A Deep Learning-Based Approach to Predict Large-Scale Dynamics of Normalized Difference Vegetation Index for the Monitoring of Vegetation Activities and Stresses Using Meteorological Data

Ying Sun, Dazhao Lao, Yongjian Ruan, Chen Huang and Qinchuan Xin ()
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Ying Sun: School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Guangzhou 510275, China
Dazhao Lao: Tandon School of Engineering, New York University, Brooklyn, New York, NY 11201, USA
Yongjian Ruan: School of Geographical Science, Guangzhou University, Guangzhou 510006, China
Chen Huang: School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Guangzhou 510275, China
Qinchuan Xin: School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-Sen University, Guangzhou 510275, China

Sustainability, 2023, vol. 15, issue 8, 1-21

Abstract: Vegetation activities and stresses are crucial for vegetation health assessment. Changes in an environment such as drought do not always result in vegetation drought stress as vegetation responses to the climate involve complex processes. Satellite-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI) have been widely used to monitor vegetation activities. As satellites only carry information for understanding past and current vegetation conditions, there is a need to model vegetation dynamics to make future predictions. Although many other factors are related, we attempt to predict the vegetation activities and stresses via simulating NDVI, based on only meteorological data and using a deep learning method (bidirectional long short-term memory model, BiLSTM). The BiLSTM is a sequence processing model that can predict NDVI by establishing the relationship between meteorological variables and vegetation activities. Experimental results show that the predicted NDVI is consistent with the reference data (R 2 = 0.69 ± 0.28). The best accuracy was achieved in the deciduous forest (R 2 = 0.87 ± 0.16). The vegetation condition index (VCI) calculated from the BiLSTM-predicted NDVI also agreed with the satellite-based ones (R 2 = 0.70 ± 0.28). Both the monitored and predicted VCI indicated an upward but insignificant trend of vegetation activity in the past decade and increased vegetation stresses in the early growing season over northern China. Based on meteorological data, the deep learning-based solution shows the potential for not only retrospective analysis, but also future prediction of vegetation activities and stresses under varied climate conditions as compared with remote sensing data.

Keywords: NDVI time series; meteorological data; deep learning; BiLSTM; vegetation activities and stresses; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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