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

Authors : Melis İNALPULAT, Neslişah CİVELEK, Metin UŞAKLI, Levent GENÇ

Pages : 96-104

Doi:10.33202/comuagri.1295054

View : 137 | Download : 138

Publication Date : 2023-07-19

Article Type : Research Paper

Abstract :Land use and land cover insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(NDVI);, Green NDVI insert ignore into journalissuearticles values(GNDVI);, and Principal Component Analysis insert ignore into journalissuearticles values(PCA); using Google Earth Engine insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(O);, perennial cultivation insert ignore into journalissuearticles values(P);, non-perennial cultivation insert ignore into journalissuearticles values(NP);, forest insert ignore into journalissuearticles values(F);, natural vegetation insert ignore into journalissuearticles values(N);, settled area-bare land insert ignore into journalissuearticles values(S);, and water surface insert ignore into journalissuearticles values(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

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