Earth Observation & Remote Sensing

Earth Observation & Remote Sensing

Earth observation leverages multispectral, hyperspectral, SAR, and optical satellite imagery to derive quantitative information about land cover, vegetation health, surface water, urban expansion, and terrain change. Sensors aboard platforms such as Sentinel-2, Landsat, and commercial very-high-resolution constellations provide systematic revisit coverage that enables both snapshot analysis and time-series monitoring.

Spectral index computation (NDVI, NDWI, NDBI, and others) transforms raw reflectance values into meaningful biophysical indicators. Supervised and unsupervised classification algorithms — including random forests, support vector machines, and deep learning architectures — assign land-cover classes to imagery pixels or objects. Change detection workflows compare multi-temporal datasets to quantify deforestation rates, coastal erosion, impervious surface growth, or post-disaster damage extents.

Analysis-ready data pipelines handle atmospheric correction, cloud masking, orthorectification, and mosaicking to produce consistent, comparable image stacks. Results are delivered as validated thematic maps, statistical summaries, and time-series dashboards that inform environmental compliance, agricultural planning, natural resource management, and infrastructure monitoring programs.