Spatial Patterns of Soil Organic Carbon in South Delhi District : A Multi-Land Use Baseline Assessment
DOI:
https://doi.org/10.53550/ijsc.v54.i1.215Keywords:
Empirical Bayesian Kriging, Land use/cover, Soil organic carbon, South Delhi, Spatial baseline, Urban soilsAbstract
Soil organic carbon (SOC) is a fundamental component of soil health and plays a critical role in climate regulation and ecosystem functioning. In rapidly urbanizing regions, spatial variation in SOC reflects complex land use patterns, yet spatially explicit assessments across diverse urban land uses remain limited. This study characterized the spatial distribution of SOC and associated soil physicochemical properties across five major land-use and land-cover (LULC) types in South Delhi District, India. Soil samples were collected from 98 locations representing agriculture, forest, scrub forest, built-up, and wasteland areas during 2021 and 2022. At each location, samples were obtained from three depth intervals (0–10, 10–20, and 20–30 cm) and analyzed for SOC, bulk density, moisture content, pH, electrical conductivity, water holding capacity, porosity, and particle density. Spatial patterns of SOC were assessed using Empirical Bayesian Kriging (EBK). SOC concentrations varied significantly across LULC types, with forest soils highest (0.62–0.66% at 0–10 cm) and wasteland soils lowest (0.15–0.17% at 0–10 cm, dropping to 0.06–0.08% at 20–30 cm). Built-up areas had similar low levels. SOC decreased with depth across all land uses, with 60–70% in the surface layer. SOC correlated strongly with bulk density, porosity, and water retention. EBK mapping revealed SOC-rich zones in forests and depleted zones in urban and degraded lands, with good validation. This study sets a baseline for SOC in South Delhi, highlighting hotspots and depleted areas for conservation. The spatial patterns suggest stability over two years. Limitations include uneven sampling, short duration, and lack of vegetation and land management data. Future work should include long-term monitoring, vegetation, soil texture, and SOC fractionation to better understand carbon dynamics.
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