Using a coupled dynamic factor - random forest analysis (DFRFA) to reveal drivers of spatiotemporal heterogeneity in the semi-arid regions of southern Africa

Understanding the drivers of large-scale vegetation change is critical to managing landscapes and key to predicting how projected climate and land use changes will affect regional vegetation patterns. This study aimed to improve our understanding of the role, magnitude, and spatial distribution of t...

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Veröffentlicht in:	PloS one 2018-12, Vol.13 (12), p.e0208400-e0208400
Hauptverfasser:	Southworth, Jane, Bunting, Erin, Zhu, Likai, Ryan, Sadie J, Herrero, Hannah V, Waylen, Peter, Muñoz-Carpena, Rafael, Campo-Bescós, Miguel A, Kaplan, David
Format:	Artikel
Sprache:	eng
Schlagworte:	Africa, Southern Air temperature Arid regions Arid zones Biodiversity Biology and Life Sciences Biophysics Catchments Classification Climate Climate and land use Climate change Computer and Information Sciences Desert Climate Discriminant analysis Earth Sciences Ecology and Environmental Sciences Ecosystem Ecosystems Environmental changes Environmental Monitoring - methods Evapotranspiration Factor analysis Factor Analysis, Statistical Forests Grasslands Heterogeneity Human influences Humans Humidity Land use Land use management Landscape Mesoscale phenomena Models, Statistical MODIS Normalized difference vegetative index Novels People and Places Physical Sciences Potential evapotranspiration Precipitation (Meteorology) Predictions Protected areas Rain Rainfall Regional development Relative humidity Remote sensing Research and Analysis Methods Savannahs Scale models Seasons Semi arid areas Semiarid lands Semiarid zones Senators Settling Social factors Socioeconomic data Socioeconomic factors Socioeconomics Soil - chemistry Soil moisture Soil settlement Soil temperature Soil types Spatial Analysis Spatial distribution Spatial heterogeneity Temperature Time series Trends Vegetation Vegetation cover Vegetation index Vegetation patterns
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