Mapping land degradation using remote sensing data and an unsupervised clustering algorithm in the eThekwini Metropolitan Area.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Land degradation is a major environmental problem facing South Africa and many other countries around the world. For proper management and adoption of best rehabilitation strategies, a compendious regional-scale assessment approach is needed to attain the full extent of the impairment. The aim of this study was to assess the spatial extent of land degradation with the use of GIS and remote sensing techniques in the eThekwini Metropolitan Area (EMA), KwaZulu-Natal, South Africa. The first objective was to review the status of land degradation in South Africa, as well as tracking of emerging trends in remote sensing and Geographic Information Systems research. Historically, in South Africa, land degradation has been associated with poverty and rurality. While conducting studies was also a challenge, demanding high human and economic resources. Although these studies were accurate and invaluable, most of them were too localized and highly difficult to replicate. The introduction of remote sensing has bought a new dimension with a timely spatial mapping of land degradation at regional scales. As a result, there thus been a sharp increase in remote sensing-based land degradation studies, this is also accompanied by the recent improvements in capabilities of remote sensors and associated GIS platforms. However, there is still a challenge of accessibility, especially for financial constricted regions such as the sub-Sahara of Africa. Most of the cutting-edge remote sensing data such as the hyperspectral and high spatial resolution imagery are highly expensive and therefore inaccessible to those not affording. However, the use of new-age medium resolution sensors is a potential solution. The second objection of this study was to detect and map the spatial distribution of land degradation in the EMA through use of Sentinel-2 derived vegetation indices (VIs) in conjunction with a hierarchical clustering algorithm. Data from Sentinel-2 was used to derive VIs used in this study, these are namely; NDVI, RVI, SAVI; and SARVI. The framework using Ward’s hierarchical clustering performed relatively good to produce 6 clusters that achieved an overall classification accuracy (OA) of 88.81% when mapping land-cover including land degradation. In this regard, land degradation achieved the highest classification accuracy of up to 100%, while water achieved the lowest at 63.33%. Although there was quite a significant difference in accuracies between different land-cover classes, overall, the results were still reasonably good with an error rate of 0.14 and Kappa Coefficient of 0.86. The results from this study, therefore, suggest that Ward’s unsupervised clustering approach is a suitable tool for mapping of complex land-cover classes, particularly land degradation

Studying Changes in the Cryosphere Using Radar Interferometry: Permafrost Surface Subsidence and Glacial Unloading Deformation

In past decades, cryospheric components such as glaciers, ice sheets, sea ice, and frozen ground have been undergoing significant and rapid changes, associated with changes in the global climate system. In this research, I present two case studies on how geodetic tools, especially Radar Interferometry (InSAR), can be used to monitor and to advance our understanding of the changing cryosphere. First, I measure seasonal and long-term surface subsidence on the North Slope of Alaska near Prudhoe Bay using InSAR. I detect a long-term surface subsidence of 1 to 4 cm per decade, which is probably caused by melting of ground ice within the permafrost, as ground temperatures have increased by 2 to 3°C in northern Alaska since the early 1980s. I also find a seasonal subsidence of 1 to 4 cm during summer thaw seasons, which is caused by the volume decrease of the top soil layer (the active layer) undergoing annual thawing-freezing cycles. A retrieval algorithm is developed to estimate the active layer thickness (ALT) using the InSAR-measured seasonal subsidence. The estimated ALT values match in situ measurements at Circumpolar Active Layer Monitoring sites within the uncertainties. I estimate an ALT of 30 to 50 cm over moist tundra areas, and a larger thickness of 50 to 80 cm over wet tundra areas. Second, I use InSAR and Global Positioning System data to measure the crustal elastic uplift near Jakobshavn Glacier in west-central Greenland caused by its rapid ice loss since 1997. These geodetic measurements place valuable constraints on the ice mass balance estimation based on altimetry measurements from NASA\u27s Airborne Topographic Mapper (ATM), as I find good agreement between the observed crustal rebound rates and the predicted rates using the ATM measurements. I also directly invert for the spatial pattern of ice thinning from the InSAR-measured crustal deformation. Overall, this research suggests that InSAR-measured surface deformation complements traditional in situ monitoring of the active layer and the permafrost, extends ALT estimates over large areas at high spatial resolution, provides new insights into the dynamics of permafrost systems and changes in permafrost conditions, and helps to study the ice mass loss of a rapidly thinning glacier and its surrounding region and to better understand a glacier\u27s rapid response to a warming climate

Integrated Applications of Geo-Information in Environmental Monitoring

This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society

Synthetic aperture radar remote sensing for landfill monitoring

Despite today’s intensive efforts directed at the recycling and recovery of solid wastes, the controlled disposal of refuse into land remains an important and necessary means of effective waste management. The work presented in this thesis investigates the use of Synthetic Aperture Radar (SAR) data to monitor solid waste landfills. The end-users’ interests vary from detecting the presence of a landfill to more specifically monitoring on-site operations and environmental conditions. Following a general literature review on the application of Earth Observation data for landfill monitoring, the identified research objectives are to: 1) assess whether SAR data can support the identification of landfill sites by distinguishing them from other disturbed areas which present similar optical spectral signatures, and 2) assess the possibility of correlating SAR data with onsite operational procedures. Data acquired for the research are: ground observations and measurements examining the spatial, temporal and biophysical characteristics of a landfill that can influence SAR data; historical and new programmed SAR scenes obtained from the ESA ERS-1 and -2 satellites and from Envisat ASAR instrument; ground based SAR (GB-SAR) acquisitions; simulations based on the RT2 backscatter model; additional space-based and airborne optical data to support the analysis and discussion. The examination of both the SAR amplitude spatial structure and the temporal decorrelation of these sites shows that there are three key characteristics that can distinguish them from other disturbed areas with similar optical spectral signatures: the presence of anisotropic features that strongly affect the SAR backscatter; the fact that the coherence magnitude images of these sites are characterised by large decorrelated areas with transient attributes; and their distinctive positive topography. The analysis highlights that one single-polarisation acquisition can hardly provide correct land-cover information, and consequently knowledge on land-use. The research demonstrates the key value of merging together complementary information derived from both the space and time dimensions, achieving fairly accurate land-use classification results. The research also provides an appreciation of the applicability of the developed techniques in an operational framework. These can suffer a number of limitations if a landfill site is located in a particular environment, and/or if meteorological conditions can significantly affect the radar signal, and/or unusual landfilling procedures are applied by the operators. Concluding remarks on the end-users needs point out that there are a number of aspects, ranging from practical and managerial matters to legal and technical issues, that often discourage the utilisation of EO data by new potential users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo