Abstract
Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is limited by the underestimation of burn scars from small, fragmented fires and high cloudiness. Moreover, systematic mapping of BA is challenged by the need for human intervention in training sample acquisition, which precludes the development of automatic-generated products over large areas and long periods. Here, we developed a multi-sensor, active fire-supervised, one-class BA mapping algorithm to address several of these limitations. Our main objective is to generate a long-term, detailed BA atlas suitable to improve fire regime characterization and validation of coarse resolution products. We use composite images derived from the Landsat satellite to generate end-of-season maps of fire-affected areas for the entire Cerrado. Validation exercises and intercomparison with BA maps from a semi-automatic algorithm and visual photo interpretation were conducted for the year 2015. Our results improve the BA mapping by reducing omission errors, especially where there is high cloud frequency, few active fires are detected, and burned areas are small and fragmented. Finally, our approach represents at least a 45% increase in BA mapped in the Cerrado, in comparison to the annual extent detected by the current coarse global product from MODIS satellite (MCD64), and thus, it is capable of supporting improved regional emissions estimates.
Highlights
The high global variability of fire occurrence makes it difficult to have a consistent fire record over space and time
The approach we propose in this work is characterized by (i) the use of detection of changes based on temporal composites and (ii) an automated procedure to produce longer time series of fire scars in large areas without human intervention, namely, (a) the integration of active fire data to collect training samples and (b) use of a single class classification based on machine learning
The OC-SVM technique is derived from the standard Support Vector Machine algorithm [58,59] and designed to solve single-class classification problems, showing positive results in burned area (BA) mapping when compared to other traditional methods [6,47,60,61,62]
Summary
The high global variability of fire occurrence makes it difficult to have a consistent fire record over space and time. This is challenging for a continental biome like the Cerrado, with large extent, low accessibility areas, and inconsistent or scarce field fire records In this context, remote sensing data, together with automatic classification techniques, are the only feasible, cost-effective, and timely source of information for systematic monitoring of fire occurrence for a broad range of spatial scales [4,5,6]. Burnt area maps over the Cerrado have been mostly produced using spatially coarse data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor on board TERRA and AQUA satellites Those maps have been systematically available since 2001 at global (e.g., MCD64-500 m [7], Fire CCI-250 m [8]) and regional (e.g., AQM-1 km [5]) scales. Other automatic BA classifiers were successfully applied to the region with diverse broad spatial coverage using other sensors, such as the Project for On-Board Autonomy-Vegetation (PROBA-V) [6] and the Visible Infrared
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