Earth observation, geospatial intelligence and environmental modelling help connect complex landscapes with the people and decisions affected by change.
These areas overlap through data, methods and shared questions about environmental change and resilience.
Using indicator-based methods, climate scenarios and geospatial models to understand exposure, sensitivity, adaptive capacity and their implications for climate resilience.
Selected applications: Agricultural vulnerability in Kashmir; district-level climate-risk assessments; adaptation pathways.
Connecting remote sensing with hydrological evidence to understand flood exposure, glacier-related hazards, wetland change and water-resource questions.
Selected applications: Jhelum River floods, Gangabal glacial-lake risk, Wular Lake monitoring and irrigation assessment.
Building reproducible environmental information from imagery, spatial databases, Google Earth Engine, modelling and AI/ML-based methods.
Selected applications: Satellite classification, geoportals, monitoring systems and environmental prediction.
Translating scientific outputs into planning decisions, environmental appraisal, climate programmes and institutional geospatial services.
Selected applications: EIA support, climate adaptation planning, forest demarcation and spatial data infrastructure.
Monitoring ecosystems and landscape transformations relevant to conservation, land resources and ecological sustainability.
Selected applications: Forest-fire vulnerability, biodiversity mapping and land-use/land-cover analysis.
Applying spatial evidence to urban environmental change, infrastructure access and land-use decisions.
Selected applications: Healthcare accessibility, urban heat, site suitability and settlement planning.
Sentinel, Landsat, field records, satellite products and spatial databases.
GIS, machine learning, climate scenarios and hydrological or risk analysis.
Compare spatial outputs with independent observations and domain knowledge.
Geospatial maps, risk indicators, planning layers and policy-ready products.
Tools and environments documented in the CV include Google Earth Engine, ArcGIS, QGIS, Python, R, ERDAS, HEC-RAS, SWAT, InVEST and MaxEnt. The choice of methodology depends on the scientific question and data quality.