# Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data
## 2 Materials and Methods
### Proposed Method  
Forest fire mapping employed by Indonesian agencies mostly use optical imagery, such as Landsat-8 and Sentinel-2 [44; 45; 46], but extensive and persistent cloud cover in Indonesia requires other sensors which are unaffected by clouds [22]. The burnt area, during the 2019 Indonesia land and forest events, was generated from the Sentinel-1a synthetic aperture radar (SAR) satellite imageries and processed using random forest, classification and regression trees (CART), and the support vector machine. One of the task of forest fire mapping is to map the fire perimeters and to predict the areas of potential fire hazards from the areas of vegetation regrowth after fires. The burn area index (BAI) highlights the burnt land between two bands of the spectrum, by emphasizing the difference of reflectance of the burnt vegetation in post-fire images. The index is computed from the spectral distance from each pixel to a reference spectral point where recently burnt areas converge, in which brighter pixels indicate burnt areas. The BAI is calculated as in Equation (3) [47]. \[BAI=\frac{1}{\left(0.1-Band_{\gamma}\right)^{2}-\left(0.06-Band_{2}\right)^{2}} \tag{3}\]The normalized burn ratio index (NBR), one of the most widely used image enhancements for mapping wildfires, is used to determine the burnt area and was improved by the machine learning classifier algorithm [48] and the basic formula, is expressed in Equation (4). \[NBR=\frac{Band_{1}-Band_{2}}{Band_{1}+Band_{2}} \tag{4}\]  
The method is used to detect burnt tracks is the burn ratio index technique and the difference ratio index technique. This index uses a temporal indication, which is the difference in the conditions before and after a fire for each polarization. The recording after a fire is selected through the October recording period, while the pre-fire condition data is the recording for the period from March to May. The backscatter value generated from each condition (before and after fire) is the average of several recordings, because it aims to minimize the backscatter anomaly or bias. The radar burn ratio (RBR) in Equation (5) is modified from the NBR and is defined as the average backscatter ratio before and after a fire occurs at a certain polarization [49], meanwhile, the radar burn difference (RBD) in Equation (6) is the difference between the average scattering conditions before and after the fire [50]. \[\text{RBR}_{\text{xy}}=\frac{\text{Postfire average backscatter}_{\text{xy}}}{ \text{Prefire average backscatter}_{\text{xy}}} \tag{5}\]  
\[\text{RBD}_{\text{xy}}=\text{Postfire average backscatter}_{\text{xy}}-\text{ Prefire average backscatter}_{\text{xy}} \tag{6}\]  
The results of the two indexes with the two polarizations, produced four variables, namely the VV polarization RBR, VH polarization RBR, VV variation RBD, and the VH polarized RBD. The four layers are used as variables to determine the pixels of the burnt land object. The supervised classification is used to obtain the pixels on the burnt land objects by referring to a sample of the selected area. To obtain the best accuracy, three types of classification algorithms were selected, namely the support vector machine, random forest, and CART. The three classification results from these different algorithms are then compared with the field data on the burnt land, as a reference image. The sample locations of this study are the peatlands in the Kalimantan Island region. The peatlands in the area often experience forest and land fires, related to the characteristics of the deep peat depth, which makes the organic content in the area very high, combined with unsustainable agricultural and plantation land conversion practices, and a dropping groundwater table, especially during the dry season [48]. The calculation of the accuracy test is carried out by taking a reference image on the burnt area, since it is in vector polygon format, the classification model was first converted into vectors to obtain a high calculation accuracy.