Alfred S. Alademomi, Mayaki Anthony Omeiza, Daramola Olagoke Emmanuel, Salami Tosin Julius, O. Bolarinwa
Deformation and subsidence measurements are very vital for stability of structures and buildings. Deformation and subsidence monitoring are easily carried out with the aid of established baselines. This study focuses on the establishment of baseline for monitoring deformation and subsidence within university of Lagos. Geodetic method of control establishment was adopted, where five (5) control stations were established on stable grounds across the university of Lagos main campus with Differential GPS observation carried out on them and data obtained were processed and analysed statistically. The result of the findings shows that the baseline established is very reliable, given that the vertical controls have their relative redundancy number rij ranging between 0.1
{"title":"Establishment of deformation and subsidence monitoring baseline in the coastal environment: A case study of University of Lagos","authors":"Alfred S. Alademomi, Mayaki Anthony Omeiza, Daramola Olagoke Emmanuel, Salami Tosin Julius, O. Bolarinwa","doi":"10.4314/sajg.v9i1.2","DOIUrl":"https://doi.org/10.4314/sajg.v9i1.2","url":null,"abstract":"Deformation and subsidence measurements are very vital for stability of structures and buildings. Deformation and subsidence monitoring are easily carried out with the aid of established baselines. This study focuses on the establishment of baseline for monitoring deformation and subsidence within university of Lagos. Geodetic method of control establishment was adopted, where five (5) control stations were established on stable grounds across the university of Lagos main campus with Differential GPS observation carried out on them and data obtained were processed and analysed statistically. The result of the findings shows that the baseline established is very reliable, given that the vertical controls have their relative redundancy number rij ranging between 0.1<rij<1.0 and the standard deviations ranges from 0.002 to 0.005. Also, the relative precision of the established baselines fell within the range of 7.36e-06ppm-2.54e-05ppm. From the findings of this research, deformation and subsidence studies can be reliably monitored within the University of Lagos and its environ using the baseline established through this research in order to safeguard lives and properties – including high rise structures within the university’s main campus.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48438533","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}
Data interpolation – construction of new data points within range of a discrete set of known data point – is an important modeling activity in geographical studies. In this study, three commonly applied interpolation methods (nearest point, kriging and moving average) were examined in an assessment of the varying dispersion of selected physical and chemical parameters of stream-borne effluents from palm oil processing area in a growing commercial centre in Ife South local government area in Nigeria. Specific objectives were to examine selected physiochemical properties of a stream that receives palm oil effluent, and compare results of a kriging interpolation using derived variogram values with that which was based on the accepted parametric default in a popular geographical information system. The study also presents visualised results of interpolation of selected parameters based on ordinary kriging, moving average and nearest point interpolation. Analysis were achieved using PAST 3 and ILWIS GIS software. Result showed that although the stream is vulnerable to contamination by the palm oil processing activities around the area, it also receives contaminants from other non-source points that were not investigated in this study. It also indicated that the different point interpolation methods did not produce similar results. Whereas the values of conductivity were interpolated to vary as 120.1 – 219.5 μScm-1 with kriging interpolation, it varied as 105.6 – 220.0 μScm-1 and 135.0 – 173.9 μScm-1, with nearest point and moving average interpolations, respectively. Also, whereas the computed variogram model produced the best fit lines with Gaussian model, the Spherical model was assumed default for all the distributions in selected GIS software, such that the value of Nugget was assumed as 0.00, when it actually varies with data locations distribution. Conclusively, procedure of estimating spatial variation always produce results that are influenced by data distribution and model assumptions, and as such the data characteristics rather than GIS software’s defaults are appropriate for consideration in geospatial evaluation.
{"title":"Water quality and influence of interpolation procedure on visualization of selected parameters in a headwater stream, in Ayepe-Olode, southwestern Nigeria","authors":"A. Eludoyin, O. S. Ijisesan","doi":"10.4314/sajg.v9i1.4","DOIUrl":"https://doi.org/10.4314/sajg.v9i1.4","url":null,"abstract":"Data interpolation – construction of new data points within range of a discrete set of known data point – is an important modeling activity in geographical studies. In this study, three commonly applied interpolation methods (nearest point, kriging and moving average) were examined in an assessment of the varying dispersion of selected physical and chemical parameters of stream-borne effluents from palm oil processing area in a growing commercial centre in Ife South local government area in Nigeria. Specific objectives were to examine selected physiochemical properties of a stream that receives palm oil effluent, and compare results of a kriging interpolation using derived variogram values with that which was based on the accepted parametric default in a popular geographical information system. The study also presents visualised results of interpolation of selected parameters based on ordinary kriging, moving average and nearest point interpolation. Analysis were achieved using PAST 3 and ILWIS GIS software. Result showed that although the stream is vulnerable to contamination by the palm oil processing activities around the area, it also receives contaminants from other non-source points that were not investigated in this study. It also indicated that the different point interpolation methods did not produce similar results. Whereas the values of conductivity were interpolated to vary as 120.1 – 219.5 μScm-1 with kriging interpolation, it varied as 105.6 – 220.0 μScm-1 and 135.0 – 173.9 μScm-1, with nearest point and moving average interpolations, respectively. Also, whereas the computed variogram model produced the best fit lines with Gaussian model, the Spherical model was assumed default for all the distributions in selected GIS software, such that the value of Nugget was assumed as 0.00, when it actually varies with data locations distribution. Conclusively, procedure of estimating spatial variation always produce results that are influenced by data distribution and model assumptions, and as such the data characteristics rather than GIS software’s defaults are appropriate for consideration in geospatial evaluation.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47385297","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 compared two methods used for agricultural statistics generation in Rwanda. The first method is area frame sampling, which is also the currently used method in Rwandan seasonal agricultural surveys; while the second method is the application of remote sensing technique using multi-temporal Normalised Difference Vegetation Index (NDVI) classes to stratify land into homogenous agriculture land classes. The analysis of the methodological flow of Rwanda area frames and the estimated homogeneity in the resulting frames was mainly based on literature review. For the delineation of homogeneous NDVI classes, the study used 10 years data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (2004 – 2014). The NDVI data were classified using ISODATA clustering technique, and the focus was put on agriculture-dominated classes, obtained through the intersection with 2010 national land use and land cover data. Analysis of Variance (ANOVA) and Fisher’s Least Significant Difference (LSD) statistical methods were applied to investigate significant differences between and within NDVI classes and the currently used Rwanda strata in terms of area coverage of four (4) dominant crops in Rwanda – banana, maize, cassava, and beans. The results of the analysis revealed homogeneity of 85% within NDVI classes, and 69% within the current Rwanda strata, at p = 0.05. The NDVI classes were also used to improve the Rwanda strata, and the homogeneity has increased by 5%; reaching 74% after NDVI-based reclassification.
本研究比较了卢旺达用于农业统计生成的两种方法。第一种方法是区域框架抽样，这也是卢旺达季节性农业调查目前使用的方法;第二种方法是应用遥感技术，利用多时相归一化植被指数(NDVI)分类将土地划分为同质的农业用地类别。对卢旺达地区框架的方法学流程和由此产生的框架的估计同质性的分析主要基于文献综述。为了描述均匀的NDVI类别，研究使用了中分辨率成像光谱仪(MODIS)传感器10年(2004 - 2014)的数据。采用ISODATA聚类技术对NDVI数据进行分类，并将其与2010年全国土地利用和土地覆盖数据进行交叉分析，得到以农业为主的类。采用方差分析(ANOVA)和Fisher 's Least Significant Difference (LSD)统计方法来调查卢旺达四种主要作物(香蕉、玉米、木薯和豆类)的面积覆盖情况，NDVI类别和卢旺达目前使用的地层之间和内部的显著差异。分析结果显示，在NDVI分类中，均匀性为85%，在卢旺达当前地层中，均匀性为69%，p = 0.05。NDVI分级也用于改善卢旺达地层，均匀性提高了5%;在基于ndi的重新分类后达到74%。
{"title":"Comparative assessment of homogeneity differences in multi-temporal NDVI strata and the currently used agricultural area frames in Rwanda","authors":"M. Mugabowindekwe, G. Rwanyiziri","doi":"10.4314/sajg.v9i1.7","DOIUrl":"https://doi.org/10.4314/sajg.v9i1.7","url":null,"abstract":"This study compared two methods used for agricultural statistics generation in Rwanda. The first method is area frame sampling, which is also the currently used method in Rwandan seasonal agricultural surveys; while the second method is the application of remote sensing technique using multi-temporal Normalised Difference Vegetation Index (NDVI) classes to stratify land into homogenous agriculture land classes. The analysis of the methodological flow of Rwanda area frames and the estimated homogeneity in the resulting frames was mainly based on literature review. For the delineation of homogeneous NDVI classes, the study used 10 years data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (2004 – 2014). The NDVI data were classified using ISODATA clustering technique, and the focus was put on agriculture-dominated classes, obtained through the intersection with 2010 national land use and land cover data. Analysis of Variance (ANOVA) and Fisher’s Least Significant Difference (LSD) statistical methods were applied to investigate significant differences between and within NDVI classes and the currently used Rwanda strata in terms of area coverage of four (4) dominant crops in Rwanda – banana, maize, cassava, and beans. The results of the analysis revealed homogeneity of 85% within NDVI classes, and 69% within the current Rwanda strata, at p = 0.05. The NDVI classes were also used to improve the Rwanda strata, and the homogeneity has increased by 5%; reaching 74% after NDVI-based reclassification.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45101923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study integrates geographic information system (GIS) and analytic hierarchy process (AHP) to evaluate land suitability for maize production in Zimbabwe using multi-criteria evaluation (MCE) process. Four thematic maps based on rainfall, temperate, soil type and slope were integrated through overlay technique in a GIS environment to produce maize production suitability map. The resultant maize suitability map was overlaid with constraints map to ‘mask out’ all non-agricultural land. The final maize suitability map shows that 3.20% of the total land is highly suitable, 16.56% is suitable, 25.34% is moderately suitable, 32.33% is marginally suitable and 9.57% is not suitable for maize production in its current form. The maize suitability classification was validated by regression analyses using measured maize grain yield of 5 key maize varieties representing 5 different maturity groups. Grain yield was regressed against suitability index (SI) of each land class. There were significant positive correlations between maize grain yield and land suitability classes (R2 = 0.63 - 0.85). Integrating GIS and AHP with MCE is effective in assessing land suitability for targeting location specific interventions for maize production and the result is a comprehensive suitability map for Zimbabwe, incorporating several critical environmental factors affecting maize adaptation. We recommend the use of this suitability map as a decision support tool in land use planning and policy making.
{"title":"Mapping land suitability for maize (Zea mays L.) production using GIS and AHP technique in Zimbabwe","authors":"W. Chivasa, O. Mutanga, Ç. Biradar","doi":"10.4314/sajg.v8i2.11","DOIUrl":"https://doi.org/10.4314/sajg.v8i2.11","url":null,"abstract":"The study integrates geographic information system (GIS) and analytic hierarchy process (AHP) to evaluate land suitability for maize production in Zimbabwe using multi-criteria evaluation (MCE) process. Four thematic maps based on rainfall, temperate, soil type and slope were integrated through overlay technique in a GIS environment to produce maize production suitability map. The resultant maize suitability map was overlaid with constraints map to ‘mask out’ all non-agricultural land. The final maize suitability map shows that 3.20% of the total land is highly suitable, 16.56% is suitable, 25.34% is moderately suitable, 32.33% is marginally suitable and 9.57% is not suitable for maize production in its current form. The maize suitability classification was validated by regression analyses using measured maize grain yield of 5 key maize varieties representing 5 different maturity groups. Grain yield was regressed against suitability index (SI) of each land class. There were significant positive correlations between maize grain yield and land suitability classes (R2 = 0.63 - 0.85). Integrating GIS and AHP with MCE is effective in assessing land suitability for targeting location specific interventions for maize production and the result is a comprehensive suitability map for Zimbabwe, incorporating several critical environmental factors affecting maize adaptation. We recommend the use of this suitability map as a decision support tool in land use planning and policy making.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45165923","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}
Simple algebraic change detection techniques viz. image difference and image ratio were applied to the South African national land use / cover (NLC) datasets of years 2000 and 2014, prepared in grid format covering the Klerksdorp–Orkney–Stilfontein–Hartebeestfontein (KOSH) region in order to assess land use/land cover changes. Both the 2000 and 2014 NLC datasets were generated from Landsat images using different classification schemes and the code values & attributes of the land cover classes of the two datasets were different/not comparable. In order to make these datasets comparable for change detection, the NLC2000 dataset was examined in ArcView GIS by superimposing it onto the NLC2014 dataset and similarities and differences were identified. For each cover type of the NLC2000 dataset, comparable cover type of the 2014 dataset was identified by making a query to the NLC2000 dataset and after viewing the spatial distributions of selected units in respect of the NLC2014 dataset. Suitable code values of NLC2014 dataset were identified for the NLC2000 dataset and it was later reclassified. The land use / cover change detection study reveals that increase in areas were observed for the cover types: Cultivated common fields (low), Cultivated common fields (med), Mines 2 semi-bare, Wetlands, Urban commercial and Plantations/woodlots mature. The Grassland, Thicket/dense bush, Urban residential (dense trees/bush), Mines 1 bare, and Cultivated common pivots (high) showed a decrease in places. During the 14 years, Grassland had decreased from 2,132.47 km2 (77.35% of the total area) to 1,629.78 km2 (59.11% of the total area) owing to landscape transformation to other land covers (e.g. Cultivated common fields and Urban residential) due to human activities. The percentage increase in areas observed for the Cultivated common fields (low and medium) were 8.21% and 2.96% while the Mines 2 semi-bare, Wetlands, Urban commercial, Plantations/woodlots mature showed increases of 0.67%, 0.32%, 0.28% and 0.23% respectively. The area of Thicket/dense bush decreased from 108.15 km2 to 56.71 km2 (change of 1.87%). Maps of land use/land cover changes and statistics obtained for the changed areas are very useful for identifying various changes occurring in different classes and for monitoring land use dynamics.
{"title":"Detection of land use / cover changes of the KOSH region over a period of 14 years using the South African National Land Cover datasets for 2000 and 2014","authors":"Abraham Thomas","doi":"10.4314/sajg.v8i2.1","DOIUrl":"https://doi.org/10.4314/sajg.v8i2.1","url":null,"abstract":"Simple algebraic change detection techniques viz. image difference and image ratio were applied to the South African national land use / cover (NLC) datasets of years 2000 and 2014, prepared in grid format covering the Klerksdorp–Orkney–Stilfontein–Hartebeestfontein (KOSH) region in order to assess land use/land cover changes. Both the 2000 and 2014 NLC datasets were generated from Landsat images using different classification schemes and the code values & attributes of the land cover classes of the two datasets were different/not comparable. In order to make these datasets comparable for change detection, the NLC2000 dataset was examined in ArcView GIS by superimposing it onto the NLC2014 dataset and similarities and differences were identified. For each cover type of the NLC2000 dataset, comparable cover type of the 2014 dataset was identified by making a query to the NLC2000 dataset and after viewing the spatial distributions of selected units in respect of the NLC2014 dataset. Suitable code values of NLC2014 dataset were identified for the NLC2000 dataset and it was later reclassified. The land use / cover change detection study reveals that increase in areas were observed for the cover types: Cultivated common fields (low), Cultivated common fields (med), Mines 2 semi-bare, Wetlands, Urban commercial and Plantations/woodlots mature. The Grassland, Thicket/dense bush, Urban residential (dense trees/bush), Mines 1 bare, and Cultivated common pivots (high) showed a decrease in places. During the 14 years, Grassland had decreased from 2,132.47 km2 (77.35% of the total area) to 1,629.78 km2 (59.11% of the total area) owing to landscape transformation to other land covers (e.g. Cultivated common fields and Urban residential) due to human activities. The percentage increase in areas observed for the Cultivated common fields (low and medium) were 8.21% and 2.96% while the Mines 2 semi-bare, Wetlands, Urban commercial, Plantations/woodlots mature showed increases of 0.67%, 0.32%, 0.28% and 0.23% respectively. The area of Thicket/dense bush decreased from 108.15 km2 to 56.71 km2 (change of 1.87%). Maps of land use/land cover changes and statistics obtained for the changed areas are very useful for identifying various changes occurring in different classes and for monitoring land use dynamics.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46574142","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 appetite for up-to-date information about the earth’s surface is ever increasing, as such information provides a basis for a large number of applications. These include the earth’s resource detection and evaluation, land cover and land use change monitoring together with other vast environmental studies such as climate change assessment. Due to the advantages of repetitive data acquisition, the synoptic view, together with the varied spatial resolution it provides, and its available historically achieved dataset, remote sensing earth observation has become the major preferred data source for various earth studies. This study assesses land cover change detection of the land cover products (2013_2014 and 2017_2018) derived from earth observation.There are vast number of change detection methodologies and techniques with some still emerging. This study embarked on post classification change detection methodology which entailed morphological and spectral filtering techniques. The 10 land cover classes that were assessed for change detection are: natural wooded land, shrubland, grassland, waterbodies, wetlands, barren lands, cultivated, built-up, planted forest together with mines and quarries. The change detection accuracy result was 74.97%. Through the likelihood analysis, the likelihood for change to occur (e.g. cultivated to grassland) and unlikelihood of change to occur (e.g. built-up to planted forest), resulted in 72.2% areas of potential realistic change.The change detection results, further depend on the quality, compatibility and accuracy of the input land cover datasets. The application of different ancillary data together with different modelling techniques for land cover classification also affect the true reflectance of land cover change detection. Therefore extra caution should be exercised when analysing change detection so as to provide true and reliable changes.
{"title":"The South African land cover change detection derived from 2013_2014 and 2017_2018 land cover products","authors":"L. Ngcofe, R. Hickson, Pradeep Singh","doi":"10.4314/sajg.v8i2.4","DOIUrl":"https://doi.org/10.4314/sajg.v8i2.4","url":null,"abstract":"The appetite for up-to-date information about the earth’s surface is ever increasing, as such information provides a basis for a large number of applications. These include the earth’s resource detection and evaluation, land cover and land use change monitoring together with other vast environmental studies such as climate change assessment. Due to the advantages of repetitive data acquisition, the synoptic view, together with the varied spatial resolution it provides, and its available historically achieved dataset, remote sensing earth observation has become the major preferred data source for various earth studies. This study assesses land cover change detection of the land cover products (2013_2014 and 2017_2018) derived from earth observation.There are vast number of change detection methodologies and techniques with some still emerging. This study embarked on post classification change detection methodology which entailed morphological and spectral filtering techniques. The 10 land cover classes that were assessed for change detection are: natural wooded land, shrubland, grassland, waterbodies, wetlands, barren lands, cultivated, built-up, planted forest together with mines and quarries. The change detection accuracy result was 74.97%. Through the likelihood analysis, the likelihood for change to occur (e.g. cultivated to grassland) and unlikelihood of change to occur (e.g. built-up to planted forest), resulted in 72.2% areas of potential realistic change.The change detection results, further depend on the quality, compatibility and accuracy of the input land cover datasets. The application of different ancillary data together with different modelling techniques for land cover classification also affect the true reflectance of land cover change detection. Therefore extra caution should be exercised when analysing change detection so as to provide true and reliable changes.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43332197","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 examined the spatiotemporal pattern of urban growth and magnitude of changes in selected Local Government Areas (LGAs) in the suburban area of Ibadan using remote sensing cum spatial metrics. Data for the study were obtained from administrative maps of the study area, population data and satellite imageries. All of these were complemented with ground validations using Global Positioning System (GPS) receiver. Periods of change analysis were divided into two epochs; 1986 to 2002 and 2002 to 2017. The imageries were classified into four landuse/cover classes based on Anderson’s modified version of supervised classification scheme. Accuracy assessment of the imageries was carried out using 100 randomly sampled ground truth points. Ten spatial metrics were selected for analysis using Fragstats program. Results show a significant loss in vegetation due to conversion (Land Consumption Rate, LCR = 0.025, 0.019 and 0.027; Land Absorption Coefficient, LAC = 0.012 and 0.048). Moreover, there was a persistent increase in Number of Patches (NP) indicating a scattered and fragmented but continuous development. However, dwindling values of Patch Density (PD = 90.94, 27.07 and 30.30) indicate expansion through conversion of other landuses at varying rates. Results also indicate an incidence of fragmented low density development in the fringe areas (Area Weighted Mean Patch Fractal Dimension, AWMPFD = 1.37 and 1.39). The study highlights the chaotic land development and unrestrained urban expansion in the study area.
{"title":"Landscape metrics analysis of land use patterns and changes in suburban local government areas of Ibadan, Nigeria","authors":"A. Olayiwola, O. Fakayode","doi":"10.4314/sajg.v8i2.7","DOIUrl":"https://doi.org/10.4314/sajg.v8i2.7","url":null,"abstract":"This study examined the spatiotemporal pattern of urban growth and magnitude of changes in selected Local Government Areas (LGAs) in the suburban area of Ibadan using remote sensing cum spatial metrics. Data for the study were obtained from administrative maps of the study area, population data and satellite imageries. All of these were complemented with ground validations using Global Positioning System (GPS) receiver. Periods of change analysis were divided into two epochs; 1986 to 2002 and 2002 to 2017. The imageries were classified into four landuse/cover classes based on Anderson’s modified version of supervised classification scheme. Accuracy assessment of the imageries was carried out using 100 randomly sampled ground truth points. Ten spatial metrics were selected for analysis using Fragstats program. Results show a significant loss in vegetation due to conversion (Land Consumption Rate, LCR = 0.025, 0.019 and 0.027; Land Absorption Coefficient, LAC = 0.012 and 0.048). Moreover, there was a persistent increase in Number of Patches (NP) indicating a scattered and fragmented but continuous development. However, dwindling values of Patch Density (PD = 90.94, 27.07 and 30.30) indicate expansion through conversion of other landuses at varying rates. Results also indicate an incidence of fragmented low density development in the fringe areas (Area Weighted Mean Patch Fractal Dimension, AWMPFD = 1.37 and 1.39). The study highlights the chaotic land development and unrestrained urban expansion in the study area.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49420118","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}
Several ecosystems have been significantly altered by anthropogenic nitrogen inputs. The timely estimation of nitrogen concentration is essential for ensuring environmental sustainability. Academic publications between 1966 and 2016 were reviewed to assess the potential of remotely sensed information to estimate nitrogen concentrations for various applications. A discriminatory keyword search and a set of inclusion criteria was used to develop a representative sample (n = 100). Results revealed that the global distribution of academic publications is skewed towards the Northern Hemisphere with the largest research gap occurring within Africa. Moreover, prior to 2006, research into the remote estimation of nitrogen had a minor presence in literature, with the agricultural sector being the most extensively researched (56%). Freely available, high spatial and temporal resolution imagery has afforded research into the remote estimation of nitrogen in the African continent, particularly in the subject area of policy and management, the capacity to grow.
{"title":"Estimation of foliar nitrogen using remotely sensed data: A quantitative review","authors":"Rowan Naicker, O. Mutanga, K. Peerbhay","doi":"10.4314/sajg.v8i2.6","DOIUrl":"https://doi.org/10.4314/sajg.v8i2.6","url":null,"abstract":"Several ecosystems have been significantly altered by anthropogenic nitrogen inputs. The timely estimation of nitrogen concentration is essential for ensuring environmental sustainability. Academic publications between 1966 and 2016 were reviewed to assess the potential of remotely sensed information to estimate nitrogen concentrations for various applications. A discriminatory keyword search and a set of inclusion criteria was used to develop a representative sample (n = 100). Results revealed that the global distribution of academic publications is skewed towards the Northern Hemisphere with the largest research gap occurring within Africa. Moreover, prior to 2006, research into the remote estimation of nitrogen had a minor presence in literature, with the agricultural sector being the most extensively researched (56%). Freely available, high spatial and temporal resolution imagery has afforded research into the remote estimation of nitrogen in the African continent, particularly in the subject area of policy and management, the capacity to grow.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42213554","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}
Lake Chad is located at the south of the Sahara Desert in an arid region. The lake’s water resources are under severe pressure due to the basic needs of the growing population around the lake, global warming, and increasing irrigation demands. Numerous land cover change studies have measured the rate of depletion of the lake’s surface water. However, the contribution of the increasing high temperatures in the region which is also a compounding factor has received little attention. In this study, an assessment of the changes in surface water extent of Lake Chad from 1973-2017 was carried out through a land cover analysis. The potential influence of the rising land surface temperatures on the water losses was also studied. The extraction of the land cover was done using maximum likelihood classification. The results show that between 1973 and 1987, the lake lost 12,796.81km2 of its surface water area. This period coincided with a season of drought and dry seasons reported to have occurred in the lake’s area during the 1970s. Between 1987 and 2003, average temperature rise and change in surface water area was +1.54˚C and +962.71km2 respectively. Between 2003 and 2017, average temperature rise and change in surface water area was +3.69˚C and -25.17km2 respectively. These results provide further evidence of the alarming rate of water loss in the lake’s environment, and suggest a link between rising land surface temperatures and diminution of the lake’s water. The findings inform efforts directed at addressing the ecological problem facing the lake.
{"title":"Spatio-temporal assessment of changing land surface temperature and depleting water in the Lake Chad area","authors":"P. Nwilo, A. A. Umar, M. Adepoju, C. Okolie","doi":"10.4314/sajg.v8i2.3","DOIUrl":"https://doi.org/10.4314/sajg.v8i2.3","url":null,"abstract":"Lake Chad is located at the south of the Sahara Desert in an arid region. The lake’s water resources are under severe pressure due to the basic needs of the growing population around the lake, global warming, and increasing irrigation demands. Numerous land cover change studies have measured the rate of depletion of the lake’s surface water. However, the contribution of the increasing high temperatures in the region which is also a compounding factor has received little attention. In this study, an assessment of the changes in surface water extent of Lake Chad from 1973-2017 was carried out through a land cover analysis. The potential influence of the rising land surface temperatures on the water losses was also studied. The extraction of the land cover was done using maximum likelihood classification. The results show that between 1973 and 1987, the lake lost 12,796.81km2 of its surface water area. This period coincided with a season of drought and dry seasons reported to have occurred in the lake’s area during the 1970s. Between 1987 and 2003, average temperature rise and change in surface water area was +1.54˚C and +962.71km2 respectively. Between 2003 and 2017, average temperature rise and change in surface water area was +3.69˚C and -25.17km2 respectively. These results provide further evidence of the alarming rate of water loss in the lake’s environment, and suggest a link between rising land surface temperatures and diminution of the lake’s water. The findings inform efforts directed at addressing the ecological problem facing the lake.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48432604","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}
Formal land registration systems have failed to accommodate the wide range of land tenure claims found in developing countries, including land rights under customary and informal tenure systems, thereby leaving the rural people with insecure tenure. To reduce poverty, empower the poor, and ensure economic growth, security of tenure is beneficial. This paper investigates how the Social Tenure Domain Model (STDM) can be applied to record customary and informal land rights at Itaji-Ekiti, Nigeria. Primary data was collected by administering questionnaires on a house-to-house basis and conducting interviews with land rights holders. Spatial data was recorded using a handheld Global Positioning System (GPS) Garmin Oregon 300 and a mobile application (Topographic Mapper). Secondary data was collected from reports, journal articles, published books and the Google Earth image repository. The data collected through the administered questionnaires was used to analyse perceptions of tenure by the holders of land rights. This study provides additional knowledge for researchers in the field of Cadastral Surveying, as it tested the applicability of the STDM. The model will also help the government of Nigeria with the necessary data for the upgrading of informal settlements, which will provide integrity in land administration.
{"title":"Using a domain model of social tenure to record land rights: A Case Study of Itaji-Ekiti, Ekiti State, Nigeria","authors":"K. H. Babalola, Simon Hull","doi":"10.4314/sajg.v8i2.8","DOIUrl":"https://doi.org/10.4314/sajg.v8i2.8","url":null,"abstract":"Formal land registration systems have failed to accommodate the wide range of land tenure claims found in developing countries, including land rights under customary and informal tenure systems, thereby leaving the rural people with insecure tenure. To reduce poverty, empower the poor, and ensure economic growth, security of tenure is beneficial. This paper investigates how the Social Tenure Domain Model (STDM) can be applied to record customary and informal land rights at Itaji-Ekiti, Nigeria. Primary data was collected by administering questionnaires on a house-to-house basis and conducting interviews with land rights holders. Spatial data was recorded using a handheld Global Positioning System (GPS) Garmin Oregon 300 and a mobile application (Topographic Mapper). Secondary data was collected from reports, journal articles, published books and the Google Earth image repository. The data collected through the administered questionnaires was used to analyse perceptions of tenure by the holders of land rights. This study provides additional knowledge for researchers in the field of Cadastral Surveying, as it tested the applicability of the STDM. The model will also help the government of Nigeria with the necessary data for the upgrading of informal settlements, which will provide integrity in land administration.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41686195","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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