No description available.

No description available.

Over the past few decades, remote sensing has emerged as a powerful tool for studying active faulting in continental regions. However, the commonly used remote sensing techniques, including radar interferometry, visual inspection of imagery, and image matching, cannot measure three-dimensional (3D) surface displacements in earthquakes, limiting our ability to investigate faulting. The improvement of very high-resolution (VHR) optical imaging systems (stereo in particular) in recent years has made it possible for earth scientists to measure 3D surface deformation remotely. In this thesis, I contribute to assessing the capability of VHR optical imagery, by determining earthquake deformation from four different types of earthquakes (different in sense of slip and date of the event). In the case of the 2010 M_w 7.2 El Mayor-Cucapah, Mexico earthquake, I show that digital elevation models (DEMs) derived from Pleiades stereo imagery are comparable to light detection and ranging (LiDAR) surveys, and differencing pre- and post-earthquake DEMs can measure 3D displacements, which will be very useful for studying future earthquakes. For the 2013 M_w 7.7 Balochistan, Pakistan earthquake, I determine the vertical motion from a post-earthquake Pleiades DEM and find constant fault kinematics throughout the Late Quaternary. This study has resolved a current controversy of the Balochistan earthquake, in which it has been argued that kinematics of the Hoshab fault switches between strike-slip and dip-slip. Applying historical aerial, KH-9 Hexagon spy satellite, SPOT-2 and modern SPOT-6 images to the 1978 M_w 7.3 Tabas-e-Golshan earthquake, I measure the coseismic and postseismic displacements, and show that the Tabas fold system in eastern Iran may exhibit characteristic slip behaviour. Combining Pleiades imagery, fieldwork and geological dating techniques, I determine slip in the 1556 Huaxian earthquake in China and the recurrence interval for similar events. These examples demonstrate the usefulness of high-resolution optical imagery in investigating past and present earthquakes.

The purpose of this thesis paper is to investigate womanist theology and method, along with restoration practices involving spirituality and healing within the context of the visual arts. The thesis exhibition will attempt to create new visual possibilities that inform womanist theological scholarship in terms of promoting contemporary female religious imagery within a metaphorical language. While womanist theology is steeped in interdisciplinary practices, it has yet to consider seriously the studio arts as a means to explore and develop the womanist language. This study will investigate how essential and natural the visual arts assist our understanding of spirituality, especially through a womanist context.

Drought is a climatic event regarding prolonged "drier than normal" conditions. Precipitation deficits, crop-moisture stress, soil-water unbalance, sudden stream flow cut-offs and low carrying capacity of ecosystems are responses to drought. Drought can occur in humid to arid climates, however, drought is more severe in arid and semi-arid areas due to the fact that in those distinctive areas, water resources are extremely limited and restricted. Additionally, local ecologies and ecosystems in arid regions are very fragile. Once a water competition occurs, critical services of ecosystems such as pure water, recreation, and land productivity will be threatened. This research focuses on prolonged drought events that have been occurring more frequently in a coastal province of South Central Vietnam – named Binh Thuan. The study area is distinctive because its climate is characterized as one of the driest provinces in Vietnam. Annual rainfall in the North and near the coast of the province is less than 800 mm per year. During 6 months of dry season, there is almost no rain, or less than 50 mm. Due to precipitation deficits and high surface temperatures in recent years, meteorological droughts have occurred more frequently, and lasted longer, thereby stressing water resources for vegetation, wildlife, households, and industry. The occurrence of prolonged droughts has constrained economic activities in the coastal areas, especially agriculture and aquaculture. Furthermore, a long duration of dry conditions coupled with unsustainable land management (such as overgrazing), "drought-sensitive" soils in areas with sand and barren lands may introduce and accelerate risks of desertification processes (land productivity deterioration and unable to recover). This research uses geospatial technologies to monitor drought severity and drought impacts on land use and land cover. Chapter 1 is a brief introduction and literature review of the drought context in Binh Thuan Province to place the research questions and objectives in content. Chapter 2 discusses the occurrence of meteorological droughts in Binh Thuan Province, then proposes climatic indices able to monitor this type of drought. Chapter 3 focuses on explaining and assessing uneven dry conditions that stressed vegetation health in the study area. This chapter investigates spatiotemporal distributions and frequencies of prolonged agricultural droughts using remotely sensed data and anomalies of precipitation distribution. Results indicate that coastal areas in the North of Binh Thuan are subject to severe droughts. Chapter 4 assesses human impacts on land management and practices in the study area during drought periods. Results show that in recent years (2010 to present), local governments and residents have implemented strategies to prevent sand dominance and to adapt to water shortages during dry seasons, such as vegetative cover, crop rotation with drought-tolerant plants and wind breaks. Accuracy was assessed using field data collected in the summer of 2016, in conjunction with Google Earth imagery. In summary, this dissertation enhances understanding of drought events and impacts in Binh Thuan Province by considering different types of drought - meteorological and agricultural drought, and interactions of drought and human impacts upon land management and land practices during dry periods. Furthermore, findings and results of this research have demonstrated the effectiveness of remotely sensed datasets, and other geospatial technologies, such as geographic information systems, in modeling drought severity and in examining efforts and drought-adaptive practices of local residents. This work is a valuable foundation on which further studies can build to support policy development to protect and reserve soil-land productivity in Binh Thuan and other coastal regions of Vietnam affected by prolonged droughts. / Doctor of Philosophy / Drought is a temporal climatic event with "drier than normal" conditions. While drought can occur in any climates, it can be more extreme in arid and semi-arid areas where annual rainfall and water resources are limited. Depending on types of drought, its presences and impacts may differ: (1) meteorological drought relates to a decrease of average rainfall/snowfall may resulting in moisture stress, (2) hydrological drought leads to a reduction of streamflow and groundwater, and (3) agricultural drought influences soil-water-crop balance or vegetation health. Prolonged drought – abnormally long duration of dry conditions, coupled with unsustainable management in water and land practice may cause losses of land productivity, promote soil erosion, and result in sand dominance in coastal areas. These land degradation processes can lead to "a desert-like condition" in impacted areas. This research concerns drought and its impacts in a coastal province in South central Vietnam, Binh Thuan. The study area is distinctive because its climate is characterized as one of the driest provinces in Vietnam. Annual rainfall in the North and near the coast is less than 800 mm per year, and during the 6 months of the dry season, there is almost no rain, or less than 50 mm. Due to precipitation deficits and high surface temperatures in recent years, meteorological droughts have occurred more frequently and lasted longer, stressing water resources for vegetation, wildlife, households, and industry. Additionally, unsustainable land management, such as overgrazing, coupled with movements of sand and barren lands from the coast inland, have accelerated the risks of land degradation. This research applies an integration of geospatial technologies for monitoring drought severity and impacts on land management and illustrates how local people have adapted to droughts.

Deep learning ship detection in satellite optical imagery suffers from false positive occurrences with clouds, landmasses, and man-made objects that interfere with correctly classifying ships. A custom U-Net is implemented to challenge this issue and aims to capture more features in order to provide a more accurate class accuracy. This model is trained with two different systematic architectures: single node architecture and a parameter server variant whose workers act as a boosting mechanism. To ex-tend this effort, a refining method of offline hard example mining aims to improve the accuracy of the trained models in both the validation and target datasets however it results in over correction and a decrease in accuracy. The single node architecture results in 92% class accuracy over the validation dataset and 68% over the target dataset. This exceeds class accuracy scores in related works which reached up to 88%. A parameter server variant results in class accuracy of 86% over the validation set and 73% over the target dataset. The custom U-Net is able to achieve acceptable and high class accuracy on a subset of training data keeping training time and cost low in cloud based solutions. / Graduate

[pt] O crescimento da população do planeta tem aumentado continuamente a demanda por produtos agrícolas. Assim, a informação quanto a áreas cultivadas e estimativas de produção se tornam cada vez mais importantes. Técnicas baseadas em imagens satelitais constituem uma das opções mais atrativas para o monitoramento agrícola sobre grandes áreas. A maior parte dos trabalhos científicos voltados a esta aplicação foram desenvolvidos para regiões temperadas do planeta, que apresentam um dinâmica muito mais simples da que se tem em regiões tropicais. Neste contexto, a presente tese propõe um novo método automático baseado em Campos Aleatórios Condicionais (CRF) para o reconhecimento de culturas agrícolas em regiões tropicais a partir de sequências de imagens multi-temporais e multiresolução produzidas por diferentes sensores orbitais. Experimentos foram realizados para validar diversas variantes do método proposto. Utilizaramse bases de dados públicas de duas regiões do Brasil que compreendem sequências de imagens óticas e de radar com diferentes resoluções espaciais. Os experimentos realizados demonstraram que o método proposto atingiu acurácias maiores do que métodos baseados em uma única imagem ou sensor. Particularmente, notou-se a redução do efeito sal-e-pimenta nos mapas gerados devido, mormente, à capacidade do método de capturar informação contextual. / [en] The earth population growth has continuously increased the demand for agricultural production. Thus, acreage and crop yield information become increasingly important. Techniques based on satellite images are one of the most attractive options for agricultural monitoring over large areas. Most of the scientific works on this application were developed for temperate regions of the planet, which present a much simpler dynamics than those in tropical regions. In this context, the present thesis proposes a new automatic method based on Conditional Random Fields (CRF) for the crop recognition in tropical regions from multi-temporal and multi-resolution image sequences from orbital multi-sensors. Experiments were performed to validate several variants of the proposed method. We used public databases from two regions of Brazil that comprise sequences of optical and radar images with different spatial resolutions. The experiments demonstrated that the proposed method achieved a higher accuracy than methods based on a single image or sensor. Particularly, the reduction of the salt-and-pepper effect in the generated maps was noticed due, mainly, to the capacity of the method to capture contextual information.

Tropical forests store more than half of the world’s forest carbon and are particularly threatened by deforestation and degradation processes, which together represent the second largest source of anthropogenic CO2 emissions. Consequently, tropical forests are the focus of international climate policies (i.e. Reducing emissions from deforestation and forest degradation, REDD) aiming at reducing forest-related CO2 emissions. The REDD initiative lies on our ability to map forest carbon stocks (i.e. spatial dynamics) and to detect deforestation and degradations (i.e. temporal dynamics) at large spatial scales (e.g. national, forested basin), with accuracy and precision. Remote-sensing is as a key tool for this purpose, but numerous sources of error along the carbon mapping chain makes meeting REDD criteria an outstanding challenge. In the present thesis, we assessed carbon (quantified through aboveground biomass, AGB) estimation error at the tree- and plot-level using a widely used pantropical AGB model, and at the landscape-level using a remote sensing method based on canopy texture features from very high resolution (VHR) optical data. Our objective was to better understand and reduce AGB estimation error at each level using information on large canopy tree structure, distribution and spatial organization. Although large trees disproportionally contributed to forest carbon stock, they are under-represented in destructive datasets and subject to an under-estimation bias with the pantropical AGB model. We destructively sampled 77 very large tropical trees and assembled a large (pantropical) dataset to study how variation in tree form (through crown sizes and crown mass ratio) contributed to this error pattern. We showed that the source of bias in the pantropical model was a systematic increase in the proportion of tree mass allocated to the crown in canopy trees. An alternative AGB model accounting for this phenomenon was proposed. We also propagated the AGB model bias at the plot-level and showed that the interaction between forest structure and model bias, although often overlooked, might in fact be substantial. We further analyzed the structural properties of crown branching networks in light of the assumptions and predictions of the Metabolic Theory of Ecology, which supports the power-form of the pantropical AGB model. Important deviations were observed, notably from Leonardo’s rule (i.e. the principle of area conservation), which, all else being equal, could support the higher proportion of mass in large tree crowns. A second part of the thesis dealt with the extrapolation of field-plot AGB via canopy texture features of VHR optical data. A major barrier for the development of a broad-scale forest carbon monitoring method based on canopy texture is that relationships between canopy texture and stand structure parameters (including AGB) vary among forest types and regions of the world. We investigated this discrepancy using a simulation approach: virtual canopy scenes were generated for 279 1-ha plots distributed on contrasted forest types across the tropics. We showed that complementing FOTO texture with additional descriptors of forest structure, notably on canopy openness (from a lacunarity analysis) and tree slenderness (from a bioclimatic proxy) allows developing a stable inversion frame for forest AGB at large scale. Although the approach we proposed requires further empirical validation, a first case study on a forests mosaic in the Congo basin gave promising results. Overall, this work increased our understanding of mechanisms behind AGB estimation errors at the tree-, plot- and landscape-level. It stresses the need to better account for variation patterns in tree structure (e.g. ontogenetic pattern of carbon allocation) and forest structural organization (across forest types, under different environmental conditions) to improve general AGB models, and in fine our ability to accurately map forest AGB at large scale.

[pt] O monitoramento oficial do desmatamento na Amazônia brasileira tem dependido tradicionalmente de especialistas humanos que avaliam visualmenteas imagens de sensoriamento remoto e rotulam cada pixel individual comodesmatamento ou não desmatamento. Essa metodologia é obviamente carae demorada devido à vasta área monitorada. A razão para não utilizar métodos totalmente automáticos para a tarefa é a necessidade da maior precisãopossível nos números oficiais de desmatamento. Neste trabalho é propostauma alternativa semi-automática baseada em aprendizagem profunda, naqual uma rede neural profunda é primeiro treinada com imagens existentes e referências de anos anteriores, e empregada para realizar detecção dedesmatamento em imagens recentes. Após a inferência, a incerteza nos resultados em nível de pixel da rede é estimada e assume-se que os resultadosda classificação com baixa incerteza podem ser confiáveis. As demais regiõesde alta incerteza, que correspondem a uma pequena porcentagem da áreade teste, são então submetidas à pós-classificação, por exemplo, um procedimento de auditoria realizado visualmente por um especialista humano.Desta forma, o esforço de etiquetagem manual é bastante reduzido.Investigamos várias estratégias de estimativa de incerteza, incluindo abordagens baseadas em confiança, Monte Carlo Dropout (MCD), conjuntosprofundos e aprendizagem evidencial, e avaliamos diferentes métricas de incerteza. Além disso, conduzimos uma análise abrangente para identificar ascaracterísticas das áreas florestais que contribuem para a elevada incerteza.Ilustramos as principais conclusões da análise em 25 polígonos selecionados em quatro locais-alvo, que exemplificam causas comuns de incerteza.Os sítios-alvo estão localizados em áreas de estudo desafiadoras nos biomasbrasileiros da Amazônia e do Cerrado. Através da avaliação experimental nesses locais, demonstramos que a metodologia semi-automática proposta atinge valores impressionantes de pontuação F1 que excedem 97 por cento, aomesmo tempo que reduz a carga de trabalho de auditoria visual para apenas 3 por cento da área alvo. O código desenvolvido para este estudo está disponível emhttps://github.com/DiMorten/deforestation_uncertainty. / [en] Official monitoring of deforestation in the Brazilian Amazon has relied traditionally on human experts who visually evaluate remote sensing images and label each individual pixel as deforestation or no deforestation. That methodology is obviously costly and time-consuming due to the vast monitored area. The reason for not using fully automatic methods for the task is the need for the highest possible accuracies in the authoritative deforestation figures. In this work, a semi-automatic, deep learning-based alternative is proposed, in which a deep neural network is first trained with existing images and references from previous years, and employed to perform deforestation detection on recent images. After inference, the uncertainty in the network s pixel-level results is estimated, and it is assumed that low-uncertainty classification results can be trusted. The remaining high-uncertainty regions, which correspond to a small percentage of the test area, are then submitted to post classification, e.g., an auditing procedure carried out visually by a human specialist. In this way, the manual labeling effort is greatly reduced. We investigate various uncertainty estimation strategies, including confidence-based approaches, Monte Carlo Dropout (MCD), deep ensembles and evidential learning, and evaluate different uncertainty metrics. Furthermore, we conduct a comprehensive analysis to identify the characteristics of forest areas that contribute to high uncertainty. We illustrate the main conclusions of the analysis upon 25 selected polygons on four target sites, which exemplify common causes of uncertainty. The target sites are located in challenging study areas in the Brazilian Amazon and Cerrado biomes. Through experimental evaluation on those sites, we demonstrate that the proposed semi-automated methodology achieves impressive F1-score values which exceeds 97 percent, while reducing the visual auditing workload to just 3 percent of the target area. The current code is available at https://github.com/DiMorten/deforestation_uncertainty.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.