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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5285

Title: Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa
Authors: Wessels, K
Steenkamp, K
Von Maltitz, G
Archibald, S
Keywords: AVHRR
Biomes
NDVI
Net primary production
Phenology
Regression tree
Vegetation mapping
Issue Date: Feb-2011
Publisher: Wiley-Blackwell
Citation: Wessels, K, Steenkamp, K, Von Maltitz and Archibald, S. 2011. Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa. Applied Vegetation Science, vol 14(1), pp 49–66
Series/Report no.: Workflow request;5377
Abstract: What are the patterns of remotely sensed vegetation phenology, including their inter-annual variability, across South Africa? What are the phenological attributes that contribute most to distinguishing the different biomes? How well can the distribution of the recently redefined biomes be predicted based on remotely sensed, phenology and productivity metrics? Ten-day, 1 km, NDVI AVHRR were analysed for the period 1985 to 2000. Phenological metrics such as start, end and length of the growing season and estimates of productivity, based on small and large integral (SI, LI) of NDVI curve, were extracted and long-term means calculated. A random forest regression tree was run using the metrics as the input variables and the biomes as the dependent variable. A map of the predicted biomes was reproduced and the differentiating importance of each metric assessed. Regression tree analysis based on remotely sensed metrics performed as good as, or better than, previous climate-based predictors of biome distribution. The results confirm that the remotely sensed metrics capture sufficient functional diversity to classify and map biome level vegetation patterns and function.
Description: Copyright: Wiley Blackwell 2011. ABSTRACT ONLY
URI: http://onlinelibrary.wiley.com/doi/10.1111/j.1654-109X.2010.01100.x/abstract
http://hdl.handle.net/10204/5285
ISSN: 1402-2001
Appears in Collections:Earth observation
Ecosystems processes & dynamics
General science, engineering & technology

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