Grassland biomes are valuable socio-economic and ecological resources. However, the invasion of grasslands by alien plant species has emerged as one of the biggest threats to their sustainability, management and conservation. Timely, cost-effective and accurate determination of invasive alien plant spatial distribution is paramount for mitigating the adverse effects of alien plants on natural grasslands. Whereas literature on use of optical bands for invasive alien plants detection and mapping is abound, there is paucity in literature on the integration of Vegetation Indices (VIs) and optical reflectance bands in invasive species mapping. Specifically, there is need to test the efficacy of improved and freely available sensors like Sentinel-2 in understanding landscape invasion. Hence, this study sought to assess the efficacy of Sentinel-2’s optical bands and VIs for improving the mapping of American Bramble (Rubus cuneifolius) within a grassland biome. Variable Importance in the Projection (VIP) was used to identify the most influential reflectance bands and VIs, which were then fused at a feature level to determine Bramble spatial distribution. To determine the optimal season for Bramble mapping, seasonal classification accuracies were executed in Support Vector Machine (SVM) learning algorithm and accuracies for Spring, Summer, Autumn and Winter seasons compared. Results show that although the highest overall accuracy was achieved using only optical bands, fused imagery increased overall classification accuracies during spring and autumn i.e. 70% to 73% and 63% to 65%, respectively. However, the fused imagery failed to improve on the benchmark of optical imagery during summer and winter. Findings from this study underline the efficacy of complementing VIs and optical bands in determining the distribution of invasive species within grasslands at specific seasons. Furthermore, this study advocates for the adoption and fusion of freely available new generation satellite imagery such as Sentinel-2 as a cost effective option in landscape mapping.
{"title":"Assessing the synergistic potential of Sentinel-2 spectral reflectance bands and derived vegetation indices for detecting and mapping invasive alien plant species","authors":"J. Odindi, O. Mutanga, Perushan Rajah","doi":"10.4314/sajg.v9i1.6","DOIUrl":"https://doi.org/10.4314/sajg.v9i1.6","url":null,"abstract":"Grassland biomes are valuable socio-economic and ecological resources. However, the invasion of grasslands by alien plant species has emerged as one of the biggest threats to their sustainability, management and conservation. Timely, cost-effective and accurate determination of invasive alien plant spatial distribution is paramount for mitigating the adverse effects of alien plants on natural grasslands. Whereas literature on use of optical bands for invasive alien plants detection and mapping is abound, there is paucity in literature on the integration of Vegetation Indices (VIs) and optical reflectance bands in invasive species mapping. Specifically, there is need to test the efficacy of improved and freely available sensors like Sentinel-2 in understanding landscape invasion. Hence, this study sought to assess the efficacy of Sentinel-2’s optical bands and VIs for improving the mapping of American Bramble (Rubus cuneifolius) within a grassland biome. Variable Importance in the Projection (VIP) was used to identify the most influential reflectance bands and VIs, which were then fused at a feature level to determine Bramble spatial distribution. To determine the optimal season for Bramble mapping, seasonal classification accuracies were executed in Support Vector Machine (SVM) learning algorithm and accuracies for Spring, Summer, Autumn and Winter seasons compared. Results show that although the highest overall accuracy was achieved using only optical bands, fused imagery increased overall classification accuracies during spring and autumn i.e. 70% to 73% and 63% to 65%, respectively. However, the fused imagery failed to improve on the benchmark of optical imagery during summer and winter. Findings from this study underline the efficacy of complementing VIs and optical bands in determining the distribution of invasive species within grasslands at specific seasons. Furthermore, this study advocates for the adoption and fusion of freely available new generation satellite imagery such as Sentinel-2 as a cost effective option in landscape mapping.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44700150","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}
LiDAR intensity and texture features have reported high accuracies for discriminating forest species, particularly with the utility of the random forest (RF) algorithm. To date, limited studies has utilized LiDAR-derived forest gap information to assist in forest species discrimination. In this study, LiDAR intensity and texture features were extracted from forest canopy gaps to discriminate Eucalyptus grandis and Eucalyptus dunnii within a forest plantation. Additionally, LiDAR intensity and texture information was extracted for both canopy gaps and forest canopy and utilized for species discrimination. Using LiDAR intensity and texture information extracted for both canopy gap and forest canopy, resulted in a model accuracy of 94.74% (KHAT = 0.88). Using only canopy gap information, the RF model obtained an overall accuracy of 90.91% (KHAT = 0.81). The results highlight the potential for using canopy gap information for commercial species discrimination and mapping.
{"title":"Modelling forest species using LiDar-derived metrics of forest canopy gaps","authors":"L. Lombard, R. Ismail, Nitesh K. Poona","doi":"10.4314/sajg.v9i1.3","DOIUrl":"https://doi.org/10.4314/sajg.v9i1.3","url":null,"abstract":"LiDAR intensity and texture features have reported high accuracies for discriminating forest species, particularly with the utility of the random forest (RF) algorithm. To date, limited studies has utilized LiDAR-derived forest gap information to assist in forest species discrimination. In this study, LiDAR intensity and texture features were extracted from forest canopy gaps to discriminate Eucalyptus grandis and Eucalyptus dunnii within a forest plantation. Additionally, LiDAR intensity and texture information was extracted for both canopy gaps and forest canopy and utilized for species discrimination. Using LiDAR intensity and texture information extracted for both canopy gap and forest canopy, resulted in a model accuracy of 94.74% (KHAT = 0.88). Using only canopy gap information, the RF model obtained an overall accuracy of 90.91% (KHAT = 0.81). The results highlight the potential for using canopy gap information for commercial species discrimination and mapping.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43099224","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}
N. Mudau, W. Mapurisa, Thomas Tsoeleng, Morwapula Mashalane
This study investigated the automation of the building extraction using SPOT 6 satellite imagery. The proposed methodology uses variance textural information derived from 1.5m panchromatic image to detect built-up areas from non-built-up areas. Once detected, detailed segmentation is performed on built-up class to create individual building objects. Canny edges, SAVI and spectral properties of the objects were used to classify building structures from other land use features using a thresholding technique. The methodology was tested in different areas including formal, rural village and informal and new development settlement types without modifying segmentation and classification parameters. The proposed methodology successfully detected built-up from non built-up areas in all different settlement types. The detection of individual structures achieved more than 70% in formal, rural village and new development areas while less than 50% of building structures in informal settlement were detected. The proposed method can contribute towards monitoring of human settlement developments over a larger area which is vital during spatial planning, service delivery and environmental management. This work will contribute towards the development of a National Human Settlement Layer developed and maintained by SANSA.
{"title":"Towards development of a national human settlement layer using high resolution imagery: a contribution to SDG reporting","authors":"N. Mudau, W. Mapurisa, Thomas Tsoeleng, Morwapula Mashalane","doi":"10.4314/sajg.v9i1.1","DOIUrl":"https://doi.org/10.4314/sajg.v9i1.1","url":null,"abstract":"This study investigated the automation of the building extraction using SPOT 6 satellite imagery. The proposed methodology uses variance textural information derived from 1.5m panchromatic image to detect built-up areas from non-built-up areas. Once detected, detailed segmentation is performed on built-up class to create individual building objects. Canny edges, SAVI and spectral properties of the objects were used to classify building structures from other land use features using a thresholding technique. The methodology was tested in different areas including formal, rural village and informal and new development settlement types without modifying segmentation and classification parameters. The proposed methodology successfully detected built-up from non built-up areas in all different settlement types. The detection of individual structures achieved more than 70% in formal, rural village and new development areas while less than 50% of building structures in informal settlement were detected. The proposed method can contribute towards monitoring of human settlement developments over a larger area which is vital during spatial planning, service delivery and environmental management. This work will contribute towards the development of a National Human Settlement Layer developed and maintained by SANSA.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48797197","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 production of land cover maps using supervised classification algorithms is one of the most common applications of remote sensing. In this study, the effectiveness of supervised classification algorithms in land cover classification using ASTER data was evaluated in the Mankweng Area and its environs. The false colour composite image generated from combination of band 1, 2 and 3 in red, green and blue, respectively, was used to generate training classes for six land cover types (waterbody, forest, vegetation, Duiwelskloof leucogranite, Turfloop granite and built-up land). These were used to construct land cover maps using eight supervised classification algorithms: Maximum Likelihood, Minimum Distance, Support Vector Machine, Mahalanobis Distance, Parallelepiped, Neural Network, Spectral Angle Mapper and Spectral Information Divergence. To evaluate the effectiveness of the algorithms, the land cover maps were subjected to accuracy assessment to determine precision of the algorithms in accurately classifying the land cover types and level of confidence that can be attributed to the land cover maps. Most algorithms poorly performed in classifying spatially overlapping land cover types without abrupt boundaries. This indicates that the environmental conditions and distribution of land cover types can affect the performance of certain classification algorithms, and thus need to be considered prior to selection of algorithms. However, Support Vector Machine and Minimum Distance proved to be the two most effective algorithms as they provided better producer’s and user’s accuracy in the range of 80-100% for all land cover types, which represent good classification.
{"title":"Evaluation of effectiveness of supervised classification algorithms in land cover classification using ASTER images-A case study from the Mankweng (Turfloop) Area and its environs, Limpopo Province, South Africa","authors":"Nndanduleni Muavhi","doi":"10.4314/sajg.v9i1.5","DOIUrl":"https://doi.org/10.4314/sajg.v9i1.5","url":null,"abstract":"The production of land cover maps using supervised classification algorithms is one of the most common applications of remote sensing. In this study, the effectiveness of supervised classification algorithms in land cover classification using ASTER data was evaluated in the Mankweng Area and its environs. The false colour composite image generated from combination of band 1, 2 and 3 in red, green and blue, respectively, was used to generate training classes for six land cover types (waterbody, forest, vegetation, Duiwelskloof leucogranite, Turfloop granite and built-up land). These were used to construct land cover maps using eight supervised classification algorithms: Maximum Likelihood, Minimum Distance, Support Vector Machine, Mahalanobis Distance, Parallelepiped, Neural Network, Spectral Angle Mapper and Spectral Information Divergence. To evaluate the effectiveness of the algorithms, the land cover maps were subjected to accuracy assessment to determine precision of the algorithms in accurately classifying the land cover types and level of confidence that can be attributed to the land cover maps. Most algorithms poorly performed in classifying spatially overlapping land cover types without abrupt boundaries. This indicates that the environmental conditions and distribution of land cover types can affect the performance of certain classification algorithms, and thus need to be considered prior to selection of algorithms. However, Support Vector Machine and Minimum Distance proved to be the two most effective algorithms as they provided better producer’s and user’s accuracy in the range of 80-100% for all land cover types, which represent good classification.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43041444","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}
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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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