Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

The Brazilian Tropical Moist Forest Biome(BTMFB)spans almost 4 million km^(2) and is subject to extensive annual fires that have been categorized into deforestation,maintenance,and forest fire types.Information on fire types is important as they have different atmospheric emissions and ecological impacts.A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer(MODIS)active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps,and using predictor variables concerned with fuel flammability,fuel load,fire behavior,fire seasonality,fire annual frequency,proximity to surface transportation,and local temperature.The fire seasonality,local temperature,and fuel flammability were the most influential on the classification.Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years(2003–2010)are presented with an overall fire type classification accuracy of 90.9%(kappa 0.824).The fire type user’s and producer’s classification accuracies were respectively 92.4%and 94.4%(maintenance fires),88.4%and 87.5%(forest fires),and,88.7%and 75.0%(deforestation fires).The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature.

Global operational land imager Landsat-8 reflectance-based active fire detection algorithm

A global operational land imager(GOLI)Landsat-8 daytime active fire detection algorithm is presented.It utilizes established contextual active fire detection approaches but takes advantage of the significant increase in fire reflectance in Landsat-8 band 7(2.20μm)relative to band 4(0.66μm).The detection thresholds are fixed and based on a statistical examination of 39 million non-burning Landsat-8 pixels.Multi-temporal tests based on band 7 reflectance and relative changes in normalized difference vegetation index in the previous six months are used to reduce commissions errors.The probabilities of active fire detection for the GOLI and two recent Landsat-8 active fire detection algorithms are simulated to provide insights into their performance with respect to the fire size and temperature.The algorithms are applied to 11 Landsat-8 images that encompass a range of burning conditions and environments.Commission and omission errors are assessed by visual interpretation of detected active fire locations and by examination of the Landsat-8 images and higher spatial resolution Google Earth imagery.The GOLI algorithm has lower omission and comparable commission errors than the recent Landsat-8 active fire detection algorithms.The GOLI algorithm has demonstrable potential for global application and is suitable for implementation with other Landsat-like reflective wavelength sensors.