South African Journal of Geomatics

Multicriteria decision method for renewable energy production: siting solar, wind and small hydropower plants in Zimbabwe

Grace Ngwenya — 2024-01-16

Energy development in Zimbabwe has not been coincident with the rising demand for energy, thus placing a large strain on existing resources. The National Renewable Energy Policy states that by 2030, Zimbabwe should to some extent be driven by clean and sustainable energy sources. In support of this initiative, this study sought to identify suitable locations for renewable energy production plants (solar, wind and small hydropower) in Zimbabwe. The Analytic Hierarchy Process (AHP) was used to evaluate the decision criteria. A raster-based suitability model was constructed using the decision criteria, and areas showing suitable sites to install wind, solar and small hydropower (SHP) plants were identified. The results showed that suitable sites for small-scale wind turbines are in the Beitbridge rural district covering a land area of approximately 12 719 km². Hwange rural was found to be the district with a large potential for siting solar power plants with a land area of approximately 26 974 km². Several river channels distributed throughout the country were identified as potential sites for establishing SHP plants. The main contributions of this paper are the identification of the evaluation criteria and suitable sites for wind, solar and SHP plants in Zimbabwe.

Evaluation of the Performance of the Water Cloud Model and Modified Water Cloud Model in Estimating Soil Moisture. A case study of Kiruuli Village

Mark Mukomazi Buyungo — 2024-02-21

In Uganda, crop yields have been constrained by recurrent droughts and reliance on rain-fed agriculture. As a straightforward measure, irrigation farming has been adopted by the government through its rehabilitation of old schemes and its assistance to farmers in the setting up of micro-irrigation farms. Of consequence is the fact that the maximization of crop yields through irrigation necessitates soil moisture data for irrigation scheduling. Both ground-based measurements and remote sensing techniques can be used to access this information, with the latter holding the advantage of gathering more information over a wider area. Because of its ability to account for vegetation cover, the Water Cloud Model (WCM) ─ a remote sensing-based model ─ has been widely used in earlier studies to estimate soil moisture content over vegetated areas. However, the accuracy of the model is limited by the assumption that vegetation is a homogenous scatterer. Thus, the Modified Water Cloud Model (MWCM) was developed in accordance with the debate that by considering the heterogeneous scattering nature of the vegetation, it would perform better than the WCM. Using Kiruuli Village (in a coffee-growing area), this study compared the performance of the WCM and the Modified Water Cloud Model (MWCM) in estimating soil moisture. The models were implemented using Sentinel 1 and 2 images acquired on 05 September 2021 and 02 August 2021, respectively. Results showed that the MWCM performed slightly better than the WCM with Root Mean Square Errors (RMSEs) of 3.3346 and 3.7482, respectively. The marginality of the results can be attributed to a relatively high vegetation fraction at the time of image acquisition and a reasonably small area of comparison. Generally, more work can be carried out to compare the models across a larger area with a sparser vegetation cover.

Validating Uav-Sfm Photogrammetry Heights for Highway Topographic Surveying in Tanzania

Nicholas Charles Batakanwa — 2024-02-15

The demand for accurate topographic surveying data to support ever-growing infrastructural development such as highway construction is huge. Topographic surveying defines a point with X, Y, and Z relative values to create a 3D earth surface model. The Z values represent the vertical height of a point from the benchmark. Vertical heights can be obtained from conventional levelling and Digital Elevations Models (DEMs), as in the case of heights from unmanned aerial vehicle structures from motion photogrammetry (SfM-P) and global navigation satellite systems (GNSSs).

GNSS-Real Time Kinematics (RTK) is the most common method used but is sometimes outscored because of limitations in terms of time consumption and physically inaccessible surfaces. Recently, SfM-P surveys appear to have been quick and effective in accessing areas that would not have been possible when applying GNSS RTK methods. SfM-P surveys have recently been reorganized through cheap, rapid and elementary methods, but few research findings have been documented. Therefore, the study for validating SfM-P surveys in topographic surveys of highways in Tanzania has proved to be most opportune.

In this study, an evaluation was performed by comparing SfM-P survey method heights to GNSS RTK method heights for an area with 3km wide and 19 km long. A total of 39 ground control points was used. The standard deviation between the SfM-P method heights and the GNSS RTK method heights was ±1.4 cm. The samples of elevation data for the preliminary surveying of highways were determined at an 80% accuracy level. However, among the respective heights, only 20% produced a +/- two-centimetre (2 cm) relative precision ─- an extremely high precision level and most satisfactory for detailed topographic surveys. This study confirms that the SfM-P survey can be most helpful in preliminary highway surveys in Tanzania and in surveys of those areas, such as the Dodoma region, with a sparser vegetation cover. However, the SfM-P survey method cannot guarantee good performance to comply with the detailed highway topographic survey height requirements of Tanzania.

Deep Learning-based Derivatives for Shoreline Change Detection in Cape Town, South Africa

Lynn Fanikiso — 2024-02-29

Coastal environments are vital for sustaining key industries, including fisheries, mining, real estate, and tourism, through the provisioning of essential ecosystem services. However, the intricate interplay of bio-chemical and physical processes in these areas is susceptible to disruption by human-driven coastal developments. This disruption has gradually led to adverse consequences such as altered water movement patterns, land quality degradation, habitat loss, pollutant introduction, greenhouse gas emissions, and subsequent sea-level rise. With Cape Town's 240-kilometre coastline serving as a prime example of this complex relationship between coastal processes and livelihoods, this study employs innovative remote sensing and deep learning techniques. We use Sentinel-2 satellite imagery and the Deep Learning-based CoastSat Python toolkit, specifically leveraging the Modified Normalized Difference Water Index (MNDWI) for sub-pixel level shoreline detection. Focusing on four critical Cape Town coastlines - Noordhoek-Kommetjie, Milnerton, Strandfontein-Monwabisi, and Strand, we assess and compare shoreline changes as detected by the CoastSat toolkit. Our findings reveal significant shoreline changes, with Strandfontein-Monwabisi experiencing the most substantial shift, approximately 284 metres. Noordhoek-Kommetjie exhibited a change of 110 metres, while Milnerton and Strand displayed similar changes, with 88 and 91 metres, respectively. This research not only offers valuable insights into Cape Town's dynamic coastlines, but also informs the strategic allocation of coastal management resources. Consistent data collection and analysis hold the potential to foster interactive coastal monitoring tools, enhancing the effectiveness and sustainability of coastal management practices.

Accuracy assessment of vertical and horizontal coordinates derived from Unmanned Aerial Vehicles over District Six in Cape Town

Thabani Thuse — 2024-03-13

Unmanned aerial vehicles (UAVs) are now an alternative for geospatial data collection for a variety of applications. One such application is terrain mapping, particularly digital elevation model (DEM) and digital surface model (DSM) products, but questions remain about its accuracy and efficiency, especially when compared to traditional ground survey methods. Thus, the purpose of this paper is to compare the traditional surveying methods for topographical mapping through datasets obtained through Total Station (Trimble M3) and a camera mounted on a quadcopter (DJI Phantom 4 Pro UAV). We obtained both datasets in the same location at the District 6 open field area in the City of Cape Town, with undulating terrain. We compared the resultant horizontal coordinates (x and y) and orthometric height (H) at 159 check points (CPs). The drone-based elevations were derived using UAV drone computer vision techniques. In using the UAV drone, the reconstructed camera positions and terrain features were used to derive ultra-high-resolution point clouds, ortho-photos, and digital surface models from the multi-view UAV camera photos taken at 120 m above mean sea level. The root-mean-square-errors (RMSEs) for the differences between the Total Station and UAV coordinates at 159 CPs are ±0.046, ±0.038 and ±0.079 m for x, y, and H coordinates, respectively. Comparisons over slope ranges show that the highest orthometric height error (±0.090 m) is at a slope steepness of >15°, while the least orthometric height error (±0.073 m) is at a slope steepness of 5 - 10°. The results also show that the highest errors in x (±0.070 m) and y (±0.074 m) occur at a slope steepness of >15° and the least errors in x (±0.035 m) and y (±0.029 m) at a 5 – 10° slope steepness. Similar comparisons on elevation ranges show that the highest orthometric height error (±0.098 m) is at an elevation range of 60 – 100 m, while the least orthometric height error (±0.052 m) is at a 40 – 60 m elevation range. The highest errors in x (±0.051 m) and y (±0.061m) occur at elevation ranges of 40 – 60 m and 0 – 20 m, respectively, while the least errors in x (±0.040 m) and y (±0.025 m) occur at elevation ranges of 80 – 100 m and 40 – 60 m, respectively. These results indicate that horizontal positions (x, y) and orthometric heights (H) obtained from UAV are accurate enough for most mapping applications.

Counting Buildings from Unmanned Aerial Vehicle Images Using a Deep Learning Based Approach

Evet Naturinda — 2024-04-10

Effective urban planning requires accurate and up-to-date spatial information. Remote sensing has contributed immensely to the efficiency of collecting this information. With remotely sensed high-spatial-resolution images, details such as buildings counted in an area can be extracted; however, traditional methods of extracting this information involve direct counting by humans, which is often demanding in terms of time. Computer vision techniques have shown promising results in handling image-related challenges in recent years. Therefore, this study aimed to adapt deep learning-based algorithms to simplify the counting of buildings from high-spatial-resolution aerial images in a fairly suburban environment. A deep learning algorithm based on convolutional neural networks, You Only Look Once (YOLO), was adapted to detect and count the buildings in the Unmanned Aerial Vehicle (UAV) sensed images. The model achieved high accuracy, with a recall rate of 0.89, an F1 score of 0.89, and an average precision of 91.12% on the validation data. When applied to new testing data, the algorithm successfully identified and counted the number of buildings with an overall accuracy of 71%. The approach presented in this research extracted building counts reliably, quickly, and accurately in a fairly suburban environment. Such information can be applied to tracking urban growth and physical planning.

A Geodetic-based Estimate of Groundwater Storage Variations in Balaka, Malawi

Mwayi Michael Taulo — 2024-03-13

Ground water is the main source of water for domestic and agricultural purposes in rural areas in Malawi. Continued exploitation of the ground water for domestic, agricultural, mining and other industrial purposes results in continued temporal changes in its levels. Understanding changes in the Groundwater Storage Capacity is crucial in development and in improving the livelihoods of people. Attempts to study groundwater storage have been made in Malawi. However, the lack of groundwater data, triggered by scarcity of ground observation facilities, hampers water resource management efforts. In this paper, the Gravity Recovery and Climate Experiment (GRACE-FO), supported by Global Land Data Assimilation System (GLDAS), has been used to determine variations in Terrestrial Water storage capacity levels, which combine the surface moisture, groundwater, snow and canopy water conditions in Balaka district, in Malawi, over a period of ten years (2012-2022). Owing to the fact that Balaka does not register any snowfall, only the surface moisture anomaly was considered in reducing the terrestrial water storage anomaly to determine the groundwater storage level changes from 2012 to 2022. Since an increasing trend, declining to levels as low as 0.002mm/year, was determined, the GRACE-based groundwater storage anomalies revealed no significant changes in groundwater levels. Influencing factors for the increasing trend were not addressed in this paper. Nonetheless, the results of this paper can contribute positively to the effective management of groundwater resources and promote the use of geodetic gravity data in water resource management.

A quantitative assessment of forest rejuvenation activities by a community on the borders of Hwange National Park using GIS and Remote Sensing

Kudzai Chirango Chirenje — 2024-03-13

Various natural and human activities have led to land degradation in the Hwange National Park and in the surrounding communal areas. The deforestation has in turn upset the natural ecosystem, with the desecrated vegetation leading to reduced soil moisture-holding capacity, reduced carbon sequestration by the forest and the subsequent loss of biodiversity. There has been attempts to rejuvenate the forest through activities that include holistic grazing methods, the use of swales, the mulching of fields, the cultivation of cover crops, the use of gabions, erosional restoration works, and the use of rocket stoves to minimise the reliance of inhabitants on firewood. These have been followed up by the application of scientific methodologies to assess the effectiveness of the various methods used. By applying the Normalized Difference Vegetation Index (NDVI) and assessing the land use / land cover changes (LULC), the impacts of the aforementioned rejuvenation activities were studied over a period of five years (2017-2022). Annual mean NDVI values were computed to reduce the bias in respect of changes in leaf phenology caused by variations in rainfall. The results show an increase of NDVI from a mean of 0.304 in 2020 to a mean of 0.345 in 2021. More so, there was a 30% increase in 2021 and a 46% increase in 2022 from the previous years for forest cover in the study area. The results show the positive impact of the performed rejuvenation activities.

Vegetation Monitoring for High Voltage Transmission and Distribution Line Corridors using Satellite Stereo Images: a Case Study of Isingiro District.

Tonny Joseph Mawanda — 2024-06-20

In Uganda, monitoring for vegetation encroachment on transmission and distribution line corridors is conducted through ground inspections. Ground inspection is carried out by either pole climbing, foot patrolling, or vehicle inspection. These procedures are time-consuming and stressful. In developed countries, aerial borne inspection methods, such as Light Detection and Ranging (LiDAR), helicopter patrols, Synthetic Aperture Radar, Unmanned Aerial Vehicles, and aerial videography, are being used. These techniques produce accurate results but they are very costly and are hardly accessible. Using satellite stereo pairs for inspecting overhead power line corridors for vegetation can be more effective than inspection through aerial methods.

In this research, 50cm spatial resolution Pléiades1A stereo imagery was used to generate a digital surface model (DSM) showing the spatial relationship between vegetation and the high-voltage power lines in the study area. The resultant Pléiades 1A stereo imagery was then segmented into a chessboard grid and processed using texture classification and Normalized Difference Vegetation Index (NDVI) threshold values to identify any tall trees. Using the DSM, together with the data layer, showing the tall trees and the high-voltage powerline corridors, the degree of vegetation encroachment on high-voltage power lines at the time of image acquisition was determined.

It was found that the DSM generated was accurate to the 17.3cm Root Mean Square Error (RMSE). This demonstrates the capability of stereoscopic techniques using Pléiades 1A data in modelling the spatial relationship between vegetation and high-voltage power lines. A line denoted by Code 4 showed a considerable tree canopy density within its growth limit zone which was further confirmed through site visits.

Application of Unmanned Aerial Photography for the Estimation of Extracted Rocks – Beautiful Rock Quarry, Nigeria Experience

A. J. Adeyemi — 2024-06-20

This research was carried out to investigate the application of unmanned aerial photography in estimating the respective tonnages of extracted rocks at Beautiful Rock Quarry, Lokoja, Nigeria. To attain the aim of the research, the aerial views of the 2D images captured were used to generate aerial views in the form of 3D models, namely, Digital Terrain Models (DTMs) and Digital Surface Models (DSMs). ImageJ and Regard3D components of Image Processing Software, were used for the processing of the images. Owing to the tilted aerial photograph, correction checks were conducted for the sensor coordinates, and the ground coordinates were established. Images of the extracted pits and stockpiles were analyzed by applying image processing software, which identified elevation spots and generated Z-plots for these feature. With the results generated from the image processing software, the extracted rocks from the pit were calculated to weigh 1,027,204.512 tons, while the total production of aggregates sold from the quarry between 2012 and 2020 was documented to weigh 1,715,800 tons. The difference between the tonnage of extracted rocks from the pit and the total production of aggregates sold was due to the swell factor of the rock (1.70), the fly rock issuing from the blasting operations and the loss of aggregates during the loading and haulage phases of the mining operation. The stockpile calculated from the 3D images weighed 44,695.39 tons. The expected tonnage of the extracted rocks from the pit after excavation and crushing into aggregates was expected to be 1,746,247.670tons, which is about two percent higher than the total aggregate weighing 30,447.67 tons produced from the quarry. Therefore, approximately two percent should be allowed for the extracted tonnage expected from the pit after excavation and crushing into aggregates, as well as for the total aggregate produced from the quarry.

Geospatial Big Data or Big Geospatial Data: A Bibliometric Review

Chidinma Godsgood Ndu — 2024-06-20

The study reviews a broad body of published literature on Geospatial big data or big geospatial data to analyse the nature of the research context, identify trends in the research area, and provide future research directions. Web of Science and Scopus databases were used to collect and convert data to information through an analysis conducted using the Bibliometrix R package and Biblioshiny. 1114 articles were retrieved after de-duplication and the analysis shows that the leading term, Geospatial big data, was first seen in the year 2012. Since then, it has captured the attention of researchers with a yearly increase in articles on the topic. The percentage decline from 2017 to 2018 was 6%, while that between 2020 and 2021 was 17%. On the other hand, the overall increase in annual scientific production between 2012 and 2021 was about 95%, which indicates a massive growth of interest in Geospatial big data. The results identify the country with the largest number of published articles, the most globally influential author, and the author with the highest average number of citations per year, while research collaborations among countries and the top leading institutions were also identified. Furthermore, the study compared author keywords plus outputs, using co-word analysis. Finally, the study identified gaps and proffered future research directions along which scholarly investigations could expand. The paper suggests that future research should focus on Geospatial big data applications to social media, urban planning, disaster management, and incorporation of artificial intelligence in addressing sustainable development challenges.

The Development of a Location Addressing System for Rural Villages in Botswana: Tlokweng Case Study

Kealeboga Moreri — 2024-06-20

Communities rely on location addressing for various purposes in order to optimize their emergency responses, services, and goods delivery. Without a well-designed location addressing system (LAS), governments and other service providers struggle to efficiently and effectively provide goods and services to local communities. Botswana has adopted a Land Administration Procedures, Capacity, and Systems (LAPCAS) project, developed by a Swedish company, to produce a unique system to address the need to pinpoint locations in the country. However, it has not solved the issue of location addressing in villages as the one currently being developed nationally has not as yet been implemented. As a developing country, Botswana needs a LAS to facilitate the identification and location of properties and to plan developments based on the existing infrastructure. Technological advancements such as Geographic Information Systems (GIS), Global Navigation and Satellite Systems (GNSS), and Web 2.0 have made it possible to develop LAS to improve emergency response and service delivery in most Western countries. Thus, a prototype LAS was developed in this study. Using the Python programming language, the GeoPandas Framework, and the Django Web Application, it has already demonstrated its potential benefits in a peri-urban village, namely, Tlokweng, in Botswana. The objective is to show that geoinformation techniques can be used as foundations for identifying locations and navigating the challenges facing rural areas in developing countries such as Botswana.

The Spatiotemporal Analysis of Drought and Impact on Millet Production across North Darfur State using Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI)

Mohammed B. Altoom — 2024-06-20

Drought severely affects the agricultural sector. Its effects have in fact been aggravated by the global warming climate scenario. Although drought has to a large extent been studied globally, it is often overlooked on the local and regional scales. Therefore, accurate and timely characterizations of agricultural drought at the local and regional scales are essential for developing adaptation and mitigation strategies. This study analyzed the spatiotemporal trend of drought events and their impact on millet production in North Darfur State, Sudan from 1981 to 2020. The Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) for the three-month (June-August), six-month (June-November), and nine-month (June-February) timescales were used to assess the relevant drought events. The drought-yield relationships were assessed using the Pearson correlation coefficient (r). The results indicate that for detecting drought trends the RDI index is more sensitive to variabilities than the SPI index. The drought events affecting North Darfur over broad spatial extents occurred particularly over the years, 1989, 1990, 1992, 1999, and 2001; the most extreme of drought events being in 2003. The correlation coefficient analysis (r) between the SPI and RDI respectively and the standardized variable of crop yield (SVCY) for millet grain yield showed a strong agreement between the respective variables. The moderate to extreme reductions in millet crop yield occurring in 1992, 1999, 2001, and 2003 corresponded with the moderate to extreme drought indicated by the RDI. Severe crop losses were experienced in Kabkabiya and Umm Kadadda. This study contributes to a clearer understanding of drought impacts on the local scale and thus to insights into developing more effective and targeted management strategies and to enhancing crop resilience.