Evaluation of Prediction Performance of Vegetation Biomass Density for Two Different Case Study Areas in Turkey with Hybrid Wavelet and Artificial Neural Network Method

Abstract

Abstract Deterioration of natural resources such as vegetation, due to urbanization and increasing population density is evident in many areas of the world. For land use planning, it is vital to assess the plant density and forecast its future changes in light of vegetation-climate interactions given the current trend of global climate change. The purpose of this article is to show how we can detect the variation of vegetation density and forecast its future values with the Artificial Intelligence (AI) methods. As case studies, we selected 2 districts namely Alanya in Antalya province and Iznik in Bursa province of Turkey, that showed the highest and lowest land cover change between 2006 and 2018 respectively, according to the CORINE land cover classification. In the analysis, we have used satellite data (Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) data from MODIS/Terra satellite) and atmospheric data (archive precipitation and temperature measurements at meteorological stations) to define vegetation changes up to 2030.We have used ANN with original data and with the data obtained by the wavelet transform application (W-ANN). The average EVI value for 2030 was calculated as 0.22 with a 5.4% error probability for Iznik, and 0.28 with a 2% error probability for Alanya. By comparing the predicted values of W-ANN for 2030 with respect those of 2018, vegetation biomass density will decrease by 21.4% in Iznik, and 6.6% in Alanya. The results were also compared with the Landsat Normalized Difference Built-up Index (NDBI).