# 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. os 2022 2022022022022022022022022022022022022022022022202202202220222022202  
land fire episodes always hit Indonesia every year, although the magnitude of the forest and land fire ignition is closely related to the El Nino event for example in 1997-1998 and 2015 [8; 9; 10]. Various impacts arise after forest and land fires, the most concerning issue about post-forest and land fire events is land degradation, due to the burning of the existing vegetation cover and fire impacts on the soil environment [11]. The effect of fire is not only directly associated with the destruction of vegetation cover but also alters the hydrological condition of the soil, i.e., reducing the resistance of soil to erosion and increasing the overdland flow during storm events [12; 13; 14]. As the consequences of the post-fire effect to the soil environment is really harmful, a rapid assessment is needed to map the distribution of the post-fire land degradation area, widely known as a burnt scar area [15]. Remote sensing in particular, due the variety of its sensors, is known as a powerful tool to assess the burnt scars rapidly [15; 16; 17]. A few studies have explored the application of remotely-sensed data for mapping the burnt scars in Indonesia, and found that the radar sensor is most suitable, rather than other optical sensors [17; 18; 19]. The combination of using radar sensors with the state-of-the-art computational techniques, using cloud computing, can improve the speed of image processing and avoid the bias of cloud cover [20; 21; 22]. The usability of the Google Earth Engine as one of the cloud computing platforms for satellite imagery processing, has grown over the last decade, since 2011 [23]. GEE is host to many satellite imageries for over 40 years, from such satellites as Landsat, MODIS, the National Oceanographic and Atmospheric Administration Advanced-Very-High Resolution Radiometer (NOAA-AVHRR), Sentinel 1,2,3, and 5-P, and the Advanced Land Observing Satellite (ALOS) [20].