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":" ","pages":""},"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":" ","pages":""},"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}
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":" ","pages":""},"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}
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":" ","pages":""},"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":" ","pages":""},"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":" ","pages":""},"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}
Robert S.B. Galatiya Suya, M. Soko, Harvey Chilembwe, Arles C. Kapachika, Chikondi Mphamba
Multipath effects are mostly regarded as a nuisance in Global Navigation Satellite System (GNSS) receiver measurements and it is of utmost relevance to expose the magnitude this error has on observations. The impact of multipath is characterized in the context of a given environment and application. In Malawi, Zomba geodynamics Continuously Operating Reference Station (CORS) is in a multipath prone environment. The GNSS observations for this station have been used in geodynamics studies in Malawi without an understanding of multipath affecting the positioning accuracy. Taking this as an advantage, this paper evaluated pseudorange multipath (MP) and signal noise ratio (SNR) on both L1 (MP1 and SNR1) and L2 (MP2 and SNR2) for the station. This was specifically addressed by computing the elevation mask with minimum and maximum multipath effects. In addition, the number of satellite vehicles (SVs) and their associated Geometric Dilution of Precision (GDOP) are also determined to define their relationship with respect to elevation angles. One week GNSS observations spanning a twenty-four hour interval for DOY 001 to DOY 007 in January 2018 were analysed in Translation Editing and Quality Check (TEQC) software at four (10º, 15º, 20º and 25º) cut-off angles. Results indicate high multipath effects for both MP1 and MP2 at 10º elevation mask among the four elevation masks. The least MP1 and MP2 multipath effects were detected at an elevation angle of 25º. In addition, MP1 multipath was worse than MP2 all the angles. Moreover, statistical results demonstrated an increase in both SNR1 and SNR2 with respect to elevation angle. For these days, L2 signal was more affected by noise than L1. Further to this, an assessment of SVs and GDOP for the CORS show that at least ten (10) satellites were observed in each day at 10º and 15º elevation cut-off. The number of satellites dropped to five (5) at the elevation angle of 25º resulting into a larger GDOP value of 4.5 (a decrease by about 38% from 1.7 at 10º and 15º elevation cut-off angles). Therefore, to increase both the number of satellites and precision, Zomba CORS may be upgraded to a multi-constellation CORS by including other navigation systems such as GLONASS, Galileo and BeiDou. While it is possible to reject GNSS satellite observations below the horizon, it is recommended that post-processing of GNSS data for Zomba geodynamics CORS be done at elevation masks above 15º. Considering that multipath repeats itself every sidereal day, it is thus recommended to model or remove multipath affecting Zomba geodynamics CORS. In addition, the study also recommends that trees very close to Zomba CORS antenna be removed to reduce signal scattering.
{"title":"Pseudorange multipath at Zomba geodynamics Continuously Operating Reference Station (CORS) in Malawi","authors":"Robert S.B. Galatiya Suya, M. Soko, Harvey Chilembwe, Arles C. Kapachika, Chikondi Mphamba","doi":"10.4314/sajg.v10i1.1","DOIUrl":"https://doi.org/10.4314/sajg.v10i1.1","url":null,"abstract":"Multipath effects are mostly regarded as a nuisance in Global Navigation Satellite System (GNSS) receiver measurements and it is of utmost relevance to expose the magnitude this error has on observations. The impact of multipath is characterized in the context of a given environment and application. In Malawi, Zomba geodynamics Continuously Operating Reference Station (CORS) is in a multipath prone environment. The GNSS observations for this station have been used in geodynamics studies in Malawi without an understanding of multipath affecting the positioning accuracy. Taking this as an advantage, this paper evaluated pseudorange multipath (MP) and signal noise ratio (SNR) on both L1 (MP1 and SNR1) and L2 (MP2 and SNR2) for the station. This was specifically addressed by computing the elevation mask with minimum and maximum multipath effects. In addition, the number of satellite vehicles (SVs) and their associated Geometric Dilution of Precision (GDOP) are also determined to define their relationship with respect to elevation angles. One week GNSS observations spanning a twenty-four hour interval for DOY 001 to DOY 007 in January 2018 were analysed in Translation Editing and Quality Check (TEQC) software at four (10º, 15º, 20º and 25º) cut-off angles. Results indicate high multipath effects for both MP1 and MP2 at 10º elevation mask among the four elevation masks. The least MP1 and MP2 multipath effects were detected at an elevation angle of 25º. In addition, MP1 multipath was worse than MP2 all the angles. Moreover, statistical results demonstrated an increase in both SNR1 and SNR2 with respect to elevation angle. For these days, L2 signal was more affected by noise than L1. Further to this, an assessment of SVs and GDOP for the CORS show that at least ten (10) satellites were observed in each day at 10º and 15º elevation cut-off. The number of satellites dropped to five (5) at the elevation angle of 25º resulting into a larger GDOP value of 4.5 (a decrease by about 38% from 1.7 at 10º and 15º elevation cut-off angles). Therefore, to increase both the number of satellites and precision, Zomba CORS may be upgraded to a multi-constellation CORS by including other navigation systems such as GLONASS, Galileo and BeiDou. While it is possible to reject GNSS satellite observations below the horizon, it is recommended that post-processing of GNSS data for Zomba geodynamics CORS be done at elevation masks above 15º. Considering that multipath repeats itself every sidereal day, it is thus recommended to model or remove multipath affecting Zomba geodynamics CORS. In addition, the study also recommends that trees very close to Zomba CORS antenna be removed to reduce signal scattering.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42554169","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}
Due to the wide use of GNSS receivers both on satellites at low earth orbit and on the ground, continuous and long-time ionospheric data with increasing accuracy have been obtained and used to study variations in the Earth’s ionosphere. Daily data from 2010.001 to 2017.365 sampled at 30 seconds from 104 African Geodetic Reference Frame (AFREF) dual-frequency GNSS Continuously Operating Reference Stations receivers distributed across Africa, were used in this study. Single Layer Model which assumes that all free electrons are concentrated in a shell of infinitesimal thickness, provides determining ionospheric total electron content value. In this study, the SLM model was used to derive total electron content values. TEC values obtained from the AFREF GNSS CORS measurements were compared with the TEC values from the global ionosphere maps provided by the Centre for Orbit Determination in Europe (CODE). The comparison was achieved by means of time series and wavelet analyses, and also by considering various model validation metrics. Comparative results for TEC estimates from both datasets based on goodness of fit measures, time series and wavelet analyses show good agreement on a statistical basis (r = 0.948) within the limits of experimental observation.
{"title":"Regional ionospheric total electron content over Africa from ground-based GNSS observations","authors":"M. Moses, J. D. Dodo, L. M. Ojigi, K. Lawal","doi":"10.4314/sajg.v10i1.2","DOIUrl":"https://doi.org/10.4314/sajg.v10i1.2","url":null,"abstract":"Due to the wide use of GNSS receivers both on satellites at low earth orbit and on the ground, continuous and long-time ionospheric data with increasing accuracy have been obtained and used to study variations in the Earth’s ionosphere. Daily data from 2010.001 to 2017.365 sampled at 30 seconds from 104 African Geodetic Reference Frame (AFREF) dual-frequency GNSS Continuously Operating Reference Stations receivers distributed across Africa, were used in this study. Single Layer Model which assumes that all free electrons are concentrated in a shell of infinitesimal thickness, provides determining ionospheric total electron content value. In this study, the SLM model was used to derive total electron content values. TEC values obtained from the AFREF GNSS CORS measurements were compared with the TEC values from the global ionosphere maps provided by the Centre for Orbit Determination in Europe (CODE). The comparison was achieved by means of time series and wavelet analyses, and also by considering various model validation metrics. Comparative results for TEC estimates from both datasets based on goodness of fit measures, time series and wavelet analyses show good agreement on a statistical basis (r = 0.948) within the limits of experimental observation.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":"29 14","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41298394","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 present study analyzes the seasonal variability of the relationship between the land surface temperature (LST) and normalized difference bareness index (NDBaI) on different land use/land cover (LULC) in Raipur City, India by using sixty-five Landsat images of four seasons (pre-monsoon, monsoon, post-monsoon, and winter) of 1991-1992, 1995-1996, 1999-2000, 2004-2005, 2009-2010, 2014-2015, and 2018-2019. The results show that the post-monsoon season indicates the best correlation (0.59), followed by the monsoon (0.56), pre-monsoon (0.47), and winter (0.44) season. The water bodies reflect a strongly positive correlation in all the four seasons (0.65 in pre-monsoon, 0.51 in monsoon, 0.53 in post-monsoon, and 0.62 in winter). On green vegetation, this correlation is also strongly positive in monsoon (0.57), post-monsoon (0.62), and winter (0.55), whereas it is moderate positive in pre-monsoon (0.37) season. The built-up area and bare land build a moderate positive correlation in all the four seasons (0.35 in pre-monsoon, 0.43 in monsoon, 0.48 in post-monsoon, and 0.39 in winter). Among the four seasons, the post-monsoon season builds the best correlation for all LULC types, whereas the pre-monsoon season has the least correlation. This research work is beneficial for land use and environmental planning of any city under similar climatic conditions.
{"title":"A seasonal relationship between land surface temperature and normalized difference bareness index","authors":"S. Guha, H. Govil","doi":"10.4314/sajg.v10i2.12","DOIUrl":"https://doi.org/10.4314/sajg.v10i2.12","url":null,"abstract":"The present study analyzes the seasonal variability of the relationship between the land surface temperature (LST) and normalized difference bareness index (NDBaI) on different land use/land cover (LULC) in Raipur City, India by using sixty-five Landsat images of four seasons (pre-monsoon, monsoon, post-monsoon, and winter) of 1991-1992, 1995-1996, 1999-2000, 2004-2005, 2009-2010, 2014-2015, and 2018-2019. The results show that the post-monsoon season indicates the best correlation (0.59), followed by the monsoon (0.56), pre-monsoon (0.47), and winter (0.44) season. The water bodies reflect a strongly positive correlation in all the four seasons (0.65 in pre-monsoon, 0.51 in monsoon, 0.53 in post-monsoon, and 0.62 in winter). On green vegetation, this correlation is also strongly positive in monsoon (0.57), post-monsoon (0.62), and winter (0.55), whereas it is moderate positive in pre-monsoon (0.37) season. The built-up area and bare land build a moderate positive correlation in all the four seasons (0.35 in pre-monsoon, 0.43 in monsoon, 0.48 in post-monsoon, and 0.39 in winter). Among the four seasons, the post-monsoon season builds the best correlation for all LULC types, whereas the pre-monsoon season has the least correlation. This research work is beneficial for land use and environmental planning of any city under similar climatic conditions.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42618337","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 digital image camera technology has revolutionized the aerial imagery capture throughout the world. It has provided high spatial and spectral resolution together with superior efficiency and reliability compared to the traditional analogue aerial imagery method. In so doing, this has provided great accuracy in various photogrammetric applications. The South African survey and mapping organisation currently known as Chief Directorate: National Geospatial Information (CD: NGI) has been capturing aerial photography dating back as early as the 1930s. However, from 2008 CD: NGI embraced a transition to digital aerial imagery capture at 50cm Ground Sample Distance (GSD) (from 2008 to 2016) and 25cm GSD (as from 2017 till date). This has resulted to 1370 digital aerial imagery already captured (this number will continue to change as there are jobs that are still being flown which are yet to be recorded). The continuous enhancement of digital camera technology opens for more possibilities of national aerial imagery capture at even greater spatial resolution such as 10cm GSD in the foreseeable future.However, the continuous digital imagery capture has not been without challenges such as the current global Covid-19 pandemic which has resulted in budget reprioritization, the organisation’s technical knowledge transfer from one generation to another together with the determination of the CD: NGI requirement for digital imagery specifications. The organisation values stakeholders’ engagement to ensure relevance, completeness and consistency on the data produced. This is possible through collaboration and partnership between State institutions, the private sector, research, and tertiary institutions through sharing of resources.
{"title":"The status of aerial photogrammetry in South Africa: a transition to digital imagery system","authors":"L. Ngcofe, Bulelwa Semoli","doi":"10.4314/sajg.v11i1.3","DOIUrl":"https://doi.org/10.4314/sajg.v11i1.3","url":null,"abstract":"The digital image camera technology has revolutionized the aerial imagery capture throughout the world. It has provided high spatial and spectral resolution together with superior efficiency and reliability compared to the traditional analogue aerial imagery method. In so doing, this has provided great accuracy in various photogrammetric applications. The South African survey and mapping organisation currently known as Chief Directorate: National Geospatial Information (CD: NGI) has been capturing aerial photography dating back as early as the 1930s. However, from 2008 CD: NGI embraced a transition to digital aerial imagery capture at 50cm Ground Sample Distance (GSD) (from 2008 to 2016) and 25cm GSD (as from 2017 till date). This has resulted to 1370 digital aerial imagery already captured (this number will continue to change as there are jobs that are still being flown which are yet to be recorded). The continuous enhancement of digital camera technology opens for more possibilities of national aerial imagery capture at even greater spatial resolution such as 10cm GSD in the foreseeable future.However, the continuous digital imagery capture has not been without challenges such as the current global Covid-19 pandemic which has resulted in budget reprioritization, the organisation’s technical knowledge transfer from one generation to another together with the determination of the CD: NGI requirement for digital imagery specifications. The organisation values stakeholders’ engagement to ensure relevance, completeness and consistency on the data produced. This is possible through collaboration and partnership between State institutions, the private sector, research, and tertiary institutions through sharing of resources.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48810865","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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