Abstract
A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGISTM libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool’s structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset.
Highlights
Fires are one of the foremost factors of land surface disturbance in diverse ecosystems causing severe economic, ecologic, and atmospheric effects [1]
Most papers dealing with validation of global burned area products recommend using medium-resolution images (Landsat TM or similar) as reference information [5,10,11,12], since national statistics are usually either not available or not reliable, making field work unfeasible
The Burned Area Mapping Software (BAMS) tool has been run in six test areas to evaluate its performance for operational burned area mapping
Summary
Fires are one of the foremost factors of land surface disturbance in diverse ecosystems causing severe economic, ecologic, and atmospheric effects [1]. In the last few decades satellite data have been used extensively to monitor biomass burning at the regional to global scale in order to overcome the problems related to large areas affected by fire, the very dynamic nature of the process, and the low accessibility of many key regions [2,3]. In the past few years several global burned area thematic products derived from low- to moderate-resolution satellite data based on various automatic publicly available algorithms have been released [4,5,6,7,8], but no global consensus exists yet on the validation procedures and error characterization methods [9,10,11]. The same recommendation is given by the standard protocol of the Committee on Earth Observation
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