Evaluation of Stochastic and Artificial Neural Network Models for Multi-step Lead Forecasting of NDVI

Abstract

Vegetation degradation is associated with human activities and climate change leading to ecosystem changes and biodiversity losses. To reduce the impacts of vegetation degradation, forecasting of vegetation condition is vital in formulating measures to prevent and reduce the losses. Vegetation indices (VI) obtained from remote sensing data, such as the normalized difference vegetation index (NDVI) are widely used to monitor and forecast vegetation condition. In the present study, a stochastic and artificial neural network (ANN) models were compared in modeling and multi-step lead forecasting of NDVI in the Middle Tana River Basin (MTRB), Kenya. Pixel-wise NDVI data for the period 2000 - 2019 was extracted from the MOD13Q1 product of the Moderate Resolution Imaging Spectroradiometer (MODIS). Time lags of NDVI was used as inputs for the models. The results showed that the ANN model outperforms the stochastic model, with a predicting accuracy of RMSE of 0.07207, MSE of 0.00589 and MAE of 0.06417. The multi-step lead forecasting produced satisfactory results indicating the suitability of the models as tools in forecasting NDVI.