- Above-ground biomass estimation with SAR and lidar fusion — SAR backscatter correlates with woody volume up to a saturation ceiling that hides the heaviest carbon stocks. Fusing P-band radar with GEDI lidar height metrics pushes that ceiling higher and produces spatially continuous biomass maps with honest uncertainty bounds.
- Active fire detection and burned-area mapping — Thermal anomaly detection from VIIRS and MODIS locates active fires within hours of ignition, while post-fire NBR differencing and SAR coherence map what burned after the smoke clears. Each method has hard limits that operational users must understand.
- Canopy height model derivation from spaceborne lidar — GEDI and ICESat-2 measure canopy height with centimetre-scale vertical precision but sparse footprints. Fusing those samples with Sentinel-1 SAR and Sentinel-2 optical covariates via ML regression produces wall-to-wall canopy height models, with honest uncertainty that varies by forest structure.
- Carbon stock estimation and REDD+ verification — Satellite lidar and SAR backscatter, fused carefully, can estimate above-ground biomass across millions of hectares and underpin credible REDD+ MRV. The methods work well in low-to-medium biomass forests; they hit hard physical limits in dense tropical canopy above roughly 100 Mg/ha.
- Fire fuel load and fire-risk mapping — Satellite-derived vegetation indices and spaceborne lidar can map canopy fuel loads and live fuel moisture proxies at landscape scale, giving fire managers a quantitative basis for suppression planning before ignition occurs.
- Forest degradation and sub-canopy damage mapping — Selective logging, fuelwood extraction and edge erosion thin a canopy without clearing it. Binary deforestation alerts stay silent. L-band SAR and spectral unmixing reveal the gradient between intact and gone.
- Post-fire and post-disturbance forest recovery monitoring — Dense Landsat and Sentinel-2 time-series, combined with spectral indices and the LandTrendr segmentation algorithm, can track canopy regrowth trajectories after fire, storm or clearance. The critical caveat: spectral recovery consistently outpaces structural and biodiversity recovery, a gap that matters enormously for carbon accounting.
- Forest phenology and seasonality mapping — Dense satellite time-series reveal when forests flush, peak and senesce, exposing differences between forest types and flagging drought stress weeks before visible dieback. This page explains the sensors, methods and honest limits of spaceborne phenology mapping.
- Forest road and logging infrastructure detection — Unpaved logging roads and skid trails are the earliest detectable signal of forest exploitation. Sub-metre optical imagery and SAR coherence methods can map them weeks before canopy loss becomes visible to coarser sensors.
- Illegal logging activity detection — Selective illegal felling leaves faint but readable signatures: coherence loss in SAR time-series, skid trails visible in very-high-resolution optical imagery, and clearance polygons that cross-check against published concession cadastres.
- National-scale land-cover classification — Wall-to-wall land-cover maps built from multi-seasonal satellite composites are the baseline every forest, agriculture and planning policy rests on. Getting the class definitions, sensor mix and accuracy reporting right is harder than the imagery makes it look.
- Mangrove extent and change detection — Mangroves occupy less than 0.5% of the world's forest area yet rank among the most carbon-dense and ecologically critical coastal ecosystems. Satellite SAR and multispectral data, combined carefully, can map their extent and track losses to aquaculture and development with sub-hectare precision.
- Near-real-time deforestation alerts — Dense time-series of optical and SAR imagery can flag new forest clearance within days of occurrence. The hard problem is not the algorithm; it is cloud cover, revisit gaps, and minimum mappable area.
- Peatland drainage and subsidence monitoring — Sentinel-1 InSAR time-series can detect millimetre-scale surface subsidence over drained peatlands, while optical imagery maps the canal networks driving that collapse. Together they underpin credible REDD+ and voluntary carbon accounting.
- Plantation species and age-class mapping — Commercial plantations of eucalyptus, acacia and pine look similar from the ground but diverge sharply in their seasonal reflectance trajectories. Multi-temporal Sentinel-2 time-series, particularly the red-edge bands, can separate species and rotation age-classes that standard NDVI misses entirely.
- Annual tree cover loss and gain tracking — Annual global tree cover change is now measurable at 30 m resolution from 2000 onwards, but the numbers mean different things to different jurisdictions. Definitional gaps between remote-sensing products and national forest inventories have direct consequences for REDD+ carbon accounting.