Dans le sud tunisien, tous les périmètres irrigués de type oasis n’ont pas connu le même développement au cours de 50 dernières années. On observe ainsi depuis plusieurs décennies, une évolution des surfaces couvertes qui dans certaines régions ont plus que doublé, alors que pour d’autres elles sont restées pratiquement stables et dans certains cas, ont même connu une régression. Face aux enjeux que ces changements impliquent tant sur le plan environnemental qu’économique et social, l’élaboration d’une typologie des oasis ainsi que l’estimation de leur état sont d’une importance stratégique aux niveaux national, régional et international. Pour connaître l'état de la végétation dans ces oasis, des systèmes de surveillance de ces écosystèmes oasiens devraient être mis au point et renseignés régulièrement par des données prises sur ces surfaces cultivées. Ces données peuvent être obtenues en partie par les systèmes d’observations satellitaires à haute et à moyenne résolution spatiale, et forte répétitivité temporelle, qui par leur vision synoptique, constituent une source d’informations particulièrement adéquate. Le travail de recherche présenté ici porte sur l’exploration de méthodes développées à partir de deux types de séries temporelles d’images d’observation de la Terre : celles produites par l’expérience SPOT-5 (Take5) et le produit MOD13Q1 du capteur MODIS, respectivement à 10m et 250 m de résolution spatiale, et avec des répétitivités de 5 et 26 jours. Ces méthodes et données ont été testées sur la région du Djérid dans le but final de mettre en place un système de surveillance des oasis basé sur l'analyse des signatures temporelles à partir d’images d’observations de la Terre prises très régulièrement dans le temps.Deux démarches différentes d’analyse ont été menées pour chaque type de données, basées sur le traitement de séries temporelles d’un indice de végétation, le NDVI. La première repose sur les variations temporelles de l’activité végétale sur une courte période d’avril à septembre 2015 à travers la série d’images SPOT-5 (Take5) : la comparaison entre oasis s’est faite à l’échelle du périmètre irrigué (une oasis peut être composée de plusieurs périmètres irrigués) en utilisant la méthode statistique de classification ascendante hiérarchique. La seconde utilise une technique de décomposition temporelle d’un signal pour extraire la tendance d’une série d’images pluriannuelles à l’échelle d’un point géographique (un pixel de 250mx250m) à travers la série temporelle MOD13Q1 de 2000 à 2016.Les résultats obtenus à partir du traitement et de l’analyse de ces séries temporelles optiques ont permis de montrer qu’il est possible d’identifier les principaux types de périmètres irrigués présents dans la région de Djérid, et retrouver rétrospectivement l’histoire récente de leur développement. Ils mettent aussi en évidence le fait que les images SPOT-5 (Take5), qui préfigurent celles actuellement disponibles avec les images produites par les satellites Sentinel2, améliorent considérablement la caractérisation spatio-temporelle du fonctionnement des oasis grâce à la finesse de leur résolution spatiale et de leur répétitivité temporelle.Les résultats de cette thèse permettent de dégager de nouvelles pistes de couplage entre télédétection, données de terrain et analyses statistiques en apportant une information continue dans le temps et dans l’espace pour le suivi et la surveillance des écosystèmes oasiens. En effet avec deux capteurs tel que Sentinel2, couplé aux données historiques de MOD13Q1, il est permis désormais de caractériser précisément les oasis d’une façon presque continue. / In southern Tunisia, not all irrigated oasis-type perimeters have undergone the same development, we observed an evolution of the covered surfaces which more than doubled in the last half-century, while for other regions they have remained practically stable and in some cases, a decrease in these areas. These changes have affected environmental and economic systems. In this context, the evaluation of the state of oases and the development of a typology of oases systems is a key-issue for sustainable agriculture. To know the state of vegetation in these oases, monitoring systems for oasis ecosystems must be informed by data on cultivated areas. These data can be obtained in part by satellite observation systems with high and moderate spatial resolution and high temporal repetitiveness, offer a synoptic vision that makes them a particularly appropriate information source for the estimation of such data. The research work presented here focuses on the exploration of methods developed from two types of time series of Earth observation images: those produced by the SPOT-5 experiment (Take5) and the MOD13Q1 product of the MODIS sensor, at 10m and 250m spatial resolution respectively. These methods and data were tested in the Djerid region with the final aim of setting up an oasis monitoring system based on the analysis of time signatures from Earth observation images made very regularly over time.Two change detection approaches based on NDVI time series. The first consists on temporal variations in vegetation activity over a short period from April to September 2015 through the SPOT-5 time series (Take5): the comparison between oases was made at the scale of the irrigated perimeter (an oasis can be composed of several irrigated perimeters) using agglomerative hierarchical clustering (AHC) method.The second uses a temporal decomposition technique to extract the trend from a multi-year time series at the scale of a geographical point (a 250mx250m pixel) across the MOD13Q1 time series (2000-2016).Results obtained from the processing and analysis of optical time series have shown that it is possible to identify the main types of irrigated perimeters present in the Djerid region, and to retrospectively trace their recent development history. They also highlight the fact that SPOT-5 (Take5) images, which prefigure those currently available with images produced by Sentinel2 satellites, significantly improve the spatio-temporal characterization of oases functioning through their 10m spatial resolution and 5-day temporal repetitiveness.The results of this thesis highlight new possibilities for the combination of remote sensing, field data and statistical analysis, delivering nonstop information in time and space on the characterization of oases systems. Indeed, with a single sensor such as Sentinel2, coupled with the historical data of MOD13Q1, it is now possible to accurately characterize oases on a continuous basis.

Anthropogenic disturbance in a landscape can take various forms, including residential development, which has substantial impact on the world’s ecosystems. Exurban development, characterized by low density residential development outside urban areas, was and continues to be one of the fastest growing forms of residential development in North America. It has disproportionately large ecological impacts relative to its footprint, yet is mostly overlooked in scientific studies. Specifically, a lack of spatially explicit (disaggregate) data on exurban development at regional level has contributed to a very limited understanding of this interspersed low density development. The main goal of this dissertation is to provide an increased understanding of exurban development in terms of its location, locating factors, and conservation and ecological implications at regional level, especially to enable incorporation of exurban information in the decision making processes. For this I asked four specific questions in this dissertation: (i) Where exactly is exurban development? (ii) What are the most likely factors that influence exurban development location? (iii) How does current and future development conflict with conservation goals? And (iv) What is the extent of the exurban development’s ecological impacts? Using a heterogeneous landscape, the County of Peterborough (Ontario, Canada), as the case study this dissertation undertook a number of separate yet related analyses that collectively provided the improved understanding of exurban development. The investigation of traditionally used surrogates for development, like roads and census data, and a more direct remote sensing method, using moderate resolution SPOT/HRVIR imagery, provided insights and contributed to development of spatially explicit data on exurban development. The evaluation of several commonly hypothesized locating factors in relation to exurban development revealed some of the major influences on the location of this development, especially in the context of Ontario. This research contributed to our understanding of the future risks of land conversion and identification of potential conflict areas between development and conservation plans in the study area. Lastly, examining the ecological impact of exurban development including associated roads, in terms of functions such as barrier effects and landscape connectivity, highlighted the importance of these seldom included anthropogenic disturbances in land and conservation planning. The contributions of this research to the existing body of knowledge are threefold. First, this dissertation reveals the limitations associated with existing methods used to map exurban development and presents a relatively easy, effective, automated and operational method to delineate exurban built areas at regional level using GIS and remote sensing. Second, the analyses conducted in this dissertation repeatedly highlights the importance of incorporating fine level details on exurban development in land and conservation planning as well as ecological impact assessments and presents methods and tools that can systematically and scientifically integrate this information in decision making framework. Third, this study conducted one of a kind, comprehensive and spatially explicit study on exurban development in Canada, where there is near absence of such research. With the rarely available exurban built footprint data delineated for the study area, this study not only identified the potential locating factors, future conversion risk, and conflict areas between development and conservation plans, but also quantified ecological impact in terms of landscape function, namely barrier effects and landscape connectivity, using a relatively novel circuit theoretic approach that can directly inform land and conservation decision planning process.

Anthropogenic disturbance in a landscape can take various forms, including residential development, which has substantial impact on the world’s ecosystems. Exurban development, characterized by low density residential development outside urban areas, was and continues to be one of the fastest growing forms of residential development in North America. It has disproportionately large ecological impacts relative to its footprint, yet is mostly overlooked in scientific studies. Specifically, a lack of spatially explicit (disaggregate) data on exurban development at regional level has contributed to a very limited understanding of this interspersed low density development. The main goal of this dissertation is to provide an increased understanding of exurban development in terms of its location, locating factors, and conservation and ecological implications at regional level, especially to enable incorporation of exurban information in the decision making processes. For this I asked four specific questions in this dissertation: (i) Where exactly is exurban development? (ii) What are the most likely factors that influence exurban development location? (iii) How does current and future development conflict with conservation goals? And (iv) What is the extent of the exurban development’s ecological impacts? Using a heterogeneous landscape, the County of Peterborough (Ontario, Canada), as the case study this dissertation undertook a number of separate yet related analyses that collectively provided the improved understanding of exurban development. The investigation of traditionally used surrogates for development, like roads and census data, and a more direct remote sensing method, using moderate resolution SPOT/HRVIR imagery, provided insights and contributed to development of spatially explicit data on exurban development. The evaluation of several commonly hypothesized locating factors in relation to exurban development revealed some of the major influences on the location of this development, especially in the context of Ontario. This research contributed to our understanding of the future risks of land conversion and identification of potential conflict areas between development and conservation plans in the study area. Lastly, examining the ecological impact of exurban development including associated roads, in terms of functions such as barrier effects and landscape connectivity, highlighted the importance of these seldom included anthropogenic disturbances in land and conservation planning. The contributions of this research to the existing body of knowledge are threefold. First, this dissertation reveals the limitations associated with existing methods used to map exurban development and presents a relatively easy, effective, automated and operational method to delineate exurban built areas at regional level using GIS and remote sensing. Second, the analyses conducted in this dissertation repeatedly highlights the importance of incorporating fine level details on exurban development in land and conservation planning as well as ecological impact assessments and presents methods and tools that can systematically and scientifically integrate this information in decision making framework. Third, this study conducted one of a kind, comprehensive and spatially explicit study on exurban development in Canada, where there is near absence of such research. With the rarely available exurban built footprint data delineated for the study area, this study not only identified the potential locating factors, future conversion risk, and conflict areas between development and conservation plans, but also quantified ecological impact in terms of landscape function, namely barrier effects and landscape connectivity, using a relatively novel circuit theoretic approach that can directly inform land and conservation decision planning process.

Thesis (MA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Image classification has long been used in earth observation and is driven by the need for accurate maps to develop conceptual and predictive models of Earth system processes. Synthetic aperture radar (SAR) imagery is used ever more frequently in land cover classification due to its complementary nature with optical data. There is therefore a growing need for reliable, accurate methods for using SAR and optical data together in land use and land cover classifications. However, combining data sets inevitably increases data dimensionality and these large, complex data sets are difficult to handle. It is therefore important to assess the benefits and limitations of using multi-temporal, dual-sensor data for applications such as land cover classification. This thesis undertakes this assessment through four main experiments based on combined RADARSAT-2 and SPOT-5 imagery of the southern part of Reunion Island. In Experiment 1, the use of feature selection for dimensionality reduction was considered. The rankings of important features for both single-sensor and dual-sensor data were assessed for four dates spanning a 6-month period, which coincided with both the wet and dry season. The mean textural features produced from the optical bands were consistently ranked highly across all dates. In the two later dates (29 May and 9 August 2014), the SAR features were more prevalent, showing that SAR and optical data have complementary natures. SAR data can be used to separate classes when optical imagery is insufficient. Experiment 2 compared the accuracy of six supervised and machine learning classification algorithms to determine which performed best with this complex data set. The Random Forest classification algorithm produced the highest accuracies and was therefore used in Experiments 3 and 4. Experiment 3 assessed the benefits of using combined SAR-optical imagery over single-sensor imagery for land cover classifications on four separate dates. The fused imagery produced consistently higher overall accuracies. The 29 May 2014 fused data produced the best accuracy of 69.8%. The fused classifications had more consistent results over the four dates than the single-sensor imagery, which suffered lower accuracies, especially for imagery acquired later in the season. In Experiment 4, the use of multi-temporal, dual-sensor data for classification was evaluated. Feature selection was used to reduce the data set from 638 potential training features to 50, which produced the best accuracy of 74.1% in comparison to 71.9% using all of the features. This result validated the use of multi-temporal data over single-date data for land cover classifications. It also validated the use of feature selection to successfully inform data reduction without compromising the accuracy of the final product. Multi-temporal and dual-sensor data shows potential for mapping land cover in a tropical, mountainous region that would otherwise be challenging to map using single-sensor data. However, accuracies Stellenbosch University https://scholar.sun.ac.za iv generally remained lower than would allow for transferability and replication of the current methodology. Classification algorithm optimisation, supervised segmentation and improved training data should be considered to improve these results. / AFRIKAANSE OPSOMMING: Beeld-klassifikasie word al ‘n geruime tyd in aardwaarneming gebruik en word gedryf deur die behoefte aan akkurate kaarte om konseptuele en voorspellende modelle van aard-stelsel prosesse te ontwikkel. Sintetiese apertuur radar (SAR) beelde word ook meer dikwels in landdekking klassifikasie gebruik as gevolg van die aanvullende waarde daarvan met optiese data. Daar is dus 'n groeiende behoefte aan betroubare, akkurate metodes vir die gesamentlike gebruik van SAR en optiese data in landdekking klassifikasies. Die kombinasie van datastelle bring egter ‘n onvermydelike verhoging in data dimensionaliteit mee, en hierdie groot, komplekse datastelle is moeilik om te hanteer. Dus is dit belangrik om die voordele en beperkings van die gebruik van multi-temporale, dubbel-sensor data vir toepassings soos landdekking-klassifikasie te evalueer. Die waarde van gekombineerde (versmelte) RADARSAT-2 en SPOT-5 beelde word in hierdie tesis deur middel van vier eksperimente geevalueer. In Eksperiment 1 is die gebruik van kenmerk seleksie vir dimensionaliteit-vermindering toegepas. Die ranglys van belangrike kenmerke vir beide enkel-sensor en 'n dubbel-sensor data is beoordeel vir vier datums wat oor 'n tydperk van 6 maande strek. Die gemiddelde tekstuur kenmerke uit die optiese lae is konsekwent hoog oor alle datums geplaas. In die twee later datums (29 Mei en 9 Augustus 2014) was die SAR kenmerke meer algemeen, wat dui op die aanvullende aard van SAR en optiese data. SAR data dus gebruik kan word om klasse te onderskei wanneer optiese beelde onvoldoende daarvoor is. Eksperiment 2 het die akkuraatheid van ses gerigte en masjien-leer klassifikasie algoritmes vergelyk om te bepaal watter die beste met hierdie komplekse datastel presteer. Die random gorest klassifikasie algoritme het die hoogste akkuraatheid bereik en is dus in Eksperimente 3 en 4 gebruik. Eksperiment 3 het die voordele van gekombineerde SAR-optiese beelde oor enkel-sensor beelde vir landdekking klassifikasies op vier afsonderlike datums beoordeel. Die versmelte beelde het konsekwent hoër algehele akkuraathede as enkel-sensor beelde gelewer. Die 29 Mei 2014 data het die hoogste akkuraatheid van 69,8% bereik. Die versmelte klassifikasies het ook meer konsekwente resultate oor die vier datums gelewer en die enkel-sensor beelde het tot laer akkuraathede gelei, veral vir die later datums. In Eksperiment 4 is die gebruik van multi-temporale, dubbel-sensor data vir klassifikasie ge-evalueer. Kenmerkseleksie is gebruik om die data stel van 638 potensiële kenmerke na 50 te verminder, wat die beste akkuraatheid van 74,1% gelewer het. Hierdie resultaat bevestig die belangrikheid van multi-temporale data vir grond dekking klassifikasies. Dit bekragtig ook die gebruik van kenmerkseleksie om data vermindering suksesvol te rig sonder om die akkuraatheid van die finale produk te belemmer. Stellenbosch University https://scholar.sun.ac.za vi Multi-temporale en dubbel-sensor data toon potensiaal vir die kartering van landdekking in 'n tropiese, bergagtige streek wat andersins uitdagend sou wees om te karteer met behulp van enkel-sensor data. Oor die algemeen het akkuraathede egter te laag gebly om vir oordraagbaarheid en herhaling van die huidige metode toe te laat. Klassifikasie algoritme optimalisering, gerigte segmentering en verbeterde opleiding data moet oorweeg word om hierdie resultate te verbeter.

Land use and land cover changes (LULCC) including deforestation for agricultural land and others are elements that contribute on global environmental change. Therefore understanding a trend of these changes in the past, current, and future is important for making proper decisions to develop in a sustainable way. This study analyzed land use and land cover (LULC) changes over time for Tuy Duc district belonging to Dak Nong province based on LULC maps classified from a set of multidate satellite images captured in year 2003, 2006, 2009, and 2013 (SPOT 5 satellite images). The LULC spatio-temporal changes in the area were classified as perennial agriculture, cropland, residential area, grassland, natural forest, plantation and water surface. Based on these changes over time, potential LULC in 2023 was predicted using Cellular Automata (CA)–Markov model. The predicted results of the change in LULC in 2023 reveal that the total area of forest will lose 9,031ha accounting of 50% in total area of the changes. This may be mainly caused by converting forest cover to agriculture (account for 28%), grassland (12%) and residential area (9%). The findings suggest that the forest conversion needs to be controlled and well managed, and a reasonable land use plan should be developed in a harmonization way with forest resources conservation. / Thay đổi sử dụng đất và thảm phủ (LULCC) bao gồm cả việc phá rừng để phát triển nông nghiệp và vì các mục đích khác là tác nhân đóng góp vào biến đổi môi trường toàn cầu. Vì vậy hiểu biết về khuynh hướng của sự thay đổi này trong quá khứ, hiện tại và tương lai là quan trọng để đưa ra những quyết định dúng đắn để phát triển bền vững. Nghiên cứu đã phân tích LULCC trong thời gian qua dựa vào các bản đồ sử dụng đất và thảm phủ (LULC) đã được phân loại từ một loạt ảnh vệ tinh đa phổ được thu chụp vào năm 2003, 2006, 2009 (ảnh SPOT 5). Những thay đổi LULC theo thời gian và không gian trong khu vực được phân loại thành đất nông nghiệp với cây dài ngày, cây ngắn ngày, thổ cư, trảng cỏ cây bụi, rừng tự nhiên, rừng trồng và mặt nước. Dựa trên sự thay đổi này theo thời gian, LULC tiềm năng cho năm 2023 đã được dự báo bằng cách sử dụng mô hình CAMarkov. Kết quả dự báo LULCC năm 2023 đã cho thấy tổng diện tích rừng bị mất khoảng 9,031 ha chiếm 50% trong tổng số diện tích thay đổi. Điều này chủ yếu là do chuyển đổi từ rừng tự nhiên sang canh tác nông nghiệp (chiếm 28%), trảng cỏ cây bụi (12%) và khu dân cư (9%). Kết quả cho thấy việc chuyển đổi rừng cần phải được kiểm soát và quản lý tốt và một kế hoạch sử dụng đất hợp lý cần được xây dựng trong sự hài hòa với bảo tồn tài nguyên rừng.

Identifying landuse management practices is important for detecting landuse change and impacts on the surrounding landscape. The Ontario Ministry of Agriculture and Rural A airs has established a database product called the Agricultural Resource Inventory (AgRI), which is used for the storage and analysis of agricultural land management practices. This thesis explores the opportunity to populate the AgRI. A comparison of two supervised classi fications using optical satellite imagery with multiple single-date classifi cations and a subsequent multi-date, multi-sensor classi fication were used to gauge the best image timing for crop classi fication. In this study optical satellite images (Landsat-5 and SPOT-4/5) were inputted into a decision tree classifi er and Maximum Likelihood Classifi er (MLC) where the decision tree performed better than the MLC in overall and class accuracies. Classifi cation experienced complications from visual diff erences in vegetation. The multi-date classifi cation performed had an accuracy of 66.52%. The lack of imagery available at crop ripening stages reduced the accuracies greatly.

De part leur influence déterminante sur les changements globaux, les changements d'occupation et d'utilisation des sols constituent un champ de recherche extrêmement actif. Néanmoins l'étude des changements de la zone côtière de leur conséquences locales est encore relativement rare. Or le littoral connaît depuis le siècle dernier des pressions anthropiques croissantes génératrices de changements d'occupation des sols. La finalité principale de ce travail était de contribuer à la connaissance des dynamiques territoriales récentes de la partie terrestre de la zone côtière, appliquée au territoire du Pays de Brest. La méthodologie retenue s'articule autour de la classification orientée-objet d'une image satellitaire SPOT 5 d'avril 2003, et de l'utilisation de données d'occupation des sols hétérogènes (IPLI-77), pour la production d'une information sur les changements d'occupation des sols survenus dans les communes littorales du Pays de Brest entre 1977 et 2003. Le résultat de l'analyse orientée-objet entreprise sur l'image SPOT 5 permet de décrire finement l'occupation des sols en 2003, à trois niveaux de précision. Puis l'analyse combinée des deux jeux de données décrit les principales évolutions de l'occupation des sols entre les deux dates. Elle montre une artificialisation sensible, notamment aux abords de la ville de Brest et sur le littoral, et un enfrichement des terres agricoles. Enfin la formulation d'hypothèses synthétisées sous forme de variables permet de mettre en évidence quelques facteurs de l'artificialisation à cette période.

海岸為海洋與陸地交界之處，風、浪與潮流等自然營力長期於此不斷的侵蝕與堆積交互演替。近年來，隨著人口快速增加，人類對於海岸地區土地利用與開發的需求急遽擴張，使得影響海岸地形的變因日益複雜且變化迅速。宜蘭特殊的沙丘性海岸因抗蝕性弱，易受到外力影響而改變地形，海岸後方的沿海平原為人口與產業集中的地區，因此自然營力與人為因素對宜蘭平原海岸地形與環境的影響，備受關注。因衛星影像具多時期與大尺度的空間特性，可提供土地覆蓋變遷分析之有效資訊，故本文使用2003年、2006年與2009年宜蘭平原沿海地區SPOT 5衛星影像，利用階層式分類程序將研究試區分為水體、建成與交通用地、沙地、農地與林地等五種土地覆蓋類別，透過土地覆蓋分類之結果，比較三個時期土地覆蓋型面積的變化；建立馬可夫轉移矩陣，了解各土地覆蓋型轉移的情況；其次，量化地景指標以了解整體土地覆蓋型區塊在空間結構上的情況，並利用Shannon多樣性指標t檢定測驗兩時期間整體地景是否有明顯的變遷。進一步，利用二項式Logit迴歸分析2003至2006年與2006年至2009年間土地覆蓋型的變化與沙丘海岸變遷的關係以及參考前人宜蘭海岸變遷之研究，選擇可能影響此區海岸變遷的自然與人為環境因子，建立二項式Logit迴歸模式，探討各項因子對於沙丘性海岸的影響，並利用海岸沙丘空間分佈預測機率圖，最後以2006年與2009年沙地主題圖作為驗證資料，探討模式的可行性。本研究透過不同的空間計量方法，了解本區土地覆蓋型的變化，期本研究成果對於此區海岸保護與管理政策制定者有一參考的依據。 / Coastal zone is at the junction of ocean and land. The area constantly experiences interchanging succession of erosion and accumulation due to natural forces such as wind, wave, and tidal currents. In recent years, associated with fast population increase, the demand of lands expanded rapidly such that the effects on topography of coastal zone became more complex and changed quickly. Coastal sand dunes are dynamic and fragile terrain often regarded as environmentally sensitive areas. Sand dunes are vulnerable to erosion by natural process and human activity. The objective of this research was to examine the effects of environmental factors and land-use changes on coastal sand dunes in I-Lan County. Satellite imageries are characterized by multi-temporal and large-scale, therefore they are ideal for providing necessary information to facilitate analysis of regional land-cover changes. In this research, three SPOT 5 images acquired in 2003, 2006 and 2009 were used to analyze land-cover changes in I-Lan coastal zone. Firstly, a hierarchical classification procedure was applied to classify the image data to five land-cover types and the land-cover changes were compared. Secondly, based on the classification results, a Markov transitional probability matrix was constructed to understand the transition among different land-cover types, and the Fragstats software was used to quantify the landscape structure of three different periods. By analyzing the spatial distributions of land-cover types in different time periods, we were able to examine to the temporal and spatial changes of land-cover in the I-Lan coastal zone. In addition, a t-test based on Shannon diversity index was used to evaluate the changes of the whole landscape in the study area. Thirdly, by selecting possible natural and man-made factors that are likely to affect coastal environment based on various prior studies, the mathematical models such as Markov chain and binomial logit regression analysis were applied to predict the future overall landscape structure and to simulate the spatial distribution of the sandy coastal zone. Thematic maps derived from satellite images obtained in 2006 and 2009 were used to verify and assess the feasibility of the models. This study integrated several spatial statistical methods to understand the patterns of land-cover changes in the study area. It is expected that the results of this study may offer a valuable reference for the policy-makers of coastal protection and management.

Thesis (MSc)--Stellenbosch University, 2012. / ENGLISH ABSTRACT: This research considered the automated remote sensing-based classification of wetland extent within the Nuwejaars and Heuningnes River systems on the Agulhas Plain. The classification process was based on meaningful image objects created through image segmentation rather than on single pixels. An expert system classifier was compared to a nearest-neighbour supervised classifier, and one multispectral (SPOT 5) image (dry season) and two C-band, VV-polarisation synthetic aperture radar (SAR: ERS-2) images (dry and wet season) were used separately and in combination. Classifications were performed within two subset areas. Final classes identified were Permanent waterbody, Other wetland and Non-wetland. Statistical accuracy assessment was performed. Validation data was derived from a combination of high-resolution aerial photographs, the SPOT 5 image, high-resolution imagery on Google Earth and observations during a field visit. Wetland extent was defined as the total extent of wetland-specific vegetation, unvegetated seasonal pans and waterbodies. More detailed classes were originally envisaged, but available validation data was not considered adequate for assessing their accuracy with any confidence. The supervised classifier was found to be more accurate overall than the developed expert system. The difference between the two was however not always significant. The two SAR images alone did not contain sufficient information for the accurate classification of Agulhas wetlands’ extent, with recorded overall accuracies not exceeding 65% regardless of the classifier used. The SPOT image alone achieved accuracies higher than 80%; this was considered a good result. In comparison, combining the SAR and SPOT data did not improve the classification accuracy. The potential of the expert system to be applied with little modification to images acquired over other areas or over the same area in other years should be further investigated. However, several reservations are noted in this regard. Future research could potentially improve the results obtained from supervised classification by augmenting it with expert system rules to identify more complicated classes. KEYWORDS ERS-2, SPOT 5, SAR, wetlands, expert system classifier, nearest-neighbour supervised classifier / AFRIKAANSE OPSOMMING: Hierdie navorsing het die geoutomatiseerde afstandswaarneminggebaseerde klassifikasie van vleilandomvang binne die Nuwejaars- en Heuningnesrivier stelsels op die Agulhasvlakte ondersoek. Die klassifikasieproses was gebaseer op betekenisvolle beeldobjekte geskep deur middel van beeldsegmentasie eerder as op enkele beeldelemente. ‘n Deskundige stelsel klassifiseerder is vergelyk met ‘n naaste-naburige gerigte klassifiseerder. Een multispektrale (SPOT 5) beeld vir die droë seisoen, sowel as twee C-band, VV-polarisasie sintetiese diafragma radar (SAR, ERS2) beelde (vir die droë en nat seisoene) is afsonderlik en in kombinasie gebruik. Klassifikasies is uitgevoer binne twee sub-areas in die beelde. Finale klasse wat geïdentifiseer is was Permanente waterliggaam, Ander vleiland en Nie-vleiland. Statistiese akkuraatheidsassessering is uitgevoer. Verwysingsdata is geskep vanuit ‘n kombinasie van hoë- resolusie lugfoto’s, die SPOT 5 beeld, hoë-resolusie beelde op Google Earth en waarnemings tydens ‘n besoek aan die studiegebied. Vleiland omvang is gedefinieer as die totale omvang van vleiland-spesifieke plantegroei, onbegroeide seisoenale panne en waterliggame. Die gerigte klassifiseerder blyk om oor die algemeen meer akkuraat as die ontwikkelde deskundige stelsel te wees. Die verskil was egter nie altyd beduidend nie. Die twee SAR beelde alleen het nie genoegsame inligting bevat vir die akkurate klassifikasie van Agulhas-vleilande se omvang nie, met behaalde algehele akkuraatheidsvlakke wat nie 65% oorskry het nie, ongeag van die klassifiseerder. Die SPOT-beeld alleenlik het algehele akkuraathede van meer as 80% behaal; wat as ‘n goeie resultaat beskou kan word. In vergelyking hiermee kon die kombinering van SAR- en SPOT-data nie ‘n verbetering teweeg bring nie. Die potensiaal van die deskundige stelsel om met min aanpassing op beelde van ander gebiede of van dieselfde gebied in ander jare toegepas te word, verg verdere ondersoek. Verskeie voorbehoude word egter in hierdie verband gemeld. Toekomstige navorsing kan potensieel die resultate van gerigte klassifikasie verbeter deur dit aan te vul met deskundige stelsel reëls vir die klassifikasie van meer komplekse klasse. TREFWOORDE ERS-2, SPOT 5, SAR, vleilande, deskundige stelsel klassifiseerder, naaste-naburige gerigte klassifiseerder.

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