Prediction of NDVI using the Holt-Winters model in high and low vegetation regions: A case study of East Africa
Mwana Said Omar, Hajime Kawamukai
Abstract
The Normalized Difference Vegetation Index (NDVI) indicates vegetation condition and is a vital index in monitoring and forecasting vegetation. Forecasting vegetation aids in decision making and land resource management. The objective of this study was to build a pixel-wise NDVI time series modelled using the Holt-Winters model. Moderate resolution NDVI data at 16 - days temporal resolution and 250 m spatial resolution was obtained from MOD13Q1 of the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Vegetation Indices from 2000 to 2019. The model was tested and its performance evaluated in a low and high vegetation regions in East Africa. The results showed that the model performed better in the high vegetation region than in the low vegetation region for 600 ✕ 600 pixels, achieving a mean absolute error (MAE) of 0.0679 and a root mean squared error (RMSE) of 0.084. 6 months mean NDVI forecast spatial maps were also generated to identify regions of potential vegetation deficit. The maps showed that the low vegetation region is predicted to experience moderate to light vegetation deficit while the high vegetation region is predicted to experience moderate to extremely high vegetation condition. Forecast error measures was computed to validate the forecast results. Our results show the suitability of using the Holt-Winters model in monitoring and forecasting NDVI using univariate data and may be used as a foundation for future studies interested in pixel-wise prediction of NDVI. The forecast maps may be useful to guide planners and decision makers to formulate mitigation plans and reduce the impacts of vegetation degradation at the local and regional level.