Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia

Abstract

This study examines whether MODIS NDVI satellite data time series can be used to detect land degradation and regeneration areas in Mongolia. Time series analysis was applied to an 11-year MODIS NDVI satellite data record, based on the hypothesis that the resulting NDVI residual trend vectors would enable successful detection of changes in photosynthetically active vegetation. We performed regression analysis, derived regression slope values, and generated a map of significant trends. We also examined land cover development and meteorological data for the same period.11-year time series of MODIS 16-day composite NDVI data proved sufficient for deriving statistically significant trend values for 50% of Mongolia's surface. MODIS land cover products proved suitable for identifying areas of vegetation cover change. Areas showing positive and negative NDVI trends mostly coincided with areas of land cover class change indicating an increase or a decrease in vegetation, respectively. Precipitation changes in the same time period seem to have had an influence on large NDVI trend areas. The NDVI time series trend analysis methodology applied successfully detected changes due to deforestation, forest fires, mining activities, urban expansion, and grassland regeneration. These findings demonstrate that NDVI time series trend analysis is suitable for detecting vegetation change areas and for identifying land degradation and regeneration.