Spatial Analysis of Future Land-Use Impacts on Carbon Sequestration and Priority Area Identification in the Kojur Watershed using Hotspot and Moran's I Techniques

Aims: Land-use and land-cover (LULC) dynamics are central drivers of terrestrial Carbon fluxes. This study aimed to assess the spatial heterogeneity of Carbon storage in the Kojur Watershed and to determine how topographic factors influence Carbon sequestration across historical and projected timeframes.Materials & Methods: LULC maps for ۲۰۰۳, ۲۰۱۳, and ۲۰۲۳ were generated using satellite imagery and the Land Change Modeler (LCM). Future scenarios for ۲۰۳۵ and ۲۰۵۰ were simulated using the Cellular Automata Markov (Ca-Markov) model under a business-as-usual assumption, which projected historical land-use transitions and socio-economic trends forward. The InVEST Carbon module estimated Carbon storage in four pools: aboveground biomass, belowground biomass, litter, and soil. Spatial clustering was analyzed using Moran’s I and Getis-Ord Gi* statistics.Findings: Carbon storage showed a consistent, statistically significant clustered pattern across all years. Forests held the most Carbon (≈۲.۵-۲.۶ million tons), rangelands moderate (≈۱.۲-۱.۵ million tons), rainfed agriculture less (≈۰.۲-۰.۳ million tons), and residential areas minimal (<۰.۰۱ million tons). Moran’s I values (۰.۳۵-۰.۵۴) confirmed strong spatial dependence. Hotspot zones expanded from ۲۸.۹۳% in ۲۰۰۳ to ۳۲.۹۰% in ۲۰۵۰, while coldspots also grew slightly, indicating an increase in spatial polarization. Higher elevations and moderate slopes generally store more Carbon due to denser forest cover and reduced human activity.Conclusion: The Kojur Watershed exhibits a stable, clustered pattern of Carbon storage. Hotspot zones serve as vital Carbon reservoirs for climate mitigation. Preserving these areas and restoring low-Carbon zones offer a strategic path for sustainable resource management and enhanced ecosystem services.

کلیدواژه ها:

Geographic Information Systems (GIS) ، Hotspot and Coldspot Analysis ، Kojur Watershed ، Spatial Autocorrelation Analysis