Burned Area Detection Using Multi-Sensor SAR, Optical, and Thermal Data in Mediterranean Pine Forest

Abstract

Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This study analyzes the contribution of different features derived from optical, thermal, and Synthetic Aperture Radar (SAR) images to extract BA information from the Turkish red pine (Pinus brutia Ten.) forest in a Mediterranean ecosystem. In addition to reflectance values of the optical images, Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) data are produced from both Sentinel-2 and Landsat-8 data. The backscatter of C-band Sentinel-1 and L-band ALOS-2 SAR images and the coherence feature derived from the Interferometric SAR technique were also used. The pixel-based random forest image classification method is applied to classify the BA detection in 24 scenarios created using these features. The results show that the L-band data provided a better contribution than C-band data and the combination of features created from Landsat LST, NBR, and coherence of L-band ALOS-2 achieved the highest accuracy, with an overall accuracy of 96% and a Kappa coefficient of 92.62%.

Algorithms for efficient multi-temporal change detection in SAR imagery

“…This proves particularly advantageous in monitoring changes to land use and land cover, especially in the context of natural catastrophes. As discussed in Section 1, pairwise NCD has major limitations within these contexts, although it can derive insights as seen in [2], [3], and [4].…”

“…At the most basic level, changes are most often classified as arrivals or departures, representing objects that became brighter or darker between the pair of images, respectively. SAR-based change detection is used for many purposes, including disaster assessment [2], construction analysis [3], and environmental monitoring [4]. These uses will be discussed in more detail in Section 2.…”

Burn severity assessment with different remote sensing products for wildfire damage analysis

“…Supervised-unsupervised classification methods, spectral indices, spectral mixture analysis, logistic regression models, basic components analysis, machine learning and deep learning algorithms are the methods generally used in the studies conducted for the detection of burning area. Additionally, cloud-based computation platforms (such as Google Earth Engine) have recently become popular [10,11,12]. In some studies, very high-resolution satellite images such as Geoeye and Pleiades have been processed with classification methods such as Spectral angle Mapper (SAM) and object-based classification, and then compared with the Composite Burn Index produced from fieldwork [13,14].…”

The Importance of Earth Observation for Monitoring Cultural Heritage Sites Affected by Fire Events: The Case Study of Arakapas, Cyprus Using Sentinel 2 Data

“…The Sentinel 2 satellite has been used in several studies to detect burnt areas in order to determine the location and extent of fire events and to monitor environmental recovery (GÜLCİ et al, 2021;Hu et al, 2021;Pádua et al, 2020;Schroeder et al, 2016). According to the wider literature (Morresi et al, 2022) (Dindaroglu et al, 2021) (Zidane et al, 2021) (Abdikan et al, 2022) (Luo and Wu, 2022) (Putra et al, 2022) (Saulino et al, 2020), the analysis of the damage that an area suffers after a fire can be investigated using spectral indices. The most widely used index for mapping the forest fire disturbance is the Normalised Burn Ratio (NBR) (García and Caselles, 1991;Key and Benson, 2006) The majority of cultural heritage and archaeological sites, especially in the Mediterranean region, are covered with vegetation -increasing the risk of firesand are located near to forest regions or in abandoned areas covered with vegetation (Grammalidis et al, 2011).…”