ABSTRACT

Different studies on predicting future green cover changes exist with various success levels. Each one focuses on a different story about which model is more appropriate. Therefore, finding a suitable model remains a difficult task due to the remotely sensed data issues and the complexity of the vegetation cover change process. Despite the unicity of vegetation indices time series, the forecasting assessment relies basically on the commonly used forecast error measurements (e.g. Mean Square Error (MSE), Root MSE (RMSE), and the symmetric Mean Absolute Percentage Error (sMAPE), etc.) which may not reflect the real potential of the fitted forecasting model. Herein, the experimentation of forecasting vegetation indices employing two univariate time series models and new accuracy metrics is discussed. Box Jenkins models (Seasonal AutoRegressive Integrated Moving Average (ARIMA) model) and neural network model (nonlinear autoregressive (NAR) model) are applied individually and coupled (NAR-NAR and NAR-ARIMA) based on a multi-resolution analysis-wavelet transform. These models’ forecasting ability is evaluated using 16-days Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index (NDVI) time series data of two different vegetation cover types of northwestern area in Tunisia. The major finding highlights firstly the importance of integration of the decomposition step to show off the hidden components of vegetation indices. The commonly used accuracy measures show that the coupled neural networks model outperforms other models. Then, interesting conclusions were drawn when phenological metrics are used as performance measures. Herein, Box Jenkins forecasting model generates a better NDVI curve shape than hybrid models despite their low RMSE measures. This is mainly due to good estimation of some phenological events, namely, the amplitude, the peak and the season’s length. Generally, Box Jenkin model excels at handling quick variations. By contrast, combined models show better phenological metrics’ estimation when the observations are complex or describe long time periods. Based on these findings, we suggest that the choice of the evaluation metric must be related to the future forecaster interest.

摘要

对于成功预测未来绿色覆盖物变化，存在不同的研究。每个人专注于一个不同的故事，说明哪种模型更合适。因此，由于遥感数据问题和植被覆盖变化过程的复杂性，找到合适的模型仍然是一项艰巨的任务。尽管植被指数时间序列的统一性，但预测评估基本上依赖于常用的预测误差测量值（例如均方误差（MSE），根MSE（RMSE）和对称平均绝对百分比误差（sMAPE）等）。这可能无法反映拟合预测模型的实际潜力。在此，讨论了使用两个单变量时间序列模型和新的精度指标来预测植被指数的实验。基于多分辨率分析-小波变换，分别应用了Box Jenkins模型（季节性自回归综合移动平均值（ARIMA）模型）和神经网络模型（非线性自回归（NAR）模型）并进行了耦合（NAR-NAR和NAR-ARIMA）。 。使用突尼斯西北部两种不同植被覆盖类型的16天中等分辨率成像光谱仪归一化差异植被指数（NDVI）时间序列数据评估了这些模型的预测能力。主要发现首先突出了整合分解步骤以显示植被指数隐藏成分的重要性。常用的精度度量表明，耦合神经网络模型优于其他模型。然后，当将物候指标用作绩效指标时，得出了有趣的结论。此处，尽管Box Jenkins预测模型的RMSE值较低，但其生成的NDVI曲线形状要比混合模型好。这主要是由于对某些物候事件的正确估计，即幅度，峰值和季节长度。通常，Box Jenkin模型擅长处理快速变化。相比之下，当观察结果很复杂或描述了较长的时间段时，组合模型显示出更好的物候指标度量。基于这些发现，我们建议评估指标的选择必须与未来的预测者兴趣相关。这主要是由于对某些物候事件的正确估计，即幅度，峰值和季节长度。通常，Box Jenkin模型擅长处理快速变化。相比之下，当观察结果很复杂或描述了较长的时间段时，组合模型显示出更好的物候指标度量。基于这些发现，我们建议评估指标的选择必须与未来的预测者兴趣相关。这主要是由于对某些物候事件的正确估计，即幅度，峰值和季节长度。通常，Box Jenkin模型擅长处理快速变化。相比之下，当观察结果很复杂或描述了较长的时间段时，组合模型显示出更好的物候指标度量。基于这些发现，我们建议评估指标的选择必须与未来的预测者兴趣相关。