Depremin Arazi Kullanım ve Arazi Örtüsü (AKAÖ) Üzerine Etkilerinin Google Earth Engine (GEE), Sentinel-2 Görüntüleri ve Makine Öğrenmesi Kullanılarak Değerlendirilmesi: Antakya Örneği

Abstract

Natural disasters, especially earthquakes, known to be the most devastating process that threating human life, ecosystems, and land properties including land use and land cover (LULC). Understanding of such changes may help for rehabilitation processes, as well as presentation of baseline to develop management strategies for further steps. Remote sensing technologies have long been used for determination of change directions and magnitudes after earthquakes while development in cloud-based platforms provided users to avoid issues in storage and processing costs, effectively. In present study, it was aimed to determine LULC changes occurred around Antakya city of Hatay after February 06, 2023 and February 20, 2023 earthquakes, which caused serious losses. Moreover changes within 5 km zone from central coordinates were also investigated by considering individual subzones with 1 km width. One of the most widely used machine learning algorithm, random forest (RF), was used classify Sentinel-2 imageries via Google Earth Engine (GEE) platform. Accuracy assessment procedures were implemented to determine reliabilities of LULC2022 and LULC2023, and accuracies were found over 0.85. Investigation of overall changes have revealed that areas of forest (F) and cultivated fields (CF) were considerably decreased while concrete (C), natural vegetation (N) and water (W) areas have increased. Dispersal of collapse buildings resulted in increase of C class not only at city level, but also within each subzone of 5 km buffer zone. Classification of Sentinel-2 imageries through RF algorithm in GEE provided rapid and reliable results for determining changes in Antakya, whereby periodically monitoring of further changes strongly suggested.