Dissertations / Theses on the topic 'Landsat MSS satellite imagery'

Knowledge of land cover is important in a variety of natural resources applications. This knowledge becomes more powerful within the spatial analysis capabilities of a geographic information system (GIS). This thesis presents a digital land cover map of Virginia, produced through interpretation of 14 Landsat Thematic Mapper (TM) scenes, circa 1991-1993. The land cover map, which has a 30m pixel size, was produced entirely with personal computers. Hypercluster aggregation, an unsupervised classification method, was used when hazy and mountainous conditions were not present. A haze correction procedure by Lavreau (1991) was used, followed by a supervised classification on coastal areas. An enhanced supervised classification, focusing on topographic shading, was performed in the mountains. Color infrared photographs, digital maplets, expert knowledge, and other maps were used as training data. Aerial videography transects were flown to acquire reference data. Due to the spatial inaccuracies inherent in the videography reference data, only homogeneous land cover areas were used in the accuracy assessment. The results of the overall accuracy for each scene determined the ordering of scenes within the statewide land cover mosaic (i.e., scenes with higher accuracy had a higher proportion of area represented). An accuracy assessment was then performed on the statewide land cover mosaic. An overall accuracy of 81.8% and a Kappa statistic of 0.81 resulted. A discussion of potential reasons for land cover class confusion and suggestions for classification improvements are presented. Overall deciduous forest was the most common land cover in Virginia. Herbaceous areas accounted for 20% of the land area, which was the second largest. Mixed forest and coastal wetlands were the cover types with the least area, each under 3%.
Master of Science

This dissertation investigated the practicality and expediency of applying remote sensing data fusion products to the analysis of dryland vegetation phenology. The objective of the first study was to verify the quality of the output products of the spatial and temporal adaptive reflectance fusion method (STARFM) over the dryland Arizona study site. Synthetic 30 m resolution images were generated from Landsat-5 Thematic Mapper (TM) data and a range of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance datasets and assessed via correlation analysis with temporally coincident Landsat-5 imagery. The accuracy of the results (0.61 < R2 < 0.94) justified subsequent use of STARFM data in this environment, particularly when the imagery were generated from Nadir Bi-directional Reflectance Factor (BRDF)-Adjusted Reflectance (NBAR) MODIS datasets. The primary objective of the second study was to assess whether synthetic Landsat data could contribute meaningful information to the phenological analyses of a range of dryland vegetation classes. Start-of-season (SOS) and date of peak greenness phenology metrics were calculated for each STARFM and MODIS pixel on the basis of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) time series over a single growing season. The variability of each metric was calculated for all STARFM pixels within 500 m MODIS extents. Colorado Plateau Pinyon Juniper displayed high amounts of temporal and spatial variability that justified the use of STARFM data, while the benefit to the remaining classes depended on the specific vegetation index and phenology metric. The third study expanded the STARFM time series to five years (2005-2009) to examine the influence of site characteristics and climatic conditions on dryland ponderosa pine (Pinus ponderosa) forest phenological patterns. The results showed that elevation and slope controlled the variability of peak timing across years, with lower elevations and shallower slopes linked to higher levels of variability. During drought conditions, the number of site variables that controlled the timing and variability of vegetation peak increased.
Ph. D.

The population, employment and housing units along the Gulf Coast of Mississippi have been increasing since the 1970s through the 2000s. In this study, an overall increasing trend in land cover was found in developed land area near interstates and highways along all three coastal counties. A strong positive correlation was observed in Hancock County between developed land and population and developed land and housing units. A strong negative correlation was observed between vegetation and housing units. Weak positive correlations were found in Harrison County between developed land and population, marsh and population, and marsh and housing units. A weak positive correlation was found in Jackson County between bare soil and population. Several study limitations such as unsupervised classification and misclassification are discussed to explain why a strong correlation was not found in Harrison and Jackson Counties.

The Urban Heat Island (UHI) effect is a function of excess heating of man-made impermeable surfaces and structures. Using Landsat satellite imagery along with its Thermal-Infrared (TIR) band, the UHI of Starkville, MS; Birmingham, AL; and Atlanta, GA were analyzed. Unsupervised classification of the Landsat imagery and temperature extraction from the TIR band revealed city size and amount of high-density urban land use are directly related to UHI intensity and higher than average surface temperatures. Vegetation analysis within the three study area cities, however, revealed an average surface temperature reduction of 2 °C with only 15% forest coverage within a 1km² area. Results obtained can be useful as a potential monitoring tool that can characterize relationships between amount and percentage of urban tree cover and surface temperature. The information can be utilized by city planners and others who are interested in mitigating UHI effects in the ever- increasing urban America.

Carried out in collaboration with the Occoquan Water Monitoring Lab, this thesis presents the results of research that sought to ascertain the spatial distribution of chlorophyll-a concentrations in the Lake Manassas Reservoir using a combination of Landsat TM satellite imagery and ground based field measurements. Images acquired on May 14, 1998 and March 8, 2000 were analyzed with chlorophyll-a measurements taken on 13, 1998 and March 7, 2000. A ratio of Landsat TM band 3: Landsat Band 4 was used in a regression with data collected at eight water quality monitoring stations run by the Occoquan Watershed Monitoring Lab. Correlation coefficients of 0.76 for the 1998 data and 0.73 for the 2000 data were achieved. Cross validation statistical analysis was used to check the accuracy of the two models. The standard error and error of the estimate were reasonable for the models from both years. In each instance, the ground data was retrieved approximately 24 hours before the Landsat Image acquisition and was a potential source of error. Other sources of error were the small sample size of chlorophyll-a concentration measurements, and the uncertainty involved in the location of the water quality sampling stations.
Master of Science

Wetlands are considered to be one of the most valuable natural occurring forms of land cover in the world. Hydrologic regulation, carbon sequestration, and habitat provision for a wide assortment of flora and fauna are just a few of the benefits associated with wetlands. The implementation of satellite remote sensing has been demonstrated to be a reliable approach to monitoring wetlands over time. Unfortunately, a national wetland inventory does not exist for Canada at this time. This study employs a supervised classification method of Landsat satellite imagery between 1976 and 2008 within the Tunkwa watershed, southwest of Kamloops, British Columbia, Canada. Images from 2005 and 2008 were repaired using a gap-filling technique due to do the failure of the scan-line corrector on the Landsat 7 satellite in 2003. Percentage pixel counts for wetlands were compared, and a diminishing trend was identified; approximately 4.8% of wetland coverage loss was recognized. The influence of the expansion of Highland Valley Copper and the forestry industry in the area may be the leading causes of wetland desiccation. This study expresses the feasibility of wetland monitoring using remote sensing and emphasizes the need for future work to compile a Canadian wetland inventory.

Mestrado em Engenharia do Ambiente - Instituto Superior de Agronomia
Computer Assisted Photo-Interpretation (CAPI) uses remotely sensed imagery to control farmers’ subsidy applications in the context of the EU’s Common Agriculture Policy. A simple and reproducible method to automatize CAPI in an operational context with the overreaching goal to reduce control costs and completion time was developed in this study. Validated control data provided by the Portuguese Control and Paying Agency for Agriculture (IFAP) and a multispectral atmospherically corrected Landsat ETM+ time series were used to calibrate and test the method. Taking advantage of the nature of subsidy declarations, object-based land cover classification for the 12 most controlled classes was carried out in the region of Ribatejo. The main feature of the presented method is that it allows choosing a confidence level on the automatic classification of farmers’ parcels. While higher confidence levels reduce the risk of misclassifications, lower levels increase the number of automatic control decisions. A confidence level of 80% is a good compromise. This confidence level leads to over 55% of automatically taken control decisions with an overall accuracy of 84%. Furthermore, over 85% of all parcels classified as maize, rice, wheat or vineyard can be controlled by the method with the optimal confidence level.

Desert wetlands, in particular those slow moving bodies of water known as cienegas, are important sites for biodiversity in arid landscapes and serve as indicators of hydrological functioning on the landscape-level. One of the most extensive systems of cienegas, historical or extant, in southeastern Arizona lies along Cienega Creek, located southeast of Tucson, Arizona. Satellite imagery analysis is heavily utilized to determine landscape-level trends, but cienegas present a challenge to traditional analysis methods. The Normalized Difference Vegetation Index (NDVI), the classic measure of vegetation greenness, reacts counter-intuitively to open water and is affected by open ground, both common occurrences in cienega habitats. Additional remote sensing indices have been developed that balance sensitivity to these environmental elements. This research explores these remote sensing indices at Cienega Creek applying one topographic index to current elevation data and five spectral indices to Thematic Mapper imagery from 1984 to 2011. Temporal trends were identified for all spectral indices and all indices were compared for suitability in cienega habitats. Temporal trends were analyzed for spatial clustering and spatial trends identified. The Normalized Difference Infrared Index utilizing Landsat Thematic Mapper band 5 outperformed other indices at differentiating between cienega, riparian, and upland habitats and is more suitable than NDVI for analyzing cienega habitats in such circumstances.

En utilisant les donnees d'une simulation spot a 7 canaux (arizona), ce travail a permis de tester l'apport qualitatif et quantitatif des donnees spot. Une etude effectuee sur les canaux complementaires a montre que les canaux moyen infra-rouge optimisent la configuration actuelle du satellite spot. Cette etude a permis de juger la fidelite incontestable des donnees spot par rapport aux observations geologiques et aux donnees radiometriques de terrain et de laboratoire. L'elaboration d'une carte geologique au 1/250 000 eme d'un secteur du sonora central, utilisant des images landsat mss a permis une etude structurale de la region. Ce qui a permis de tester la faisabilite, le gain de temps et la fiabilite de la carte geologique ainsi elaboree, dont sont temoins les ameliorations apportees par rapport aux cartes preexistantes. Par des methodes de traitement d'images (manipulation sur console pericolor) et l'utilisation de la cartographie elaboree precedemment, on a pu mettre au point une methode de prospection utilisant le traitement d'image et les donnees radiometriques de terrain dans le domaine de l'exploration miniere (testee dans le cadre de trois zones metallogeniques de reference)

Land cover change detection in urban areas provides valuable data on loss of forest and agricultural land to residential and commercial development. Using Landsat 5 Thematic Mapper (1991) and Landsat 7 ETM+ (2000) imagery of Gwinnett County, GA, change images were obtained using image differencing of Normalized Difference Vegetation Index (NDVI), principal components analysis (PCA), and Tasseled Cap-transformed images. Ground truthing and accuracy assessment determined that land cover change detection using the NDVI and Tasseled Cap image transformation methods performed best in the study area, while PCA performed the worst of the three methods assessed. Analyses on vegetative and vegetation changes from 1991- 2000 revealed that these methods perform well for detecting changes in vegetation and/or vegetative characteristics but do not always correspond with changes in land use. Gwinnett County lost an estimated 13,500 hectares of vegetation cover during the study period to urban sprawl, with the majority of the loss coming from forested areas.

Thesis (M.A.)--Georgia State University, 2005.
Title from title screen. Zhi-Yong Yin, committee chair; Paul Knapp, Truman Hartshorn, committee members. Electronic text (135 p. : col. ill., col. maps)) : digital, PDF file. Description based on contents viewed Aug. 2, 2007. Includes bibliographical references (p. 125-133).

Biomass burning in southern Africa has brought significant challenges to the research society as a fundamental driver of climate and land cover changes. Burned area mapping approaches have been developed that generate large-scale low and moderate resolution products made with different satellite data. This consequently afford the remote sensing community a unique opportunity to support their potential applications in e.g., examining the impact of fire on natural resources, estimating the quantities of burned biomass and gas emissions. Generally, the satellite-derived burned area products produced with dissimilar algorithms provide mapped burned areas at different levels of accuracy, as the environmental and remote sensing factors vary both spatially and temporally. This study focused on the inter-comparison and accuracy evaluation of the 500-meter Moderate Resolution Imaging Spetroradiomter (MODIS) burned area product (MCD45A1) and the Backup MODIS burned area product (hereafter BMBAP) across the main-fire prone South African biomes using reference data independently-derived from multi-temporal 30-meter Landsat 5 Thematic Mapper (TM) imagery distributed over six validation sites. The accuracy of the products was quantified using confusion matrices, linear regression and subpixel burned area measures. The results revealed that the highest burned area mapping accuracies were reported in the fynbos and grassland biomes by the MCD45A1 product, following the BMBAP product across the pine forest and savanna biomes, respectively. Further, the MCD45A1 product presented higher subpixel detection probabilities for the burned area fractions <= 50% than the BMBAP product, which appeared more reliable in detecting burned area fractions > 50% of a MODIS pixel. Finally the results demonstrated that the probability of identifying a burned area within a MODIS pixel is directly related to the proportion of the MODIS pixel burned and thus, highlights the relevance of fractional burned area during classification accuracy assessment of lower resolution remotely-sensed products using data with higher spatial resolution.
Dissertation (MSc)--University of Pretoria, 2011.
Geography, Geoinformatics and Meteorology
unrestricted

Soil attributes directly influence on its surface temperature. Although there are several studies using soil spectra obtained from satellites, soil evaluation through Land Surface Temperature (LST) is still scarce. The broad availability of satellite thermal data and the development of algorithms to retrieve LST facilitated its use in soil studies. The objective of this study was to evaluate soil LST variations due to its composition and verify the potential of using LST on soil attributes quantification, also integrated with reflectance spectra and elevation data. The study area (198 ha) is located in Sao Paulo state, Brazil, and had plowed bare soil during the satellite image acquisition date. Soil samples were collected in a regular grid of 100 x 100 m (depths: 0-0.2 m and 0.8- 1.0 m); soil granulometry, organic matter (OM) and iron oxides were determined by wet chemistry analysis. In this study, an image of Landsat 5 was used for extracting LST using the inversion of Planck\'s function in band 6 (10,400 - 12,500 nm), and land surface emissivity was estimated using Normalized Difference Vegetation Index threshold method. Reflectance values were extracted from bands 1, 2, 3, 4, 5 and 7. Models for soil attributes quantification were performed using Linear Regression (LR), with samples from 62 auger points distributed in 14 toposequences. Simple LR was applied for generating prediction models based on LST and on elevation data (extracted from a Digital Elevation Model). Multiple LR was applied in order to generate prediction models using atmospherically corrected spectral reflectance from Visible, Near-Infrared and Shortwave infrared (Vis-NIR-SWIR) bands as predictors, and also for the prediction of soil attributes using simultaneously Vis-NIR-SWIR, LST and elevation data, and only significant variables identified by T-tests were used. Predictive performance of models was assessed based on adjusted coefficient of determination (R2adj), Root Mean Squared Error (RMSE, g kg-1) and Ratio of Performance to Interquartile Range (RPIQ) obtained in validation. Ordinary kriging was also performed and the resulted interpolated surfaces were compared to the maps obtained from the best LR model. There was significant correlation between soil attributes and reflectance, LST and elevation data, and soils with clay texture were differentiated from sandy soils based on LST mean values. For all soil attributes, models using only elevation presented the worst performance; models using only LST, moderate performance; and using Vis-NIR-SWIR bands, good predictive performance. For clay, the best model obtained had bands 4-7, LST and elevation as predictors; for sand and iron oxides, the best model had bands 4-7 and LST; for OM, band 4, band 7 and LST. The use of LST for estimating soil attributes increases the predictive performance of multiple LR models when associated with other variables obtained through remote sensing, particularly surface reflectance data, improving the validation of models reaching high R2adj, high RPIQ and low RMSE values. Maps for sand, OM and iron oxides obtained through ordinary kriging outperformed those obtained for the same attributes using LR models based on RS co-variables, and for clay, both approaches reached the same accuracy level. Mapping of soil clay, sand, OM and iron oxides contents through multiple LR models using Landsat 5 products is a simple and easy to reproduce technique, appropriate for soil attributes mapping in bare soil agricultural areas.
Os atributos do solo influenciam diretamente na sua temperatura de superfície. Apesar de existir vários estudos utilizando espectros de solos obtidos de satélite, a avaliação do solo por meio da Temperatura de Superfície Terrestre (em inglês Land Surface Temperature, LST) ainda é escassa. A ampla disponibilidade de dados termais de satélite e o desenvolvimento de algoritmos para derivar a LST facilitou o seu uso em estudos de solos. O objetivo desse trabalho foi avaliar variações da LST do solo devidas à sua composição e verificar o potencial de uso da LST na quantificação de atributos do solo, também integrada com dados de espectros de reflectância e elevação. A área de estudo (198 ha) está localizada no estado de São Paulo, Brasil, e estava com solo exposto e arado na data de aquisição da imagem de satélite. Amostras de solo foram coletadas em um grid regular de 100 x 100 m (profundidades: 0.02 m e 0.8-1.0 m); a granulometria do solo, matéria orgânica (MO) e óxidos de ferro foram determinados via análises físicas e químicas laboratoriais. Neste estudo, uma imagem do Landsat 5 foi utilizada para extrair a temperatura de superfície usando a inversão da função da Lei de Planck na banda 6 (10.400 - 12.500 nm), e a emissividade de superfície foi estimada utilizando o método do limiar do Índice de Vegetação da Diferença Normalizada. Valores de reflectância das bandas 1, 2, 3, 4, 5 e 7 foram extraídos. Modelos para quantificação de atributos do solo foram feitos usando Regressão Linear (RL), com amostras de 62 pontos de tradagem distribuídos em 14 topossequências. A RL simples foi aplicada para gerar modelos de predição baseados na LST e também na elevação (extraída de um modelo digital de elevação). A RL múltipla foi aplicada para gerar modelos de predição usando os espectros de reflectância com correção atmosférica das bandas do Visível, Infravermelho próximo e Infravermelho de ondas curtas (Vis-NIR-SWIR) como preditores; também foi aplicada para predição de atributos do solo usando simultaneamente dados do Vis-NIR-SWIR, LST e elevação, e apenas variáveis significativas identificadas por teste T foram usadas. A performance preditiva dos modelos foi avaliada baseada no coeficiente de determinação ajustado (R2adj), raiz do erro quadrático médio (RMSE, g kg-1) e razão de desempenho do intervalo interquartil (RPIQ) obtidos na validação. A krigagem ordinária também foi feita e as superfícies interpoladas resultantes foram comparadas com o melhor modelo de RL. Houve correlação significativa entre os atributos do solo e dados de reflectância, LST e elevação, e solos com textura argilosa foram diferenciados de solos arenosos com base em valores médios de LST. Para todos os atributos do solo, os modelos usando apenas elevação apresentaram a pior performance, modelos usando somente LST, performance moderada, e usando as bandas do Vis-NIR-SWIR, boa performance preditiva. Para argila, o melhor modelo obtido teve as bandas 4-7, LST e elevação como preditores; para areia e óxidos de ferro, o melhor modelo teve as bandas 4-7 e LST; para MO, banda 4, banda 7 e LST. O uso da LST para estimar atributos do solo aumenta a performance preditiva de modelos de RL múltipla quando associada a outras variáveis obtidas via sensoriamento remoto (SR), particularmente dados de reflectância de superfície, melhorando a validação dos modelos atingindo altos valores de R2adj e RPIQ e baixos valores de RMSE. Os mapas para areia, MO e óxidos de ferro obtidos via krigagem ordinária superaram aqueles obtidos para os mesmos atributos usando modelos de RL baseados em co-variáveis obtidas via SR, e para argila, ambas abordagens atingiram o mesmo nível de acurácia. O mapeamento dos conteúdos de argila, areia, matéria orgânica e óxidos de ferro do solo via modelos de RL múltipla utilizando produtos do Landsat 5 é uma técnica simples e fácil de reproduzir, apropriada para o mapeamento de atributos do solo em áreas de agricultura com solo exposto.

The political history of U.S. commercial remote sensing began in 1984 when the U.S. government first attempted to commercialize its civil earth observation satellite system " Landsat. Since then, the high technology of earth imaging satellite systems has generated intense debates and policy conflicts, primarily centered on U.S. government concerns over the national security and foreign policy implications of high-resolution commercial satellite systems. Conversely, proponents of commercial observation satellites have urged U.S. policymakers to recognize the scientific and socio-economic utility of commercial remote sensing and thus craft and implement regulatory regimes that allow for a greater degree of information openness and transparency in using earth observation satellite imagery. This dissertation traces and analyzes that tumultuous political history and examines the policy issues and social construction of commercial remote sensing to determine the role of knowledge in the effective crafting and execution of commercial remote sensing laws and policies. Although individual and organizational perspectives, interests, missions, and cultures play a significant role in the social construction of commercial observation satellite systems and programs, the problem of insufficient knowledge of the myriad dimensions and complex nature of commercial remote sensing is a little studied but important component of this social construction process. Knowledge gaps concerning commercial remote sensing extend to various dimensions of the subject matter, such as the global, economic, technical, and legal/policy aspects. Numerous examples of knowledge voids are examined to suggest a connection between deficient knowledge and divergent policy perceptions as they relate to commercial remote sensing. Relevant knowledge voids are then structurally categorized to demonstrate the vastness and complexity of commercial remote sensing policy issues and to offer recommendations on how to fill such knowledge gaps to effect increased collaboration between the US government and the U.S. commercial remote sensing industry. Finally, the dissertation offers suggestions for future STS studies on policy issues, particularly those that focus on the global dimensions of commercial remote sensing or on applying the knowledge gap concept advanced by this dissertation to other areas of science and technology policymaking.
Ph. D.

Leaf area index (LAI) is an important variable in models that attempt to simulate carbon, nutrient, water and energy fluxes for forest ecosystems. LAI can be measured either directly (destructive sampling) or by using indirect techniques that involve estimation of LAI from light penetration through canopies. Destructive sampling techniques are laborious, expensive and can only be carried out for small plots. Although indirect techniques are non-destructive and less time consuming, they assume a random foliage distribution that rarely occurs in nature. Thus a technique is required that would allow for rapid estimation of LAI at the stand level. A means of getting this information is via remotely sensed measurements of reflected energy with an airborne or satellite-based sensor. Such information on an important plant species such as Acacia mearnsii (Black Wattle) is vital as it provides an insight into its water use. Landsat ETM+ images covering four study sites In KwaZulu-Natal midlands encompassing pure stands of Acacia mearnsii were processed to obtain four types of vegetation indices (VIs). The indices included: normalized difference vegetation index (NDVI), ratio vegetation index (RVI), transformed vegetation index (TVI) and vegetation index 3 (VB). Ground based measurements of LAI were made using destructive sampling (actual LAI) and LAI-2000 optical instrument, (plant area index, PAl). Specific leafarea (SLA) and leaf area (LA) were measured in the field for the entire sample stands to estimate their LAI values. The relationships between the various VIs and SLA, actual LAI and PAl values measured by LAI-2000 were evaluated using correlation and regression statistical analyses. Results showed that the overall mean SLA value of Acacia mearnsii was 8.28 m2kg-1 SLA showed strong correlations with NDVI (r=0.71, pThesis (M.Sc.)-University of Natal, Pietermaritzburg, 2003.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Twenty-five years after the implementation of the Birds Directive in 1979, Europe‟s farmland bird species and long-distance migrants continue to decrease at an alarming rate. Farmland supports more bird species of conservation concern than any other habitat in Europe. Therefore, it is imperative to understand farmland species‟ relationship with their habitats. Bird conservation requires spatial information; this understanding not only serves as a check on the individual species‟ populations, but also as a measure of the overall health of the ecosystem as birds are good indicators of the state of the environment. The target species in this study is the corn bunting Miliaria calandra, a bird whose numbers in northern and central Europe have declined sharply since the mid-1970s. This study utilizes public domain data, namely Landsat imagery and CORINE land cover, along with the corn bunting‟s presence-absence data, to create a predictive distribution map of the species based on habitat preference. Each public domain dataset was preprocessed to extract predictor variables. Predictive models were built in R using logistic regression.(...)

Optimal scale of study and scaling are fundamental to ecological research, and have been made easier with remotely sensed (RS) data. With access to RS data at multiple scales, it is important to identify how they compare and how effectively information at a specific scale will potentially transfer between scales. Therefore, my research compared the spatial, spectral, and temporal aspects of scale of RS data to study biophysical properties and spatio-temporal dynamics of the northern mixed prairie vegetation. I collected ground cover, dominant species, aboveground biomass, and leaf area index (LAI) from 41 sites and along 3 transects in the West Block of Grasslands National Park of Canada (GNPC; +49°, -107°) between June-July of 2006 and 2007. Narrowband (VIn) and broadband vegetation indices (VIb) were derived from RS data at multiple scales acquired through field spectroradiometry (1 m) and satellite imagery (10, 20, 30 m). VIs were upscaled from their native scales to coarser scales for spatial comparison, and time-series imagery at ~5-year intervals was used for temporal comparison. Results showed VIn, VIb, and LAI captured the spatial variation of plant biophysical properties along topographical gradients and their spatial scales ranged from 35-200 m. Among the scales compared, RS data at finer scales showed stronger ability than coarser scales to estimate ground vegetation. VIn were found to be better predictors than VIb in estimating LAI. Upscaling at all spatial scales showed similar weakening trends for LAI prediction using VIb, however spatial regression methods were necessary to minimize spatial effects in the RS data sets and to improve the prediction results. Multiple endmember spectral mixture analysis (MESMA) successfully captured the spatial heterogeneity of vegetation and effective modeling of sub-pixel spectral variability to produce improved vegetation maps. However, the efficiency of spectral unmixing was found to be highly dependent on the identification of optimal type and number of region-specific endmembers, and comparison of spectral unmixing on imagery at different scales showed spectral resolution to be important over spatial resolution. With the development of a comprehensive endmember library, MESMA may be used as a standard tool for identifying spatio-temporal changes in time-series imagery. Climatic variables were found to affect the success of unmixing, with lower success for years of climatic extremes. Change-detection analysis showed the success of biodiversity conservation practices of GNPC since establishment of the park and suggests that its management strategies are effective in maintaining vegetation heterogeneity in the region. Overall, my research has advanced the understanding of RS of the northern mixed prairie vegetation, especially in the context of effects of scale and scaling. From an eco-management perspective, this research has provided cost- and time-effective methods for vegetation mapping and monitoring. Data and techniques tested in this study will be even more useful with hyperspectral imagery should they become available for the northern mixed prairie.

Snow occurs in the High Drakensberg of southern Africa approximately eight times per annum. Snow cover is frequently captured by Landsat satellite imagery, which provide data for the monitoring of snow cover in other regions of the world. Together with a digital elevation model, repetitive snow cover data are used to analyse the distribution of snow cover in the High Drakensberg study area. The effect that the regional and local topography, latitude, and climatic conditions have on the spatial distribution of snow and the function that temperature, wind, altitude, aspect and slope gradient play in the preservation of snow cover are examined. The results of the spatial study allow for the identification of sites that support the accumulation of snow. Specific active and relict geomorphological features were surveyed and correlated spatially to the contemporary snow cover. Among such features are linear debris ridges on south-facing valley slopes in the High Drakensberg. These appeared similar to glacial features found elsewhere in the world and are thus significant in a long-standing and highly conjectured debate over the validity of possible plateau, cirque and niche glaciation in the region. Late-lying snow cover favours gently sloping south- and southeast-facing aspects at altitudes from 3000 m ASL to just below the highest peaks in the region near 3450 m ASL, above which higher insolation levels on the flat mountain summits provides unfavourable conditions. Snow cover immediately adjacent to the Drakensberg escarpment ablates quickly whilst snow cover at high altitudes in the Lesotho interior experiences better preservation conditions. Latitude has no obvious impact on the distribution of snow cover due to the dominant role of topography in the High Drakensberg other than a limiting of snowfall to regions south of 29°S in late spring. Various synoptic conditions produce snowfall in the region, with cold fronts associated with midlatitude cyclones producing the majority of snow cover. A strong correlation exists between the spatial distribution of snow cover and specific geomorphological features. Observed linear debris ridges are located on slopes that experience frequent contemporary snow cover, lending credence for a glacial origin of the ridges during a period of colder environmental conditions.