1. AbstractThe 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.

4. Remotely Sensed DataAn extensive set of field data acquired for the Okavango Delta are available for this research through the University of Texas Center for Space Research, collaboration with the University of Botswana Harry Oppenheimer Okavango Research Center (HOORC -located in Maun, Botswana), and other international partners. This project is utilizing 85 Landsat TM and ETM+ images beginning in April 1989 and extending through October 2002 with an average time step of every 2-3 months.

Research Question: Can vegetation structure be mapped using multitemporal, multispectral remote sensing data? Vegetation structure is defined as the horizontal and vertical distribution and organization of the vegetation and for the purposes of this research it is equivalent to the ecological definition of vegetation formation. Vegetation formations (e.g. open woodland or grass/shrub savanna) are defined as the hierarchical level below vegetation types (e.g. grassland or woodland). A common approach to map land cover change from medium resolution multispectral data (such as Landsat) is the “from-to” change analysis where relative changes of land cover between two scenes are extracted. Rather than mapping static land cover change between two distinct times, this research will utilize the total temporal signal from the available Landsat dataset and map temporal classes, or classes that spectrally behave similarly throughout the time series. The use of remotely sensed time series data will capture the subtle stages of vegetation structure on the landscape that is driven by flooding and fire as well as identification of vegetation formations due differences in phenology. By characterizing the successional changes in vegetation formation, it becomes possible to track land cover change trajectories due to disturbance and gain insight into these landscape processes. For example, a change of floodplain vegetation to upland shrub-dominated vegetation can be an indicator of an important shift in the functioning of the system and have subsequent land use impacts (Ringrose et al., 2005). These drying shifts can impact the carbon source/sink pools (Hudak and Wessman, 1998; Asner et al., 2004) as well as affect the hydrology and soil biogeochemistry (Asner et al., 2004).

5. Research Methodology1. Flooding and fire signatures were identified and extracted for each scene to create a disturbance map using an unsupervised ISODATA classifier (Heinl et al., 2006). Disturbance information from observed fire and flooding signatures from multispectral, remotely sensed data have been combined to create a spatially explicit map of disturbance histories and return intervals. These return intervals were tested against vegetation type and structure information derived from a 2000 Landsat ETM+ based landcover map produced by HOORC.

2. NDVI was computed for each image in the time-series which were subsequently used as input into an ISODATA classifier. The result was 35 clusters that have a similar temporal trajectory through the 14-year time period. The mean NDVI value, percent of flooding, and percent of burning for each of the 35 clusters throughout the time-series were examined for periodicities of the temporal trajectory. McCarthy et al. (2000) report a weak 3-year and a slightly higher 8-, 18- and 80-year oscillation in precipitation data.

14-year Fire History

Number of Years Burned

14-year Flood History

Number of Years Flooded

7. Wavelet AnalysisWavelet analysis has been widely used in many fields including geophysics and climate studies for characterizing the temporal and frequency variability in time series data. Wavelets work by decomposing the input signal into a set of dominant frequencies and then determining when the variability fluctuations occur in time (Torrence and Compo, 1998; Kestin et al., 1998). Wavelet analysis was performed on NDVI, NDVI residual (annual and semi-annual signal removed), flooding, and burning time-series data for identified temporal clusters.

Cluster 32 is found in the upper channels of the Boro and Santantatibe Rivers and is likely comprised of sedges and reeds. The NDVI signal indicates a strong semi-annual and annual signal beginning in 1993. The flooding power spectrum time-series plots shows that at least 30% of all pixels are continuously flooded from 1989 – 1993, however the continual flooding serves to dampen the signal in the wavelet power spectrum. This is due to the fact that the wavelets are looking for variability in signal rather than a continuous response. In 1994, the continuous flooding is replaced with a strong annual flood pulse which is reflected in the wavelet power spectrum for flooding. Fire is less of a factor for this cluster with 20% of the pixels burning in 1994 and again in 2002. The multitemporal analysis for this cluster indicate a possible shift from a continuously inundated region to one that is seasonally inundated via an annual pulse. The implications of this potential shift on the vegetation is unclear at this point, however, wavelet analysis provides a methodology for identifying when shifts occur. There are weak 3-year signals observed in some of the temporal trajectories, however the 14-year extent of the time series is insufficient to test longer term cycles.

Herbaceous Savanna Woodland

6. Disturbance Trend ResultsThe 14-year history of flooding and fires were extracted from the Landsat and ALI imagery dataset. Each individual disturbance map was combined to create an annual flooding or annual fire map. These annual fires were then combined to create a 14-year flooding and fire history map as well as a flooding and fire return time map. The areas with the greatest amount of burning in the lower Delta occur in hunting concessions where burning vegetation is common to attract wildlife to graze on new grasses. The disturbance return trends were tested against vegetation type and structure information derived from a Landsat-based landcover map produced by the Harry Oppenheimer Okavango Research Center (HOORC).

Disturbance Return Clusters

Shades of blue indicate flood only regime

Shades of orange/red indicate fire only regime

Shades of green indicate both flooding and fire

Multitemporal data provides a way to examine the dynamism on the landscape beyond a static landcover classification. The disturbance trends for savanna and woodland are largely fire-only, whereas herbaceous vegetation is influenced by both flooding and fire. Isolating the disturbance trends at a lower hierarchical level reveals that both flooding and fire patterns coexist in tall dense grasslands, whereas many of the other herbaceous classes have a fire-only regime. Vegetation structure yields different disturbance trends compared with vegetation type. A-spatial analysis of disturbance patterns provide one piece of information regarding how this ecosystem functions, however the spatial configuration of classes on the landscape is important.

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Fire Return Intervals (yrs)

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Woodlands

2. The Role of DisturbanceThe Okavango Delta system, located in northern Botswana is experiencing both human-induced change and natural changes at a variety of spatio-temporal scales. An uncertainty of ecosystem sustainability for the Okavango exists and drives much of the research focused on the region. The temporal variability of the localized precipitation from year to year is reported to follow 3-, 8-, and 18-year precipitation cycles common to southern Africa (McCarthy et al., 2000). A thorough understanding of ecosystems requires knowledge of the disturbance history, as disturbances impact all levels of biologic organization in a system. Environmental remote sensing is well suited to provide information on land cover characteristics and the associated dynamic changes at the landscape level in response to disturbance. The Okavango Delta is an ecologically compelling area in terms of its inter- and intra-annual variability, disturbance regimes, and responsiveness to changing climatic conditions. While the Okavango Delta is especially important to the communities who rely upon its resources and the wildlife that utilize it for habitat, understanding the complex interaction between natural and anthropogenic changes in this area will also contribute to the general understanding of vegetation response to disturbance regimes (in this case, fire and flooding).

Surface fire burns grasses and although the leaves on trees are burned, trees are otherwise unharmed and will fully resprout next year.

Acacia trees that had encroached a former floodplain will likely die due to the 2006 flood.

8. Next StepsMultitemporal analysis of remotely sensed data using wavelets appears to be a promising methodology for detecting the dominant trends and periodicities in ecosystem cycles. This research will continue by examining patterns in the wavelet power spectrum for all detected temporal clusters and linking to landcover trajectories observed in the field. Once behavior of ecosystem response is identified and known, it is possible to model future scenarios of stressors to the Okavango including climate change and increased anthropogenic pressures.

References1. Asner, G.P., Elmore, A.J., Olander, L.P., Martin, R.E., and

Harris, A.T., 2004. Grazing systems, ecosystem responses, and global change. Annual Review of Environmental Resources, 29:261-299.

2. Heinl, M., Neuenschwander, A., Silva, J., and Vanderpost, C., 2006. Interactions between fire and flooding in a southern African floodplain system. Landscape Ecology; 21(5): 699-709.

3. Hudak, A.T. and Wessman, C.A., 1998. Textural analysis of historical aerial photography to characterize woody encroachment in south African savanna. Remote Sensing of Environment; 66(3): 317-330.

4. McCarthy, T.S., G.R.J. Cooper, P.D. Tyson, and W.N. Ellery, 2000. Seasonal flooding in the Okavango Delta, Botswana – recent history and future prospects. South African Journal of Science, 96: 25 – 33.

5. Ringrose, S., Jellema, A.,Huntsman-Mapila, P., Baker, L., Brubaker, K., 2005. Use of remotely sensed data in the analysis of soil-vegetation changes along a drying gradient peripheral to the Okavango Delta, Botswana. Int. J. Remote Sensing; 26(19): 4293-4319.

6. Torrence, C. and Compo, G.P., 1998. A practical guide to wavelets analysis. Bull. Of Amer. Meteorological Society; 79; 61-78.

AcknowledgementsThis work was supported by the National Aeronautics and Space Administration under the Earth System Science Fellowship Program; data provided by the EO-1 program and SAFARI 2000 program and Technische Universitat Munchen. Field travel was made possible by the National Science Foundation under the Doctoral Dissertation Research Initiative. Many thanks to Susan Ringrose, Wilma Matheson, Thoralf Meyer, and Lawrence Drotsky for their field assistance this summer.

3. Land Use in the Okavango DeltaThe majority of the lands within the Okavango Delta are protected for conservation under the designation of the Moremi Game Reserve (MGR) or Wildlife Management Areas (WMA). The lands within the MGR exist under the highest levels of protection and permits are required for entry into the game reserve. In an effort to avoid disease, a 250 km long and 1.5 m high veterinary fence dubbed the “Buffalo Fence” was erected in 1982 to separate the wildlife management areas from the communal lands of the villages. This fence separates wildlife populations from the domestic livestock populations and today the WMA’s are almost entirely fenced. The wildlife management areas have restrictions on the extraction of natural resources and are designated as either commercial or community hunting or photography concessions. Tourism is a major economic factor in Botswana.

Cheetah out for afternoon hunt at Moremi Game Reserve

Tourists experiencing the beauty of the Okavango on a Mokoro