South African Journal of Geomatics

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

Grace Ngwenya, Simon Antony Hull — Tue, 16 Jan 2024 00:00:00 +0000

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, Ivan Bamweyana — Wed, 21 Feb 2024 00:00:00 +0000

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 — Thu, 15 Feb 2024 00:00:00 +0000

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, Moreblessings Shoko — Thu, 29 Feb 2024 00:00:00 +0000

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, Kevin Musungu, Patroba Achola Odera — Wed, 13 Mar 2024 00:00:00 +0000

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

Wed, 10 Apr 2024 00:00:00 +0000

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 — Wed, 13 Mar 2024 00:00:00 +0000

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

Wed, 13 Mar 2024 00:00:00 +0000

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.