Modeling NPP and NDVI time series in different bioclimatic regions of Iran

Vegetation is one of the important components of ecosystems that usually changes seasonally. An accurate parameterization of vegetation cover dynamics by developing time series models can strengthen our understanding of vegetation change. This research is aims to investigate and model the temporal changes of Net Primary Production (NPP) and Normalized Difference Vegetation Index (NDVI) across bioclimatic regions of Iran, including the Khazari, Baluchi, semi-desert, steppe, semi-steppe and Arid forests. We used Moderate Resolution Imaging Spectroradiometer (MODIS) sensor products for NPP and NDVI time series (MOD17A2 and MOD13Q1, respectively). The SARIMA (Seasonal Autoregressive Integrated Moving Average) time series model is developed for NPP and NDVI time series. The investigation of Auto Correlation Functions (ACF) showed a strong seasonality in NPP and NDVI at the 12-month lag time. Comparing the lag times from 1 to 24 month for different regions shows that the NPP variable has a stronger seasonality. The evaluation of error criteria showed NPP time series models based on RMSE, R ² , MRE, and CE criteria was better, while based on the ME criteria, the models performs better for NDVI time series (For example, in Khazari region for NPP and NDVI time series respectively, ME = 3.67, 0.05, RMSE = 0.12, 0.18, R2 = 0.87, 0.63, MRE = 0.02, 0.12, and CE = 0.84, 0.12). The selected models provided a short-term forecasting of the NPP and NDVI index for study regions at 24-month time, that useful for the planning and management to reduce vegetation degradation and preserve ecosystem and biodiversity.