Satellite remote sensing detection of forest vegetation land cover changes and their potential drivers

Abstract

Climate changes and rapid urbanization are the main factors affecting forest vegetation land cover around the globe. Satellite remote sensing data provide important information to detect changes in forest landscapes over long time periods in contrast to conventional approaches. Satellite remote sensing provides a useful tool to capture the temporal dynamics of forest vegetation change in response to climate shifts, at spatial resolutions fine enough to capture the spatial heterogeneity. In this paper, we present an integrated, standardized approach that aims at combining remote sensing data provided by different sensors, available for a long-term period (2000-2018). This multi-sensor and multi-temporal approach detects Cernica-Branesti periurban forest vegetation dynamics based on derived biophysical parameters within the highly dynamic city of Bucharest, as a test case. Landsat TM/ETM/OLI, MODIS Terra/Aqua, and Sentinel 2 data are combined in an integrated procedure to locate forest disturbances in relation with potential climate and anthropogenic drivers. To apply the approach for detecting forest land cover changes, the MODIS Normalized Difference Vegetation Index/Enhanced Vegetation Index (NDVI/EVI), and Leaf Area Index (LAI) data are used to provide forest vegetation change detection information in relation with land surface temperature (LST) and climate stressors and to monitor forest vegetation phenological variations. Correlations between NDVI/EVI time series and climatic variables were computed. Forest vegetation dynamics at seasonal and longer timescales reflect large-scale interactions between the terrestrial biosphere and the climate system.