Jaco Kemp

Parameters Affecting Interferometric Coherence—The Case of a Dynamic Agricultural Region

In South Africa, the need exists for a long-term monitoring system to detect deformation because of mining. Differential radar interferometry (dInSAR) is recognized for its ability to detect deformation without the need for extensive field observations. However, phase noise (assessed by interferometric coherence) is known to adversely affect interferometric measurements. In this paper, 12 polarimetric RADARSAT-2 SAR images are used to statistically analyze the sensitivity of coherence to perpendicular baseline, Doppler centroid (DC) difference, temporal baseline, land surface evolution, and polarization. The results suggest that the average scene coherence for HH and VV polarizations is most sensitive to the effects of temporal baseline if RADARSAT-2 data with small perpendicular baselines and DC differences are considered. However, an increase in the sensitivity to dc difference and perpendicular baseline is observed after analysis of ERS-2 data for which higher values of these parameters are available. The choice of optimal polarization is seasonally dependent, with VV polarization data being more suitable at the end of the growing season after harvesting, while HH polarization is more suitable during the peak of the growing season. Image pairs with at most 24-day temporal baselines are required during the peak of the growing season while successful interferogram generation is possible with temporal baselines of up to 120 days after the peak of the growing season. Subsidence basins are successfully detected, demonstrating that the dInSAR techniques would be suitable for the long-term monitoring of surface subsidence in this dynamic commercial agricultural environment.

Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques

Machine learning can be employed to accurately differentiate between crops (95%).Pan-sharpening Landsat 8 imagery dramatically improves crop classification accuracy (15%).Pan-sharpening Landsat 8 imagery effects classification accuracy more than image analysis method does. This study evaluates the potential of pan-sharpening multi-temporal Landsat 8 imagery for the differentiation of crops in a Mediterranean climate. Five Landsat 8 images covering the phenological stages of seven major crops types in the study area (Cape Winelands, South Africa) were acquired. A statistical pan-sharpening algorithm was used to increase the spatial resolution of the 30m multispectral bands to 15m. The pan-sharpened images and original multispectral bands were used to generate two sets of input features at 30 and 15m resolutions respectively. The two sets of spatial variables were separately used as input to decision trees (DTs), k-nearest neighbour (k-NN), support vector machine (SVM), and random forests (RF) machine learning classifiers. The analyses were carried out in both the object-based image analysis (OBIA) and pixel-based image analysis (PBIA) paradigms. For the OBIA experiments, three image segmentation scenarios were tested (good, over and under segmentation). The PBIA experiments were carried out at 30m and 15m resolutions. The results show that pan-sharpening led to dramatic (15%) improvements in classification accuracies in both the PBIA and OBIA approaches. Compared to the other classifiers, SVM consistently produced superior results. When applied to the pan-sharpened imagery SVM produced an overall accuracy of nearly 96% using OBIA, while PBIAs overall accuracy was 1.63% lower. We conclude that pan-sharpening Landsat 8 imagery is highly beneficial for classifying agricultural fields whether an object- or pixel-based approach is used.

Hazards and vulnerabilities among informal wetland communities in Kampala, Uganda

Population pressure, urbanization and industrial developments, among other factors, have resulted in severe degradation of environmental resources such as wetlands. In the face of increased climate variability, several hazards continue to emerge, affecting the vulnerable sectors of society, especially the poor. Risks due to hazards and vulnerabilities are context specific; they are shaped by causal mechanisms and local conditions, which need to be understood if risks are to be reduced. In this paper, a range of hazards, perceived vulnerabilities and associated factors among wetland communities in Kampala have been analysed. The analysis is based on a survey of 551 households using semi-structured interviews, focus group discussions and key informant interviews. The study focused on communities living in four wetlands that drain the city’s wastewater into Murchison Bay in Lake Victoria. Results show floods and waterlogging as the principal hazards; however, secondary effects of floods and waterlogging such as disease vectors and diseases affect more people than the floods. Tenants were more likely than landlords/homeowners to be exposed to floods, and households that spend more than US

Community-level adaptation to minimize vulnerability and exploit opportunities in Kampala’s wetlands

80 per month were less likely than households that spend less to be exposed to floods. Households that had been exposed to floods before were more likely to perceive themselves as vulnerable. Variations in exposure to hazards and perceived vulnerabilities could be due to differences in the capacity to resist, cope with, or adapt to minimize vulnerability. An investigation of adaptation mechanisms responding to the various hazards identified in this paper would enrich understanding of the elements that shape risk in this context.

A Simple Normalized Difference Approach to Burnt Area Mapping Using Multi-Polarisation C-Band SAR

This paper discusses benefits that informal wetland communities in Kampala, Uganda derive from their location in the wetland and how they adapt to minimize vulnerability to hazards such as floods and disease vectors. We focus on the mechanisms, preferences and ability to adapt. A total of 551 households were interviewed in addition to four focus group discussions and five key informant interviews. Free water from spring wells and cheaper rental units topped the benefits from location, while the main benefit associated with the wetland is that it supports crop farming. Tenure status was significantly associated with the preference and perceived ability to adapt: tenants were less likely to prefer to adapt, and less likely to perceive themselves as able to afford adaptation, than landlords. There is a need for coordinated adaptation strategies that involve all stakeholders and that enhance equitable utilization of wetland resources without compromising their ecosystem services and economic benefits.

Detection of Sinkhole Precursors Through SAR Interferometry: Radar and Geological Considerations

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.

Detection of sinkhole precursors through SAR interferometry: First results from South Africa

Sinkholes are an unpredictable geohazard that endanger life and property in dolomitic terrains. Sinkholes are a significant threat in Gauteng, South Africa’s most populated and urbanized 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 synthetic aperture radar (SAR) differential 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 letter 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 six 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 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.

The phenology of an agricultural region as expressed by polarimetric decomposition and vegetation indices

Sinkholes are an unpredictable geohazard that endangers life and property in dolomitic terrains. They are a significant threat in Gauteng, South Africas most populated and urbanized 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 indicators. Spaceborne SAR interferometry is able to monitor small-scale surface deformation over large areas and was exploited to detect and measure precursors to sinkhole development. The first results of DInSAR-based monitoring of areas associated with sinkhole development using TerraSAR-X is presented here. Subsidence activity was detected on three successive interferograms over a 55 day period. The subsidence basin was approximately 100 m in diameter with a maximum vertical displacement of 66.7 mm. Field surveys revealed tension cracks along the edges of the subsidence basin. Dramatically, four months after the subsidence event, a high pressure water supply pipeline burst meters downslope of the basin. The first results indicate that high resolution, X-band interferometry is able to monitor dolomite-induced instability in an urban environment.

Predicting modis EVI from SAR parameters using random forests algorithms

The monitoring of vegetation biophysical characteristics in agricultural regions has traditionally been achieved using visible and infrared data. However, limitations to long-term monitoring are associated with cloud-cover. In this paper we consider the use of fully polarimetric SAR data and H-A-α decomposition to characterize the link between the different phenological stages of crops and the change in their scattering behaviour over time. The results demonstrated that the analysis of scattering mechanisms observed on fully polarimetric data can provide information regarding the peak of the growing season, the time-frame associated with harvesting and the monitoring of regrowth of crops. Additionally, changes in the roughness conditions of soils associated with harvesting, tilling and planting was observed. This suggests that polarimetric data is complementary to traditional vegetation indices derived from visible and near infrared sensors.

A review of spatial planning in Ghana's socio-economic development trajectory: A sustainable development perspective

Vegetation indices derived from optical and multispectral data, such as Enhanced Vegetation Index (EVI), have been widely used to monitor the health and productivity of vegetation. However, their use is limited in areas with persistent cloud-cover and inclement weather. Due to the all-weather observations provided by Synthetic Aperture Radar (SAR) sensors, an alternative would be to derive vegetation descriptors from SAR data. This study investigates the use of C-band SAR parameters to simulate EVI derived from optical and multispectral data using Random Forest algorithms. The results suggest that the relationship between SAR observables and EVI was weak although the radar backscatter in the HV and HH polarisation as well as Van Zyl volume scattering are potential predictors for EVI.