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Remote Sens. 2016, 8(5), 370; doi:10.3390/rs8050370

Linking Land Surface Phenology and Vegetation-Plot Databases to Model Terrestrial Plant α-Diversity of the Okavango Basin

1
Department of Biodiversity, Ecology and Evolution of Plants, University of Hamburg, Biocentre Klein Flottbek, Ohnhorststr. 18, 22609 Hamburg, Germany
2
Department of Environmental Remote Sensing and Geoinformatics, Faculty of Regional and Environmental Sciences, Trier University, Behringstraße 21, 54296 Trier, Germany
3
School of Natural Resources and Spatial Sciences, Namibia University of Science and Technology, P/Bag 13388 Windhoek, Namibia
*
Author to whom correspondence should be addressed.
Academic Editors: Susan L. Ustin, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 19 November 2015 / Revised: 4 April 2016 / Accepted: 18 April 2016 / Published: 29 April 2016
(This article belongs to the Special Issue Remote Sensing of Biodiversity)
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Abstract

In many parts of Africa, spatially-explicit information on plant α-diversity, i.e., the number of species in a given area, is missing as baseline information for spatial planning. We present an approach on how to combine vegetation-plot databases and remotely-sensed land surface phenology (LSP) metrics to predict plant α-diversity on a regional scale. We gathered data on plant α-diversity, measured as species density, from 999 vegetation plots sized 20 m × 50 m covering all major vegetation units of the Okavango basin in the countries of Angola, Namibia and Botswana. As predictor variables, we used MODIS LSP metrics averaged over 12 years (250-m spatial resolution) and three topographic attributes calculated from the SRTM digital elevation model. Furthermore, we tested whether additional climatic data could improve predictions. We tested three predictor subsets: (1) remote sensing variables; (2) climatic variables; and (3) all variables combined. We used two statistical modeling approaches, random forests and boosted regression trees, to predict vascular plant α-diversity. The resulting maps showed that the Miombo woodlands of the Angolan Central Plateau featured the highest diversity, and the lowest values were predicted for the thornbush savanna in the Okavango Delta area. Models built on the entire dataset exhibited the best performance followed by climate-only models and remote sensing-only models. However, models including climate data showed artifacts. In spite of lower model performance, models based only on LSP metrics produced the most realistic maps. Furthermore, they revealed local differences in plant diversity of the landscape mosaic that were blurred by homogenous belts as predicted by climate-based models. This study pinpoints the high potential of LSP metrics used in conjunction with biodiversity data derived from vegetation-plot databases to produce spatial information on a regional scale that is urgently needed for basic natural resource management applications. View Full-Text
Keywords: Angola; Botswana; dry tropical forests; EVI; Miombo; MODIS; Namibia; phenological metrics; predictive modeling; species density Angola; Botswana; dry tropical forests; EVI; Miombo; MODIS; Namibia; phenological metrics; predictive modeling; species density
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Revermann, R.; Finckh, M.; Stellmes, M.; Strohbach, B.J.; Frantz, D.; Oldeland, J. Linking Land Surface Phenology and Vegetation-Plot Databases to Model Terrestrial Plant α-Diversity of the Okavango Basin. Remote Sens. 2016, 8, 370.

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