This study determines and predicts multi-temporal Land-Use-Land-Cover Change (LULC) in a peripheral urban landscape over a 22 year period in relation to the study area‘s greenery. A change detection analysis using post classification Maximum Likelihood algorithm on three multispectral SPOT-4 images was used to determine land-cover transformation. To predict future land coverage, a Land-Cover Change Modeler (LCM) and a Markov Chain were used. Results show that between the year 2000-2006, 2006-2011 and 2000-2011 the study area experienced varied changes in the different LULCs. Built-up areas increased by 10.08%, 3.15% and 13.23% in 2000-2006, 2006-2011, and 2000-2011 respectively. Areas covered by thicket decreased by 0.59% in 2000-2006 but increased by 0.56%, 0.07% in 2006-2011 and 2000-2011 respectively. Forest land-cover increased by 2.59% in 2000-2006, 2.82% in 2006-2011, and 5.41% in 2000-2011. Grassland declined by 8.46% and 2.64% in 2000-2006 and 2000-2011 respectively while degraded grassland declined by 3.62%, 12.45% and 16.07% in 2000-2006, 2006-2011, and 2000-2011 respectively. Projection results indicate a consistent pattern of growth or decline to those experienced between 2000-2011. This study provides insight into LULC patterns within the eThekwini metro area and offers invaluable understanding of the transformation of the urban green spaces. Key words: Land-Use-Land-Cover Change, Change detection, Land-Cover Change Modeler, Markov Chain Process, Land-Cover Change Prediction. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

In order to manage the often highly dynamic urban landscapes, it is important to map different themes from time to time. This study made use of Geographical Information System and aerial photographs to determine LULC transformation in the eastern suburbs of Pietermaritzburg in KwaZulu-Natal, South Africa. Land use land cover maps for the eastern suburbs (Copesville, Eastwood, Raisethorpe and Willowton) for the years 1989 to 2009 were generated and transformations based on twelve LULCs determined. Results in this study showed that the most significant increase were in residential (formal and informal) and industrial LULCs while the most significant decrease were recorded in the cultivated and open LULC. Generally, results in this study further show that urban LULC attributed to human influx has been at the expense of internal open green spaces and peripheral cultivated and uncultivated lands. The study concludes that aerial photographs in concert with GIS are valuable tools in mapping rapidly changing urban landscapes. / Thesis (M.Env.Dev.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.

Abstract<p>Since 1999, Very High spatial Resolution satellite data (Ikonos-2, QuickBird and OrbView-3) represent the surface of the Earth with more detail. However, information extraction by multispectral pixel-based classification proves to have become more complex owing to the internal variability increase in the land-cover units and to the weakness of spectral resolution. <p>Therefore, one possibility is to consider the internal spectral variability of land-cover classes as a valuable source of spatial information that can be used as an additional clue in characterizing and identifying land cover. Moreover, the spatial resolution gap that existed between satellite images and aerial photographs has strongly decreased, and the features used in visual interpretation transposed to digital analysis (texture, morphology and context) can be used as additional information on top of spectral features for the land cover classification.<p>The difficulty of this approach is often to transpose the visual features to digital analysis.<p>To overcome this problem region-based classification could be used. Segmentation, before classification, produces regions that are more homogeneous in themselves than with nearby regions and represent discrete objects or areas in the image. Each region becomes then a unit analysis, which makes it possible to avoid much of the structural clutter and allows to measure and use a number of features on top of spectral features. These features can be the surface, the perimeter, the compactness, the degree and kind of texture. Segmentation is one of the only methods which ensures to measure the morphological features (surface, perimeter.) and the textural features on non-arbitrary neighbourhood. In the pixel-based methods, texture is calculated with mobile windows that smooth the boundaries between discrete land cover regions and create between-class texture. This between-class texture could cause an edge-effect in the classification.<p><p>In this context, our research focuses on the potential of land cover region-based classification of VHR satellite data through the study of the object extraction capacity of segmentation processes, and through the study of the relevance of region features for classifying the land-cover classes in different kinds of Belgian landscapes; always keeping in mind the parallel with the visual interpretation which remains the reference.<p><p>Firstly, the results of the assessment of four segmentation algorithms belonging to the two main segmentation categories (contour- and region-based segmentation methods) show that the contour detection methods are sensitive to local variability, which is precisely the problem that we want to overcome. Then, a pre-processing like a filter may be used, at the risk of losing a part of the information. The “region-growing” segmentation that uses the local variability in the segmentation process appears to be the best compromise for the segmentation of different kinds of landscape.<p>Secondly, the features calculated thanks to segmentation seem to be relevant to identify some land-cover classes in urban/sub-urban and rural areas. These relevant features are of the same type as the features selected visually, which shows that the region-based classification gets close to the visual interpretation. <p>The research shows the real usefulness of region-based classification in order to classify the land cover with VHR satellite data. Even in some cases where the features calculated thanks to the segmentation prove to be useless, the region-based classification has other advantages. Working with regions instead of pixels allows to avoid the salt-and-pepper effect and makes the GIS integration easier.<p>The research also highlights some problems that are independent from the region-based classification and are recursive in VHR satellite data, like shadows and the spatial resolution weakness for identifying some land-cover classes.<p><p>Résumé<p>Depuis 1999, les données satellitaires à très haute résolution spatiale (IKONOS-2, QuickBird and OrbView-3) représentent la surface de la terre avec plus de détail. Cependant, l’extraction d’information par une classification multispectrale par pixel devient plus complexe en raison de l’augmentation de la variabilité spectrale dans les unités d’occupation du sol et du manque de résolution spectrale de ces données. Cependant, une possibilité est de considérer cette variabilité spectrale comme une information spatiale utile pouvant être utilisée comme une information complémentaire dans la caractérisation de l’occupation du sol. De plus, de part la diminution de la différence de résolution spatiale qui existait entre les photographies aériennes et les images satellitaires, les caractéristiques (attributs) utilisées en interprétation visuelle transposées à l’analyse digitale (texture, morphologie and contexte) peuvent être utilisées comme information complémentaire en plus de l’information spectrale pour la classification de l’occupation du sol.<p><p>La difficulté de cette approche est la transposition des caractéristiques visuelles à l’analyse digitale. Pour résoudre ce problème la classification par région pourrait être utilisée. La segmentation, avant la classification, produit des régions qui sont plus homogène en elles-mêmes qu’avec les régions voisines et qui représentent des objets ou des aires dans l’image. Chaque région devient alors une unité d’analyse qui permet l’élimination de l’effet « poivre et sel » et permet de mesurer et d’utiliser de nombreuses caractéristiques en plus des caractéristiques spectrales. Ces caractéristiques peuvent être la surface, le périmètre, la compacité, la texture. La segmentation est une des seules méthodes qui permet le calcul des caractéristiques morphologiques (surface, périmètre, …) et des caractéristiques texturales sur un voisinage non-arbitraire. Avec les méthodes de classification par pixel, la texture est calculée avec des fenêtres mobiles qui lissent les limites entre les régions d’occupation du sol et créent une texture interclasse. Cette texture interclasse peut alors causer un effet de bord dans le résultat de la classification.<p><p>Dans ce contexte, la recherche s’est focalisée sur l’étude du potentiel de la classification par région de l’occupation du sol avec des images satellitaires à très haute résolution spatiale. Ce potentiel a été étudié par l’intermédiaire de l’étude des capacités d’extraction d’objet de la segmentation et par l’intermédiaire de l’étude de la pertinence des caractéristiques des régions pour la classification de l’occupation du sol dans différents paysages belges tant urbains que ruraux. / Doctorat en sciences agronomiques et ingénierie biologique / info:eu-repo/semantics/nonPublished