Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data

Abstract

Forest and field fires have become a frequent phenomenon in recent years caused by human activities in Indonesia, affecting all forms of forest and field cover. Forest fire-degraded land is more prone to burn again, due to the nature of peatland in Kalimantan. Rapid mapping technology for burnt areas affected by forest fires is needed to obtain this information. The use of remote sensing technology, in the form of synthetic aperture radar (SAR) images, combined with cloud computing processing speeds up data processing and is not affected by the existing cloud cover. The Quick-Mapping employed in this research provides faster mapping time, compared to the currently employed method, based on field report data, to enable a better and more efficient firefighting effort. The data processing is carried out using cloud computing, enabling the processing of large amounts of data. The processing is carried out starting with importing the data, preprocessing to classification running, simultaneously, using the JavaScript programming language. The research classifies the burnt area from backscatter patterns before and after the event in two measurements, namely the radar burn ratio (RBR) and the radar burn difference (RBD). The RBR is defined as the average backscatter ratio at a certain polarization, while RBD is the difference between the average scattering conditions. The composite image for the classification utilizes images from the RBR and RBD with co-polarized (VV) and cross-polarized (VH) backscatter. The burnt area difference is −1.9 for VH and −1.7 for VV, which indicates a lower backscatter, due to forest fire. The classification of the burnt area yields the best overall accuracy of 88.26% with a support vector machine and processing time of 1 h, compared to the currently 12 h needed to provide burnt area maps from field observation data.