Urban areas around the world are changing constantly and therefore it is necessary to update urban land cover maps regularly. Remote sensing techniques have been used to monitor changes and update land-use/land-cover information in urban areas for decades. Optical imaging systems have received most of the attention in urban studies. The development of SAR applications in urban monitoring has been accelerated with more and more advanced SAR systems operating in space.   This research investigated object-based and rule-based classification methodologies for extracting urban land-cover information from high resolution SAR data. The study area is located in the north and northwest part of the Greater Toronto Area (GTA), Ontario, Canada, which has been undergoing rapid urban growth during the past decades. Five-date RADARSAT-1 fine-beam C-HH SAR images with a spatial resolution of 10 meters were acquired during May to August in 2002. Three-date RADARSAT-2 ultra-fine-beam C-HH SAR images with a spatial resolution of 3 meters were acquired during June to September in 2008.   SAR images were pre-processed and then segmented using multi-resolution segmentation algorithm. Specific features such as geometric and texture features were selected and calculated for image objects derived from the segmentation of SAR images. Both neural network (NN) and support vector machines (SVM) were investigated for the supervised classification of image objects of RADARSAT-1 SAR images, while SVM was employed to classify image objects of RADARSAT-2 SAR images. Knowledge-based rules were developed and applied to resolve the confusion among some classes in the object-based classification results.   The classification of both RADARSAT-1 and RADARSAT-2 SAR images yielded relatively high accuracies (over 80%). SVM classifier generated better result than NN classifier for the object-based supervised classification of RADARSAT-1 SAR images. Well-designed knowledge-based rules could increase the accuracies of some classes after the object-based supervised classification. The comparison of the classification results of RADARSAT-1 and RADARSAT-2 SAR images showed that SAR images with higher resolution could reveal more details, but might produce lower classification accuracies for certain land cover classes due to the increasing complexity of the images. Overall, the classification results indicate that the proposed object-based and rule-based approaches have potential for operational urban land cover mapping from high-resolution space borne SAR images. / QC 20101209

The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pixels, size, shape, compactness, and asymmetry improved classification accuracy.Chapter 2. We sought to understand if patterns of gas exchange to changes in temperature and CO2 can explain why C3 shrubs are increasing, and C3 and C4 grasses are decreasing in the southwestern U.S. We conducted seasonal, leaf-level gas exchange surveys, and measured temperature response curves and A-Ci response curves of common shrub, forb, and grass species in perennial grassland ecosystems over the year. We found that the functional trait of being evergreen is increasingly more successful in climate changing conditions with warmer winter months. Grass species in our study did not differentiate by photosynthetic pathway; they were physiologically the same in all of our measurements. Increasing shrub species, Ephedra viridis and Coleogyne ramosissima displayed functional similarities in response to increasing temperature and CO2.

The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pixels, size, shape, compactness, and asymmetry improved classification accuracy.Chapter 2. We sought to understand if patterns of gas exchange to changes in temperature and CO2 can explain why C3 shrubs are increasing, and C3 and C4 grasses are decreasing in the southwestern U.S. We conducted seasonal, leaf-level gas exchange surveys, and measured temperature response curves and A-Ci response curves of common shrub, forb, and grass species in perennial grassland ecosystems over the year. We found that the functional trait of being evergreen is increasingly more successful in climate changing conditions with warmer winter months. Grass species in our study did not differentiate by photosynthetic pathway; they were physiologically the same in all of our measurements. Increasing shrub species, Ephedra viridis and Coleogyne ramosissima displayed functional similarities in response to increasing temperature and CO2.

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O presente estudo desenvolve uma metodologia que agrega os produtos do sensoriamento remoto, em especial imagens provenientes de satélites de alta resolução espacial, como é o caso do satélite IKONOS, com os recursos dos sistemas de informações geográficas - SIG, para planejamento de Engenharia de Transportes. O parâmetro considerado é a acessibilidade. As cidades brasileiras, notadamente as de grande e médio porte, a partir da segunda metade do século passado até os dias atuais, vêm passando por uma expansão urbana rápida, crescente e desordenada, devido à ausência do planejamento urbano. Em virtude disso ocorrem sérios problemas sociais e econômicos. As medidas de acessibilidade podem ser utilizadas pelo administrador público para promover um adequado planejamento urbano dos municípios, principalmente o planejamento da infraestrutura viária e dos sistemas de transporte público coletivo. O objetivo é apresentar um arcabouço metodológico que se vale dos produtos de sensoriamento remoto e análises em ambiente SIG, para a extração e obtenção dos dados necessários para a determinação do índice de acessibilidade do setor empresarial do município de Osasco, localizado na Região Metropolitana de São Paulo. A partir de uma classificação baseada em objetos da imagem do município adquirida pelo sensor multiespectral do satélite IKONOS II, extraiu-se informações pertinentes quanto à localização das atividades comerciais e industriais do município, bem como sua posição em relação ao sistema viário da cidade. Os dados são convertidos e manipulados em um ambiente SIG. Após essa manipulação a medida de acessibilidade referente aos estabelecimentos comerciais e industriais de Osasco pode ser determinada. Tem-se, portanto, o índice de acessibilidade da atividade comercial e industrial do município. Índice esse, útil, por exemplo, para futuros investimentos e empreendimentos nesse setor para o município. A proposição desta metodologia se justifica, pois a detecção remota dos dados diminui o custo e o tempo despendidos em pesquisas de campo e atualizações de dados cadastrais. Desta forma, ela é uma alternativa interessante e bastante conveniente para localidades que não possuam dados em cadastro ou estes estão desatualizados. A validação desse método se verifica com o cálculo dessa mesma acessibilidade pelo método convencional, ou seja, com os dados do cadastro da Prefeitura do Município de Osasco, partindo da hipótese de que esses dados são corretos e confiáveis. Ao se comparar essas duas acessibilidades pode-se concluir sobre a aplicabilidade da metodologia proposta. Ao final, é possível verificar que a metodologia proposta pode ser aplicada, e é uma alternativa viável para localidades que não possuam dados cadastrais passíveis de serem usados para a determinação do índice de acessibilidade. / Considering the continuous urban growth, the lack of urban planning produces serious consequences whatever the subject. The accelerated and disordered urban sprawl faced by Brazilian cities since the 1950\'s has levered serious social and economic problems. In this context, the accessibility measures can be used as one of the several indicators to promote urban planning, mainly the highway network infrastructure and the public transportation system planning. This paper addresses a methodology for getting an accessibility index based on Remote Sensing and GIS technologies. Thus, objects of interest are detected from high resolution satellite images and then computed using GIS tools in order to refine the contextual classification process. Moreover, based on these objects as well as those based on the existing transportation road system, an accessibility index map is generated. Metrics of accessibility have been employed to validate the effective use of the proposed methodology for transportation planning. The accessibility measures are described and analyzed through a case of study in the city of Osasco, in the Metropolitan Region of São Paulo, Brazil. The proposed methodology consists in using the IKONOS II images to extract all the information needed to estimate accessibility. In order to do this, the first step is to do an object-based classification of the IKONOS II images. The goal of this classification is to find the commercials and industrial establishments located in the study area, and to extract the highway network of the city. These spatial data are analyzed within a GIS environment and an accessibility index is calculated using the parameters mined from the satellite images. This index is called Commercial and Industrial (C&I) accessibility, and it can be compared with C&I accessibility of other localities. The use of this methodology can be justified based on the fact that there are places with no recorded data or with outdated recorded data (highway network or C&I establishments). In this case, Remote Sensing Technologies can provide support for estimating the accessibility map index. Moreover, Remote Sensing can offer a significant reduction in cost and time for getting the database. The validation of this method is done by calculating the C&I accessibility index of the same study area through recorded data available in the Osasco municipality. These two accessibility index are compared, so it is possible to conclude about the efficiency of the proposed methodology. Therefore, the proposed methodology can be applied and it leads to satisfactory results.

O presente estudo desenvolve uma metodologia que agrega os produtos do sensoriamento remoto, em especial imagens provenientes de satélites de alta resolução espacial, como é o caso do satélite IKONOS, com os recursos dos sistemas de informações geográficas - SIG, para planejamento de Engenharia de Transportes. O parâmetro considerado é a acessibilidade. As cidades brasileiras, notadamente as de grande e médio porte, a partir da segunda metade do século passado até os dias atuais, vêm passando por uma expansão urbana rápida, crescente e desordenada, devido à ausência do planejamento urbano. Em virtude disso ocorrem sérios problemas sociais e econômicos. As medidas de acessibilidade podem ser utilizadas pelo administrador público para promover um adequado planejamento urbano dos municípios, principalmente o planejamento da infraestrutura viária e dos sistemas de transporte público coletivo. O objetivo é apresentar um arcabouço metodológico que se vale dos produtos de sensoriamento remoto e análises em ambiente SIG, para a extração e obtenção dos dados necessários para a determinação do índice de acessibilidade do setor empresarial do município de Osasco, localizado na Região Metropolitana de São Paulo. A partir de uma classificação baseada em objetos da imagem do município adquirida pelo sensor multiespectral do satélite IKONOS II, extraiu-se informações pertinentes quanto à localização das atividades comerciais e industriais do município, bem como sua posição em relação ao sistema viário da cidade. Os dados são convertidos e manipulados em um ambiente SIG. Após essa manipulação a medida de acessibilidade referente aos estabelecimentos comerciais e industriais de Osasco pode ser determinada. Tem-se, portanto, o índice de acessibilidade da atividade comercial e industrial do município. Índice esse, útil, por exemplo, para futuros investimentos e empreendimentos nesse setor para o município. A proposição desta metodologia se justifica, pois a detecção remota dos dados diminui o custo e o tempo despendidos em pesquisas de campo e atualizações de dados cadastrais. Desta forma, ela é uma alternativa interessante e bastante conveniente para localidades que não possuam dados em cadastro ou estes estão desatualizados. A validação desse método se verifica com o cálculo dessa mesma acessibilidade pelo método convencional, ou seja, com os dados do cadastro da Prefeitura do Município de Osasco, partindo da hipótese de que esses dados são corretos e confiáveis. Ao se comparar essas duas acessibilidades pode-se concluir sobre a aplicabilidade da metodologia proposta. Ao final, é possível verificar que a metodologia proposta pode ser aplicada, e é uma alternativa viável para localidades que não possuam dados cadastrais passíveis de serem usados para a determinação do índice de acessibilidade. / Considering the continuous urban growth, the lack of urban planning produces serious consequences whatever the subject. The accelerated and disordered urban sprawl faced by Brazilian cities since the 1950\'s has levered serious social and economic problems. In this context, the accessibility measures can be used as one of the several indicators to promote urban planning, mainly the highway network infrastructure and the public transportation system planning. This paper addresses a methodology for getting an accessibility index based on Remote Sensing and GIS technologies. Thus, objects of interest are detected from high resolution satellite images and then computed using GIS tools in order to refine the contextual classification process. Moreover, based on these objects as well as those based on the existing transportation road system, an accessibility index map is generated. Metrics of accessibility have been employed to validate the effective use of the proposed methodology for transportation planning. The accessibility measures are described and analyzed through a case of study in the city of Osasco, in the Metropolitan Region of São Paulo, Brazil. The proposed methodology consists in using the IKONOS II images to extract all the information needed to estimate accessibility. In order to do this, the first step is to do an object-based classification of the IKONOS II images. The goal of this classification is to find the commercials and industrial establishments located in the study area, and to extract the highway network of the city. These spatial data are analyzed within a GIS environment and an accessibility index is calculated using the parameters mined from the satellite images. This index is called Commercial and Industrial (C&I) accessibility, and it can be compared with C&I accessibility of other localities. The use of this methodology can be justified based on the fact that there are places with no recorded data or with outdated recorded data (highway network or C&I establishments). In this case, Remote Sensing Technologies can provide support for estimating the accessibility map index. Moreover, Remote Sensing can offer a significant reduction in cost and time for getting the database. The validation of this method is done by calculating the C&I accessibility index of the same study area through recorded data available in the Osasco municipality. These two accessibility index are compared, so it is possible to conclude about the efficiency of the proposed methodology. Therefore, the proposed methodology can be applied and it leads to satisfactory results.

Sand and mud traversed by tidal inlets and channels, which split in subtle branches, salt marshes at the coast, the tide, harsh weather conditions and a high diversity of fauna and flora characterize the ecosystem Wadden Sea. No other landscape on the Earth changes in such a dynamic manner. Therefore, land cover classification and monitoring of vulnerable ecosystems is one of the most important approaches in remote sensing and has drawn much attention in recent years. The Wadden Sea in the southeastern part of the North Sea is one such vulnerable ecosystem, which is highly dynamic and diverse. The tidal flats of the Wadden Sea are the zone of interaction between marine and terrestrial environments and are at risk due to climate change, pollution and anthropogenic pressure. Due to that, the European Union has implemented various directives, which formulate objectives such as achieving or maintaining a good environmental status respectively a favourable conservation status within a given time. In this context, a permanent observation for the estimation of the ecological condition is needed. Moreover, changes can be tracked or even foreseen and an appropriate response is possible. Therefore, it is important to distinguish between short-term changes, which are related to the dynamic manner of the ecosystem, and long-term changes, which are the result of extraneous influences. The accessibility both from sea and land is very poor, which makes monitoring and mapping of tidal flat environments from in situ measurements very difficult and cost-intensive. For the monitoring of big areas, time-saving applications are needed. In this context, remote sensing offers great possibilities, due to its provision of a large spatial coverage and non-intrusive measurements of the Earth’s surface. Previous studies in remote sensing have focused on the use of electro-optical and radar sensors for remote sensing of tidal flats, whereas microwave systems using synthetic aperture radar (SAR) can be a complementary tool for tidal flat observation, especially due to their high spatial resolution and all-weather imaging capability. Nevertheless, the repetitive tidal event and dynamic sedimentary processes make an integrated observation of tidal flats from multi-sourced datasets essential for mapping and monitoring. The main challenge for remote sensing of tidal flats is to isolate the sediment, vegetation or shellfish bed features in the spectral signature or backscatter intensity from interference by water, the atmosphere, fauna and flora. In addition, optically active materials, such as plankton, suspended matter and dissolved organics, affect the scattering and absorption of radiation. Tidal flats are spatially complex and temporally quite variable and thus mapping tidal land cover requires satellites or aircraft imagers with high spatial and temporal resolution and, in some cases, hyperspectral data. In this research, a hierarchical knowledge-based decision tree applied to multi-sensor remote sensing data is introduced and the results have been visually and numerically evaluated and subsequently analysed. The multi-sensor approach comprises electro-optical data from RapidEye, SAR data from TerraSAR-X and airborne LiDAR data in a decision tree. Moreover, spectrometric and ground truth data are implemented into the analysis. The aim is to develop an automatic or semi-automatic procedure for estimating the distribution of vegetation, shellfish beds and sediments south of the barrier island Norderney. The multi-sensor approach starts with a semi-automatic pre-processing procedure for the electro-optical data of RapidEye, LiDAR data, spectrometric data and ground truth data. The decision tree classification is based on a set of hierarchically structured algorithms that use object and texture features. In each decision, one satellite dataset is applied to estimate a specific class. This helps to overcome the drawbacks that arise from a combined usage of all remote sensing datasets for one class. This could be shown by the comparison of the decision tree results with a popular state-of-the-art supervised classification approach (random forest). Subsequent to the classification, a discrimination analysis of various sediment spectra, measured with a hyperspectral sensor, has been carried out. In this context, the spectral features of the tidal sediments were analysed and a feature selection method has been developed to estimate suitable wavelengths for discrimination with very high accuracy. The developed feature selection method ‘JMDFS’ (Jeffries-Matusita distance feature selection) is a filter-based supervised band elimination technique and is based on the local Euclidean distance and the Jeffries-Matusita distance. An iterative process is used to subsequently eliminate wavelengths and calculate a separability measure at the end of each iteration. If distinctive thresholds are achieved, the process stops and the remaining wavelengths are applied in the further analysis. The results have been compared with a standard feature selection method (ReliefF). The JMDFS method obtains similar results and runs 216 times faster. Both approaches are quantitatively and qualitatively evaluated using reference data and standard methodologies for comparison. The results show that the proposed approaches are able to estimate the land cover of the tidal flats and to discriminate the tidal sediments with moderate to very high accuracy. The accuracies of each land cover class vary according to the dataset used. Furthermore, it is shown that specific reflection features can be identified that help in discriminating tidal sediments and which should be used in further applications in tidal flats.

<p>Three prescribed burn sites and seven selective timber harvest sites were surveyed using a UAS equipped with a PPK-triggered RGB sensor to determine optimal image collection parameters surrounding each type of disturbance and land cover. The image coordinates were corrected with a third-party base station network (CORS) after the flight, and photogrammetrically processed to produce high-resolution georeferenced orthomosaics. This addressed the first objective of this study, which was to <i>establish effective data procurement methods from both before and after planned </i>disturbances. <br></p><p>Orthomosaic datasets surrounding both a prescribed burn and a selective timber harvest, were used to classify land covers through geographic image-based analysis (GEOBIA). The orthomosaic datasets were segmented into image objects, before classification with a machine-learning algorithm. Land covers for the prescribed prairie burn were 1) bare ground, 2) litter, 3) green vegetation, and 4) burned vegetation. Land covers for the selective timber harvest were 1) mature canopy, 2) understory vegetation, and 3) bare ground. 65 samples per class were collected for prairie burn datasets, and 80 samples per class were collected for timber harvest datasets to train the classifier. A supported vector machines (SVM) algorithm was used to produce four land cover classifications for each site surrounding their respective planned disturbance. Pixel counts for each class were multiplied by the ground sampled distance (GSD) to obtain area calculations for land covers. Accuracy assessments were conducted by projecting 250 equalized stratified random (ESR) reference points onto the georeferenced orthomosaic datasets to compare the classification to the imagery through visual interpretation. This addressed the second objective of this study, which was to <i>establish effective data classification methods from both before and after planned </i>disturbances.<br></p><p>Finally, a two-tailed t-Test was conducted with the overall accuracies for each disturbance type and land cover. Results showed no significant difference in the overall accuracy between land covers. This was done to address the third objective of this study which was to <i>determine if a significant difference exists between the classification accuracies between planned disturbance types</i>. Overall, effective data procurement and classification parameters were established for both <i>before </i>and <i>after </i>two common types of <i>planned </i>disturbances within the CHF region, with slightly better results for prescribed burns than for selective timber harvests.<br></p>