Fire regimes have the potential to disturb ecological aspects of a landscape and/or contribute to the maintenance of the biological diversity. Thus, a gauge of the impact of planned and unplanned fire regimes is vital to South Africa’s national reserves. The Jonkershoek Nature Reserve in the Western Cape is characterized by the occurrence of indigenous Fynbos and Afromontane Forest vegetation. Geographical Information Systems (GIS) and Remote Sensing (RS) can aid the management and preservation of indigenous vegetational species. This study used knowledge of the ecological conditions of the Reserve, historical fire data, Landsat TM and Landsat OLI imagery, and geospatial analysis to investigate the impact of the fire regimes in the Reserve. Image classification was carried out from 2005 to 2015 to determine the burn patterns, with the process being aided by the fire regime history from 1970 to 2015. Ordinary Least Squares (OLS) analysis was carried out to determine how abiotic factors, such as elevation, slope and aspect, impact fires in the Reserve. The assessment of fires included the ascertainment of their location, coverage, and frequency, the Normalised Burn Ratio (NBR), the differenced Normalised Burn Ratio (dNBR) and the Normalised Difference Vegetation Index (NDVI). There were 39 fires recorded in the Jonkershoek Nature Reserve from 1970 to 2015. The largest fire events were recorded in 1999 (26503.6 ha.) and 2015 (8363.0 ha.). The lowest area of fire impact recorded occurred in the years 2010 (0.15ha.), 1973 (1.1 ha.) and 1987 (3.1 ha.). With an overall classification accuracy of 94.17%, the Landsat OLI imagery performed better with an overall classification accuracy of 94.17% than the Landsat TM at 75.83%. The OLS regression showed that fire severity was positively correlated to NDVI and elevation. This may suggest that regions of healthy vegetation at any altitude may be susceptible to burnings if there is sufficient vegetation to fuel a fire. The OLS was negatively correlated to slope and aspect. This may impact fire risk as steeper slopes may have vegetation growing in their fire shadow.
{"title":"GIS investigation of the fire history of Jonkershoek Nature Reserve","authors":"S. Mashele, K. Singh","doi":"10.4314/sajg.v11i2.2","DOIUrl":"https://doi.org/10.4314/sajg.v11i2.2","url":null,"abstract":"Fire regimes have the potential to disturb ecological aspects of a landscape and/or contribute to the maintenance of the biological diversity. Thus, a gauge of the impact of planned and unplanned fire regimes is vital to South Africa’s national reserves. The Jonkershoek Nature Reserve in the Western Cape is characterized by the occurrence of indigenous Fynbos and Afromontane Forest vegetation. Geographical Information Systems (GIS) and Remote Sensing (RS) can aid the management and preservation of indigenous vegetational species. This study used knowledge of the ecological conditions of the Reserve, historical fire data, Landsat TM and Landsat OLI imagery, and geospatial analysis to investigate the impact of the fire regimes in the Reserve. Image classification was carried out from 2005 to 2015 to determine the burn patterns, with the process being aided by the fire regime history from 1970 to 2015. Ordinary Least Squares (OLS) analysis was carried out to determine how abiotic factors, such as elevation, slope and aspect, impact fires in the Reserve. The assessment of fires included the ascertainment of their location, coverage, and frequency, the Normalised Burn Ratio (NBR), the differenced Normalised Burn Ratio (dNBR) and the Normalised Difference Vegetation Index (NDVI). There were 39 fires recorded in the Jonkershoek Nature Reserve from 1970 to 2015. The largest fire events were recorded in 1999 (26503.6 ha.) and 2015 (8363.0 ha.). The lowest area of fire impact recorded occurred in the years 2010 (0.15ha.), 1973 (1.1 ha.) and 1987 (3.1 ha.). With an overall classification accuracy of 94.17%, the Landsat OLI imagery performed better with an overall classification accuracy of 94.17% than the Landsat TM at 75.83%. The OLS regression showed that fire severity was positively correlated to NDVI and elevation. This may suggest that regions of healthy vegetation at any altitude may be susceptible to burnings if there is sufficient vegetation to fuel a fire. The OLS was negatively correlated to slope and aspect. This may impact fire risk as steeper slopes may have vegetation growing in their fire shadow.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49387584","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}
Remote Sensing evapotranspiration models are critical in order to understand the cycling of water in the environment. Initially, an outline of the concepts related to evapotranspiration, as well as the shortcomings of land-based methods, is presented. The aim of the study was based on reviewing remote sensing evapotranspiration models which provide an alternative data source. These models have proved to be a cheaper alternative to mapping and estimating spatiotemporal evapotranspiration measurements across local and regional scales. This paper reviews the single-source energy balance model, which differs from the two-source model, for estimating spatiotemporal measurements of evapotranspiration. The single-source energy balance model is underpinned by mathematical equations which differentiate the various single-source evapotranspiration models (Surface Energy Balance Systems, Simplified Surface Energy Systems, Surface Energy Balance Algorithm, and Mapping Evapotranspiration at high Resolution and with Internalised Calibration). The soil surface and forest canopy components were observed to be the major difference between the single and dual-source models. Further advice was discussed on the implementation of the OpenET tool, which provides an open and accessible satellite-based estimation of evapotranspiration for improved water management.
{"title":"Remote sensing-based evapotranspiration determination: A review of single-source energy balance models","authors":"Lehlohonolo Sello, Akhona Maqhubela, Gaathier Mahed","doi":"10.4314/sajg.v11i2.7","DOIUrl":"https://doi.org/10.4314/sajg.v11i2.7","url":null,"abstract":"Remote Sensing evapotranspiration models are critical in order to understand the cycling of water in the environment. Initially, an outline of the concepts related to evapotranspiration, as well as the shortcomings of land-based methods, is presented. The aim of the study was based on reviewing remote sensing evapotranspiration models which provide an alternative data source. These models have proved to be a cheaper alternative to mapping and estimating spatiotemporal evapotranspiration measurements across local and regional scales. This paper reviews the single-source energy balance model, which differs from the two-source model, for estimating spatiotemporal measurements of evapotranspiration. The single-source energy balance model is underpinned by mathematical equations which differentiate the various single-source evapotranspiration models (Surface Energy Balance Systems, Simplified Surface Energy Systems, Surface Energy Balance Algorithm, and Mapping Evapotranspiration at high Resolution and with Internalised Calibration). The soil surface and forest canopy components were observed to be the major difference between the single and dual-source models. Further advice was discussed on the implementation of the OpenET tool, which provides an open and accessible satellite-based estimation of evapotranspiration for improved water management.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48609408","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}
R. Suya, Charles Kapachika, M. Soko, Vincent Luhanga, J. Ogwang, Harvey Chilembwe, Francis Gitau
Global Navigation Satellite System (GNSS) signals in the L-band are affected by the non-dispersive neutral atmosphere. Regardless of their center frequency, the L-band code and phase observations are affected by the same measure of delay. GNSS receivers play a significant role in quantifying the zenith tropospheric delay (ZTD) from satellite signals. Malawi has a Continuously Operating Reference Stations (CORS) network which was established to support research in geophysical geodesy and geodynamics. However, the quality of the observations tracked by the CORS has never been tested in terms of its meteorological application. In this paper, the ZTD estimation approach and the evaluation of results from the Global Positioning System (GPS) measurements are presented. The optimal approach of precise point positioning (PPP) was used to estimate ZTD from one-week datasets which were collected from six CORS monuments distributed in the northern and southern regions of Malawi. In addition, the zenith wet delay (ZWD) and zenith hydrostatic delay (ZHD) were also estimated to determine their respective contributions to the total delay in all the stations. Alongside the meteorological parameters, the positioning repeatabilities were also established for all stations. Results indicate that the averaged ZTD, ZWD and ZHD can reach as high as 247mm, 47 mm, and 199 mm, respectively. The minimum ZTD, ZWD, and ZHD for the stations can drop to as low as 220 mm, 24 mm, and 181 mm, respectively. This indicates that the ZHD contributes to more than 90% of the total delay at the stations. For the positioning performance, there was no obvious disparity in the latitude (less than 0.5 cm), longitude (less than 1 cm), and ellipsoidal height repeatabilities (less than 1.5 cm). Thus, the results clearly demonstrate that the Malawi CORS network may be used for GNSS-based meteorological applications using the available geodetic receivers. However, for high precision meteorological applications, Malawi may consider densifying the available network with geodetic grade receivers.
{"title":"Applying Malawi Continuously Operating Reference Stations (CORS) in GNSS Meteorology","authors":"R. Suya, Charles Kapachika, M. Soko, Vincent Luhanga, J. Ogwang, Harvey Chilembwe, Francis Gitau","doi":"10.4314/sajg.v11i2.4","DOIUrl":"https://doi.org/10.4314/sajg.v11i2.4","url":null,"abstract":"Global Navigation Satellite System (GNSS) signals in the L-band are affected by the non-dispersive neutral atmosphere. Regardless of their center frequency, the L-band code and phase observations are affected by the same measure of delay. GNSS receivers play a significant role in quantifying the zenith tropospheric delay (ZTD) from satellite signals. Malawi has a Continuously Operating Reference Stations (CORS) network which was established to support research in geophysical geodesy and geodynamics. However, the quality of the observations tracked by the CORS has never been tested in terms of its meteorological application. In this paper, the ZTD estimation approach and the evaluation of results from the Global Positioning System (GPS) measurements are presented. The optimal approach of precise point positioning (PPP) was used to estimate ZTD from one-week datasets which were collected from six CORS monuments distributed in the northern and southern regions of Malawi. In addition, the zenith wet delay (ZWD) and zenith hydrostatic delay (ZHD) were also estimated to determine their respective contributions to the total delay in all the stations. Alongside the meteorological parameters, the positioning repeatabilities were also established for all stations. Results indicate that the averaged ZTD, ZWD and ZHD can reach as high as 247mm, 47 mm, and 199 mm, respectively. The minimum ZTD, ZWD, and ZHD for the stations can drop to as low as 220 mm, 24 mm, and 181 mm, respectively. This indicates that the ZHD contributes to more than 90% of the total delay at the stations. For the positioning performance, there was no obvious disparity in the latitude (less than 0.5 cm), longitude (less than 1 cm), and ellipsoidal height repeatabilities (less than 1.5 cm). Thus, the results clearly demonstrate that the Malawi CORS network may be used for GNSS-based meteorological applications using the available geodetic receivers. However, for high precision meteorological applications, Malawi may consider densifying the available network with geodetic grade receivers. \u0000 ","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47154082","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 unprecedented influx of people into urban areas has led to the horizontal and vertical growth of urban environments. One of the notable impacts of urbanisation is the encroachment of urban-like environments into non-urban areas. This is common in both developed and developing countries, and South Africa’s City of Tshwane, the administrative capital of the country, has been affected by urbanisation because of migration. One of the parameters or proxies used to quantify urban growth is vegetation cover. There is a consensus that with the increase in the population of urban dwellers, vegetation cover will decrease. To assess and monitor vegetation cover, the Normalised Difference Vegetation Index (NDVI) is commonly used. In this study, MODIS NDVI data with a 250m spatial resolution was used to assess the impact of urban growth on vegetation. A time series analysis of the MODIS NDVI with a spatial resolution of 250m was used to establish the patterns of vegetation cover. Trends in vegetation change were determined in newly developed residential areas, informal settlements, and various vegetated areas. Sen's slope estimator and Mann-Kendall’s statisticwere used to analyse the spatial trends and variations in trends among different land cover classes. The slope of the trends differs significantly but there is a general decline in vegetation cover. The temporal profiles revealed the high and low NDVI values, respectively showing greening (high vegetation) and browning (low vegetation) trends from 2000 to 2016. It is concluded that urban growth has an impact on vegetation cover. However, this does not disturb the seasonal changes in vegetation where high NDVI values prevail in summer and low values in winter.
{"title":"Monitoring vegetation phenology using MODIS NDVI 250m in the City of Tshwane, South Africa","authors":"J. Magidi, Fethi Ahmed","doi":"10.4314/sajg.v11i2.1","DOIUrl":"https://doi.org/10.4314/sajg.v11i2.1","url":null,"abstract":"The unprecedented influx of people into urban areas has led to the horizontal and vertical growth of urban environments. One of the notable impacts of urbanisation is the encroachment of urban-like environments into non-urban areas. This is common in both developed and developing countries, and South Africa’s City of Tshwane, the administrative capital of the country, has been affected by urbanisation because of migration. One of the parameters or proxies used to quantify urban growth is vegetation cover. There is a consensus that with the increase in the population of urban dwellers, vegetation cover will decrease. To assess and monitor vegetation cover, the Normalised Difference Vegetation Index (NDVI) is commonly used. In this study, MODIS NDVI data with a 250m spatial resolution was used to assess the impact of urban growth on vegetation. A time series analysis of the MODIS NDVI with a spatial resolution of 250m was used to establish the patterns of vegetation cover. Trends in vegetation change were determined in newly developed residential areas, informal settlements, and various vegetated areas. Sen's slope estimator and Mann-Kendall’s statisticwere used to analyse the spatial trends and variations in trends among different land cover classes. The slope of the trends differs significantly but there is a general decline in vegetation cover. The temporal profiles revealed the high and low NDVI values, respectively showing greening (high vegetation) and browning (low vegetation) trends from 2000 to 2016. It is concluded that urban growth has an impact on vegetation cover. However, this does not disturb the seasonal changes in vegetation where high NDVI values prevail in summer and low values in winter.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46651490","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}
Urban expansion, mainly occasioned by poorly controlled physical development, continues to pose severe threats to sustainable food production. While studies have concentrated more on food production in the hinterlands of Nigeria, there is a dearth of information on empirical investigations into urban food supply. This study, therefore, examined the effect of poorly controlled physical development on urban food production in Ibadan. An ecological footprint model was used to provide its theoretical anchor, while a longitudinal survey was the research design of choice. Both primary and secondary data were sourced. Geographical and remote sensing methods of analysis were used, with the primary focus being on Ibadan City and the dairy farm that has been converted to non-agricultural uses. This research revealed that Ibadan’s total urban area increased from 70.3584 ha in 1986 to 411.8877 ha in 2019. This expansion was accompanied by the loss of agricultural land, the depletion of water bodies, and agricultural land conversion. Validation of the research findings revealed a relatively high accuracy in terms of the Kappa value of 0.72 and an overall classification accuracy of 79.17% for 1986, of 0.84 and 88.33% for 2000, and of 0.91 and 92.5% for 2019. This studyrecommends that farmers should be trained on soilless farming practices such as aeroponics and hydroponics which both require relatively small portions of land to produce food.
{"title":"The effect of poorly controlled physical development on urban food production in Ibadan, Nigeria","authors":"U. Jimoh, K. Otokiti","doi":"10.4314/sajg.v11i2.6","DOIUrl":"https://doi.org/10.4314/sajg.v11i2.6","url":null,"abstract":"Urban expansion, mainly occasioned by poorly controlled physical development, continues to pose severe threats to sustainable food production. While studies have concentrated more on food production in the hinterlands of Nigeria, there is a dearth of information on empirical investigations into urban food supply. This study, therefore, examined the effect of poorly controlled physical development on urban food production in Ibadan. An ecological footprint model was used to provide its theoretical anchor, while a longitudinal survey was the research design of choice. Both primary and secondary data were sourced. Geographical and remote sensing methods of analysis were used, with the primary focus being on Ibadan City and the dairy farm that has been converted to non-agricultural uses. This research revealed that Ibadan’s total urban area increased from 70.3584 ha in 1986 to 411.8877 ha in 2019. This expansion was accompanied by the loss of agricultural land, the depletion of water bodies, and agricultural land conversion. Validation of the research findings revealed a relatively high accuracy in terms of the Kappa value of 0.72 and an overall classification accuracy of 79.17% for 1986, of 0.84 and 88.33% for 2000, and of 0.91 and 92.5% for 2019. This studyrecommends that farmers should be trained on soilless farming practices such as aeroponics and hydroponics which both require relatively small portions of land to produce food.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44977002","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}
Osman Mohammed Abukari, Akwasi Acheampong, I. Dadzie, S. Osah
In this study, we determined three-dimensional (3D) position coordinates for eight new Continuous Operating Reference Stations (CORS) in Ghana through three different GNSS positioning techniques. The three GNSS positioning techniques whereby the network of CORS was tied to ITRF14 and War Office 1926 datums included:1) Precise Point Positioning (PPP); 2) Precise Differential GNSS (PDGNSS), using reference stations based on ITRF14; and 3) PDGNSS, using reference stations based on War Office. The PPP solutions were computed using the Canadian Spatial Reference System Precise Point Positioning software (CSRS-PPP), available online and as an open source GNSS laboratory tool software (gLAB). The PDGNSS solutions were obtained from OPUS and AUSPOS online services, as well as from self-post-processing using Topcon Tools software v8.2.3. All solutions were computed using 24-hour data for twelve consecutive days in the month of October 2018 (GPS DoY 284 to GPS DoY 295). The quality, reliability, and acceptability of position solutions were measured by computing the average positioning error, the rate of ambiguity resolution and the repeatability ratios of the solutions. The variability of coordinate differences for each pair of different positioning techniques was computed to determine their solution congruences. Ultimately, , the average positioning errors in northing, easting, and height were 0.003m, 0.005m and 0.009m, respectively. The rate of ambiguity resolution was between 75.3% and 90.3%. Repeatability ratios ranged between 1: 68,500,000 and 1: 411,100,000. Finally, the minimum and maximum range of variability in coordinate differences for each pair of positioning techniques was 1mm to 16mm for horizontal positions and 2mm to 137mm for vertical positions.
{"title":"Congruence through repeatability of position solutions by different GNSS survey techniques","authors":"Osman Mohammed Abukari, Akwasi Acheampong, I. Dadzie, S. Osah","doi":"10.4314/sajg.v11i2.8","DOIUrl":"https://doi.org/10.4314/sajg.v11i2.8","url":null,"abstract":"In this study, we determined three-dimensional (3D) position coordinates for eight new Continuous Operating Reference Stations (CORS) in Ghana through three different GNSS positioning techniques. The three GNSS positioning techniques whereby the network of CORS was tied to ITRF14 and War Office 1926 datums included:1) Precise Point Positioning (PPP); 2) Precise Differential GNSS (PDGNSS), using reference stations based on ITRF14; and 3) PDGNSS, using reference stations based on War Office. The PPP solutions were computed using the Canadian Spatial Reference System Precise Point Positioning software (CSRS-PPP), available online and as an open source GNSS laboratory tool software (gLAB). The PDGNSS solutions were obtained from OPUS and AUSPOS online services, as well as from self-post-processing using Topcon Tools software v8.2.3. All solutions were computed using 24-hour data for twelve consecutive days in the month of October 2018 (GPS DoY 284 to GPS DoY 295). The quality, reliability, and acceptability of position solutions were measured by computing the average positioning error, the rate of ambiguity resolution and the repeatability ratios of the solutions. The variability of coordinate differences for each pair of different positioning techniques was computed to determine their solution congruences. Ultimately, , the average positioning errors in northing, easting, and height were 0.003m, 0.005m and 0.009m, respectively. The rate of ambiguity resolution was between 75.3% and 90.3%. Repeatability ratios ranged between 1: 68,500,000 and 1: 411,100,000. Finally, the minimum and maximum range of variability in coordinate differences for each pair of positioning techniques was 1mm to 16mm for horizontal positions and 2mm to 137mm for vertical positions.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46240647","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}
Urbanisation has been identified as a major threat to the environment as it increases demand for urban spaces and transforms natural landscapes to impervious surfaces, leading to the Urban Heat Island (UHI) phenomenon. Natural landscapes such as vegetation and water bodies act as thermal sinks that absorb heat while impervious surfaces such as buildings and concrete pavements act as thermal sources that retain and emit heat. The thermal emission results in several negative effects such as temperature inversion, compromised human health, pollution, species loss, high energy consumption and climate change at a local, regional and global scales. Whereas studies on UHI are abound, there is paucity in literature on the influence of seasonal urban Land Use Land Cover (LULC) transformation on urban thermal characteristics. Specifically, the proportional seasonal variability and contribution of individual LULCs to urban heat is often poorly understood. Using the freely available Landsat 8 optical and thermal bands, this study examined the seasonal characteristics of the UHI phenomenon in relation to LULCs in the Pietermaritzburg city, South Africa. Results in this study revealed that UHIs exist in both winter and summer, but with more intensity in summer. The study also established that LULCs varied with seasons. Bare surfaces and dense vegetation had the most thermal influence during winter while dense vegetation and low density buildings had the most thermal influence during summer. These findings provide a better understanding of thermal distribution based on LULC seasonality changes, valuable for sustainable urban planning and climate change mitigation.
{"title":"The influence of seasonal land-use-land-cover transformation on thermal characteristics within the city of Pietermaritzburg","authors":"J. Odindi","doi":"10.4314/sajg.v9i2.23","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.23","url":null,"abstract":"Urbanisation has been identified as a major threat to the environment as it increases demand for urban spaces and transforms natural landscapes to impervious surfaces, leading to the Urban Heat Island (UHI) phenomenon. Natural landscapes such as vegetation and water bodies act as thermal sinks that absorb heat while impervious surfaces such as buildings and concrete pavements act as thermal sources that retain and emit heat. The thermal emission results in several negative effects such as temperature inversion, compromised human health, pollution, species loss, high energy consumption and climate change at a local, regional and global scales. Whereas studies on UHI are abound, there is paucity in literature on the influence of seasonal urban Land Use Land Cover (LULC) transformation on urban thermal characteristics. Specifically, the proportional seasonal variability and contribution of individual LULCs to urban heat is often poorly understood. Using the freely available Landsat 8 optical and thermal bands, this study examined the seasonal characteristics of the UHI phenomenon in relation to LULCs in the Pietermaritzburg city, South Africa. Results in this study revealed that UHIs exist in both winter and summer, but with more intensity in summer. The study also established that LULCs varied with seasons. Bare surfaces and dense vegetation had the most thermal influence during winter while dense vegetation and low density buildings had the most thermal influence during summer. These findings provide a better understanding of thermal distribution based on LULC seasonality changes, valuable for sustainable urban planning and climate change mitigation.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42987575","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}
Humankind often needs to accurately model, identify and spatially quantify aboveground phenomena on the Earth’s surface for informed decision-making. Height data derived from digital elevation models (DEMs) is often used to achieve this. This study conducted a deterministic assessment of three normalised digital surface models (nDSMs) of different spatial resolutions, namely 2m, 4m and 12m, derived from VHR digital stereo aerial photography, tri-stereo Pléiades imagery and Tandem-X InSAR data, respectively. Covering a predominantly built-up area within a city landscape, the nDSMs were vertically and volumetrically compared to assess their quality and fit-for-use. In each case a consistent systematic evaluation was accomplished against a lidar derived reference surface at matching spatial resolutions (co-registered) using a semi-automated GIS routine. The relative height and volumetric errors were statistically analysed and described, including those computed individually over nine urban land cover/land use (LCLU) classes and several selected large buildings. Higher vertical accuracies were reported across single storey structures and areas with no to little or short vegetation, as apposed to substantially lower accuracies obtained over multi-levelled buildings and tall (dense) woody vegetation. Here significant underestimations of volumes exacerbated by lower spatial resolutions were also observed across each nDSM. Conversely, notable volume overestimations were found over predominantly grass-covered areas in especially the finer-scaled nDSMs. VHR elevation data is recommended to model and quantify aboveground elements spatially in 3D (e.g. buildings, earthworks and woody vegetation) in urban landscapes, but a sensitivity test beforehand remains critical to ensure more reliable outcomes for users and stakeholders alike.
{"title":"3D Evaluation of fine-scale normalised DSMs in urban settings","authors":"A. Breytenbach","doi":"10.4314/sajg.v9i2.26","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.26","url":null,"abstract":"Humankind often needs to accurately model, identify and spatially quantify aboveground phenomena on the Earth’s surface for informed decision-making. Height data derived from digital elevation models (DEMs) is often used to achieve this. This study conducted a deterministic assessment of three normalised digital surface models (nDSMs) of different spatial resolutions, namely 2m, 4m and 12m, derived from VHR digital stereo aerial photography, tri-stereo Pléiades imagery and Tandem-X InSAR data, respectively. Covering a predominantly built-up area within a city landscape, the nDSMs were vertically and volumetrically compared to assess their quality and fit-for-use. In each case a consistent systematic evaluation was accomplished against a lidar derived reference surface at matching spatial resolutions (co-registered) using a semi-automated GIS routine. The relative height and volumetric errors were statistically analysed and described, including those computed individually over nine urban land cover/land use (LCLU) classes and several selected large buildings. Higher vertical accuracies were reported across single storey structures and areas with no to little or short vegetation, as apposed to substantially lower accuracies obtained over multi-levelled buildings and tall (dense) woody vegetation. Here significant underestimations of volumes exacerbated by lower spatial resolutions were also observed across each nDSM. Conversely, notable volume overestimations were found over predominantly grass-covered areas in especially the finer-scaled nDSMs. VHR elevation data is recommended to model and quantify aboveground elements spatially in 3D (e.g. buildings, earthworks and woody vegetation) in urban landscapes, but a sensitivity test beforehand remains critical to ensure more reliable outcomes for users and stakeholders alike.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46461494","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}
A new height datum for Uganda is computed using the corrective surface principle. It is based on a combination of the Uganda Gravimetric Quasigeoid model (UGQ) 2014 and GNSS/levelling. UGQ2014 was derived from the Uganda Gravimetric Geoid model (UGG) 2014, which was computed from sparse terrestrial gravity data from the International Gravimetric Bureau, the 3 arc second Shuttle Radar Topography Mission digital elevation model and the GOCE – only global geopotential model GO_CONS_GCF_2_TIM_R5. The corrective surface was constructed based on 21 discrete GNSS/levelling points and then evaluated with 4 independent points. Three interpolation techniques were tested for the creation of the corrective surface with the Kriging method giving the lowest standard deviation and noise level suggesting that it is the best method for interpolation. In absolute terms, the Root Mean Square of the fit between the known and computed normal-orthometric heights based on the new height datum is 11cm, which is about 5cm (31%) better than using UGQ2014 alone. For relative heights an average precision of 29 ppm is computed for all baselines tested. Both the absolute and relative tests show that the new height datum satisfies the precision and accuracy requirements of third order precise levelling. Therefore, UGQ2014C represents a significant step towards the determination of a precise new height datum for Uganda.
{"title":"Towards a new height datum for Uganda","authors":"R. Ssengendo, A. Gidudu","doi":"10.4314/sajg.v9i2.8","DOIUrl":"https://doi.org/10.4314/sajg.v9i2.8","url":null,"abstract":"A new height datum for Uganda is computed using the corrective surface principle. It is based on a combination of the Uganda Gravimetric Quasigeoid model (UGQ) 2014 and GNSS/levelling. UGQ2014 was derived from the Uganda Gravimetric Geoid model (UGG) 2014, which was computed from sparse terrestrial gravity data from the International Gravimetric Bureau, the 3 arc second Shuttle Radar Topography Mission digital elevation model and the GOCE – only global geopotential model GO_CONS_GCF_2_TIM_R5. The corrective surface was constructed based on 21 discrete GNSS/levelling points and then evaluated with 4 independent points. Three interpolation techniques were tested for the creation of the corrective surface with the Kriging method giving the lowest standard deviation and noise level suggesting that it is the best method for interpolation. In absolute terms, the Root Mean Square of the fit between the known and computed normal-orthometric heights based on the new height datum is 11cm, which is about 5cm (31%) better than using UGQ2014 alone. For relative heights an average precision of 29 ppm is computed for all baselines tested. Both the absolute and relative tests show that the new height datum satisfies the precision and accuracy requirements of third order precise levelling. Therefore, UGQ2014C represents a significant step towards the determination of a precise new height datum for Uganda.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48960359","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}
Coping with rapid urbanisation and the impacts of climate change requires effective land management. Quality land information is essential for this. A land information infrastructure is a collaborative and coordinated initiative aimed at providing land information from different organisations, such as municipalities, government departments and private companies, to diverse user communities. A land information infrastructure is complex, spanning information streams through many organisations and technical systems, and presenting challenges for managing and monitoring the production of land information. In the manufacturing field, a supply chain refers to the stream of activities from the initial source to the delivery of end products to customers, and supply chain management is directed at optimising the creation of the products of such a chain. The Supply Chain Operations Reference (SCOR) model is widely used for analysing supply chain processes in order to quantify and improve product and service delivery, and it has also been applied to geographical information supply chains. In this study, the SCOR model is applied to the supply chain processes in a South African case study of a land information infrastructure focusing on the production of cadastral information products. The supply chain comprises a land developer, a land surveying firm, the Surveyor General’s and Deeds Offices, a geospatial data vendor and the end customer. This supply chain is mapped and analysed using supply chain mapping and the SCOR model, and based on this, the complexity of the land information infrastructure is revealed. The study shows that supply chain management and the SCOR model can be used to analyse, monitor and manage the production processes of land information within a land information infrastructure.
{"title":"Towards monitoring and managing the production of cadastral information in land information infrastructures using supply chain mapping and the Supply Chain Operations Reference (SCOR) model","authors":"Edward Kurwakumire, S. Coetzee, P. Schmitz","doi":"10.4314/SAJG.V9I2.12","DOIUrl":"https://doi.org/10.4314/SAJG.V9I2.12","url":null,"abstract":"Coping with rapid urbanisation and the impacts of climate change requires effective land management. Quality land information is essential for this. A land information infrastructure is a collaborative and coordinated initiative aimed at providing land information from different organisations, such as municipalities, government departments and private companies, to diverse user communities. A land information infrastructure is complex, spanning information streams through many organisations and technical systems, and presenting challenges for managing and monitoring the production of land information. In the manufacturing field, a supply chain refers to the stream of activities from the initial source to the delivery of end products to customers, and supply chain management is directed at optimising the creation of the products of such a chain. The Supply Chain Operations Reference (SCOR) model is widely used for analysing supply chain processes in order to quantify and improve product and service delivery, and it has also been applied to geographical information supply chains. In this study, the SCOR model is applied to the supply chain processes in a South African case study of a land information infrastructure focusing on the production of cadastral information products. The supply chain comprises a land developer, a land surveying firm, the Surveyor General’s and Deeds Offices, a geospatial data vendor and the end customer. This supply chain is mapped and analysed using supply chain mapping and the SCOR model, and based on this, the complexity of the land information infrastructure is revealed. The study shows that supply chain management and the SCOR model can be used to analyse, monitor and manage the production processes of land information within a land information infrastructure.","PeriodicalId":43854,"journal":{"name":"South African Journal of Geomatics","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46301505","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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