Prediction of NDVI in the Yarlung Zangbo River Using Artificial Neural Networks

China Rural Water and Hydropower ›› 2021 ›› (1) : 84-89.

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The Yarlung Zangbo River Basin is not only a treasure of ecological resources, but also a sensitive area for global climate change. Based on the MODIS data from 2000 to 2015 and meteorological data of 30 ground stations, the spatial-temporal variation characteristics of the NDVI (the normalized difference vegetation index) in the Yarlung Zangbo River Basin are analyzed. The partial climatic analysis and principal component analysis are adopted to identify the dominant climatic factors that affect the NDVI change in each subzone. On this basis, the NDVI prediction models based on artificial neural networks are proposed and applied to Yarlung Zangbo River Basin. The results show: ①The NDVI in the Yarlung Zangbo River Basin is gradually increasing from the upstream to the downstream. ②The results of principal component analysis (PCA) and partial correlation analysis (PAR) show that rainfall and temperature in the first three months are the main factors affecting vegetation. ③The ANN-PCA, ANN-PAR and ANN models are proposed and applied in the Yarlung Zangpo River Basin. The average Nash coefficients are 0.75, 0.71 and 0.63 in calibration period respectively, and 0.73, 0.69 and 0.62 in the verification period respectively. The results show that climatic factor identification can improve the model accuracy significantly. The proposed models achieve satisfied accuracy and can be applied to predict the spatial and temporal trend of NDVI in the Yarlung Zangbo River Basin.

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artificial neural network / Yarlung Zangbo River / NDVI / principal component analysis / partial correlation analysis

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. Prediction of NDVI in the Yarlung Zangbo River Using Artificial Neural Networks. China Rural Water and Hydropower. 2021, 0(1): 84-89

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Received	Revised	Published
2020-01-09	2020-05-27	2021-01-15
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2021-02-21

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