My relationship with the Earth is complicated; I study it, question it, and occasionally yell at my screen when the cloud cover ruins a perfectly good satellite image. At its core, my research applies remote sensing, geospatial analysis, and machine learning to address pressing challenges in environmental monitoring, ecological risk assessment, and conservation planning. Using Google Earth Engine, ArcGIS, QGIS, and R, I integrate multi-sensor satellite data, including Landsat, Sentinel-2, Sentinel-5P TROPOMI, and MODIS, with machine learning methods to generate spatially explicit insights across diverse ecological and environmental contexts.

My work spans forest fire susceptibility mapping, urban heat island dynamics, atmospheric pollution modelling, biomass estimation, and species vulnerability modelling under climate change. Essentially, I ask difficult questions of the planet and let the algorithms figure it out. I have applied these approaches across varied landscapes, from the Western Ghats to urban centres, generating outputs that inform both scientific understanding and practical conservation and management decisions.

Research Profiles:

Scopus: https://www.scopus.com/authid/detail.uri?authorId=59982284100

ORCID: https://orcid.org/0009-0000-9815-3504

ResearchGate: https://www.researchgate.net/profile/Riaz-Sheriff