South African Journal of Geomatics

Temporal Characterization of Land Use Change and Land-scape Processes in Informal Settlements in the City of Cape Town, South Africa

Perpetua I Okoye — 2024-07-10

This study conducted a Land Use Change (LUC) analysis on informal settlements in Cape Town, South Africa, using bi-temporal steps, S1 (2010) and S2 (2016), to characterize land use (LU) conversions and landscape processes for informed policymaking. Utilizing the 2011 national land cover dataset and post-classification methods, two LU datasets and maps, D1 for S1 and D2 for S2, were derived. These classifications achieved an overall accuracy exceeding 95%, with Kappa coefficients above 0.9. The analysis employed change trajectories and conversion labels to evaluate LU changes and landscape dynamics, providing a thematic representation of LUC within informal settlements. Landscape reclamation processes, including abandonment, urban development, and RDP (Reconstruction and Development Programme) development, constituted approximately five percent of the total LU conversions, while degradation processes like persistence and intensification dominated, affecting approximately 93% of the area. Partial reclamation, notably through interspersed RDP (RDPi), accounted for about two percent of conversions. These findings highlight the importance of accurate and timely LUC data reporting in informal settlements to address socioeconomic challenges effectively and support policy decisions to enhance these communities' physical and socioeconomic infrastructure.

Error Analysis in Multibeam Hydrographic Survey System

Basil Daniel Devote — 2024-07-10

Hydrographic surveying involves the integration of a depth-measuring sonar (Sound navigation and ranging) with a positioning system or Global Navigation Satellite System (GNSS); a motion sensor or Inertia Measuring Unit (IMU); and an azimuth sensor (gyroscope). The various sensors acquire data in terms of their respective reference frame and time. The challenge lies in integrating the various sensor frames and time, and in transforming the vessel frame coordinate system into a terrestrial reference frame. The integration of the various sensor frames and time is necessary to minimize systematic errors in the bathymetric data that result from latency, and calibration uncertainty. The focus of this research is to model the systematic bias associated with the integration of the various sensor reference frames. In so doing, the quality of the acquired data is enhanced, and error budgeting and uncertainty prediction can be effectively carried out during the preparation, acquisition, and processing stages of the bathymetric exercise. As such, the required project specification and hydrographic standards, as defined by the International Hydrographic Organization (IHO), are met.

Characterising the evolution of the urban form of zones that accommodate warehousing clusters in the City of Cape Town municipality

Masilonyane Mokhele — 2024-07-10

Modern economies are characterised by increasing globalisation, e-commerce, and a growing number of logistics facilities. Despite insightful research on the changing locational patterns of logistics facilities epitomised by logistics sprawl, there is a lack of literature analysing changes in the urban form of areas that accommodate warehousing clusters. The paper, therefore, aims to analyse changes in the urban form of zones that accommodate warehousing clusters in the City of Cape Town municipality. The study was based on three main types of secondary data: georeferenced 1:50 000 topographical maps from 1942 to 2010, current and historical spatial planning policy applicable to the City of Cape Town, and historical literature on the spatial economic characteristics of the zones that accommodate warehousing clusters. The topographical maps were loaded onto ArcGIS 3.10, after which large buildings were traced to ascertain changes in the urban form of the warehousing cluster areas over the respective decades. The study found that changes in the urban form of the contemporary warehousing cluster areas were linked to the growth of industrial zones and the transport infrastructure. However, the spatial policy for the time under consideration, although cementing the growth of industrial zones in the municipality, did not explicitly consider the placement of warehousing facilities. In light of the findings, the City of Cape Town municipality is urged to anticipate and plan for the growth of warehousing relative to the industrial zones and transport infrastructure. To ensure efficient and sustainable land utilisation, derelict industrial buildings in accessible areas could be redeveloped to accommodate warehousing facilities.

Classification of 3D Sonar Point Clouds derived Underwater using Machine and Deep Learning (CANUPO and RandLA-Net) Approaches

Simiso Ntuli — 2024-07-12

The techniques of point cloud classification in aquatic environments have various applications such as landslide hazard mapping, recovery of lost objects, underwater infrastructure inspection, exploration of mineral resources on the seabed, underwater cultural heritage documentation, environmental preservation and conservation purposes. This study combines acoustic (Sonar) and laser-based (Lidar) remote sensing technologies in an aquatic environment with two machine and deep learning approaches to illustrate the techniques to identify submerged objects. Firstly, the relative accuracy of the underwater imaging system, the BlueView BV5000 Mechanical Scanning Sonar, is evaluated at close range. Secondly, the supervised CANUPO and RandLA-Net classification approaches are used to classify submerged sonar point clouds. Common objects of interest, namely tyres and chairs, were selected for classification. Relative accuracy measurement results showed a centimetre-level root mean square error (RMSE) value, with good accuracies recorded when the scanner is positioned close to objects. The best results were achieved when the target objects were placed at a minimum distance of 2 m from the acoustic scanner. Subsequently, the results of point cloud classification were satisfactory for both approaches. An overall accuracy of 79.81% and an F₁ score of 79.80% were achieved using the CANUPO classification approach. On the other hand, an 80.72% overall accuracy and an 80.63% F₁ score were obtained using a RandLA-Net approach. These analyses provide a reasonable framework for the parameters that can be used when applying these techniques in natural aquatic environments.

A holistic categorisation of address purposes as an analytical entry point to finding solutions for addressing governance in South Africa

Sharthi Laldaparsad — 2024-08-06

An address is structured information allowing one to locate a building or other feature in the physical world; yet, in large parts of the world, including South Africa, many people must get by without such a utility. Although addresses are being rolled out, there is still no clarity concerning stakeholder responsibilities in the governance of addresses and address data in South Africa. In this paper, we present a categorisation of address purposes based on a holistic analysis of the many purposes of an address found in literature. Supported by this categorisation, stakeholders who could or should be involved in addressing governance in South Africa are identified. This first hierarchical categorisation of address purposes can be extended with additional levels of categorisation of new and diverse uses and purposes of addresses as they emerge. The categorisation confirms the significant value of addresses to society, governance, and the economy, sanctioning the need for investments for implementing an effective addressing infrastructure. The study serves as the analytical entry point to finding solutions for more coherent policy formulation and governance.

Impact of Landuse change on Urban Thermal Variance in Umuahia Urban, Nigeria: a Remote Sensing-based Approach

Felix Ike — 2024-08-06

Urbanization is directly related to changes in land surface temperature (LST). However, little is known about the spatial and temporal impact of urbanization on Urban Thermal Variance (UTV) in Umuahia. To this end, we quantified the spatiotemporal associations of UTV intensity between 1986 and 2017. We calculated LST change by using a land-use change map and computed the level of vegetation coverage based on the Normalized Diﬀerence Vegetation Index (NDVI), and the Urban Thermal Field Variance Index (UTFVI). In so doing, we could determine the ecological index from multi-temporal Landsat data. Results showed that, at the expense of other types of land cover, the built-up portions of the study area were progressively increasing in surface area with a concomitant increase in temperature. The transfer matrix developed in this study reveals that within the 31-year period there was a transformation of about 59.88% and 8.23% from vegetation and bareland, respectively, to built-up cover. The spatio-temporal distribution of surface temperature showed that the built-up areas recorded the highest annual mean temperatures of 21.50^oC in 1986, 22.20^oC in 2001, and 26.01^oC in 2017. Results of the UTFVI showed that more areas were undergoing deteriorating ecological change and imbalances, thus leading to an increase in the area affected by the strong heat island phenomenon, which accounted for 0.065% of the total study area in 1986, 1.02% in 2000, and 32.91% in 2017. We concluded that urbanization has increased the overall surface temperature of the city. However, owing to the re-location of the city’s main market, there has been a decline in UTFV in the vicinity of the city centre. The research findings indicate that the implementations of effective plans to mitigate the heat island effects are imperative for the promotion of sustainable urban development.

Mapping smallholder maize farm distribution using multi-temporal Sentinel-1 data integrated with Sentinel-2, DEM and CHIRPS precipitation data in Google Earth Engine

Colette de Villiers — 2024-08-06

Mapping smallholder maize farms in complex and uneven rural terrain is a major barrier to accurately documenting the spatial representation of the farming units. Remote sensing technologies rely on various satellite products for differentiating maize cropland cover from other land cover types. The potential for multi-temporal Sentinel-1 synthetic aperture radar (SAR), Sentinel-2, digital elevation model (DEM) and precipitation data obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) version 2.0 was investigated for mapping maize crop distributions during the growing seasons, 2015–2021, in the Sekhukhune municipal area of Limpopo, a province in South Africa. Sentinel-1 variables, including monthly VH, VV, VV+VH (V = vertical, H = horizontal) polarization band data and data issuing from the principal component analysis of VH polarization were integrated with Sentinel-2-derived normalized difference vegetation index (NDVI), DEM terrain, and precipitation data. The random forest (RF) algorithm was applied to distinguish maize crops from four other land cover types, including bare soil, natural vegetation, built-up area, and water. The findings indicated that the models that used only Sentinel-1 data as input data had overall accuracies below 71%. The best performing models producing overall accuracies above 83% for 2015–2021 were those where Sentinel-1 (VV+VH) data were integrated with all the ancillary data. Overall, the McNemar test indicated enhanced performance for models where all other ancillary input data had been incorporated. The results of our study show considerable temporal variation in maize area estimates, with 59 240.84 ha in the 2018/2019 growing season compared to 18 462.51 ha in the 2020/2021 growing season. The spatial information gathered through these models proved to be valuable and is essential for addressing food security, one of the objectives of the Sustainable Development Goals.

Assessing the Impact of Spatial Planning on the Spread of COVID-19 within Kampala City

Brendah Nagula — 2024-08-06

Spatial planning has previously had an impact on the dynamics of pandemics. However, its influence on the spread of COVID-19 has not been explored. This research therefore focused on assessing the impact of spatial planning on the spread of COVID-19 in Kampala City. The research was based on the confirmed COVID-19 cases registered between 21 March 2020 and 27 March 2021 and was conducted in conjunction with the spatial analytical methods of the Global Moran’s Index, Anselin’s Local Indicator of Spatial Association (LISA) and the Geographically Weighted Regression model (GWR). Global Moran’s I and Anselin’s LISA were used to determine the spatial distribution of COVID-19 cases. The GWR was used to model the relationship between conformance to spatial planning and the spatial distribution of COVID-19 cases. Results attained through these methods showed a random distribution of cases, with LISA results indicating parishes located in the Central Division as major disease risk sites of COVID-19. Furthermore, results from the GWR revealed a negative relationship, with an R² value of 0.51, between spatial planning and the spatial distribution of Covid-19. This means that variations in spatial planning initiatives could explain 51 per cent of the variations in COVID-19 cases in Kampala City. Therefore, to change Kampala into a pandemic-resilient city, there is a need to develop appropriate compact spatial planning designs, especially in the parishes of Nakasero 1, Nakasero 11, Nakasero III, and Kagugube.

Assessment of Spatial-temporal Lake Victoria Shoreline Variations using Synthetic Aperture Radar

Lydia Mazzi Kayondo — 2024-09-18

In the context of their dynamic and linear character, shorelines feature as among the most important features on the earth’s surface. They change shape and position over multiple spatial and temporal scales, with their water levels serving as a key indicator to characterize the expansion or shrinkage of the water body in question. In the context of Lake Victoria, the central focus of this article, there has been an uncontrolled increase in its water levels which has ultimately contributed to variations in the lake shoreline. This variation has in turn led to unpredictable flooding along the shores of the lake, claiming human property. Therefore, environmental management authorities, such as NEMA, require accurate and up-to-date information about shoreline changes. The main objective of this study was to assess the spatial-temporal variations of the Lake Victoria shoreline in the Southern Buganda sub-region for the period 2015 - 2021, by employing microwave remote sensing. Sentinel 1 and Sentinel 2 imagery were used. The study also assessed the performance of HH and VH polarizations in shoreline delineation. Different image processing techniques such as thresholding and band math were used in both SNAP and ArcGIS software. Based on the DSAS evaluation statistics, VH polarization performed a better delineation of the shoreline than HH polarization. The study also found that the lake shoreline in the Southern Buganda sub-region was subject to entirely low erosion rates, ranging from 0.5m/yr to 2m/yr, as observed in the sub-counties of Buwunga, Kyanamukaaka, and Kabira. High erosion rates of above 5m/yr were observed in some areas in the Bukakata and Kyebe sub-counties. This study recommends that VH polarization be used. Further studies could integrate predictive analytics to attain future shoreline positions.

Leveraging on GIS to Enhance Efficiency and Promote Good Governance in Subsidized Fertilizer Distribution: Case Study of Njoro Sub-County, Kenya

S.S.W Ngugi — 2024-09-18

In line with its interventions to tackle food insecurity and achieve Sustainable Development Goal (SDG) 2, the Government of Kenya in 2022 allocated 3.55 billion Kenya Shillings as a subsidy to support farmers in purchasing fertilizers. Despite this initiative, however, corruption has led to the diversion of fertilizers to private shops, thus jeopardizing the intended distribution to needy farmers. This study aims to demonstrate the application of Geographic Information Systems (GIS) to enhance efficiency and promote good governance in the distribution of subsidized fertilizer. The GIS approach addressed multiple factors, namely, reducing farmers' travel distance to access fertilizers, ensuring proper accountability for distributed fertilizers, guiding farmers in their use of the appropriate type and quantity of fertilizer based on soil and crop types, and increasing overall food production. Data applicable to eligible farmers such as land parcel details, soil information and crop types were obtained from the Ministry of Agriculture and integrated into a geodatabase. Spatial and statistical analyses revealed that most farmers operate on a small-scale and are located more than 40 km from the main government fertilizer depot, making transportation costs prohibitive. The proposed solution is to establish sub-depots at the ward level within a three-kilometer radius for easier access. A user-friendly dashboard displaying farmers' locations and farm data was created to enhance transparency and accountability, while optimizing fertilizer distribution logistics. The study showed that GIS is a powerful tool for enhancing the efficiency and promoting good governance in the distribution of agricultural inputs, thus ultimately contributing to improved food security and sustainable development.

Application of Ordinary Kriging in Mapping Soil Organic Carbon in Chad using SoilGrids data

Kella Douzouné — 2024-09-18

The quantification of the pattern and spatial distribution of soil organic carbon (SOC) is fundamental to understanding many ecosystem processes. This study aimed to apply ordinary kriging (OK) to model the spatial distribution of SOC in Chad. A total of 995 sampling locations from the region were used to extract soil organic carbon from three raster layers. Those raster layers represented the SOC of 0-5 cm, 5–15 cm, and 15-30 cm of soil horizon and were downloaded from the SoilGrids website. The mean value of the soil carbon derived from the three horizons was used as 0-30cm horizon data and analysed using R-4.1.3 version software and ArcGIS 10.5. Different variogram models were first examined on the variogram cloud, and, based on RMSE, MSE, and MAE criteria, the best fit was selected. The results indicated that the Gaussian model is the best fit to the data, with 27.84, -3.35, and 20.95 obtained, respectively, for RMSE, MAE, and ME. The short-range spatial dependence of SOC was strong, with a nugget close to zero. The spatial dependency of the data was medium, with a nugget-to-sill ratio of 0.36. The southern portion of the country has a higher concentration of SOC than the northern portion. It can be concluded that the generated map could serve as a proxy for SOC in the region where evidence of spatial structure and quantitative estimates of uncertainty are reported. Therefore, the maps produced can be used for many applications, including soil sampling optimization.