Abstract. The focus of this paper is on the pre-packaged version of SEBS in ILWIS and the sensitivity of SEBS to some parameters over which the user has some control when using this version of the model, in order to make informed choices to limit uncertainties. The sensitivities of SEBS to input parameters are related to daily ET rather than energy flux results since this is of interest to water managers and other users of the results of the SEBS model. This paper describes some of the uncertainties introduced by the sensitivity of the SEBS model to (a) land surface temperature and air temperature gradient, (b) the choice of fractional vegetation formula, (c) displacement height and the height at which wind speed is measured, and (d) study area heterogeneity. It was shown that SEBS is sensitive to land surface temperature and air temperature gradient and the magnitude of this sensitivity depended on the land cover and whether or not the wetlimit had been reached. The choice of fractional vegetation cover formula was shown to influence the daily ET results by up to 0.7 mm. It was shown that the height of the vegetation canopy should be considered in relation to the weather station reference height to avoid the sensible heat flux from becoming unsolvable due to a negative ln calculation. Finally the study area was shown to be heterogeneous although the resolution at which fluxes were calculated did not significantly impact on energy partitioning results. The differences in the upscaling from evaporative fraction to daily ET at varying resolutions observed implies that the heterogeneity may play the biggest role in the upscaling and the influence of albedo on this calculation should be studied.

Geological hazards and their effects are often geographically widespread. Consequently, their effective mapping and monitoring is best conducted using satellite and airborne imaging platforms to obtain broad scale, synoptic coverage. With a multitude of hazards and effects, potential data types, and processing techniques, it can be challenging to determine the best approach for mapping and monitoring. It is therefore critical to understand the spatial and temporal effects of any particular hazard on the environment before selecting the most appropriate data type/s and processing techniques to apply. This review is designed to assist the decision-making and selection process when embarking on a hazard mapping or monitoring exercise. It focuses on the application of optical, LiDAR, and synthetic aperture RADAR technologies for the assessment of pre-event risk and post-event damage. Geological hazards of global interest summarized here are landslides and erosion; seismic and tectonic hazards; ground subsidence; and flooding and tsunami

Abstract-Sinkholes are an unpredictable geohazard that endangers life and property in dolomitic terrains. Sinkholes are a significant threat in Gauteng, South Africa's most populated and urbanised province. Small-scale surface subsidence is frequently present prior to the collapse of a sinkhole. Therefore, the presence of precursory surface deformation can be exploited to develop early warning systems. Spaceborne SAR interferometry (DInSAR) is able to monitor small-scale surface deformation over large areas and can be used to detect and measure precursors to sinkhole development. This paper investigates the use of repeat-pass DInSAR to detect sinkhole precursors in the Gauteng Province. Twenty stripmap acquisitions from TerraSAR-X were acquired over a full year. DInSAR results revealed the presence of three previously unknown deformation features, one of which could be confirmed by subsequent field investigations. Furthermore, a water supply pipeline ruptured 6 months after the initial observation. The detection of the deformation, therefore, provided a viable early warning to landowners who were unaware of the subsidence. Detected deformation features were between 40 m and 100 m in diameter. The maximum displacement measured was 50 mm over 55 days. Despite the successful detection, seven sinkhole events occurred in the observation period for which no deformation could be detected. The results indicate that high-resolution, X-band interferometry is able to monitor dolomite-induced instability in an urban environment. However, considerations related to SAR interferometry and physical sinkhole properties need to be addressed before DInSAR can be used in an operational early warning system. Index Terms-Geophysical measurements, hazardous areas, interferometry, land surface, spaceborne radar, synthetic aperture radar (SAR).

Sinkholes are global phenomena with significant consequences on the natural-and built environment. Significant efforts have been devoted to the assessment of sinkhole hazards to predict the spatial and temporal occurrence of future sinkholes as well as to detect small-scale deformation prior to collapse. Sinkhole hazard maps are created by considering the distribution of past sinkholes in conjunction with their geomorphic features, controlling conditions and triggering mechanisms. Quantitative risk assessment then involves the statistical analysis of sinkhole events in relation to these conditions with the aim of identifying high risk areas. Remote sensing techniques contribute to the field of sinkhole hazard assessment by providing tools for the population of sinkhole inventories and lend themselves to the monitoring of precursory deformation prior to sinkhole development. In this paper, we outline the background to sinkhole formation and sinkhole hazard assessment. We provide a review of earth observation techniques, both for the compilation of sinkhole inventories as well as the monitoring of precursors to sinkhole development. We discuss the advantages and limitations of these approaches and conclude by highlighting the potential role of radar interferometry in the early detection of sinkhole-induced instability resulting in a potential decrease in the risk to human lives and infrastructure by enabling proactive remediation.

Abstract:In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle (NDαI) approach to burn-scar mapping using C-band data. Polarimetric decompositions are used to derive α-angles from pre-burn and post-burn scenes and NDαI is calculated to identify decreases in vegetation between the scenes. The technique was tested in an area affected by a wildfire in January 2016 in the Western Cape, South Africa. The quad-pol H-A-α decomposition was applied to RADARSAT-2 data and the dual-pol H-α decomposition was applied to Sentinel-1A data. The NDαI results were compared to a burn scar extracted from Sentinel-2A data. High overall accuracies of 97.4% (Kappa = 0.72) and 94.8% (Kappa = 0.57) were obtained for RADARSAT-2 and Sentinel-1A, respectively. However, large omission errors were found and correlated strongly with areas of high local incidence angle for both datasets. The combined use of data from different orbits will likely reduce these errors. Furthermore, commission errors were observed, most notably on Sentinel-1A results. These errors may be due to the inability of the dual-pol H-α decomposition to effectively distinguish between scattering mechanisms. Despite these errors, the results revealed that burnt areas could be extracted and were in good agreement with the results from Sentinel-2A. Therefore, the approach can be considered in areas where persistent cloud cover or smoke prevents the extraction of burnt area information using conventional multispectral approaches.

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We present a workflow for investigating large, slow-moving landslides which combines the synthetic aperture radar (SAR) technique, GIS post-processing, and airborne laser scanning (ALS), and apply it to Fels landslide in Alaska, US. First, we exploit a speckle tracking (ST) approach to derive the easting, northing, and vertical components of the displacement vectors across the rock slope for two five-year windows, 2010–2015 and 2015–2020. Then, we perform post-processing in a GIS environment to derive displacement magnitude, trend, and plunge maps of the landslide area. Finally, we compare the ST-derived displacement data with structural lineament maps and profiles extracted from the ALS dataset. Relying on remotely sensed data, we estimate that the thickness of the slide mass is more than 100 m and displacements occur through a combination of slumping at the toe and planar sliding in the central and upper slope. Our approach provides information and interpretations that can assist in optimizing and planning fieldwork activities and site investigations at landslides in remote locations.

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