Aarifah Williams, J. Berkland, Bongeka Maphumulo, Gaathier Mahed, Keegan Stokes
We present the measurement of fractures near the town of Beaufort West, South Africa. A field visit was conducted to examine the dip and azimuth of rock outcrops in and around the town. The locations of these various fractures were mapped and their orientation, which included the dip and strike of the rock surface, was measured using a geological compass (i.e., Brunton Truarc 15 Compass). The geological compass measurements were then compared to three mobile devices. These mobile devices, namely an iPad 2 and two smartphones (Samsung S8 and Huawei P10 Lite), all had the same application for standardization and the mobile device results were individually compared to the geological compass. The data stemming from the various mobile devices and the geological compass were then compared in terms of their variance. This statistical analysis was performed using the Correlated T-test method, as well as the Pearson Correlation Coefficient formula. To visually examine the main fracture orientations, the data obtained using the geological compass was plotted on a rose diagram. Results show that the relationship between the geological compass and the mobile device readings had little to no correlation, when using both the correlation and t-tests as combined determinants. This highlights the importance of ensuring measurement accuracy in the field as well as instrument calibration in situ.
{"title":"Accuracy assessment of smart devices for Geoscience field mapping","authors":"Aarifah Williams, J. Berkland, Bongeka Maphumulo, Gaathier Mahed, Keegan Stokes","doi":"10.4314/sajg.v11i1.4","DOIUrl":"https://doi.org/10.4314/sajg.v11i1.4","url":null,"abstract":"We present the measurement of fractures near the town of Beaufort West, South Africa. A field visit was conducted to examine the dip and azimuth of rock outcrops in and around the town. The locations of these various fractures were mapped and their orientation, which included the dip and strike of the rock surface, was measured using a geological compass (i.e., Brunton Truarc 15 Compass). The geological compass measurements were then compared to three mobile devices. These mobile devices, namely an iPad 2 and two smartphones (Samsung S8 and Huawei P10 Lite), all had the same application for standardization and the mobile device results were individually compared to the geological compass. The data stemming from the various mobile devices and the geological compass were then compared in terms of their variance. This statistical analysis was performed using the Correlated T-test method, as well as the Pearson Correlation Coefficient formula. To visually examine the main fracture orientations, the data obtained using the geological compass was plotted on a rose diagram. Results show that the relationship between the geological compass and the mobile device readings had little to no correlation, when using both the correlation and t-tests as combined determinants. This highlights the importance of ensuring measurement accuracy in the field as well as instrument calibration in situ.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46405950","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 implementation of Unmanned Aerial Vehicles (UAVs) and Structure-from-Motion (SfM) photogrammetry in assessing forest structures for forest inventory and biomass estimations has shown great promise in reducing costs and labour intensity while providing relative accuracy. Tree Height (TH) and Diameter at Breast Height (DBH) are two major variables in biomass assessment. UAV-based TH estimations depend on reliable Digital Terrain Models (DTMs), while UAV-based DBH estimations depend on reliable dense photogrammetric point cloud. The main aim of this study was to evaluate the performance of multi-rotor UAV photogrammetric point cloud in estimating homogeneous and heterogeneous forest structures, and their comparison to more accurate LiDAR data obtained from Aerial Laser Scanners (ALS), Terrestrial Laser Scanners (TLS), and more conventional means like manual field measurements. TH was assessed using UAVSfM and LiDAR point cloud derived DTMs, while DBH was assessed by comparing UAVSfM photogrammetric point cloud to LiDAR point cloud, as well as to manual measurements. The results obtained in the study indicated that there was a high correlation between UAVSfM TH and ALSLiDAR TH (R2 = 0.9258) for homogeneous forest structures, while a lower correlation between UAVSfM TH and TLSLiDAR TH (R2 = 0.8614) and UAVSfM TH and ALSLiDAR TH (R2 = 0.8850) was achieved for heterogeneous forest structures. A moderate correlation was obtained between UAVSfM DBH and field measurements (R2 = 0.5955) for homogenous forest structures, as well as between UAVSfM DBH and TLSLiDAR DBH (R2 = 0.5237), but a low correlation between UAVSfM DBH and UAVLiDAR DBH (R2 = 0.1114). The study demonstrated that UAV acquired imagery can be used to accurately estimate TH in both forest types, but has challenges estimating DBH. The research does not suggest that UAVSfM serves as a replacement for more high-cost and accurate LiDAR data, but rather as a cheaper adequate alternative in forestry management depending on accuracy requirements.
{"title":"Estimating the performance of multi-rotor unmanned aerial vehicle structure-from-motion (UAVsfm) imagery in assessing homogeneous and heterogeneous forest structures: a comparison to airborne and terrestrial laser scanning","authors":"Kenechukwu C. Onwudinjo, J. Smit","doi":"10.4314/sajg.v11i1.6","DOIUrl":"https://doi.org/10.4314/sajg.v11i1.6","url":null,"abstract":"The implementation of Unmanned Aerial Vehicles (UAVs) and Structure-from-Motion (SfM) photogrammetry in assessing forest structures for forest inventory and biomass estimations has shown great promise in reducing costs and labour intensity while providing relative accuracy. Tree Height (TH) and Diameter at Breast Height (DBH) are two major variables in biomass assessment. UAV-based TH estimations depend on reliable Digital Terrain Models (DTMs), while UAV-based DBH estimations depend on reliable dense photogrammetric point cloud. The main aim of this study was to evaluate the performance of multi-rotor UAV photogrammetric point cloud in estimating homogeneous and heterogeneous forest structures, and their comparison to more accurate LiDAR data obtained from Aerial Laser Scanners (ALS), Terrestrial Laser Scanners (TLS), and more conventional means like manual field measurements. TH was assessed using UAVSfM and LiDAR point cloud derived DTMs, while DBH was assessed by comparing UAVSfM photogrammetric point cloud to LiDAR point cloud, as well as to manual measurements. The results obtained in the study indicated that there was a high correlation between UAVSfM TH and ALSLiDAR TH (R2 = 0.9258) for homogeneous forest structures, while a lower correlation between UAVSfM TH and TLSLiDAR TH (R2 = 0.8614) and UAVSfM TH and ALSLiDAR TH (R2 = 0.8850) was achieved for heterogeneous forest structures. A moderate correlation was obtained between UAVSfM DBH and field measurements (R2 = 0.5955) for homogenous forest structures, as well as between UAVSfM DBH and TLSLiDAR DBH (R2 = 0.5237), but a low correlation between UAVSfM DBH and UAVLiDAR DBH (R2 = 0.1114). The study demonstrated that UAV acquired imagery can be used to accurately estimate TH in both forest types, but has challenges estimating DBH. The research does not suggest that UAVSfM serves as a replacement for more high-cost and accurate LiDAR data, but rather as a cheaper adequate alternative in forestry management depending on accuracy requirements.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47690129","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 statistical qualities of census output areas are of great importance especially when the purpose of output areas is to understand the statistical properties of the population rather than mapping. If the purpose of creating census output areas is solely for displaying results in a map format, shape compactness of output areas is prioritised. In that case, other statistical characteristics such as population, population mean and social homogeneity are often ignored. This paper explored the statistical qualities of the Automated Zone-design Tool (AZTool) generated census output areas using the 2001 census Enumeration Areas (EAs) as building blocks in South Africa. The statistical qualities were mainly based on population target mean, minimum population threshold, social homogeneity as well as shape compactness. The homogeneity variables that were selected from the 2001 census data were dwelling type and geotype. The results showed that the AZTool generated output areas substantially outperformed the original EAs and Small Area Layers (SALs) in terms of the minimum population threshold and population distribution statistical qualities. It is worth noting though that the AZTool output areas were less compact and homogeneous than the original EAs in both urban and rural settings. The fact that a minimum population threshold of 500 was respected by the AZTool output areas in both rural and urban settings was a huge success from confidentiality point of view. It was concluded that the AZTool could be utilized to produce robust and high-quality optimised output areas for population census dissemination in South Africa.
人口普查产出地区的统计质量非常重要，特别是当产出地区的目的是了解人口的统计特性而不是绘图时。如果创建普查输出区域的目的仅仅是为了以地图格式显示结果，则优先考虑输出区域的形状紧凑性。在这种情况下，其他统计特征，如人口、人口均值和社会同质性往往被忽略。本文探讨了使用2001年南非人口普查枚举区(EAs)作为构建块的自动区域设计工具(AZTool)生成的人口普查输出区域的统计质量。统计质量主要基于人口目标均值、最小人口阈值、社会同质性和形状紧密性。从2001年人口普查数据中选取的同质性变量为居住类型和地理类型。结果表明，AZTool生成的输出区域在最小种群阈值和种群分布统计质量方面明显优于原始ea和Small Area Layers (SALs)。值得注意的是，AZTool的输出区域在城市和农村环境中都不如原来的ea紧凑和均匀。从保密性的角度来看，AZTool输出地区在农村和城市环境中都遵守了500人的最低人口门槛，这是一个巨大的成功。最后得出的结论是，可以利用人口普查工具为南非的人口普查传播提供可靠和高质量的最佳产出领域。
{"title":"The statistical qualities of the zone design census output areas","authors":"T. Mokhele, O. Mutanga, F. Ahmed","doi":"10.4314/sajg.v11i1.1","DOIUrl":"https://doi.org/10.4314/sajg.v11i1.1","url":null,"abstract":"The statistical qualities of census output areas are of great importance especially when the purpose of output areas is to understand the statistical properties of the population rather than mapping. If the purpose of creating census output areas is solely for displaying results in a map format, shape compactness of output areas is prioritised. In that case, other statistical characteristics such as population, population mean and social homogeneity are often ignored. This paper explored the statistical qualities of the Automated Zone-design Tool (AZTool) generated census output areas using the 2001 census Enumeration Areas (EAs) as building blocks in South Africa. The statistical qualities were mainly based on population target mean, minimum population threshold, social homogeneity as well as shape compactness. The homogeneity variables that were selected from the 2001 census data were dwelling type and geotype. The results showed that the AZTool generated output areas substantially outperformed the original EAs and Small Area Layers (SALs) in terms of the minimum population threshold and population distribution statistical qualities. It is worth noting though that the AZTool output areas were less compact and homogeneous than the original EAs in both urban and rural settings. The fact that a minimum population threshold of 500 was respected by the AZTool output areas in both rural and urban settings was a huge success from confidentiality point of view. It was concluded that the AZTool could be utilized to produce robust and high-quality optimised output areas for population census dissemination in South Africa.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42956130","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}
Processing of Global Navigational Satellite System (GNSS) data forms the basis for the usage of differential systems for obtaining spatial data. All open sources or commercial software packages developed for data processing give specific details to suit the intended purpose of the software. To obtain a uniform format for submitted survey data, Survey and Mapping Division (SMD) in various jurisdictions have specified formats for data submission for all kinds of surveys. In this regard, “GNSS Ghana” Software (GGS), a GNSS standalone Windows-based application with a modern user-friendly interface was developed for geodetic applications such as, projection and datum transformation worldwide, GNSS data post-processing of Receiver Independent Exchange Format (RINEX) files, and generating reports to meet Ghana SMD reporting standards including cadastral computations and reports for submission. To assess the developed software, GNSS data from two International GNSS Service (IGS) stations (BJCO and YKRO) were processed using GGS and three other commercial software such as GNSS Solution Software (GSS), Spectrum Survey Software (SSS), and Leica Geo Office (LGO), and the positional results compared against the existing coordinate. The results revealed that the GGS outperformed the remaining three commercial software packages with a sub-meter level of accuracy. Further assessment was conducted on datum transformation using the coordinates of 21 existing geodetic control points in Ghana. Utilizing the 7-transformation parameters of Ghana, the results gave uncertainties of [0.10ft. ± 0.99ft.] in the eastings and [0.02ft. ± 1.61ft.] in the northings with a 99% confidence level.
{"title":"Development of GNSS software for Ghana Survey and Mapping Division","authors":"Gameti Charles, Acheampong Akwasi Afrifa, J. Ayer","doi":"10.4314/sajg.v11i1.10","DOIUrl":"https://doi.org/10.4314/sajg.v11i1.10","url":null,"abstract":"Processing of Global Navigational Satellite System (GNSS) data forms the basis for the usage of differential systems for obtaining spatial data. All open sources or commercial software packages developed for data processing give specific details to suit the intended purpose of the software. To obtain a uniform format for submitted survey data, Survey and Mapping Division (SMD) in various jurisdictions have specified formats for data submission for all kinds of surveys. In this regard, “GNSS Ghana” Software (GGS), a GNSS standalone Windows-based application with a modern user-friendly interface was developed for geodetic applications such as, projection and datum transformation worldwide, GNSS data post-processing of Receiver Independent Exchange Format (RINEX) files, and generating reports to meet Ghana SMD reporting standards including cadastral computations and reports for submission. To assess the developed software, GNSS data from two International GNSS Service (IGS) stations (BJCO and YKRO) were processed using GGS and three other commercial software such as GNSS Solution Software (GSS), Spectrum Survey Software (SSS), and Leica Geo Office (LGO), and the positional results compared against the existing coordinate. The results revealed that the GGS outperformed the remaining three commercial software packages with a sub-meter level of accuracy. Further assessment was conducted on datum transformation using the coordinates of 21 existing geodetic control points in Ghana. Utilizing the 7-transformation parameters of Ghana, the results gave uncertainties of [0.10ft. ± 0.99ft.] in the eastings and [0.02ft. ± 1.61ft.] in the northings with a 99% confidence level.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41888211","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, D. Omar, C. Okolie, O. Daramola, Tosin J. Salami
The computation of geodetic coordinates is the basis of geodetic surveying and foundation to modern techniques for geodetic network analyses and design of integrated survey schemes for monitoring and detecting structural deformations. The positional accuracy achievable by Direct and Indirect models of geodetic position determination depends on the varying lengths, azimuths and latitude of the first point of the network of stations. Existing knowledge gaps preclude a comprehensive understanding of the relative accuracies of these methods. Therefore, the aim of this study is to determine the achievable accuracies of three models (Bowring, Chord and Power Series) for direct and indirect position determination vis-a-vis the network configuration. The data comprised of 33 controls in the D-Chain geodetic network located in North-Central Nigeria, with a range of network of lines between 15.530km and 113.254km. Various attributes of the network such as azimuth, angle, distance, and coordinates were computed to a high accuracy and precision using a program written in the Matlab software environment. The results of the direct and indirect computation were summarised using descriptive statistics. Also, the accuracies of the computed coordinates were assessed by comparisons with the provisional (initial) coordinates of the controls. In the analysis of coordinate differences, the positional root mean square error (RMSE) for each of the three models in decreasing order of accuracies are: 4.572639341′′ (Chord), 4.601685022′′ (Power Series) and 4.601701034′′ (Bowring). The positional mean absolute deviation (MAD) for the three models in decreasing order of accuracies are 3.788841258′′ (Chord), 3.813184934′′ (Power Series) and 3.813198679′′ (Bowring) and this agrees with the RMSE trend for the network. This study has shown that the D-chain network configuration favours the use of Chord model for position determination based on the adopted configuration.
{"title":"Comparative accuracy assessment of the Bowring, Chord and Power series models for direct and indirect determination of geodetic coordinates","authors":"O. G. Omogunloye, D. Omar, C. Okolie, O. Daramola, Tosin J. Salami","doi":"10.4314/sajg.v10i2.9","DOIUrl":"https://doi.org/10.4314/sajg.v10i2.9","url":null,"abstract":"The computation of geodetic coordinates is the basis of geodetic surveying and foundation to modern techniques for geodetic network analyses and design of integrated survey schemes for monitoring and detecting structural deformations. The positional accuracy achievable by Direct and Indirect models of geodetic position determination depends on the varying lengths, azimuths and latitude of the first point of the network of stations. Existing knowledge gaps preclude a comprehensive understanding of the relative accuracies of these methods. Therefore, the aim of this study is to determine the achievable accuracies of three models (Bowring, Chord and Power Series) for direct and indirect position determination vis-a-vis the network configuration. The data comprised of 33 controls in the D-Chain geodetic network located in North-Central Nigeria, with a range of network of lines between 15.530km and 113.254km. Various attributes of the network such as azimuth, angle, distance, and coordinates were computed to a high accuracy and precision using a program written in the Matlab software environment. The results of the direct and indirect computation were summarised using descriptive statistics. Also, the accuracies of the computed coordinates were assessed by comparisons with the provisional (initial) coordinates of the controls. In the analysis of coordinate differences, the positional root mean square error (RMSE) for each of the three models in decreasing order of accuracies are: 4.572639341′′ (Chord), 4.601685022′′ (Power Series) and 4.601701034′′ (Bowring). The positional mean absolute deviation (MAD) for the three models in decreasing order of accuracies are 3.788841258′′ (Chord), 3.813184934′′ (Power Series) and 3.813198679′′ (Bowring) and this agrees with the RMSE trend for the network. This study has shown that the D-chain network configuration favours the use of Chord model for position determination based on the adopted configuration.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46547896","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 aim of the study described in this article was to investigate the vegetation health and drought response of naturally occurring Albany thicket and neighbouring farmland vegetation, that appears in an area of the Eastern Cape, South Africa. Google Earth Engine was used to manipulate Landsat 5, 7 and 8 datasets to produce a 30-year temporal dataset, after which the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) were then applied to create a time series analysis. The Mann-Kendall and Spearman correlation statistical tests were used on the time series to observe trends and correlations between the NDVI and the NDWI datasets. The Spearman correlation test results showed that there were high correlations between the NDVI and the NDWI datasets (all above 0.805). Furthermore, the Man-Kendall test showed that all the datasets had positively increasing trends, while the NDVI datasets all had monotonic positive trends. Large differences in the NDVI and the NDWI were seen for the different vegetation types during times of drought, and farmland was the most severely affected with an average of 19% decrease in the NDVI and an average of 71% decrease in the NDWI.
{"title":"Using remote sensing to assess plant health and drought response in game reserves and adjacent farmland overtime in the Eastern Cape, South Africa","authors":"Cameron B. Wesson, W. Britz","doi":"10.4314/sajg.v10i2.15","DOIUrl":"https://doi.org/10.4314/sajg.v10i2.15","url":null,"abstract":"The aim of the study described in this article was to investigate the vegetation health and drought response of naturally occurring Albany thicket and neighbouring farmland vegetation, that appears in an area of the Eastern Cape, South Africa. Google Earth Engine was used to manipulate Landsat 5, 7 and 8 datasets to produce a 30-year temporal dataset, after which the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) were then applied to create a time series analysis. The Mann-Kendall and Spearman correlation statistical tests were used on the time series to observe trends and correlations between the NDVI and the NDWI datasets. The Spearman correlation test results showed that there were high correlations between the NDVI and the NDWI datasets (all above 0.805). Furthermore, the Man-Kendall test showed that all the datasets had positively increasing trends, while the NDVI datasets all had monotonic positive trends. Large differences in the NDVI and the NDWI were seen for the different vegetation types during times of drought, and farmland was the most severely affected with an average of 19% decrease in the NDVI and an average of 71% decrease in the NDWI.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45529492","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 Jukskei River catchment is one of the urban catchments in the central part of Gauteng province covering a large part of City of Johannesburg Metropolitan Municipality and small part of Tshwane and Ekurhuleni Metropolitan Municipalities that have witnessed tremendous land use/land cover (LULC) change over time. Jukskei River catchment is one of the fastest growing catchments in terms of population and change in LULC over time. Therefore, it is very important to detect the nature and extent of these changes in order to identify the direction and future expansion of LULC within the catchment area. To accomplish that, multi-temporal satellite remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) 1987, Landsat-5 Thematic Mapper (TM) 2001 and Landsat-8 Operational Land imager (OLI) 2015 were used to detect LULC change in Jukskei River catchment area. The Jukskei River catchment was classified into four major LULC classes including: Built-up area, bare surface, sparse vegetation and intact vegetation. The analysis of the results revealed that for the past 28 years (i.e., 1987-2015), built-up and bare surface areas have increased by 56.2% (42713.1 ha) and 8,2% (6225.1 ha) while intact and sparse vegetation have decreased by 9.8% (7455.0 ha) and 25.8% (19659.6 ha), respectively. The overall accuracies for 1987, 2001, and 2015, were 85.9%, 87.5%, and 92.5% respectively, with Kappa Index of Agreement (KIA) of 81.3%, 83.3%, and 90% which indicates the accuracy of classified images with the reference images. The results revealed by this study can be used for decision-making activities and policy development regarding land use management within the catchment.
{"title":"Detecting land use and land cover change for a 28-year period using multi-temporal Landsat satellite images in the Jukskei River catchment, Gauteng, South Africa","authors":"T. Mawasha, W. Britz","doi":"10.4314/sajg.v11i1.2","DOIUrl":"https://doi.org/10.4314/sajg.v11i1.2","url":null,"abstract":"The Jukskei River catchment is one of the urban catchments in the central part of Gauteng province covering a large part of City of Johannesburg Metropolitan Municipality and small part of Tshwane and Ekurhuleni Metropolitan Municipalities that have witnessed tremendous land use/land cover (LULC) change over time. Jukskei River catchment is one of the fastest growing catchments in terms of population and change in LULC over time. Therefore, it is very important to detect the nature and extent of these changes in order to identify the direction and future expansion of LULC within the catchment area. To accomplish that, multi-temporal satellite remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) 1987, Landsat-5 Thematic Mapper (TM) 2001 and Landsat-8 Operational Land imager (OLI) 2015 were used to detect LULC change in Jukskei River catchment area. The Jukskei River catchment was classified into four major LULC classes including: Built-up area, bare surface, sparse vegetation and intact vegetation. The analysis of the results revealed that for the past 28 years (i.e., 1987-2015), built-up and bare surface areas have increased by 56.2% (42713.1 ha) and 8,2% (6225.1 ha) while intact and sparse vegetation have decreased by 9.8% (7455.0 ha) and 25.8% (19659.6 ha), respectively. The overall accuracies for 1987, 2001, and 2015, were 85.9%, 87.5%, and 92.5% respectively, with Kappa Index of Agreement (KIA) of 81.3%, 83.3%, and 90% which indicates the accuracy of classified images with the reference images. The results revealed by this study can be used for decision-making activities and policy development regarding land use management within the catchment.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47265056","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}
L. Singh, O. Mutanga, P. Mafongoya, K. Peerbhay, S. Dovey
Nutrient deficiencies in commercial forest trees often lead to stunted growth and reduced chances of field survival, resulting in a loss of time, productivity, and trees that can become more susceptible to a host of infections. While conventional foliar analytical methods provide accurate results, they are not time and cost-effective in a high productivity environment. This study aims to test the capability of remote sensing to detect macronutrient and micronutrient deficiencies rapidly in juvenile trees. We acquired full-waveform hyperspectral data (350nm-2500nm) from 135 young trees planted in individual pots in a controlled forestry nursery environment. We quantified nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), and boron (B) in young commercially planted forest variety. This study identified the most critical wavebands for detecting nutrient deficiencies using built-in random forest (RF) measures of variable importance. The random forest algorithm's robustness significantly reduced the dataset's noise whilst producing promising results for certain macronutrients such as P and N (0.95 and 0.89, respectively) and micronutrients such as Mn and Cu (0.90 and 0.86, respectively). We identified the red-edge, near-infrared (NIR), visible and short-wave infrared-2 (SWIR-2) regions of the electromagnetic spectrum as the most effective regions for detecting macronutrients and micronutrients in this study. We recommend testing the use of strategic portions of the electromagnetic spectrum for reducing noise and enabling faster computing time, such as portable near-infrared technology.
{"title":"Detecting nutrient deficiencies in Eucalyptus grandis trees using hyperspectral remote sensing and random forest","authors":"L. Singh, O. Mutanga, P. Mafongoya, K. Peerbhay, S. Dovey","doi":"10.4314/sajg.v10i2.14","DOIUrl":"https://doi.org/10.4314/sajg.v10i2.14","url":null,"abstract":"Nutrient deficiencies in commercial forest trees often lead to stunted growth and reduced chances of field survival, resulting in a loss of time, productivity, and trees that can become more susceptible to a host of infections. While conventional foliar analytical methods provide accurate results, they are not time and cost-effective in a high productivity environment. This study aims to test the capability of remote sensing to detect macronutrient and micronutrient deficiencies rapidly in juvenile trees. We acquired full-waveform hyperspectral data (350nm-2500nm) from 135 young trees planted in individual pots in a controlled forestry nursery environment. We quantified nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), and boron (B) in young commercially planted forest variety. This study identified the most critical wavebands for detecting nutrient deficiencies using built-in random forest (RF) measures of variable importance. The random forest algorithm's robustness significantly reduced the dataset's noise whilst producing promising results for certain macronutrients such as P and N (0.95 and 0.89, respectively) and micronutrients such as Mn and Cu (0.90 and 0.86, respectively). We identified the red-edge, near-infrared (NIR), visible and short-wave infrared-2 (SWIR-2) regions of the electromagnetic spectrum as the most effective regions for detecting macronutrients and micronutrients in this study. We recommend testing the use of strategic portions of the electromagnetic spectrum for reducing noise and enabling faster computing time, such as portable near-infrared technology.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45801180","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}
Road asset mapping has the potential of reducing: costs in keeping all assets data, time-consuming activities like retrieving asset attribute from large files, risks associated with losing all the data by using Geographic Information Systems (GIS). Traditional road data has been stored in the form of hard copy maps showing the different road infrastructure. The World Wide Web (WWW), has revolutionized the provision, dissemination, and data access to people in different geographical locations. Web-GIS based applications have gained popularity because their low cost, ease of use and availability to a large population – that is anyone with a web-browser. Through browsers, web-GIS based applications can display a map with useful information. The design and development of an interactive web-GIS based digital road infrastructure management tool not only allows users to visualize the road infrastructure content but also help in decision making. It makes use of open source GIS tools, PostgreSQL and PostGIS (to manage spatial and non-spatial data), Geoserver (to connect the database to the client mapping application) and Apache Tomcat (to build and deploy the application). The maps are published through Geoserver with their associated information using JavaScript libraries (Open Layers and Geoext). Further spatial analysis (attribute queries) can be done online. Results show that a web-GIS was developed that manages road asset infrastructure like road signs, bridges, animal grids, rest areas. A user can query precise assets they want to visualize for instance damaged bridges. HoweGIS:here is still need to further improve the application for instance allowing user to put complaints about damaged road assets. Thus, the development of the application will help decision makers as well as other users to utilize the information for the benefit of the country.
{"title":"Design and Implementation of a Web-GIS for the management of road infrastructure in Zimbabwe","authors":"A. Mazhindu, Honest K. Madamombe","doi":"10.4314/sajg.v11i1.5","DOIUrl":"https://doi.org/10.4314/sajg.v11i1.5","url":null,"abstract":"Road asset mapping has the potential of reducing: costs in keeping all assets data, time-consuming activities like retrieving asset attribute from large files, risks associated with losing all the data by using Geographic Information Systems (GIS). Traditional road data has been stored in the form of hard copy maps showing the different road infrastructure. The World Wide Web (WWW), has revolutionized the provision, dissemination, and data access to people in different geographical locations. Web-GIS based applications have gained popularity because their low cost, ease of use and availability to a large population – that is anyone with a web-browser. Through browsers, web-GIS based applications can display a map with useful information. The design and development of an interactive web-GIS based digital road infrastructure management tool not only allows users to visualize the road infrastructure content but also help in decision making. It makes use of open source GIS tools, PostgreSQL and PostGIS (to manage spatial and non-spatial data), Geoserver (to connect the database to the client mapping application) and Apache Tomcat (to build and deploy the application). The maps are published through Geoserver with their associated information using JavaScript libraries (Open Layers and Geoext). Further spatial analysis (attribute queries) can be done online. Results show that a web-GIS was developed that manages road asset infrastructure like road signs, bridges, animal grids, rest areas. A user can query precise assets they want to visualize for instance damaged bridges. HoweGIS:here is still need to further improve the application for instance allowing user to put complaints about damaged road assets. Thus, the development of the application will help decision makers as well as other users to utilize the information for the benefit of the country.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45875126","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}
Flooding in urban areas is a major natural disaster causing damage to infrastructure, properties and loss of life. In urban areas the major causes behind the changing hydrological processes (i.e., floods) include topography, increase in precipitation due to climate change and change in land-use/land-cover (LULC) over time. The objective of this study is to evaluate the spatial and temporal LULC change impacts on flooding along the Jukskei River in Alexandra Township, Johannesburg, South Africa. The LULC images of 1987 MSS and 2015 OLI derived from Landsat satellite were pre-processed and classified using a supervised classification method. The analysis of LULC revealed that, there is an increase in built-up area from 934,2 ha to 1277,2 ha and reduction in intact and sparse vegetation from 190,5 ha to 62,4 ha and 380,8 ha to 142,1 ha, respectively, between the years 1987 and 2015. The flood depth map, velocity map and flood depth-velocity for different return periods and LULC scenarios have been developed by using an integrated approach of the Hydrological Engineering Centre-River Analysis System (HEC-RAS) and the HEC-GeoRAS with the geographic information system (GIS) and remote sensing data. From the analysis, it is observed that there is an increase in flood depth and flood velocity from 2,3 m to 3,0 m and 1,4 m/s to 3,4 m/s, whereas the depth-velocity for the last 28-years increased by 3,4 m2/s from 2,9 m2/s to 6,3 m2/s for the 1987 LULC and the 2015 LULC conditions, respectively. The flood hazard maps generated in this study can be used by local authorities and municipalities for flood disaster management.
{"title":"Hydrological impacts of land use - land cover change on urban flood hazard: A case study of the Jukskei River in Alexandra Township, Johannesburg, South Africa.","authors":"T. Mawasha, W. Britz","doi":"10.4314/sajg.v10i2.11","DOIUrl":"https://doi.org/10.4314/sajg.v10i2.11","url":null,"abstract":"Flooding in urban areas is a major natural disaster causing damage to infrastructure, properties and loss of life. In urban areas the major causes behind the changing hydrological processes (i.e., floods) include topography, increase in precipitation due to climate change and change in land-use/land-cover (LULC) over time. The objective of this study is to evaluate the spatial and temporal LULC change impacts on flooding along the Jukskei River in Alexandra Township, Johannesburg, South Africa. The LULC images of 1987 MSS and 2015 OLI derived from Landsat satellite were pre-processed and classified using a supervised classification method. The analysis of LULC revealed that, there is an increase in built-up area from 934,2 ha to 1277,2 ha and reduction in intact and sparse vegetation from 190,5 ha to 62,4 ha and 380,8 ha to 142,1 ha, respectively, between the years 1987 and 2015. The flood depth map, velocity map and flood depth-velocity for different return periods and LULC scenarios have been developed by using an integrated approach of the Hydrological Engineering Centre-River Analysis System (HEC-RAS) and the HEC-GeoRAS with the geographic information system (GIS) and remote sensing data. From the analysis, it is observed that there is an increase in flood depth and flood velocity from 2,3 m to 3,0 m and 1,4 m/s to 3,4 m/s, whereas the depth-velocity for the last 28-years increased by 3,4 m2/s from 2,9 m2/s to 6,3 m2/s for the 1987 LULC and the 2015 LULC conditions, respectively. The flood hazard maps generated in this study can be used by local authorities and municipalities for flood disaster management.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47258731","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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