Hydrogeology Journal

, Volume 15, Issue 1, pp 19–28 | Cite as

Remote sensing and GIS based determination of groundwater dependent ecosystems in the Western Cape, South Africa

  • Zahn Münch
  • Julian Conrad


Finding the location of groundwater dependent ecosystems (GDEs) is important in determining the extent of restrictions that need to be placed upon the abstraction of groundwater. Remote sensing was combined with geographical information system (GIS) modelling to produce a GDE probability rating map for the Sandveld region, South Africa. Landsat TM imagery identified the areas indicating the probable presence of GDEs and GIS assisted in their delineation. Three GIS models were generated: a GIS model predicting landscape wetness potential (LWP model) based on terrain morphological features; the LWP model was modified to highlight groundwater generated landscape wetness potential (the resulting GglWP model); and a groundwater elevation model was interpolated, combining groundwater level measurements in boreholes in the region with digital elevation model data. Biomass indicators generated from Landsat were classified and combined with the GIS models, followed by field verification of riverine and wetland GDEs. The LWP model provided the most accurate results of the three models tested for GDEs in this region.


Remote sensing Geographical information systems Groundwater dependent ecosystems Groundwater management Sandveld region 


Dans le cadre de la délimitation géographique des restrictions sur les prélèvements en eau souterraine, la localisation des écosystèmes dépendant des eaux souterraines (EDES, ou GDE) a son importance. La télédétection a été combinée à une modélisation sous SIG (Système d’Information Géographique) afin de produire une carte de probabilité de présence des EDES sur la région de Sandveld, en Afrique du Sud. Les images Landsat TM ont identifié les secteurs indicateurs de présence de EDES, et les SIG ont contribué à leur délimitation. Trois modèles SIG ont été créés: un premier modèle prédisant le potentiel d’humidité des terrains d’après la morphologie locale (modèle LWP); ce modèle a été modifié dans le but de souligner le potentiel d’humidité généré par les eaux souterraines (modèle résultant GglWP); enfin, un modèle piézométrique a été interpolé par combinaison des levés piézométriques dans les forages du secteur et des données du modèle numérique de terrain. Les indicateurs de biomasse issus des données Landsat ont été classés et combinés aux modèles SIG, puis les EDES de zones humides et de cours d’eau ont été vérifiés sur le terrain. Sur les trois modèles testés pour ce secteur, le modèle LWP a donné les résultats les plus probants.


Definir la localización de los ecosistemas dependientes de las aguas subterráneas (GDEs) es importante para determinar la extensión de las restricciones que hay que tomar en la extracción de aguas subterráneas. La teledetección se ha combinado con la modelación utilizando Sistemas de Información Geográfica (GIS), para producir un mapa de valoración de probabilidades para la región Sandveld, en Sudáfrica. Las imágenes Landsat TM identificaron las áreas con presencia de GDEs y el GIS ayudó en su delineación. Se generaron tres modelos GIS: un Modelo GIS para predecir los paisajes húmedos potenciales (LWP model) basado en las características morfológicas del terreno; el modelo LWP fue modificado para destacar los paisajes húmedos potenciales producidos por las aguas subterráneas (dando lugar al modelo GgIWP); y se interpoló un modelo de niveles de aguas subterráneas, combinando medidas de niveles de agua subterránea en sondeos en la región con datos de un modelo digital de niveles. Los indicadores de biomasa generados mediante Landsat fueron clasificados y combinados con los modelos GIS, seguidos por verificaciones de campo en GDEs de rivera y humedales. El model LWP proporciona los resultados más precisos de los tres modelos probados por GDEs en esta región.



The following people and organisations are thanked for contributing to this work: Barry Low and Uschi Pond, COASTEC (Coastal and Environmental Consultants) for botanical field mapping, Department of Water Affairs and Forestry for funding, Roger Parsons, Parsons and Associates Specialist Groundwater Consultants CC for technical input and Water Research Commission for funding.


  1. Biasi F (2001) ELU AML pack: annotated AML code and documentation for generating ecological land units (ELUs) from a DEM and geology grid using ArcInfo. The Nature Conservancy (website). Cited 27 July 2005
  2. Chuanyan Z, Zhongren N, Guodong C, Junhua Z and Zhaodong F (2006) GIS-assisted modeling of the spatial distribution of Qinghai spruce (Picea crassifolia) in the Qilian Mountains, northwestern China based on biophysical parameters. Ecological Modelling 191:487–500Google Scholar
  3. Colvin C, le Maitre D, Hughes S (2002) Assessing terrestrial groundwater dependent ecosystems in South Africa. WRC Report No. 1090–2/2/03, Water Research Commission, Pretoria, South AfricaGoogle Scholar
  4. Conrad JE, Low AB, Münch Z, Pond U (2005) Remote sensing based botany and groundwater dependency study: northern Sandveld. DWAF report no. RDM/G300/02/CON/0505, Department of Water Affairs and Forestry, Pretoria, South AfricaGoogle Scholar
  5. Cowan GI (1995) Wetland Regions of South Africa. In: Cowan GI (ed) Wetlands of South Africa. Department of Environmental Affairs and Tourism, Pretoria, South AfricaGoogle Scholar
  6. Crist EP, Cicone RC (1984) Application of the Tasseled Cap concept to simulated Thematic Mapper data. Photogramm Eng Remote Sensing 50:343–352Google Scholar
  7. De Beer CH (2003) The geology of the Sandveld area between Lambert’s Bay and Piketberg (Project 5510). Report No. 2003–0032, Council for Geoscience, Western Cape Unit, Bellville, South AfricaGoogle Scholar
  8. DWAF (1998) The National Water Act (Act 36 of 1998). The Department of Water Affairs and Forestry (DWAF), Pretoria, South AfricaGoogle Scholar
  9. DWAF (2003) Sandveld preliminary (Rapid) reserve determinations. Langvlei, Jakkals and Verlorenvlei Rivers, Olifants-Doorn WMA G30, vol 2: specialist reports. Prepared by GEOSS, Southern Waters and Coastec. DWAF Project Number: 2002–227. Department of Water Affairs and Forestry (DWAF), Pretoria, South AfricaGoogle Scholar
  10. Eamus D, Froend R (2006) Groundwater-dependent ecosystems: the where, what and why of GDEs. Aust J Bot 54:91–96CrossRefGoogle Scholar
  11. Eamus D, Froend R, Loomes R, Hose G, Murray B (2006) A functional methodology for determining the groundwater regime needed to maintain the health of groundwater-dependent vegetation. Aust J Bot 54:97–114CrossRefGoogle Scholar
  12. Elmore AJ, Mustard JF, Manning SJ (2003) Regional patterns of plant community response to changes in water: Owens Valley, California. Ecol Appl 13(2):443–460CrossRefGoogle Scholar
  13. Elumnoh A, Shrestha RP (2000) Application of DEM data to Landsat image classification: evaluation in a tropical wet-dry landscape of Thailand. Photogramm Eng Remote Sensing 66(3):297–304Google Scholar
  14. ERDAS (2002) Field guide, 6th edn. ERDAS LLC, Atlanta, GeorgiaGoogle Scholar
  15. Gritzner ML, Marcus WA, Aspinall R and Custer SG (2001) Assessing landslide potential using GIS, soil wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology 37:149–165CrossRefGoogle Scholar
  16. Harvey KR, Hill GJE (2001) Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: a comparison of aerial photography, Landsat TM and SPOT satellite imagery. Int J Remote Sens 22(15):2911–2925CrossRefGoogle Scholar
  17. Hatton T, Evans R (1998) Dependence of ecosystems on groundwater and its significance to Australia. Occasional Paper no. 12/98, Land and Water Resources Research and Development Corporation, CSIRO, Clayton, AustraliaGoogle Scholar
  18. Jensen JR, Hodgson ME, Christensen E, Mackey HE, Tinney LR, Sharitz R (1986) Remote sensing inland wetlands: a multispectral approach. Photogramm Eng Remote Sensing 52(1):87–100Google Scholar
  19. Jensen JR, Rutchey K, Koch MS, Narumalani S (1995) Inland wetland change detection in the Everglades water conservation area 2A using a time series of normalised remotely sensed data. Photogramm Eng Remote Sensing 61(2):199–209Google Scholar
  20. Lillesand TM, Kiefer RW (1987) Remote sensing and image interpretation. Wiley, New YorkGoogle Scholar
  21. Lunetta RS, Balogh ME (1999) Application of Multi-temporal Landsat 5 imagery for wetland identification. Photogramm Eng Remote Sensing 65(11):1303–1310Google Scholar
  22. Münch Z, Conrad JE (2005) Remote sensing based determination of groundwater dependent ecosystems: E10 catchment, Western Cape. GEOSS Report No:G2005/07–01, Prepared for Parsons and Associates. GEOSS, Stellenbosch, South AfricaGoogle Scholar
  23. Munyati C (2000) Wetland change detection on the Kafue Flats, Zambia, by classification of a multi-temporal remote sensing image dataset. Int J Remote Sens 21(9):1787–1806CrossRefGoogle Scholar
  24. Murray BR, Zeppel MJB, Hose GC, Eamus D (2003) Groundwater-dependent ecosystems in Australia: it’s more than just water for rivers. Ecol Manage Rest 4(2):110–113CrossRefGoogle Scholar
  25. Thompson M, Marneweck G, Bell S, Kotze D, Muller J, Cox D, Clark R (2002) A methodology proposed for a South African national wetland inventory. Report prepared for John Dini, Wetlands Conservation Programme, Department Environmental Affairs and Tourism, Pretoria, South AfricaGoogle Scholar
  26. Van Sandwyk L, van Tonder GJ, de Waal DJ, Botha JF (1992) A comparison of spatial Bayesian estimation and classical Bayesian Kriging procedures. WRC report no. 271/3/92. Water Research Commission, Pretoria, South AfricaGoogle Scholar
  27. Wilkie D, Finn J (1996) Remote sensing imagery for natural resources monitoring: a guide for first-time users. Columbia University Press, New YorkGoogle Scholar
  28. Xu Y, Colvin C, van Tonder G, Hughes S, le Maitre D, Zhang J, Mafanya T, Braune E (2003) Towards the resource directed measures: groundwater component (Version 1.1). WRC Report No. 1090–2/1/03. Projects K5/1090–1092, Water Research Commission, Pretoria, South AfricaGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.GEOSS-Geohydrological and Spatial Solutions International (Pty) LtdStellenboschSouth Africa

Personalised recommendations