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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%.

<p><strong>Abstract.</strong> Recently, global climate change is one of the biggest challenges in the world. Dense downfall and following catastrophic floods are one of the most destructive natural hazards among all. Consequences do not only risk human life but also cause economical damage. It is critical rapid mapping of flooding for decision making and emergency services in river management. In this study, we apply a multi-temporal change detection analysis to investigate the flooded areas occurred in Edirne province of Turkey. The study area is located at the lower course of Meric River (Evros in Greece or Maritsa in Bulgarian) which is the border between Turkey and Greece. The river basin is dominated by cropland which suffers from strong catastrophic precipitation. This situation cause overflow of capacity of the dams located along the river and serious flooding occur. Due to its dynamic structure the region exposed to heavy flooding in the past. One of the biggest inundations was occurred at 2nd February 2015 which resulted severe devastation in both urban and rural areas. For the analyses of the temporal and spatial dynamics of the disaster we use Sentinel-1 Synthetic Aperture Radar (SAR) data due to its systematic frequent acquisition. A dataset of pre-event and post-event Sentinel-1 images within the January and February of 2015 period was acquired. Flooded areas were extracted with threshold, random forest and deep learning approaches.</p>

Building extraction from high resolution (HR) satellite imagery is one of the most significant issue for remote sensing community. Manual extraction process is onerous and time consuming that's why the improvement of the best automation is a crucial topic for the researchers. In this study, we aimed to expose the significant contribution of normalized digital surface model (nDSM) to the automatic building extraction from mono HR satellite imagery performing two-step application in an appropriate study area which includes various terrain formations. In first step, the buildings were manually and object-based automatically extracted from ortho-rectified pan-sharpened IKONOS and Quickbird HR imagery that have 1 m and 0.6 m ground sampling distances (GSD), respectively. Next, the nDSM was created using available aerial photos to represent the height of individual non-terrain objects and used as an additional channel for segmentation. All of the results were compared with the reference data, produced from aerial photos that have 5 cm GSD. With the contribution of nDSM, the number of extracted buildings was increased and more importantly, the number of falsely extracted buildings occurred by automatic extraction errors was sharply decreased, both are the main components of precision, completeness and overall quality.