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25 October 2016Forest vegetation dynamics and its response to climate changes
Maria A. Zoran,1 Liviu Florin V. Zoran,2 Adrian I. Dida3
1National Institute of Research and Development for Optoelectronics (Romania) 2Univ. Politehnica of Bucharest (Romania) 3Univ. Transylvania of Brasov (Romania)
Forest areas are experiencing rapid land cover change caused by human-induced land degradation and extreme climatic events. 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. Frequent satellite data products, for example, can provide the basis for studying time-series of biophysical parameters related to vegetation dynamics. Vegetation index time series provide a useful way to monitor forest vegetation phenological variations. In this study, we used MODIS Terra/Aqua time-series data, along with yearly and monthly net radiation, air temperature, and precipitation data to examine the feedback mechanisms between climate and forest vegetation. Have been quantitatively described Normalized Difference Vegetation Index(NDVI) /Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Evapotranspiration (ET) and Gross Primary Production (GPP) temporal changes for Cernica- Branesti forest area, a periurban zone of Bucharest city in Romania, from the perspective of vegetation phenology and its relation with climate changes and extreme climate events (summer heat waves). A time series from 2000 to 2016 of the MODIS Terra was analyzed to extract forest biophysical parameters anomalies. Forest vegetation phenology analyses were developed for diverse forest land-covers providing a useful way to analyze and understand the phenology associated to those landcovers. Correlations between NDVI/EVI , LAI, ET and GPP time series and climatic variables have been computed.
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Maria A. Zoran, Liviu Florin V. Zoran, Adrian I. Dida, "Forest vegetation dynamics and its response to climate changes," Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 99981V (25 October 2016); https://doi.org/10.1117/12.2241374