This study evaluated habitat parameters for the red-cockaded woodpecker (RCW; Picoides borealis) on three tracts in Hoke County, North Carolina. Multi-spectral imagery was used to classify shadow, non-vegetation, herbaceous, hardwoods, and loblolly and longleaf pine trees. Field data were collected for image classification training and validation. Overall classification accuracy for separating hardwood from pine trees, was 80.8%. When separating longleaf (Pinus palustris Mill.) and loblolly (Pinus taeda L.) pine from hardwoods the accuracy was 73.7%. Field-based height/diameter relationships were applied to LiDAR-identified trees to predict diameter classes. Due to differences in management regimes and site conditions, each tract had different majority pine diameter classes. Average height, diameter, basal area, and stem density per plot were reported from matched, unmatched, and total LiDAR trees to field trees. Differences between the height, diameter, basal area, and stem density values occurred between the matched and unmatched LiDAR- and field-identified trees.

Les zones humides, à l’interface entre terre et eau, sont des milieux riches et diversifiés, aux fonctions et valeurs multiples aujourd’hui largement reconnues. Face à la sensibilité grandissante des organisations gouvernementales, régionales et du public aux effets néfastes, directs ou indirects, de la régression, voire dans certains cas de la disparition des zones humides, l’inventaire, la délimitation, mais aussi la caractérisation et le suivi de ces milieux sont devenus une priorité. Si leur délimitation est aujourd’hui opérationnelle, l’évaluation de leurs fonctions n’a été opérée que sur des sites de quelques hectares, alors qu’il est nécessaire d’évaluer l’état fonctionnel des zones humides sur des territoires plus étendus pour les gérer. Les objectifs de cette thèse sont de développer une méthode permettant de spatialiser les fonctions des zones humides à l’échelle de territoires d’une centaine de Km² au minimum, d’évaluer des données de télédétection optiques à très haute résolution spatiale afin de produire des indicateurs de l’état fonctionnel des zones humides, et d’évaluer l’impact de changements d’occupation des sols sur ces fonctions. Pour cela, la démarche FAP a été adaptée et appliquée sur deux sites de 130 et 650 km² localisés en Bretagne et en Dordogne. Après avoir délimités et caractérisés les zones humides à partir de données de télédétection, des indicateurs spatialisés dérivés de ces données ont été utilisés pour évaluer des fonctions hydrologiques, biogéochimiques et écologiques. L’évolution de ces fonctions a ensuite été simulée selon différents scénarios de changements d’occupation des sols. Les résultats montrent l’intérêt des données de télédétection, en particulier LiDAR, pour caractériser avec précision la micro-topographie, le réseau hydrographique et la végétation des zones humides. Ces données permettent de cartographier le potentiel fonctionnel des zones humides à différentes échelles allant de la parcelle à l’ensemble du site, et ce pour différentes fonctions. La simulation des changements d’occupation des sols à l’horizon 2030 et l’évaluation de ceux-ci sur les fonctions des zones humides peuvent constituer un outil d’aide à la gestion de ces milieux. / Interfacing between land and water systems, wetlands perform multiple functions and values that are now widely recognized. Inventory, delineation, but also characterization and monitoring of wetlands are now a priority to address the regression and in some cases the loss of these ecosystems. While wetland delineation is widely performed, the assessment of their functions has been only made on small sites of several hectares, whereas it is necessary to evaluate wetland functional status on larger areas to manage them. The objectives of this thesis are to develop a method to map wetland functions on areas greater than a hundred square kilometers, evaluate optical remote sensing data with very high spatial resolution to produce indicators of functional status of wetlands, and assess the impact of land use change on these functions. For this, the FAP approach has been adapted and applied to two sites located in Brittany and Dordogne. Once having defined and characterized wetlands from remotely sensed data, the spatial indicators derived from these data were used to evaluate hydrological, biogeochemical and ecological wetland functions. The evolution of these functions was then simulated under different scenarios of land use changes. The results show the usefulness of remotely sensed data, especially LiDAR data, to accurately characterize the micro-topography, drainage network and vegetation of wetlands. The functional potential of wetlands can therefore be mapped at different scales from the plot to the whole site for various functions. The simulation of land-use changes for the period 2000–2030 and the evaluation of their impact on wetland functions can be a tool for managing these environments

O mapeamento da vegetação urbana tem se restringido às áreas públicas ou a área total, não deixando de forma evidente a contribuição dos quintais privados para a vegetação urbana. Alguns trabalhos indicam que essas áreas teriam potencial para mitigar os impactos causados pela urbanização. Neste trabalho testaram-se diversos métodos para tentar mapear as áreas de quintais permeáveis com o intuito de desenvolver um método que possa auxiliar o planejamento urbano e na investigação da contribuição dessas áreas para a vegetação urbana. O trabalho conseguiu resultados com acurácia bastante elevada (Kappa = 0,9553) e apresentou técnicas inovadoras de filtragem e classificação, destacando-se o aplicativo desenvolvido para realizar a filtragem Kuwahara que demonstrou melhorar bastante os resultados de classificações por abordagem de pixels. As classificações de abordagem por pixel levaram a resultados estatisticamente até melhores que a classificação orientada a objetos, no entanto, a apresentação visual dos resultados da classificação orientada a objetos é superior pela redução do ruído. / Urban vegetation mapping has been restricted to the public areas or the total area, not showing the contribution of the private yards to the urban greening. Some studies indicate that these areas have potential to mitigate the impacts caused by the urbanization. In this work, many methods for mapping pervious gardens were tested aiming the development of a method that could aid urban planning and investigating the contribution of these areas to the urban greening. This work achieved highly accurate results (kappa = 0.9553) and presented novel techniques for filtering and classification. We highlight the development of a simple application to perform the Kuwahara filter, which improved the results of the classification by pixel approach. The classification algorithms by pixel approach resulted in statistically more accurate products, although visually the results presented by object-oriented approach is closer to landscape features.

O mapeamento da vegetação urbana tem se restringido às áreas públicas ou a área total, não deixando de forma evidente a contribuição dos quintais privados para a vegetação urbana. Alguns trabalhos indicam que essas áreas teriam potencial para mitigar os impactos causados pela urbanização. Neste trabalho testaram-se diversos métodos para tentar mapear as áreas de quintais permeáveis com o intuito de desenvolver um método que possa auxiliar o planejamento urbano e na investigação da contribuição dessas áreas para a vegetação urbana. O trabalho conseguiu resultados com acurácia bastante elevada (Kappa = 0,9553) e apresentou técnicas inovadoras de filtragem e classificação, destacando-se o aplicativo desenvolvido para realizar a filtragem Kuwahara que demonstrou melhorar bastante os resultados de classificações por abordagem de pixels. As classificações de abordagem por pixel levaram a resultados estatisticamente até melhores que a classificação orientada a objetos, no entanto, a apresentação visual dos resultados da classificação orientada a objetos é superior pela redução do ruído. / Urban vegetation mapping has been restricted to the public areas or the total area, not showing the contribution of the private yards to the urban greening. Some studies indicate that these areas have potential to mitigate the impacts caused by the urbanization. In this work, many methods for mapping pervious gardens were tested aiming the development of a method that could aid urban planning and investigating the contribution of these areas to the urban greening. This work achieved highly accurate results (kappa = 0.9553) and presented novel techniques for filtering and classification. We highlight the development of a simple application to perform the Kuwahara filter, which improved the results of the classification by pixel approach. The classification algorithms by pixel approach resulted in statistically more accurate products, although visually the results presented by object-oriented approach is closer to landscape features.

No description available.

O monitoramento, a previsão e o controle de eventos extremos, como as inundações, é imprescindível, principalmente em áreas urbanas, devido à maior densidade populacional, bens materiais, saneamento e infraestruturas envolvidos no processo. O objetivo deste estudo, consistiu em classificar de forma automática o uso e cobertura da terra em área urbana, em um ortofotomosaico com altíssima resolução espacial (16 cm), cobrindo a área do bairro centro de Lajeado (Estado do Rio Grande do Sul – Brasil) e sua posterior aplicação, com dados dos arruamentos e de um Modelo Digital de Elevação (MDE), para a estruturação de um framework automatizado, baseado em plataformas livres, com capacidade de monitorar níveis de inundações sobre essa área urbana, em escala espacial e temporal. Para a classificação do uso e cobertura da terra foram testados os classificadores por árvore de decisão Boosted C5.0, Random Forests e Classification and Regression Trees (CART). Primeiramente, foram identificadas as seguintes classes: vegetação arbórea; vegetação herbácea (gramíneas); solo exposto; sistema viário (calçamento); telhados metálicos e telhados cerâmicos claros; telhados de concreto e fibrocimento; telhados metálicos e cerâmicos escuros; e sombra. Por meio do programa eCognition foram aplicados sete níveis de segmentação do ortofotomosaico, coletadas as amostras e definidos os atributos para cada classe O treinamento para os classificadores foi realizado no programa R. Para a análise da exatidão de classificação, foram gerados pontos de checagem aleatórios, que foram comparados com as classes das três imagens classificadas, para o cálculo da matriz de erros e do índice Kappa. A imagem classificada pelo algoritmo Random Forests apresentou a maior Exatidão Global (EG = 82,20%) e Índice Kappa (K = 0,79), seguido pela imagem classificada pelo algoritmo Boosted C5.0 (EG = 80,4%; K = 0,77) e pelo CART (EG = 64,90%; K = 0,57). Já o framework foi baseado na equação de regressão fluviométrica Encantado/Lajeado. Os resultados dessa equação podem ser visualizados como mapas em uma interface WEBSIG, onde estão simuladas as áreas e infraestruturas inundadas no bairro centro de Lajeado. Foram projetados diferentes níveis históricos de inundações e esse modelo foi validado a partir da comparação dos dados simulados com os medidos de uma inundação ocorrida em 10 de outubro de 2015. O erro altimétrico obtido foi inferior a 1 m. O framework deste estudo realiza o monitoramento do nível de inundação para a área urbana de Lajeado com até 6 horas de antecedência, demonstrando a eficácia desta simulação. / The monitoring, forecasting and control of extreme events, such as floods, is essential, especially in urban settlements, due to the greater population density, material assets, sanitation and infrastructures on these areas. This work aims to classify automatically the urban land use and land cover in an orthophoto mosaic with very high spatial resolution (16 cm) covering the central district area of Lajeado (Rio Grande do Sul State - Brazil) and its subsequent application, with data of the streets and a Digital Elevation Model (MDE), for the structuring of an automated framework, based on free platforms, with capacity to monitor flood levels in this urban area, on a spatial and temporal scale. We tested the decision tree classifiers Boosted C5.0, Random Forests and Classification and Regression Trees (CART). First, the following classes of land use and land cover were identified: forest land; herbaceous land (grasses); bare soil; road system (pavement); metal roofs and clear ceramic roofs; concrete and fiber cement roofs; dark metallic and ceramic roofs; and shade. The eCognition software were used to processes seven levels of segmentation of the orthophoto mosaic, and for collecting samples and attributes from each one these classes The decision tree methods were performed in R software. For the classification accuracy assessment, we generated random check points, which were compared with the classes of the three classified images, in order to calculate the error matrix and Kappa index. The image classified by the algorithm Random Forests presented the highest Global Accuracy (GA = 82.20%) and Kappa Index ( = 0.79), followed by the image classified by the Boosted C5.0 (GA = 80.4%; = 0.77) and the CART algorithm (GA = 64.90%,  = 0.57). The Framework was based on the fluviometric regression equation Encantado/Lajeado. The results of this equation can be visualized as maps in a WEBGIS interface, where the flooded areas in the downtown neighborhood of Lajeado are simulated. Different historical flood levels were projected and this model was validated by comparing the simulated data with those measured from a flood occurred on October 10th, 2015. The altimetric error obtained was less than 1 m. The framework of this study carries out the monitoring of the flood level for the Lajeado urban area with 6 hours in advance, demonstrating the effectiveness of this simulation.

O monitoramento, a previsão e o controle de eventos extremos, como as inundações, é imprescindível, principalmente em áreas urbanas, devido à maior densidade populacional, bens materiais, saneamento e infraestruturas envolvidos no processo. O objetivo deste estudo, consistiu em classificar de forma automática o uso e cobertura da terra em área urbana, em um ortofotomosaico com altíssima resolução espacial (16 cm), cobrindo a área do bairro centro de Lajeado (Estado do Rio Grande do Sul – Brasil) e sua posterior aplicação, com dados dos arruamentos e de um Modelo Digital de Elevação (MDE), para a estruturação de um framework automatizado, baseado em plataformas livres, com capacidade de monitorar níveis de inundações sobre essa área urbana, em escala espacial e temporal. Para a classificação do uso e cobertura da terra foram testados os classificadores por árvore de decisão Boosted C5.0, Random Forests e Classification and Regression Trees (CART). Primeiramente, foram identificadas as seguintes classes: vegetação arbórea; vegetação herbácea (gramíneas); solo exposto; sistema viário (calçamento); telhados metálicos e telhados cerâmicos claros; telhados de concreto e fibrocimento; telhados metálicos e cerâmicos escuros; e sombra. Por meio do programa eCognition foram aplicados sete níveis de segmentação do ortofotomosaico, coletadas as amostras e definidos os atributos para cada classe O treinamento para os classificadores foi realizado no programa R. Para a análise da exatidão de classificação, foram gerados pontos de checagem aleatórios, que foram comparados com as classes das três imagens classificadas, para o cálculo da matriz de erros e do índice Kappa. A imagem classificada pelo algoritmo Random Forests apresentou a maior Exatidão Global (EG = 82,20%) e Índice Kappa (K = 0,79), seguido pela imagem classificada pelo algoritmo Boosted C5.0 (EG = 80,4%; K = 0,77) e pelo CART (EG = 64,90%; K = 0,57). Já o framework foi baseado na equação de regressão fluviométrica Encantado/Lajeado. Os resultados dessa equação podem ser visualizados como mapas em uma interface WEBSIG, onde estão simuladas as áreas e infraestruturas inundadas no bairro centro de Lajeado. Foram projetados diferentes níveis históricos de inundações e esse modelo foi validado a partir da comparação dos dados simulados com os medidos de uma inundação ocorrida em 10 de outubro de 2015. O erro altimétrico obtido foi inferior a 1 m. O framework deste estudo realiza o monitoramento do nível de inundação para a área urbana de Lajeado com até 6 horas de antecedência, demonstrando a eficácia desta simulação. / The monitoring, forecasting and control of extreme events, such as floods, is essential, especially in urban settlements, due to the greater population density, material assets, sanitation and infrastructures on these areas. This work aims to classify automatically the urban land use and land cover in an orthophoto mosaic with very high spatial resolution (16 cm) covering the central district area of Lajeado (Rio Grande do Sul State - Brazil) and its subsequent application, with data of the streets and a Digital Elevation Model (MDE), for the structuring of an automated framework, based on free platforms, with capacity to monitor flood levels in this urban area, on a spatial and temporal scale. We tested the decision tree classifiers Boosted C5.0, Random Forests and Classification and Regression Trees (CART). First, the following classes of land use and land cover were identified: forest land; herbaceous land (grasses); bare soil; road system (pavement); metal roofs and clear ceramic roofs; concrete and fiber cement roofs; dark metallic and ceramic roofs; and shade. The eCognition software were used to processes seven levels of segmentation of the orthophoto mosaic, and for collecting samples and attributes from each one these classes The decision tree methods were performed in R software. For the classification accuracy assessment, we generated random check points, which were compared with the classes of the three classified images, in order to calculate the error matrix and Kappa index. The image classified by the algorithm Random Forests presented the highest Global Accuracy (GA = 82.20%) and Kappa Index ( = 0.79), followed by the image classified by the Boosted C5.0 (GA = 80.4%; = 0.77) and the CART algorithm (GA = 64.90%,  = 0.57). The Framework was based on the fluviometric regression equation Encantado/Lajeado. The results of this equation can be visualized as maps in a WEBGIS interface, where the flooded areas in the downtown neighborhood of Lajeado are simulated. Different historical flood levels were projected and this model was validated by comparing the simulated data with those measured from a flood occurred on October 10th, 2015. The altimetric error obtained was less than 1 m. The framework of this study carries out the monitoring of the flood level for the Lajeado urban area with 6 hours in advance, demonstrating the effectiveness of this simulation.

O monitoramento, a previsão e o controle de eventos extremos, como as inundações, é imprescindível, principalmente em áreas urbanas, devido à maior densidade populacional, bens materiais, saneamento e infraestruturas envolvidos no processo. O objetivo deste estudo, consistiu em classificar de forma automática o uso e cobertura da terra em área urbana, em um ortofotomosaico com altíssima resolução espacial (16 cm), cobrindo a área do bairro centro de Lajeado (Estado do Rio Grande do Sul – Brasil) e sua posterior aplicação, com dados dos arruamentos e de um Modelo Digital de Elevação (MDE), para a estruturação de um framework automatizado, baseado em plataformas livres, com capacidade de monitorar níveis de inundações sobre essa área urbana, em escala espacial e temporal. Para a classificação do uso e cobertura da terra foram testados os classificadores por árvore de decisão Boosted C5.0, Random Forests e Classification and Regression Trees (CART). Primeiramente, foram identificadas as seguintes classes: vegetação arbórea; vegetação herbácea (gramíneas); solo exposto; sistema viário (calçamento); telhados metálicos e telhados cerâmicos claros; telhados de concreto e fibrocimento; telhados metálicos e cerâmicos escuros; e sombra. Por meio do programa eCognition foram aplicados sete níveis de segmentação do ortofotomosaico, coletadas as amostras e definidos os atributos para cada classe O treinamento para os classificadores foi realizado no programa R. Para a análise da exatidão de classificação, foram gerados pontos de checagem aleatórios, que foram comparados com as classes das três imagens classificadas, para o cálculo da matriz de erros e do índice Kappa. A imagem classificada pelo algoritmo Random Forests apresentou a maior Exatidão Global (EG = 82,20%) e Índice Kappa (K = 0,79), seguido pela imagem classificada pelo algoritmo Boosted C5.0 (EG = 80,4%; K = 0,77) e pelo CART (EG = 64,90%; K = 0,57). Já o framework foi baseado na equação de regressão fluviométrica Encantado/Lajeado. Os resultados dessa equação podem ser visualizados como mapas em uma interface WEBSIG, onde estão simuladas as áreas e infraestruturas inundadas no bairro centro de Lajeado. Foram projetados diferentes níveis históricos de inundações e esse modelo foi validado a partir da comparação dos dados simulados com os medidos de uma inundação ocorrida em 10 de outubro de 2015. O erro altimétrico obtido foi inferior a 1 m. O framework deste estudo realiza o monitoramento do nível de inundação para a área urbana de Lajeado com até 6 horas de antecedência, demonstrando a eficácia desta simulação. / The monitoring, forecasting and control of extreme events, such as floods, is essential, especially in urban settlements, due to the greater population density, material assets, sanitation and infrastructures on these areas. This work aims to classify automatically the urban land use and land cover in an orthophoto mosaic with very high spatial resolution (16 cm) covering the central district area of Lajeado (Rio Grande do Sul State - Brazil) and its subsequent application, with data of the streets and a Digital Elevation Model (MDE), for the structuring of an automated framework, based on free platforms, with capacity to monitor flood levels in this urban area, on a spatial and temporal scale. We tested the decision tree classifiers Boosted C5.0, Random Forests and Classification and Regression Trees (CART). First, the following classes of land use and land cover were identified: forest land; herbaceous land (grasses); bare soil; road system (pavement); metal roofs and clear ceramic roofs; concrete and fiber cement roofs; dark metallic and ceramic roofs; and shade. The eCognition software were used to processes seven levels of segmentation of the orthophoto mosaic, and for collecting samples and attributes from each one these classes The decision tree methods were performed in R software. For the classification accuracy assessment, we generated random check points, which were compared with the classes of the three classified images, in order to calculate the error matrix and Kappa index. The image classified by the algorithm Random Forests presented the highest Global Accuracy (GA = 82.20%) and Kappa Index ( = 0.79), followed by the image classified by the Boosted C5.0 (GA = 80.4%; = 0.77) and the CART algorithm (GA = 64.90%,  = 0.57). The Framework was based on the fluviometric regression equation Encantado/Lajeado. The results of this equation can be visualized as maps in a WEBGIS interface, where the flooded areas in the downtown neighborhood of Lajeado are simulated. Different historical flood levels were projected and this model was validated by comparing the simulated data with those measured from a flood occurred on October 10th, 2015. The altimetric error obtained was less than 1 m. The framework of this study carries out the monitoring of the flood level for the Lajeado urban area with 6 hours in advance, demonstrating the effectiveness of this simulation.

La télédétection est une technique de plus en plus utilisée dans le domaine fluvial, et si des images acquises à haute, voire très haute altitude via des vecteurs aéroportés et satellites sont traditionnellement utilisées, l’imagerie in situ (ou « imagerie de terrain ») constitue un outil complémentaire qui présente de nombreux avantages (facilité de mise en place, coûts réduits, point de vue oblique, etc.). Les possibilités de programmer les prises de vue fixes à des fréquences relativement élevées (de quelques dixièmes de secondes dans le cas de vidéos, à quelques heures par exemple) mais aussi de pouvoir observer les évènements au moment où ils surviennent, est sans commune mesure avec les contraintes associées à l’acquisition de l’imagerie « classique » (dont les plus hautes fréquences s’élèvent à quelques jours). Cela permet de produire des jeux de données conséquents, dont l’analyse automatisée est nécessaire et constitue l’un des enjeux de cette thèse. Le traitement et l’analyse de jeux de données produits sur cinq sites test français et québécois ont permis de mieux évaluer les potentialités et les limites liées à l’utilisation de l’imagerie in situ dans le cadre de l’étude des milieux fluviaux. La définition des conditions optimales d’installation des capteurs en vue de l’acquisition des données constitue la première étape d’une démarche globale, présentée sous forme de modules optionnels, à prendre en compte selon les objectifs de l’étude. L’extraction de l’information radiométrique, puis le traitement statistique du signal ont été évalués dans plusieurs situations tests. La classification orientée-objet avec apprentissage supervisé des images a notamment été expérimentée via des random forests. L’exploitation des jeux de données repose principalement sur l’analyse de séries temporelles à haute fréquence. Cette thèse expose les forces et les faiblesses de cette approche et illustre des usages potentiels pour la compréhension des dynamiques fluviales. Ainsi, l’imagerie in situ est un très bon outil pour l’étude et l’analyse des cours d’eau, car elle permet la mesure de différents types de temporalités régissant les processus biophysiques observés. Cependant, il est nécessaire d’optimiser la qualité des images produites et notamment de limiter au maximum l’angle de vue du capteur, ou la variabilité des conditions de luminosité entre clichés, afin de produire des séries temporelles pleinement exploitables. / Remote sensing is more and more used in river sciences, mainly using satellite and airborne imagery. Ground imagery constitutes a complementary tool which presents numerous advantages for the study of rivers. For example, it is easy to set up; costs are limited; it allows an oblique angle; etc. It also presents the possibility to set up the triggering with very high frequency, ranging, for instance, from a few seconds to a few hours. The possibility to monitor events at the instant they occur makes ground imagery extremely advantageous compared to aerial or spatial imagery (whose highest acquisition frequency corresponds to a few days). Such frequencies produce huge datasets, which require automated analyses. This is one of the challenges addressed in this thesis. Processing and analysis of data acquired at five study sites located in France and Québec, Canada, facilitated the evaluation of ground imagery potentials, as well as its limitations with respect to the study of fluvial systems. The identification of optimal conditions to set up the cameras and to acquire images is the first step of a global approach, presented as a chain of optional modules. Each one is to be taken into account according to the objectives of the study. The extraction of radiometric information and the subsequent statistical analysis of the signal were tested in several situations. In particular, random forests were applied, as a supervised object-oriented classification method. The datasets were principally exploited using high frequency time series analyses, which allowed demonstrating strengths and weaknesses of this approach, as well as some potential applications. Ground imagery is a powerful tool to monitor fluvial systems, as it facilitates the definition of various kinds of time characteristics linked with fluvial biophysical processes. However, it is necessary to optimize the quality of the data produced. In particular, it is necessary to minimize the acquisition angle and to limit the variability of luminosity conditions between shots in order to acquire fully exploitable datasets.

Thesis (MSc)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: A land cover classification showing the landscape structure of a given area is necessary to make appropriate measures for environmental planning. The environmental impacts from insensitive human activities have led to a severe loss of biodiversity in the Cape Floristic Region over time. The natural biodiversity of the North-West Sandveld in particular, has suffered severe loss and a high level of fragmentation. The rapid growth of certain agricultural practices in the Sandveld has led not only to loss of biodiversity, but the secondary effects of excessive water extraction, invasive alien fauna and flora species and harmful run-off from toxic herbicides and pesticides. To plan effectively, an accurate map of a suitable resolution needs to be created to effectively display spatial information. The primary aim was to demonstrate that a semi-automated fine-scale, land cover classification using object-oriented image analysis is possible for a large local area to examine the environmental issues pertaining to the Sandveld. Towards this aim, a model to classify land cover of the study area was developed and its effectiveness analysed and interpreted. To meet these requirements, pre-processed SPOT 5 satellite imagery was used to digitize certain classes and to generate frame, border and Normalized Differentiation Vegetation Index (NDVI) layers for the object-oriented classification in eCognition. The accuracy of the results was determined using the Kappa coefficient which gave an accuracy level of 70%. The environmental impacts were determined after area calculations were done on each class. The results showed that the natural areas still made up the greatest percentage of the Sandveld but that it is highly fragmented, especially along the coast and many areas, although left in a natural state, were being overgrazed by livestock. The temporary irrigated, temporary non-irrigated strip agriculture and permanent agriculture classes made up the most of the remainder of the areas and had the largest impact on the Sandveld‟s biodiversity. For the biodiversity of the Sandveld to remain intact, a balance between enforcing the law and encouraging inhabitants of the Sandveld to encourage more environmentally balanced practices needs to be created. Stewardship programmes and education will greatly enhance the effectiveness of any conservation efforts. / AFRIKAANSE OPSOMMING:'n Landbedekkingsklassifikasie wat die landskapstruktuur van 'n gegewe gebied aandui, is noodsaaklik om gepaste omgewingsbeplanning toe te pas. Die effek van intensiewe menslike aktiwiteite op die omgewing oor 'n lang tydperk, het gelei tot die verlies van biodiversiteit in die Kaapse Blommestreek. Die natuurlike biodiversiteit van veral die Noordwes Sandveld is deur ernstige verliese, asook 'n vlak van fragmentasie beïnvloed. Behalwe dat die vinnige toename van sekere landboupraktyke in die Sandveld gelei het tot die verlies van biodiversiteit. Sekondêre faktore soos die oormatige water-ontrekking, indringerspesies van fauna en flora en die afloop van skadelike onkruiddoder en pes-weerende middels in rivierstelsels is ook verantwoordelik vir verlies van biodiversiteit. Om effektiewe beplanning moontlik te maak, is die skepping van 'n akkurate kaart met 'n geskikte resolusie wat die ruimtelike inligting effektief voorstel, nodig. Die hoof doel van hierdie studie was om 'n semi-geoutomatiseerde fynskaal landbedekkingsklassifikasie in 'n groot plaaslike gebied wat die omgewingskwessies rakende die Sandveld te bestudeer en demonstreer, deur gebruik te maak van objek-gerigte beeldanalise. Die effektiwiteit van 'n model wat ontwikkel is om die studiegebied te klassifiseer, was ontleed en geïnterpreteer. Om hierdie vereistes te bevredig, is voorverwerkte SPOT 5 satellietbeelde gebruik om sekere klasse te versyfer, asook om raam-, grens- en genormaliseerde plantegroei differensiasie indekslae vir beeldgerigte klassifikasie in eCognition te skep. Die akkuraatheid van die resultate was bepaal deur die Kappa-koëffisiënt wat 'n akkuraatheidsvlak van 70% gelewer het. Die omgewingsimpakte was bepaal deur opperlakteberekeninge vir elke klas te maak. Die resultate dui aan dat natuurlike gebiede steeds die grootste persentasie van die Sandveld beslaan, maar dat dit hoogs gefragmenteerd is, veral langs die kuslyn en dat baie gebiede, wat wel steeds in 'n natuurlike toestand is, oorbewei word deur vee. Die tydelike spilpuntbesproeide landbougebiede, tydelike nie-besproeide strooklandbou en permanente landbouklasse, beslaan die meeste van die oorblywende landbedekking van die Sandveld. Hierdie klasse het ook die grootste impak op die studiegebied se biodiversiteit. Om die biodiversiteit van die Sandveld te behou, moet 'n balans tussen die afdwing van wetgewing en die aanmoedig van meer omgewingsgebalanseerde praktyke in die Sandveld gevind word. Rentmeesterskap programme en opvoeding sal die effektiwiteit van enige bewaringspogings aansienlik verbeter.