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Remote Sensing-derived hydroperiod as a predictor of floodplain vegetation composition

Wetlands Ecology and Management volume 23, pages 603–616 (2015)Cite this article

Abstract

Characterising hydroperiod and vegetation for flood-pulsed wetlands is a critical first step towards understanding their ecology. In large, data-poor wetlands such as Botswana’s Okavango Delta, quantifying hydrology and ecology presents great logistic and financial challenges, yet relationships between hydrology and floodplain ecology are essential inputs to management. This paper describes an approach to improving ecological understanding by seeking relationships between archival remote sensing data and floodplain vegetation data. We produced a high spatial resolution (30 × 30 m) time series of annual flood frequency from Landsat 5TM imagery for the period 1989–2009. A second, lower spatial resolution (250 × 250 m) series of monthly flood extent was developed from a band 1 (0.62–0.67 μm) threshold of MODIS (MOD09Q1) imagery for the period 2000–2012. Vegetation composition and abundance was sampled in 30 floodplain sites, using a modified Braun-Blanquet approach. Interpreted flood extent from MODIS was 92 % accurate compared to the Landsat interpretation, and 89 % accurate when assessed against field data. Three major classes of floodplain vegetation were identified from ordination and cluster analysis: Occasionally flooded savanna, Seasonally flooded grassland, and Seasonally flooded sedgeland. Relationships identified between hydroperiod and vegetation communities were tested against five validation sites, in four of which indicator species occurrence was predicted with ≥60 % accuracy. The methods used are simple, objective, repeatable and inexpensive. Relating floodplain vegetation to hydrological history provides a means of predicting shifts in species composition and abundance for given changes in hydrology.

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Acknowledgments

The research on which this article is based was funded and supported by many and various agencies: The University of Botswana, University of Florida (Adaptive Management: Water, Wetlands and Watersheds program funded by the National Science Foundation), and the Biokavango project (Global Environment Facility). The University of Botswana also provided funding for the costs of studying and living abroad. Their support is gratefully acknowledged. In addition the support of Wilfred Khaneguba, Moagisi Diare, Florian Bendsen and Aulter Karumendu, for their unflagging enthusiasm, willingness to do transects chest deep in crocodile-infested waters, and very fine goat stews in very remote places. Thanks are also due to the following hunting and photographic safari operators for their cooperation: Harry Charalambous (Johan Calitz Safaris), Horseback Safaris, Elephant Back Safaris, and Rann Hunting Safaris. The MODIS data were obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (https://lpdaac.usgs.gov/data_access).

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Authors and Affiliations

  1. University of Botswana Okavango Research Institute, Private Bag 285, Maun, Botswana

    M. Murray-Hudson, K. Thito, K. Kashe & E. Mosimanyana

  2. Climate Systems Analysis Group, University of Cape Town, Private Bag X3, Rondebosch, 7701, South Africa

    P. Wolski

  3. Ecosurv Environmental Consultants, P.O. Box 20374, Maun, Botswana

    L. Cassidy

  4. Howard T. Odum Center for Wetlands, University of Florida, Gainesville, FL, 32601, USA

    M. T. Brown

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  1. M. Murray-Hudson
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  2. P. Wolski
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  3. L. Cassidy
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  4. M. T. Brown
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  5. K. Thito
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  6. K. Kashe
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  7. E. Mosimanyana
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Correspondence to M. Murray-Hudson.

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Murray-Hudson, M., Wolski, P., Cassidy, L. et al. Remote Sensing-derived hydroperiod as a predictor of floodplain vegetation composition. Wetlands Ecol Manage 23, 603–616 (2015). https://doi.org/10.1007/s11273-014-9340-z

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  • DOI: https://doi.org/10.1007/s11273-014-9340-z

Keywords

  • Wetland vegetation
  • Hydroperiod
  • Spatial flood distribution
  • Remote sensing