Forecast of NDVI in coniferous areas using temporal ARIMA analysis and climatic data at a regional scale

Important issues such as the prediction of drought, fire risk and forest disease are based on analysis of forest vegetation response. A method of forecasting the short-term response of forest vegetation on the basis of an autoregressive integrated moving average (ARIMA) analysis was designed in this study. We used 10-day maximum value composite (MVC) bands of the Normalized Difference Vegetation Index (NDVI) obtained from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data from 1993 to 1997. Using the theory of stochastic processes (Box-Jenkins), the MVC-NDVI series was analysed and a seasonal ARIMA (SARIMA) model was developed for forecasting NDVI in the following 10-day periods. The SARIMA model identified a moving-average regular term with a 10-day lag and an autoregressive 37 10-day period seasonal term with a one-season (1-year) component. The study also demonstrated a slight relationship between the NDVI and the precipitation level in some species of conifers by using climatic time series and the analysis of dynamic models and allowed us to elaborate an image of the immediate future NDVI for the study area (Castile and Leon, Spain).