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Abstract：
The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.
摘要：
植被健康指数（VHI）是用于评估植被健康和状况的指标，基于卫星衍生数据。它提供了压力或活力的综合指标，常用于农业，生态学,和环境监测，以预测植被健康变化。尽管有其优势,很少有关于预测VHI作为未来预测的研究，特别是使用最新有效的机器学习方法。因此，本研究的主要目的是利用遥感图像预测VHI值。为了实现这一目标,该研究提出采用组合的卷积神经网络(CNN)和一种称为长短期记忆(LSTM)的特定类型的循环神经网络(RNN)，被称为ConvLSTM。VHI时间序列图像是根据从Terra和Aqua卫星上的中分辨率成像光谱仪（MODIS）获得的归一化植被指数（NDVI）和地表温度（LST）数据计算的。除了传统的基于图像的计算，该研究建议使用NDVI和LST时间序列的全球最小值和全球最大值（全球范围）来计算VHI。研究结果表明，具有1层结构的ConvLSTM通常比2层和3层结构提供更好的预测。1步的平均均方根误差(RMSE)值，2步，和提前3步的VHI预测分别为0.025、0.026和0.026，每个步骤代表一个8天的预测范围。此外，所提出的使用应用的ConvLSTM结构的全局比例模型优于传统的VHI计算方法。
