Research Projects

State of the Art Rapid urbanization, land-use changes, and population growth necessitate quantifying these modifications to inform sustainable development and policy decisions,Remote sensing offers a powerful means of capturing these changes over extensive spatial and temporal scales. This study aims to map and quantify human modifications across India between 2000 and 2022 using a composite approach that integrates key parameters influencing anthropogenic impact on natural systems. The study encompasses the entirety of India, covering diverse geographic zones, urban, agricultural lands, and natural landscapes. This national-scale approach aims to capture regional variations and identify areas with concentrated human impacts over the two-decade study period. Data and Parameters The study will utilize not less than six parameters, normalized and weighted, to derive a spatial index of human modifications: Land Use and Land Cover (LULC) Change (2000-2022): LULC changes are primary indicators of human modification, capturing transformations in agriculture, urbanization, deforestation, and land development. Temperature Anomalies (Observed - Reanalysis, 2000-2022): Differences between observed and reanalysis temperatures highlight anthropogenic heating and microclimate modifications. Population Change: Population density and growth data represent demographic pressure on land and resources. Night Lights Change: Nighttime light data serves as a proxy for economic and urban growth, capturing the expansion of human activities. Urbanization Change: This metric will assess the degree of urbanization, considering infrastructure expansion and settlement changes. NDVI Change (Sen’s Slope, 2000-2022): Vegetation health and cover changes, derived using Sen’s slope on NDVI trends, reflect ecological stress from land development. Methodology Data Acquisition: Obtain datasets for each parameter from reputable remote sensing sources as mentioned in table above. Data Normalization: Normalize each parameter on a 0 to 1 scale, where 0 represents low human modification and 1 represents high human modification. This scaling ensures comparability across parameters with different units and magnitudes. Parameter Weighting: Assign weights to each parameter based on its influence on human modification, using expert judgment or Fuzzy - analytical hierarchy process (AHP) or Principal component-based weight or combinations. The weighting scheme will be calibrated to optimize the accuracy of the final modification index. Index Calculation: Multiply each parameter by its respective weight and aggregate them to calculate a composite human modification index, representing cumulative anthropogenic impacts across India. Validation and Sensitivity Analysis: Validate the human modification index using ground truth data, such as local land use records and case study regions. Perform a sensitivity analysis to evaluate the impact of different parameter weights on the final index output.