Landscape Approach to Normalized Difference Vegetation Index Forecast by Artificial Neural Network: Example of Diyala River Basin

2019. Т. 19, No 3. С. 5–19 5 Introduction Modern Earth sciences are not conceivable without the analysis of multispectral satellite data. The Normalized Difference Vegetation Index (NDVI) and other proxies of primary biological productivity are important products of this analysis [1]. The values of NDVI are highly depended on environmental conditions, so that NDVI is one of principal indicators for evaluating climate impact onto terrestrial ecosystems [2–12]. Particularly, extent and evolution NDVI are often used to estimate climate changes global and vegetation activity [2, 3] and net primary production and vegetation dynamics overlarge arid regions such as Sahel [4, 5], arid regions of Central Asia and Kazakhstan [6, 7], Mongolia and arid area of China [8, 9], Tibetan Plateau [10]. In addition to monitoring arid zones in the works [11, 12] explores the long-period changes in forest-steppe, forest and tundra vegetation of the Russian Federation. Mapping of NDVI dynamics is one of the main instruments for evaluation and prediction of agricultural productivity [13–18]. First, as in the case of natural biomes, some work explore the impact of climate change on productivity of rain-fed zones [13] and other use NDVI data to model of ecological regimes of rural territory [14]. The changes in vegetation indices of rural areas allows to separate healthy vegetation crops from weak developed fields in irrigated agriculture [15]; to monitor droughts [16, 17] and, with availability of additional surface data, to implement crop forecast [18]. From this point, the capabilities to predict vegetation index under an appropriate spatiotemporal scale [13–18] are critical for decision making to adapt agricultural techniques or to limit socio-economic losses associated with urbanization [19, 20]. Информатика и вычислительная техника