Landscape Approach to Normalized Difference Vegetation Index Forecast by Artificial Neural Network: Example of Diyala River Basin
DOI:
https://doi.org/10.14529/ctcr190301Keywords:
remote sensing, NDVI forecast, perceptron, bioclimatic landscapes, precipitation, temperature, climatic responseAbstract
This study examines the perspective of artificial neural networks for forecast Normalized Differential Vegetation Index (NDVI) on Diyala River basin and also how information about of bioclimatic landscapes will affect to forecasting performance. To do this, in the first stage of the experiment, a total of 20 perceptrons with different one hidden layer architectures were trained with sitespecific variables (latitude, longitude, minimal, maximal and mean height, landcover type) and seasonal meteorological variables (precipitation sum, and minimal, maximal and average daily temperatures) by error back propagation algorithm on the data of 2000–2010 years and tested on data for 2011–2016 years. It has been shown that the best performance, with determination coefficient R2 of 0.78, was achieved by perceptron model with 12 hidden neurons the activated by logistic activation function and hyperbolic tangential activation of output value of NDVI. The large spatial heterogeneity of forecasting performance of the best perceptron was detected: in upper part of basin characterized according to Köppen – Trewartha bioclimatic classification, as landscapes of temperate mountain climate and the subtropical climate with dry summers, R2 was 0.76–0.80, whereas in dry steppe landscapes and semi-desert landscapes of Diyala downstream R2 was 0.6–0.7. The second stage of experiments with 20 models of perceptrons where the type of landscape was added as input variable or where 150 individual perceptrons were selected for each landscape, have shown that these approaches allows to R2 increase up to 0.73–0.85. However, the strong contrast between characteristics of individual models complicates their use in the practice and requires to finding of new forecasting approaches.
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