Agricultural Land Classification Using Vegetation Indices, PCA, and Google Earth Engine: Case Study of Söke/Aydın

Authors : Melis Inalpulat, Neslişah Civelek, Metin Uşakli, Levent Genç

Pages : 96-104

Doi:10.33202/comuagri.1295054

View : 99 | Download : 139

Publication Date : 2023-07-19

Article Type : Research Article

Abstract :Land use and land cover (LULC) classification is known to be one of the most widely used indicators of environmental change and degradation all over the world. There are various algorithms and methods for LULC classification, whereby reliability of the classification maps presents the principal concern. The study focused on evaluation of accuracies of LULC maps produced from original bands of Sentinel-2 imageries together with Normalized Difference Vegetation Index (NDVI), Green NDVI (GNDVI), and Principal Component Analysis (PCA) using Google Earth Engine (GEE) platform to identify best enhancing method for agricultural land classification. Moreover, short-term LULC changes aimed to be identified in the specified area. To achieve the aims, all available imageries acquired in the same month of different years with less than 10% cloud contamination were used to compose averaged images for May 2018 and May 2022 for generating LULC2018 and LULC2022 maps. The area has separated into seven main classes, namely, olive (O), perennial cultivation (P), non-perennial cultivation (NP), forest (F), natural vegetation (N), settled area-bare land (S), and water surface (W) via random forest algorithym. Reliabilities of LULC maps were evaluated through accuracy assessment procedures considering stratified randomized control points. Transitions between each LULC classes were identified.
Keywords : Ana Bileşenler Analizi, Google Earth Enine, Sentinel-2, sınıflama doğruluğu, tarımsal alan, vejetasyon indeksleri.