The report herein consisted of two objectives, the first of which was a data collection effort in John H. Kerr Reservoir. Ten field monitoring trips were performed between March 30, 1981 and March 3, 1982. The temperature, velocity, and depth data from those trips are contained in Appendix A. Plots of temperature versus depth at the stations chosen in the reservoir are contained in Appendix B. The second objective was an application of the database to Landsat MSS data available during the same period of record. The effects of temperature and total suspended solids on Landsat MSS reflectance were investigated. The effect of increasing temperature was a notable decrease in reflectance especially in Bands 4 and 5. This temperature effect may have been influenced by other water quality parameters that were not measured. The effect of increasing total suspended solids was a pronounced reflectance increase in Band 5. / M.S.

The purpose of this research was to explore the potential of using LANDSAT MSS data in the study of land use/cover patterns and turbidity relationships within the Lake Anna watershed. Two premises of this research are that a relationship exists between land use/cover patterns and turbidity levels, and that LANDSAT MSS data can be used to study this relationship. Turbidity levels within Lake Anna were estimated by the chromaticity technique used by Munday et al and were correlated to two groups of ground-based data -- surface turbidity levels and the product of the Universal Soil Loss Equation (USLE). Estimated turbidity levels correlated moderately well with surface data, but only a slight relationship could be established between land use/cover patterns and estimated turbidity. Possible explanations for these results were grouped into two categories, practical and conceptual problems. Practical problems were defined as data collection problems and included LANDSAT system and data accuracy problems. Conceptual problems were problems based on theoretical issues of using LANDSAT MSS data to study relationships between land use/cover patterns and turbidity levels. Conceptual problems remained even after the practical problems were solved. The accomplishments of this research included the application of chromaticity analysis to small man-made reservoirs, further exploration of the relationship between land use/cover patterns, and turbidity levels, and extension of LANDSAT MSS data in watershed management. Most importantly, this research exposed some of the limitations in using LANDSAT MSS data to study relationships between land use/cover patterns and turbidity levels. / Master of Science

South Africa’s growing elephant populations are concentrated in relatively small enclosed protected areas resulting in the over utilisation of the available food sources. Elephants and other herbivores as well as other natural disturbances such as fires and droughts play an important role in maintaining savannah environments. When these disturbances become too concentrated in a particular area the vegetation composition may be negatively affected. Excessive damage to the vegetation would result from exceeding the capacity of a protected area to provide food resources. The effect of the 120 elephants on the vegetation of Welgevonden Private Game Reserve, is not known. The rugged terrain of this reserve makes it a difficult, time consuming and labour intensive exercise to conduct ground studies. Satellite images can be used as a monitoring tool for vegetation change and improve the quantity and quality of environmental data to be collected significantly, allowing more informed management decision-making. This study evaluated the use of satellite imagery for monitoring elephant induced vegetation change on Welgevonden Private Game Reserve. The LANDSAT Thematic Mapper multispectral images, acquired at two yearly intervals from 1993 until 2007 were used. However, no suitable images were available for the years 1997, 2001 and 2003. A series of vegetation change maps was produced and the distribution of water sources and fire occurrences mapped. The areas of change were then correlated with the spatial distribution of water points and fire occurances, with uncorrelated areas of change. This was analysed using large animal population trends, weather data and management practices. On the visual comparison of the vegetation maps, it was seen that over this time period there was some decrease and thinning of woodland, but the most notable change was the increase of open woodland and decrease in grasslands. Using only the digital change detection for the period 1993 to 2007, a general increase in vegetation cover is seen. But this generalisation is misleading, since comparing the digital change detection to the vegetation maps indicates that while vegetation cover may have increased, significant changes occurred in the vegetation types. Most of the areas of significant change that were identified showed a strong positive correlation with burnt areas. The distribution of the water sources could not be directly linked to the vegetation change although rainfall fluctuations seemed to have accelerated vegetation changes. Years with high game counts, such as 1999, also coincide with very low rainfall making it difficult to differentiate between the effects of heavy utilisation of vegetation and low rainfall. Furthermore, many of the initial vegetation changes could be the result of land use changes due to the introduction of browsers, selective grazers and elephants that allow for more natural utilisation of the vegetation. Remote sensing makes it possible to successfully track changes in vegetation and identify areas of potential elephant induced vegetation change. Vegetation changes caused by disturbances, such as fire and anthropogenic activities, can be accounted for but it is not possible to conclude with a high level of certainty that the further changes seen are solely a result of elephant damage. Further work is required to reliably isolate elephant induced vegetation changes, as well as to establish the effects these changes have on the ecosystem as a whole. / Environmental Sciences / (M. Sc. (Environmetal Sciences))

Savannahs regulate an agro-ecosystem crucial for the production of domestic livestock, one of the main sources of income worldwide as well as in South African rural communities. Nevertheless, globally these ecosystem functions are threatened by intense human exploitation, inappropriate land use and environmental changes. Leaf area index (LAI) defined as one half the total green leaf area per unit ground surface area, is an inventory of the plant green leaves that defines the actual size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data could serve as an indicator of rangeland productivity. Consequently, the accurate and rapid estimation of LAI is a key requirement for farmers and policy makers to devise sustainable management strategies for rangeland resources. In this study, the main focus was to assess the utility and the accuracy of the PROSAILH radiative transfer model (RTM) to estimate LAI in the South African rangeland on the recently launched Landsat 8 sensor data. The Landsat 8 sensor has been a promising sensor for estimating grassland LAI as compared to its predecessors Landsat 5 to 7 sensors because of its increased radiometric resolution. For this purpose, two PROSAIL inversion methods and semi- empirical methods such as Normalized difference vegetation index (NDVI) were utilized to estimate LAI. The results showed that physically based approaches surpassed empirical approach with highest accuracy yielded by artificial neural network (ANN) inversion approach (RMSE=0.138), in contrast to the Look-Up Table (LUT) approach (RMSE=0.265). In conclusion, the results of this study proved that PROSAIL RTM approach on Landsat 8 data could be utilized to accurately estimate LAI at regional scale which could aid in rapid assessment and monitoring of the rangeland resources. / Environmental Sciences / M. Sc. (Environmental Science)

Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Anthropological influences are threatening the state of many savanna ecosystems in most rural landscapes around the world. Effective monitoring and management of these landscapes requires up to date maps and data on the state of the environment. Degradation data over a range of scales is often not readily available due to a lack of financial resources, time and technical capabilities. The aim of this research was to use a medium resolution multispectral SPOT 5 image from 2010 and Landsat 8 images from 2014 to map habitat intactness in the Bushbuckridge and Kruger National Park (KNP) region. The images were pre-processed and segmented into meaningful image objects using an object based image analysis (OBIA) approach. Five image derivatives namely: brightness, compactness, NIR standard deviation, area and the normalised difference vegetation index (NDVI) were evaluated for their capability to model habitat intactness. A habitat intactness index was generated by combining the five derivatives and rescaling them to a data range of 0 to 10, with 0 representing completely transformed areas, 10 being undisturbed natural vegetation. Field data were collected in October 2014 using a field assessment form consisting of 10 questions related to ecosystem state, in order to facilitate comparisons with the remote sensing habitat intactness index. Both satellite data sets yielded low overall accuracies below 30%. The results were improved by applying a correction factor to the reference data. The results significantly improved with SPOT 5 producing the highest overall accuracy of 62.6%. The Landsat 8 image for May 2014 achieved an improved accuracy of 60.2%. The SPOT 5 results showed to be a better predictor of habitat intactness as it assigned natural vegetation with better accuracy, while Landsat 8 correctly assigned mostly degraded areas. These findings suggest that the method was not easily transferable between the different satellite sensors in this savanna landscape, with a high occurrence of forest plantations and rural settlements too. These areas caused high omission errors in the reference data, resulting in the moderate overall accuracies obtained. It is recommended that these sites be clipped out of the analysis in order to obtain acceptable accuracies for non-transformed areas. The study nevertheless demonstrated that the habitat intactness index maps derived can be a useful data source for mapping general patterns of degradation especially on a regional scale. Therefore, the methods tested in this study can be integrated in habitat mapping projects for effective conservation planning. / AFRIKAANSE OPSOMMING: Antropologiese invloede bedreig die toestand van savanna-ekostelsels in die meeste landelike landskappe regoor die wêreld. Doeltreffende monitering en bestuur van hierdie landskappe vereis op datum kaarte en inligting oor die toestand van die omgewing. Agteruitgangsdata van verskillende skale is dikwels nie geredelik beskikbaar nie weens 'n gebrek aan finansiële hulpbronne, tyd en tegniese vermoëns. Die doel van hierdie navorsing was om ‘n hoë resolusie multispektrale SPOT 5 beeld van 2010 en Landsat 8 beelde van 2014 te gebruik om die habitatongeskondenheid in die Bushbuckridge en Kruger Nasionale Park (KNP) streek te karteer. Die beelde is voorverwerk en gesegmenteer om sinvolle beeldvoorwerpe te skep deur die gebruik van ‘n voorwerp gebaseerde beeldanalise (OBIA) benadering. Vyf beeldafgeleides naamlik: helderheid, kompaktheid, NIR standaardafwyking, area en die genormaliseerde verskil plantegroei-indeks (NDVI) is geëvalueer vir hul vermoë om habitat ongeskondenheid te modelleer. ‘n Habitatongeskondenheidsindeks is gegenereer deur die kombinasie van die vyf afgeleides wat herskaal is na 'n datareeks van 0 tot 10, met 0 om totaal getransformeerde gebiede te verteenwoordig en 10 om ongestoorde natuurlike plantegroei voor te stel. Velddata is versamel in Oktober 2014 met gebruik van 'n veldassesseringsvorm, bestaande uit 10 vrae wat verband hou met die toestand van die ekostelsel, om vergelykings met die afstandswaarneming habitatongeskondenheidsindeks te fasiliteer. Beide satellietdatastelle het lae algehele akkuraatheid onder 30% opgelewer. Die resultate is deur die toepassing van 'n regstellingsfaktor tot die verwysing data verbeter. Die resultate het aansienlik verbeter met SPOT 5 wat die hoogste algehele akkuraatheid van 62.6% gelewer het. Die Landsat 8 beeld vir Mei 2014 bereik 'n verbeterde akkuraatheid van 60.2%. Die SPOT 5 resultate het geblyk om ‘n beter voorspeller van habitatongeskondenheid te wees as gevolg van ‘n beter akkuraatheid vir natuurlike plantegroei, terwyl Landsat meestal gedegradeerde gebiede kon voorspel. Hierdie bevindinge dui daarop dat die metode nie maklik oordraagbaar was tussen die verskillende satelliet sensors in hierdie savanna landskap nie, veral as gevolg van ‘n hoë voorkoms van bosbouplantasies en landelike nedersettings. Hierdie gebiede veroorsaak hoë weglatingsfoute in die verwysing data, wat lei tot gematigde algehele akkuraatheid. Dit word aanbeveel dat hierdie areas gemasker word tydens die ontleding om aanvaarbare akkuraatheid te verkry vir nie-getransformeerde gebiede. Nogtans het die studie getoon dat die afgeleide habitatongeskondenheidsindekskaarte ‘n nuttige bron van data kan wees vir die kartering van algemene patrone van agteruitgang, veral op 'n plaaslike skaal. Daarom kan die getoetsde metodes in die studie in habitatkarteringsprojekte vir doeltreffende bewaring beplanning geïntegreer word. Stellenbosch University https://scholar.sun.ac.za

South Africa’s growing elephant populations are concentrated in relatively small enclosed protected areas resulting in the over utilisation of the available food sources. Elephants and other herbivores as well as other natural disturbances such as fires and droughts play an important role in maintaining savannah environments. When these disturbances become too concentrated in a particular area the vegetation composition may be negatively affected. Excessive damage to the vegetation would result from exceeding the capacity of a protected area to provide food resources. The effect of the 120 elephants on the vegetation of Welgevonden Private Game Reserve, is not known. The rugged terrain of this reserve makes it a difficult, time consuming and labour intensive exercise to conduct ground studies. Satellite images can be used as a monitoring tool for vegetation change and improve the quantity and quality of environmental data to be collected significantly, allowing more informed management decision-making. This study evaluated the use of satellite imagery for monitoring elephant induced vegetation change on Welgevonden Private Game Reserve. The LANDSAT Thematic Mapper multispectral images, acquired at two yearly intervals from 1993 until 2007 were used. However, no suitable images were available for the years 1997, 2001 and 2003. A series of vegetation change maps was produced and the distribution of water sources and fire occurrences mapped. The areas of change were then correlated with the spatial distribution of water points and fire occurances, with uncorrelated areas of change. This was analysed using large animal population trends, weather data and management practices. On the visual comparison of the vegetation maps, it was seen that over this time period there was some decrease and thinning of woodland, but the most notable change was the increase of open woodland and decrease in grasslands. Using only the digital change detection for the period 1993 to 2007, a general increase in vegetation cover is seen. But this generalisation is misleading, since comparing the digital change detection to the vegetation maps indicates that while vegetation cover may have increased, significant changes occurred in the vegetation types. Most of the areas of significant change that were identified showed a strong positive correlation with burnt areas. The distribution of the water sources could not be directly linked to the vegetation change although rainfall fluctuations seemed to have accelerated vegetation changes. Years with high game counts, such as 1999, also coincide with very low rainfall making it difficult to differentiate between the effects of heavy utilisation of vegetation and low rainfall. Furthermore, many of the initial vegetation changes could be the result of land use changes due to the introduction of browsers, selective grazers and elephants that allow for more natural utilisation of the vegetation. Remote sensing makes it possible to successfully track changes in vegetation and identify areas of potential elephant induced vegetation change. Vegetation changes caused by disturbances, such as fire and anthropogenic activities, can be accounted for but it is not possible to conclude with a high level of certainty that the further changes seen are solely a result of elephant damage. Further work is required to reliably isolate elephant induced vegetation changes, as well as to establish the effects these changes have on the ecosystem as a whole. / Environmental Sciences / (M. Sc. (Environmetal Sciences))

Savannahs regulate an agro-ecosystem crucial for the production of domestic livestock, one of the main sources of income worldwide as well as in South African rural communities. Nevertheless, globally these ecosystem functions are threatened by intense human exploitation, inappropriate land use and environmental changes. Leaf area index (LAI) defined as one half the total green leaf area per unit ground surface area, is an inventory of the plant green leaves that defines the actual size of the interface between the vegetation and the atmosphere. Thus, LAI spatial data could serve as an indicator of rangeland productivity. Consequently, the accurate and rapid estimation of LAI is a key requirement for farmers and policy makers to devise sustainable management strategies for rangeland resources. In this study, the main focus was to assess the utility and the accuracy of the PROSAILH radiative transfer model (RTM) to estimate LAI in the South African rangeland on the recently launched Landsat 8 sensor data. The Landsat 8 sensor has been a promising sensor for estimating grassland LAI as compared to its predecessors Landsat 5 to 7 sensors because of its increased radiometric resolution. For this purpose, two PROSAIL inversion methods and semi- empirical methods such as Normalized difference vegetation index (NDVI) were utilized to estimate LAI. The results showed that physically based approaches surpassed empirical approach with highest accuracy yielded by artificial neural network (ANN) inversion approach (RMSE=0.138), in contrast to the Look-Up Table (LUT) approach (RMSE=0.265). In conclusion, the results of this study proved that PROSAIL RTM approach on Landsat 8 data could be utilized to accurately estimate LAI at regional scale which could aid in rapid assessment and monitoring of the rangeland resources. / Environmental Sciences / M. Sc. (Environmental Science)

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-05-10T18:06:15Z No. of bitstreams: 1 WOSLLEY SIDNEY NOGUEIRA DE OLIVEIRA - DISSERTAÇÃO PPGSA ACADÊMICO 2018..pdf: 7059892 bytes, checksum: 1ab51771320e5bbd6c88d3c01b4b7aeb (MD5) / Made available in DSpace on 2018-05-10T18:06:15Z (GMT). No. of bitstreams: 1 WOSLLEY SIDNEY NOGUEIRA DE OLIVEIRA - DISSERTAÇÃO PPGSA ACADÊMICO 2018..pdf: 7059892 bytes, checksum: 1ab51771320e5bbd6c88d3c01b4b7aeb (MD5) Previous issue date: 2018-02-22 / Os perímetros irrigados implantados no Estado da Paraíba são considerados uma alternativa econômica bastante rentável, promove a geração de empregos e aumenta a disponibilidade de alimentos. Devido ao manejo inadequado do solo e da água, isso têm causado perdas na qualidade do solo desses perímetros, degradando-os principalmente por salinização. O sensoriamento remoto é uma alternativa tecnológica de baixo custo, boa frequência temporal e possui a capacidade de mapear áreas em processo de desertificação. Essa pesquisa têm por objetivo identificar possíveis áreas afetadas por sais no Perímetro Irrigado de São Gonçalo (PISG), Sousa- PB, por meio de técnicas de sensoriamento remoto. Para esse estudo foi utilizado imagens do satélite LANDSAT 8/OLI (média resolução espacial), órbita 216 / ponto 65 da data de 23/11/2016; imagem do software Google Earth Pro® da data de 29/02/2016 para servir como imagem auxiliar e registros fotográficos das áreas in loco. Realizou-se a técnica de classificação supervisionada, utilizando o SCP (semi- automatic plugin) no software QGIS (Quantum Gis). A aferição da qualidade da classificação se deu por meio da validação cruzada, utilizando de parâmetros estatísticos como a exatidão do produtor (EP), exatidão do usuário (EU), exatidão global (EG) e índice Kappa. A classe área supostamente salinizada (ASS) apresentou EP e EU de 89.15% e 88.88%, respectivamente. O índice Kappa resultou em um valor de 0.8684, a classe ASS foi classificada como sendo de qualidade excelente. A qualidade geral da classificação é avaliada tanto pela EG que apresentou um valor de 0.9350 como pelo índice Kappa geral com valor de 0.9252, sendo valores que representam uma classificação de qualidade excelente. A classe ASS apresentou os maiores valores mínimos e máximos de fator de refletância em todas as bandas da imagem, destacando a banda 6 de valores 0.47 e 0.67, respectivamente. O valor da área classificada como sendo da classe ASS foi de 1736.75 hectares, 31% da área total do PISG. As imagens analisadas possibilitaram discriminar áreas salinizadas e não salinizadas mediante as diferenças de tonalidade e de refletância. As imagens analisadas com o plugin SCP possibilitaram a realização de um mapa de classificação supervisionada, indicando a variabilidade espacial das áreas propícias ao processo de salinização. No entanto, recomenda- se a análise dos parâmetros físicos e químicos do solo dessas áreas para o aumento da confiabilidade na qualidade desse tipo de mapeamento. / The irrigated perimeters implemented in the State of Paraiba are considered a costeffective alternative quite profitable, promotes the generation of jobs and increases the availability of food. Due to inadequate management of soil and water, that have caused losses in soil quality of these perimeters, degrading them mainly by salinization. Remote sensing is an alternative low-cost technology, good temporal and frequency has the ability to map areas in process of desertification. This research aim to identify potential areas affected by salts in the irrigated perimeter of São Gonçalo (PISG), Sousa-PB, through remote sensing techniques. For this study we used LANDSAT satellite images 8/OLI (average spatial resolution), 216/orbit point 65 of 07/11/2016 date; image of the Google Earth Pro software® from date of 29/02/2016 to serve as auxiliary image and photographic records of the areas on the spot. The supervised classification technique, using the SCP (semi-automatic plugin) in software QGIS (Quantum Gis). The measurement of the quality of the classification took place by means of cross-validation, using statistical parameters such as the accuracy of the producer (EP), accuracy of the user (EU), global (EG) accuracy and Kappa index. The area class supposedly salinated (.ASS) presented EP and I of 89.15% and 88.88%, respectively. The Kappa index resulted in a value of .ASS class 0.8684 was classified as being of excellent quality. The overall quality of the classification is assessed both by EG who presented a 0.9350 value as the Kappa index 0.9252 valued General, being values that represent a rating of excellent quality. The class ASS presented the largest minimum and maximum values of reflectance factor in all the bands in the image, highlighting the band 6 0.47 values and 0.67, respectively. The value of the area classified as being of .ASS class was 1736.75 acres, 31% of the total area of the PISG. The images reviewed discriminate salinated areas and not allowed saline through the variations of shade and reflectance. The images analyzed with the SCP plugin enabled the creation of a map of supervised classification, indicating the spatial variability of the areas prone to salinization process. However, it is recommended that the analysis of the physical and chemical soil parameters of these areas for increased reliability in the quality of this type of mapping.

Indiana University-Purdue University Indianapolis (IUPUI) / Over the past two decades, northern parts of the United States have experienced extreme heat conditions. Some of the notable heat wave impacts have occurred in Chicago in 1995 with over 600 reported deaths and in Philadelphia in 1993 with over 180 reported deaths. The distribution of extreme heat events in Indianapolis has varied since the year 2000. The Urban Heat Island effect has caused the temperatures to rise unusually high during the summer months. Although the number of reported deaths in Indianapolis is smaller when compared to Chicago and Philadelphia, the heat wave in the year 2010 affected primarily the vulnerable population comprised of the elderly and the lower socio-economic groups. Studying the spatial distribution of high temperatures in the vulnerable areas helps determine not only the extent of the heat affected areas, but also to devise strategies and methods to plan, mitigate, and tackle extreme heat. In addition, examining spatial patterns of vulnerability can aid in development of a heat warning system to alert the populations at risk during extreme heat events. This study focuses on the qualitative and quantitative methods used to measure extreme heat events. Land surface temperatures obtained from the Landsat TM images provide useful means by which the spatial distribution of temperatures can be studied in relation to the temporal changes and socioeconomic vulnerability. The percentile method used, helps to determine the vulnerable areas and their extents. The maximum temperatures measured using LST conversion of the original digital number values of the Landsat TM images is reliable in terms of identifying the heat-affected regions.