Browsing by Author "Bangira, Tsitsi"
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- ItemComparing thresholding with machine learning classifiers for mapping complex water(MDPI, 2019-06) Bangira, Tsitsi; Alfieri, Silvia Maria; Menenti, Massimo; van Niekerk, AdriaanSmall reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. 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) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found 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 main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polarisation and the S2 NDWI, respectively, provided better overall accuracies than when thresholding was applied to any of the individual features considered. The accuracies of this dual thresholding technique were comparable to those of machine learning and may thus offer a viable solution for automatic mapping of waterbodies.
- ItemMapping 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.
- ItemA spectral unmixing method with ensemble estimation of endmembers : application to flood mapping in the Caprivi floodplain(MDPI, 2017) Bangira, Tsitsi; Alfieri, Silvia Maria; Menenti, Massimo; Van Niekerk, Adriaan; Vekerdy, ZoltanThe Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data at high revisit frequency is preferred for accurate flood monitoring. Currently, EO data has either high temporal or coarse spatial resolution. Accurate methodologies for the estimation and monitoring of flooding extent using coarse spatial resolution optical image data are needed in order to capture spatial details in heterogeneous areas such as Caprivi. The objective of this work was the retrieval of the fractional abundance of water ( γw ) by applying a new spectral indices-based unmixing algorithm to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) data using a minimum number of spectral bands. These images are technically similar to the OLCI image data acquired by the Sentinel-3 satellite, which are to be systematically provided in the near future. The normalized difference wetness index (NDWI) was applied to delineate the water surface and combined with normalized difference vegetation index (NDVI) to account for emergent vegetation within the water bodies. The challenge to map flooded areas by applying spectral unmixing is the estimation of spectral endmembers, i.e., pure spectra of land cover features. In our study, we developed and applied a new unmixing method based on the use of an ensemble of spectral endmembers to capture and take into account spectral variability within each endmember. In our case study, forty realizations of the spectral endmembers gave a stable frequency distribution of γw . Quality of the flood map derived from the Envisat MERIS (MERIS) data was assessed against high (30 m) spatial resolution Landsat Thematic Mapper (TM) images on two different dates (17 April 2008 and 22 May 2009) during which floods occurred. The findings show that both the spatial and the frequency distribution of the γw extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. The use of conventional linear unmixing, instead, applied using the entire available spectra for each image, resulted in relatively large differences between TM and MERIS retrievals.