Dinamik Bitki Örtüsü Tahmini Yapay Sinir Ağı Uygulaması: Düzce İli Örneği Üzerinde Çalışma
Worldwide, vegetation cover functioning as the secure region for wild life, and natural water, air filter from pollution. Forecasting the vegetation dynamics assist the governments and managements to decrease the negative influence of vegetation dynamic fluctuations. In recent years, forecasting of precise vegetation dynamics become and highly important issue, due to rapid vegetation changings and the needs to protect this natural resource. The aim of this article is to forecasting the vegetation dynamics by applying neural networks (NN). Düzce region utilized as case study, which situated in the north west region of Turkey. Normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) were employed to create vegetation time series. From United States Geological Survey website, 300 NDVI interval data acquired and processed in ArcGIS software. The dataset of vegetation time series built based on required neural networks data structure. Spatiotemporal pixel based sampling strategy performed to forecast the vegetation dynamics. A number of geospatial data handling steps employed using Python and Matlab programing languages. Forecasting data separated to two subdivisions (training set, and testing set). Mean squared error (MSE) utilized as performance accuracy assessment metric. Neural networks effectively implemented using spatiotemporal data and achieve high testing accuracy. Consequences reveals the fitness of neural networks to forecast vegetation dynamics maps.