S. Ntuli, Mulemwa Akombelwa, Angus Forbes, Mayshree Singh
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.
{"title":"Classification of 3D Sonar Point Clouds derived Underwater using Machine and Deep Learning (CANUPO and RandLA-Net) Approaches","authors":"S. Ntuli, Mulemwa Akombelwa, Angus Forbes, Mayshree Singh","doi":"10.4314/sajg.v13i2.4","DOIUrl":"https://doi.org/10.4314/sajg.v13i2.4","url":null,"abstract":"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.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141834195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Error Analysis in Multibeam Hydrographic Survey System","authors":"Basil Daniel Devote","doi":"10.4314/sajg.v13i2.2","DOIUrl":"https://doi.org/10.4314/sajg.v13i2.2","url":null,"abstract":"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.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Temporal Characterization of Land Use Change and Land-scape Processes in Informal Settlements in the City of Cape Town, South Africa","authors":"P. I. Okoye, Jörg Lalk","doi":"10.4314/sajg.v13i2.1","DOIUrl":"https://doi.org/10.4314/sajg.v13i2.1","url":null,"abstract":"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.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Characterising the evolution of the urban form of zones that accommodate warehousing clusters in the City of Cape Town municipality","authors":"Masilonyane Mokhele, Brian Fisher-Holloway","doi":"10.4314/sajg.v13i2.3","DOIUrl":"https://doi.org/10.4314/sajg.v13i2.3","url":null,"abstract":"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.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141835248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Philemon Tsele, Ludwig Combrinck, Roelf Botha, Bongani Ngcobo
The Hartebeesthoek Radio Astronomy Observatory (HartRAO) of South Africa is developing a Lunar Laser Ranging (LLR) system to achieve sub-centimetre range precision to the Moon. Key to this high precision expectation, which includes improving the overall operational performance of its telescope, is the thermal analysis of the telescope structure. In this study, thermal sensors were mounted on the thermally- important areas of the tube structure to measure the tube displacements emanating from the varying ambient air temperatures. A laser distance-measurement system was used for this purpose. Results showed that while the optical tube undergoes structural changes with changes in temperature, the tube position closer to the place where the spider assembly is mounted is unevenly displaced in three directions. In particular, for the time period considered in this study, it was found that the relative displacements on average at prisms 1, 2 and 3 in the vertical direction were 2.5540 ± 0.0007 m, 1.3750 ± 0.0008 m and 1.9780 ± 0.0007 m, respectively. The corresponding standard deviation (SD) values of ±0.0007 m, ±0.0008 m and ±0.0007 m denotes the average deviations that occurred in the vertical direction at the centre of prisms 1, 2 and 3, respectively. The generally higher SD of relative displacements in the vertical direction rather than in the easting and northing directions, suggest that the tube experienced greater variations in the vertical direction. Furthermore, the lower arc of the tube front, was found to have increased variability, and therefore it was hypothesised to introduce more elevation pointing offsets than azimuth for the LLR. This information constitutes an important input for guiding the efforts to determine the extent of the correction needed to be fed into the LLR telescope pointing model to counteract expected thermally induced pointing offsets.
{"title":"Analysis of thermally-induced displacements of the HartRAO Lunar Laser Ranger optical tube: impact on pointing","authors":"Philemon Tsele, Ludwig Combrinck, Roelf Botha, Bongani Ngcobo","doi":"10.4314/sajg.v12i.2.8","DOIUrl":"https://doi.org/10.4314/sajg.v12i.2.8","url":null,"abstract":"The Hartebeesthoek Radio Astronomy Observatory (HartRAO) of South Africa is developing a Lunar Laser Ranging (LLR) system to achieve sub-centimetre range precision to the Moon. Key to this high precision expectation, which includes improving the overall operational performance of its telescope, is the thermal analysis of the telescope structure. In this study, thermal sensors were mounted on the thermally- important areas of the tube structure to measure the tube displacements emanating from the varying ambient air temperatures. A laser distance-measurement system was used for this purpose. Results showed that while the optical tube undergoes structural changes with changes in temperature, the tube position closer to the place where the spider assembly is mounted is unevenly displaced in three directions. In particular, for the time period considered in this study, it was found that the relative displacements on average at prisms 1, 2 and 3 in the vertical direction were 2.5540 ± 0.0007 m, 1.3750 ± 0.0008 m and 1.9780 ± 0.0007 m, respectively. The corresponding standard deviation (SD) values of ±0.0007 m, ±0.0008 m and ±0.0007 m denotes the average deviations that occurred in the vertical direction at the centre of prisms 1, 2 and 3, respectively. The generally higher SD of relative displacements in the vertical direction rather than in the easting and northing directions, suggest that the tube experienced greater variations in the vertical direction. Furthermore, the lower arc of the tube front, was found to have increased variability, and therefore it was hypothesised to introduce more elevation pointing offsets than azimuth for the LLR. This information constitutes an important input for guiding the efforts to determine the extent of the correction needed to be fed into the LLR telescope pointing model to counteract expected thermally induced pointing offsets.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135461267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The accurate and appropriate monitoring of the spatial distribution of fruit tree crops is crucial for crop management and yield forecasting. Owing to both inter- and intra-farm fragmentation and overlapping phenological cycles, the classification of fruit tree crops in subtropical agriculture using single-date images is challenging. Therefore, this research aimed to identify the optimal temporal window in which the crucial phenological stages can be used to classify fruit tree crops in Levubu, Limpopo province, using a random forest (RF) classifier. Phenological metrics were extracted from 12-month Multispectral Instrument (MSI) images from Sentinel-2 (S2). The RF classification algorithm attained an overall accuracy of 84.89% and a kappa coefficient of 83%. The user accuracy ranged from 62 to 100%, while the producer accuracy ranged from 60 to 100%. An analysis of variance was used to assess whether the overall accuracies among the S2 monthly composites were statistically significant. The results showed distinct spectral differences between fruit trees. In April, there were differences observed during the harvesting and senescence of the mango and macadamia nut crops. In May, there were differences observed during the senescence of the macadamia nut, mango, and guava crops. In June and July, there were distinct spectral differences during the peak flowering stage of the avocado, macadamia nut, and mango crops, as well as in the fruiting stage of the banana crops. Followed by the red-edge bands, the shortwave infrared bands were significant in differentiating between the respective fruit tree crops. The results of this research provide evidence-based information that can assist farm managers and horticulturists in making informed decisions. This is critical in achieving effective agricultural management and in ensuring the sustainability of local horticultural systems.
{"title":"Identifying the optimal phenological period for discriminating subtropical fruit tree crops using multi-temporal Sentinel-2 data and Google Earth Engine","authors":"Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali","doi":"10.4314/sajg.v12i.2.10","DOIUrl":"https://doi.org/10.4314/sajg.v12i.2.10","url":null,"abstract":"The accurate and appropriate monitoring of the spatial distribution of fruit tree crops is crucial for crop management and yield forecasting. Owing to both inter- and intra-farm fragmentation and overlapping phenological cycles, the classification of fruit tree crops in subtropical agriculture using single-date images is challenging. Therefore, this research aimed to identify the optimal temporal window in which the crucial phenological stages can be used to classify fruit tree crops in Levubu, Limpopo province, using a random forest (RF) classifier. Phenological metrics were extracted from 12-month Multispectral Instrument (MSI) images from Sentinel-2 (S2). The RF classification algorithm attained an overall accuracy of 84.89% and a kappa coefficient of 83%. The user accuracy ranged from 62 to 100%, while the producer accuracy ranged from 60 to 100%. An analysis of variance was used to assess whether the overall accuracies among the S2 monthly composites were statistically significant. The results showed distinct spectral differences between fruit trees. In April, there were differences observed during the harvesting and senescence of the mango and macadamia nut crops. In May, there were differences observed during the senescence of the macadamia nut, mango, and guava crops. In June and July, there were distinct spectral differences during the peak flowering stage of the avocado, macadamia nut, and mango crops, as well as in the fruiting stage of the banana crops. Followed by the red-edge bands, the shortwave infrared bands were significant in differentiating between the respective fruit tree crops. The results of this research provide evidence-based information that can assist farm managers and horticulturists in making informed decisions. This is critical in achieving effective agricultural management and in ensuring the sustainability of local horticultural systems.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135665852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Implementing a watershed erosion control programme requires resource-intensive and time-consuming preliminary studies to prioritize such interventions and to focus on those sub-catchments where they are most likely to yield the most effective results.
In this study, we explore and document the effectiveness of using hypsometric analysis as a method to prioritize erosion control measures and apply it to the Yanze watershed located in central Rwanda.
Based on a 30m-resolution DEM of the watershed and using ArcGIS and R software, we made estimates of hypsometric integral values and calculated soil loss estimates through RUSLE modelling and by using data from different sources, namely the Rwanda Meteorological Agency (rainfall data), ISRIC (soil data), and Sentinel-2 images (land cover maps).
The hypsometric integral values of the Yanze sub-catchments were high, ranging from 0.5 to 0.936. This, combined with the overall convex upward hypsometric curves, indicates that the Yanze watershed is still at a youthful stage in its erosional cycle.
The results of the RUSLE model showed that the average potential soil loss in the Yanze watershed is 55.63 tonnes.ha-1.year-1, which is comparable to the national average estimated at 62 tonnes.ha-1.year-1.
The correlation analysis that we conducted between the hypsometric integral values of the Yanze sub-catchments and their respective mean soil loss values revealed no correlation between the two variables. From the results of this study, we conclude that in watersheds where lithology affects soil erosion significantly, morphology can indeed indicate the potential for erosion. However, we further concluded that future studies to characterize erosion potential using morphometry should employ additional morphometric parameters in the regression model.
实施流域侵蚀控制方案需要进行资源密集和耗时的初步研究，以确定这些干预措施的优先次序，并将重点放在最有可能产生最有效结果的子集水区。在本研究中，我们探索并记录了使用假设分析作为优先考虑侵蚀控制措施的方法的有效性，并将其应用于位于卢旺达中部的延泽流域。基于30m分辨率的流域DEM，利用ArcGIS和R软件，我们通过RUSLE建模和使用不同来源的数据，即卢旺达气象局(降雨数据)、ISRIC(土壤数据)和Sentinel-2图像(土地覆盖图)，估算了拟测积分值，并计算了土壤流失量。扬子子流域的半对称积分值较高，在0.5 ~ 0.936之间。结合整体上凸向上的坡度曲线，表明扬子流域仍处于侵蚀旋回的年轻阶段。
RUSLE模型结果表明，延泽流域平均潜在土壤流失量为55.63 t .ha . 1。1年，这与全国平均估计的62吨相当。对延泽子流域的拟拟积分值与各自的平均土壤流失量进行了相关分析，结果表明两者之间没有相关性。根据本研究的结果，我们得出结论，在岩性对土壤侵蚀影响显著的流域，形态确实可以表明侵蚀的潜力。然而，我们进一步得出结论，未来使用形态计量学表征侵蚀潜力的研究应该在回归模型中加入额外的形态计量参数。
{"title":"Assessing the importance of hypsometry for catchment soil erosion: A case study of the Yanze watershed, Rwanda","authors":"Faustin Gashakamba, Umaru Garba Wali, Vaillant Rutazuyaza Byizigiro","doi":"10.4314/sajg.v12i.2.9","DOIUrl":"https://doi.org/10.4314/sajg.v12i.2.9","url":null,"abstract":"Implementing a watershed erosion control programme requires resource-intensive and time-consuming preliminary studies to prioritize such interventions and to focus on those sub-catchments where they are most likely to yield the most effective results.
 In this study, we explore and document the effectiveness of using hypsometric analysis as a method to prioritize erosion control measures and apply it to the Yanze watershed located in central Rwanda.
 Based on a 30m-resolution DEM of the watershed and using ArcGIS and R software, we made estimates of hypsometric integral values and calculated soil loss estimates through RUSLE modelling and by using data from different sources, namely the Rwanda Meteorological Agency (rainfall data), ISRIC (soil data), and Sentinel-2 images (land cover maps).
 The hypsometric integral values of the Yanze sub-catchments were high, ranging from 0.5 to 0.936. This, combined with the overall convex upward hypsometric curves, indicates that the Yanze watershed is still at a youthful stage in its erosional cycle.
 The results of the RUSLE model showed that the average potential soil loss in the Yanze watershed is 55.63 tonnes.ha-1.year-1, which is comparable to the national average estimated at 62 tonnes.ha-1.year-1.
 The correlation analysis that we conducted between the hypsometric integral values of the Yanze sub-catchments and their respective mean soil loss values revealed no correlation between the two variables. From the results of this study, we conclude that in watersheds where lithology affects soil erosion significantly, morphology can indeed indicate the potential for erosion. However, we further concluded that future studies to characterize erosion potential using morphometry should employ additional morphometric parameters in the regression model.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135666080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O.G. Omogunloye, S. Bawa, O. E. Abiodun, O. A. Olunlade, T. J. Salami, A. O. Alabi
Natural disasters pose global challenges and can result in social, economic, and environmental damage, substantial loss of life, and even pose a threat to geopolitical stability. The study of such disasters through deformation modeling and analyses has found application in the disciplines of Geodesy and Geodynamics. The strain method has in fact been used to model deformation. The strain deformation parameters, namely, dilatancy, total shear strain and differential rotation, of this finite elemental model were calculated by using the baseline ratios of the coordinates of a classical traverse observed using the Global Positioning System (space technique), in the Minna datum platform. Computation was undertaken in a MATLAB programme and a MONTE CARLO environment, after the ill-conditioned triangles in the network were excluded. Statistical analysis was used to determine the significance levels of the respective deformation parameters at the 95%, 97.5% and 99.5% confidence intervals. After the statistical testing of the deformation parameters, it was observed that some of the controls were unstable in terms of their computed dilatancy and their total shear strain values. For the differential rotation of the network, the significance levels at the 95%, 97.5% and 99.5% confidence intervals were found to be 1.8743908, 0.9651796 and 0.4338522, respectively, while, on the other hand, the controls or centroids that did not respond to the network rotation had a mean value of approximately -0.99999.The minimal and maximal principal strain levels occurring at Centroids 11 and 36 with their triangulated station identities were found to be (36-12, 30-84, 43-34A) and (34-30A, 34-32A, 34-36A), respectively. The method adopted for this research proved to be very effective for a deformation study and analysis.
{"title":"An Investigation into the Stability of some selected Geodetic Controls in Lagos State of Nigeria using the Strain Analysis Technique","authors":"O.G. Omogunloye, S. Bawa, O. E. Abiodun, O. A. Olunlade, T. J. Salami, A. O. Alabi","doi":"10.4314/sajg.v12i.2.4","DOIUrl":"https://doi.org/10.4314/sajg.v12i.2.4","url":null,"abstract":"Natural disasters pose global challenges and can result in social, economic, and environmental damage, substantial loss of life, and even pose a threat to geopolitical stability. The study of such disasters through deformation modeling and analyses has found application in the disciplines of Geodesy and Geodynamics. The strain method has in fact been used to model deformation. The strain deformation parameters, namely, dilatancy, total shear strain and differential rotation, of this finite elemental model were calculated by using the baseline ratios of the coordinates of a classical traverse observed using the Global Positioning System (space technique), in the Minna datum platform. Computation was undertaken in a MATLAB programme and a MONTE CARLO environment, after the ill-conditioned triangles in the network were excluded. Statistical analysis was used to determine the significance levels of the respective deformation parameters at the 95%, 97.5% and 99.5% confidence intervals. After the statistical testing of the deformation parameters, it was observed that some of the controls were unstable in terms of their computed dilatancy and their total shear strain values. For the differential rotation of the network, the significance levels at the 95%, 97.5% and 99.5% confidence intervals were found to be 1.8743908, 0.9651796 and 0.4338522, respectively, while, on the other hand, the controls or centroids that did not respond to the network rotation had a mean value of approximately -0.99999.The minimal and maximal principal strain levels occurring at Centroids 11 and 36 with their triangulated station identities were found to be (36-12, 30-84, 43-34A) and (34-30A, 34-32A, 34-36A), respectively. The method adopted for this research proved to be very effective for a deformation study and analysis.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136000223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study aimed to determine the efficacy and capabilities of using high-resolution aerial imagery and a convolutional neural network (CNN) to identify plant species and monitor land cover and land change in the context of remote sensing. The full capabilities of a CNN were examined, including testing whether the platform could be used for land cover and the evaluation of land change over time. An unmanned aerial vehicle (UAV) was used to collect the aerial data of the study area. The CNN was encoded and operated in RStudio, while digitised data from the input imagery were used by the programme as training and validation data. The object in this respect was to learn about the relevant features of the landscape, and thereafter to classify the Opuntia invasive plant species. Accuracy assessments were carried out on the results to test the efficacy of the aerial imagery in terms of its accuracy and reliability. The classification achieved an overall accuracy of 93%, while the kappa coefficient score was 0.86. CNN was also able to predict the land coverage area of Opuntia to be within four percent (4%) of the ground truthing data. A change in land cover over time was detected by the programme after the manual clearing of the plant had been undertaken. This research has determined that the use of a CNN in remote sensing is a very powerful tool for supervised image classifications. It can be used for monitoring land cover in that it is able to accurately estimate the spatial distribution of plant species and to monitor the growth or decline in the species over time. As such, it is an efficient methodology and its use in remote sensing could be extended.
{"title":"Investigating the efficiency and capabilities of UAVs and Convolutional Neural Networks in the field of remote sensing as a land classification tool","authors":"Cameron Wesson, Wilma Britz, Robbert Duker","doi":"10.4314/sajg.v12i.2.5","DOIUrl":"https://doi.org/10.4314/sajg.v12i.2.5","url":null,"abstract":"The study aimed to determine the efficacy and capabilities of using high-resolution aerial imagery and a convolutional neural network (CNN) to identify plant species and monitor land cover and land change in the context of remote sensing. The full capabilities of a CNN were examined, including testing whether the platform could be used for land cover and the evaluation of land change over time. An unmanned aerial vehicle (UAV) was used to collect the aerial data of the study area. The CNN was encoded and operated in RStudio, while digitised data from the input imagery were used by the programme as training and validation data. The object in this respect was to learn about the relevant features of the landscape, and thereafter to classify the Opuntia invasive plant species. Accuracy assessments were carried out on the results to test the efficacy of the aerial imagery in terms of its accuracy and reliability. The classification achieved an overall accuracy of 93%, while the kappa coefficient score was 0.86. CNN was also able to predict the land coverage area of Opuntia to be within four percent (4%) of the ground truthing data. A change in land cover over time was detected by the programme after the manual clearing of the plant had been undertaken. This research has determined that the use of a CNN in remote sensing is a very powerful tool for supervised image classifications. It can be used for monitoring land cover in that it is able to accurately estimate the spatial distribution of plant species and to monitor the growth or decline in the species over time. As such, it is an efficient methodology and its use in remote sensing could be extended.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135998923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The classification of three-dimensional (3D) point clouds derived through the use of cost-effective and time-efficient photogrammetric technologies can provide helpful information for applications, particularly in the mapping context. This paper presents a practical study of 3D Unmanned Aerial System (UAS) – Structure-from-Motion (SfM) point cloud classification using mainly open-source software. Following a supervised classification approach that makes use of only the dimensionality of points, the entire scene was classified into three land-cover categories: ground, high vegetation, and buildings. By applying the above-mentioned approach, the level of competence in classifying a 3D point cloud of a heterogeneous scene situated in the University of KwaZulu-Natal, South Africa, was evaluated. The resulting overall classification accuracy of 81.3%, with a Kappa coefficient of 0.70, was determined by means of a confusion matrix. The results achieved indicate the potential use of open-source software and 3D UAS-SfM point cloud classification in mapping and monitoring complex environments and in other applications that might arise.
{"title":"Classification of 3D UAS-SfM Point Clouds in the Urban Environment","authors":"Simiso Ntuli, Angus Forbes","doi":"10.4314/sajg.v12i.2.6","DOIUrl":"https://doi.org/10.4314/sajg.v12i.2.6","url":null,"abstract":"The classification of three-dimensional (3D) point clouds derived through the use of cost-effective and time-efficient photogrammetric technologies can provide helpful information for applications, particularly in the mapping context. This paper presents a practical study of 3D Unmanned Aerial System (UAS) – Structure-from-Motion (SfM) point cloud classification using mainly open-source software. Following a supervised classification approach that makes use of only the dimensionality of points, the entire scene was classified into three land-cover categories: ground, high vegetation, and buildings. By applying the above-mentioned approach, the level of competence in classifying a 3D point cloud of a heterogeneous scene situated in the University of KwaZulu-Natal, South Africa, was evaluated. The resulting overall classification accuracy of 81.3%, with a Kappa coefficient of 0.70, was determined by means of a confusion matrix. The results achieved indicate the potential use of open-source software and 3D UAS-SfM point cloud classification in mapping and monitoring complex environments and in other applications that might arise.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135998922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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