Multitemporal Assessment of Vegetation Disturbance in the Okavango Delta, Botswana
Amy
L
Neuenschwander, University of Texas at Austin, amy@csr.utexas.edu
(Presenting)
Kelley
A
Crews-Meyer, University of Texas at Austin, kacm@uts.cc.utexas.edu
The Okavango Delta, located in northern Botswana, is RAMSAR-status wetland home to 650 bird species and over 1000 floral species. The Delta provides critical habitat and resources to wildlife (including large mammal populations) and humans. But it faces potentially ecologically damaging consequences due to natural and anthropogenic change. Changes in land use such as extraction of natural resources (water, fish, wood and reeds), increased burning, over-grazing of domestic livestock, and a growing tourism industry increasingly pressure the wetland-savanna ecosystem. The Delta experiences two remotely observable disturbance regimes, flooding and fire. Additionally, oscillations in precipitation cycles of 3, 8, 18, and 80 years have been reported for southern Africa. The impact of those oscillations on flooding (amount and distribution) across this alluvial fan is unclear.
This research utilizes 85 Landsat TM and ETM+ scenes from 1989 through 2002 covering the southeastern distal portion of the Delta. Extracted patterns of flooding and fire were tested against a 2000 Landsat-based vegetation structure classification created by local researchers at the University of Botswana’s Harry Oppenheimer Okavango Research Center. Preliminary results suggest that 1) flooding and fire regimes manifest very different spatial and temporal patterns, 2) the co-occurrence of these disturbances occurs primarily in floodplain grasses, 3) fire regimes differ between management regimes (photography versus wildlife concessions), and 4) climatic trends reported in the literature are moderately correlated with Landsat-derived vegetation indices. These early findings suggest that seasonal, annual, and longer-term anthropogenic and climatic impacts on ecologically critical disturbance regimes can be effectively assessed with seasonally rich optical time-series data.