文章基本信息

标题：SPATIO-TEMPORAL SIMULATION AND ANALYSIS OF REGIONAL ECOLOGICAL SECURITY BASED ON LSTM
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作者：C. Gong ; L. Qi ; L. Heming 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2017
卷号：IV-4/W2
页码：153-160
出版社：Copernicus Publications
摘要：Region is a complicated system, where human, nature and society interact and influence. Quantitative modeling and simulation of ecology in the region are the key to realize the strategy of regional sustainable development. Traditional machine learning methods have made some achievements in the modeling of regional ecosystems, but it is difficult to determine the learning characteristics and to realize spatio-temporal simulation. Deep learning does not need prior identification of training characteristics, have excellent feature learning ability, can improve the accuracy of model prediction, so the use of deep learning model has a significant advantage. Therefore, we use net primary productivity (NPP), atmospheric optical depth (AOD), moderate-resolution imaging spectrometer (MODIS), Normalized Difference Vegetation Index (NDVI), landcover and population data, and use LSTM to do spatio-temporal simulation. We conduct spatial analysis and driving force analysis. The conclusions are as follows: the ecological deficit of northwestern Henan and urban communities such as Zhengzhou is higher. The reason of former lies in the weak land productivity of the Loess Plateau, the irrational crop cultivation mode. The latter lies in the high consumption of resources in the large urban agglomeration; The positive trend of Henan ecological development from 2013 is mainly due to the effective environmental protection policy in the 12th five-year plan; The main driver of the sustained ecological deficit growth of Henan in 2004-2013 is high-speed urbanization, increasing population and goods consumption. This article provides relevant basic scientific support and reference for the regional ecological scientific management and construction.
关键词：Deep learning; Machine learning; LSTM; Ecological deficit; Spatio-temporal simulation