Wetlands Ecology and Management

, Volume 23, Issue 4, pp 603–616 | Cite as

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

  • M. Murray-HudsonEmail author
  • P. Wolski
  • L. Cassidy
  • M. T. Brown
  • K. Thito
  • K. Kashe
  • E. Mosimanyana
Original Paper


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.


Wetland vegetation Hydroperiod Spatial flood distribution Remote sensing 



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 (


  1. Adam E, Utanga O, Rugege D (2010) Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetlands Ecol Manage 18(3):281–296CrossRefGoogle Scholar
  2. Andersson L, Gumbricht T, Hughes D, Kniveton D, Ringrose S, Savenije H, Todd M, Wilk J, Wolski P (2003) Water flow dynamics in the Okavango River Basin and Delta–a prerequisite for the ecosystems of the Delta. Phys Chem Earth, Parts A/B/C 28(20–27):1165–1172CrossRefGoogle Scholar
  3. Andersson L, Wilk J, Todd MC, Hughes DA, Earle A, Kniveton D, Layberry R, Savenije HHG (2006) Impact of climate change and development scenarios on flow patterns in the Okavango River. J Hydrol: Water Resources in Regional Development: The Okavango River 331(1–2):43–57Google Scholar
  4. Ashton P, Neal M (2003) An overview of key strategic issues in the Okavango basin. In: Turton A, Ashton P, Cloete E (eds) Transboundary rivers, sovereignty and development: hydropolitical drivers in the Okavango River basin. African Water Issues Research Unit & Green Cross International, Pretoria, pp 31–63Google Scholar
  5. Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157(2–3):101–118CrossRefGoogle Scholar
  6. Benger SN (2007) Remote sensing of ecological responses to changes in the hydrological cycles of the Tonle Sap, Cambodia. In: Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International. pp 5028–5031Google Scholar
  7. Cassidy L (2007) Mapping the annual area burned in the wetlands of the Okavango panhandle using a hierarchical classification approach. Wetlands Ecol Manage 15(4):253–268CrossRefGoogle Scholar
  8. Dinçer T, Child S, Khupe B (1987) A simple mathematical model of a complex hydrologic system–Okavango Swamp, Botswana. J Hydrol 93:41–65CrossRefGoogle Scholar
  9. Dufrene M, Legendre P (1997) Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr 61:53–73Google Scholar
  10. Gao B-C (1996) NDWI–A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water From Space. Remote Sens Environ 58:257–266CrossRefGoogle Scholar
  11. Gibbes C, Adhikari S, Rostant L, Southworth J, Qiu Y (2010) Application of objected based classification and high resolution satellite imagery for savanna ecosystem analysis. Remote Sensing 2:2748–2772CrossRefGoogle Scholar
  12. Gumbricht T, McCarthy TS, Bauer P (2004a) The micro-topography of the wetlands of the Okavango Delta, Botswana. Earth Surf Process Landf 30(1):27–39CrossRefGoogle Scholar
  13. Gumbricht T, Wolski P, Frost P, McCarthy TS (2004b) Forecasting the spatial extent of the annual flood in the Okavango delta, Botswana. J Hydrol 290(3–4):178–191CrossRefGoogle Scholar
  14. Henry CP, Amoros C, Bornette G (1996) Species traits and recolonization processes after flood disturbances in riverine macrophytes. Plant Ecol 122(1):13–27CrossRefGoogle Scholar
  15. Jensen JR (2005) Introductory Digital Image Processing–A Remote Sensing Perspective. Prentice Hall Series in Geographic Information Science, 3 edn. Pearson Prentice Hall, Upper Saddle River, NJGoogle Scholar
  16. Johnson L, Gage S (1997) Landscape approaches to the analysis of aquatic ecosystems. Freshw Biol 37(1):113–132CrossRefGoogle Scholar
  17. Junk WJ, Piedade MTF (1993) Herbaceous plants of the Amazon floodplain near Manaus: Species diversity and adaptations to the flood pulse. Amazoniana XII (3/4):467–484Google Scholar
  18. Kim J, Hogue TS (2012) Evaluation and sensitivity testing of a coupled Landsat-MODIS downscaling method for land surface temperature and vegetation indices in semi-arid regions. Journal of Applied Remote Sensing 6(1) art: 063569. doi: 10.1117/1.JRS.6.063569
  19. Kruskal JB (1964) Nonmetric Multidimensional Scaling: a numerical method. Psychometrika 29:115–129CrossRefGoogle Scholar
  20. Leica-Geosystems (2006) ERDAS Imagine v 9.1. Heerbrugge, St Galen, SwitzerlandGoogle Scholar
  21. Mather PM (1976) Computational methods of multi-variate analysis in physical geography. J. Wiley and Sons, LondonGoogle Scholar
  22. McCune B, Mefford MJ (2006) PC-ORD. Multivariate Analysis of Ecological Data. Ver 5.10. MjM Software, Gleneden Beach, Oregon, U.S.AGoogle Scholar
  23. McCune B, Grace JB, Urban DL (2002) Analysis of Ecological Communities. Oregon State University, MjM Software DesignGoogle Scholar
  24. Mertes LAK (2002) Remote sensing of riverine landscapes. Freshw Biol 47(4):799–816CrossRefGoogle Scholar
  25. Michishita R, Gong P, Xu B (2012) Spectral mixture analysis for bi-sensor wetland mapping using Landsat TM and Terra MODIS data. Int J Remote Sens 33(11):3373–3401CrossRefGoogle Scholar
  26. Morgan RS, Ghabour ThK, Abd El-Wahed MS (2013) New Approach for Mapping the Wetlands along the Mediterranean Sea Coast of Egypt using Remote Sensing and Geographic Information Systems. J Appl Sci Res 9(2):1266–1276Google Scholar
  27. Murray-Hudson M, Wolski P, Ringrose S (2006) Scenarios of the impact of local and upstream changes in climate and water use on hydro-ecology in the Okavango Delta, Botswana. J Hydrol Water Resources in Regional Development: The Okavango River 331(1–2):73–84Google Scholar
  28. Ozesmi SL, Bauer ME (2002) Satellite remote sensing of wetlands. Wetlands Ecol Manage 10(5):381–402CrossRefGoogle Scholar
  29. Peck JE (2010) Multivariate Analysis for Community Ecologists: Step by Step using PC-ORD. MjM Software Design, Gleneden BeachGoogle Scholar
  30. Porter JW, Muzila IL (1989) Aspects of Swamp Hydrology in the Okavango. Botswana Notes and Records 21:73–91Google Scholar
  31. Powell SJ, Croke BFW, King EA Modelling Ecosystem Response to Flooding: a Remote Sensing Approach. In: Oxley L, Kurasiri D (eds) MODSIM 2007 International Congress on Modelling and Simulation, Christchurch, New Zealand, 2007. Modelling and Simulation Society of Australia and New Zealand, pp 2590–2596Google Scholar
  32. Ramachandra TV, Kumar U (2008) Wetlands of Greater Bangalore, India: Automatic Delineation through Pattern Classifiers. Electronic Green Journal 1(26)
  33. Ringrose S, Matheson W, Boyle T (1988) Differentiation of Ecological Zones in the Okavango Delta, Botswana by Classification and Contextural Analyses of Landsat MSS data. Photogrammetric Engineering and Remote Sensing 54(5):601–608Google Scholar
  34. Ringrose S, Vanderpost C, Matheson W (2003) Mapping ecological conditions in the Okavango delta, Botswana using fine and coarse resolution systems including simulated SPOT vegetation imagery. Int J Remote Sens 24(5):1029–1052CrossRefGoogle Scholar
  35. Roy DP, Ju J, Lewis P, Schaaf C, Gao F, Hansen M, Lindquist E (2008) Multi-temporal MODIS–Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sens Environ 112(6):3112–3130CrossRefGoogle Scholar
  36. Shao G, Wu J (2008) On the accuracy of landscape pattern analysis using remote sensing data. Landscape Ecol 23(5):505–511. doi: 10.1007/s10980-008-9215-x CrossRefGoogle Scholar
  37. Teillet PM, Fedosejevs G, Thome KJ, Barker JL (2007) Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain. Remote Sens Environ 110(3):393–409CrossRefGoogle Scholar
  38. Townsend P, Walsh S (2001) Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA. Plant Ecol 157(2):129–149CrossRefGoogle Scholar
  39. Wolski P, Murray-Hudson M (2006) Reconstruction of 1989-2005 inundation history in the Okavango Delta. Paper presented at the Globwetland Symposium, Frascati, Italy. ESA-ESRIN, Botswana from archival Landsat imageryGoogle Scholar
  40. Wolski P, Savenije HHG, Murray-Hudson M, Gumbricht T (2006) Modelling of the flooding in the Okavango Delta, Botswana, using a hybrid reservoir-GIS model. J Hydrol–Water Resources in Regional Development: The Okavango River 331(1–2):58–72Google Scholar
  41. Yu K, Hu C (2013) Changes in vegetative coverage of the Hongze Lake national wetland nature reserve: a decade-long assessment using MODIS medium-resolution data. J Appl Remote Sens 7(1):073589CrossRefGoogle Scholar
  42. Zhan X, Sohlberg R, Townshend JRG, DiMiceli C, Carroll ML, Eastman JC, Hansen MC, DeFries RS (2002) Detection of land cover changes using MODIS 250 m data. Remote Sens Environ 83(1):336–350CrossRefGoogle Scholar
  43. Zhang S, Na X, Kong B, Wang Z, Jiang H, Yu H, Zhao Z, Li X, Liu C, Dale P (2009) Identifying wetland change in China’s Sanjiang Plain using remote sensing. Wetlands 29(1):302–313Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • M. Murray-Hudson
    • 1
    Email author
  • P. Wolski
    • 2
  • L. Cassidy
    • 3
  • M. T. Brown
    • 4
  • K. Thito
    • 1
  • K. Kashe
    • 1
  • E. Mosimanyana
    • 1
  1. 1.University of Botswana Okavango Research InstituteMaunBotswana
  2. 2.Climate Systems Analysis GroupUniversity of Cape TownRondeboschSouth Africa
  3. 3.Ecosurv Environmental ConsultantsMaunBotswana
  4. 4.Howard T. Odum Center for WetlandsUniversity of FloridaGainesvilleUSA

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