Risks and uncertainties are unavoidable in agriculture in Sudan, due to its dependence on climatic factors and to the imperfect nature of the agricultural decisions and policies attributed to land cover and land use changes that occur. The current study was conducted in the Gash Agricultural Scheme (GAS) - Kassala State, as a semi-arid land in eastern Sudan. The scheme has been established to contribute to the rural development, to help stability of the nomadic population in eastern Sudan, particularly the local population around the Gash river areas, and to facilitate utilizing the river flood in growing cotton and other cash crops. In the last decade, the scheme production has declined, because of drought periods, which hit the region, sand invasion and the spread of invasive mesquite trees, in addition to administrative negligence. These have resulted also in poor agricultural productivity and the displacement of farmers away from the scheme area. Recently, the scheme is heavily disturbed by human intervention in many aspects. Consequently, resources of cultivated land have shrunk and declined during the period of the study, which in turn have led to dissatisfaction and increasing failure of satisfying increasing farmer’s income and demand for local consumption. Remote sensing applications and geospatial techniques have played a key role in studying different types of hazards whether they are natural or manmade. Multi-temporal satellite data combined with ancillary data were used to monitor, analyze and to assess land use and land cover (LULC) changes and the impact of land degradation on the scheme production, which provides the managers and decision makers with current and improved data for the purposes of proper administration of natural resources in the GAS. Information about patterns of LULC changes through time in the GAS is not only important for the management and planning, but also for a better understanding of human dimensions of environmental changes at regional scale. This study attempts to map and assess the impacts of LULC change and land degradation in GAS during a period of 38 years from 1972-2010. Dry season multi-temporal satellite imagery collected by different sensor systems was selected such as three cloud-free Landsat (MSS 1972, TM 1987 and ETM+ 1999) and ASTER (2010) satellite imagery. This imagery was geo-referenced and radiometrically and atmospherically calibrated using dark object subtraction (DOS). Two approaches of classification (object-oriented and pixel-based) were applied for classification and comparison of LULC. In addition, the study compares between the two approaches to determine which one is more compatible for classification of LULC of the GAS. The pixel-based approach performed slightly better than the object-oriented approach in the classification of LULC in the study area. Application of multi-temporal remote sensing data proved to be successful for the identification and mapping of LULC into five main classes as follows: woodland dominated by dense mesquite trees, grass and shrubs dominated by less dense mesquite trees, bare and cultivated land, stabilized fine sand and mobile sand. After image enhancement successful classification of imagery was achieved using pixel and object based approaches as well as subsequent change detection (image differencing and change matrix), supported by classification accuracy assessments and post-classification. Comparison of LULC changes shows that the land cover of GAS has changed dramatically during the investigated period. It has been discovered that more significant of LULC change processes occurred during the second studied period (1987 to 1999) than during the first period (1972-1987). In the second period nearly half of bare and cultivated lands was changed from 41372.74 ha (20.22 %) in 1987 to 28020.80 ha (13.60 %) in 1999, which was mainly due to the drought that hit the region during the mentioned period. However, the results revealed a drastic loss of bare and cultivated land, equivalent to more than 40% during the entire period (1972-2010). Throughout the whole period of study, drought and invasion of both mesquite trees and sand were responsible for the loss of more than 40% of the total productive lands. Change vector analysis (CVA) as a useful approach was applied for estimating change detection in both magnitude and direction of change. The promising approach of multivariate alteration detection (MAD) and subsequent maximum autocorrelation factor (MAD/MAF) transformation was used to support change detection via assessment of maximum correlation between the transformed variates and the specific original image bands related to specific land cover classes. However, both CVA and MAD/MAD strongly prove the fact that bare and cultivated land have dramatically changed and decreased continuously during the studied period. Both CVA and MAD/MAD demonstrate adequate potentials for monitoring, detecting, identifying and mapping the changes. Moreover, this research demonstrated that CVA and MAD/MAF are superior in providing qualitative details about the nature of all kinds of change. Vegetation indices (VI) such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified adjusted vegetation index (MSAVI) and grain soil index (GSI) were applied to measure the quantitative characterization of temporal and spatial vegetation cover patterns and change. All indices remain very sensitive to structure variation of LULC. The results reveal that the NDVI is more effective for detecting the amount and status of the vegetation cover in the study area than SAVI, MSAVI and GSI. Therefore, it can be stated that NDVI can be used as a response variable to identify drought disturbance and land degradation in semi-arid land such as the GAS area. Results of detecting vegetation cover observed by using SAVI were found to be more reasonable than using MSAVI, although MSAVI reduces the background of bare soil better than SAVI. GSI proves high efficiency in determining the different types of surface soils, and producing a change map of top soil grain size, which is useful in assessment of land degradation in the study area. The linkage between socio-economic data and remotely sensed data was applied to determine the relationships between the different factors derived and to analyze the reasons for change in LULC and land degradation and its effects in the study area. The results indicate a strong relationship between LULC derived from remotely sensed data and the influencing socioeconomic variables. The results obtained from analyzing socioeconomic data confirm the findings of remote sensing data analysis, which assure that the decline and degradation of agricultural land is a result of further spread of mesquite trees and of increased invasion of sand during the study period. High livestock density and overgrazing, drought, invasion of sand, spread of invasive mesquite trees, overexploitation of land, improper management, and population growth were considered as the main direct factors responsible for degradation in the study area.

In recent decades expanding land-use change has caused extensive deforestation of the tropical rainforestinducing large-scale transformation of the landscape patterns across the South American continent. Landscapechange is a modification process of the natural forest cover into fragments which generate various ecologicalimpacts. Habitat loss is identified to be a major threat to biodiversity, as it exposes species to the risk ofextinction. This study investigates 80 locations within tropical rainforest biomes to examine the landscape changewhich has occurred from 1993 – 2014. The intention is to identify the impacts of landscape fragmentation onhummingbird species diversity by spatial landscape analysis in GIS and regression modeling. The analysis foundthat there is no relationship between deforestation and reduction of hummingbird diversity. The results indicatethat hummingbird species are not particularly sensitive to landscape change as they have high resilience in regardto forest fragmentation. A potential threshold value of deforestation degree could be identified, up to whichhummingbird species richness increased, but locations subjected to over 40% fragmentation were estimated tohave lower hummingbird diversity. However, by using the spatial explicit biological data, the analysis indicatethat an extinction debt may exist in the landscape, and that future extinctions may be expected to occur in thefollowing decades as consequence of deforestation. Other factors may be as important determining variables forspecies richness: the spatial scale of the study, the habitat connectivity, hummingbird generalist tendencies.Conclusively, identification of the key factors of deforestation impacts on species diversity is essential for futureefficiency in conservation planning and sustainability of the tropical rainforest biodiversity.

The mapping of land cover using remotely sensed data is most effective when a robust classification method is employed. Random forest is a modern machine learning algorithm that has recently gained interest in the field of remote sensing due to its non-parametric nature, which may be better suited to handle complex, high-dimensional data than conventional techniques. In this study, the random forest method is applied to remote sensing data from the European Space Agency’s new Sentinel-2 satellite program, which was launched in 2015 yet remains relatively untested in scientific literature using non-simulated data. In a study site of boreo-nemoral forest in Ekerö mulicipality, Sweden, a classification is performed for six forest classes based on CadasterENV Sweden, a multi-purpose land covermapping and change monitoring program. The performance of Sentinel-2’s Multi-SpectralImager is investigated in the context of time series to capture phenological conditions, optimal band combinations, as well as the influence of sample size and ancillary inputs.Using two images from spring and summer of 2016, an overall map accuracy of 86.0% was achieved. The red edge, short wave infrared, and visible red bands were confirmed to be of high value. Important factors contributing to the result include the timing of image acquisition, use of a feature reduction approach to decrease the correlation between spectral channels, and the addition of ancillary data that combines topographic and edaphic information. The results suggest that random forest is an effective classification technique that is particularly well suited to high-dimensional remote sensing data.

The objective of this study is to investigate land-use changes and water quality trends within the headwaters of the Alafia River watershed. Water quality data were obtained from the Environmental Protection Commission of Hillsborough County (EPCHC). Eleven water quality parameters selected for analysis included: temperature (˚C), dissolved oxygen (DO), percent saturation of DO, conductivity, pH, total phosphorous (TP), total nitrogen (TN), ammonium, chlorophyll-a (uncorrected), fecal coliforms, and enterococci. ArcMap® & SWFWMD data were used to map EPCHC sampling stations, calculate contributing watershed size, and determine land-use changes over the course of the sampling period; 17 stations were chosen for this study. The annual average for each of the water quality parameters was calculated along with a Mann-Kendall Trend Analysis in order to determine if any of the observed trends were statistically significant. A non-parametric Kendall’s tau-b correlation and stepwise multiple linear regression tests were conducted in SPSS to determine if any statistically significant relationships between water quality data, land-use and basin size exist. The land-use results showed every basin consisted of some percentage of Low Density Residential, Cropland & Pastureland, Reservoirs, and Streams & Lake Swamps. In addition, no basin comprised of more than 20% wetlands and often it appears urbanization was at the sacrifice of agricultural lands, as opposed to wetlands. The trends in water quality showed eight of the 17 basins had at least one statistically significant trend. Analysis of the data used for this study has shown instances where water quality measurements were in violation of state standards. Changes in water quality can be statistically related to changes in land-use and basin size as both the correlation and the regression showed consistent relationships between several LULC types and water quality parameters: increases in Commercial & Services causes increased nutrients (TP and TN); Cropland & Pastureland causes decreased DO and DO% Saturation; increases in Tree Crops causes a decrease in pH; increasing Other Open Lands Rural causes a decrease in temperature; and increases in Shrub & Brushland cause decreases in conductivity and pH. As these relationships are based on the results from both analyses, it would seem that these relationships are the most reliable, and are key results of the study. These key relationships might be areas that future water resource managers may want to focus on in order to more efficiently improve or regulate water quality within headwater streams.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The Southwest of Rio Grande do Sul State is characterized by presenting areas of environmental vulnerability and intense degradation, with extensive portions of land covered with sand subjected to local conditions, climatic, geological and geomorphological. These areas are known as sands and the process arenization. In this context, the objective of this research was to analyze the evolution of arenization in the River Basin of Arroio Puitã in the period of 1984-2014, through multi-temporal mapping of 10 in 10 years. Thus it was proposed to verify the best classifier method for mapping the sand, draw up a mapping of land use and occupation of basin area from LANDSAT satellite images of the past 30 years, and analyze the main changes in the land use and occupation in around of the areas of sand. To accomplish such research have been followed some methodological steps described below: first happened data collection; to follow happened the geoprocessing stage initially with the test with five classifiers algorithms supervised pixel by pixel (Minimum Distance, Mahalanobis, MAXVER, Parelelepípedo and SAM) evaluating them from the Kappa coefficient, happened after the making of the use and land cover maps for the years 1984, 1994, 2004 and 2014 and finally were done the geographical analysis with the intersection of the information obtained in the mappings; and the stage corresponding to field work to target recognition of the surface that occurred concurrently with the geoprocessing step. The results revealed as the most appropriate classifier algorithm to map the sandy desertification in Southwest RS, the classifier MAXVER applied to mapping of use and land cover of the river basin for all dates. The mapping for all years considered reveals to us the predominance of fields in the study area, decreasing with the passage of time giving way mainly to agriculture and forestry. There was an expansion of area with arenization at first two periods 1984-1994 and 1994-2004, however the reduction of area in a third period, 2004-2014. Analyzing the whole period it has been the expansion of arenization area 1.87 square kilometers. The expansions and retractions of areas of sand in the period of analysis are directly related to climatic conditions and local relief since the main natural agents responsible for the maintenance of this process are water and wind. / O Sudoeste do Estado do Rio Grande do Sul é caracterizado por apresentar áreas de vulnerabilidade ambiental e de intensa degradação do solo, com extensas porções de terra recobertas por areias submetidos aos condicionantes climáticos, geológicos e geomorfológicos locais. Essas áreas são denominadas de areais e o processo de arenização. Nesse contexto, o objetivo dessa pesquisa foi analisar a evolução dos areais da Bacia Hidrográfica do Arroio Puitã no período 1984 a 2014, através de mapeamento multitemporal de 10 em 10 anos. Assim foi proposto verificar o melhor método classificador para mapeamento dos areais, elaborar um mapeamento do uso e ocupação da terra da bacia da área a partir de imagens de satélite LANDSAT dos últimos 30 anos, e, analisar as principais mudanças no uso e ocupação da terra no entorno das áreas dos areais. Para realizar tal pesquisa se seguiram algumas etapas metodológicas descritas a seguir: primeiramente se deu a coleta de dados; a seguir se deu a etapa de geoprocessamento inicialmente com o teste com cinco algoritmos classificadores supervisionados pixel a pixel (Mínima Distância, Mahalanobis, MAXVER, Parelelepípedo e SAM) avaliando-os a partir do coeficiente Kappa, após se deu a elaboração dos mapas de uso e cobertura da terra para os anos de 1984, 1994, 2004 e 2014, e, por fim as análises geográficas com o cruzamento das informações obtidas nos mapeamentos; e a etapa de trabalho de campo a fim de reconhecimento dos alvos da superfície que ocorreu concomitantemente com a etapa de geoprocessamento. Os resultados revelaram como algoritmo classificador mais adequado ao mapeamento da arenização no Sudoeste do RS o classificador MAXVER aplicado no mapeamento do uso e cobertura da terra da bacia para todas as datas. O mapeamento para todos os anos considerados nos revela a predominância de campos na área de estudo, apresentando redução com o decorrer do período cedendo lugar principalmente à agricultura e à silvicultura. Verificou-se uma expansão de área dos areais nos períodos correspondentes de 1984-1994 e 1994-2004, porém a redução de área num terceiro período, de 2004-2014. Analisando-se todo o período tem-se a expansão dos areais em 1,87 km² de área. As expansões e retrações dos areais no período estão diretamente relacionadas com os condicionantes climáticos e de relevo do local, visto que os principais agentes naturais responsáveis pela manutenção desse processo são a água e o vento.

The main purpose of thesis is creation and evaluation of models for change detection of arable land to grassland by Hybrid-based Change Detection method, which combined approaches based on the Vegetation Indices, Image Differencing and Principal Component Analysis. Six locations with different seasonal configuration of images with high resolution and one locality covered by image with very high resolution were used. The areas were spread across the foothill areas of the Czech Republic. The selection of predictors and the most suitable model was supported by statistical calculation. Application selected models were carried out using a multi-temporal object classification and their accuracy were verified using reference data. The benefit of this thesis is finding generally applicable model useful to investigate the land cover change and evaluation of the potentially most appropriate seasonal configuration of images. Valuable is also methodology in this thesis which focus on selection of predictors and calculation the order of the most appropriate models, which is unique in the available literature. The thesis provides useful findings fitting to insufficiently explored issue of Change Detection arable land to grassland. Powered by TCPDF (www.tcpdf.org)

This diploma thesis deals with the change of the landscape cover and the use of the landscape of the former Ralsko military training area after 1990. The former VVP Ralsko is located on the area of 250 km2 between Česká Lípa, Stráží pod Ralskem and Mnichovo Hradištěm. Most of its territory lies in the Liberec region and only its southern part reaches the Central Bohemian region. After the departure of the Soviet troops in 1991, the military training area was abolished and on 1 January 1992 the village of Ralsko, which currently has 171 km2 , was formed by merging nine municipalities in its former territory. The town of Ralsko is thus an area with the largest area in the Czech Republic. Between 1993 and 2004, the former military area was decontaminated, during which more than 120,000 pieces of ammunition were found and destroyed. Decontamination of contaminated soils and groundwater is ongoing. Some former military buildings are currently commercially used as warehouses for raw materials. The Military Airport at Hradčany is partly used for sports purposes, and a range of rare game species has been set up in the area of the Židlov tank space, of which the most interesting is the European Tooth (Bison bonasus). In the first part of the thesis there is a search of specialized literature dealing with...

Cette thèse vise à identifier et évaluer les impacts biogéophysiques des changements d'usage des sols depuis les 150 dernières années jusqu'à la fin du XXIe siècle sur le climat en Afrique de l’Ouest à partir des modèles LUCID et des scénarios CMIP5 utilisés dans le contexte LUCID-CMIP5. Les analyses menées dans cette thèse se sont d'abord basées dans le Sahel et dans le Golfe de Guinée où les changements passés de la couverture terrestre sont supérieurs à 5%. Les simulations LUCID ont été d'abord évaluées dans cette thèse en comparant les précipitations et la température de l'air simulées par les modèles aux données d'observation. Les analyses ont montré que la moyenne et la variabilité inter-annuelle observées des précipitations et de la température sont respectivement sous-estimées et surestimées par la plupart des modèles de climat LUCID même si la température semble mieux simulée que les précipitations. Dans cette étude, les deux simulations actuelles forcées respectivement par une distribution actuelle et pré-industrielle de la couverture terrestre ont été comparées. Les résultats montrent qu'il n'y a pas de différence évidente entre ces deux simulations par rapport aux valeurs moyennes climatiques des précipitations et de la température dans les modèles comme si les changements de la couverture terrestre n'ont pas vraiment d'importance sur la représentation de ces variables. Dans le Golfe de Guinée, les analyses montrent que l'expansion des surfaces cultivées et des pâturages s'est effectuée au détriment d'une déforestation entraînant une diminution du LAI, une augmentation d'albédo et une diminution de la rugosité de surface. Les analyses montrent que les impacts historiques des changements d'occupation des sols sur le climat dans ces régions restent très petits par rapport aux changements induits par l'augmentation des gaz à effet de serre dans l’atmosphère. Le LAI simulé par les modèles de surface LUCID et leur relation avec le climat en Afrique de l'Ouest ont été évalués, les résultats montrent que les précipitations sont fortement et positivement corrélées à la densité de feuillage avec des valeurs supérieures ou égales à 0.8 dans les deux régions. La plupart des modèles de climat montrent que la corrélation entre le LAI et la température de l'air est positive dans le Sahel et négative dans le Golfe de Guinée et suggèrent que plus de LAI dans le golfe de Guinée conduit plus d'évapotranspiration et donc une surface plus froide, alors que dans le Sahel l'effet d'albédo de l'augmentation du LAI peut dominer et augmenter la température de surface.Dans un second temps, l'impact biophysique des changements futurs de la couverture terrestre sur le climat de surface du XXIe siècle a été évalué à l'aide des simulations spécifiques similaires aux scénarios RCP8.5 mais avec une végétation fixe en 2006. Les analyses révèlent qu'à l’échelle régionale, les impacts biophysiques des changements d'occupation des sols dans les scénarios ont été globalement faibles mais statistiquement significatifs au Sahel et en Afrique centrale où la déforestation est prescrite dans le futur (>10%), mais avec une large dispersion sur la réponse du climat résultant aux différentes paramétrisations de la surface terrestre dans les modèles de climat. / By climate models developed in the LUCID project and CMIP5 models used in the LUCID-CMIP5 projet, this thesis aims to identify and evaluate biogeophysical impacts of LULCC of the past 150 years and the end of XXIst century on surface climate in West Africa. Focusing analysis in two contrasted regions of West Africa: Sahel and Guinea where land cover change is above 5% since pre-industrial times, results reveal expansion of crops and pasture and deforestation in Guinea in all LUCID models. In this work, simulations of present-day rainfall and surface air temperature have been compared with observed datasets. Results show that the observed mean and inter-annual variability of rainfall are respectively underestimated and overestimated by most of the seven climate models. Overall surface air temperature is better simulated than precipitation.Two simulations of rainfall and surface air temperature, forced respectively with present-day and pre-industrial land cover distribution are also compared. Results show that there is no obvious/visible difference between the two simulations with respect to mean climatic values of both rainfall and temperature as if the changes in land cover did not really matter for the good representation of those variables. Finally, this thesis evaluates leaf area index (LAI) in the LUCID models and its relationships with surface climate. Observations reveal that precipitation is highly and positively correlated to foliage density with values larger or equal to 0.8 in both the Sahel and Guinea. Five out of seven models show positive correlations, but not as large as in the observations. However none of the models is able to capture a larger correlation between precipitation and LAI in Guinea than in the Sahel. Most of climate models show that correlation between LAI and surface air temperature is positive in the Sahel and negative in Guinea. It suggests that more LAI in Guinea will lead to more evapotranspiration and therefore cooler surface, while in the Sahel the albedo effect of increased LAI may dominate and increase surface temperature. Finally, analysis reveals that historical effects of land-use changes are not regionally significant among the seven climate models due to a small land-cover change prescribed in these regions compared to the changes induced by large scale forcing such as sea surface temperatures changes and CO2 concentration increase.Furthermore, biogeophysical impact of land-use change in the XXIst Century climate were evaluated using specific simulations similar to RCP8.5 scenarios but with a prescribed fixed land cover map on 2006. The analysis reveals, that in contrast of last 150 years, deforestation continues in the coming years in tropical region in scenarios resulting from the extension of the cultivated area reaching 15 million km2 in 2100 over tropical Africa. Regionally, the biogeophysical impacts of projected changes in land cover in RCP8.5 scenarios were generally small but statistically significant in the Sahel and Central Africa regions where deforestation is more than 10% with a wide dispersion of climate response due to differents parameterizations of land surface in climate models.

The role and scale of human impact on the global environment is a question of special importance to the scientific community and the world as a whole. This impact has dramatically increased since the beginning of industrialisation, yet its understanding remains patchy. The sub-Arctic plays a central role in forming the global environment due to the vast territory of boreal forest and tundra. Severe climatic conditions make its ecosystems highly sensitive to any natural and human disturbances. In this context, the dynamics of boreal vegetation, and of the forest/tundra interface (the treeline), is the most representative indicator of environmental changes in the sub-Arctic. For some time now, monitoring land cover and vegetation changes using remote sensing techniques have been a powerful method for studying human impact on environment from landscape to global scales. It is particularly efficient when applied to the sub-Arctic ecosystems. Remote sensing gives access to accurate and specific information about distant and hard-to-reach areas across forest and tundra. Despite all the e orts, there is a lack of uniformity in studying human impact, a shortage of mapping of impact over large territories and a lack of understanding of the relation between human activity and environmental response. This dissertation develops a systematic approach to monitoring land cover and vegetation changes under human impact over northern Fennoscandia. The study area extends north and south of the treeline and covers around 400,000km2 reaching from Finnmark in Norway, through Norrbotten in Sweden, Lapland in Finland up to the Murmansk region in Russia. This is the most populated and industrially developed region of the whole sub-Arctic and, therefore, suffering most from human impact. This dissertation identifies industrial atmospheric pollution, reindeer herding, forest logging, forest fires and infrastructure development as the primary types of human impact close to the treeline. For each type characteristic hotspots are identified and human impact is analysed in the context of physical environment as well as cultural, economical and political development of the area. This dissertation presents an automated workflow enabling large-scale land cover mapping in northern Fennoscandia with high throughput. It starts with automated image pre-processing using image metadata and ends with automated mapping of classification results. A single classifier for multispectral Landsat data is trained on extensive field data collected across the whole region. Open source tools are used extensively to set up the processing scripts enabling rapid and reproducible analysis. Using the developed advanced remote sensing methodology land cover maps are constructed for all identified hotspots and types of human impact. Changes in vegetation are analysed using three or four historical land cover maps for each hotspot. More than 35 Landsat TM and ETM+ images covering the period from the 1980s until 2011 are processed in an automated manner. A strong correlation between the level of impact and the scale of vegetation change is confirmed and analysed. The structure and dynamics of the local treeline and the quality of environment are analysed and assessed in the context of changing levels of impact at each hotspot and regionally.

Canadian remote areas are characterized by a minimal human footprint, restricted accessibility, ubiquitous lichen/snow cover (e.g. Arctic) or continuous forest with water bodies (e.g. Sub-Arctic). Effective mapping of earth surface cover and land cover changes using free medium-resolution Landsat images in remote environments is a challenge due to the presence of spectrally mixed pixels, restricted field sampling and ground truthing, and the often relatively homogenous cover in some areas. This thesis investigates how remote sensing methods can be applied to improve the capability of Landsat images for mapping earth surface features and land cover changes in Canadian remote areas. The investigation is conducted from the following four perspectives: 1) determining the continuity of Landsat-8 images for mapping surficial materials, 2) selecting classification algorithms that best address challenges involving mixed pixels, 3) applying advanced image fusion algorithms to improve Landsat spatial resolution while maintaining spectral fidelity and reducing the effects of mixed pixels on image classification and change detection, and, 4) examining different change detection techniques, including post-classification comparisons and threshold-based methods employing PCA(Principal Components Analysis)-fused multi-temporal Landsat images to detect changes in Canadian remote areas. Three typical landscapes in Canadian remote areas are chosen in this research. The first is located in the Canadian Arctic and is characterized by ubiquitous lichen and snow cover. The second is located in the Canadian sub-Arctic and is characterized by well-defined land features such as highlands, ponds, and wetlands. The last is located in a forested highlands region with minimal built-environment features. The thesis research demonstrates that the newly available Landsat-8 images can be a major data source for mapping Canadian geological information in Arctic areas when Landsat-7 is decommissioned. In addition, advanced classification techniques such as a Support-Vector-Machine (SVM) can generate satisfactory classification results in the context of mixed training data and minimal field sampling and truthing. This thesis research provides a systematic investigation on how geostatistical image fusion can be used to improve the performance of Landsat images in identifying surface features. Finally, SVM-based post-classified multi-temporal, and threshold-based PCA-fused bi-temporal Landsat images are shown to be effective in detecting different aspects of vegetation change in a remote forested region in Ontario. This research provides a comprehensive methodology to employ free Landsat images for image classification and change detection in Canadian remote regions.