areas, highlighting urban expansion pressures. Built-up areas exhibited substantial growth, maintaining 234.05 km² consistently, with further conversions from dense vegetation (288.01 km²) and sparse vegetation (157.95 km²). Dense vegetation transitioned largely to agricultural land (187.29 km²) and built-up areas, while sparse vegetation increased due to secondary succession and degradation processes. Waterbodies remained relatively stable with minor transitions. Overall, the rapid urbanization has significantly reduced agricultural and forested land, indicating urgent needs for sustainable land management (Fig. 7). 4 Discussion The primary objective of this research was to comprehensively quantify the magnitude and spatial patterns of urbanization impacts on the Kamrup district landscape during the 2014–2024 study decade. The results indicate a significant urban expansion, with built-up areas increasing by more than 500 km² (+ 132.19%), marking one of the most rapid urbanization rates documented in Northeast India during this period. This urban growth considerably surpasses regional averages in Assam, establishing Kamrup as an important case study for understanding urbanization dynamics in biodiversity hotspots. Spatial analysis revealed multiple urbanization patterns, including radial expansion from Guwahati’s metropolitan core, linear corridor development along major transportation routes (NH-27, NH-37), and cluster development around economic nodes. These patterns reflect both planned metropolitan growth and spontaneous peri-urban expansion driven by economic opportunities, with Guwahati’s role as Northeast India’s primary economic hub being a critical underlying factor. The district’s population growth of over 40% during 2001–2024 further fuels development pressures on former agricultural and forest lands. Almost all expansion occurred at the expense of agricultural land and forests, evidencing substitution rather than complementary land-use dynamics. The preferential conversion of peri-urban agricultural lands—chosen for their accessibility, flatness, and partial prior modification—concentrates impacts on productive agricultural areas and ecologically sensitive transitional landscapes, with substantial implications for habitat connectivity, ecosystem services, and regional development trajectories. Continued urbanization at current rates may result in built-up areas occupying 30–40% of Kamrup by 2050 under high-growth scenarios. The second major objective assessed agricultural land loss and its food security implications. Agricultural land contracted by approximately 229 km² (− 36.00%), reducing its share from 15.53% to 9.94%, representing a severe and essentially irreversible loss of productive farmland historically crucial for regional food supply and rural employment. The loss is primarily focused in peri-urban areas, targeting prime agricultural lands, thereby amplifying per-hectare productivity loss. Reduced local agricultural capacity heightens dependency on food imports, increasing vulnerability to disruptions and price scandals, disproportionately affecting low-income rural communities dependent on farming livelihoods. Economic incentives driven by urban land values 5–15 times higher than agricultural land encourage land conversion, disrupting rural livelihoods, especially those of marginalized, older, and female farmers with limited mobility and skill transferabilities. Urban job opportunities generally require different competencies, leading to unemployment or precarious informal work, perpetuating poverty despite economic growth. The third objective evaluated forest ecosystem loss and biodiversity conservation impacts. Forest cover declined by 725.05 km² (− 39.02%), exceeding regional deforestation rates and reducing forest cover from 45.31% to 27.63%. Deforestation predominantly occurred near urban expansion and infrastructure corridors, driven by direct clearing and indirect edge effects. This substantial loss threatens biodiversity, with estimated species richness reductions of 20–40%, endangering endemic species such as Asian elephants, Bengal tigers, and clouded leopards. Habitat fragmentation disrupts migration and increases extinction risks, especially for species dependent on large continuous habitats or specialized niches. Forest loss also diminishes critical ecosystem services, including carbon sequestration (estimated loss of 72.5–217.5 million tonnes of stored carbon), hydrological regulation (increased runoff and flood risk), soil formation, and culturally significant resources, impacting ecosystem resilience and human well-being. Collectively, these outcomes reflect a landscape transformation driven by socioeconomic priorities favoring urban expansion over agricultural and forest lands, highlighting substitution relationships, systemic feedbacks, and multiple sustainability challenges encompassing food security, biodiversity loss, climate vulnerability, and livelihood disruptions. These findings underscore the necessity for integrated land management policies that reconcile urban growth with agricultural and conservation priorities to promote sustainable development trajectories in Kamrup and comparable rapidly urbanizing biodiversity hotspots (Deka, 2019 ; IGES, 2024 ; Sentinel Assam, 2025 ; Kumar et al., 2021 ; Assam Government, 2024 ). 5 Conclusion This comprehensive spatiotemporal analysis of Land Use Land Cover (LULC) dynamics in Kamrup district during 2014–2024 has generated robust evidence documenting unprecedented landscape transformation driven by rapid urbanization, agricultural contraction, and forest degradation. The research has accomplished three primary objectives: First, quantifying urbanization impacts revealed built-up areas expanding by 504.32 km² (+ 132.19%), representing one of the most rapid urbanization rates documented in Northeast India. Second, agricultural land contraction of 229.27 km² (− 36.00%) has eliminated productive farmland crucial for regional food security and rural livelihoods. Third, forest cover loss of 725.05 km² (− 39.02%) threatens biodiversity conservation objectives in this critical biogeographic hotspot. These findings provide the most comprehensive decadal assessment of Kamrup's landscape transformation, establishing essential baseline information for sustainable development planning and policy intervention. The research demonstrates that landscape change in rapidly developing biodiversity hotspots cannot be understood as isolated land cover transitions but rather as integrated, systemic transformations reflecting fundamental reorientation of land use functions from production (agriculture, forestry) to consumption (urban development). The spatial analysis reveals urbanization manifesting through multiple patterns including radial expansion from metropolitan cores, linear corridor development along transportation networks, and cluster development around economic nodes. These patterns reflect both planned metropolitan development and spontaneous peri-urban expansion responding to economic opportunities, with preferential targeting of peri-urban agricultural lands and accessible forest zones. The research establishes that urbanization drives substitution rather than complementary dynamics relative to agricultural and forest covers, indicating direct conversion of productive and natural landscapes to built-up uses. The simultaneous increase in sparse vegetation (+ 77.93%) reveals complex ecological processes involving secondary succession on abandoned agricultural lands and forest degradation transitions. This finding suggests that landscape dynamics extend beyond simple conversion patterns to encompass qualitative ecosystem transitions, with implications for biodiversity conservation strategies requiring nuanced approaches addressing multiple degradation pathways. The achievement of 89.2% overall classification accuracy with Kappa coefficient of 0.847 confirms the reliability of documented changes and supports confident policy recommendations based on research findings. 5.2 Implications for Sustainable Development and Policy The research findings underscore fundamental sustainability challenges requiring urgent policy intervention at district, state, and national levels. The documented urban expansion rate, if sustained, will produce landscapes dominated by built-up areas covering 30–40% of district territory by 2050, with cascading implications for food security, biodiversity conservation, and ecosystem service provision. Current development trajectories prioritize urban growth without adequate compensation or mitigation for agricultural land loss and forest degradation, creating asymmetric impacts where economic gains concentrate in urban sectors while costs fall disproportionately on rural communities and ecosystem-dependent populations. The severe contraction of productive agricultural lands threatens local food security, requiring urgent policy attention to distinguish between sustainable agricultural intensification within existing cultivated areas versus allowing continued conversion to non-agricultural uses. Current practice of agricultural land loss without compensatory productivity improvements creates direct vulnerability to food insecurity, particularly for low-income populations lacking purchasing power to absorb food price increases. The documented forest loss threatens regionally endemic species, disrupts wildlife corridors essential for landscape-scale conservation, and reduces carbon sequestration capacity critical for climate mitigation objectives. Evidence-based policy responses must integrate conservation of existing agricultural productivity capacity with controlled urban expansion that prioritizes infill development within existing urban boundaries rather than extensive peri-urban expansion. Urban green infrastructure strategies including green roofs, urban forests, and community gardens can provide habitat, ecosystem services, and livelihood opportunities while addressing urban sustainability challenges. Agricultural intensification within existing cultivated zones through improved agronomic practices, crop diversification, and value-added processing can enhance productivity without requiring landscape expansion. Forest conservation strategies must combine protection of remaining intact forests with restoration of degraded areas through natural regeneration, assisted regeneration, and community-based restoration efforts. 5.3 Recommendations for Integrated Land Management Integrated land management frameworks addressing interconnections between urbanization, agricultural sustainability, and ecosystem conservation must replace piecemeal approaches focusing on isolated objectives. The following recommendations are derived from research evidence and international best practices: Urban Planning and Development Management District planners must adopt containment strategies limiting urban expansion to minimize agricultural and forest conversion. Infill development prioritizing vacant lands, underutilized sites, and redevelopment of degraded urban areas must be preferred over outward expansion. Green infrastructure mandates requiring urban green space provisions (minimum 5% urban area, distributed throughout city fabric) will support biodiversity, ecosystem services, and human well-being. Zoning regulations must segregate incompatible land uses, protecting core agricultural zones and forest reserves from developmental pressure. Building codes requiring green roofs, vertical gardens, and water-sensitive design will enhance urban biodiversity and reduce environmental impacts. Agricultural Land Conservation and Livelihood Security Productive agricultural land protection mechanisms must establish legal frameworks preventing conversion to non-agricultural uses except under strictly controlled circumstances. Land consolidation programs can address fragmentation limiting agricultural mechanization and productivity. Agricultural intensification through improved varieties, integrated nutrient management, and integrated pest management can enhance productivity within existing cultivated areas. Livelihood diversification programs providing training and credit access can support livelihood transitions for displaced agricultural populations while maintaining community ties to productive land management. Forest Conservation and Ecosystem Restoration Protected area network expansion and enforcement strengthening must prevent further forest conversion within core conservation areas. Degraded forest restoration through natural regeneration, assisted regeneration, and reforestation can expand forest cover while providing livelihood opportunities for local communities. Community-based Forest management programs recognizing traditional resource use rights while incorporating conservation objectives can reconcile livelihood security with ecosystem protection. Biodiversity-sensitive development standards incorporating wildlife corridor protection, habitat connectivity enhancement, and species-sensitive infrastructure design must guide development planning. Multi-stakeholder governance structures bringing together government, civil society, research institutions, and private sector actors can coordinate efforts across urban planning, agricultural development, and environmental conservation. Transparent decision-making processes ensuring community participation, particularly of indigenous and traditional communities with historic resource relationships, will enhance social acceptability and implementation success. Integration of REDD + mechanisms, payments for ecosystem services, and biodiversity offset programs can generate financial resources supporting conservation and livelihood objectives simultaneously. 5.4 Directions for Future Research This research establishes foundational evidence on landscape dynamics but identifies critical knowledge gaps requiring future investigation. Detailed driver analysis exploring specific mechanisms and timing of LULC transitions will enhance understanding of decision-making processes and intervention opportunities. Ecosystem service valuation quantifying economic value of losses in carbon sequestration, hydrological regulation, biodiversity habitat, and cultural services can support cost-benefit analysis of development scenarios. Livelihood impact assessments documenting specific consequences for displaced agricultural populations, informal sector workers, and ecosystem-dependent communities will inform social protection policies. Prospective modeling examining alternative development scenarios—high urbanization versus compact development, agricultural intensification, forest conservation—can illustrate consequences of different policy choices. Community-based participatory research integrating local knowledge with scientific analysis will enhance relevance and implementation feasibility of recommendations. 5.5 Final Synthesis This research demonstrates that Kamrup district is experiencing one of the most landscape transformations documented in contemporary Northeast India, driven by economic development concentrated in Guwahati's metropolitan expansion. The simultaneous occurrence of unprecedented urban growth, severe agricultural contraction, and substantial forest loss reflects fundamental reorientation of land use priorities from production to consumption. Without timely policy intervention, current trajectories will produce landscapes fundamentally altered in composition, structure, and function, with cascading consequences for food security, biodiversity conservation, ecosystem service provision, and human well-being. However, the research also demonstrates that evidence-based policy interventions can steer development toward more sustainable trajectories. Integrated land management frameworks addressing interconnections between urbanization, agricultural sustainability, and ecosystem conservation can balance legitimate development aspirations with essential ecological and livelihood sustainability imperatives. The documented changes provide scientific foundation for urgent action, but implementation success depends on political will, financial commitment, institutional capacity, and community engagement. Kamrup district's landscape transformation reflects challenges characterizing many rapidly developing regions worldwide, making findings relevant to broader global sustainability challenges. The research methodology and analytical approaches provide replicable frameworks applicable to similar contexts. The evidence base established here supports advocacy for policy changes balancing development with sustainability, creating opportunities for transformational change aligning economic growth with environmental protection and social equity. The critical period between now and 2030—coinciding with SDG implementation deadline and Kunming-Montreal Global Biodiversity Framework implementation window—represents the last opportunity for policy course corrections that could significantly alter development trajectories and outcomes. Declarations Author contribution: The concept was developed by Achintya Pran Hazarika. Formatting and review were carried out by Achintya Pran Hazarika. The spatiotemporal and GIS-based analyses were conducted by Achintya Pran Hazarika. The results and discussion were contributed by all the authors. The final version was reviewed and approved by all the authors Conflict of interest: The authors have no conflict of interest. Funding: No funding has been acquired. Financial Interest: The authors have no financial interest to disclose. Clinical trial number : not applicable Consent to Publish declaration: not applicable Consent to Participate declaration : not applicable Ethics declaration: not applicable References Ahmed S, Das P, Sarma R. (2025). A checklist of bryophytes from Kamrup district of Assam, Northeastern Himalayas. Biodiversity Data Journal , 13, e49.1.2. https://doi.org/10.3897/BDJ.13.e.49.1.2 Anderson JR, Hardy EE, Roach JT, Witmer RE. A land use and land cover classification system for use with remote sensor data. Professional Paper. Volume 964. U.S. Geological Survey; 1976. Assam Government. (2024). 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1","display":"","copyAsset":false,"role":"figure","size":157566,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the study AreaMap showing the study Area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7988134/v1/0b9673b5f74e0fd4aab17130.png"},{"id":95923529,"identity":"b0d463b7-fab2-4026-8149-ed40f3505755","added_by":"auto","created_at":"2025-11-14 13:08:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52629,"visible":true,"origin":"","legend":"\u003cp\u003eWork process flowchart\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7988134/v1/b9cfcb5d7dcc9f6c0e64de03.png"},{"id":95923531,"identity":"6331b5a7-9ad2-4ba6-968d-312d407c5270","added_by":"auto","created_at":"2025-11-14 13:08:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56332,"visible":true,"origin":"","legend":"\u003cp\u003eArea vs Change plot and Percentage Composition change plot\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7988134/v1/1f71ff892a038310762a2092.png"},{"id":95923532,"identity":"f0d63491-b275-44aa-af2c-a4e9a7080b1d","added_by":"auto","created_at":"2025-11-14 13:08:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71203,"visible":true,"origin":"","legend":"\u003cp\u003eComparative area distribution charts\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7988134/v1/b177b56883d806ccc4fb5287.png"},{"id":96242663,"identity":"862a42f3-f8dc-4c01-9586-52ca2b98c198","added_by":"auto","created_at":"2025-11-19 07:13:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":72999,"visible":true,"origin":"","legend":"\u003cp\u003eLand Cover profile and Normalized data heatmap for area change\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7988134/v1/671dff79b479545d8169098f.png"},{"id":95923533,"identity":"8452709c-9d11-44b2-9906-a1d8e62bb4c6","added_by":"auto","created_at":"2025-11-14 13:08:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":206740,"visible":true,"origin":"","legend":"\u003cp\u003eLULC maps of 2014 and 2024 respectively\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7988134/v1/0c70f310748587bcf46f068b.png"},{"id":96243785,"identity":"4bd59148-e7c5-4736-a904-bf2de66012d4","added_by":"auto","created_at":"2025-11-19 07:17:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":285475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChange detection graph and Map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7988134/v1/068ba5616412220d542a553e.png"},{"id":98422672,"identity":"36c116fa-e05d-46d4-9c7e-4ad62fe894b1","added_by":"auto","created_at":"2025-12-17 16:31:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1645963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7988134/v1/57b537bc-bf94-41e0-acf8-a7bd894be33c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Dynamics of Land Use Land Cover Change: Quantifying Urbanization Impacts on Agricultural and Forest Landscapes","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLand Use and Land Cover Change (LULCC) represent one of the most profound anthropogenic transformations of Earth's terrestrial systems in the 21st century, fundamentally altering ecosystem functions, biodiversity patterns, climate regulation, and socioeconomic development trajectories (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pushpalatha et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The accelerating pace of landscape modification, driven by complex interactions between demographic pressures, economic development imperatives, and climate variability, has emerged as a critical force reshaping natural and human-dominated landscapes globally (Tahir et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Contemporary research demonstrates that LULCC processes are intensifying with particular severity in rapidly developing regions, where urbanization pressures intersect with agricultural expansion and natural resource extraction (Mahendra et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The integration of advanced remote sensing technologies with sophisticated Geographic Information Systems (GIS) has revolutionized our capacity to monitor, analyze, and predict these complex spatiotemporal dynamics with unprecedented precision (Khan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recent methodological advances, including the deployment of high-resolution satellite constellations such as Landsat 8/9 and Sentinel-1/2, coupled with artificial intelligence-driven classification algorithms and cloud-computing platforms like Google Earth Engine, have enhanced our understanding of LULCC processes while providing essential tools for sustainable land management and environmental conservation (Bairwa et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Rawat et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These technological innovations enable detection of subtle landscape changes at multiple temporal and spatial scales, facilitating evidence-based policy interventions and adaptive management strategies.\u003c/p\u003e\u003cp\u003eUrbanization represents the most driver of contemporary LULCC globally, with urban areas expanding at rates that significantly outpace population growth (Mahendra et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The global urban population has surpassed 55% and is projected to reach 68% by 2050, with Asia and Africa experiencing the most rapid urban transitions (UN-Habitat, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This unprecedented urban expansion exerts profound pressures on agricultural and forest landscapes, manifesting through direct conversion of productive lands to built-up areas, infrastructure development, and indirect effects including habitat fragmentation, altered hydrological regimes, and increased resource extraction (Sethi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Agricultural landscapes bear particularly severe impacts from urbanization, with prime agricultural lands often targeted for development due to their favorable topographic characteristics, soil quality, and proximity to existing infrastructure networks (Mahendra et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent assessments document alarming trends of agricultural land conversion globally, with estimates suggesting annual losses exceeding 10\u0026nbsp;million hectares to urbanization and other non-agricultural uses (FAO, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In rapidly developing regions, this conversion threatens food security, disrupts traditional livelihood systems, and diminishes ecosystem services provided by agricultural landscapes including pollination, nutrient cycling, and carbon sequestration (Bairwa et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Forest ecosystems face equally severe pressures from urban expansion and associated development activities. Urban-driven deforestation occurs through multiple pathways: direct clearing for residential and commercial development, infrastructure corridor establishment, and intensified resource extraction to meet urban demands for timber, fuel, and non-timber forest products (Marchang, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Down to Earth, 2024). Beyond direct conversion, urbanization induces forest degradation through edge effects, altered fire regimes, invasive species proliferation, and disrupted wildlife movement corridors (Elahi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These impacts are particularly consequential in biodiversity hotspots, where rapid development threatens irreplaceable biological diversity and critical ecosystem services.\u003c/p\u003e\u003cp\u003eIndia exemplifies the complex challenges of balancing rapid economic development with environmental sustainability, experiencing some of the world's most LULCC patterns (Singh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mahendra et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The country's urban population has grown from 286\u0026nbsp;million in 2001 to over 460\u0026nbsp;million by 2021, with projections suggesting continued rapid urbanization through mid-century (Census of India, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This demographic transition has catalysed extensive landscape transformations, with built-up areas expanding by 45% between 2001\u0026ndash;2011 alone (NRSC, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Simultaneously, agricultural land faces mounting pressures from urbanization, infrastructure development, and changing land tenure patterns, while forest cover experiences both gains through afforestation programs and losses from development activities (FSI, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Northeast India, encompassing eight states including Assam, represents a region of exceptional ecological and socioeconomic significance within this national context (Bhattacharya et al., 2025; UN Global Compact Network India, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The region harbors over 50% of India's flora and fauna within just 8% of the country's land area, positioning it as a critical biodiversity hotspot at the confluence of the Himalayan and Indo-Burma biogeographic realms (Elahi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This extraordinary biological richness occurs alongside complex social dynamics, including diverse indigenous communities maintaining traditional resource management systems and cultural landscapes of global significance. However, this ecological wealth faces unprecedented pressures from development initiatives positioning Northeast India as the nation's gateway to Southeast Asia (Bhattacharya et al., 2025). Recent assessments reveal alarming LULCC trends across the region, with forest cover reductions ranging from 5\u0026ndash;14% between 2001\u0026ndash;2020, and Nagaland experiencing the most decline of 17% (Marchang, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Assam, the region's largest and most populous state, contributed to a 14.1% decrease in forest cover during this period while simultaneously experiencing rapid urban expansion concentrated in metropolitan centers like Guwahati (Down to Earth, 2024). These transformations reflect broader patterns of development-driven landscape change but occur within ecosystems of exceptional conservation value, creating particularly acute sustainability challenges.\u003c/p\u003e\u003cp\u003eWithin Northeast India's complex LULCC landscape, Kamrup district emerges as a particularly compelling and representative case study for understanding contemporary urbanization impacts on agricultural and forest landscapes. The district encompasses Guwahati, Northeast India's largest metropolitan centre and primary economic hub, making it a focal point for regional development pressures and associated landscape transformations (Baruah et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Kamrup's strategic position along major transportation corridors, combined with its role as the region's educational, commercial, and administrative center, has catalyzed unprecedented growth dynamics that exemplify broader urban expansion patterns across rapidly developing biodiversity hotspots globally. Recent assessments document the district's demographic and spatial transformations. The Kamrup Metropolitan District has experienced over 40% population growth between 2001\u0026ndash;2024, with urban transformation exceeding 12% during this period (Kamrup Metropolitan District, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Current projections indicate built-up areas will reach 36.2% of the district by 2032 and 40.54% by 2052, representing one of the most rapid urbanization trajectories documented in Northeast India (Baruah et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This expansion has generated significant environmental consequences, including ground deformation with subsidence patterns reaching 71.64 mm/year in central areas due to intensive development and groundwater exploitation (Goswami et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The district's ecological significance extends well beyond its urban centers, encompassing critical biodiversity resources and ecosystem services. Recent biological inventories document 126 bryophyte species from 76 genera, including 50 new records for Assam and four species reported for the first time in the Northeastern Himalayas (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The district contains multiple protected areas including Deepor Beel Ramsar wetland, supporting 219 bird species and serving as a critical stopover for migratory waterfowl, and Amchang Wildlife Sanctuary, harbouring diverse mammalian fauna including threatened species like leopards and elephants (Das et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This juxtaposition of intensive urban development and high conservation value creates complex management challenges requiring sophisticated analytical approaches and evidence-based planning frameworks.\u003c/p\u003e\u003cp\u003eDespite significant technological and methodological advances in LULCC analysis, critical knowledge gaps persist in understanding landscape change dynamics in biodiversity hotspots undergoing rapid development. Current literature lacks comprehensive decadal assessments integrating high-resolution remote sensing with detailed socioeconomic and ecological analysis for Northeast India's critical landscapes (Khan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Baruah et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The complex interactions between urbanization pressures, traditional land use systems, agricultural transformation, forest degradation, and biodiversity conservation require integrated analytical frameworks that move beyond simple change detection to explore causal mechanisms, spatial patterns, and future scenarios (Yang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bairwa et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The period 2014\u0026ndash;2024 represents a particularly critical decade for Northeast India, characterized by major policy initiatives including Smart Cities Mission, infrastructure investments under the Act East Policy, and climate change impacts that have fundamentally altered regional development trajectories (Bhattacharya et al., 2025). This temporal window coincides with availability of high-quality satellite data from multiple sensors and advanced analytical tools including cloud-computing platforms and machine learning algorithms, creating unprecedented opportunities for comprehensive landscape analysis (Khan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, systematic assessments of LULCC patterns during this critical period remain limited, particularly at the district scale where local planning decisions most directly influence landscape outcomes and where intervention opportunities are greatest. Furthermore, existing studies often focus on single aspects of LULCC\u0026mdash;either urbanization, agricultural change, or deforestation\u0026mdash;without examining their complex interactions and cumulative impacts on landscape structure and function (Mahendra et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Integrated analyses quantifying urbanization's simultaneous impacts on both agricultural and forest landscapes, while considering spatial heterogeneity and temporal dynamics, remain scarce. Such integrated approaches are essential for developing holistic understanding of landscape transformation processes and for designing effective interventions that address multiple sustainability dimensions simultaneously. To address these the following objectives are framed:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eConduct comprehensive spatiotemporal analysis of LULCC dynamics in Kamrup district during the decade 2014\u0026ndash;2024, with particular emphasis on quantifying urbanization impacts on agricultural and forest landscapes.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEmploy state-of-the-art remote sensing and GIS techniques to analyse transformations across six major LULC categories: Agricultural Land, Barren Land, Built-up Area, Forest Cover, Sparse Vegetation, and Waterbodies.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEstablish baseline information for sustainable development planning, biodiversity conservation strategies, and climate adaptation initiatives in Kamrup district.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThis research holds profound significance for both scientific understanding and practical application of sustainable development in biodiversity-rich, rapidly urbanizing regions. By documenting unprecedented urban expansion (+\u0026thinsp;132.19%), severe agricultural land loss (-36.00%),(Fig.\u0026nbsp;3) and substantial forest degradation (-39.02%) in Kamrup district over the critical decade 2014\u0026ndash;2024, the study provides essential baseline information for evidence-based policy interventions, conservation planning, and climate adaptation strategies in Northeast India's most dynamic metropolitan region (Baruah et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The comprehensive six-class LULC analysis reveals complex ecological responses including substantial sparse vegetation increase (+\u0026thinsp;77.93%), illuminating intricate processes of secondary succession, forest degradation, and land abandonment that require nuanced conservation approaches beyond simple protection strategies (Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Elahi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Beyond local importance, this research contributes to global understanding of landscape transformation dynamics in biodiversity hotspots where economic development pressures intersect with critical conservation imperatives, providing a replicable methodological framework applicable to similar regions worldwide (Yang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bairwa et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The findings establish robust scientific foundation for addressing the fundamental sustainability challenge of balancing rapid urbanization with food security, biodiversity conservation, and environmental resilience\u0026mdash;a challenge that characterizes contemporary development trajectories across the Global South and carries implications for achieving Sustainable Development Goals related to sustainable cities, life on land, and climate action.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003e\u003cb\u003e2.1 Study Area Description\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure 1: Map showing the study Area\u003c/p\u003e\u003cp\u003eKamrup district is strategically located in the Lower Brahmaputra valley of Assam, Northeast India, at coordinates between 25\u0026deg;46\u0026prime; and 26\u0026deg;49\u0026prime; North latitude and 90\u0026deg;48\u0026prime; and 91\u0026deg;50\u0026prime; East longitude. The district encompasses approximately 4,100 km\u0026sup2; of diverse terrain, representing one of Northeast India's most ecologically and economically significant regions (Kumar \u0026amp; Pant, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Baruah et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The mighty Brahmaputra River, Asia's second-largest river by water discharge, flows through the district from east to west, dividing it into northern and southern portions and profoundly influencing landscape ecology, settlement patterns, and livelihood systems. The district's physiography reflects its position at the convergence of multiple geomorphological systems. Northern areas comprise the alluvial plains of the Brahmaputra Valley, characterized by low-lying, fertile plains frequently subjected to seasonal inundation. In contrast, southern portions encompass the foothills of the Khasi-Jaintia and Garo hills, rising to elevations of 800\u0026ndash;1,000 meters above mean sea level (msl), representing northward projections of the Shillong Plateau (Borpujari et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; DCMSME, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This topographic heterogeneity creates diverse ecological zones, from alluvial wetlands to mixed deciduous forests, supporting exceptionally high biodiversity. (Fig.\u0026nbsp;1)\u003c/p\u003e\u003cp\u003eKamrup district experiences a subtropical humid monsoon climate characteristic of the Lower Brahmaputra Valley, with annual rainfall ranging from 1,500\u0026ndash;2,600 mm, concentrated during the Southwest Monsoon (June\u0026ndash;September) (DCMSME, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Mean daily temperatures fluctuate between 7.0\u0026deg;C (winter minimum) and 39.5\u0026deg;C (pre-monsoon maximum), creating distinct seasonal variations that influence agricultural patterns, phenological cycles, and water availability. The district receives rainfall from multiple sources: the Southwest Monsoon accounts for approximately 60\u0026ndash;70% of annual precipitation, with substantial contributions from pre-monsoon (March\u0026ndash;May) thunderstorms and winter precipitation events. The Brahmaputra River and its tributaries form the district's primary hydrological network, with major tributaries including the Manas, Chaul Khoya, and Barnadi rivers. Seasonal flooding represents a critical environmental feature, with extensive wetland systems including the internationally important Deepor Beel (42.6 km\u0026sup2;), designated as a Ramsar wetland site supporting 219 bird species and serving as a critical stopover for migratory waterfowl (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eKamrup district falls within Critical Biogeographic Zones (9A and 9B) of the Northeast Brahmaputra valley, representing a convergence of Himalayan, Indo-Burma, and biogeographic elements of exceptional conservation importance. Recent biodiversity assessments document 126 bryophyte species from 76 genera, including 50 new records for Assam and four species never previously recorded in the Northeastern Himalayas (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The district encompasses critical protected areas including Amchang Wildlife Sanctuary (78.64 km\u0026sup2;) harboring diverse mammalian fauna including threatened species like Asian elephants, Bengal tigers, and clouded leopards; Deepor Beel Ramsar site; and multiple Forest Reserves protecting remnant natural ecosystems (Elahi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Kamrup district serves as Assam's and Northeast India's primary economic hub, anchored by Guwahati city, the region's largest metropolitan center with over 2.7\u0026nbsp;million residents (Census of India, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The district experienced rapid population growth from 3.7\u0026nbsp;million (2001) to over 4.5\u0026nbsp;million (2021), with urban population concentration in Guwahati Metropolitan area creating intense development pressures on surrounding agricultural and forest landscapes (Kamrup Metropolitan, 2024). This demographic expansion has catalyzed economic diversification beyond agriculture, including substantial growth in services, education, healthcare, and manufacturing sectors, driving extensive land use transformations documented in this study.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Satellite Data and Preprocessing\u003c/h2\u003e\u003cp\u003eHigh-resolution Landsat 8 OLI (Operational Land Imager) multispectral satellite imagery served as the primary data source for LULC classification. Two cloud-free scenes were acquired for 2014 (Path/Row 135/042) and 2024 (same Path/Row), ensuring consistent spatial coverage and temporal comparability. Landsat 8 OLI provides 11 spectral bands with spatial resolutions of 30 meters for optical bands (visible, near-infrared, shortwave-infrared), 100 meters for thermal bands, and 15 meters for panchromatic band (USGS, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The sensor operates on a sun-synchronous, near-polar orbit at 705 km altitude with an 8-day repeat cycle when combined with Landsat 7, providing systematic global coverage with geometric accuracy of \u0026plusmn;\u0026thinsp;12 meters RMSE (Wulder et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Landsat 8 OLI specifications particularly relevant for LULC classification include spectral bands covering visible (Blue 0.43\u0026ndash;0.45 \u0026micro;m; Green 0.45\u0026ndash;0.51 \u0026micro;m; Red 0.53\u0026ndash;0.59 \u0026micro;m), near-infrared (NIR 0.85\u0026ndash;0.88 \u0026micro;m), shortwave-infrared (SWIR-1 1.57\u0026ndash;1.65 \u0026micro;m; SWIR-2 2.11\u0026ndash;2.29 \u0026micro;m), and coastal/aerosol (0.43\u0026ndash;0.45 \u0026micro;m) regions. The data achieved geometric accuracy of 12-meter radial root mean square error (RMSE), meeting Tier 1 accuracy standards, and radiometric accuracy within 5% absolute spectral radiance and 3% top-of-atmosphere reflectance. The data is freely available through the USGS Earth Explorer platform with 16-day single-mission repeat and 8-day combined constellation temporal resolution, enabling systematic monitoring capabilities.\u003c/p\u003e\u003cp\u003eSystematic preprocessing ensured data quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e), geometric consistency, and atmospheric correction. Both 2014 and 2024 Landsat scenes underwent geometric correction to minimize positional errors. Ground Control Points (GCPs) were selected from permanent features (highway intersections, bridges, building complexes) with known coordinates obtained from Survey of India topographic maps and differential GPS surveys. Second-order polynomial transformation with nearest-neighbor resampling was applied, maintaining root mean square error (RMSE) below one pixel (30 meters) to ensure acceptable geometric accuracy for change detection analysis. Radiometric normalization addressed differences in atmospheric conditions, solar angles, and sensor calibration between acquisition dates. The Dark Object Subtraction (DOS) method was applied to correct for atmospheric effects by identifying dark objects (water bodies, shadows) with zero or near-zero spectral reflectance and subtracting their recorded values from all pixels as atmospheric offset (Chander et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Top-of-atmosphere reflectance calculation converted raw digital numbers to surface reflectance values, enabling direct spectral comparison between temporal datasets. Automated cloud and shadow detection using Landsat's Quality Assessment (QA) band and spectral indices (Normalized Difference Moisture Index) identified and masked cloud-contaminated pixels. Manual visual interpretation verified automated results and corrected misclassified pixels, ensuring that only high-quality data pixels contributed to classification analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 LULC Classification Scheme and Methodology\u003c/h2\u003e\u003cp\u003eA comprehensive six-class LULC classification scheme was developed specifically for Kamrup district's landscape characteristics, modifying the Anderson Level I classification system to enhance thematic resolution (Anderson et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; NRSC, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The first class, Agricultural Land, encompasses cultivated areas including crop fields (rice paddies, wheat fields, pulses, oilseeds), irrigated and rain-fed agriculture, agricultural settlements, and farm boundaries, characterized by moderate-to-high NDVI values during growing season, typically ranging from 0.4\u0026ndash;0.6. Barren Land, the second class, includes exposed soil, construction sites, mining areas, degraded lands without vegetation cover, and waste disposal sites, characterized by low NDVI values (\u0026lt;\u0026thinsp;0.2), high visible band reflectance, and distinctive spectral patterns in visible and shortwave-infrared bands. Built-up Area, the third class, comprises urban settlements, residential complexes, commercial zones, industrial facilities, and infrastructure (roads, railways, utilities, airports), characterized by high reflectance in visible bands, distinctive spectral patterns from concrete and asphalt, and moderate thermal signature. Forest Cover, the fourth class, represents dense forests with \u0026ge;\u0026thinsp;40% tree canopy coverage, representing mature forest ecosystems including evergreen, semi-evergreen, and deciduous forests, characterized by high NDVI values (\u0026gt;\u0026thinsp;0.6), distinctive spectral signatures across near-infrared and shortwave-infrared bands, and low visible band reflectance. Sparse Vegetation, the fifth class, includes open forests, grasslands, scrublands, secondary vegetation with 10\u0026ndash;40% vegetation coverage, areas undergoing forest degradation, land abandonment with secondary vegetation recovery, and transitional zones, characterized by NDVI values between 0.3\u0026ndash;0.5 and intermediate spectral signatures between agricultural land and dense forest. Finally, Waterbodies, the sixth class, encompasses rivers, ponds, lakes, reservoirs, wetland systems, and inundated areas, characterized by low reflectance across visible and near-infrared bands, high absorptivity in shortwave-infrared, and distinctive spectral signature with Modified Normalized Difference Water Index (MNDWI)\u0026thinsp;\u0026gt;\u0026thinsp;0.3.\u003c/p\u003e\u003cp\u003eRepresentative training samples were collected through integrated approach combining visual interpretation of high-resolution satellite imagery, expert field knowledge, and ground-truthing information. Training polygon development followed strict protocols with 450 training polygons (approximately 75 polygons per class) manually delineated representing diverse environmental conditions, topographic settings, and spectral variations within each class. Training samples were stratified across the study area to represent geographic variability and ensure representative coverage of spectral characteristics, with training polygons maintaining minimum 0.5-hectare size (approximately 555 pixels at 30-meter resolution) to minimize mixed-pixel effects and representativeness issues. Spectral signature analysis employed multivariate statistical approaches. For each class, spectral signatures were computed calculating mean values and covariance matrices across all spectral bands for each training sample. Jeffries-Matusita (J-M) distance was calculated to assess spectral separability between class pairs, confirming adequate class discrimination (J-M distance\u0026thinsp;\u0026gt;\u0026thinsp;1.8 indicating good separability; \u0026gt;1.9 indicating excellent separability). Classes demonstrating\u0026thinsp;\u0026lt;\u0026thinsp;1.8 J-M distance were evaluated for potential merging or refinement of spectral signatures.\u003c/p\u003e\u003cp\u003eSupervised Maximum Likelihood Classifier (MLC) was employed for LULC classification within ArcGIS 10.8 Spatial Analyst environment. MLC assigns each pixel to the class with highest probability of belonging based on Bayesian decision theory, assuming multivariate normal probability distribution within each class (ESRI, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rawat et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The classifier calculates likelihood functions for each class and assigns pixels to the class with maximum probability value, producing discrete classification map. Prior probabilities were established based on relative training sample sizes for each class, incorporating class frequency information into decision rule. While contemporary machine learning algorithms (Random Forest, Support Vector Machine, Deep Learning) have achieved marginally higher classification accuracies in recent studies, the Maximum Likelihood Classifier was selected for this study based on several methodologically sound justifications. MLC provides robust statistical foundation based on established Bayesian decision theory and multivariate normal distribution assumptions, which are commonly satisfied by multispectral satellite data (Rawat et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kotsiantis, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). MLC consistently achieves classification accuracies of 82\u0026ndash;89% for similar multi-temporal LULC analysis in comparable biogeographic regions, making it highly suitable for operational change detection applications. Recent comparative studies confirm MLC's reliability for regional-scale mapping despite marginal accuracy differences with machine learning algorithms. The ArcGIS MLC implementation offers superior integration with comprehensive GIS analytical capabilities, enabling seamless post-classification processing, change detection analysis, and spatial statistics within unified platform. Computational efficiency and reduced parameter tuning requirements make it optimal for operational monitoring in resource-limited environments. Extensively validated confusion matrix protocols and well-established accuracy metrics enable rigorous, comparable accuracy assessment aligned with international standards. MLC's extensive validation in similar Northeast Indian biogeographic contexts provides confidence in methodology transferability and results comparability with existing literature.\u003c/p\u003e\u003cp\u003ePost-classification processing enhanced classification quality through iterative refinement. A 3\u0026times;3 kernel majority filter was applied, replacing isolated pixels with class assignments differing from surrounding 8-pixel neighbourhood, effectively eliminating \"salt-and-pepper\" noise while preserving legitimate small features and improving overall classification coherence. Raster-to-vector conversion with minimum mapping unit (MMU) of 0.5 hectares eliminated spurious small patches. Any connected class patches smaller than MMU were merged with largest adjacent class, preventing false fragmentation and improving interpretability. Experienced analysts conducted careful visual quality control comparing classified maps with original multispectral imagery and high-resolution Google Earth imagery. Obvious misclassifications (e.g., water classified as built-up, forest as agricultural) were manually corrected based on visual interpretation and contextual analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Accuracy Assessment and Validation\u003c/h2\u003e\u003cp\u003eIndependent ground truth reference dataset was systematically generated for objective accuracy assessment. Stratified random sampling with proportional allocation across all six LULC classes generated 600 validation points (100 points per class). Reference labels were assigned through detailed visual interpretation of multi-temporal Google Earth imagery at sub-meter resolution, which provided primary reference information, personal field investigations conducted during pre-monsoon (April\u0026ndash;May 2024) that verified ground conditions for representative sample locations, and trained remote sensing analysts with 10\u0026thinsp;+\u0026thinsp;years\u0026rsquo; experience who provided professional judgment on class assignments for ambiguous cases. Multiple complementary accuracy metrics provided comprehensive assessment. Overall Accuracy (OA) calculated the percentage of correctly classified pixels by summing diagonal elements of the confusion matrix and dividing by total number of reference pixels, multiplied by 100. Producer's Accuracy (PA) indicated class-specific omission error, calculated as correctly classified pixels of each class divided by total reference pixels of that class, multiplied by 100. User's Accuracy (UA) indicated class-specific commission error, calculated as correctly classified pixels of each class divided by total classified pixels of that class, multiplied by 100. Kappa Coefficient (κ) measured agreement accounting for chance agreement using the formula: κ = (PO - PE) / (1 - PE), where PO equals observed proportion of agreement and PE equals expected proportion of agreement by chance. The classification achieved Overall Accuracy of 89.2% with Kappa coefficient of 0.847, confirming acceptable classification quality for operational LULC analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Change Detection Analysis\u003c/h2\u003e\u003cp\u003ePost-classification comparison methodology enabled detailed change detection analysis through pixel-by-pixel comparison of 2014 and 2024 classified maps, generating comprehensive transition matrices documenting area converted from each 2014 LULC class to each 2024 class. Rows represented 2014 classifications, columns represented 2024 classifications, and matrix cells contained area values for each transition. Total area of each LULC class for both dates was computed by multiplying pixel count by pixel area (900 m\u0026sup2;). Area changes were calculated as difference between 2024 and 2024 values, providing absolute change metrics. Percentage changes were calculated as [(2024 Area \u0026minus;\u0026thinsp;2014 Area) / 2014 Area] \u0026times; 100, providing scale-independent change metrics suitable for comparison across classes of different sizes. Decadal changes were converted to annual rates using compound interest formula for temporal standardization and comparability with other studies. Advanced spatial analysis techniques revealed change patterns and hotspots through multiple approaches. The Getis-Ord Gi* statistic identified statistically significant change clusters, distinguishing high-change hotspots from background change patterns. Fragmentation indices (patch density, mean patch size, edge density) quantified landscape structure changes using FRAGSTATS software, revealing implications for habitat connectivity and biodiversity conservation. Distance-based analysis examined relationships between change patterns and proximity to urban centres, transportation networks, protected areas, and natural features, identifying drivers of spatial variation. Classification and analysis were conducted using ArcGIS 10.8 Spatial Analyst for image for descriptive statistics and data organization, ArcGIS 10.8 for change detection, distance analysis, and map generation, and confusion matrix analysis using standard remote sensing accuracy protocols for accuracy assessment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Overview of Land Use Land Cover Changes\u003c/h2\u003e\n \u003cp\u003eThe spatiotemporal analysis reveals landscape transformations across Kamrup district during the 2014\u0026ndash;2024 study decade, characterized by unprecedented urban expansion coupled with simultaneous loss of agricultural and forest resources. Quantitative change statistics for all six LULC classes are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, which demonstrates the magnitude and direction of transformations across the 4,100.67 km\u0026sup2; study area. The results unveil a landscape in profound transition, driven primarily by socioeconomic development cantered on Guwahati\u0026apos;s metropolitan expansion and associated infrastructure development. The cumulative effect of individual class changes reflects broader regional development trajectories and highlights the critical nexus between urban growth, agricultural sustainability, and environmental conservation in rapidly developing biodiversity hotspots (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLULC area distribution and changes in Kamrup district (2014\u0026ndash;2024) using six-class classification scheme\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLULC Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014 Area (km\u0026sup2;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2024 Area (km\u0026sup2;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2024 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea Change (km\u0026sup2;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage Change (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgricultural Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e638.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e407.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-229.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarren Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-159.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuilt up Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e381.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e885.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e504.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForest Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1857.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1132.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-725.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSparse Vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e803.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1429.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e625.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWaterbodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-16.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4102.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4100.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Urban Expansion: Built-Up Area Dynamics\u003c/h2\u003e\n \u003cp\u003eThe most striking and spatially extensive transformation documented in this study is the unprecedented expansion of built-up areas. Built-up area increased by 504.32 km\u0026sup2; over the decade, representing a remarkable 132.19% change rate, more than doubling from 381.51 km\u0026sup2; (9.30% of district area) in 2014 to 885.84 km\u0026sup2; (21.60%) in 2024. This expansion rate positions Kamrup district among the most rapidly urbanizing regions documented in Northeast India during this period, far exceeding regional average urbanization rates. The dominant driver of this transformation is Guwahati\u0026apos;s emergence as Northeast India\u0026apos;s primary metropolitan center and economic hub, with its population concentration creating intense developmental pressure on surrounding agricultural and natural landscapes. Urban expansion has manifested through multiple spatial patterns including radial development from the metropolitan core, linear growth along major transportation corridors (particularly NH-27 and NH-37), and cluster development around nodes of economic activity including the airport, industrial parks, and commercial zones. The spatial concentration of urban growth in peri-urban zones has created particularly intense pressures on erstwhile agricultural lands, fragmenting remaining productive farmland and disrupting traditional agricultural systems. This urbanization trajectory, if sustained, will substantially reshape the district\u0026apos;s landscape structure and functional characteristics within the coming decades.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Agricultural Land Contraction\u003c/h2\u003e\n \u003cp\u003eAgricultural lands experienced severe contraction (Fig. 3) during the study period, declining by 229.27 km\u0026sup2; (\u0026minus;\u0026thinsp;36.00%), from 636.78 km\u0026sup2; in 2014 to 407.51 km\u0026sup2; in 2024. This represents a reduction from 15.53% to 9.94% of the total district area, indicating significant conversion pressure concentrated on the most productive farmlands. The spatial pattern of agricultural loss reveals preferential conversion in peri-urban areas, valley bottoms with optimal accessibility and fertility, and along transportation corridors where development pressures are most intense. This agricultural contraction carries profound socioeconomic implications, threatening food security in a district where agriculture remains an important sector for rural livelihoods despite declining relative economic importance. The conversion of productive farmland to non-agricultural uses represents an essentially irreversible loss of agricultural capital, particularly concerning given global pressures on food production and the region\u0026apos;s agricultural heritage. The majority of lost agricultural land has been converted to built-up areas, though some portions have transitioned to sparse vegetation through natural succession or abandonment. The loss of agricultural land also has cascading effects on agricultural communities, local food systems, rural employment, and ecosystem services including pollination and nutrient cycling that agricultural landscapes provide.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Forest Cover Degradation\u003c/h2\u003e\n \u003cp\u003eForest ecosystems in Kamrup district suffered substantial losses during the study decade, with forest cover decreasing by 725.05 km\u0026sup2; (\u0026minus;\u0026thinsp;39.02%), from 1,857.96 km\u0026sup2; (45.31%) in 2014 to 1,132.90 km\u0026sup2; (27.63%) in 2024. This deforestation rate substantially exceeds regional average forest loss patterns and raises critical concerns for biodiversity conservation in this biogeographic zone of exceptional ecological significance. The spatial distribution of forest loss reveals concentration in areas adjacent to urban expansion zones, particularly in southern hill areas undergoing development pressure, and along infrastructure corridors including roads and utility lines. The mechanisms of forest loss include both direct clearing for development purposes and indirect degradation from increased edge effects, altered fire regimes, illegal harvesting, and hunting pressures associated with proximity to expanding settlements. This magnitude of forest loss represents a fundamental transformation of the district\u0026apos;s ecological character, with implications extending beyond local biodiversity to include impacts on watershed functions, carbon sequestration, microclimate regulation, and cultural ecosystem services. The fragmentation of remaining forest patches into isolated fragments reduces habitat connectivity, increases extinction risks for area-sensitive species, and degrades ecosystem integrity. The loss of forest cover in a region harbouring exceptional biodiversity, including threatened species of global conservation significance, represents a particularly acute conservation challenge requiring immediate policy and management interventions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Sparse Vegetation Expansion\u003c/h2\u003e\n \u003cp\u003eNotably, sparse vegetation increased significantly during the study period by 625.89 km\u0026sup2; (+77.93%), expanding from 803.19 km\u0026sup2; (19.59%) in 2014 to 1,429.07 km\u0026sup2; (34.85%) in 2024. This substantial increase represents the highest relative growth rate among all LULC classes and reflects complex ecological processes and land use transitions. The expansion of sparse vegetation reflects multiple underlying mechanisms, including secondary vegetation colonization on abandoned or transitioned agricultural lands following farm abandonment and land use shifts; forest degradation processes where dense forests transition to open, degraded forest conditions due to harvesting, edge effects, or disturbance; and managed grassland or open vegetation systems emerging on formerly cultivated or cleared areas. In many locations, particularly on steep slopes and marginal lands, the expansion of sparse vegetation may represent natural ecological recovery and secondary succession following agricultural abandonment. However, in other contexts, particularly in areas of intensive forest pressure, the transition from dense forest to sparse vegetation represents degradation rather than recovery. The net balance between recovery and degradation processes varies spatially across the district, requiring differentiated management responses. The expansion of sparse vegetation has important implications for landscape heterogeneity, species habitat provision, and functional ecosystem characteristics, with consequences depending on the specific ecological and socioeconomic drivers in different locations (Figure 4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Barren Land and Waterbodies Changes\u003c/h2\u003e\n \u003cp\u003eBarren land decreased by 159.51 km\u0026sup2; (\u0026minus;\u0026thinsp;53.12%), from 300.29 km\u0026sup2; in 2014 to 140.78 km\u0026sup2; in 2024. This reduction reflects land use transitions away from bare, unvegetated conditions, with conversion proceeding through multiple pathways including development to built-up areas through urban expansion in zones with existing barren conditions, and vegetation colonization through natural succession or active restoration in degraded areas. The decline of barren land, while representing a modest absolute area, indicates ecosystem recovery processes or intensification of land use through development. When combined with agricultural land loss, the barren land reduction suggests significant conversion to either developed areas (contributing to built-up area expansion) or to vegetated categories through ecological succession. Waterbodies marginally contracted by 16.86 km\u0026sup2; (\u0026minus;\u0026thinsp;13.94%), from 120.94 km\u0026sup2; to 104.08 km\u0026sup2;. While this represents a relatively modest change in percentage terms, the persistence and integrity of wetland and riverine systems remain vital for regional hydrological function, groundwater recharge, biodiversity provision, and ecosystem services including water purification and flood mitigation. Even small reductions in waterbody extent can disproportionately impact ecosystem function and species dependent on aquatic habitats, particularly migratory waterfowl and other aquatic fauna utilizing these systems as critical stopover points or breeding grounds (Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Summary of Decadal Transformations\u003c/h2\u003e\n \u003cp\u003eOver the decade from 2014 to 2024, Kamrup district experienced profound land use and land cover (LULC) changes marked by rapid urban expansion coupled with transformation of natural and agricultural landscapes. The comprehensive spatial reorganization is characterized by built-up areas more than doubling (+\u0026thinsp;132.19%), agricultural land contracting severely (\u0026minus;\u0026thinsp;36.00%), and forest cover declining substantially (\u0026minus;\u0026thinsp;39.02%). In contrast, sparse vegetation showed remarkable expansion (+\u0026thinsp;77.93%), suggesting complex secondary vegetation dynamics. Simultaneously, barren land reduced significantly (\u0026minus;\u0026thinsp;53.12%) and waterbodies slightly contracted (\u0026minus;\u0026thinsp;13.94%). These changes collectively illustrate fundamental shifts in land use dynamics driven by intensive socioeconomic development concentrated on metropolitan expansion. The pattern of change reveals a district in transition from traditionally agricultural and forest-dominated landscape to increasingly urbanized system, with cascading implications for food security, biodiversity conservation, ecosystem services, and livelihood sustainability. The magnitude, pace, and spatial pattern of these changes underscore the urgent need for integrated land management strategies and sustainable development planning that consciously addresses the interconnected challenges of urbanization, agricultural sustainability, and environmental conservation (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Change detection analysis\u003c/h2\u003e\n \u003cp\u003eThe analysis of land use and land cover change in Kamrup district over the decade revealed significant transformations. Agricultural land decreased notably, with approximately 86.96 km\u0026sup2; of agricultural fields changing to other classes, including 93.78 km\u0026sup2; converting to built-up areas, highlighting urban expansion pressures. Built-up areas exhibited substantial growth, maintaining 234.05 km\u0026sup2; consistently, with further conversions from dense vegetation (288.01 km\u0026sup2;) and sparse vegetation (157.95 km\u0026sup2;). Dense vegetation transitioned largely to agricultural land (187.29 km\u0026sup2;) and built-up areas, while sparse vegetation increased due to secondary succession and degradation processes. Waterbodies remained relatively stable with minor transitions. Overall, the rapid urbanization has significantly reduced agricultural and forested land, indicating urgent needs for sustainable land management (Fig. 7).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe primary objective of this research was to comprehensively quantify the magnitude and spatial patterns of urbanization impacts on the Kamrup district landscape during the 2014\u0026ndash;2024 study decade. The results indicate a significant urban expansion, with built-up areas increasing by more than 500 km\u0026sup2; (+\u0026thinsp;132.19%), marking one of the most rapid urbanization rates documented in Northeast India during this period. This urban growth considerably surpasses regional averages in Assam, establishing Kamrup as an important case study for understanding urbanization dynamics in biodiversity hotspots. Spatial analysis revealed multiple urbanization patterns, including radial expansion from Guwahati\u0026rsquo;s metropolitan core, linear corridor development along major transportation routes (NH-27, NH-37), and cluster development around economic nodes. These patterns reflect both planned metropolitan growth and spontaneous peri-urban expansion driven by economic opportunities, with Guwahati\u0026rsquo;s role as Northeast India\u0026rsquo;s primary economic hub being a critical underlying factor. The district\u0026rsquo;s population growth of over 40% during 2001\u0026ndash;2024 further fuels development pressures on former agricultural and forest lands. Almost all expansion occurred at the expense of agricultural land and forests, evidencing substitution rather than complementary land-use dynamics. The preferential conversion of peri-urban agricultural lands\u0026mdash;chosen for their accessibility, flatness, and partial prior modification\u0026mdash;concentrates impacts on productive agricultural areas and ecologically sensitive transitional landscapes, with substantial implications for habitat connectivity, ecosystem services, and regional development trajectories. Continued urbanization at current rates may result in built-up areas occupying 30\u0026ndash;40% of Kamrup by 2050 under high-growth scenarios. The second major objective assessed agricultural land loss and its food security implications. Agricultural land contracted by approximately 229 km\u0026sup2; (\u0026minus;\u0026thinsp;36.00%), reducing its share from 15.53% to 9.94%, representing a severe and essentially irreversible loss of productive farmland historically crucial for regional food supply and rural employment. The loss is primarily focused in peri-urban areas, targeting prime agricultural lands, thereby amplifying per-hectare productivity loss. Reduced local agricultural capacity heightens dependency on food imports, increasing vulnerability to disruptions and price scandals, disproportionately affecting low-income rural communities dependent on farming livelihoods. Economic incentives driven by urban land values 5\u0026ndash;15 times higher than agricultural land encourage land conversion, disrupting rural livelihoods, especially those of marginalized, older, and female farmers with limited mobility and skill transferabilities. Urban job opportunities generally require different competencies, leading to unemployment or precarious informal work, perpetuating poverty despite economic growth. The third objective evaluated forest ecosystem loss and biodiversity conservation impacts. Forest cover declined by 725.05 km\u0026sup2; (\u0026minus;\u0026thinsp;39.02%), exceeding regional deforestation rates and reducing forest cover from 45.31% to 27.63%. Deforestation predominantly occurred near urban expansion and infrastructure corridors, driven by direct clearing and indirect edge effects. This substantial loss threatens biodiversity, with estimated species richness reductions of 20\u0026ndash;40%, endangering endemic species such as Asian elephants, Bengal tigers, and clouded leopards. Habitat fragmentation disrupts migration and increases extinction risks, especially for species dependent on large continuous habitats or specialized niches. Forest loss also diminishes critical ecosystem services, including carbon sequestration (estimated loss of 72.5\u0026ndash;217.5\u0026nbsp;million tonnes of stored carbon), hydrological regulation (increased runoff and flood risk), soil formation, and culturally significant resources, impacting ecosystem resilience and human well-being. Collectively, these outcomes reflect a landscape transformation driven by socioeconomic priorities favoring urban expansion over agricultural and forest lands, highlighting substitution relationships, systemic feedbacks, and multiple sustainability challenges encompassing food security, biodiversity loss, climate vulnerability, and livelihood disruptions. These findings underscore the necessity for integrated land management policies that reconcile urban growth with agricultural and conservation priorities to promote sustainable development trajectories in Kamrup and comparable rapidly urbanizing biodiversity hotspots (Deka, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; IGES, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sentinel Assam, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Assam Government, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis comprehensive spatiotemporal analysis of Land Use Land Cover (LULC) dynamics in Kamrup district during 2014\u0026ndash;2024 has generated robust evidence documenting unprecedented landscape transformation driven by rapid urbanization, agricultural contraction, and forest degradation. The research has accomplished three primary objectives: First, quantifying urbanization impacts revealed built-up areas expanding by 504.32 km\u0026sup2; (+\u0026thinsp;132.19%), representing one of the most rapid urbanization rates documented in Northeast India. Second, agricultural land contraction of 229.27 km\u0026sup2; (\u0026minus;\u0026thinsp;36.00%) has eliminated productive farmland crucial for regional food security and rural livelihoods. Third, forest cover loss of 725.05 km\u0026sup2; (\u0026minus;\u0026thinsp;39.02%) threatens biodiversity conservation objectives in this critical biogeographic hotspot. These findings provide the most comprehensive decadal assessment of Kamrup's landscape transformation, establishing essential baseline information for sustainable development planning and policy intervention.\u003c/p\u003e\u003cp\u003eThe research demonstrates that landscape change in rapidly developing biodiversity hotspots cannot be understood as isolated land cover transitions but rather as integrated, systemic transformations reflecting fundamental reorientation of land use functions from production (agriculture, forestry) to consumption (urban development). The spatial analysis reveals urbanization manifesting through multiple patterns including radial expansion from metropolitan cores, linear corridor development along transportation networks, and cluster development around economic nodes. These patterns reflect both planned metropolitan development and spontaneous peri-urban expansion responding to economic opportunities, with preferential targeting of peri-urban agricultural lands and accessible forest zones. The research establishes that urbanization drives substitution rather than complementary dynamics relative to agricultural and forest covers, indicating direct conversion of productive and natural landscapes to built-up uses.\u003c/p\u003e\u003cp\u003eThe simultaneous increase in sparse vegetation (+\u0026thinsp;77.93%) reveals complex ecological processes involving secondary succession on abandoned agricultural lands and forest degradation transitions. This finding suggests that landscape dynamics extend beyond simple conversion patterns to encompass qualitative ecosystem transitions, with implications for biodiversity conservation strategies requiring nuanced approaches addressing multiple degradation pathways. The achievement of 89.2% overall classification accuracy with Kappa coefficient of 0.847 confirms the reliability of documented changes and supports confident policy recommendations based on research findings.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Implications for Sustainable Development and Policy\u003c/h2\u003e\u003cp\u003eThe research findings underscore fundamental sustainability challenges requiring urgent policy intervention at district, state, and national levels. The documented urban expansion rate, if sustained, will produce landscapes dominated by built-up areas covering 30\u0026ndash;40% of district territory by 2050, with cascading implications for food security, biodiversity conservation, and ecosystem service provision. Current development trajectories prioritize urban growth without adequate compensation or mitigation for agricultural land loss and forest degradation, creating asymmetric impacts where economic gains concentrate in urban sectors while costs fall disproportionately on rural communities and ecosystem-dependent populations.\u003c/p\u003e\u003cp\u003eThe severe contraction of productive agricultural lands threatens local food security, requiring urgent policy attention to distinguish between sustainable agricultural intensification within existing cultivated areas versus allowing continued conversion to non-agricultural uses. Current practice of agricultural land loss without compensatory productivity improvements creates direct vulnerability to food insecurity, particularly for low-income populations lacking purchasing power to absorb food price increases. The documented forest loss threatens regionally endemic species, disrupts wildlife corridors essential for landscape-scale conservation, and reduces carbon sequestration capacity critical for climate mitigation objectives.\u003c/p\u003e\u003cp\u003eEvidence-based policy responses must integrate conservation of existing agricultural productivity capacity with controlled urban expansion that prioritizes infill development within existing urban boundaries rather than extensive peri-urban expansion. Urban green infrastructure strategies including green roofs, urban forests, and community gardens can provide habitat, ecosystem services, and livelihood opportunities while addressing urban sustainability challenges. Agricultural intensification within existing cultivated zones through improved agronomic practices, crop diversification, and value-added processing can enhance productivity without requiring landscape expansion. Forest conservation strategies must combine protection of remaining intact forests with restoration of degraded areas through natural regeneration, assisted regeneration, and community-based restoration efforts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Recommendations for Integrated Land Management\u003c/h2\u003e\u003cp\u003eIntegrated land management frameworks addressing interconnections between urbanization, agricultural sustainability, and ecosystem conservation must replace piecemeal approaches focusing on isolated objectives. The following recommendations are derived from research evidence and international best practices:\u003c/p\u003e\u003cp\u003eUrban Planning and Development Management\u003c/p\u003e\u003cp\u003eDistrict planners must adopt containment strategies limiting urban expansion to minimize agricultural and forest conversion. Infill development prioritizing vacant lands, underutilized sites, and redevelopment of degraded urban areas must be preferred over outward expansion. Green infrastructure mandates requiring urban green space provisions (minimum 5% urban area, distributed throughout city fabric) will support biodiversity, ecosystem services, and human well-being. Zoning regulations must segregate incompatible land uses, protecting core agricultural zones and forest reserves from developmental pressure. Building codes requiring green roofs, vertical gardens, and water-sensitive design will enhance urban biodiversity and reduce environmental impacts.\u003c/p\u003e\u003cp\u003eAgricultural Land Conservation and Livelihood Security\u003c/p\u003e\u003cp\u003eProductive agricultural land protection mechanisms must establish legal frameworks preventing conversion to non-agricultural uses except under strictly controlled circumstances. Land consolidation programs can address fragmentation limiting agricultural mechanization and productivity. Agricultural intensification through improved varieties, integrated nutrient management, and integrated pest management can enhance productivity within existing cultivated areas. Livelihood diversification programs providing training and credit access can support livelihood transitions for displaced agricultural populations while maintaining community ties to productive land management.\u003c/p\u003e\u003cp\u003eForest Conservation and Ecosystem Restoration\u003c/p\u003e\u003cp\u003eProtected area network expansion and enforcement strengthening must prevent further forest conversion within core conservation areas. Degraded forest restoration through natural regeneration, assisted regeneration, and reforestation can expand forest cover while providing livelihood opportunities for local communities. Community-based Forest management programs recognizing traditional resource use rights while incorporating conservation objectives can reconcile livelihood security with ecosystem protection. Biodiversity-sensitive development standards incorporating wildlife corridor protection, habitat connectivity enhancement, and species-sensitive infrastructure design must guide development planning. Multi-stakeholder governance structures bringing together government, civil society, research institutions, and private sector actors can coordinate efforts across urban planning, agricultural development, and environmental conservation. Transparent decision-making processes ensuring community participation, particularly of indigenous and traditional communities with historic resource relationships, will enhance social acceptability and implementation success. Integration of REDD\u0026thinsp;+\u0026thinsp;mechanisms, payments for ecosystem services, and biodiversity offset programs can generate financial resources supporting conservation and livelihood objectives simultaneously.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Directions for Future Research\u003c/h2\u003e\u003cp\u003eThis research establishes foundational evidence on landscape dynamics but identifies critical knowledge gaps requiring future investigation. Detailed driver analysis exploring specific mechanisms and timing of LULC transitions will enhance understanding of decision-making processes and intervention opportunities. Ecosystem service valuation quantifying economic value of losses in carbon sequestration, hydrological regulation, biodiversity habitat, and cultural services can support cost-benefit analysis of development scenarios. Livelihood impact assessments documenting specific consequences for displaced agricultural populations, informal sector workers, and ecosystem-dependent communities will inform social protection policies. Prospective modeling examining alternative development scenarios\u0026mdash;high urbanization versus compact development, agricultural intensification, forest conservation\u0026mdash;can illustrate consequences of different policy choices. Community-based participatory research integrating local knowledge with scientific analysis will enhance relevance and implementation feasibility of recommendations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.5 Final Synthesis\u003c/h2\u003e\u003cp\u003eThis research demonstrates that Kamrup district is experiencing one of the most landscape transformations documented in contemporary Northeast India, driven by economic development concentrated in Guwahati's metropolitan expansion. The simultaneous occurrence of unprecedented urban growth, severe agricultural contraction, and substantial forest loss reflects fundamental reorientation of land use priorities from production to consumption. Without timely policy intervention, current trajectories will produce landscapes fundamentally altered in composition, structure, and function, with cascading consequences for food security, biodiversity conservation, ecosystem service provision, and human well-being.\u003c/p\u003e\u003cp\u003eHowever, the research also demonstrates that evidence-based policy interventions can steer development toward more sustainable trajectories. Integrated land management frameworks addressing interconnections between urbanization, agricultural sustainability, and ecosystem conservation can balance legitimate development aspirations with essential ecological and livelihood sustainability imperatives. The documented changes provide scientific foundation for urgent action, but implementation success depends on political will, financial commitment, institutional capacity, and community engagement.\u003c/p\u003e\u003cp\u003eKamrup district's landscape transformation reflects challenges characterizing many rapidly developing regions worldwide, making findings relevant to broader global sustainability challenges. The research methodology and analytical approaches provide replicable frameworks applicable to similar contexts. The evidence base established here supports advocacy for policy changes balancing development with sustainability, creating opportunities for transformational change aligning economic growth with environmental protection and social equity. The critical period between now and 2030\u0026mdash;coinciding with SDG implementation deadline and Kunming-Montreal Global Biodiversity Framework implementation window\u0026mdash;represents the last opportunity for policy course corrections that could significantly alter development trajectories and outcomes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e The concept was developed by Achintya Pran Hazarika. Formatting and review were carried out by Achintya Pran Hazarika. The spatiotemporal and GIS-based analyses were conducted by Achintya Pran Hazarika. The results and discussion were contributed by all the authors. The final version was reviewed and approved by all the authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding has been acquired.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Interest:\u003c/strong\u003e The authors have no financial interest to disclose. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration:\u003c/strong\u003e not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed S, Das P, Sarma R. 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J Geog Sci. 2024;34(9):1751\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11947/j.JGGS.2024.0301\u003c/span\u003e\u003cspan address=\"10.11947/j.JGGS.2024.0301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":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":"Land Use Land Cover Change, Remote Sensing, Urbanization, Agricultural Land Loss, Forest Degradation, Biodiversity Conservation, Kamrup District, Northeast India","lastPublishedDoi":"10.21203/rs.3.rs-7988134/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7988134/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRapid urbanization in biodiversity-rich regions poses critical challenges for sustainable development. This study analyses Land Use Land Cover (LULC) changes in Kamrup district, Assam, Northeast India, during 2014\u0026ndash;2024 using Landsat 8 OLI multispectral satellite imagery and Maximum Likelihood Classification in ArcGIS. Six LULC classes were mapped: Agricultural Land, Barren Land, Built-up Area, Forest Cover, Sparse Vegetation, and Waterbodies. Results reveal landscape transformations with built-up areas experiencing unprecedented expansion of 504.32 km\u0026sup2; (+\u0026thinsp;132.19%), representing one of the most rapid urbanization rates in Northeast India. Concurrently, agricultural land declined by 229.27 km\u0026sup2; (\u0026minus;\u0026thinsp;36.00%) and forest cover decreased by 725.05 km\u0026sup2; (\u0026minus;\u0026thinsp;39.02%), indicating severe pressure on productive and natural landscapes. Notably, sparse vegetation increased by 625.89 km\u0026sup2; (+\u0026thinsp;77.93%), suggesting complex ecological processes involving secondary succession and forest degradation. The classification achieved 89.2% overall accuracy (κ\u0026thinsp;=\u0026thinsp;0.847), validating result reliability. The documented changes underscore fundamental landscape reorganization driven by metropolitan expansion, threatening food security, biodiversity conservation, and ecosystem service provision. These findings establish essential baseline information for integrated land management strategies balancing urbanization, agricultural sustainability, and environmental conservation in biodiversity hotspots. The research demonstrates that evidence-based policy interventions addressing interconnections between urban development, agricultural protection, and forest conservation can guide sustainable development trajectories in rapidly changing regions.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal Dynamics of Land Use Land Cover Change: Quantifying Urbanization Impacts on Agricultural and Forest Landscapes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 13:07:56","doi":"10.21203/rs.3.rs-7988134/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":"c99ed737-0b8a-4ece-a450-05c2f26e59d1","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T08:24:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 13:07:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7988134","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7988134","identity":"rs-7988134","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}