Evaluating methods to map burned area at 30-meter resolution in forests and agricultural areas of Central India

Abstract

Biomass burning is a major phenomenon that plays an important role in small-scale ecological processes such as vegetation dynamics and soil erosion, and global processes such as hydrological cycles and climate change. However, global fire databases have low accuracies for burned area detection in areas with small fires, low biomass and in woodlands and open forests that characterize Central India. The present study uses higher resolution (30 meter) Landsat imagery to test accuracies for burned area detection using spectral indices (SI), machine learning (ML) algorithms and supervised classification. We find that detection of burned area by global fire product Fire Information for Resource Management System (FIRMS) is very low (<20%). Accuracies are higher for Landsat-based classification of burned area using supervised classification, random forest (RF) and Support Vector Machines (SVM). Accuracies are higher in April–May than in February–March and vary by azimuth angle on the day of image acquisition. RF produced the most consistently high classification accuracies for April (>80%), but had a tendency to misclassify less frequently available land covers; SVM had similar classification accuracies but had a tendency to overfit the model. Both lead to the potential for increasing commission errors and need to be used carefully when predicting burned area. Inclusion of SI had high relative importance in predicting burned area and reduced commission errors. Given these caveats, we recommend using ML algorithms for mapping burned area in the future, as it requires less time investment than classification and can yield consistent results. Accurate mapping of high-resolution fires is important for more accurate inputs into carbon inventories and ecological understanding of land-use dynamics and drivers.