Opportunities for Increasing Societal Value of Remote Sensing Data in South Africa’s Strategic Development Priorities: A Review

Despite the enormous capital required to fund remote sensing initiatives, governments worldwide are increasingly adopting earth observation technologies to optimise operational efficiency and societal benefit. However, the value of information derived from earth observation will increase substantially if augmented by socio-economic data within contextualised focus areas of direct societal relevance. Within the framework of the key strategic development priorities designed by the South African government, the objective of this paper was to review existing and emerging remote sensing applications and their relevance to South Africa’s development priorities. Whereas there is potential for adoption of remote sensing techniques in other prioritised areas, this paper identifies health, crime analysis, rural planning and agriculture, natural resource management and physical planning as areas with considerable potential. However, to realise the set strategic priorities and outcomes, decision support systems that incorporate information derived from remote sensing should be maximised. To achieve this, it will be necessary to link patterns and processes from expert knowledge to emerging and existing societal challenges identified and to develop requisite policies of governance. The paper concludes that remote sensing technology has considerable potential to support sustainable socio-economic strategic priorities set by the South African government

Extraction of low cost houses from a high spatial resolution satellite imagery using Canny edge detection filter

Since its democratic dispensation in 1994, the South African government enacted a number of legislative and policy interventions aimed at availing equal housing opportunities to the previously marginalized citizens. Mismanagement and unreliable reporting has been widely reported in publicly funded housing programmes which necessitated the government to audit and monitor housing development projects in municipalities using more robust and independent methodologies. The objective of this study was therefore to test and demonstrate the effectiveness of high spatial resolution satellite imagery in validating the presence of government funded houses using an object-oriented classification technique that applies a Canny edge detection filter. The results of this study demonstrate that object-orientated classification applied on pan-sharpened SPOT 6 satellite imagery can be used to conduct a reliable inventory and validate the number of houses. The application of the multi-resolution segmentation and Canny edge detection filtering technique proved to be an effective means of mapping individual houses as shown by the high detection accuracy of 99% and quality percentage of 96%.Keywords: Houses, Remote Sensing, SPOT 6, Canny edge detection, Multi-resolution Segmentation, Object-Oriented Classificatio

Monitoring urban growth around Rustenburg, South Africa, using SPOT 5

Understanding urban spatial growth is critical for sustainable urban infrastructure and service planning. Urban growth information is an important input into predicting future land cover and land use change and sustainable growth management. Rapid urbanization has resulted in expanded urban land use and has led to population explosions in urban areas and undesirable environmental impacts in South Africa. This research is aimed at studying urban spatial growth of Rustenburg city in South Africa from 2007 to 2012 using temporal imagery acquired by Satellite Pour l’Observation de la Terra (SPOT) 5 satellite. Multi-temporal images acquired in 2007, 2009 and 2012 were used to assess urban spatial growth of Rustenburg. Post-classification change detection method was used to quantify urban growth. For the purpose of this study, only two classes, urban and non-urban land use classes, were mapped and assessed. The urban spatial growth between 2007 and 2009 was 16.8% while 8.7% urban growth was observed between 2009 and 2012. The total spatial urban growth between 2007 and 2012 was 25.5%. This information can be used by the municipal authorities and decision makers as input during urban and environmental planning

Land use/cover change modelling and land degradation assessment in the Keiskamma catchment using remote sensing and GIS

Land degradation in most communal parts of the Keiskamma catchment has reached alarming proportions. The Keiskamma catchment is particularly predisposed to severe land degradation associated with soil erosion, thicket degradation and deteriorating riparian vegetation. There is a close coupling between land use/cover dynamics and degradation trends witnessed in the catchment. Soil erosion is prevalent in most of the communal areas in the catchment. The principal aim of this study was to investigate land use/cover trends, model the spatial patterns of soil loss and predict future land use/cover scenarios as a means of assessing land degradation in the Keiskamma catchment. Multi-temporal Landsat satellite imagery from 1972 to 2006 was used for land use/cover change analyses using object-oriented post-classification comparison. Fragmentation analysis was performed by computing and analyzing landscape metrics in the riparian and adjacent hillslope areas to determine the land cover structural changes that have occurred since 1972. The landscape function analysis was used to validate the current rangeland conditions in the communal areas and the former commercial farms. The current condition of the riparian zones and proximal hillslopes was assessed using the Rapid Appraisal of Riparian Condition and future land use/cover scenarios were simulated using the Markovcellular automata model. Spatial patterns of soil loss in the Keiskamma catchment were determined using the Sediment Assessment Tool for Effective Erosion Control (SATEEC), which is a GIS based RUSLE model that integrates sediment delivery ratios. Object oriented classification was used to map soil erosion surfaces and valley infill in ephemeral stream channels as a means of demonstrating the major sediment transfer processes operating in the Keiskamma catchment. The Mahalanobis distance method was used to compute the topographic thresholds for gully erosion. To understand the effect of soil characteristics in severe forms of erosion, laboratory analyses were undertaken to determine the physico-chemical soil properties. iv The temporal land use/cover analysis done using the post-classification change detection indicated that intact vegetation has undergone a significant decline from 1972 to 2006. The temporal changes within the intermediate years are characterized by cyclic transitions of decline and recovery of intact vegetation. An overall decline in intact vegetation cover, an increase in degraded vegetation and bare eroded soil was noted. Fragmentation analyses done in the communal villages of the central Keiskamma catchment indicated increasing vegetation fragmentation manifested by an increase in smaller and less connected vegetation patches, and a subsequent increase of bare and degraded soil patches which are much bigger and more connected. The Landscape Organisation Index revealed very low vegetation connectivity in the communal rangelands that have weak local traditional institutions. Fragmentation analyses in the riparian and proximal hillslopes revealed evidence of increasing vegetation fragmentation from 1972 to 2006. The Markov Cellular Automata simulation predicted a decline in intact vegetation and an increase in bare and degraded soil in 2019. The Keiskamma catchment was noted as experiencing high rates of soil loss that are above provincial and national averages. The classification of erosion features and valley infill showcased the vegetation enrichment in the ephemeral streams which is occurring at the expense of high soil losses from severe gully erosion on the hillslopes. This in turn has led to an inversion of grazing patterns within the catchment, such that grazing is now concentrated within the ephemeral stream channels. Soil chemical analyses revealed a high sodium content and low soluble salt concentration, which promote soil dispersion, piping and gully erosion. The presence of high amounts of illite-smectite in the catchment also accounts for the highly dispersive nature of the soil even at low SAR values. Significant amounts of swelling 2:1 silicate clays such as smectites cause cracking and contribute to the development of piping and gullying in the catchment. Given the worsening degradation trends in the communal areas, a systematic re-allocation of state land in sections of the catchment that belonged to the former commercial farms is recommended to alleviate anthropogenic pressure. Strengthening local institutions that effectively monitor and manage natural resources will be required in order to maintain v optimum flow regimes in rivers and curb thicket degradation. Measures to curb environmental degradation in the Keiskamma catchment should encompass suitable ecological interventions that are sensitive to the socio-economic challenges facing the people in communal areas

A comparison of Normalised Difference Snow Index (NDSI) and Normalised Difference Principal Component Snow Index (NDPCSI) techniques in distinguishing snow from related land cover types

Snow is a common global meteorological phenomenon known to be a critical component of the hydrological cycle and an environmental hazard. In South Africa, snow is commonly limited to the country’s higher grounds and is considered one of the most destructive natural hazards. As a result, mapping of snow cover is an important process in catchment management and hazard mitigation. However, generating snow maps using survey techniques is often expensive, tedious and time consuming. Within the South African context, field surveys are therefore not ideal for the often highly dynamic snow covers. As an alternative, thematic cover–types based on remotely sensed data-sets are becoming popular. In this study we hypothesise that the reduced dimensionality using Principal Components Analysis (PCA) in concert Normalized Difference Snow Index (NDSI) is valuable for improving the accuracy of snow cover maps. Using the recently launched 11 spectral band Landsat 8 dataset, we propose a new technique that combines the principal component imager generated using PCA with commonly used NDSI, referred to as Normalised Difference Principal Component Snow Index (NDPCSI) to improve snow mapping accuracy. Results show that both NDPCSI and NDSI with high classification accuracies of 84.9% and 76.8% respectively, were effective in mapping snow. Results from the study also indicate that NDSI was sensitive to water bodies found on lower grounds within the study area while the PCA was able to de-correlate snow from water bodies and shadows. Although the NDSI and NDPCSI produced comparable results, the NDPCSI was capable of mapping snow from other related land covers with better accuracy. The superiority of the NDPCSI can particularly be attributed to the ability of principal component analysis to de-correlate snow from water bodies and shadows. The accuracy of both techniques was evaluated using a higher spatial resolution Landsat 8 panchromatic band and Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired on the same day. The findings suggest that NDPCSI is a viable alternative in mapping snow especially in heterogeneous landscape that includes water bodies

Validation of sentinel-2 leaf area index (LAI) product derived from SNAP toolbox and its comparison with global LAI products in an African semi-arid agricultural landscape

This study validated SNAP-derived LAI from Sentinel-2 and its consistency with existing global LAI products. The validation and intercomparison experiments were performed on two processing levels, i.e., Top-of-Atmosphere and Bottom-of-Atmosphere reflectances and two spatial resolutions, i.e., 10 m, and 20 m. These were chosen to determine their effect on retrieved LAI accuracy and consistency. The results showed moderate R2, i.e., ~0.6 to ~0.7 between SNAPderived LAI and in-situ LAI, but with high errors, i.e., RMSE, BIAS, and MAE >2 m2 m–2 with marked differences between processing levels and insignificant differences between spatial resolutions. In contrast, inter-comparison of SNAP-derived LAI with MODIS and Proba-V LAI products revealed moderate to high consistencies, i. e., R2 of ~0.55 and ~0.8 respectively, and RMSE of ~0.5 m2 m–2 and ~0.6 m2 m–2, respectively. The results in this study have implications for future use of SNAP-derived LAI from Sentinel-2 in agricultural landscapes, suggesting its global applicability that is essential for large-scale agricultural monitoring. However, enormous errors in characterizing field-level LAI variability indicate that SNAP-derived LAI is not suitable for precision farming. In fact, from the study, the need for further improvement of LAI retrieval arises, especially to support farm-level agricultural management decisions

Empirical validation of the UNAIDS Spectrum model for subnational HIV estimates: case-study of children and adults in Manicaland, Zimbabwe

Background: More cost-effective HIV control may be achieved by targeting geographical areas with high infection rates. The AIDS Impact model of Spectrum – used routinely to produce national HIV estimates – could provide the required subnational estimates but is rarely validated with empirical data, even at a national level. Design: The validity of the Spectrum model estimates were compared to empirical estimates. Methods: Antenatal surveillance and population survey data from a population HIV cohort study in Manicaland, east Zimbabwe, were input into Spectrum 5.441 to create a simulation representative of the cohort population. Model and empirical estimates were compared for key demographic and epidemiological outcomes. Alternative scenarios for data availability were examined and sensitivity analyses were conducted for model assumptions considered important for subnational estimates. Results: Spectrum estimates generally agreed with observed data but HIV incidence estimates were higher than empirical estimates while estimates of early age all-cause adult mortality were lower. Child HIV prevalence estimates matched well with the survey prevalence among children. Estimated paternal orphanhood was lower than empirical estimates. Including observations from earlier in the epidemic did not improve the HIV incidence model fit. Migration had little effect on observed discrepancies - possibly because the model ignores differences in HIV prevalence between migrants and residents. Conclusions: The Spectrum model, using subnational surveillance and population data, provided reasonable subnational estimates although some discrepancies were noted. Differences in HIV prevalence between migrants and residents may need to be captured in the model if applied to subnational epidemics

Factors Associated with Ever Being HIV-Tested in Zimbabwe: An Extended Analysis of the Zimbabwe Demographic and Health Survey (2010-2011).

INTRODUCTION: Zimbabwe has a high human immunodeficiency virus (HIV) burden. It is therefore important to scale up HIV-testing and counseling (HTC) as a gateway to HIV prevention, treatment and care. OBJECTIVE: To determine factors associated with being HIV-tested among adult men and women in Zimbabwe. METHODS: Secondary analysis was done using data from 7,313 women and 6,584 men who completed interviewer-administered questionnaires and provided blood specimens for HIV testing during the Zimbabwe Demographic and Health Survey (ZDHS) 2010-11. Factors associated with ever being HIV-tested were determined using multivariate logistic regression. RESULTS: HIV-testing was higher among women compared to men (61% versus 39%). HIV-infected respondents were more likely to be tested compared to those who were HIV-negative for both men [adjusted odds ratio (AOR) = 1.53; 95% confidence interval (CI) (1.27-1.84)] and women [AOR = 1.42; 95% CI (1.20-1.69)]. However, only 55% and 74% of these HIV-infected men and women respectively had ever been tested. Among women, visiting antenatal care (ANC) [AOR = 5.48, 95% CI (4.08-7.36)] was the most significant predictor of being tested whilst a novel finding for men was higher odds of testing among those reporting a sexually transmitted infection (STI) in the past 12 months [AOR = 1.86, 95%CI (1.26-2.74)]. Among men, the odds of ever being tested increased with age ≥ 20 years, particularly those 45-49 years [AOR = 4.21; 95% CI (2.74-6.48)] whilst for women testing was highest among those aged 25-29 years [AOR = 2.01; 95% CI (1.63-2.48)]. Other significant factors for both sexes were increasing education level, higher wealth status and currently/formerly being in union. CONCLUSIONS: There remains a high proportion of undiagnosed HIV-infected persons and hence there is a need for innovative strategies aimed at increasing HIV-testing, particularly for men and in lower-income and lower-educated populations. Promotion of STI services can be an important gateway for testing more men whilst ANC still remains an important option for HIV-testing among pregnant women

Gully cut- and- fill cycles as related to agromanagement : a historical curve number simulation in the Tigray Highlands

Gully cut-and-fill dynamics are often thought to be driven by climate and/or deforestation related to population pressure. However, in this case-study of nine representative catchments in the Northern Ethiopian Highlands, we find that neither climate changes nor deforestation can explain gully morphology changes over the twentieth century. Firstly, by using a Monte Carlo simulation to estimate historical catchment-wide curve numbers, we show that the landscape was already heavily degraded in the nineteenth and early twentieth century – a period with low population density. The mean catchment-wide curve number (> 80) one century ago was, under the regional climatic conditions, already resulting in considerable simulated historical runoff responses. Secondly, twentieth century land-cover and runoff coefficient changes were confronted with twentieth century changing gully morphologies. As the results show, large-scale land-cover changes and deforestation cannot explain the observed processes. The study therefore invokes interactions between authigenic factors, small-scale plot boundary changes, cropland management and sociopolitical forces to explain the gully cut processes. Finally, semi-structured interviews and sedistratigraphic analysis of three filled gullies confirm the dominant impact of (crop)land management (tillage, check dams in gullies and channel diversions) on gully cut-and-fill processes. Since agricultural land management – including land tenure and land distribution – has been commonly neglected in earlier related research, we argue therefore that it can be a very strong driver of twentieth century gully morphodynamics

Estimating the Population Size of Female Sex Workers in Zimbabwe: Comparison of Estimates Obtained Using Different Methods in Twenty Sites and Development of a National-Level Estimate.

BACKGROUND: National-level population size estimates (PSEs) for hidden populations are required for HIV programming and modelling. Various estimation methods are available at the site-level, but it remains unclear which are optimal and how best to obtain national-level estimates. SETTING: Zimbabwe. METHODS: Using 2015-2017 data from respondent-driven sampling (RDS) surveys among female sex workers (FSW) aged 18+ years, mappings, and program records, we calculated PSEs for each of the 20 sites across Zimbabwe, using up to 3 methods per site (service and unique object multipliers, census, and capture-recapture). We compared estimates from different methods, and calculated site medians. We estimated prevalence of sex work at each site using census data available on the number of 15-49-year-old women, generated a list of all "hotspot" sites for sex work nationally, and matched sites into strata in which the prevalence of sex work from sites with PSEs was applied to those without. Directly and indirectly estimated PSEs for all hotspot sites were summed to provide a national-level PSE, incorporating an adjustment accounting for sex work outside hotspots. RESULTS: Median site PSEs ranged from 12,863 in Harare to 247 in a rural growth-point. Multiplier methods produced the highest PSEs. We identified 55 hotspots estimated to include 95% of all FSW. FSW nationally were estimated to number 40,491, 1.23% of women aged 15-49 years, (plausibility bounds 28,177-58,797, 0.86-1.79%, those under 18 considered sexually exploited minors). CONCLUSION: There are large numbers of FSW estimated in Zimbabwe. Uncertainty in population size estimation should be reflected in policy-making