Polarimetric synthetic aperture radar (POLSAR) above ground biomass estimation in communal African savanna woodlands
- Authors: Paradzayi, Charles
- Date: 2012
- Subjects: Polarimetric synthetic aperture radar , Biomass energy , Polarimetric remote sensing , Radar in earth sciences , Energy crops , Fuelwood , Charcoal , Savannas
- Type: Thesis (D. Phil.)
- Identifier: uj:9530 , http://hdl.handle.net/10210/5958
- Description: D. Phil. (Energy Studies) , Woody biomass resource, mostly in the form of fuelwood and charcoal, is the predominant source of basic domestic energy for low-income rural and urban households in sub-Saharan Africa. In most developing economies, quantitative information on available woody biomass resources, at scales appropriate for energy planning purposes is often lacking. The continued reliance on biomass resources to meet the sustenance and livelihood needs in poor economies is exerting unsustainable pressure on the resources. The VW Foundation initiated a multi-institutional and inter-disciplinary bioenergy modelling project that sought to provide stakeholders with quantitative information on available woody biomass and its sustainable utilisation in rural communal woodlands in three countries in southern Africa. The overall project themes related to: (i) remote sensing approaches for quantifying woody biomass (ii) modelling rural energy at the village level (iii) biomass conversion technological pathways (iv) environmental and socio-economic analysis of fuelwood consumption (v) spatial-temporal analysis of the communal woodland dynamics. The exploitation of traditional biomass resources, for cash and mercantile purposes, is leading to accelerated losses of carbon sinks, natural forests and biodiversity, as well as creating local scarcity of woody biomass. Although communal woodlands are important sinks for carbon sequestration, for which developed countries are willing to pay, many poor communities are often caught between conserving communal woodlands and meeting their immediate domestic energy needs. Carbon stocks in communal woodlands are becoming crucial input for the reporting requirements of international conventions such as Reducing Emissions from Degradation and forest Deforestation (REDD) and the Kyoto Protocol on Climate Change (KPCC). This research investigated the capability of using full polarimetric spaceborne ALOS PALSAR retrievals for mapping and quantifying above ground woody biomass in semi-open savanna woodlands in South Africa, Mozambique and Zambia. Existing allometric equations were used to estimate above ground biomass densities from tree parameter measurements in selected training plots. The optimum polarisation channels for estimating standing woody biomass in savanna woodlands were ascertained by investigating the correlation between above ground biomass densities and normalised backscattering coefficient ( σ o ) from retrievals acquired using the horizontal transmit and horizontal receive (HH), horizontal transmit and vertical receive (HV), and vertical transmit and vertical receive (VV) polarisation states, under both wet and dry conditions. The training datasets were bootstrapped since the number of the training plots was limited. Regression and prediction equations have been established between the above ground biomass densities and backscatter intensities for the resampled training dataset, with the highest correlation coefficient for each polarisation. The method developed in this work identifies woody vegetation from the interaction of full polarimetric radar signals with terrain scattering mechanisms and maps the distribution of woody vegetation at any required scale. Terrain scattering mechanisms were classified by (i) performing an unsupervised entropy/alpha (H/α) Wishart classification procedure on ALOS PALSAR full beam imagery, based on the Cloude-Pottier decomposition, and (ii) using scattering classes from the unsupervised classification as training input for the maximum likelihood classification procedure on Freeman decomposition data. The classification results were used to delineate woody and non-woody vegetation classes. Equations to predict above ground biomass densities were developed by inverting the regression equations, which were established from the relationship between backscatter intensities and above ground biomass densities from selected training plots. The predicted biomass densities were reclassified into five categories and the mean biomass density values for the categories were used to compute the available woody biomass resources at the desired scale. The woody vegetation classes were used to mask biomass densities estimated from prediction equations. The research has contributed to an improved understanding of the interpretation and analysis of full polarimetric spaceborne retrievals acquired over African semi-open savanna woodlands by extending approaches developed previously for boreal and temperate region forests. The research work has succeeded to map and to some extent, quantify above ground woody biomass at landscape scale, using full polarimetry spaceborne ALOS PALSAR retrievals validated against plot-scale measurements. The results contribute to the estimation of woody biomass resources for national and global energy and carbon sequestration initiatives. However, the training dataset was limited; hence, the resultant biomass estimation equations are site specific. The approach developed in this work needs refinement before it can be utilised for operational monitoring of savanna woodlands and extrapolation of landscape scale biomass. Estimating woody biomass from the polarimetric retrievals is an improvement on techniques based on optical remote sensing methods because the response of the radar signal is responsive to the physical parameters being surveyed (mass/volume of woody biomass) rather than a surrogate (top of canopy greenness). The purpose of the VW Foundation Bioenergy Modelling project was to estimate available and accessible biomass within village precincts. Results from this work were used to develop GIS-based spatial models for estimating fuelwood collection times and the associated 'least-cost' collection routes between households and selected woodlands. The models took into account the constraints imposed by land tenure systems and geophysical factors such as terrain and watercourses to compute the time spend on fuelwood collection. The fuelwood collection effort is used to estimate the balance between rates of exploitation and woodland regeneration in order to determine the point at which harvesting fuelwood becomes unsustainable. An important output from the GIS models is a woodlands at risk map, which ranks the vulnerability of woody biomass resources in terms of travel times from surrounding villages.
- Full Text:
- Authors: Paradzayi, Charles
- Date: 2012
- Subjects: Polarimetric synthetic aperture radar , Biomass energy , Polarimetric remote sensing , Radar in earth sciences , Energy crops , Fuelwood , Charcoal , Savannas
- Type: Thesis (D. Phil.)
- Identifier: uj:9530 , http://hdl.handle.net/10210/5958
- Description: D. Phil. (Energy Studies) , Woody biomass resource, mostly in the form of fuelwood and charcoal, is the predominant source of basic domestic energy for low-income rural and urban households in sub-Saharan Africa. In most developing economies, quantitative information on available woody biomass resources, at scales appropriate for energy planning purposes is often lacking. The continued reliance on biomass resources to meet the sustenance and livelihood needs in poor economies is exerting unsustainable pressure on the resources. The VW Foundation initiated a multi-institutional and inter-disciplinary bioenergy modelling project that sought to provide stakeholders with quantitative information on available woody biomass and its sustainable utilisation in rural communal woodlands in three countries in southern Africa. The overall project themes related to: (i) remote sensing approaches for quantifying woody biomass (ii) modelling rural energy at the village level (iii) biomass conversion technological pathways (iv) environmental and socio-economic analysis of fuelwood consumption (v) spatial-temporal analysis of the communal woodland dynamics. The exploitation of traditional biomass resources, for cash and mercantile purposes, is leading to accelerated losses of carbon sinks, natural forests and biodiversity, as well as creating local scarcity of woody biomass. Although communal woodlands are important sinks for carbon sequestration, for which developed countries are willing to pay, many poor communities are often caught between conserving communal woodlands and meeting their immediate domestic energy needs. Carbon stocks in communal woodlands are becoming crucial input for the reporting requirements of international conventions such as Reducing Emissions from Degradation and forest Deforestation (REDD) and the Kyoto Protocol on Climate Change (KPCC). This research investigated the capability of using full polarimetric spaceborne ALOS PALSAR retrievals for mapping and quantifying above ground woody biomass in semi-open savanna woodlands in South Africa, Mozambique and Zambia. Existing allometric equations were used to estimate above ground biomass densities from tree parameter measurements in selected training plots. The optimum polarisation channels for estimating standing woody biomass in savanna woodlands were ascertained by investigating the correlation between above ground biomass densities and normalised backscattering coefficient ( σ o ) from retrievals acquired using the horizontal transmit and horizontal receive (HH), horizontal transmit and vertical receive (HV), and vertical transmit and vertical receive (VV) polarisation states, under both wet and dry conditions. The training datasets were bootstrapped since the number of the training plots was limited. Regression and prediction equations have been established between the above ground biomass densities and backscatter intensities for the resampled training dataset, with the highest correlation coefficient for each polarisation. The method developed in this work identifies woody vegetation from the interaction of full polarimetric radar signals with terrain scattering mechanisms and maps the distribution of woody vegetation at any required scale. Terrain scattering mechanisms were classified by (i) performing an unsupervised entropy/alpha (H/α) Wishart classification procedure on ALOS PALSAR full beam imagery, based on the Cloude-Pottier decomposition, and (ii) using scattering classes from the unsupervised classification as training input for the maximum likelihood classification procedure on Freeman decomposition data. The classification results were used to delineate woody and non-woody vegetation classes. Equations to predict above ground biomass densities were developed by inverting the regression equations, which were established from the relationship between backscatter intensities and above ground biomass densities from selected training plots. The predicted biomass densities were reclassified into five categories and the mean biomass density values for the categories were used to compute the available woody biomass resources at the desired scale. The woody vegetation classes were used to mask biomass densities estimated from prediction equations. The research has contributed to an improved understanding of the interpretation and analysis of full polarimetric spaceborne retrievals acquired over African semi-open savanna woodlands by extending approaches developed previously for boreal and temperate region forests. The research work has succeeded to map and to some extent, quantify above ground woody biomass at landscape scale, using full polarimetry spaceborne ALOS PALSAR retrievals validated against plot-scale measurements. The results contribute to the estimation of woody biomass resources for national and global energy and carbon sequestration initiatives. However, the training dataset was limited; hence, the resultant biomass estimation equations are site specific. The approach developed in this work needs refinement before it can be utilised for operational monitoring of savanna woodlands and extrapolation of landscape scale biomass. Estimating woody biomass from the polarimetric retrievals is an improvement on techniques based on optical remote sensing methods because the response of the radar signal is responsive to the physical parameters being surveyed (mass/volume of woody biomass) rather than a surrogate (top of canopy greenness). The purpose of the VW Foundation Bioenergy Modelling project was to estimate available and accessible biomass within village precincts. Results from this work were used to develop GIS-based spatial models for estimating fuelwood collection times and the associated 'least-cost' collection routes between households and selected woodlands. The models took into account the constraints imposed by land tenure systems and geophysical factors such as terrain and watercourses to compute the time spend on fuelwood collection. The fuelwood collection effort is used to estimate the balance between rates of exploitation and woodland regeneration in order to determine the point at which harvesting fuelwood becomes unsustainable. An important output from the GIS models is a woodlands at risk map, which ranks the vulnerability of woody biomass resources in terms of travel times from surrounding villages.
- Full Text:
Acidic deposition emanating from the South African Highveld: a critical levels and critical loads assessment
- Authors: Josipovic, Miroslav (Micky)
- Date: 2010-10-04T08:59:34Z
- Subjects: Acid deposition , Ozone research , Highveld (Region) South Africa
- Type: Thesis (D. Phil.)
- Identifier: uj:6926 , http://hdl.handle.net/10210/3436
- Description: D.Phil. , Please refer to full text to view abstract
- Full Text:
- Authors: Josipovic, Miroslav (Micky)
- Date: 2010-10-04T08:59:34Z
- Subjects: Acid deposition , Ozone research , Highveld (Region) South Africa
- Type: Thesis (D. Phil.)
- Identifier: uj:6926 , http://hdl.handle.net/10210/3436
- Description: D.Phil. , Please refer to full text to view abstract
- Full Text:
The application of regression analysis to forecast transaction volumes on ATM devices
- Authors: Keyter, Martin
- Date: 2009-02-05T07:11:16Z
- Subjects: Automated tellers , Regression analysis
- Type: Thesis (D. Phil.)
- Identifier: uj:8077 , http://hdl.handle.net/10210/2007
- Description: D.Litt. et Phil. , The Self Service Channel (SSC) of First National Bank does not have a consistent and scientific method whereby the potential of a site could be evaluated for the placement of ATM devices. Estimating the potential number of transactions that could be generated at any particular site will enable FNB to determine whether or not an ATM would be viable at that site. Subsequently, the problem, or reason why this study was conducted, could be formulated as follows: There exist no uniform, consistent and scientific method that can be applied to predict the potential number of transactions that could be generated at an ATM, at any potential site. Therefore, the primary objective of this study was to develop a transaction-forecasting model (in the form of a regression model) whereby the potential number of ATM transactions at any given site can be estimated. Secondly, it was essential that this model must be developed in such a way that it is practical and easy to apply in the working environment. Employees of FNB must be able to use the model as part of their daily workflow and therefore it had to be made as user-friendly as possible. As the basis of this study, the first chapters focus on the study of available literature regarding location analysis and site selection. Literature regarding ATM location analysis as such is extremely limited, and therefore literature regarding the location of retail facilities was used as the foundation of the study. As with ATMs, most retail facilities are dependent on high pedestrian and traffic volumes to increase their sales. Variables that relate to, or that could be an indicator of pedestrian or traffic volumes at a particular site, was used (where possible) as variables in this study. ATM sites can be classified according to the type of facility where it is located, e.g. shopping centres, garages, etc. Each of the site classifications that were identified in the study, is influenced by different variables, which means that a different combination of variables could be included or used for each site classification. This resulted in the development of separate regression models for each site classification, depending on the specific set of variables influencing ATM transaction volumes at that site classification. When the variables were selected, one of the criteria that had to be kept in mind was the availability of data. One of the objectives of the study was to provide a model that is relatively simple to apply in practice. Data that needs to be gathered for input into the model (independent variables) must be easy to obtain. Furthermore, to develop a regression model, the variables used in the regression analysis must be quantifiable. Certain of the variables already had values that could be used. An example of such a variable was the Gross Lettable Area (GLA). The GLA of a shopping centre is measured in m², and this figure was used to measure the correlation between GLA and transaction volumes at shopping centres. Other variables, more specifically accessibility, visibility, competitors in the vicinity and FNB ATMs in the vicinity were not easy to quantify, and methods had to be developed to quantify these. After identifying the criteria that was used to quantify the independent variables, a sample of ATM sites - for each site classification - were selected. For each of these sites the relevant data were then collected. Data were collected by means of site visits to each of the sites included in the sample, GIS databases and requests for information from petrol garage owners, shopping centre management and retail store owners where the ATMs in the sample are located. Multiple regression analysis was applied to the variables in each classification that proved to have the strongest influence on transactions at that particular site classification. Of the 14 site classifications, models could be developed for 5 classifications. These 5 classifications, however, which include shopping centres, business nodes, convenience stores, garages and industrial areas, accounts for over 90% of all the ATM locations at FNB. The other classifications were excluded due to a lack of sufficient information for regression purposes. These models provide a scientific approach to ATM placements. It will also improve the decision-making process in FNB’s Self Service Channel, and result into cost savings by preventing the installation of ATMs at unprofitable locations. This study has made a significant contribution to the banking industry and the field of urban geography in terms of new knowledge, models and methods that were developed. There are various factors that impact on the performance of ATM devices at different locations. However, it is important to keep in mind that any scientific/statistical model could be applied only with limited success in practice, as human behaviour - and anything depending on it - cannot be fully explained or predicted by statistical models. Therefore, when these models are applied, the discretion and experience of the person will always play a role in reaching a final decision. The models and methods that were developed in this thesis will be developed and refined further on a continual basis to increase its accuracy and prediction ability.
- Full Text:
- Authors: Keyter, Martin
- Date: 2009-02-05T07:11:16Z
- Subjects: Automated tellers , Regression analysis
- Type: Thesis (D. Phil.)
- Identifier: uj:8077 , http://hdl.handle.net/10210/2007
- Description: D.Litt. et Phil. , The Self Service Channel (SSC) of First National Bank does not have a consistent and scientific method whereby the potential of a site could be evaluated for the placement of ATM devices. Estimating the potential number of transactions that could be generated at any particular site will enable FNB to determine whether or not an ATM would be viable at that site. Subsequently, the problem, or reason why this study was conducted, could be formulated as follows: There exist no uniform, consistent and scientific method that can be applied to predict the potential number of transactions that could be generated at an ATM, at any potential site. Therefore, the primary objective of this study was to develop a transaction-forecasting model (in the form of a regression model) whereby the potential number of ATM transactions at any given site can be estimated. Secondly, it was essential that this model must be developed in such a way that it is practical and easy to apply in the working environment. Employees of FNB must be able to use the model as part of their daily workflow and therefore it had to be made as user-friendly as possible. As the basis of this study, the first chapters focus on the study of available literature regarding location analysis and site selection. Literature regarding ATM location analysis as such is extremely limited, and therefore literature regarding the location of retail facilities was used as the foundation of the study. As with ATMs, most retail facilities are dependent on high pedestrian and traffic volumes to increase their sales. Variables that relate to, or that could be an indicator of pedestrian or traffic volumes at a particular site, was used (where possible) as variables in this study. ATM sites can be classified according to the type of facility where it is located, e.g. shopping centres, garages, etc. Each of the site classifications that were identified in the study, is influenced by different variables, which means that a different combination of variables could be included or used for each site classification. This resulted in the development of separate regression models for each site classification, depending on the specific set of variables influencing ATM transaction volumes at that site classification. When the variables were selected, one of the criteria that had to be kept in mind was the availability of data. One of the objectives of the study was to provide a model that is relatively simple to apply in practice. Data that needs to be gathered for input into the model (independent variables) must be easy to obtain. Furthermore, to develop a regression model, the variables used in the regression analysis must be quantifiable. Certain of the variables already had values that could be used. An example of such a variable was the Gross Lettable Area (GLA). The GLA of a shopping centre is measured in m², and this figure was used to measure the correlation between GLA and transaction volumes at shopping centres. Other variables, more specifically accessibility, visibility, competitors in the vicinity and FNB ATMs in the vicinity were not easy to quantify, and methods had to be developed to quantify these. After identifying the criteria that was used to quantify the independent variables, a sample of ATM sites - for each site classification - were selected. For each of these sites the relevant data were then collected. Data were collected by means of site visits to each of the sites included in the sample, GIS databases and requests for information from petrol garage owners, shopping centre management and retail store owners where the ATMs in the sample are located. Multiple regression analysis was applied to the variables in each classification that proved to have the strongest influence on transactions at that particular site classification. Of the 14 site classifications, models could be developed for 5 classifications. These 5 classifications, however, which include shopping centres, business nodes, convenience stores, garages and industrial areas, accounts for over 90% of all the ATM locations at FNB. The other classifications were excluded due to a lack of sufficient information for regression purposes. These models provide a scientific approach to ATM placements. It will also improve the decision-making process in FNB’s Self Service Channel, and result into cost savings by preventing the installation of ATMs at unprofitable locations. This study has made a significant contribution to the banking industry and the field of urban geography in terms of new knowledge, models and methods that were developed. There are various factors that impact on the performance of ATM devices at different locations. However, it is important to keep in mind that any scientific/statistical model could be applied only with limited success in practice, as human behaviour - and anything depending on it - cannot be fully explained or predicted by statistical models. Therefore, when these models are applied, the discretion and experience of the person will always play a role in reaching a final decision. The models and methods that were developed in this thesis will be developed and refined further on a continual basis to increase its accuracy and prediction ability.
- Full Text:
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