EO·Atlas
Multi-temporal burned-area mapping
Maps the extent and timing of fire-affected area by tracking persistent post-fire changes in surface reflectance across a time-series, often anchored by coincident active-fire detections. Produces systematic burned-area records.
Multi-temporal burned-area mapping answers the question: where did fire occur, and on which date? The method detects burned surfaces by tracking persistent post-fire changes in surface reflectance across a satellite time-series, distinguishing fire-induced spectral change from transient events such as cloud shadows and phenological shifts.
The MODIS MCD64A1 Collection 6 algorithm (Giglio et al. 2018) uses a hybrid approach.[1] A burn-sensitive vegetation index (VI) is computed from MODIS shortwave-infrared bands 5 (1240 nm) and 7 (2130 nm), which are sensitive to the spectral signature of post-fire char and ash. A temporal texture metric, measuring variance across the compositing window, distinguishes persistent spectral change from transient artefacts. A dynamic threshold is applied to the combined VI and texture composite. Active fire detections from MODIS thermal anomaly products serve as training samples, anchoring the detection where fire was directly observed from space: 44% of burned pixels in the product coincide with an active fire detection on the same day, and 68% within two days of the mapped burn date.[1] Collection 6 substantially improved detection of small burns relative to Collection 5.1.[2]
MCD64A1 v061 provides 500m monthly global burned area with per-pixel burn date assignment and quality flags, covering 2000 to present.[3] The product is used in IPCC greenhouse gas inventories and the Global Fire Emissions Database (GFED).
The method fails under persistent cloud cover, where burned area may go undetected during the satellite overpass. Rapid savanna re-greening can obscure the burn scar within days of the fire. Small and patchy burns in heterogeneous landscapes are significantly under-detected at 500m pixel resolution.
Detects land-cover or structural change by comparing backscatter intensity across dates; good for flood mapping, deforestation alerts, and disaster damage assessment.
Flags surface change by testing each new optical observation against a per-pixel statistical baseline built from the historical time-series, accumulating evidence across dates before confirming a change. Underlies near-real-time deforestation and disturbance alert systems.
Detects fine-scale surface disturbance by measuring the loss of interferometric phase coherence between two SAR acquisitions, sensitive to changes too subtle to alter backscatter amplitude. Used for damage assessment and disturbed-ground mapping.
- [1]The Collection 6 MODIS burned area mapping algorithm and productpeer reviewed2026-06-04(2018) MCD64A1 C6 algorithm paper: SWIR bands 5+7 VI; temporal texture; active fire training samples; 44%/68% coincidence with AF; improved small burn detection over C5.1
- [2]MODIS Active Fire and Burned Area Products - Burned Areaagency doc2026-06-04(2023) Official algorithm description page; hybrid approach; burn-sensitive VI; active fire guidance
- [3]MCD64A1.061 MODIS Burned Area Monthly Global 500mcommunity2026-06-04(2024) Product specification: 500m monthly global, burn date per pixel, QA flags; Collection 6.1
Edited from public sources. Last reviewed date pending by SpectraWorks editorial. See the data dictionary for field definitions.