The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps.
{"title":"A multi-temporal phenology based classification approach for Crop Monitoring in Kenya","authors":"G. Laneve, R. Luciani, M. Jahjah","doi":"10.4314/sajg.v8i2.10","DOIUrl":"https://doi.org/10.4314/sajg.v8i2.10","url":null,"abstract":"The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42559530","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}
T.T. Mazarire, Phathutshedzo Eugene Ratshiedana, A. Nyamugama, E. Adam, G. Chirima
Accurate and detailed studies in crop mapping are crucial in precision agriculture, yield estimations, and crop monitoring. This study focused on exploring the utility of Sentinel-2 data in mapping of crop types and testing the two machine learning algorithms which are Random Forest and Support Vector Machine performance in classifying crop types in a heterogeneous agriculture landscape in Free state province, South Africa. Nine crop types were successfully classified. The utility and contribution of different bands for classification were evaluated using RF mean decrease GINI for variable importance. Validation of results was done using a confusion matrix which produced overall accuracy, errors and prediction measures. The best performance was attained by SVM with an overall accuracy of 95% and a kappa value of 94%. RF also performed fairly well with 85% of overall accuracy and kappa value of 83%. It was concluded that Sentinel-2 data performs better using the SVM classifier compared to RF classifier.
{"title":"Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa","authors":"T.T. Mazarire, Phathutshedzo Eugene Ratshiedana, A. Nyamugama, E. Adam, G. Chirima","doi":"10.4314/sajg.v9i2.22","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.22","url":null,"abstract":"Accurate and detailed studies in crop mapping are crucial in precision agriculture, yield estimations, and crop monitoring. This study focused on exploring the utility of Sentinel-2 data in mapping of crop types and testing the two machine learning algorithms which are Random Forest and Support Vector Machine performance in classifying crop types in a heterogeneous agriculture landscape in Free state province, South Africa. Nine crop types were successfully classified. The utility and contribution of different bands for classification were evaluated using RF mean decrease GINI for variable importance. Validation of results was done using a confusion matrix which produced overall accuracy, errors and prediction measures. The best performance was attained by SVM with an overall accuracy of 95% and a kappa value of 94%. RF also performed fairly well with 85% of overall accuracy and kappa value of 83%. It was concluded that Sentinel-2 data performs better using the SVM classifier compared to RF classifier.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46009996","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 evaluates land use change within the upper catchment area of Ala river, Akure, Ondo State over a period of 31 years (1986-2017) using Landsat Thematic Mapper (TM) 5 of 1986, Landsat Enhanced Thematic Mapper Plus (ETM+) 7 of 2002, and Landsat 8 OLI / TIRS of 2017. The normalized difference vegetation index (NDVI), surface runoff and terrain configuration within the study area were analysed using ArcGIS 10.5 software. Three land use types were identified namely; built up, vegetation and bareland/outcrop. The built up covered 29.39 % in 1986 and increased to 43.64% in 2017. Vegetation revealed a reverse trend with a continuous decline from 49.21% in 1986 to 14.16% in 2017 while bareland/outcrop increased from 19.60% in 1986 to 42.20% in 2017. The NDVI values range between -0.03 and 0.4; -0.3 and 0.3; and 0.04 and 0.3 in 1986, 2002, 2017, respectively as an indicator of vegetation degradation. The analysis showed that the study area has been considerably degraded, mainly due to the depletion of vegetation and uncontrolled urbanization process. It is therefore recommended that the government through its physical planning agency, put in place effective control mechanism to guide physical development that will enhance sustainable development in the study area and other parts of the town.
{"title":"An evaluation of the change in land use/land cover and terrain characteristics of Ala River catchment, Akure, Nigeria.","authors":"M. Ibitoye","doi":"10.4314/sajg.v9i2.18","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.18","url":null,"abstract":"This study evaluates land use change within the upper catchment area of Ala river, Akure, Ondo State over a period of 31 years (1986-2017) using Landsat Thematic Mapper (TM) 5 of 1986, Landsat Enhanced Thematic Mapper Plus (ETM+) 7 of 2002, and Landsat 8 OLI / TIRS of 2017. The normalized difference vegetation index (NDVI), surface runoff and terrain configuration within the study area were analysed using ArcGIS 10.5 software. Three land use types were identified namely; built up, vegetation and bareland/outcrop. The built up covered 29.39 % in 1986 and increased to 43.64% in 2017. Vegetation revealed a reverse trend with a continuous decline from 49.21% in 1986 to 14.16% in 2017 while bareland/outcrop increased from 19.60% in 1986 to 42.20% in 2017. The NDVI values range between -0.03 and 0.4; -0.3 and 0.3; and 0.04 and 0.3 in 1986, 2002, 2017, respectively as an indicator of vegetation degradation. The analysis showed that the study area has been considerably degraded, mainly due to the depletion of vegetation and uncontrolled urbanization process. It is therefore recommended that the government through its physical planning agency, put in place effective control mechanism to guide physical development that will enhance sustainable development in the study area and other parts of the town.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46602713","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}
Accurate documentation of land-use/land-cover (LULC) change and evaluating its hydrological impact are of great interest for catchment hydrological management. Jukskei River catchment has undergone a rapid infrastructural and residential development which had an influence on runoff depth. The objective of the study is to integrate Geographical Information System (GIS) and remote sensing (RS) techniques with Soil and Water Assessment Tool (SWAT) model to quantify the spatial and temporal changes in surface runoff depth resulting from LULC change. Landsat images of 1987 MSS, 2001 TM and 2015 OLI were pre-processed and classified using a supervised classification method with maximum likelihood. Results indicated that, there was a significant increase in built-up area from 28700.4ha in 1987 LULC to 36313.6ha in 2001 and 42713.1ha in 2015 at the expense of bare surface, intact vegetation and sparsed vegetation. However, during hydrological modelling, soil, DEM and climatic data were kept constant except LULC images which were interchanged during each simulation phase. Calibrated with observed hydrological data at the catchment outlets, SWAT model was used to evaluate the effect of LULC change on surface runoff depth. The analysis of SWAT model showed increases surface runoff depth from 70.5mm in 1987 LULC to 134.2mm in 2001 and 199.3mm in 2015 LULC. The SWAT model indicated satisfactorily results based on model calibration and validation results. Therefore, this study concluded that, integration of GIS and RS techniques with SWAT model can help in formulating policy guidelines for land-use practices thereby reducing hydrological impacts associated with LULC changes.
{"title":"Simulating change in surface runoff depth due to LULC change using soil and water assessment tool for flash floods prediction","authors":"T. Mawasha, W. Britz","doi":"10.4314/sajg.v9i2.19","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.19","url":null,"abstract":"Accurate documentation of land-use/land-cover (LULC) change and evaluating its hydrological impact are of great interest for catchment hydrological management. Jukskei River catchment has undergone a rapid infrastructural and residential development which had an influence on runoff depth. The objective of the study is to integrate Geographical Information System (GIS) and remote sensing (RS) techniques with Soil and Water Assessment Tool (SWAT) model to quantify the spatial and temporal changes in surface runoff depth resulting from LULC change. Landsat images of 1987 MSS, 2001 TM and 2015 OLI were pre-processed and classified using a supervised classification method with maximum likelihood. Results indicated that, there was a significant increase in built-up area from 28700.4ha in 1987 LULC to 36313.6ha in 2001 and 42713.1ha in 2015 at the expense of bare surface, intact vegetation and sparsed vegetation. However, during hydrological modelling, soil, DEM and climatic data were kept constant except LULC images which were interchanged during each simulation phase. Calibrated with observed hydrological data at the catchment outlets, SWAT model was used to evaluate the effect of LULC change on surface runoff depth. The analysis of SWAT model showed increases surface runoff depth from 70.5mm in 1987 LULC to 134.2mm in 2001 and 199.3mm in 2015 LULC. The SWAT model indicated satisfactorily results based on model calibration and validation results. Therefore, this study concluded that, integration of GIS and RS techniques with SWAT model can help in formulating policy guidelines for land-use practices thereby reducing hydrological impacts associated with LULC changes.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46607310","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}
Tata Herbert, Nzelibe Ifechukwu Ugochukwu, R. Olatunji
With the rapid establishment of free online processing services to provide users with reliable solutions, it is important to determine the reliability of using free online processing software for the Global Navigation Satellite System post-processing. The study aim at assessing the accuracy of two (2) free online processing software, AUSPOS, and CSRS-PPP and two (2) commercial software, compass post-processing, and GNSS solutions. Field observations were carried out on seven (7) control points using static GNSS observation techniques with an observation period of 1hr for three (3) consecutive days and conventional surveying using total station instruments to establish a closed traverse. The acquired data were post-processed using both online and commercial software. The co-ordinates generated from each software were then compared with the ones obtained using total station instruments to determine their relative discrepancies and accuracies. Root mean square error and T-test were used for the analysis of the result. The result obtained is (0.004m, 0.003m and 0.007m) for compass post-processing software and (0.015m, 0.012m and 0.016m) for GNSS solutions software and the online software had the Root Mean Square Error (RMSE) values of (0.025m, 0.023m and 0.027m) for AUSPOS and (0.034m, 0.037m and 0.041m) for CSRS-PPP both in X, Y, and Z direction i.e. UTM East, North and ellipsoidal height respectively. Analysis at a 5% level of significance shows no significant difference between the two methods. Online GNSS processing services are easy to use, do not require the knowledge of GNSS data processing and can be adopted for engineering and geodetic applications.
{"title":"Assessing the accuracy of online GNSS processing services and commercial software on short baselines","authors":"Tata Herbert, Nzelibe Ifechukwu Ugochukwu, R. Olatunji","doi":"10.4314/sajg.v9i2.21","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.21","url":null,"abstract":"With the rapid establishment of free online processing services to provide users with reliable solutions, it is important to determine the reliability of using free online processing software for the Global Navigation Satellite System post-processing. The study aim at assessing the accuracy of two (2) free online processing software, AUSPOS, and CSRS-PPP and two (2) commercial software, compass post-processing, and GNSS solutions. Field observations were carried out on seven (7) control points using static GNSS observation techniques with an observation period of 1hr for three (3) consecutive days and conventional surveying using total station instruments to establish a closed traverse. The acquired data were post-processed using both online and commercial software. The co-ordinates generated from each software were then compared with the ones obtained using total station instruments to determine their relative discrepancies and accuracies. Root mean square error and T-test were used for the analysis of the result. The result obtained is (0.004m, 0.003m and 0.007m) for compass post-processing software and (0.015m, 0.012m and 0.016m) for GNSS solutions software and the online software had the Root Mean Square Error (RMSE) values of (0.025m, 0.023m and 0.027m) for AUSPOS and (0.034m, 0.037m and 0.041m) for CSRS-PPP both in X, Y, and Z direction i.e. UTM East, North and ellipsoidal height respectively. Analysis at a 5% level of significance shows no significant difference between the two methods. Online GNSS processing services are easy to use, do not require the knowledge of GNSS data processing and can be adopted for engineering and geodetic applications.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42750029","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 paper focuses on the mapping of alteration minerals and linear structures associated with gold mineralization in the Giyani Greenstone Belt (GGB). Spectral Information Divergence (SID) and Linear Spectral Unmixing (LSU) classification algorithms were applied to the ASTER data using image extracted endmember spectra which match the spectral profiles of predominant alteration minerals (biotite and calcite) related to gold mineralization in the GGB. Directional convolution filters were also applied to the ASTER data to extract structural lineaments that may be related to gold mineralization. In addition, Fry analysis of known gold occurrences in the GGB was conducted to determine the major distribution patterns along which gold mineralization have occurred. LSU and SID results show several pixels classified as alteration minerals corresponding to known gold occurrences. SID results also show minerals overlapping into the gneissic rock, which is a rare host of gold mineralization in the study area. On the other hand, LSU results show high abundances of calcite and biotite generally confined within the mafic-ultramafic greenstone rocks, which have been hypothesized as the source of gold-mineralized fluids in the GGB. In general, LSU show distinct broad regions classified as alteration minerals corresponding to known gold occurrences that have been previously reported to host these alteration minerals. Directional convolution filters enabled the extraction of predominately NE-SW oriented linear structures, including the major shear zone which is associated with gold mineralization in the GGB. Fry analysis revealed three major distribution patterns: N-S, NE-SW, ENE-WSW along which gold mineralization have occurred. These patterns correspond to major lineaments associated with gold mineralization reported in the previous structural studies of the GGB.
{"title":"Mapping of gold-related alteration minerals and linear structures using ASTER data in the Giyani Greenstone Belt, South Africa","authors":"Nndanduleni Muavhi, Mbofholowo Emmanuel Mavhungu","doi":"10.4314/sajg.v9i2.17","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.17","url":null,"abstract":"This paper focuses on the mapping of alteration minerals and linear structures associated with gold mineralization in the Giyani Greenstone Belt (GGB). Spectral Information Divergence (SID) and Linear Spectral Unmixing (LSU) classification algorithms were applied to the ASTER data using image extracted endmember spectra which match the spectral profiles of predominant alteration minerals (biotite and calcite) related to gold mineralization in the GGB. Directional convolution filters were also applied to the ASTER data to extract structural lineaments that may be related to gold mineralization. In addition, Fry analysis of known gold occurrences in the GGB was conducted to determine the major distribution patterns along which gold mineralization have occurred. LSU and SID results show several pixels classified as alteration minerals corresponding to known gold occurrences. SID results also show minerals overlapping into the gneissic rock, which is a rare host of gold mineralization in the study area. On the other hand, LSU results show high abundances of calcite and biotite generally confined within the mafic-ultramafic greenstone rocks, which have been hypothesized as the source of gold-mineralized fluids in the GGB. In general, LSU show distinct broad regions classified as alteration minerals corresponding to known gold occurrences that have been previously reported to host these alteration minerals. Directional convolution filters enabled the extraction of predominately NE-SW oriented linear structures, including the major shear zone which is associated with gold mineralization in the GGB. Fry analysis revealed three major distribution patterns: N-S, NE-SW, ENE-WSW along which gold mineralization have occurred. These patterns correspond to major lineaments associated with gold mineralization reported in the previous structural studies of the GGB.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43881530","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 focused on analyzing the trend of food insecurity in the Northern regions of Ghana. It applied the GIS-based Multi-Criteria Evaluation approach to the criteria of rainfall, land cover, population density, road networks, slope, market centres, potable water access, access to sanitation facilities, and disaster and conflict hotspots. The Weighted Linear Combination (WLC) technique, was used to standardize a set of criteria for each of the four dimensions of food security (availability, accessibility, utilization, and stability) into an ordinary numeric scale after which those factors were accumulated via weighted averaging to determine a composite index for all the districts within the study area. The research found that the food insecurity situation is relatively high, as 174,509 people (6.3 percent) are moderately food insecure while 25,246 people (0.9 percent) are severely food insecure. Overall, 199,755 people, representing 7.2 percent of the population were food insecure (both severely and moderately food insecure). The proportion of the food insecure population was highest in the Tamale metropolis (37.2 percent) and lowest in the Zabzugu and Tatale Sanguli districts (0 percent). A correlation analysis also revealed that the composite food security index was mainly influenced by food utilization (0.75) and stability (0.64). Also, there was no significant relationship between Food stability and the other three dimension of food security (food availability, accessibility, and utilization), implying that it did not influence domestic food production or market access due to the short-term nature of its effect.
{"title":"Spatial dimensions of food and nutrition security in the Northern region of Ghana","authors":"Moses Yao Korbli, Akwasi Acheampong","doi":"10.4314/sajg.v9i2.27","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.27","url":null,"abstract":"This study focused on analyzing the trend of food insecurity in the Northern regions of Ghana. It applied the GIS-based Multi-Criteria Evaluation approach to the criteria of rainfall, land cover, population density, road networks, slope, market centres, potable water access, access to sanitation facilities, and disaster and conflict hotspots. The Weighted Linear Combination (WLC) technique, was used to standardize a set of criteria for each of the four dimensions of food security (availability, accessibility, utilization, and stability) into an ordinary numeric scale after which those factors were accumulated via weighted averaging to determine a composite index for all the districts within the study area. The research found that the food insecurity situation is relatively high, as 174,509 people (6.3 percent) are moderately food insecure while 25,246 people (0.9 percent) are severely food insecure. Overall, 199,755 people, representing 7.2 percent of the population were food insecure (both severely and moderately food insecure). The proportion of the food insecure population was highest in the Tamale metropolis (37.2 percent) and lowest in the Zabzugu and Tatale Sanguli districts (0 percent). A correlation analysis also revealed that the composite food security index was mainly influenced by food utilization (0.75) and stability (0.64). Also, there was no significant relationship between Food stability and the other three dimension of food security (food availability, accessibility, and utilization), implying that it did not influence domestic food production or market access due to the short-term nature of its effect.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43989772","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}
Positional accuracy is one of the important factors which determines acceptability of survey work. Apart from the equipment and method used which affect the accuracy of surveys, time of the day in which the equipment operates can equally affect the accuracy of a survey. In this study, the performance of Unmanned Aerial Vehicle (UAV) surveys as well as the appropriate time in the day to apply the technology in Tarkwa, Ghana, has been investigated. The paper assessed the positional accuracies of ground features on UAV-based orthophotos (with emphasis on horizontal coordinates), captured at different times of the day, keeping all other parameters unchanged for capturing, production and processing of all orthophotos each time. The positional accuracies of selected features on the orthophotos were determined by calculating the Root Mean Square Error (RMSE) between the feature coordinates on the ground measured with GNSS Receivers and those derived from the UAV-based orthophotos. The results show that coordinates derived from orthophotos captured in the morning, with average temperatures between 21 ℃ and 23 ℃, and average wind speed of not more than 10 m/s, produced images with the highest positional accuracies, with RMSE values between 0.0047 m and 0.0283 m. These RMSE are within the range of values recommended for standard mapping surveys as well as GIS.
{"title":"Assessment of positional accuracies of UAV-based coordinates derived from orthophotos at varying times of the day- A case study","authors":"S. Mantey, M. S. Aduah","doi":"10.4314/sajg.v10i1.4","DOIUrl":"https://doi.org/10.4314/sajg.v10i1.4","url":null,"abstract":"Positional accuracy is one of the important factors which determines acceptability of survey work. Apart from the equipment and method used which affect the accuracy of surveys, time of the day in which the equipment operates can equally affect the accuracy of a survey. In this study, the performance of Unmanned Aerial Vehicle (UAV) surveys as well as the appropriate time in the day to apply the technology in Tarkwa, Ghana, has been investigated. The paper assessed the positional accuracies of ground features on UAV-based orthophotos (with emphasis on horizontal coordinates), captured at different times of the day, keeping all other parameters unchanged for capturing, production and processing of all orthophotos each time. The positional accuracies of selected features on the orthophotos were determined by calculating the Root Mean Square Error (RMSE) between the feature coordinates on the ground measured with GNSS Receivers and those derived from the UAV-based orthophotos. The results show that coordinates derived from orthophotos captured in the morning, with average temperatures between 21 ℃ and 23 ℃, and average wind speed of not more than 10 m/s, produced images with the highest positional accuracies, with RMSE values between 0.0047 m and 0.0283 m. These RMSE are within the range of values recommended for standard mapping surveys as well as GIS.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47478687","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 exploitation and sustainable use of groundwater has received much attention with the sudden decline in quantity and quality of surface water. Knowledge on the current status of the physico-chemical parameters of groundwater becomes important in ensuring the sustainable use of the resource. This study used Geographic Information System (GIS) to assess groundwater quality in Ahafo-Kenyasi with particular focus on determining the spatial distribution of groundwater quality parameters and also produce groundwater quality map of the area. Physico-chemical analyses of groundwater quality parameters were made after collection of water samples from 24 community boreholes. The results of analysis carried out showed the following concentration ranges: pH (5.12-6.54), EC (71.6-952μS/cm), TDS (35.08-465.59mg/l), Turbidity (0-6.25NTU), Ammonia (0.01-0.61mg/l), Nitrate (0.1-4.12mg/l), Sulphate (1-65.5mg/l). All the samples analysed were above the guidelines set by World Health Organization (WHO, 2011) except for pH and Turbidity. Spatial distribution maps of the individual water quality parameters were developed using kriging interpolation technique and accepted based on the prediction performances of Stable, Exponential, K-Bessel semivariogram models. Overall water quality of the study area was assessed using Water Quality Index (WQI). The results showed that groundwater quality in the area decreases from north-western to south-eastern. However, groundwater from Ahafo-Kenyasi is good for domestic purposes.
{"title":"Geospatial analysis of groundwater quality using GIS: A case study of Ahafo Kenyasi, Ghana","authors":"Ankomah Ernest, Dadzie Isaac","doi":"10.4314/sajg.v10i1.3","DOIUrl":"https://doi.org/10.4314/sajg.v10i1.3","url":null,"abstract":"The exploitation and sustainable use of groundwater has received much attention with the sudden decline in quantity and quality of surface water. Knowledge on the current status of the physico-chemical parameters of groundwater becomes important in ensuring the sustainable use of the resource. This study used Geographic Information System (GIS) to assess groundwater quality in Ahafo-Kenyasi with particular focus on determining the spatial distribution of groundwater quality parameters and also produce groundwater quality map of the area. Physico-chemical analyses of groundwater quality parameters were made after collection of water samples from 24 community boreholes. The results of analysis carried out showed the following concentration ranges: pH (5.12-6.54), EC (71.6-952μS/cm), TDS (35.08-465.59mg/l), Turbidity (0-6.25NTU), Ammonia (0.01-0.61mg/l), Nitrate (0.1-4.12mg/l), Sulphate (1-65.5mg/l). All the samples analysed were above the guidelines set by World Health Organization (WHO, 2011) except for pH and Turbidity. Spatial distribution maps of the individual water quality parameters were developed using kriging interpolation technique and accepted based on the prediction performances of Stable, Exponential, K-Bessel semivariogram models. Overall water quality of the study area was assessed using Water Quality Index (WQI). The results showed that groundwater quality in the area decreases from north-western to south-eastern. However, groundwater from Ahafo-Kenyasi is good for domestic purposes.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49446893","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}
Forest fire is a devastating phenomenon in real life, causing huge losses of lives, properties and ecologies. A risk assessment model to identify, classify and map forest fire risk areas is presented in this paper. This model considers four risk models, i.e. ignition model, detection model, response model and fuel model analysis. The first model concentrates on human influence factors in forest fires, including the land use, distance from roads, and distance from settlements and the second model is made up of the possibility of fire visibility from road and settlement viewpoint. The forest fire response included distance from fire stations and motion resistance is the third model. The type of fuel (dry or wet), fuel moisture content, health of the forest vegetation and topography of the area were analysed as the fourth model. The study results indicate that very high-risk zones covered 38.8km2 representing 25.6% of the total forest area. Findings of the research are helpful in developing forest fire management systems. Fast and appropriate direction could be used by management to stop the spread of fire effectively. It also helps to provide effective means for protecting forests from fires as well as to formulate appropriate methods to control and manage forest fire damages and its spread. Recommendations were made at the end of the work to implement fire towers, break lines and employ the use of modern detection techniques such drones, etc to improve fire detection and response.
{"title":"Modelling the risk of forest to fire for the Bosomkese Forest Reserve, Ahafo Region, Ghana","authors":"Adams Elias Dadzie, A. Mary","doi":"10.4314/sajg.v10i1.5","DOIUrl":"https://doi.org/10.4314/sajg.v10i1.5","url":null,"abstract":"Forest fire is a devastating phenomenon in real life, causing huge losses of lives, properties and ecologies. A risk assessment model to identify, classify and map forest fire risk areas is presented in this paper. This model considers four risk models, i.e. ignition model, detection model, response model and fuel model analysis. The first model concentrates on human influence factors in forest fires, including the land use, distance from roads, and distance from settlements and the second model is made up of the possibility of fire visibility from road and settlement viewpoint. The forest fire response included distance from fire stations and motion resistance is the third model. The type of fuel (dry or wet), fuel moisture content, health of the forest vegetation and topography of the area were analysed as the fourth model. The study results indicate that very high-risk zones covered 38.8km2 representing 25.6% of the total forest area. Findings of the research are helpful in developing forest fire management systems. Fast and appropriate direction could be used by management to stop the spread of fire effectively. It also helps to provide effective means for protecting forests from fires as well as to formulate appropriate methods to control and manage forest fire damages and its spread. Recommendations were made at the end of the work to implement fire towers, break lines and employ the use of modern detection techniques such drones, etc to improve fire detection and response.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43811142","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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