Mapping Vegetation Morphology Types in Southern Africa Savanna Using MODIS Time-Series Metrics: A Case Study of Central Kalahari, Botswana

Niti B. Mishra, Kelley A. Crews, Jennifer A. Miller and Thoralf Meyer
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Niti B. Mishra: Department of Geography & the Environment, University of Texas, Austin, 305 E 23rd St, CLA 3.306, Austin, TX 78712, USA
Kelley A. Crews: Department of Geography & the Environment, University of Texas, Austin, 305 E 23rd St, CLA 3.306, Austin, TX 78712, USA
Jennifer A. Miller: Department of Geography & the Environment, University of Texas, Austin, 305 E 23rd St, CLA 3.306, Austin, TX 78712, USA
Thoralf Meyer: Department of Geography & the Environment, University of Texas, Austin, 305 E 23rd St, CLA 3.306, Austin, TX 78712, USA

Land, 2015, vol. 4, issue 1, 1-19

Abstract: Savanna ecosystems are geographically extensive and both ecologically and economically important; they therefore require monitoring over large spatial extents. There are, in particular, large areas within southern Africa savanna ecosystems that lack consistent geospatial data on vegetation morphological properties, which is a prerequisite for biodiversity conservation and sustainable management of ecological resources. Given the challenges involved in distinguishing and mapping savanna vegetation assemblages using remote sensing, the objective of this study was to develop a vegetation morphology map for the largest protected area in Africa, the central Kalahari. Six vegetation morphology classes were developed and sample training/validation pixels were selected for each class by analyzing extensive in situ data on vegetation structural and functional properties, in combination with existing ancillary data and coarse scale land cover products. The classification feature set consisted of annual and intra annual matrices derived from 14 years of satellite-derived vegetation indices images, and final classification was achieved using an ensemble tree based classifier. All vegetation morphology classes were mapped with high accuracy and the overall classification accuracy was 91.9%. Besides filling the geospatial data gap for the central Kalahari area, this vegetation morphology map is expected to serve as a critical input to ecological studies focusing on habitat use by wildlife and the efficacy of game fencing, as well as contributing to sustainable ecosystem management in the central Kalahari.

Keywords: semi-arid savanna; vegetation morphology; NDVI; MODIS time-series; random forest; spatial heterogeneity; CKGR; Kalahari (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2015
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