Abstract On September 24, 1999, the first high-resolution commercial nature resource satellite, IKONOS-2, had been successfully launched. This started a new era to the applications of remote sensing. The best resolution of the IKONOS imageries is 0.82m. This imagery provides more detail spatial information than previous satellites. Many high spatial resolution satellites with hyper-spectral imageries will be launched successively by the year of 2002. When the time comes, the application of remotely sensed images in the area of land and sea will certainly be more widespread. Those imageries will be the fundamental data source for digital earth. The main purpose of this paper is to apply the IKONOS multi-spectral satellite imageries to derive the shallow water depth. Two key studies will be included as follows. The first is to discuss the high-resolution characteristics of IKONOS images and its precise geometrically correction. The other is applying the multi-spectral images to calculate water depth by regression with few field-measured bathymetry. It is anticipated that the high-resolution remote sensing technology will be an alternative tool to the shallow water bathymetric surveying. The rugged terrain imageries of CARTERRA Geo level in Taipei County were selected and the bundle adjustment was used for precise images geometrically correction. The positioning accuracy is approximate 1.83m for east-west direction, 1.35m for north-south direction, and 1.6m for elevation. If the orthophoto is been rectified by using bundle adjustment method, the horizontal position accuracy of the check points is about 2.04m. In accordance with these results, using bundle adjustment in the CARTERRA Geo level imagery rectification has proved feasible. In the study of using muti-spectral images to derive the shallow water depth, both simulated data, IKONOS and SPOT satellite images of South Bay in KenTing are used to verify the influence of wave effect in the satellite imageries. By means of the concept of multi-resolution analysis in wavelet theory, the Daubechies D4 coefficients is tried to filter out the wave effect. Significant improvement on the shallow water depth calculation after filtering wave effect is shown in the result. The accuracy of water depth derivation using high resolution is about 30cm for the water depth shallower than 10m. This research proves that derivation of shallow water depth by using high-resolution satellite imagery is feasibility.

The past years has seen the advent of the availability of high resolution commercial satellite imagery. This study shows that whilst high resolution commercial satellite imagery is capable of producing reasonable spatial data both in quality and cost for use in an urban GIS the challenges of supplying this data commercially is not limited to simply the provision of the imagery. Since a significant amount of work has been done by others to examine and quantify the technical suitability and limitations of high resolution commercial satellite imagery, this study examines the practical limitations and opportunities presented with the arrival of this new spatial data source. In order to do this a number of areas are examined; the historical development of the satellite systems themselves, the business evolution of the owning commercial ventures, Geographical Information Systems (GIS) data and service requirements for a diverse range of spatial data applications and finally the evaluation and comparison of the imagery as a spatial data source. The study shows that high resolution commercial satellite imagery is capable of providing spatial data and imagery for a variety of uses at different levels of accuracy as well as opening up a new era in the supply and application of metric imagery. From a technical approach high resolution commercial satellite imagery provides remote access, one metre or better resolution, 11 bit imagery and a multispectral capability not previously available from space. Equally as challenging is the process or achievement in making the technical capability a reality in a commercial world requiring a financial return at all levels; from the image vendors to the spatial science professional providing a service to a paying customer. The imagery must be financially viable for all concerned.

Made available in DSpace on 2014-06-11T19:23:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2001-11-26Bitstream added on 2014-06-13T20:50:28Z : No. of bitstreams: 1 ishikawa_mi_me_prud.pdf: 658123 bytes, checksum: e1d7f4d23437dc8bb19a8805ed344335 (MD5) / O grande avanço tecnológico desta década, na área de Sensoriamento Remoto, pode ser percebido quando são observadas as grandes mudanças nas características dos sistemas orbitais mais tradicionais, bem como da nova geração de sistemas sensores desenvolvidos com o intuito de auxiliar, cada vez mais, as tarefas de identificação de alvos na superfície terrestre, devido à grande melhoria na resolução espacial. Produtos orbitais de alta resolução, com grau de detalhamento em torno do metro, permitem um melhor aproveitamento das imagens em aplicações cartográficas. O mercado de mapeamento urbano atualmente é ainda quase inteiramente baseado em fotografias aéreas. Porém, o Sensoriamento Remoto orbital vem passando por uma grande evolução tecnológica desde o final de 1999, quando foi lançado pela empresa norte-americana Space Imaging o satélite IKONOS. Este satélite possui sensores capazes de gerar imagens com lmetro de resolução espacial no modo pancromático e 4 metros no modo multiespectral. Estas imagens permitem o mapeamento da cobertura e uso do solo de maneira detalhada e continuada, desde que sejam usados métodos e/ou técnicas apropriadas. Este trabalho teve como objetivo fazer um estudo do potencial de uso das imagens geradas pelo satélite IKONOS, produto Geo, no que diz respeito a escala máxima de utilização em aplicações cartográficas. O procedimento para verificar a exatidão cartográfica baseou-se na análise estatística das discrepâncias entre as coordenadas de pontos no terreno, obtidas através do GPS, e as coordenadas dos pontos homólogos extraídas da imagem IKONOS, através da análise da existência de tendências e da precisão. Como resultado final, chegou-se a conclusão que a imagem IKONOS/Geo utilizada é adequada a escala 1:50000 e menores. / The huge technological advancement that occurred in this decade, in the field of Remote Sensing, can be well perceived when we observe the great changes that occurred in the characteristics of the more traditional orbital systems, as well as of those which belong to the new generation of sensor systems developed with the aim of helping, more and more, the tasks of identification of targets on the Earth surface, due to the improvement on the spatial resolution. Orbital products of high resolution with the possibility of showing details of about one meter in size allow a better employment of imagery in cartographic applications. The urban mapping market is nowadays almost totally based on aerial photography. However, the orbital Remote Sensing is getting through a immense technological evolution since the end of 1999, when the satellite IKONOS was launched by a north American company called Space Imaging. This satellite has sensors capable of generating images with 1 meter resolution in the panchromatic mode and 4 meter resolution in the multispectral mode. These imagery allow mapping the land cover and use in a detailed and continuous manner, providing the appropriate methods and/or techniques are used. This dissertation aimed at studying the potential use of such imagery obtained by IKONOS satellite, Geo Product, specially with respect to the maximum scale of employment for cartographic applications. The approach for the checking the cartographic accuracy was based upon the statistical analysis of discrepancies between the coordinates on the ground, obtained by the use of GPS, and the coordinates of homologue points extracted from the IKONOS imagery, through the analysis of existence of trend and also by the analysis of precision. As a final result, it has been found that the IKONOS/Geo imagery is useful for mapping at 1:50.000 and smaller scales.

Submitted by Nathália Faria da Silva (nathaliafsilva.ufv@gmail.com) on 2017-06-29T13:50:41Z No. of bitstreams: 1 texto completo.pdf: 8017325 bytes, checksum: d629f140a9c8dc8b022cd34baa6561d4 (MD5) / Made available in DSpace on 2017-06-29T13:50:41Z (GMT). No. of bitstreams: 1 texto completo.pdf: 8017325 bytes, checksum: d629f140a9c8dc8b022cd34baa6561d4 (MD5) Previous issue date: 2005-02-21 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / A partir de uma imagem de satélite IKONOS II, de uma área no Médio Rio Doce, localizada entre os municípios de Governador Valadares e Tumiritinga, com 56,73 km 2 , foi realizada uma identificação da intensidade de degradação do solo e da cobertura vegetal. A análise da imagem foi realizada pelo sistema de fotointerpretação cujos resultados foram processados em softwares de SIG ArcInfo e ArcView (ESRI, 1996). Paralelamente foram empregados dados históricos da área e dados do meio físico contidos nos mapas de solos e mapas planialtimétricos, dos quais foram obtidas informações sobre hidrografia; relevo e rede viária. De forma complementar, foram efetuadas visitas ao campo para coleta de informações, compondo uma fonte primária de dados. Como resultados, foram obtidos os mapas de Geoformas, Cobertura Vegetal e Geoambientes e Degradação. Para obtenção do mapa de Geoambientes e Degradação foram identificadas as intensidades de degradação do solo e vegetação, tendo como referência as características presentes e pretéritas da área considerada. Foram efetuadas observações de campo sobre os processos e intensidades de degradação desses ambientes. Para tanto, foram elaboradas tabelas que correlacionam a intensidade de degradação com os indicadores presentes no solo e na vegetação da área estudada. Foram identificadas quatro classes de degradação (cd): cd 1 - muito baixa ou leve, que corresponde a 7,5% da área; cd 2 - baixa ou moderada, observada em 7,2% da área; cd 3 - alta ou forte - presente em 47,5% da área e cd 4 - muito alta ou muito forte - cuja ocorrência foi identificada em 37,8% da área. / An identification of the intensity of soil degradation and vegetal covering of an area in the Médio Rio Doce, located between the cities of Governador Valadares and Tumiritinga, with 56,73 km 2 , was established based on an image of satellite IKONOS II. The analysis of the image was performed by means of the system of photointerpretation whose results were processed using softwares of SIG ArcInfo and ArcView (ESRI, 1996). Parallelly, historic data of the land area and data of the physical environment contained in soil maps and in planialtimetric maps were used. Information about hydrography, relief and roads were collected from planialtimetric maps. As a complementary approach, field trips were made in order to collect information, composing this way, a primary source of data. As a result, maps of Geoforms, Vegetal Covering, Geoenvironment and Degradation were attained. In order to obtain the map of Geoenvironment and Degradation, intensities of soil degradation as well as vegetation were identified having the present and preterit characteristics of the considered land area as reference. In addition, field observation about the degradation intensity process of these environments was performed. For such, tables which correlate the intensity of degradation to the indicative elements present in the soil and in the vegetation of the analyzed area were elaborated. According to the undertaken studies, four classes of degradation were identified (cd): cd 1 - very low or slight, which corresponds to 7,5% of the area; cd 2 - low or moderate, observed in 7,2% of the area; cd 3 - high or strong, present in 47,5% of the area and cd 4 - very high or very strong, whose occurrence was identified in 37,8% of the area.

The construction of Baku-Tbilisi-Ceyhan (BTC) Oil and South Caucasus Gas (SCP) pipelines was completed in 2005. The Azerbaijan section of BTC Oil and SCP Gas pipelines is 442 km long and 44 m wide corridor named as the Right-of-Way. BTC and SCP pipelines are aligned parallel to each other within the same 44m corridor. The construction process of the pipelines significantly disturbed vegetation and soil cover along Right-of-Way of pipelines. The revegetation and erosion control measures were conducted after the completion of construction to restore the disturbed footprints of construction activities. The general goals of the present studies, dedicated to the environmental monitoring of revegetation and planning of erosion control measures were: to evaluate the status of the revegetation in 2007 since the completion of the construction activities in 2005, to determine the climate and ground factors controlling the vegetation regrowth and to predict erosion-prone areas along Right-of-Way of pipelines. Regression and root mean square error analysis between the Normalized Difference Vegetation Index (NDVI) of IKONOS images acquired in 2007 and in-situ estimations of vegetation cover percentage revealed R2 equal to 0.80 and RMSE equal to 6% which were optimal for the normalization of NDVI to vegetation cover. The total area of restored vegetation cover between 2005 and 2007 was 8.9 million sq. m. An area of 10.7 million sq. m. of ground vegetation needed restoration in order to comply with the environmental acceptance criteria. Based on the Global Spatial Regression Model, precipitation, land surface temperature and evapotranspiration were determined as the main climate factors controlling NDVI of grasslands along Right-of-Way of pipelines. In case of croplands, precipitation, evapotranspiration and annual minimum temperature were determined as the main factors controlling NDVI of croplands. The regression models predicting NDVI for grasslands and croplands were also formulated. The Geographically Weighted Regression analyses in comparison with the global regression models results clearly revealed that the relationship between NDVI of grasslands and croplands and the predictor variables was spatially non-stationary along the corridor of pipelines. Even though the observed R2 value between elevation and NDVI of grasslands was low (R2= 0.14), the accumulation of the largest NDVI patterns was observed higher than 150m elevation. This revealed that elevation has non-direct control of NDVI of grasslands through its control of precipitation and temperature along the grasslands of Right-of-Way. The spatial distribution percentage of NDVI classes within slope aspect categories was decreasing in the southern directions of slope faces. Land surface temperature was decreasing with elevation but no particular patterns of land surface temperature in the relationship with NDVI accumulation within the aspect categories were observed. Aspect categories have non-direct control of NDVI and there are some other factors apart from land surface temperature which require further investigations. Precipitation was determined to be controlling the formation of topsoil depth and the topsoil obviously controls the VC growth of grasslands as one of the main ground factors. The regression analysis between NDVI of grasslands and croplands with groundwater depth showed very low correlation. But the clustered patterns of vegetation cover were observed in the relationship with groundwater depth and soil moisture for both grasslands and croplands. The modeling of groundwater depth relative to soil moisture and MODIS NDVI of grasslands determined that the threshold of groundwater depth for vegetation growth is in the range of 1-5 m. MODIS NDVI and soil moisture did not reveal a significant correlation. Soil moisture revealed R2 equal to 0.34 with elevation, R2 equal to 0.23 with evapotranspiration, R2 equal to 0.57 with groundwater depth and R2 equal to 0.02 with precipitation. This allowed to suspect that precipitation is not the main factor controlling soil moisture whereas elevation, evapotranspiration and groundwater depth have non-direct control of soil moisture. Therefore, soil moisture has also non-direct control of vegetation cover growth along the corridor of pipelines. The variations of soil moisture in the 1-3 m soil depth range may have the threshold of depth controlling vegetation cover regrowth and this requires more detailed soil moisture data for further investigations. The reliability of the Global Spatial Regression Model and Geographically Weighted Regression predictions is limited by the MODIS images spatial resolution equal to 250 m and spectral characteristics. The Morgan-Morgan-Finney (MMF) and Universal Soil Loss Equation (USLE) predictions revealed non-similarity in the spatial distribution of soil loss rates along Right-of-Way. MMF model revealed more clustered patterns of predicted critical erosion classes with soil loss more than 10 ton/ha/year in particular ranges of pipelines rather than Universal Soil Loss Equation model with the widespread spatial distribution. Paired-Samples T-Test with p-value less than 0.05 and Bivariate correlation with the Pearson\'s correlation coefficient equal to 0.23 showed that the predictions of these two models were significantly different. Verification of USLE- and MMF- predicted erosion classes against in-situ 316 collected erosion occurrences collected in the period of 2005-2012 revealed that USLE performed better than MMF model along pipeline by identifying of 192 erosion occurrences out of 316, whereas MMF identified 117 erosion sites. USLE revealed higher ratio of frequencies of erosion occurrences within the critical erosion classes (Soil Loss > 10 t/ha), what also showed higher reliability of soil loss predictions by USLE. The validation of quantitative soil loss predictions using the measurements from 48 field erosion plots revealed higher R2 equal to 0.67 by USLE model than by MMF. This proved that USLE-predicted soil loss rates were more reliable than MMF not only in terms of spatial distributions of critical erosion classes but also in the quantitative terms of soil loss rates. The total number of erosion-prone pipeline segments with the identified erosion occurrences was 316 out of 38376. The number of erosion-prone pipeline segments realistically predicted by USLE model e.g. soil loss more than 10 t/ha was 97 whereas MMF predicted only 70 erosion-prone pipeline segments. The regression analysis between 354 USLE and MMF erosion-prone segments revealed R2 equal to 0.36 what means that the predictions by USLE and MMF erosion models are significantly different on the level of pipeline segments. The average coefficients of variation of predicted soil loss rates by USLE and MMF models and the number of accurately predicted erosion occurrences within the geomorphometric elements of terrain, vegetation cover and landuse categories were larger in the USLE model. This supported the hypothesis that larger spatial variations of erosion prediction models can contribute to the better soil loss prediction performance and reliability of erosion prediction models. This also supported the hypothesis that better understanding of spatial variations within geomorphometric elements of terrain, land-use and vegetation cover percentage classes can support in the selection of the appropriate erosion models with better performance in the particular areas of pipelines. Qualitative multi-criteria assessment for the determination of erosion-prone areas revealed stronger relations with the USLE predictions rather than with MMF. Multi-criteria assessment identified 35 of erosion occurrences but revealed more reliable predictions on the level of terrain units. Predicted erosion-prone areas by USLE revealed higher correlation coefficient with erosion occurrences than MMF model within terrain units what proved higher reliability of the USLE predictions and its stronger relation with the multi-criteria assessment.

Made available in DSpace on 2015-03-26T13:53:16Z (GMT). No. of bitstreams: 1 texto completo.pdf: 2076422 bytes, checksum: 848e731a5bb4cc826cf7c41614e4a0c5 (MD5) Previous issue date: 2010-02-23 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / This study evaluated the potential of automatic classifiers, and methodology of the classification of the resident community in the basin to use mapping and land cover under agroecological management. The study area is the São Joaquim River Basin in the municipality of Araponga, Zona da Mata mineira. In the method, at first, was held to a field trip where they were collected ground control points to georeference image IKONOS II and the training samples and validation of the use classes and soil covered by GPS. In the laboratory, supervised classifications were performed by automatic algorithms Maximum Likelihood, Neural Networks and Bhattacharya.For each algorithm, two ratings were made 17 and 14 classes. A use classification and land cover was done by the residents of the basin where the classes were identified for use and land cover. The classified images were taken to the laboratory and turned into digital format. The results show that among the automatic classifiers, the Bhattachaya shows better result, Kappa 0.76, very good result for classification of the area. Already Kappa image classified by the community was 0.55, considered good result according to the literature. These results demonstrate that the algorithm Batacharya is the most efficient for the mapping and it is possible that the local community to interpret the environment in which to live and perform with autonomy mappings to map future strategies. Therefore, thefindings of this study in addition to being useful for future planning of action research in the basin under study, will serve as universal knowledge for classification of land use in other areas of agroecological management. / Este trabalho objetivou avaliar o potencial de classificadores automáticos e da metodologia empregada na classificação da comunidade residente na bacia para mapeamento do uso e cobertura do solo sob manejo agroecológico. A área de estudo é a Bacia do Rio São Joaquim, no município de Araponga, Zona da Mata mineira. Na metodologia, no primeiro momento, foi realizada a ida a campo onde foram coletados os Pontos de Controle Terrestre para georreferenciar imagem IKONOS II e as amostras de treinamento e validação das classes de uso e cobertura do solo através de GPS. Em laboratório, foram realizadas classificações supervisionadas automáticas pelos algoritmos da Máxima Verossimilhança, Redes Neurais Artificiais e Bhattacharya. Para cada algoritmo, foram feitas duas classificações, 17 e 14 classes. Uma classificação do uso e cobertura do solo foirealizada pelos moradores da bacia onde foram identificadas as classes de uso e cobertura do solo. As imagens classificadas foram levadas ao laboratório e transformadas em formato digital. Os resultados demonstram que dentre os classificadores automáticos, o Bhattachaya apresentou melhor resultado, Kappa 0,76, resultado muito bom para classificação da área em questão. Já o Kappa da imagem classificada pela comunidade foi de 0,55, resultado considerado bom de acordo com a literatura. Estes resultados demonstram que o algoritimo Bhatacharya é o mais eficiente para o mapeamento e que é possível que a comunidade local interprete o meio em que vive e possa realizar com autonomia mapeamentos para traçar estratégias futuras. Sendo assim, os resultados encontrados nesta pesquisa além de serem úteis para futuros planejamentos de pesquisa-ação na bacia hidrográfica em estudo, servirão como conhecimento universal para classificação do uso do solo em outras áreas com manejo agroecólogico.

The construction of Baku-Tbilisi-Ceyhan (BTC) Oil and South Caucasus Gas (SCP) pipelines was completed in 2005. The Azerbaijan section of BTC Oil and SCP Gas pipelines is 442 km long and 44 m wide corridor named as the Right-of-Way. BTC and SCP pipelines are aligned parallel to each other within the same 44m corridor. The construction process of the pipelines significantly disturbed vegetation and soil cover along Right-of-Way of pipelines. The revegetation and erosion control measures were conducted after the completion of construction to restore the disturbed footprints of construction activities. The general goals of the present studies, dedicated to the environmental monitoring of revegetation and planning of erosion control measures were: to evaluate the status of the revegetation in 2007 since the completion of the construction activities in 2005, to determine the climate and ground factors controlling the vegetation regrowth and to predict erosion-prone areas along Right-of-Way of pipelines. Regression and root mean square error analysis between the Normalized Difference Vegetation Index (NDVI) of IKONOS images acquired in 2007 and in-situ estimations of vegetation cover percentage revealed R2 equal to 0.80 and RMSE equal to 6% which were optimal for the normalization of NDVI to vegetation cover. The total area of restored vegetation cover between 2005 and 2007 was 8.9 million sq. m. An area of 10.7 million sq. m. of ground vegetation needed restoration in order to comply with the environmental acceptance criteria. Based on the Global Spatial Regression Model, precipitation, land surface temperature and evapotranspiration were determined as the main climate factors controlling NDVI of grasslands along Right-of-Way of pipelines. In case of croplands, precipitation, evapotranspiration and annual minimum temperature were determined as the main factors controlling NDVI of croplands. The regression models predicting NDVI for grasslands and croplands were also formulated. The Geographically Weighted Regression analyses in comparison with the global regression models results clearly revealed that the relationship between NDVI of grasslands and croplands and the predictor variables was spatially non-stationary along the corridor of pipelines. Even though the observed R2 value between elevation and NDVI of grasslands was low (R2= 0.14), the accumulation of the largest NDVI patterns was observed higher than 150m elevation. This revealed that elevation has non-direct control of NDVI of grasslands through its control of precipitation and temperature along the grasslands of Right-of-Way. The spatial distribution percentage of NDVI classes within slope aspect categories was decreasing in the southern directions of slope faces. Land surface temperature was decreasing with elevation but no particular patterns of land surface temperature in the relationship with NDVI accumulation within the aspect categories were observed. Aspect categories have non-direct control of NDVI and there are some other factors apart from land surface temperature which require further investigations. Precipitation was determined to be controlling the formation of topsoil depth and the topsoil obviously controls the VC growth of grasslands as one of the main ground factors. The regression analysis between NDVI of grasslands and croplands with groundwater depth showed very low correlation. But the clustered patterns of vegetation cover were observed in the relationship with groundwater depth and soil moisture for both grasslands and croplands. The modeling of groundwater depth relative to soil moisture and MODIS NDVI of grasslands determined that the threshold of groundwater depth for vegetation growth is in the range of 1-5 m. MODIS NDVI and soil moisture did not reveal a significant correlation. Soil moisture revealed R2 equal to 0.34 with elevation, R2 equal to 0.23 with evapotranspiration, R2 equal to 0.57 with groundwater depth and R2 equal to 0.02 with precipitation. This allowed to suspect that precipitation is not the main factor controlling soil moisture whereas elevation, evapotranspiration and groundwater depth have non-direct control of soil moisture. Therefore, soil moisture has also non-direct control of vegetation cover growth along the corridor of pipelines. The variations of soil moisture in the 1-3 m soil depth range may have the threshold of depth controlling vegetation cover regrowth and this requires more detailed soil moisture data for further investigations. The reliability of the Global Spatial Regression Model and Geographically Weighted Regression predictions is limited by the MODIS images spatial resolution equal to 250 m and spectral characteristics. The Morgan-Morgan-Finney (MMF) and Universal Soil Loss Equation (USLE) predictions revealed non-similarity in the spatial distribution of soil loss rates along Right-of-Way. MMF model revealed more clustered patterns of predicted critical erosion classes with soil loss more than 10 ton/ha/year in particular ranges of pipelines rather than Universal Soil Loss Equation model with the widespread spatial distribution. Paired-Samples T-Test with p-value less than 0.05 and Bivariate correlation with the Pearson\'s correlation coefficient equal to 0.23 showed that the predictions of these two models were significantly different. Verification of USLE- and MMF- predicted erosion classes against in-situ 316 collected erosion occurrences collected in the period of 2005-2012 revealed that USLE performed better than MMF model along pipeline by identifying of 192 erosion occurrences out of 316, whereas MMF identified 117 erosion sites. USLE revealed higher ratio of frequencies of erosion occurrences within the critical erosion classes (Soil Loss > 10 t/ha), what also showed higher reliability of soil loss predictions by USLE. The validation of quantitative soil loss predictions using the measurements from 48 field erosion plots revealed higher R2 equal to 0.67 by USLE model than by MMF. This proved that USLE-predicted soil loss rates were more reliable than MMF not only in terms of spatial distributions of critical erosion classes but also in the quantitative terms of soil loss rates. The total number of erosion-prone pipeline segments with the identified erosion occurrences was 316 out of 38376. The number of erosion-prone pipeline segments realistically predicted by USLE model e.g. soil loss more than 10 t/ha was 97 whereas MMF predicted only 70 erosion-prone pipeline segments. The regression analysis between 354 USLE and MMF erosion-prone segments revealed R2 equal to 0.36 what means that the predictions by USLE and MMF erosion models are significantly different on the level of pipeline segments. The average coefficients of variation of predicted soil loss rates by USLE and MMF models and the number of accurately predicted erosion occurrences within the geomorphometric elements of terrain, vegetation cover and landuse categories were larger in the USLE model. This supported the hypothesis that larger spatial variations of erosion prediction models can contribute to the better soil loss prediction performance and reliability of erosion prediction models. This also supported the hypothesis that better understanding of spatial variations within geomorphometric elements of terrain, land-use and vegetation cover percentage classes can support in the selection of the appropriate erosion models with better performance in the particular areas of pipelines. Qualitative multi-criteria assessment for the determination of erosion-prone areas revealed stronger relations with the USLE predictions rather than with MMF. Multi-criteria assessment identified 35 of erosion occurrences but revealed more reliable predictions on the level of terrain units. Predicted erosion-prone areas by USLE revealed higher correlation coefficient with erosion occurrences than MMF model within terrain units what proved higher reliability of the USLE predictions and its stronger relation with the multi-criteria assessment.

No description available.

Vegetation community patterns and processes are indicators and integrators of climate. Recently, scientists have shown that climate change is most pronounced in circumpolar regions. Arctic ecosystems have traditionally been sequestering carbon and accumulating large carbon stores. However, given enhanced warming in the Arctic, the potential exists for intensified global climate change if these ecosystems transition from sinks to sources of atmospheric CO2. In the Mid and High Arctic, ecosystems exhibit extreme levels of spatial heterogeneity, particularly at landscape scales. High spatial-resolution (e.g., 4m) remote sensing data capture heterogeneous vegetation patterns of the Arctic landscape and have the potential to model ecosystem biophysical properties and CO2 fluxes. The following conditions are required to model arctic ecosystem processes: (i) unique spectral signatures that correspond to variations in the landscape pattern; (ii) models that transform remote sensing data into derivative values pertaining to the landscape; and (iii) field measures of the variables to calibrate and validate the models. First, this research creates an ecosystem classification scheme through ordination, clustering, and spectral-separability of ground cover data to generate ecologically meaningful and spectrally distinct image classifications. Classifications had overall accuracies between 69% - 79% and Kappa values of 0.54 - 0.69. Secondly, biophysical variable models of percent vegetation cover, aboveground biomass, and soil moisture are calibrated and validated using a k-fold cross-validation linear bivariate regression methodology. Percent vegetation cover and percent soil moisture produce the strongest and most consistent results (r2 ≥ 0.84 and 0.73) across both study sites. Finally, in situ CO2 exchange rate data, an NDVI model for each component flux, which explains between 42% and 95% of the variation at each site, is generated. Analysis of coincidence indicates that a single model for each component flux can be applied, independent of site. This research begins to fill a gap in the application of high spatial-resolution remote sensing data for modelling Arctic ecosystem biophysical variables and carbon dioxide exchange, particularly in the Canadian Arctic. The results of this research also indicate high levels of functional convergence in ecosystem-level structure and function within Arctic landscapes. / Thesis (Ph.D, Geography) -- Queen's University, 2013-01-03 22:24:20.157

Approved for public release, distribution is unlimited / Terrain classification is studied here using the tool of texture analysis of high-spatial resolution panchromatic imagery. This study analyzes the impact and effectiveness of texture analysis on terrain classification within the Elkhorn Slough Estuary and surrounding farmlands within the central California coastal region. Ikonos panchromatic (1 meter) and multispectral (4 meter) imagery data are examined to determine the impact of adding texture analysis to the standard MSI classification approaches. Spectral Angle Mapper and Maximum Likelihood classifiers are used. Overall accuracy rates increased with the addition of the texture processing. The classification accuracy rate rose from 81.0% for the MSI data to 83.9% when the additional texture measures were added. Modest accuracy (55%) was obtained from texture analysis alone. The addition of textural data also enhanced the classifier's ability to discriminate between several different woodland classes contained within the image. / Lieutenant Commander, United States Navy