Development of a numerical geohydrological model for a fractured rock aquifer in the Karoo, near Sutherland, South Africa
- Authors: Maqhubela, Akhona
- Date: 2024-04
- Subjects: Groundwater -- South Africa -- Northern Cape , Hydrogeology -- South Africa -- Northern Cape , Remote sensing , Geographic information systems
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64163 , vital:73659
- Description: The regional scale method in groundwater storage observation introduces uncertainties that hinder the evaluation of the remaining lifespan of depleted aquifers. The scarcity of precipitation data presents significant global challenge, especially in semi-arid regions. This study constructs a regional numerical hydrogeological model that identifies the potential impacts of climate change on the water balance for the South African Gravimetric Observation Station in Sutherland. The purpose of this study is to understand mechanisms controlling groundwater in the fractured rock aquifer. The climate data from the Weather forecast data over the last ten years was collected from the South African Weather Service. and groundwater levels data assessed the potential impacts of climate change on water balance components, especially precipitation and evapotranspiration. Precipitation is the primary recharge parameter in this study and had the highest level recorded in winter, with May having the highest precipitation rates of 24,62mm. The instrument conducted two profile investigations in a single day to detect geological abnormalities at various depths, achieving an impressive accuracy of up to 0.001 mV. The fact that groundwater flows from regions of higher hydraulic heads to areas of lower hydraulic charges, confirms that riverbeds in Sutherland act as preferential conduits for subsurface recharge. The profile and processed geophysical maps show low chances of getting groundwater in this observed area due to extensively great depth, approximately 150 – 210 m. The river package from MODFLOW model shows little inflow to the study nearby well locations. These model results showed a negative difference between water flowing in and out of the system of about -7m3 between 2002 and 2020. Groundwater flows faster at borehole five, where the hydraulic conductivity is large. The resulting regional hydrogeological model offered valuable insights into how climate change might influence the distribution and accessibility of groundwater resources. In the context of Sutherland, a negative groundwater budget value signaled that groundwater extraction or consumption surpassed the natural replenishment or recharge of the aquifer. , Thesis (MSc) -- Faculty of Science, School of Environmental Sciences, 2022
- Full Text:
- Date Issued: 2024-04
- Authors: Maqhubela, Akhona
- Date: 2024-04
- Subjects: Groundwater -- South Africa -- Northern Cape , Hydrogeology -- South Africa -- Northern Cape , Remote sensing , Geographic information systems
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64163 , vital:73659
- Description: The regional scale method in groundwater storage observation introduces uncertainties that hinder the evaluation of the remaining lifespan of depleted aquifers. The scarcity of precipitation data presents significant global challenge, especially in semi-arid regions. This study constructs a regional numerical hydrogeological model that identifies the potential impacts of climate change on the water balance for the South African Gravimetric Observation Station in Sutherland. The purpose of this study is to understand mechanisms controlling groundwater in the fractured rock aquifer. The climate data from the Weather forecast data over the last ten years was collected from the South African Weather Service. and groundwater levels data assessed the potential impacts of climate change on water balance components, especially precipitation and evapotranspiration. Precipitation is the primary recharge parameter in this study and had the highest level recorded in winter, with May having the highest precipitation rates of 24,62mm. The instrument conducted two profile investigations in a single day to detect geological abnormalities at various depths, achieving an impressive accuracy of up to 0.001 mV. The fact that groundwater flows from regions of higher hydraulic heads to areas of lower hydraulic charges, confirms that riverbeds in Sutherland act as preferential conduits for subsurface recharge. The profile and processed geophysical maps show low chances of getting groundwater in this observed area due to extensively great depth, approximately 150 – 210 m. The river package from MODFLOW model shows little inflow to the study nearby well locations. These model results showed a negative difference between water flowing in and out of the system of about -7m3 between 2002 and 2020. Groundwater flows faster at borehole five, where the hydraulic conductivity is large. The resulting regional hydrogeological model offered valuable insights into how climate change might influence the distribution and accessibility of groundwater resources. In the context of Sutherland, a negative groundwater budget value signaled that groundwater extraction or consumption surpassed the natural replenishment or recharge of the aquifer. , Thesis (MSc) -- Faculty of Science, School of Environmental Sciences, 2022
- Full Text:
- Date Issued: 2024-04
Quantifying the impact of the spatio-temporal variability of land use/land cover on surface run-off generation and groundwater recharge in the luvuvhu river catchment area as a study area
- Ramuhovhi, Dakalo Ndivhuwo Stella
- Authors: Ramuhovhi, Dakalo Ndivhuwo Stella
- Date: 2024-04
- Subjects: Remote sensing , Geographic information systems , Groundwater flow
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64322 , vital:73675
- Description: Assessing the spatio-temporal dynamics of land use land cover (LULC) change on hydrological response is vital for catchment sustainability and developing proper management strategies. The study aimed to assess the spatiotemporal effects and implications of LULC dynamics on surface runoff in the Luvuvhu River Catchment, Limpopo Province, using the Soil and Water Assessment Tool (SWAT) model. Satellite images of Landsat 5-thematic mapper and Landsat 8 operational land imager for the years 1990 and 2021 were used to explore the characteristics of LULC for this study by adopting the maximum likelihood (ML) supervised classification method. Five LULC classes were classified in this study; namely, water, built-up area, bare surface, dense vegetation, and sparse vegetation. The classification results show good accuracy values in the range of 76% (1990) and 84% (2021) with overall kappa of 63.8% and 72.8% for 1990 and 2021, respectively. For the purpose of this study, integration of geospatial technique and SWAT model were configured to operate at a monthly time interval over a span of 34 years, specifically from 1979 to 2013 to simulate surface runoff. The SWAT simulation process was executed using a digital elevation model, soil, LULC, and weather data. The analysis of LULC for 1990 and 2021 runoff modelling, it was found that, the runoff depth increased gradually from 3249 mm to 5162.5 mm during 1990 and 2021 LULC change, respectively. The R2, ENS, PBIAS, and RSR values for the calibration and the validation were 0.81 and 0.76, and 0.72 and 0.68, 0.64 and 0.58, 0.54 and 0.63 respectively. These values indicate good correlation between the observed and simulated stream flow data Therefore, suitable and timely management measures must be taken by policy decision-makers to enable sustainable development and to protect the catchment’s natural resources in order to reduce the severity of the changes. , Thesis (MSc) -- Faculty of Science, School of Environmental Sciences, 2024
- Full Text:
- Date Issued: 2024-04
- Authors: Ramuhovhi, Dakalo Ndivhuwo Stella
- Date: 2024-04
- Subjects: Remote sensing , Geographic information systems , Groundwater flow
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/64322 , vital:73675
- Description: Assessing the spatio-temporal dynamics of land use land cover (LULC) change on hydrological response is vital for catchment sustainability and developing proper management strategies. The study aimed to assess the spatiotemporal effects and implications of LULC dynamics on surface runoff in the Luvuvhu River Catchment, Limpopo Province, using the Soil and Water Assessment Tool (SWAT) model. Satellite images of Landsat 5-thematic mapper and Landsat 8 operational land imager for the years 1990 and 2021 were used to explore the characteristics of LULC for this study by adopting the maximum likelihood (ML) supervised classification method. Five LULC classes were classified in this study; namely, water, built-up area, bare surface, dense vegetation, and sparse vegetation. The classification results show good accuracy values in the range of 76% (1990) and 84% (2021) with overall kappa of 63.8% and 72.8% for 1990 and 2021, respectively. For the purpose of this study, integration of geospatial technique and SWAT model were configured to operate at a monthly time interval over a span of 34 years, specifically from 1979 to 2013 to simulate surface runoff. The SWAT simulation process was executed using a digital elevation model, soil, LULC, and weather data. The analysis of LULC for 1990 and 2021 runoff modelling, it was found that, the runoff depth increased gradually from 3249 mm to 5162.5 mm during 1990 and 2021 LULC change, respectively. The R2, ENS, PBIAS, and RSR values for the calibration and the validation were 0.81 and 0.76, and 0.72 and 0.68, 0.64 and 0.58, 0.54 and 0.63 respectively. These values indicate good correlation between the observed and simulated stream flow data Therefore, suitable and timely management measures must be taken by policy decision-makers to enable sustainable development and to protect the catchment’s natural resources in order to reduce the severity of the changes. , Thesis (MSc) -- Faculty of Science, School of Environmental Sciences, 2024
- Full Text:
- Date Issued: 2024-04
A multiscale remote sensing assessment of subtropical indigenous forests along the wild coast, South Africa
- Authors: Blessing, Sithole Vhusomuzi
- Date: 2015
- Subjects: Forests and forestry -- South Africa -- Remote sensing , Forest conservation , Remote sensing , Geographic information systems
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:10677 , http://hdl.handle.net/10948/d1021169
- Description: The subtropical forests located along South Africa’s Wild Coast region, declared as one of the biodiversity hotspots, provide benefits to the local and national economy. However, there is evidence of increased pressure exerted on the forests by growing population and reduced income from activities not related to forest products. The ability of remote sensing to quantify subtropical forest changes over time, perform species discrimination (using field spectroscopy) and integrating field spectral and multispectral data were all assessed in this study. Investigations were conducted at pixel, leaf and sub-pixel levels. Both per-pixel and sub-pixel classification methods were used for improved forest characterisation. Using SPOT 6 imagery for 2013, the study determined the best classification algorithm for mapping sub-tropical forest and other land cover types to be the maximum likelihood classifier. Maximum likelihood outperformed minimum distance, spectral angle mapper and spectral information divergence algorithms, based on overall accuracy and Kappa coefficient values. Forest change analysis was made based on spectral measurements made at top of the atmosphere (TOC) level. When applied to the 2005 and 2009 SPOT 5 images, subtropical forest changes between 2005-2009 and 2009-2013 were quantified. A temporal analysis of forest cover trends in the periods 2005-2009 and 2009-2013 identified a decreasing trend of -3648.42 and -946.98 ha respectively, which translated to 7.81 percent and 2.20 percent decrease. Although there is evidence of a trend towards decreased rates of forest loss, more conservation efforts are required to protect the Wild Coast ecosystem. Using field spectral measurements data, the hierarchical method (comprising One-way ANOVA with Bonferroni correction, Classification and Regression Trees (CART) and Jeffries Matusita method) successfully selected optimal wavelengths for species discrimination at leaf level. Only 17 out of 2150 wavelengths were identified, thereby reducing the complexities related to data dimensionality. The optimal 17 wavelength bands were noted in the visible (438, 442, 512 and 695 nm), near infrared (724, 729, 750, 758, 856, 936, 1179, 1507 and 1673 nm) and mid-infrared (2220, 2465, 2469 and 2482 nm) portions of the electromagnetic spectrum. The Jeffries-Matusita (JM) distance method confirmed the separability of the selected wavelength bands. Using these 17 wavelengths, linear discriminant analysis (LDA) classified subtropical species at leaf level more accurately than partial least squares discriminant analysis (PLSDA) and random forest (RF). In addition, the study integrated field-collected canopy spectral and multispectral data to discriminate proportions of semi-deciduous and evergreen subtropical forests at sub-pixel level. By using the 2013 land cover (using MLC) to mask non-forested portions before sub-pixel classification (using MTMF), the proportional maps were a product of two classifiers. The proportional maps show higher proportions of evergreen forests along the coast while semi-deciduous subtropical forest species were mainly on inland parts of the Wild Coast. These maps had high accuracy, thereby proving the ability of an integration of field spectral and multispectral data in mapping semi-deciduous and evergreen forest species. Overall, the study has demonstrated the importance of the MLC and LDA and served to integrate field spectral and multispectral data in subtropical forest characterisation at both leaf and top-of-atmosphere levels. The success of both the MLC and LDA further highlighted how essential parametric classifiers are in remote sensing forestry applications. Main subtropical characteristics highlighted in this study were species discrimination at leaf level, quantifying forest change at pixel level and discriminating semi-deciduous and evergreen forests at sub-pixel level.
- Full Text:
- Date Issued: 2015
- Authors: Blessing, Sithole Vhusomuzi
- Date: 2015
- Subjects: Forests and forestry -- South Africa -- Remote sensing , Forest conservation , Remote sensing , Geographic information systems
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:10677 , http://hdl.handle.net/10948/d1021169
- Description: The subtropical forests located along South Africa’s Wild Coast region, declared as one of the biodiversity hotspots, provide benefits to the local and national economy. However, there is evidence of increased pressure exerted on the forests by growing population and reduced income from activities not related to forest products. The ability of remote sensing to quantify subtropical forest changes over time, perform species discrimination (using field spectroscopy) and integrating field spectral and multispectral data were all assessed in this study. Investigations were conducted at pixel, leaf and sub-pixel levels. Both per-pixel and sub-pixel classification methods were used for improved forest characterisation. Using SPOT 6 imagery for 2013, the study determined the best classification algorithm for mapping sub-tropical forest and other land cover types to be the maximum likelihood classifier. Maximum likelihood outperformed minimum distance, spectral angle mapper and spectral information divergence algorithms, based on overall accuracy and Kappa coefficient values. Forest change analysis was made based on spectral measurements made at top of the atmosphere (TOC) level. When applied to the 2005 and 2009 SPOT 5 images, subtropical forest changes between 2005-2009 and 2009-2013 were quantified. A temporal analysis of forest cover trends in the periods 2005-2009 and 2009-2013 identified a decreasing trend of -3648.42 and -946.98 ha respectively, which translated to 7.81 percent and 2.20 percent decrease. Although there is evidence of a trend towards decreased rates of forest loss, more conservation efforts are required to protect the Wild Coast ecosystem. Using field spectral measurements data, the hierarchical method (comprising One-way ANOVA with Bonferroni correction, Classification and Regression Trees (CART) and Jeffries Matusita method) successfully selected optimal wavelengths for species discrimination at leaf level. Only 17 out of 2150 wavelengths were identified, thereby reducing the complexities related to data dimensionality. The optimal 17 wavelength bands were noted in the visible (438, 442, 512 and 695 nm), near infrared (724, 729, 750, 758, 856, 936, 1179, 1507 and 1673 nm) and mid-infrared (2220, 2465, 2469 and 2482 nm) portions of the electromagnetic spectrum. The Jeffries-Matusita (JM) distance method confirmed the separability of the selected wavelength bands. Using these 17 wavelengths, linear discriminant analysis (LDA) classified subtropical species at leaf level more accurately than partial least squares discriminant analysis (PLSDA) and random forest (RF). In addition, the study integrated field-collected canopy spectral and multispectral data to discriminate proportions of semi-deciduous and evergreen subtropical forests at sub-pixel level. By using the 2013 land cover (using MLC) to mask non-forested portions before sub-pixel classification (using MTMF), the proportional maps were a product of two classifiers. The proportional maps show higher proportions of evergreen forests along the coast while semi-deciduous subtropical forest species were mainly on inland parts of the Wild Coast. These maps had high accuracy, thereby proving the ability of an integration of field spectral and multispectral data in mapping semi-deciduous and evergreen forest species. Overall, the study has demonstrated the importance of the MLC and LDA and served to integrate field spectral and multispectral data in subtropical forest characterisation at both leaf and top-of-atmosphere levels. The success of both the MLC and LDA further highlighted how essential parametric classifiers are in remote sensing forestry applications. Main subtropical characteristics highlighted in this study were species discrimination at leaf level, quantifying forest change at pixel level and discriminating semi-deciduous and evergreen forests at sub-pixel level.
- Full Text:
- Date Issued: 2015
Monitoring carbon stocks in the sub-tropical thicket biome using remote sensing and GIS techniques : the case of the Great Fish River Nature Reserve and its environs, Eastern Cape province, South Africa
- Authors: Nyamugama, Adolph
- Date: 2013
- Subjects: Fragmented landscapes -- South Africa -- Eastern Cape -- Remote sensing , Environmental degradation -- South Africa -- Eastern Cape , Remote sensing , Geographic information systems
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:10672 , http://hdl.handle.net/10948/d1020303
- Description: The subtropical thicket biome in the Eastern Cape Province of South Africa has been heavily degraded and transformed due overutilization during the last century. The highly degraded and transformed areas exhibit a significant loss of above ground carbon stocks (AGC) and loss of SOC content. Information about land use /cover change and fragmentation dynamics is a prerequisite for measuring carbon stock changes. The main aim of this study is to assess the trends of land use/cover change, fragmentation dynamics, model the temporal changes of AGC stocks in the Great Fish River Nature Reserve and its environs from 1972 to 2010, quantify and map the spatial distribution of SOC concentrations in the partial subtropical thicket cover in the Great Fish River Nature Reserve and environs (communal rangelands). Multi-temporal analyses based on 1972 Landsat MSS, 1982 and 1992 Landsat TM, 2002 Landsat ETM and 2010 SPOT 5 High Resolution images were used for land use/cover change detection and fragmentation analysis. Object oriented post-classification comparison was applied for land use/cover change detection analysis. Fragmentation dynamics analysis was carried out by computing and analyzing landscape metrics in land use/cover classes. Landscape fragmentation analyses revealed that thicket vegetation has increasingly become fragmented, characterized by smaller less linked patches of intact thicket cover. Landscape metrics for intact thicket and degraded thicket classes reflected fragmentation, as illustrated by the increase in the Number of Patches (NP), Patch Density (PD), Landscape Shape Index (LSI), and a decrease in Mean Patch Size (MPS). The use of remote sensing techniques and landscape metrics was vital for the understanding of the dynamics of land use/cover change and fragmentation. Baseline land use/cover maps produced for 1972, 1982, 1992 2002 and 2010 and fragmentation analyses were then used for analyzing carbon stock changes in the study area. To model the temporal changes of AGC stocks in the Great Fish River Nature Reserve and its environs from 1972 to 2010, a method based on the integration of RS and GIS was employed for the estimation of AGC stocks in a time series. A non-linear regression model was developed using NDVI values generated from SPOT 5 HRG satellite imagery of 2010 as the independent variable and AGC stock estimates from field plots as the dependent variable. The regression model was used to estimate AGC stocks for the entire study area on the 2010 SPOT 5 HRG and also extrapolated to the 1972 Landsat MSS, 1982 and 1992 Landsat TM, and 2002 Landsat ETM. The AGC stocks for the period 1972 -1982, 1982-1992, 1992-200) and 2002-2010 were compared by means of change detection analysis. The comparison of AGC stocks was carried out at subtropical thicket class level. The results showed a decline of AGC stocks in all the classes from 1972 to 2010. Degraded and transformed thicket classes had the highest AGC stock losses. The decline of AGC stocks was attributed to thicket transformation and degradation which were caused by anthropogenic activities. To map and quantify SOC concentration in partial (fractional) thicket vegetation cover, the spectral reflectance of both thicket vegetation and bare-soils was measured in situ. Soil samples were collected from the sampling sites and transported to the laboratory for spectral reflectance and SOC measurements. Thicket vegetation and bare soil reflectance were measured using spectroscopy both in situ and under laboratory conditions. Their respective endmembers were extracted from ASTER imagery using the Pixel Purity Index (PPI). The endmembers were validated with in situ and laboratory thicket and bare-soil reflectance signatures. The spectral unmixing technique was applied to ASTER imagery to discriminate pure pixels of thicket vegetation and bare-soils; a residual spectral image was produced. The Residual Spectral Unmixing (RSU) procedure was applied to the residual spectral image to produce an RSU soil spectrum image. Partial Least Squares Regression (PSLR) model was developed using spectral signatures of a residual soil spectrum image as the independent variable and SOC concentration measured from soil samples as the dependent variable. The PSLR prediction model was used to predict SOC concentration on the RSU soil spectral image. The predicted SOC concentration was then validated with SOC concentration measured from the field plots. A Strong correlation (R2 = 0.82) was obtained between the predicted SOC concentration and the SOC concentration measured from field samples. The PSLR was then used to generate a map of SOC concentration for the Great Fish River Nature Reserve and its environs. Areas with very low SOC concentrations were found in the degraded communal villages, as opposed to the higher SOC values in the protected area. The results confirmed that RS techniques are key to estimating and mapping the spatial distribution of SOC concentration in partial subtropical thicket vegetation. Partial thicket vegetation has a huge influence on the soil spectra; it can influence the prediction of SOC concentration. The use of the RSU approach eliminates partial thicket vegetation cover from bare soil spectra. The residual soil spectrum image contains enough information for the mapping of SOC concentration. The technique has the potential to augment the applicability of airborne imaging spectroscopy for soil studies in the sub-tropical thicket biome and similar environments.
- Full Text:
- Date Issued: 2013
- Authors: Nyamugama, Adolph
- Date: 2013
- Subjects: Fragmented landscapes -- South Africa -- Eastern Cape -- Remote sensing , Environmental degradation -- South Africa -- Eastern Cape , Remote sensing , Geographic information systems
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:10672 , http://hdl.handle.net/10948/d1020303
- Description: The subtropical thicket biome in the Eastern Cape Province of South Africa has been heavily degraded and transformed due overutilization during the last century. The highly degraded and transformed areas exhibit a significant loss of above ground carbon stocks (AGC) and loss of SOC content. Information about land use /cover change and fragmentation dynamics is a prerequisite for measuring carbon stock changes. The main aim of this study is to assess the trends of land use/cover change, fragmentation dynamics, model the temporal changes of AGC stocks in the Great Fish River Nature Reserve and its environs from 1972 to 2010, quantify and map the spatial distribution of SOC concentrations in the partial subtropical thicket cover in the Great Fish River Nature Reserve and environs (communal rangelands). Multi-temporal analyses based on 1972 Landsat MSS, 1982 and 1992 Landsat TM, 2002 Landsat ETM and 2010 SPOT 5 High Resolution images were used for land use/cover change detection and fragmentation analysis. Object oriented post-classification comparison was applied for land use/cover change detection analysis. Fragmentation dynamics analysis was carried out by computing and analyzing landscape metrics in land use/cover classes. Landscape fragmentation analyses revealed that thicket vegetation has increasingly become fragmented, characterized by smaller less linked patches of intact thicket cover. Landscape metrics for intact thicket and degraded thicket classes reflected fragmentation, as illustrated by the increase in the Number of Patches (NP), Patch Density (PD), Landscape Shape Index (LSI), and a decrease in Mean Patch Size (MPS). The use of remote sensing techniques and landscape metrics was vital for the understanding of the dynamics of land use/cover change and fragmentation. Baseline land use/cover maps produced for 1972, 1982, 1992 2002 and 2010 and fragmentation analyses were then used for analyzing carbon stock changes in the study area. To model the temporal changes of AGC stocks in the Great Fish River Nature Reserve and its environs from 1972 to 2010, a method based on the integration of RS and GIS was employed for the estimation of AGC stocks in a time series. A non-linear regression model was developed using NDVI values generated from SPOT 5 HRG satellite imagery of 2010 as the independent variable and AGC stock estimates from field plots as the dependent variable. The regression model was used to estimate AGC stocks for the entire study area on the 2010 SPOT 5 HRG and also extrapolated to the 1972 Landsat MSS, 1982 and 1992 Landsat TM, and 2002 Landsat ETM. The AGC stocks for the period 1972 -1982, 1982-1992, 1992-200) and 2002-2010 were compared by means of change detection analysis. The comparison of AGC stocks was carried out at subtropical thicket class level. The results showed a decline of AGC stocks in all the classes from 1972 to 2010. Degraded and transformed thicket classes had the highest AGC stock losses. The decline of AGC stocks was attributed to thicket transformation and degradation which were caused by anthropogenic activities. To map and quantify SOC concentration in partial (fractional) thicket vegetation cover, the spectral reflectance of both thicket vegetation and bare-soils was measured in situ. Soil samples were collected from the sampling sites and transported to the laboratory for spectral reflectance and SOC measurements. Thicket vegetation and bare soil reflectance were measured using spectroscopy both in situ and under laboratory conditions. Their respective endmembers were extracted from ASTER imagery using the Pixel Purity Index (PPI). The endmembers were validated with in situ and laboratory thicket and bare-soil reflectance signatures. The spectral unmixing technique was applied to ASTER imagery to discriminate pure pixels of thicket vegetation and bare-soils; a residual spectral image was produced. The Residual Spectral Unmixing (RSU) procedure was applied to the residual spectral image to produce an RSU soil spectrum image. Partial Least Squares Regression (PSLR) model was developed using spectral signatures of a residual soil spectrum image as the independent variable and SOC concentration measured from soil samples as the dependent variable. The PSLR prediction model was used to predict SOC concentration on the RSU soil spectral image. The predicted SOC concentration was then validated with SOC concentration measured from the field plots. A Strong correlation (R2 = 0.82) was obtained between the predicted SOC concentration and the SOC concentration measured from field samples. The PSLR was then used to generate a map of SOC concentration for the Great Fish River Nature Reserve and its environs. Areas with very low SOC concentrations were found in the degraded communal villages, as opposed to the higher SOC values in the protected area. The results confirmed that RS techniques are key to estimating and mapping the spatial distribution of SOC concentration in partial subtropical thicket vegetation. Partial thicket vegetation has a huge influence on the soil spectra; it can influence the prediction of SOC concentration. The use of the RSU approach eliminates partial thicket vegetation cover from bare soil spectra. The residual soil spectrum image contains enough information for the mapping of SOC concentration. The technique has the potential to augment the applicability of airborne imaging spectroscopy for soil studies in the sub-tropical thicket biome and similar environments.
- Full Text:
- Date Issued: 2013
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