Assessment of Land use land cover changes in Kangpokpi district, Manipur using Geospatial Technology

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Seitinthang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7451328/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 Land use and land cover dynamics play a critical role in shaping ecological stability and socio-economic development, particularly in environmentally sensitive regions like Kangpokpi district, Manipur. This study employs geospatial technology to assess temporal changes in LULC patterns over the past tow decades, integrating satellite imagery, remote sensing techniques, and GIS- based spatial analysis. Using multi-temporal Sentinel dataset (2017,2020, and 2024), supervised classification was performed to delineate major LULC categories including forest, Agricultural land, Settlement, Shrubland and Water. The results reveal a marked decline in forest cover, accompanied by an expansion of agricultural land and settlement, driven by population growth, shifting cultivation practices, and infrastructural development. Change detection and Gain-and Loss analysis highlights spatial hotspots of land transformation. This research offers a replicable framework for monitoring landscape changes and informing sustainable land use planning in hill districts. The findings underscore the urgency of policy measures that balance development with ecological conservation, and advocate for region-specific strategies to mitigate land degradation and promote resilient land stewardship in Kangpokpi. Land use Land cover Geospatial technology Remote sensing change detection Projection sustainable land management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Land has always been essential for sustaining life and enabling huma civilization. alterations in land use and land cover (LULC) are major drivers of environmental degradation, including climate change, biodiversity loss, and hydrological disruption (Sanu and Sumana, 2023; C. Shwetha et al., 2024; Nazar et al., 2023). LULC broadly refers to natural land cover and anthropogenic land use- factors that shape a region’s socioeconomic status (Mohibul S.K et al., 2022; Arif et al., 2023). Rapid population growth, urbanization, industrial expansion, and deforestation have intensified land transformation, especially in mountainous and developing regions (Upadhyay and Dixit, 2024; Dutta et al., 2020). These changes lead to declining water quality, altered soil moisture, disrupted rainfall patterns, and reduced carbon storage due to forest loss, thereby exacerbating climate risks (Silva et al., 2023; Ulrish et al., 2023; Anand et al., 20230. Urban sprawl further impacts land dynamics, inflates real estate costs, and triggers unsustainable settlement growth (Sarkar et al., 2025). Remote sensing techniques are crucial for detecting rapid land cover changes and supporting effective land use planning (Thapliyal and Prabhakar, 2024). Studying LULC trends helps evaluate past and present land transformations, predict future scenarios, and guide policy making (Paul et al., 2022; Sampath et al., 2023). LULC and climate change influence streamflow, drought intensity, vegetation cover (NDVI), air pollution dispersion, and habitat fragmentation especially in ecologically sensitive zones like high altitude catchment (Ahmed et al., 2021; He et al., 2023; Mikaeili et al., 2022; Afrifa et al., 2022). Understanding the dynamic and non-linear interplay between humans and nature through LULC patterns is critical for sustainable agricultural landscape management in regions such as Churachandpur district of Manipur (Singh et al., 2024; Deka et al., 2024; Zhang et al., 2024). Study Area Kangpokpi district, located in the northern region of Manipur, encompasses a prominent mountainous watershed covering approximately 1698 sq. km. administratively, the district comprises nine sub-divisions and 534 villages, with a population of 193,744 as per the 2011 Census. Elevation ranges from 661 meters to a peak of 2691 meters above mean sea level, contributing to its diverse topography and hydrological profile. The district is interlaced with a network of major streams and rivers that play a crucial role in its watershed dynamics- The Kanggui river flows southward through the northern region and the district capital, Kangpokpi town, eventually draining into the Imphal valley. The Twilang river traverse the western corridor from north to south. The Gun River originates in the northern sector, flowing southward along the eastern region and also draining towards the Imphal valley. Topographically, the region displays slopes ranging from gentle ( 60%). The gentle slopes account for 51.5% of the total area, predominantly spread across the central valley (southwest to northeast), the western zone, and scattered patches in the east. Moderate slopes (20–40%) cover approximately 46.1%, dominating extensive portions of the eastern and western regions. The steep and very steep slopes represent 2.32% and 0.02% of the area respectively, mostly occurring in isolated segments of the western hills. Regarding the aspect orientation, about 51.4% of the terrain faces southward, while 47% faces northward, indicating a balance distribution of slope exposure which can influence microclimatic and erosion patterns. Geographically the district spans coordinate between 25°8′75″N to 25°9′14″N latitude and 93°58′17″E to 92°58′28″ E longitude, situating it within a dynamic and ecologically sensitive mountainous landscape. Materials and Methods The study focuses on the major river basins within the Kangpokpi district, which were delineated using hydrological modeling tools in ArcGIS 10.7.1 and ArcGIS Pro 2.8.4. the catchment boundaries were extracted based on flow direction and accumulation derived from Digital Elevation Models (DEM), enabling a comprehensive spatial analysis of basin characteristics. A multi-source dataset was compiled to support land use /land cover (LULC) analysis and terrain modeling. Satellite Imagery of Sentinel-2 imagery with a spatial resolution of 10 meters was obtained from Esri’s Living Atlas for the period 2017 to 2024. The imagery was atmospherically corrected and clipped to the study area. Digital Elevation Models (DEM) were sourced from the US Geological Survey (USGS) Earth Explorer, providing elevation and slope information essential for hydrological modeling. Vector road data were acquired from GeoSadak, under the PMGSY National GIS initiative of the Ministry of Rural Development, Government of India, offering insights into infrastructure distribution and accessibility. All the datasets were projected to the Universal Transverse Mercator (UTM), WGS 1984, zone 44N, ensuring spatial consistency across layers. The terrain variables such as slope, elevation, and stream order were derived from the DEM using spatial analyst tools. These factors were integrated with the road network to assess their influence on land use dynamics and accessibility. Stream ordering was performed using the Strahler method to classify river hierarchy within each basin. The LULC maps were generated using supervised classification techniques applied to Sentinel-2 imagery. Training samples were selected based on visual interpretation and ancillary data. The classified maps were validated using ground truth points and high-resolution imagery. To simulate future land use/ land cover transitions, the MOLUSCE (Modules for Land Use Change Evaluation) plugin in QGIS and Land Change Modeler (LCM) in TerrSet liberaGIS were employed. These tools utilize machine learning algorithms and transition potential modeling to predict spatial patterns of change. Model validation was conducted using Kappa statistics, which measure agreement between predicted and actual LUCL maps. A Kappa value above 0.75 was considered indicative of strong model performance. Results Land cover distribution patterns The temporal analysis of LULC changes between 2017 and 2024 reveals significant shifts in land cover patterns, reflecting both natural processes and anthropogenic influences. These changes carry important implications for environmental sustainability, socio-economic development, and regional land management strategies. Below is a detailed interpretation of each major LULC class and its broader significance. Forest Cover Expansion: Ecological Recovery and Policy Impact The increase in forest cover from 219,380.41 ha (88.05%) in 2017 to 225,570.8 ha (89.59%) in 2024 marks a notable positive trend in ecological restoration. This expansion may be attributed to afforestation programs under national schemes such as the Green India Mission, natural regeneration in previously degraded lands, and community-based forest governance (Agrawal and Ostrom, 2001). From an ecological standpoint, increased forest cover enhances carbon sequestration, contributing to India’s climate mitigation commitments under the Paris Agreement. Forest also plays a vital role in soil stabilization, hydrological regulation, and biodiversity conservation, especially in the Indo-Burma biodiversity hotspot (Myers et al., 2000). Pan et al. (2011) estimate that global forests absorb approximately 2.4 ± 0.4 Pg C yr⁻¹, underscoring their role in climate regulation. However, the quality of forest cover must assess whether the increase represents dense, ecologically functional forests or secondary growth. Remote sensing-based classification should be complemented with ground truthing to evaluate forest health, species composition, and canopy density (Roy et al., 2015). Shrubland Decline: Transition Zones Under Ecological Pressure Shrubland area decreased from 18,809.19 ha (7.55%) in 2017 to 14,850.12 ha (5.90%) in 2024, indicating a loss of transitional vegetation. Shrublands serve as ecotones and provide habitat heterogeneity, supporting small mammals, birds, and pollinators (Laurance et al., 2014). The decline may reflect ecological succession, where shrublands transition into forest due to reduced disturbance, or land conversion pressures from settlements and agriculture. While succession can be positive, the loss of shrubland may fragment ecological networks and reduce landscape resilience (Forman and Godron, 1986). Conservation planning should aim to preserve these zones, especially where they support unique flora and fauna. Built-up Arae Growth: Urbanization and Infrastructure Development Built-up areas expanded from 4,380 ha (1.76%) in 2017 to 6,212.93 ha (2.47%) in 2024, reflecting a 42% increase. This growth suggests gradual urbanization driven by population growth, infrastructure development, and economic diversification (Seto et al., 2012). This urban expansion improves access to services but also increases impervious surfaces, leading to higher runoff, reduced groundwater recharge, and urban heat island effects (Grimm et al., 2008). Unplanned development can fragment habitats and encroach on agricultural and forest lands. Sustainable urban planning must integrate green infrastructure and zoning regulations to balance development with ecological integrity (McDonald et al., 2020). Agricultural Land Contraction: Shifting Livelihoods and Land Suitability Agricultural land decline from 4,670 ha (1.87%) in 2017 to 4,167.47 ha (1.66%) in 2024, suggesting a gradual contraction. This may be due to topographic constraints, policy driven land reclassification, and changing livelihood patterns (Pretty et al., 2018). While reduced agricultural expansion supports ecological goals, it raises concerns about food security and rural employment. In regions where agriculture is a primary livelihood, contraction may lead to economic vulnerability unless alternative income sources are available. Promoting sustainable intensification and agroforestry can maintain productivity without expanding the agricultural footprint (Tilman et al., 2002). The land capability assessments should guide agricultural zoning to ensure cultivation occurs on suitable lands, minimizing erosion and degradation (FAO,1976). Water Bodies: Hydrological Stability and Climate Sensitivity The water bodies remained relatively stable, fluctuating between 970 ha and 981.59 ha. this consistency suggests hydrological resilience, possibly die to protected catchments and stable precipitation patterns. However, the low percentage of water cover (0.39%-0.41%) highlights vulnerability to climate variability (Vorosmarty et al., 2000). The surface water bodies are critical for irrigation, drinking water, and ecosystem services. Integrated watershed management, rainwater harvesting, and wetland restoration are essential strategies to enhance water security (Mitsch and Gosselink, 2000). Monitoring water quality and seasonal dynamics using remote sensing and field surveys can provide early warning to hydrological stress. Table 1 Land use Land cover of 2017, 2020, and 2024 (hectare) Land use Classes 2017 2020 2024 2030 Prediction Area in (ha) % Area in (ha) % Area in (ha) % Area in (ha) % Water 676.13 0.47 345.08 0.24 494.44 0.34 1.51 0.0 Forest 114112.6 79.55 99954.39 69.68 110682.5 77.16 115499.92 77.19 Agricultural Land 6133.9 4.27 6746.13 4.70 6238.27 4.34 6587.15 4.40 Settlement 4522.75 3.15 5327.8 3.71 6398.12 4.46 7180.71 4.79 Shrubland 17991.61 12.54 31063.59 21.65 19623.64 13.68 20349.62 13.60 Change Detection The LULC changes presented in Table.1., demonstrates that the distribution of primary transitions across the five LULC categories varied significantly between 2017, 2020, and 2024 respectively. According to the study’s findings, notable shifts and transitions occurred among these categories over time. Figure 4 . Changes of LULC from 2020 to 2024 of Kangpokpi. In 2017, analysis of the classified image revealed that forest occupied approximately 79.55% (114112.6 hectares) of the total geographical area, reflecting the widespread hilly terrain. Shrubland was the second most prevalent land cover, accounting for 12.54% (17991.61 hectares), while agricultural land covered 4.27% (6133.9 hectares), indicating limited agricultural activities and sparse populated. Settlement areas comprised 3.15% (4522.75 hectares), and water bodies occupied the remaining 1% (676.13 hectares). By 2020, forest land remained the dominant cover but declined to 69.68% (99954.39 hectares) suggesting a reduction in forested areas. Shrubland increased significantly to 21.65% (31.63.59 hectares), marking a notable expansion since 2017. Agricultural land and Settlement area also grew, covering 4.70% (6746.13 hectares) and 3.71% (5327.8 hectares) respectively. Water bodies decreased to 1% (345.08 hectares), possibly due to deforestation and the expansion of settlements and agricultural zones, which may have contributed to local climate changes. In 2024, forest land once again became the dominant land cover, increasing to 77.16% (110682.5 hectares), likely due to widespread reforestation and plantation farming efforts. Water bodies also expanded slightly to 1% (494.44 hectares) compared to 2020. Settlement area continued to grow, reaching 4.46% (6398.12 hectares), reflecting population growth and urban development. Shrubland cover 13.68% (19623.64 hectares), and Agricultural land accounted for 4.34% (6238.27 hectares), both showing increases from 2020. Gain-Loss Analysis Based on the results of SVMs classification, a change analysis process in LCM on IDRISI was used to obtain the data on land gain and loss as well as the net change for each LUCL type for the period of 2017–2020 and 2020–2024 respectively. For the periods 2017–2020, the landscape underwent notable transformations, with forest experiencing the most significant loss (14000 ha), primarily transitioning into shrubland and agricultural land. This suggests deforestation pressures, possibly driven by shifting cultivation, logging or land degradation. Shrubland saw the largest gain (15000 ha), likely due to forest conversion or land abandonment. Agricultural land expanded modestly (3000 ha) indicating either intensification or encroachment into forest zones. Settlement growth (2000 ha) reflects urban expansion, though its spatial footprint remains relatively contained. These transitions have direct implications for erosion modeling and sustainable land management in the area. The forest-to-shrubland shift, in particular, may signal increased vulnerability to soil erosion, especially in sloped terrains of Kangpokpi. Table 2 Estimated LULC Transition Matrix between 2017–2020 (hectare) From-To Shrubland Agricultural land Settlement Forest Water Total Loss Shrubland - 1000 500 1500 - 3000 Agricultural land 2000 - 500 500 - 3000 Settlement 500 500 - 1000 - 2000 Forest 10000 2000 1500 - 500 14000 Water - - - - - 500 Total Gain 15000 3000 2000 - - - The land use/land cover analysis using TerrSet reveals a significant spatial transition in Kangpokpi district between 2020 and 2024. The pronounced change was observed in shrubland, which experienced a net loss of 12000 hectares. This decline was primarily due to conversions into forest and water bodies, suggesting active ecological restoration, land abandonment, or reclassification of transitional vegetation zones. Forest areas exhibited high turnover, with roughly 12000 hectares both gained and lost. This bidirectional change indicates simultaneous afforestation and deforestation processes, potentially driven by shifting cultivation, community forestry initiatives, or natural succession. Agricultural and settlement categories remained relatively stable, each showing balanced gains and losses (4000 ha), implying spatial redistribution rather than net expansion. These patterns may reflect seasonal land use shifts, zoning adjustment, or infrastructure development. Water bodies showed the most substantial net gain (12000 ha) likely attributable to reservoir expansion, wetland restoration, or improved hydrological mapping. This increase holds ecological significance, particularly in mitigating erosion and enhancing water retention in upland catchments. The estimated transition matrix was constructed based on TerrSet outputs. It highlights dominant flows from shrubland to forest and water, and minor exchanges among agricultural, settlement, and forest categories. These findings underscore the dynamic nature of land systems in Kangpokpi and their implications for erosion modeling, hydrological assessments, and sustainable and management. Table 3 Estimated Transition Matrix between 2020–2024 (hectare) From - To Shrubland Agricultural Land Settlement Forest Water Total Loss Shrubland - 3000 1000 6000 6000 16000 Agricultural land 1000 - 1000 1000 1000 4000 Settlement 500 500 - 1000 2000 4000 Forest 6000 2000 2000 - 2000 12000 Water 1000 1000 1000 - - 4000 Total Gain 4000 4000 4000 12000 16000 Prediction of LULC The LULC projection for Kangpokpi district by 2030 reveals a marked transition from forest and shrubland ecosystems toward intensified agricultural use and expanding settlements. This shift is spatially concentrated in valley regions and along emerging transport corridors, driven by demographic pressure, infrastructure development, and land conversion practices. Forest fragmentation and shrubland decline signal potential ecological stress, particularly in upland zones vulnerable to erosion. The expansion of agriculture into marginal slopes and settlement encroachment near riparian zones heightens the risk of soil degradation and hydrological disruption. These trends underscore the urgency for integrated land management strategies, including agroforestry adoption, zoning enforcement, and erosion mitigation planning, to safeguard ecological integrity while accommodating socio-economic growth. Discussion The observed LLULC changes between 2017 and 2024 in the study area reflect a complex interplay of ecological processes, socio-economic transitions, and policy interventions. The increase in forest cover and concurrent decline in shrubland and agricultural land suggest a landscape undergoing ecological recovery, possibly driven by afforestation efforts, reduced shifting cultivation, and natural succession. These trends align with national and global priorities for climate mitigation, biodiversity conservation, and sustainable land management (Pan et al., 2011; Myers et al.,2000). However, the decline shrubland raises concerns about the loss of transitional habitats that support ecological connectivity and species diversity. Shrublands often serve as buffer zones and corridors, and their reduction may fragment ecosystems, especially in hilly terrains with high endemism (Laurance et al., 2014). Future land management strategies must recognize the ecological value of these zones and incorporate them into conservation planning. The expansion of built-up areas, though moderate, reflects ongoing urbanization and infrastructure development. This trend underscores the need for integrated spatial planning that balances development with environmental sustainability. Unregulated urban growth can lead to habitat loss, increased surface runoff, and reduced ground water recharge (Grimm et al., 2008). Incorporating green infrastructure and enforcing zoning regulations can mitigate these impacts and promote resilient urban landscapes (McDonald et al., 2020). The contradiction of agricultural land may indicate shifting livelihood patterns, land abandonment, or reclassification of marginal lands. While this supports forest recovery, it also raises questions about food security and rural livelihoods. Sustainable intensification and agroforestry practices can help maintain agricultural productivity while minimizing environmental degradation (Pretty et al., 2018; Tilman et al.,2002). Water bodies remained relatively stable, suggesting hydrological resilience. However, their low spatial coverage highlights vulnerability to climate variability and increasing demand. Integrated watershed management and wetland restoration are essential to safeguard water resources and support both ecological and human needs (Vorosmarty et al., 2000; Mitsch and Gosselink, 2000). Overall, the LULC dynamics reveal a landscape in transition, shaped by ecological restoration, urban expansion, and evolving land use priorities. These changes offer opportunities for sustainable development but also demand proactive planning and multi sectoral coordination. Conclusions This study provides a comprehensive assessment of LULC changes in the region between 2017 and 2024, highlighting key trends and their implications for environmental sustainability and land governance. The findings reveal a significant increase in forest cover, indicating ecological recovery and successful conservation efforts. The decline in shrubland and agricultural land, reflecting land use transitions and potential ecological succession. Moderate expansion of built-up areas, pointing to urbanization and infrastructure growth. The stable water body coverage, suggesting hydrological resilience but also climate sensitivity. These patterns underscore the importance of integrated land use planning that balances ecological integrity with socio-economic development. Forest expansion contributes to climate mitigation and biodiversity conservation, while urban growth and agricultural contraction require careful management to avoid unintended consequences. Some of the policy recommendations emerging from the study include: Strengthening community-based forest management and afforestation programs. Protecting transitional ecosystems such as shrublands through ecological zoning. Promoting sustainable urban development with green infrastructure and land use regulations. Supporting climate resilient agriculture and agroforestry to sustain rural livelihoods. Enhancing water resource management through watershed protection and wetland restoration. Future research should focus on ground level validation of remote sensing data, socio economic drivers of land use change, and the integration of climate projections into land management strategies. Participatory approaches involving local communities, planners and policymakers will be crucial to ensure that land use transitions support both environmental resilience and human well-being. Declarations The Author declares there are no financial or non-financial interest that are directly or indirectly related to the work. Author Contribution The author is the sole contributor of this manuscript References Afrifa Joseph K., Monney K. A., and Deikumah J. P. 2022. Effects of urban land use types on avifauna assemblage in a rapidly developing urban settlement in Ghana. Urban Ecosystems. Springer. https://doi.org/10.1007/s11252-022-01281-0. Agrawal, A., and Ostrom, E. 2001. Collective action, property rights, and decentralization in resource use in India and Nepal. 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A., et al. 2002. Agricultural sustainability and intensive production practices. Nature,418(6898), 671–677. Ulrich Veit, Schultz Michael, Lautenbach Sven, and Zipf Alexander. 2023. Carbon fluxes related to land use and land cover change in Biden-Wurttemberg. Environ Monit Assess. Springer. https://doi.org/10.1007/s10661-023-11141-9. Upadhyay V. and Dixit H. 2024. Land use and land cover as a conditioning factor in landslide susceptibility: A literature review. Gopal Krishna Panda, Rajib Shaw, Subodh Chandra Pal, Uday Chatterjee, and Asish Saha (edn.). Landslide: Susceptibility, Risk Assessment and Sustainability. Application of Geostatistical and Geospatial modeling. Advances in Natural and Technological Hazards Research. Volume 52. Springer. https://doi.org/10.1007/978-3-031-56591-5. Verburg, P. H., Schot, P. P., Dijst, M. J., and Veldkamp, A. 2009. Land use change modelling: Current practice and research priorities. GeoJournal, 61(4), 309–324. Vorosmarty, C. J., Green, P., Salisbury, J., and Lammers, R. B. 2000. Global water resources: Vulnerability from climate change and population growth. Science, 289(5477),284 − 235. Zhang C., Li Y., Wang W., Gao Z., Liu H., and Nie Y. 2024. Combined effects of climate and land use changes on the alpha and beta functional diversities of terrestrial mammals in China. Science China Life Sciences. Vol. 67 No. 10. Springer. https://doi.org/10.1007/s11427-023-2574-0. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7451328","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505678968,"identity":"bb015da6-2871-415b-843b-fdcf883504e6","order_by":0,"name":"Dr. Lh. Seitinthang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYBAC+QbGBoYEBhBiYDjw4YcNkGJsPIBPi8EBxsaGBAYDkBbGgzN70kB0A34tIBUMEC3Mh3nYDoNF8Wthb25/8HDHnzz+2e0XDvDwnLdb234YaEuNTTROv/QcbGxIPGNQLHHnTMEBCYvbydvOJAK1HEvLbcCl50YiUEubQWLDjZyEAwY8t5PNDgC1MDYcxq3l/kOIlvkgLQls55LNzj8koOUGI0TLhhvpBw4cYDtgZ3aDgC0GZxIbZyS2GSduvJHDcLCxJznB7AbQlgQ8fpFvP/7g4882ucR5N9Iff/7zw87e7Hz6wwcfamxwOwwBeAxAZCJYZQJh5SDA/gBE2hOneBSMglEwCkYSAAB3QXWp8fBfgAAAAABJRU5ErkJggg==","orcid":"","institution":"Modern College","correspondingAuthor":true,"prefix":"Dr.","firstName":"Lh.","middleName":"","lastName":"Seitinthang","suffix":""}],"badges":[],"createdAt":"2025-08-25 08:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7451328/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7451328/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89976726,"identity":"a4e5205a-cd45-4841-9082-b609702ef789","added_by":"auto","created_at":"2025-08-27 06:05:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":994723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Area Map of Kangpokpi District,\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7451328/v1/32c856f66fe779b2b4cd8740.png"},{"id":89976711,"identity":"b7325853-b61a-413d-bb35-fce1074c5de2","added_by":"auto","created_at":"2025-08-27 06:05:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1385006,"visible":true,"origin":"","legend":"\u003cp\u003eLand use/ Land cover Maps of Kangpokpi\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7451328/v1/2b98ea8aab572e314bb46a73.png"},{"id":89978093,"identity":"32edc37b-2c11-4111-a7a7-3d463d139239","added_by":"auto","created_at":"2025-08-27 06:13:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59022,"visible":true,"origin":"","legend":"\u003cp\u003eLULC Classes and their percentage cover of Kangpokpi.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7451328/v1/487a137b4ea558896e0e1864.png"},{"id":89976725,"identity":"f1d3d4fc-bc06-4ac4-9017-e659cd358be4","added_by":"auto","created_at":"2025-08-27 06:05:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":359206,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of LULC from 2020 to 2024 of Kangpokpi.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7451328/v1/3bf61637509997268dc0be0d.png"},{"id":89976743,"identity":"ec034c4b-0959-4b97-b55c-ce8ed3258541","added_by":"auto","created_at":"2025-08-27 06:05:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":138780,"visible":true,"origin":"","legend":"\u003cp\u003eFig 4. Prediction of LULC, 2030 in Kangpokpi.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7451328/v1/5d0281ed95557dbd2bd3be92.png"},{"id":89976731,"identity":"e153c65d-2c65-480c-a696-7b7eaa5e0c4c","added_by":"auto","created_at":"2025-08-27 06:05:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":92817,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Materials and Methods section.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7451328/v1/f10aac3d0e32a8b486163b0e.png"},{"id":90488626,"identity":"463c9ca1-54c2-44ff-a5f9-9afa8c7a9053","added_by":"auto","created_at":"2025-09-03 09:17:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3397175,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7451328/v1/1156813c-776e-468a-8a10-1dde13b9dc7d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Land use land cover changes in Kangpokpi district, Manipur using Geospatial Technology","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLand has always been essential for sustaining life and enabling huma civilization. alterations in land use and land cover (LULC) are major drivers of environmental degradation, including climate change, biodiversity loss, and hydrological disruption (Sanu and Sumana, 2023; C. Shwetha et al., 2024; Nazar et al., 2023).\u003c/p\u003e\u003cp\u003eLULC broadly refers to natural land cover and anthropogenic land use- factors that shape a region\u0026rsquo;s socioeconomic status (Mohibul S.K et al., 2022; Arif et al., 2023). Rapid population growth, urbanization, industrial expansion, and deforestation have intensified land transformation, especially in mountainous and developing regions (Upadhyay and Dixit, 2024; Dutta et al., 2020).\u003c/p\u003e\u003cp\u003eThese changes lead to declining water quality, altered soil moisture, disrupted rainfall patterns, and reduced carbon storage due to forest loss, thereby exacerbating climate risks (Silva et al., 2023; Ulrish et al., 2023; Anand et al., 20230. Urban sprawl further impacts land dynamics, inflates real estate costs, and triggers unsustainable settlement growth (Sarkar et al., 2025).\u003c/p\u003e\u003cp\u003eRemote sensing techniques are crucial for detecting rapid land cover changes and supporting effective land use planning (Thapliyal and Prabhakar, 2024). Studying LULC trends helps evaluate past and present land transformations, predict future scenarios, and guide policy making (Paul et al., 2022; Sampath et al., 2023).\u003c/p\u003e\u003cp\u003eLULC and climate change influence streamflow, drought intensity, vegetation cover (NDVI), air pollution dispersion, and habitat fragmentation especially in ecologically sensitive zones like high altitude catchment (Ahmed et al., 2021; He et al., 2023; Mikaeili et al., 2022; Afrifa et al., 2022).\u003c/p\u003e\u003cp\u003eUnderstanding the dynamic and non-linear interplay between humans and nature through LULC patterns is critical for sustainable agricultural landscape management in regions such as Churachandpur district of Manipur (Singh et al., 2024; Deka et al., 2024; Zhang et al., 2024).\u003c/p\u003e\n\u003ch3\u003eStudy Area\u003c/h3\u003e\n\u003cp\u003eKangpokpi district, located in the northern region of Manipur, encompasses a prominent mountainous watershed covering approximately 1698 sq. km. administratively, the district comprises nine sub-divisions and 534 villages, with a population of 193,744 as per the 2011 Census. Elevation ranges from 661 meters to a peak of 2691 meters above mean sea level, contributing to its diverse topography and hydrological profile.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe district is interlaced with a network of major streams and rivers that play a crucial role in its watershed dynamics- The Kanggui river flows southward through the northern region and the district capital, Kangpokpi town, eventually draining into the Imphal valley. The Twilang river traverse the western corridor from north to south. The Gun River originates in the northern sector, flowing southward along the eastern region and also draining towards the Imphal valley.\u003c/p\u003e\u003cp\u003eTopographically, the region displays slopes ranging from gentle (\u0026lt;\u0026thinsp;20%) to very steep (\u0026gt;\u0026thinsp;60%). The gentle slopes account for 51.5% of the total area, predominantly spread across the central valley (southwest to northeast), the western zone, and scattered patches in the east. Moderate slopes (20\u0026ndash;40%) cover approximately 46.1%, dominating extensive portions of the eastern and western regions. The steep and very steep slopes represent 2.32% and 0.02% of the area respectively, mostly occurring in isolated segments of the western hills. Regarding the aspect orientation, about 51.4% of the terrain faces southward, while 47% faces northward, indicating a balance distribution of slope exposure which can influence microclimatic and erosion patterns. Geographically the district spans coordinate between 25\u0026deg;8\u0026prime;75\u0026Prime;N to 25\u0026deg;9\u0026prime;14\u0026Prime;N latitude and 93\u0026deg;58\u0026prime;17\u0026Prime;E to 92\u0026deg;58\u0026prime;28\u0026Prime; E longitude, situating it within a dynamic and ecologically sensitive mountainous landscape.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe study focuses on the major river basins within the Kangpokpi district, which were delineated using hydrological modeling tools in ArcGIS 10.7.1 and ArcGIS Pro 2.8.4. the catchment boundaries were extracted based on flow direction and accumulation derived from Digital Elevation Models (DEM), enabling a comprehensive spatial analysis of basin characteristics. A multi-source dataset was compiled to support land use /land cover (LULC) analysis and terrain modeling.\u003c/p\u003e\u003cp\u003eSatellite Imagery of Sentinel-2 imagery with a spatial resolution of 10 meters was obtained from Esri\u0026rsquo;s Living Atlas for the period 2017 to 2024. The imagery was atmospherically corrected and clipped to the study area. Digital Elevation Models (DEM) were sourced from the US Geological Survey (USGS) Earth Explorer, providing elevation and slope information essential for hydrological modeling. Vector road data were acquired from GeoSadak, under the PMGSY National GIS initiative of the Ministry of Rural Development, Government of India, offering insights into infrastructure distribution and accessibility. All the datasets were projected to the Universal Transverse Mercator (UTM), WGS 1984, zone 44N, ensuring spatial consistency across layers. The terrain variables such as slope, elevation, and stream order were derived from the DEM using spatial analyst tools. These factors were integrated with the road network to assess their influence on land use dynamics and accessibility. Stream ordering was performed using the Strahler method to classify river hierarchy within each basin.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe LULC maps were generated using supervised classification techniques applied to Sentinel-2 imagery. Training samples were selected based on visual interpretation and ancillary data. The classified maps were validated using ground truth points and high-resolution imagery. To simulate future land use/ land cover transitions, the MOLUSCE (Modules for Land Use Change Evaluation) plugin in QGIS and Land Change Modeler (LCM) in TerrSet liberaGIS were employed. These tools utilize machine learning algorithms and transition potential modeling to predict spatial patterns of change. Model validation was conducted using Kappa statistics, which measure agreement between predicted and actual LUCL maps. A Kappa value above 0.75 was considered indicative of strong model performance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eLand cover distribution patterns\u003c/h2\u003e\u003cp\u003eThe temporal analysis of LULC changes between 2017 and 2024 reveals significant shifts in land cover patterns, reflecting both natural processes and anthropogenic influences. These changes carry important implications for environmental sustainability, socio-economic development, and regional land management strategies. Below is a detailed interpretation of each major LULC class and its broader significance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eForest Cover Expansion: Ecological Recovery and Policy Impact\u003c/h3\u003e\n\u003cp\u003eThe increase in forest cover from 219,380.41 ha (88.05%) in 2017 to 225,570.8 ha (89.59%) in 2024 marks a notable positive trend in ecological restoration. This expansion may be attributed to afforestation programs under national schemes such as the Green India Mission, natural regeneration in previously degraded lands, and community-based forest governance (Agrawal and Ostrom, 2001). From an ecological standpoint, increased forest cover enhances carbon sequestration, contributing to India\u0026rsquo;s climate mitigation commitments under the Paris Agreement. Forest also plays a vital role in soil stabilization, hydrological regulation, and biodiversity conservation, especially in the Indo-Burma biodiversity hotspot (Myers et al., 2000). Pan et al. (2011) estimate that global forests absorb approximately 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 Pg C yr⁻\u0026sup1;, underscoring their role in climate regulation. However, the quality of forest cover must assess whether the increase represents dense, ecologically functional forests or secondary growth. Remote sensing-based classification should be complemented with ground truthing to evaluate forest health, species composition, and canopy density (Roy et al., 2015).\u003c/p\u003e\n\u003ch3\u003eShrubland Decline: Transition Zones Under Ecological Pressure\u003c/h3\u003e\n\u003cp\u003eShrubland area decreased from 18,809.19 ha (7.55%) in 2017 to 14,850.12 ha (5.90%) in 2024, indicating a loss of transitional vegetation. Shrublands serve as ecotones and provide habitat heterogeneity, supporting small mammals, birds, and pollinators (Laurance et al., 2014). The decline may reflect ecological succession, where shrublands transition into forest due to reduced disturbance, or land conversion pressures from settlements and agriculture. While succession can be positive, the loss of shrubland may fragment ecological networks and reduce landscape resilience (Forman and Godron, 1986). Conservation planning should aim to preserve these zones, especially where they support unique flora and fauna.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBuilt-up Arae Growth: Urbanization and Infrastructure Development\u003c/h2\u003e\u003cp\u003eBuilt-up areas expanded from 4,380 ha (1.76%) in 2017 to 6,212.93 ha (2.47%) in 2024, reflecting a 42% increase. This growth suggests gradual urbanization driven by population growth, infrastructure development, and economic diversification (Seto et al., 2012). This urban expansion improves access to services but also increases impervious surfaces, leading to higher runoff, reduced groundwater recharge, and urban heat island effects (Grimm et al., 2008). Unplanned development can fragment habitats and encroach on agricultural and forest lands. Sustainable urban planning must integrate green infrastructure and zoning regulations to balance development with ecological integrity (McDonald et al., 2020).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAgricultural Land Contraction: Shifting Livelihoods and Land Suitability\u003c/h3\u003e\n\u003cp\u003eAgricultural land decline from 4,670 ha (1.87%) in 2017 to 4,167.47 ha (1.66%) in 2024, suggesting a gradual contraction. This may be due to topographic constraints, policy driven land reclassification, and changing livelihood patterns (Pretty et al., 2018). While reduced agricultural expansion supports ecological goals, it raises concerns about food security and rural employment. In regions where agriculture is a primary livelihood, contraction may lead to economic vulnerability unless alternative income sources are available. Promoting sustainable intensification and agroforestry can maintain productivity without expanding the agricultural footprint (Tilman et al., 2002). The land capability assessments should guide agricultural zoning to ensure cultivation occurs on suitable lands, minimizing erosion and degradation (FAO,1976).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eWater Bodies: Hydrological Stability and Climate Sensitivity\u003c/h3\u003e\n\u003cp\u003eThe water bodies remained relatively stable, fluctuating between 970 ha and 981.59 ha. this consistency suggests hydrological resilience, possibly die to protected catchments and stable precipitation patterns. However, the low percentage of water cover (0.39%-0.41%) highlights vulnerability to climate variability (Vorosmarty et al., 2000). The surface water bodies are critical for irrigation, drinking water, and ecosystem services. Integrated watershed management, rainwater harvesting, and wetland restoration are essential strategies to enhance water security (Mitsch and Gosselink, 2000). Monitoring water quality and seasonal dynamics using remote sensing and field surveys can provide early warning to hydrological stress.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand use Land cover of 2017, 2020, and 2024 (hectare)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLand use Classes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e2030 Prediction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea in (ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea in (ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eArea in (ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eArea in (ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e676.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e345.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e494.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114112.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99954.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e69.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e110682.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e77.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e115499.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e77.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural Land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6133.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6746.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6238.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6587.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSettlement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4522.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5327.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6398.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7180.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17991.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31063.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19623.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e20349.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eChange Detection\u003c/h2\u003e\u003cp\u003eThe LULC changes presented in Table.1., demonstrates that the distribution of primary transitions across the five LULC categories varied significantly between 2017, 2020, and 2024 respectively. According to the study\u0026rsquo;s findings, notable shifts and transitions occurred among these categories over time.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Changes of LULC from 2020 to 2024 of Kangpokpi.\u003c/p\u003e\u003cp\u003eIn 2017, analysis of the classified image revealed that forest occupied approximately 79.55% (114112.6 hectares) of the total geographical area, reflecting the widespread hilly terrain. Shrubland was the second most prevalent land cover, accounting for 12.54% (17991.61 hectares), while agricultural land covered 4.27% (6133.9 hectares), indicating limited agricultural activities and sparse populated. Settlement areas comprised 3.15% (4522.75 hectares), and water bodies occupied the remaining 1% (676.13 hectares).\u003c/p\u003e\u003cp\u003eBy 2020, forest land remained the dominant cover but declined to 69.68% (99954.39 hectares) suggesting a reduction in forested areas. Shrubland increased significantly to 21.65% (31.63.59 hectares), marking a notable expansion since 2017. Agricultural land and Settlement area also grew, covering 4.70% (6746.13 hectares) and 3.71% (5327.8 hectares) respectively. Water bodies decreased to 1% (345.08 hectares), possibly due to deforestation and the expansion of settlements and agricultural zones, which may have contributed to local climate changes.\u003c/p\u003e\u003cp\u003eIn 2024, forest land once again became the dominant land cover, increasing to 77.16% (110682.5 hectares), likely due to widespread reforestation and plantation farming efforts. Water bodies also expanded slightly to 1% (494.44 hectares) compared to 2020. Settlement area continued to grow, reaching 4.46% (6398.12 hectares), reflecting population growth and urban development. Shrubland cover 13.68% (19623.64 hectares), and Agricultural land accounted for 4.34% (6238.27 hectares), both showing increases from 2020.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGain-Loss Analysis\u003c/h2\u003e\u003cp\u003eBased on the results of SVMs classification, a change analysis process in LCM on IDRISI was used to obtain the data on land gain and loss as well as the net change for each LUCL type for the period of 2017\u0026ndash;2020 and 2020\u0026ndash;2024 respectively. For the periods 2017\u0026ndash;2020, the landscape underwent notable transformations, with forest experiencing the most significant loss (14000 ha), primarily transitioning into shrubland and agricultural land. This suggests deforestation pressures, possibly driven by shifting cultivation, logging or land degradation. Shrubland saw the largest gain (15000 ha), likely due to forest conversion or land abandonment. Agricultural land expanded modestly (3000 ha) indicating either intensification or encroachment into forest zones. Settlement growth (2000 ha) reflects urban expansion, though its spatial footprint remains relatively contained. These transitions have direct implications for erosion modeling and sustainable land management in the area. The forest-to-shrubland shift, in particular, may signal increased vulnerability to soil erosion, especially in sloped terrains of Kangpokpi.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated LULC Transition Matrix between 2017\u0026ndash;2020 (hectare)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrom-To\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShrubland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgricultural land\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSettlement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal Loss\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSettlement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Gain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe land use/land cover analysis using TerrSet reveals a significant spatial transition in Kangpokpi district between 2020 and 2024. The pronounced change was observed in shrubland, which experienced a net loss of 12000 hectares. This decline was primarily due to conversions into forest and water bodies, suggesting active ecological restoration, land abandonment, or reclassification of transitional vegetation zones. Forest areas exhibited high turnover, with roughly 12000 hectares both gained and lost. This bidirectional change indicates simultaneous afforestation and deforestation processes, potentially driven by shifting cultivation, community forestry initiatives, or natural succession. Agricultural and settlement categories remained relatively stable, each showing balanced gains and losses (4000 ha), implying spatial redistribution rather than net expansion. These patterns may reflect seasonal land use shifts, zoning adjustment, or infrastructure development. Water bodies showed the most substantial net gain (12000 ha) likely attributable to reservoir expansion, wetland restoration, or improved hydrological mapping. This increase holds ecological significance, particularly in mitigating erosion and enhancing water retention in upland catchments. The estimated transition matrix was constructed based on TerrSet outputs. It highlights dominant flows from shrubland to forest and water, and minor exchanges among agricultural, settlement, and forest categories. These findings underscore the dynamic nature of land systems in Kangpokpi and their implications for erosion modeling, hydrological assessments, and sustainable and management.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated Transition Matrix between 2020\u0026ndash;2024 (hectare)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrom - To\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShrubland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgricultural Land\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSettlement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal Loss\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShrubland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSettlement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e12000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Gain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of LULC\u003c/h2\u003e\u003cp\u003eThe LULC projection for Kangpokpi district by 2030 reveals a marked transition from forest and shrubland ecosystems toward intensified agricultural use and expanding settlements. This shift is spatially concentrated in valley regions and along emerging transport corridors, driven by demographic pressure, infrastructure development, and land conversion practices. Forest fragmentation and shrubland decline signal potential ecological stress, particularly in upland zones vulnerable to erosion. The expansion of agriculture into marginal slopes and settlement encroachment near riparian zones heightens the risk of soil degradation and hydrological disruption. These trends underscore the urgency for integrated land management strategies, including agroforestry adoption, zoning enforcement, and erosion mitigation planning, to safeguard ecological integrity while accommodating socio-economic growth.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe observed LLULC changes between 2017 and 2024 in the study area reflect a complex interplay of ecological processes, socio-economic transitions, and policy interventions. The increase in forest cover and concurrent decline in shrubland and agricultural land suggest a landscape undergoing ecological recovery, possibly driven by afforestation efforts, reduced shifting cultivation, and natural succession. These trends align with national and global priorities for climate mitigation, biodiversity conservation, and sustainable land management (Pan et al., 2011; Myers et al.,2000). However, the decline shrubland raises concerns about the loss of transitional habitats that support ecological connectivity and species diversity. Shrublands often serve as buffer zones and corridors, and their reduction may fragment ecosystems, especially in hilly terrains with high endemism (Laurance et al., 2014). Future land management strategies must recognize the ecological value of these zones and incorporate them into conservation planning.\u003c/p\u003e\u003cp\u003eThe expansion of built-up areas, though moderate, reflects ongoing urbanization and infrastructure development. This trend underscores the need for integrated spatial planning that balances development with environmental sustainability. Unregulated urban growth can lead to habitat loss, increased surface runoff, and reduced ground water recharge (Grimm et al., 2008). Incorporating green infrastructure and enforcing zoning regulations can mitigate these impacts and promote resilient urban landscapes (McDonald et al., 2020). The contradiction of agricultural land may indicate shifting livelihood patterns, land abandonment, or reclassification of marginal lands. While this supports forest recovery, it also raises questions about food security and rural livelihoods. Sustainable intensification and agroforestry practices can help maintain agricultural productivity while minimizing environmental degradation (Pretty et al., 2018; Tilman et al.,2002).\u003c/p\u003e\u003cp\u003eWater bodies remained relatively stable, suggesting hydrological resilience. However, their low spatial coverage highlights vulnerability to climate variability and increasing demand. Integrated watershed management and wetland restoration are essential to safeguard water resources and support both ecological and human needs (Vorosmarty et al., 2000; Mitsch and Gosselink, 2000). Overall, the LULC dynamics reveal a landscape in transition, shaped by ecological restoration, urban expansion, and evolving land use priorities. These changes offer opportunities for sustainable development but also demand proactive planning and multi sectoral coordination.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides a comprehensive assessment of LULC changes in the region between 2017 and 2024, highlighting key trends and their implications for environmental sustainability and land governance. The findings reveal a significant increase in forest cover, indicating ecological recovery and successful conservation efforts. The decline in shrubland and agricultural land, reflecting land use transitions and potential ecological succession. Moderate expansion of built-up areas, pointing to urbanization and infrastructure growth. The stable water body coverage, suggesting hydrological resilience but also climate sensitivity.\u003c/p\u003e\u003cp\u003eThese patterns underscore the importance of integrated land use planning that balances ecological integrity with socio-economic development. Forest expansion contributes to climate mitigation and biodiversity conservation, while urban growth and agricultural contraction require careful management to avoid unintended consequences. Some of the policy recommendations emerging from the study include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStrengthening community-based forest management and afforestation programs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eProtecting transitional ecosystems such as shrublands through ecological zoning.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePromoting sustainable urban development with green infrastructure and land use regulations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSupporting climate resilient agriculture and agroforestry to sustain rural livelihoods.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnhancing water resource management through watershed protection and wetland restoration.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFuture research should focus on ground level validation of remote sensing data, socio economic drivers of land use change, and the integration of climate projections into land management strategies. Participatory approaches involving local communities, planners and policymakers will be crucial to ensure that land use transitions support both environmental resilience and human well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe Author declares there are no financial or non-financial interest that are directly or indirectly related to the work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author is the sole contributor of this manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfrifa Joseph K., Monney K. A., and Deikumah J. P. 2022. Effects of urban land use types on avifauna assemblage in a rapidly developing urban settlement in Ghana. Urban Ecosystems. Springer. https://doi.org/10.1007/s11252-022-01281-0.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgrawal, A., and Ostrom, E. 2001. 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Land use change modelling: Current practice and research priorities. GeoJournal, 61(4), 309\u0026ndash;324.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVorosmarty, C. J., Green, P., Salisbury, J., and Lammers, R. B. 2000. Global water resources: Vulnerability from climate change and population growth. Science, 289(5477),284\u0026thinsp;\u0026minus;\u0026thinsp;235.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang C., Li Y., Wang W., Gao Z., Liu H., and Nie Y. 2024. Combined effects of climate and land use changes on the alpha and beta functional diversities of terrestrial mammals in China. Science China Life Sciences. Vol. 67 No. 10. Springer. https://doi.org/10.1007/s11427-023-2574-0.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land use Land cover, Geospatial technology, Remote sensing, change detection, Projection, sustainable land management","lastPublishedDoi":"10.21203/rs.3.rs-7451328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7451328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand use and land cover dynamics play a critical role in shaping ecological stability and socio-economic development, particularly in environmentally sensitive regions like Kangpokpi district, Manipur. This study employs geospatial technology to assess temporal changes in LULC patterns over the past tow decades, integrating satellite imagery, remote sensing techniques, and GIS- based spatial analysis. Using multi-temporal Sentinel dataset (2017,2020, and 2024), supervised classification was performed to delineate major LULC categories including forest, Agricultural land, Settlement, Shrubland and Water. The results reveal a marked decline in forest cover, accompanied by an expansion of agricultural land and settlement, driven by population growth, shifting cultivation practices, and infrastructural development. Change detection and Gain-and Loss analysis highlights spatial hotspots of land transformation. This research offers a replicable framework for monitoring landscape changes and informing sustainable land use planning in hill districts. The findings underscore the urgency of policy measures that balance development with ecological conservation, and advocate for region-specific strategies to mitigate land degradation and promote resilient land stewardship in Kangpokpi.\u003c/p\u003e","manuscriptTitle":"Assessment of Land use land cover changes in Kangpokpi district, Manipur using Geospatial Technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:05:29","doi":"10.21203/rs.3.rs-7451328/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":"c451e291-8013-4244-b7e5-859320ff2857","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-03T09:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 06:05:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7451328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7451328","identity":"rs-7451328","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}