Global Burned Area Detection: MODIS and beyond - IGAC Project

Global Burned Area Detection:MODIS and beyondLuigi Boschetti*, Chris Justice*, David Roy**, Louis Giglio*Diane Davies*, Steve Stehman****Department of Geographic Sciences University of Maryland**South Dakota State University *** SUNY Syracuse

Fire detection from satellite• Active fires = fires actively burning at theoverpass time of the satellite; detectionmostly based on temperature.• Burned areas = areas affected by firewithin a certain time interval; detectionbased on the effects of fire on vegetation(removal of photosintetic activity, charcoalon the ground, exposed soil).

• Global Product 500m• Monthly Composite –showing date of burning( Roy and Boschetti

Algorithm• Automated, without training data or human intervention• Rolling BRDF based expectation change detection: semi-Physicallybased; less dependent upon imprecise but noise tolerantclassification techniques; very few thresholds• Applied independently per pixel to daily gridded MODIS 500mland surface reflectance time series=> globally map 500m location and approximate day of burningRoy et al,2002, 2005Roy, Boschetti, Ju, Justice 2008

Conceptual Scheme (one pixel, time series)observedrtime

Conceptual Schemeobservedrt-1time

Conceptual SchemeBRDF Inversion windowobservedrt-1time

Conceptual SchemeBRDF Inversion windowobservedpredictedrr (t|t-1)t-1time

Conceptual SchemeBRDF Inversion windowobservedpredictedrr (t|t-1)r (t|t-1)t-1time

Conceptual SchemerBRDF Inversion windowobservedpredictedr (t+1|t)r (t+1|t)ttime

Animation: 5 Months of burning,Okavango Delta, Botswana, 2002.Produced using multitemporal rollingBRDF-based change detection approach,Roy et al. 2002

Example of Quality Assessment:Comparison with polygons by theEuropean Forest Fire ServiceBoschetti et al, 2008

Validation Protocol• Landsat-based validation protocol– Developed in SAFARI2000 with SAFNet– Expanded to other GOFC-GOLD regional networks– Protocol advocated & now adopted by the CEOSCal/Val program• Multi-temporal Landsat data– interpreted by regional experts– map the area burned between acquisitions– generate independent reference data set• All independent reference data to be made available tothe community via the MODIS fire web site

Time 1:Landsat ETM+Sept. 4th

Time 2:Landsat ETM+Oct 6thYellow vectors = ETM+ interpretedburned areas occurring between thetwo ETM+ acquisitions

MODIS500mBurned AreasTime 1 Sept. 4toTime 2 Oct. 6White vectors = ETM+ interpretedburned areas occurring between thetwo ETM+ acquisitions

Validation Metrics• Regression – regional spatial accuracyassessment5 x 5 km cellsRoy and Boschetti 2009• Confusion matrix statistics (overall, user’s &producer’s accuracy) – pixel level accuracy assessment

VALIDATION SITESAfrica

VALIDATION SITES

VALIDATION SITESCentral and South America

VALIDATION SITESEurope

VALIDATION SITESSiberia

Validation of Burned Area ProductTemporal Reporting Accuracy• To date we have concentrated on product spatialreporting accuracy• The product also reports the ~day of detection• The nominal uncertainty due to the daily rollingBRDF inversion window is 8 days• Temporal product accuracy increasingly relevantto user community– near real time air quality– atm. transport models (weather on day of burn, plume injection height)– some regional assessment applications (nat. resource, disaster management)

C6 validation strategy• Make use of the free availability of Landsat data• Sampling in time and space, automatic selectionof all the suitable pairs of cloud-free data• Existing Stage 2 dataset used for preliminaryestimation of the variance• Will allow calibration of the moderate resolutionestimates• Product independent – suitable for internationalcollaboration• Basis for operational assessment (VIIRS LDCMera)

… from MODIS to Landsat

… we have used Landsat data forvalidation and quality assessment ofmoderate resolution burned areaproducts.

… we have used Landsat data forvalidation and quality assessment ofmoderate resolution burned areaproducts.Why not to map burned areas fromLandsat using the same fully automatic,systematic approach of MODIS?

MODIS-Landsat data fusion for highspatial resolution multi-annual wall towall burned area mapping of theconterminous United StatesBoschetti, L. (Principal Investigator, University of Maryland),Roy, D.P. (Co-Investigator, South Dakota State University GIScCE),Funded by NASA NNH09ZDA001N-TERRAQUA The Science of Terraand Aqua - Multi-Platform and Sensor Data Fusion,February 2011 – January 2014

Input dataset: WELD Landsat• NASA-funded (David Roy P.I)• Preprocessed, mosaiced, tiled Landsatdata• Whole Landsat 7 US dataset processed• Makes it possible to implement aautomatic processing chain for thematicproducts (MODIS approach)• Pontentially expanding to global

WELD Tile Map (CONUS has 501 5000x5000 30m pixel tiles in Albers)

Burned area polygons for 2002 (MTBS) – thebiggest fire is on the California-Oregon border

0 10 205KilometersWELD tileh01v05WELD tileh02v05Summer2002composite

WELD tileh01v05WELD tileh02v05Summer2002compositeMOD14 Global Active Fire Product

WELD tileh01v05WELD tileh02v05Fall2002composite

WELD tileh01v05WELD tileh02v05Fall2002compositeLandsat Burned Area Map

WELD tileh01v05WELD tileh02v05Fall2002composite

The methodologyFIRST PASS: PIXEL BASED SEMANTIC CHANGEDETECTION•The weekly WELD products are classified using the SIAM automatic classifier into96 spectral categories (Baraldi et al. 2010). SIAM is a physical model-based, fullyautomatic (no training) decision-tree classifier based on prior spectral knowledge ofsurface types observed from space.•The 52 SIAM classified weekly products per WELD tile enables the adoption of achange detection strategy based on semantics applied to the SIAM spectralcategories: a set of explicit rules applied to the spectral category time series, detectingall the transitions between categories that are compatible with burning, while avoidingpotentially spurious changes, is developed.• The changes considered compatible with burning are:Vegetation → SoilVegetation with high LAI → Vegetation with low LAIVegetation → CharcoalLight Soil → Dark Soil

SECOND PASS: SEGMENTBASED DATA FUSIONThe candidate burned areas detected by temporal analysis of the SIAMspectral categories are segmented using an approach based on theirproximity in space and time, and integrated with the MODIS active fireproduct.A burned area segment is confidently detected if:a) it contains a MODIS active fire detection in the sametemporal interval of the spectral changesORb) it is adjacent to a segment already confirmed as confident, and the spectralchanges in the two segments are detected in the same temporal interval.The contextual analysis is iterated until no new segments are identified asconfidently burned.

Global WELD April 20101.8km TOA true color browse, each browse pixel generated from 60 x 60 30m WELD pixels, sinusoidal projection

Timeline for futuredevelopments

• MCD45 is currently undergoing a fullreprocessing with improved code(Collection 5.1). The results will beavailable in the fall.• Redesigned code with integration of activefires will be ready by the end of the year(Collection 6).• VIIRS (MODIS successor) successfullylaunched. The active fire product is part ofthe standard product suite, no funding atthe moment for burned area product

More informationMODIS fire products: http://modis-fire.umd.eduInternational coordination on fire monitoring:http://gofc-fire.umd.edu/International coordination on product validation:http://lpvs.gsfc.nasa.gov/GOFC REDD Sourcebook:http://www.gofc-gold.uni-jena.de/redd/