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Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa
Affiliation:1. College of Forestry, 231 Peavy Hall, Corvallis, OR 97331, United States;2. NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, United States;3. Joint Center for Earth System Technology, University of Maryland Baltimore County, Baltimore MD, United States;4. Boston University Earth & Environment, 685 Commonwealth Avenue, Boston, MA 02215, United States
Abstract:Currently there is a lack of knowledge on spatio-temporal patterns of land surface dynamics at medium spatial scale in southern Africa, even though this information is essential for better understanding of ecosystem response to climatic variability and human-induced land transformations. In this study, we analysed vegetation dynamics across a large area in southern Africa using the 14-years (2000–2013) of medium spatial resolution (250 m) MODIS-EVI time-series data. Specifically, we investigated temporal changes in the time series of key phenometrics including overall greenness, peak and timing of annual greenness over the monitoring period and study region. In order to specifically capture spatial and per pixel vegetation changes over time, we calculated trends in these phenometrics using a robust trend analysis method. The results showed that interannual vegetation dynamics followed precipitation patterns with clearly differentiated seasonality. The earliest peak greenness during 2000–2013 occurred at the end of January in the year 2000 and the latest peak greenness was observed at the mid of March in 2012. Specifically spatial patterns of long-term vegetation trends allowed mapping areas of (i) decrease or increase in overall greenness, (ii) decrease or increase of peak greenness, and (iii) shifts in timing of occurrence of peak greenness over the 14-year monitoring period. The observed vegetation decline in the study area was mainly attributed to human-induced factors. The obtained information is useful to guide selection of field sites for detailed vegetation studies and land rehabilitation interventions and serve as an input for a range of land surface models.
Keywords:Time-series data  Land surface phenology  Trend analysis  Vegetation patterns  Africa
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