GDEs rely on groundwater

Transcription

1 Surveying Remote sensing for groundwater dependent ecosystems by Zahn Münch and Julian Conrad, GEOSS Identifying groundwater dependent ecosystems (GDEs) is important to define what restriction should be placed on the available groundwater allocation from the reserve. GDEs rely on groundwater for some, or all, of their water requirements. Since ecosystems will use whatever resources are available, the availability of resources will determine the structure, composition and dynamics of the particular ecosystem (Colvin et al., 00). Where groundwater is accessible, ecosystems will develop some degree of dependence which is likely to increase with increased aridity of the associated environment (Hatton and Evans, 998). Understanding the nature of groundwater dependent ecosystems (GDEs) is crucial where ecosystem survival might be critically affected by even a small change in groundwater levels. According to Hatton and Evans (998) there are four kinds of ecosystem dependence on groundwater: Terrestrial vegetation where groundwater is within the rooting depth of the plants. River base flow systems where aquatic and riparian ecosystems are dependent on groundwater contribution to river base flow. Wetlands and spring systems which are dependent on groundwater discharge, either over wide areas or at a definite point. Aquifer and cave ecosystems which refer to subterranean living organisms and the groundwaterbody itself. As part of a national assessment of terrestrial groundwater dependent ecosystems (TGDE), a national map of potential GDEs, based upon groundwater levels and soil moisture availability has been produced (Colvin et al., 00). During a recent study in the northern Sandveld botanical field mapping and remote sensing combined with GIS modelling were used to detect probable GDEs (Conrad et al., 00). Fig. Study area. Botanical field-mapping produced high quality, high resolution results, but with associated high costs. GDEs were delineated to the widest extent of identifiable indicator species. The method developed during the remote sensing component of the Sandveld study was adapted for GDE determination in the adjacent E0 catchment. In this study a desktop approach was followed. The existing GIS model was expanded to include indicators of the occurrence of groundwater i.e. the existence of a shallow unconfined aquifer using the depth to water table and secondary aquifer potential as defined by lineaments and fracture zones. In addition groundwater levels were classified and combined with the spectral delineations as an alternative method. Results from a brief field visit, as well as information from :0 000 topographical maps, have been used PositionIT - Sept/Oct 006

2 for validation. However, conclusive proof of groundwater dependence can only be determined using water chemistry indicators (Colvin et al., 00). This report reviews the image analysis and GIS processing used to delineate probable groundwater dependent ecosystems and suggests future enhancements to improve the methodology. The objectives of these studies were the following: To undertake image analysis using satellite imagery and remote sensing techniques to identify areas of elevated vegetation, wetness and greenness which may be an indication of the presence of GDEs. To use GIS processing to assist the delineation of GDEs by implementing two processes: - spatial modelling independent of present land-cover - spatial interpolation of groundwater levels To combine the spectrally predicted GDEs with the GIS derived data sets so that the extent of GDE probabilities could be defined. To identify enhancements to improve the methodology, thereby reducing the cost of field verification. Materials and methods Fig. : Spectrally defined GDE boundaries before supervised classification of indicator communities, showing both over - and under -classification. In the northern Sandveld, situated on the west coast of South Africa, three quaternary catchments (G0E, G0F and G0G), part of a longitudinal wetland system, were selected for analysis. Bordering these catchments to the east, ten quaternary catchments (E0A to E0K) along the Olifants River, were analysed (Fig. ). The area is characterised by low rainfall at the coast (00-00 mm/a) with higher rainfall ( mm/a) in the mountainous areas at catchment boundaries and in the Cederberg mountains on the eastern border of the E0 catchment. Agriculture (potatoes, citrus, vineyards), supported by irrigation, places increased pressure on groundwater resources. Study design The methodology used was based on a framework developed by Thompson et al. (00) as part of the National Wetland Inventory project using Landsat imagery as a generic presence or absence mapping of core wetland areas, combined with GIS terrain modelling. This methodology was expanded to include determining factors influencing Fig. : GglWP - GDE probability classes. groundwater dependence into the GIS terrain model, i.e. depth to water table (Münch and Conrad, 00) and geological fractures as indicator of secondary aquifer potential (Colvin et al., 00). The landscape wetness potential model (LWP) devised by Thompson et al. (00) was modified to show groundwater generated landscape wetness potential (GglWP). An additional step was added for comparison: GIS processing was used to create a groundwater level data set which was classified and used instead of the spatially derived model. Image analysis The analysis of the satellite imagery consisted of image preparation, extraction of biomass indicators (Normalised Differentiated Vegetation Index (NDVI) and Tasseled Cap (TC) wetness and greenness) and classification. Basic land cover classification was done on multiseasonal images to maximise land cover variety. Biomass indicators data sets were extracted and combined to create a new image, which was classified to highlight areas of high GDE probability. Image analysis was accomplished using ERDAS. GIS modeling Terrain-based spatial modelling was accomplished using Arcview GIS Spatial Analyst. The objective of the spatial modelling was to determine areas where groundwater may be likely to accumulate for use by GDEs, largely based on surface hydrological accumulation and landscape or terrain features. For the G0 catchments, the LWP suggested by Thompson et al. (00) was used without enhancement, since the detailed botanical field PositionIT - Sept/Oct 006

3 mapping which comprised part of the process, provided excellent reference material for comparison and validation. For the E0 catchments, limited field mapping could be done and an enhanced model was designed in order to provide a cost effective methodology at desktop level. The input data sets for the GglWP model were: Imageclassified GDE probability Classes Spatially modelled wetness classes High (,) Medium () Low (,) Low Medium Low Low Medium High Medium Low High High High Medium Table : Probability table used to combine image-classified spectral GDE probability with spatially defined wetness potential. Groundwater levels derived by Bayesian interpolation using TRIPOL (TRIPOL, 99); Lineaments and faults obtained for the study area from the Council for Geoscience (:0 000); and A hydrologically corrected 0 m DEM obtained from DWAF. For GglWP, ArcView. ModelBuilder was used to integrate six parameters: depth to water table; lineament density; groundwater convergence zones; slope steepness; groundwater flow accumulation; and relative slope position or topographic index (TPI). TRIPOL was used to generate the depth to water table data set, while Arcview Spatial Analyst was used to create the other input layers. For the LWP slope steepness, flow accumulation and relative slope position were considered. Several configurations of input Parameter Depth to water table Lineament density Relative slope position Slope steepness GW flow accumulation Parameter values ,0,0-,0,0-, Ridge Valley Flat Footslope Midslope Upperslope > Imageclassified GDE probability Classes parameters, reclassifications, ratings and weightings were tested. The final weights and ratings for GglWP can be found in Table. The output surfaces generated from both the GglWP and LWP models were smoothed to create more homogeneous zones by applying mean and maximum statistical focal functions using a x grid cell size neighbourhood. The maximum statistic enhanced the higher potential zones. Modelling of GglWP and LWP were Rating ( to ) 0 Weight factor (%) GW con-vergence zones Table : GglWP model weighted overlay function parameters. 0 0 >0 0-0 m Groundwater level classes 0-0 m 0-0 m 0-0 m Low Medium 6 High 6 7 Table : Probability table used to combine image-classified spectral GDE probability with groundwater levels. completely independent from image analysis and was not influenced by any spectrally defined parameters. Spatial modelling was used to combine the output from the image analysis with (a) the result from the GIS modelling (GglWP/LWP); and (b) the rated groundwater level layer, to produce a GDE probability layer. A probability table was constructed which combined spectral GDE classes with the GIS delineated classes, in order to determine the best combinations representing probable locations of GDEs in the landscape. Table illustrates this approach for the spatial model and Table shows the values for the groundwater levels. Groundwater levels combined with spectral analysis were only considered in the E0 catchments. For the G0 catchments, extensive field verification compared to botanical field mapping was done, with the associated cost implications. For the E0 catchments, the GDE probability layers were compared to the results from the TGDE investigation done by Colvin et al. (00). After comparison of the different GDE probability data sets, enhancements to the process were suggested. Results Spectral analysis In both study areas a basic land cover classification data set was created from multi-temporal satellite images. This was done to maximise the variety of classes that can be identified using unsupervised classification. For the G0 catchments, three image dates, coinciding with vegetative response due to seasonal climatic PositionIT - Sept/Oct 006

4 change highlighting unimpacted areas, were combined for optimal biomass indicator extraction. In the E0 study a single available dry season image was used for analysis. Other available images did not add significantly to the quality of the analysis. NDVI and TC wetness and greenness indices were generated for the selected image(s). Index values obtained for the biomass indicators were stretched between 0 - for processing with ERDAS (6 colours). The combined NDVI-TC data set was classified using unsupervised classification only with 0 output classes. According to Thompson et al. (00) alternative data combinations could have been used, determined by the format of the imagery used, and the experience of the analyst. The actual allocation of the spectral classes generated during the unsupervised classification of the GDE probability areas, was a subjective procedure. The classes identified were low, medium and high based on greenness regardless of land-use. Spatial models For the G0 catchments, a LWP model based primarily on slope morphology with respect to hydrological flow accumulation was devised. In the case of low relief areas like the northern Sandveld, Thompson et al. (00) found that additional information such as specific geological structures could be used to enhance the wetness potential identified. GglWP, used in the E0 catchment, is a combination of rated and weighted GIS layers, combined into classes. In the -class scale classes and represented low GDE occurrence probability, class represented possible GDE occurrence, and class and high GDE occurrence probability. By applying neighbourhood statistics in a x cell sampling area, the maximum class value was Fig. : GW level - GDE probability classes. selected to ensure that in any given grid unit, the highest probability was automatically selected. The GglWP model for the E0 catchment with high relief areas shows more variation than the LWP model for low relief areas. A groundwater level data set was generated using the Inverse Distance Weighted (IDW) method. Groundwater levels range from 0 to 98 mbgl. The quality of this type of data set is related to the availability of suitable borehole data. It is important to note that the distribution of boreholes within the study area is biased towards the location of anthropogenic development and may not accurately reflect the groundwater levels in unsampled areas. An interpolated surface depicting the water table may not necessarily give an accurate reflection of groundwater flow. Combining spectral and spatial data Using the ERDAS matrix function, spectrally defined probable GDE classes were combined with the various spatial models. The spatially defined classes were simplified into classes (low, medium and high). The data was combined into new classes according to Table. Discussion Using remote sensing techniques, a land-cover data set could be generated to use as a masking layer to show anthropogenic development in the study area. The quality of the landcover classification would determine the accuracy with which the final GDE potential layer could be masked to show the current GDEs. Identifying areas with elevated vegetation, wetness and greenness could be done successfully using a single-date image, or a multi-temporal image. Image data was classified to indicate GDE probability. Three classes (low, medium and high) were selected. GDEs too fragmented, with spectral characteristics similar to the adjacent land-cover, covering a small area, could not be detected using Landsat images due to the resolution of 0 m. It would require 0 - pixels to uniquely and reliably identify a particular feature Fig. : Photographs at field site marked on map. 6 PositionIT - Sept/Oct 006

5 (Wilkie and Finn, 996). Using the biomass indicators (NDVI and Tasselled Cap) all classes of vegetation are identified, not only groundwater dependent vegetation. Using GIS and spatial modelling, a spatial model could be developed based on the physical landscape characteristics, to predict the probability of the occurrence of GDEs. A shortcoming of the GglWP spatial model is that the input data for the groundwater elevation grid is dependent on the borehole distribution within the area, which is not representative of the whole area. By expanding the GglWP model to contain reference to underlying geology and soils, it can be enhanced. The output from the GglWP model must be subjected to accuracy and sensitivity testing. The final delineation of GDE boundaries should be field verified and chemical analysis applied. G0 catchments: comparisons to botanical field mapping The preliminary land-cover classification defined areas, such as open water and natural vegetation as potentially containing GDEs and excluded all other areas (agriculture, urban areas, sand). Using this approach, some GDEs that are located within non-natural vegetation landcovers were excluded from further mapping. After vegetation mapping was completed, some of these areas had to be added. Wetlands and open water were more easily identified. The occurrence of dense populations of Willdenowia incurvata sonkwasriet, a non GWD species as well as tall shrubs such as non-indigenous Acacia saligna Port Jackson willow and Acacia cyclops rooikrans, gave spectrally false positive results. This can be seen in Fig., which shows the spectrally defined GDE boundaries before supervised classification of indicator communities. Lower correlation of remotely sensed boundaries with botanically mapped boundaries were found in the Jakkals and Langvlei River systems, which are more severely modified anthropogenically, with an indicator specie footprint less than the Landsat resolution. Comparing the models The LWP model used in the Northern Sandveld combined with the spectrally classified data, produced adequate results at regional scale and could be validated accurately against the botanically mapped GDE boundaries. The limitations of the resolution of Landsat images were highlighted during this exercise where many of the botanically identified indicator communities were smaller than the identifiable resolution of Landsat. An important consideration here is what are we trying to achieve in finding GDEs - is the objective to find the original, unmodified footprint of a particular ecosystem before modification, or are we trying to determine the current status of ecosystems that exist within the particular study area. Given enough time and resources, the particular GDE would be able to recover to its previous glory, if not affected by alien vegetation. In Fig. and Fig., the GglWP and groundwater level models, combined with the appropriate spectral data sets, indicating GDE probability are compared for a selected area in the E0 catchment. Fig. shows a photograph of the selected position on the map, which is located within an area identified during the TGDE mapping as having a high probability of hosting GDEs. The GglWP model correctly highlights the GDE probability along the riverbank, but overestimates GDE probability in cultivated areas as indicated by the :0 000 background map. The groundwater level model doesn t indicate GDE probability along this stretch, due to very deep groundwater levels found at boreholes in the area. The only conclusive way of proving the correctness of any one of the models, would be to investigate the chemical composition of particular plants in the selected sites. Conclusion Remote sensing based classification combined with spatial GIS based modelling is a cost effective solution which can provide useful results at a regional scale. However, a footprint of greater than 0, ha for indicator communities is required to enhance mapping accuracy using Landsat satellite images. Other image types may provide more accurate results. As suggested by Thompson et al. (00), there are various other biomass indicator combinations that may be used to more accurately or efficiently interpret the data spectrally which beg investigation. Prior knowledge of the study area (in terms of expected landscape structure and associated land-cover / use characteristics) is a key factor in the speed of data processing, especially in allocating of spectral classes to landcover and GDE classes, since spectral classification remains a subjective process. In conjunction with vegetation mapping and field verification, which may be expensive and time consuming, remote sensing combined with spatial modelling becomes a powerful tool for managing areas that are increasingly under ecological stress due to cultivation and urbanisation. As a desktop predictive tool, this methodology produces results that appear accurate, but need to be tested in the field and chemically verified. Once this has been done, the model can be enhanced to include additional predictive parameters to identify the hydrogeological type-setting determining the location of the groundwater dependent ecosystems. The cost-effective nature of a predictive model of this nature cannot be overstated. Acknowledgement This paper was presented at the AfricaGIS 00 Conference in Pretoria in October 00 and is republished with permission. References [] C Colvin, D le Maitre, and S Hughes, 00. Assessing terrestrial groundwater dependent ecosystems in South Africa. Water Research Commission, Pretoria. WRC Report No. 090-//0. [] J E Conrad, A B Low, Z Munch and U Pond, 00. Remote sensing based botany and groundwater dependency study: northern Sandveld. Department of Water Affairs and Forestry, Pretoria. DWAF report No. RDM/G00/0/CON/00. [] T Hatton, and R Evans, 998. Dependence of ecosystems on groundwater and its significance to Australia. Occasional Paper No /98, Land and Water Resources Research and Development Corporation, CSIRO Australia [] Z Munch and J E Conrad, 00. Remote Sensing based determination of groundwater dependent ecosystems: E0 catchment, Western Cape. Prepared for Parsons & Associates. GEOSS, Stellenbosch. GEOSS Report No: G00/07-0. [] M Thompson, G Marneweck, S Bell, D Kotze, J Muller, D Cox and R Clark, 00. A methodology proposed for a South African national wetland inventory. CSIR Environmentek, Pretoria. [6] TRIPOL, 99. Institute for Groundwater Studies (IGS) and the Water Research Commission. [7] D Wilkie, and J Finn, 996. Remote Sensing Imagery for Natural Resources Monitoring: A guide for first-time users Columbia University Press, New York. PositionIT - Sept/Oct 006 7