Doctoral Degrees (Geography and Environmental Studies)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/617

Browse

Browsing Doctoral Degrees (Geography and Environmental Studies) by Author "Bangira, Tsitsi"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Mapping surface water in complex and heterogeneous environments using remote sensing
    (Stellenbosch : Stellenbosch University, 2019-04) Bangira, Tsitsi; Van Niekerk, Adriaan; Menenti, Massimo; Verkedy, Zoltan; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH ABSTRACT: Global climate change characterised by rising temperatures and changes in the magnitude and intensity of precipitation is projected to affect the spatial and temporal distribution of land surface water (LSW) resources. Accurate and reliable information on the dynamics of LSW is valuable in understanding and monitoring the occurrence and impacts of floods and droughts. This knowledge is also critical for appropriate planning and impact assessment. Research has showed that droughts and floods are the two major hydrological disasters in developing countries such as southern Africa. This is mainly due to the lack of accurate and robust methods and reliable data sources necessary for monitoring the spatial and temporal dynamics of LSW resources. Satellite remote sensing (RS) technology is a promising primary data source and provides techniques suitable for repeated mapping water bodies and flood plains. However, many flood plains and water bodies are characterised by the presence of submerged vegetation, dissolved and suspended substances. These characteristics limit the application of RS in monitoring LSW resources. This study evaluated the potential of remotely sensed data with different temporal, spatial and radiometric properties to map LSW in such challenging environments. Three experiments were carried out. The first experiment evaluated a new spectral indices-based unmixing algorithm that uses a minimum number of spectral bands. The algorithm was applied to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) imagery to map open water and partly submerged vegetation. MERIS FR imagery has high (three days) temporal, but low (300 m) spatial resolution. The quality of the flood map derived from MERIS data was compared to high (30 m) spatial, but low (16 day) temporal resolution Landsat Thematic Mapper (TM) images on two different flooding dates (17 April 2008 and 22 May 2009). The findings show that, despite the low resolution of MERIS, both the spatial and frequency distribution of the water fraction extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. This suggests that the proposed technique can be used to produce reliable and frequent flood maps using low spatial resolution imagery. The use of synthetic aperture radar (SAR) has become increasingly relevant for mapping and monitoring flooded vegetation (FV). In a second experiment, a procedure was constructed and validated based on a time series of Sentinel-1 SAR data for mapping floods in a vegetated floodplain. For each newly available image, the probability of temporary flooded conditions is tested against the probability of not-flooded conditions. The changes in land cover characteristics are considered by the technique. The modelling and testing components were applied independently to the vertical transmit and horizontal receive (VH) polarisation, vertical transmit and vertical receive (VV) and VH/VV ratio. The resulting flood maps were compared to those obtained from Landsat-8 Operational Land Imager (OLI) and ground truthing. Overall classification accuracies showed that the maps produced from the fused Sentinel-1 products (VH and VH/VV) were most accurate (84.5%) and significantly better than when only the VH polarisation was used (78.7%). These results demonstrate that the fusion of VH/VV and VV polarisations can improve flood mapping in vegetated floodplains. The third experiment involved using automatic thresholding of near-concurrent normalized difference water index (NDWI) (generated from Sentinel-2) and VH backscatter bands (generated from Sentinel-1) to map waterbodies with diverse spectral and spatial characteristics. The resulting maps were compared to the classification performances of five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM). The results show that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The LSW maps and techniques developed in this study are critical for flood status monitoring, water resources planning and disaster management, and will as such reduce the impact of floods and droughts on vulnerable communities living in southern Africa. Furthermore, the results of this study will hopefully inspire the remote sensing community to make use of the new generation of freely available multispectral and SAR data (such as those provided by the Sentinel constellations) for operational drought and flood monitoring.