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Bejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.
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The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with the distribution is through the process of sampling. GANs have found to be useful in applications such as image-to-image translation, in-painting, and text-to-image synthesis. In this thesis, we propose to capitalize on the power of GANs for different remote sensing problems.
The first problem is a new research track to the remote sensing community that aims to generate remote sensing images from text descriptions. More specifically, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, and convert them the equivalent remote sensing images. The proposed method is composed of a text encoder and an image synthesis module. The text encoder is tasked with converting a text description into a vector. To this end, we explore two encoding schemes: a multilabel encoder and a doc2vec encoder. The multilabel encoder takes into account the presence or absence of objects in the encoding process whereas the doc2vec method encodes additional information available in the text. The encoded vectors are then used as conditional information to a GAN network and guide the synthesis process. We collected satellite images and ancient text descriptions for training in order to evaluate the efficacy of the proposed method. The qualitative and quantitative results obtained suggest that the doc2vec encoder-based model yields better images in terms of the semantic agreement with the input description. In addition, we present open research areas that we believe are important to further advance this new research area.
The second problem we want to address is the issue of semi-supervised domain adaptation. The goal of domain adaptation is to learn a generic classifier for multiple related problems, thereby reducing the cost of labeling. To that end, we propose two methods. The first method uses GANs in the context of image-to-image translation to adapt source domain images into target domain images and train a classifier using the adapted images. We evaluated the proposed method on two remote sensing datasets. Though we have not explored this avenue extensively due to computational challenges, the results obtained show that the proposed method is promising and worth exploring in the future. The second domain adaptation strategy borrows the adversarial property of GANs to learn a new representation space where the domain discrepancy is negligible, and the new features are discriminative enough. The method is composed of a feature extractor, class predictor, and domain classifier blocks. Contrary to the traditional methods that perform representation and classifier learning in separate stages, this method combines both into a single-stage thereby learning a new representation of the input data that is domain invariant and discriminative. After training, the classifier is used to predict both source and target domain labels. We apply this method for large-scale land cover classification and cross-sensor hyperspectral classification problems. Experimental results obtained show that the proposed method provides a performance gain of up to 40%, and thus indicates the efficacy of the method.
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Lewis, Ryan H. "Topological & network theoretic approaches in hyperspectral remote sensing /." Online version of thesis, 2010. http://ritdml.rit.edu/handle/1850/12274.
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Slade, Jr Wayne Homer. "Computational Intelligence Approaches to Ocean Color Inversion." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/SladeWH2004.pdf.
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Johnson, Michele K. "Remote sensing and the South, a critical evaluation of common approaches." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq37557.pdf.
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Sumaryono, Sumaryono. "Assessing Building Vulnerability to Tsunami Hazard Using Integrative Remote Sensing and GIS Approaches." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-123909.
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Kearney, Sean Patrick. "Integrating field and remote sensing approaches to evaluate ecosystem services from agriculture in smallholder landscapes." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62110.
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Agriculture now covers over a third of the Earth’s terrestrial surface, and smallholder farmers alone manage over a billion hectares globally. As stewards of the land, smallholders do much more for human well-being than just harvest useful products. However, a conventionally narrow focus on productivity over the last half- century now threatens ecosystem health and long-term agricultural production, particularly as global climate change accelerates. Agroecological and ‘climate-smart’ agricultural (CSA) practices have been proposed to both mitigate climate change and build resilience by enhancing multiple ecosystem services (ES), and policies are emerging to incentivize the adoption of such practices. In order to (1) better understand how agroecological and CSA management alternatives impact multiple ES, and (2) contribute to operationalizing monitoring of ES in smallholder landscapes, I present research from El Salvador combining field methods and remote sensing analysis to evaluate multiple ES. Using data from on-farm field trials, I developed composite ES indices to demonstrate distinct benefits and synergies among multiple ES from agroforestry and, to a lesser extent, organic management (i.e., CSA) compared to conventional management. I also identified a subset of easy-to-measure field proxies that correlate well with multiple ES, and proposed an improved method to compare relative erosion resulting from different land management practices. At the landscape scale, I focused on emerging techniques to map aboveground woody biomass (AGWB) – a large terrestrial carbon sink and indicator of agroforestry management – using high-spatial-resolution satellite imagery and airborne laser scanning (ALS). I showed how satellite data could be used to quantify AGWB at the watershed to landscape scale with uncertainties of less than 5%, and suggest that a singular focus on plot-scale uncertainty limits the operationalization of satellite-based approaches to monitor AGWB. I also present a novel approach to using ALS that improves the accuracy of measuring AGWB in trees outside of forests (e.g., agroforestry, hedgerows) and apply it to show that these trees contain substantial AGWB within smallholder landscapes, further demonstrating the ES benefits of agroforestry. This dissertation contributes to designing simple and cost-effective monitoring strategies to help operationalize policies promoting management practices that enhance multiple ES in smallholder agriculture.Land and Food Systems, Faculty of
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Ali, Fadi. "Urban classification by pixel and object-based approaches for very high resolution imagery." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-23993.
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Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV imagery for land use and land cover classification. Remote sensing imagery comes in various characteristics and format from different sources, most commonly from satellite and airborne platforms. Recently, unmanned aerial vehicles (UAVs) have become a very good potential source to collect geographic data with new unique properties, most important asset is the VHR of spatiotemporal data structure. UAV systems are as a promising technology that will advance not only remote sensing but GIScience as well. UAVs imagery has been gaining popularity in the last decade for various remote sensing and GIS applications in general, and particularly in image analysis and classification. One of the concerns of UAV imagery is finding an optimal approach to classify UAV imagery which is usually hard to define, because many variables are involved in the process such as the properties of the image source and purpose of the classification. The main objective of this research is evaluating land use / land cover (LULC) classification for urban areas, whereas the data of the study area consists of VHR imagery of RGB bands collected by a basic, off-shelf and simple UAV. LULC classification was conducted by pixel and object-based approaches, where supervised algorithms were used for both approaches to classify the image. In pixel-based image analysis, three different algorithms were used to create a final classified map, where one algorithm was used in the object-based image analysis. The study also tested the effectiveness of object-based approach instead of pixel-based in order to minimize the difficulty in classifying mixed pixels in VHR imagery, while identifying all possible classes in the scene and maintain the high accuracy. Both approaches were applied to a UAV image with three spectral bands (red, green and blue), in addition to a DEM layer that was added later to the image as ancillary data. Previous studies of comparing pixel-based and object-based classification approaches claims that object-based had produced better results of classes for VHR imagery. Meanwhile several trade-offs are being made when selecting a classification approach that varies from different perspectives and factors such as time cost, trial and error, and subjectivity. Classification based on pixels was approached in this study through supervised learning algorithms, where the classification process included all necessary steps such as selecting representative training samples and creating a spectral signature file. The process in object-based classification included segmenting the UAV’s imagery and creating class rules by using feature extraction. In addition, the incorporation of hue, saturation and intensity (IHS) colour domain and Principle Component Analysis (PCA) layers were tested to evaluate the ability of such method to produce better results of classes for simple UAVs imagery. These UAVs are usually equipped with only RGB colour sensors, where combining more derived colour bands such as IHS has been proven useful in prior studies for object-based image analysis (OBIA) of UAV’s imagery, however, incorporating the IHS domain and PCA layers in this research did not provide much better classes. For the pixel-based classification approach, it was found that Maximum Likelihood algorithm performs better for VHR of UAV imagery than the other two algorithms, the Minimum Distance and Mahalanobis Distance. The difference in the overall accuracy for all algorithms in the pixel-based approach was obvious, where the values for Maximum Likelihood, Minimum Distance and Mahalanobis Distance were respectively as 86%, 80% and 76%. The Average Precision (AP) measure was calculated to compare between the pixel and object-based approaches, the result was higher in the object-based approach when applied for the buildings class, the AP measure for object-based classification was 0.9621 and 0.9152 for pixel-based classification. The results revealed that pixel-based classification is still effective and can be applicable for UAV imagery, however, the object-based classification that was done by the Nearest Neighbour algorithm has produced more appealing classes with higher accuracy. Also, it was concluded that OBIA has more power for extracting geographic information and easier integration within the GIS, whereas the result of this research is estimated to be applicable for classifying UAV’s imagery used for LULC applications.
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Wang, Xiaozhen. "LITE aerosol retrievals with improved calibration and retrieval approaches in support of CALIPSO." Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/280757.
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Two of the biggest uncertainties in understanding and predicting climate change are the effects of aerosols and clouds. NASA's satellite mission, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, will provide vertical, curtain-like images of the atmosphere on a global scale and assist scientists in better determining how aerosols and clouds affect the Earth's radiation budget. The data from a previous space shuttle mission, LITE (Lidar In-space Technology Experiment, launched in Sept., 1994), have been employed to develop algorithms (e.g., spaceborne lidar system calibration and aerosol retrievals) in support of CALIPSO. In this work, a new calibration approach for 1064 nm lidar channel has been developed via comparisons of the 532 nm and 1064 nm backscatter signals from cirrus clouds. Some modeling analyses and simulations have also been implemented for CALIPSO's narrow bandwidth receiver filter to quantitatively distinguish Cabannes scattering from the full bandwidth Rayleigh scattering and correct the calibration of 532 nm channel. LITE data were also employed in some analyses with the aim of recovering the estimates of the backscatter ratio, R, of clean air regions. The uncertainties in aerosol retrieval due to different error sources, especially the bias and random errors of the extinction-to-backscatter ratio, Sa, have been investigated. A revised Sa table look-up approach is incorporated with two notable revisions for improved S a selection, which, as a consequence enable more bounded aerosol retrievals. Approximate but quantitatively useful multiple-scattering corrections are reported using a modeled multiple scattering factor, eta, which approximates the reduced attenuation caused by multiple scattering. Assessment of multiple scattering effects for a reasonable range of eta values is included for a combination of retrieval approaches.
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Powers, Stephanie Thompson. "Multi-scale Approaches for Evaluating the Success of Habitat Restoration in Tampa Bay, Florida." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6747.
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This research aims to further the understanding of ecological restoration success in the Tampa Bay, Florida, region. Although over four hundred restoration projects have been completed in the bay area, knowledge of their success has been hindered by the lack of assessment and transfer of information concerning project outcomes. Without comprehensive project assessment, local science will be limited in its ability to inform practice because we lack the advantage of past knowledge.
Using a multi-scaled approach, a diverse set of restoration projects are evaluated, providing information on how the projects are contributing to defined targets established by the Tampa Bay Estuary Program’s guiding documents. Through execution of habitat field assessments and completion of geographic information system, remote sensing, and aerial and terrestrial laser scanning analyses, the feasibility and effectiveness of these projects is investigated.
Additionally, the research provides innovative techniques for monitoring projects with relative ease, allowing project evaluation to be conducted on a more regular basis across a range of temporal and spatial scales. A cost matrix, created from this toolbox, is provided to offer land managers with a means of evaluating, regulating, and conserving restored critical coastal habitats in Tampa Bay, thus saving public dollars that may otherwise be wasted on failed projects.
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Holman, Kiyomi. "Testing Approaches and Sensors for Satellite-Derived Bathymetry in Nunavut." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41402.
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Nearshore bathymetry in the Canadian Arctic is poorly surveyed, but is vital knowledge for coastal communities that rely on marine transportation for resources and development. Nautical charts currently available are often outdated and surveying by traditional methods is both time consuming and expensive. Satellite-derived bathymetry (SDB) offers a significantly cheaper and faster option to provide information on nearshore bathymetry. The two most common approaches to SDB are empirical and physics-based. The empirical approach is simple and typically does well when calibrated with high-quality in-situ data, whereas the physics-based approach is more difficult to implement and requires precise atmospheric correction. This project tests the practical use of five methods within the empirical and physics-based approaches to SDB, using Landsat 8 and Sentinel-2 satellite imagery, at seven sites across Nunavut. Methods tested include: the Ratio-Transform, Multiband, and Random Forest Regression methods (empirical) and radiative transfer modeling (physics-based) using two atmospheric correction models: ACOLITE and Deep Water Correction. All methods typically use geolocated water depth data for validation, as well as calibration for the empirical methods. Spectral reflectance for model inputs were collected in Cambridge Bay, NU. Water depth data were acquired from the Canadian Hydrographic Service. All processing was conducted within the framework of plugins developed for the open-source GIS software, QGIS. Results from the empirical methods were typically poor due to poor calibration data, though Random Forest Regression performed well when good calibration data were available. Due to poor quality validation data, error for the physics-based results cannot be adequately quantified in most places. Additionally, atmospheric correction remains a challenge for the physics-based methods. Overall, results indicate that where large, high-quality calibration datasets are available, Random Forest Regression performs best of all methods tested, with little bias and low mean absolute error in water less than 10 m deep. As such datasets are rare in the Arctic, the physics-based method is often the only option for SDB and is an excellent qualitative tool for informing communities of shallow bathymetry features and assessing navigation risk.
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Shatnawi, Nawras [Verfasser], and S. [Akademischer Betreuer] Hinz. "Assessment of Groundwater Potential Zones in the Lower Jordan Valley Using Remote Sensing Approaches / Nawras Shatnawi. Betreuer: S. Hinz." Karlsruhe : KIT-Bibliothek, 2014. http://d-nb.info/1053703988/34.
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Nouri, Hamideh, Edward Glenn, Simon Beecham, Boroujeni Sattar Chavoshi, Paul Sutton, Sina Alaghmand, Behnaz Noori, and Pamela Nagler. "Comparing Three Approaches of Evapotranspiration Estimation in Mixed Urban Vegetation: Field-Based, Remote Sensing-Based and Observational-Based Methods." MDPI AG, 2016. http://hdl.handle.net/10150/618720.
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Despite being the driest inhabited continent, Australia has one of the highest per capita water consumptions in the world. In addition, instead of having fit-for-purpose water supplies (using different qualities of water for different applications), highly treated drinking water is used for nearly all of Australia's urban water supply needs, including landscape irrigation. The water requirement of urban landscapes, particularly urban parklands, is of growing concern. The estimation of evapotranspiration (ET) and subsequently plant water requirements in urban vegetation needs to consider the heterogeneity of plants, soils, water, and climate characteristics. This research contributes to a broader effort to establish sustainable irrigation practices within the Adelaide Parklands in Adelaide, South Australia. In this paper, two practical ET estimation approaches are compared to a detailed Soil Water Balance (SWB) analysis over a one year period. One approach is the Water Use Classification of Landscape Plants (WUCOLS) method, which is based on expert opinion on the water needs of different classes of landscape plants. The other is a remote sensing approach based on the Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra satellite. Both methods require knowledge of reference ET calculated from meteorological data. The SWB determined that plants consumed 1084 mmyr(-1) of water in ET with an additional 16% lost to drainage past the root zone, an amount sufficient to keep salts from accumulating in the root zone. ET by MODIS EVI was 1088 mmyr(-1), very close to the SWB estimate, while WUCOLS estimated the total water requirement at only 802 mmyr(-1), 26% lower than the SWB estimate and 37% lower than the amount actually added including the drainage fraction. Individual monthly ET by MODIS was not accurate, but these errors were cancelled out to give good agreement on an annual time step. We conclude that the MODIS EVI method can provide accurate estimates of urban water requirements in mixed landscapes large enough to be sampled by MODIS imagery with 250-m resolution such as parklands and golf courses.
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Usman, Muhammad. "Performance Assessment and Management of Groundwater in an Irrigation Scheme by Coupling Remote Sensing Data and Numerical Modeling Approaches." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-203578.
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The irrigated agriculture in the Lower Chenab Canal (LCC) of Pakistan is characterized by huge water utilization both from surface and groundwater resources. Need of utilization of water from five rivers in Punjab province along with accelerated population growth has forced the construction of world’s largest irrigation network.
Nevertheless, huge irrigation infrastructure, together with inappropriate drainage infrastructure, led to a build-up of shal-low groundwater levels, followed by waterlogging and secondary salinization in the soil profile. Following this era, decreased efficiency of irrigation supply system along with higher food demands had increased burdens on groundwater use, which led to a drop in groundwater levels in major parts of LCC. Previous studies in the study region revealed lacking management and maintenance of irrigation system, inflexible irrigation strategies, poor linkages between field level water supply and demands. No future strategy is present or under consideration to deal with this long time emerged groundwater situation particularly under unchanged irrigation water supply and climate change. Therefore, there is an utmost importance to assess the current profile of water use in the irrigation scheme and to device some workable strategies under future situations of land use and climate change. This study aims to investigate the spatio-temporal status of water utilization and performance of irrigation system using remote sensing data and techniques (SEBAL) in combination with other point data.
Different irrigation performance indicators including equity, adequacy and reliability using evaporation fraction as main input parameter are utilized. Current profiles of land use/land cover (LULC) areas are assessed and their change detections are worked out to establish realistic future scenarios. Spatially distributed seasonal net recharge, a very important input parameter for groundwater modeling, is estimated by employing water balance approaches using spatial data from remote sensing and local norms. Such recharge results are also compared with a water table fluctuation approach. Following recharge estimation, a regional 3-D groundwater flow model using FEFLOW was set up. This model was calibrated by different approaches ranging from manual to automated pilot point (PP) approach. Sensitivity analysis was performed to see the model response against different model input parameters and to identify model regions which demand further improvements. Future climate parameters were downscaled to establish scenarios by using statistical downscaling under IPCC future emission scenarios. Modified recharge raster maps were prepared under both LULC and climate change scenarios and were fed to the groundwater model to investigate groundwater dynamics.
Seasonal consumptive water use analysis revealed almost double use for kharif as compared to rabi cropping seasons with decrease from upper LCC to lower regions. Intra irrigation subdivision analysis of equity, an important irrigation performance indicator, shows less differences in water consumption in LCC. However, the other indicators (adequacy and reliability) indicate that the irrigation system is neither adequate nor reliable. Adequacy is found more pronounced during kharif as compared to rabi seasons with aver-age evaporation fraction of 0.60 and 0.67, respectively. Similarly, reliability is relatively higher in upper LCC regions as compared to lower regions. LULC classification shows that wheat and rice are major crops with least volatility in cultivation from season to season. The results of change detection show that cotton exhibited maximum positive change while kharif fodder showed maximum negative change during 2005-2012. Transformation of cotton area to rice cultivation is less conspicuous. The water consumption in upper LCC regions with similar crops is relatively higher as compared to lower regions. Groundwater recharge results revealed that, during the kharif cropping seasons, rainfall is the main source of recharge followed by field percolation losses, while for rabi cropping seasons, canal seepage remains the major source. Seasonal net groundwater recharge is mainly positive during all kharif seasons with a gradual increase in groundwater level in major parts of LCC. Model optimization indicates that PP is more flexible and robust as compared to manual and zone based approaches. Different statistical indicators show that this method yields reliable calibration and validation as values of Nash Sutcliffe Efficiency are 0.976 and 0.969, % BIAS are 0.026 and -0.205 and root mean square errors are 1.23 m and 1.31 m, respectively. Results of model output sensitivity suggest that hydraulic conductivity is a more influential parameter in the study area than drain/fillable porosity. Model simulation results under different scenarios show that rice cultivation has the highest impact on groundwater levels in upper LCC regions whereas major negative changes are observed for lower parts under decreased kharif fodder area in place of rice, cotton and sugarcane. Fluctuations in groundwater level among different proposed LULC scenarios are within ±1 m, thus showing a limited potential for groundwater management. For future climate scenarios, a rise in groundwater level is observed for 2011 to 2025 under H3A2 emission regime. Nevertheless, a drop in groundwater level is expected due to increased crop consumptive water use and decreased precipitations under H3A2 scenario for the periods 2026-2035 and 2036-2045. Although no imminent threat of groundwater shortage is anticipated, there is an opportunity for developing groundwater resources in the lower model regions through water re-allocation that would be helpful in dealing water shortages. The groundwater situation under H3B2 emission regime is relatively complex due to very low expectation of rise in groundwater level through precipitation during 2011-2025. Any positive change in groundwater under such scenarios is mainly associated with changes in crop consumptive water uses. Consequently, water management under such situation requires revisiting of current cropping patterns as well as augmenting water supply through additional surface water resources.
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Villarreal, Miguel Luis. "Land Use and Disturbance Interactions in Dynamic Arid Systems: Multiscale Remote Sensing Approaches for Monitoring and Analyzing Riparian Vegetation Change." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/195061.
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Riparian systems are comprised of interacting aquatic and terrestrial elements that contribute distinctively to the natural capital of arid landscapes. Riparian vegetation is a major component of riparian systems, providing the ecosystem services required to support watershed health. The spatial and temporal distributions of riparian vegetation are influenced by hydrologic and disturbance processes operating at scales from local to regional. I believe both these processes are well suited to monitoring using synoptic and multitemporal approaches.The research in this dissertation is presented as 3 related studies. The first study focused on historical riparian dynamics related to natural disturbance and land use. Using current and historical riparian vegetation maps, we examined vegetation change within catchments of varying land use intensity. Results suggest that land use activities and wastewater subsidy affect the rate of development and diversity of riparian community typesThe second study used moderate resolution satellite imagery to monitor changes in riparian structure and pattern within a land cover change framework. We classified Landsat Thematic Mapper satellite imagery of the Upper Santa Cruz River watershed using Classification and Regression Tree (CART) models. We tested the ability of our models to capture change at landscape, floodplain, and catchment scales, centering our change detection efforts on a riparian tree die-off episode and found they can be used to describe both general landscape dynamics and disturbance-related riparian change.The third study examined historical and environmental factors contributing to spatial patterns of vegetation following two riparian tree die-offs. We used high resolution aerial imagery to map locations of individual live and dead trees and collected a suite of environmental variables and historical variables related directly and indirectly to land use and disturbance history. We tested for differences between groups of live and dead trees using Multi-response Permutation Procedures and found strong relationships between historical factors and mortality incidence.The results from these studies demonstrate the importance of examining historical information and spatial linkages across scales when monitoring riparian vegetation. From a land management perspective, the results identify the need for landscape-level, ecosystem-based management programs to maintain functioning and spatially connected riparian systems.
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Repaka, Sunil Reddy. "Comparing spectral-object based approaches for extracting and classifying transportation features using high resolution multi-spectral satellite imagery." Master's thesis, Mississippi State : Mississippi State University, 2004. http://library.msstate.edu/etd/show.asp?etd=etd-11082004-231712.
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Powers, Ryan Paul. "Integration of remote sensing and spatial conservation prioritization approaches for aiding large-area, multi-jurisdictional biodiversity conservation in Canada’s boreal forest." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/52903.
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Remote sensing is an important complementary data source to enable cost effective monitoring and mapping of biodiversity indicators over large extents in a consistent and repeatable manner. As such, remote sensing is capable of supporting the information needs of modern biodiversity conservation efforts. However, a number of critical challenges and opportunities deserve greater attention. The aim of this research is to advance the use of remote sensing and other geospatial techniques for large-area, multi-jurisdictional conservation of Canada’s boreal forest. Outcomes of this dissertation contributed to progress in each of four research themes: (i) assessing biodiversity across broad areas, (ii) identifying areas of high conservation priority (iii) evaluating the efficacy of current and hypothetical reserve networks, and (iv) incorporating future vegetation variability in conservation planning. The overall research findings indicate the tremendous capacity of the Canadian boreal forest to provide suitable areas for conservation investment and demonstrate the usefulness of these coarse-scale approaches for guiding ongoing research aimed at boreal conservation planning. Key findings included: (a) Reserves that were restricted to only intact forest landscapes were less flexible and efficient (more costly), (b) Reserves using accessibility (distance from road and human settlement) as a cost surrogate were able to satisfy a range of conservation targets and compactness levels while remaining remote from human influence, (c) Reserves (≥1000 km2; <10000 km2) were relatively less variable, (d) Climate change impacts (estimated vegetation productivity variability) greatly influences the cost of reserve networks and the amount of area required to meet conservation targets, (e) Conservation of more sites spread across locations with higher variable vegetation probability values, yet low cost (wilderness areas), proved most efficient, and (f) Reserve networks optimized under “current” or “least change (B1)” conditions are unlikely to maintain their current representative targets in 2080Forestry, Faculty of
Graduate
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Farrell, Michael D. Jr. "Analysis of Modeling, Training, and Dimension Reduction Approaches for Target Detection in Hyperspectral Imagery." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7505.
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Whenever a new sensor or system comes online, engineers and analysts responsible for processing the measured data turn first to methods that are tried and true on existing systems. This is a natural, if not wholly logical approach, and is exactly what has happened in the advent of hyperspectral imagery (HSI) exploitation. However, a closer look at the assumptions made by the approaches published in the literature has not been undertaken.
This thesis analyzes three key aspects of HSI exploitation: statistical data modeling, covariance estimation from training data, and dimension reduction. These items are part of standard processing schemes, and it is worthwhile to understand and quantify the impact that various assumptions for these items have on target detectability and detection statistics.
First, the accuracy and applicability of the standard Gaussian (i.e., Normal) model is evaluated, and it is shown that the elliptically contoured t-distribution (EC-t) sometimes offers a better statistical model for HSI data. A finite mixture approach for EC-t is developed in which all parameters are estimated simultaneously without a priori information. Then the effects of making a poor covariance estimate are shown by including target samples in the training data. Multiple test cases with ground targets are explored. They show that the magnitude of the deleterious effect of covariance contamination on detection statistics depends on algorithm type and target signal characteristics. Next, the two most widely used dimension reduction approaches are tested. It is demonstrated that, in many cases, significant dimension reduction can be achieved with only a minor loss in detection performance.
In addition, a concise development of key HSI detection algorithms is presented, and the state-of-the-art in adaptive detectors is benchmarked for land mine targets. Methods for detection and identification of airborne gases using hyperspectral imagery are discussed, and this application is highlighted as an excellent opportunity for future work.
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Lopez, Radcenco Manuel. "Data-driven approaches for ocean remote sensing : from the non-negative decomposition of operators to the reconstruction of satellite-derived sea surface dynamics." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0107/document.
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Au cours des dernières années, la disponibilité toujours croissante de données de télédétection multi-source de l'océan a été un facteur clé pour améliorer notre compréhension des dynamiques de la surface de l'océan. A cet égard, il est essentiel de mettre au point des approches efficaces pour exploiter ces ensembles de données. En particulier, la décomposition des processus géophysiques en modes pertinents est une question clé pour les problèmes de caractérisation, de prédiction et de reconstruction. Inspirés par des progrès récents en séparation aveugle des sources, nous visons, dans la première partie de cette thèse, à étendre les modèles de séparation aveugle de sources sous contraintes de non-négativité au problème de la caractérisation et décomposition d'opérateurs ou fonctions de transfert entre variables d'intérêt. Nous développons des schémas computationnels efficaces reposant sur des fondations mathématiques solides. Nous illustrons la pertinence des modèles de décomposition proposés dans différentes applications impliquant l'analyse et la prédiction de dynamiques géophysiques. Par la suite, étant donné que la disponibilité toujours croissante d'ensembles de données multi-sources supporte l'exploration des approches pilotées par les données en tant qu'alternative aux formulations classiques basées sur des modèles, nous explorons des approches basées sur les données récemment introduits pour l'interpolation des champs géophysiques à partir d'observations satellitaires irrégulièrement échantillonnées. De plus, en vue de la future mission SWOT, la première mission satellitaire à produire des observations d'altimétrie par satellite complètement bidimensionnelles et à large fauchée, nous nous intéressons à évaluer dans quelle mesure les données SWOT permettraient une meilleure reconstruction des champs altimétriquesIn the last few decades, the ever-growing availability of multi-source ocean remote sensing data has been a key factor for improving our understanding of upper ocean dynamics. In this regard, developing efficient approaches to exploit these datasets is of major importance. Particularly, the decomposition of geophysical processes into relevant modes is a key issue for characterization, forecasting and reconstruction problems. Inspired by recent advances in blind source separation, we aim, in the first part of this thesis dissertation, at extending non-negative blind source separation models to the problem of the observation-based characterization and decomposition of linear operators or transfer functions between variables of interest. We develop mathematically sound and computationally efficient schemes. We illustrate the relevance of the proposed decomposition models in different applications involving the analysis and forecasting of geophysical dynamics. Subsequently, given that the ever-increasing availability of multi-source datasets supports the exploration of data-driven alternatives to classical model-driven formulations, we explore recently introduced data-driven models for the interpolation of geophysical fields from irregularly sampled satellite-derived observations. Importantly, with a view towards the future SWOT mission, the first satellite mission to produce complete two-dimensional wide-swath satellite altimetry observations, we focus on assessing the extent to which SWOT data may lead to an improved reconstruction of altimetry fields
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Troya-Galvis, Andrès. "Approche collaborative et qualité des données et des connaissances en analyse multi-paradigme d'images de télédétection." Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAD040/document.
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L'interprétation automatique d'images de télédétection à très haute résolution spatiale est une tâche complexe mais nécessaire. Les méthodes basées objet sont couramment employées pour traiter ce type d'images. Elles consistent à construire les objets d'intérêt au moyen d'une étape de segmentation puis à les classifier en utilisant des méthodes de fouille de données. La majorité des travaux entrepris dans ce domaine considèrent la segmentation et la classification de manière indépendante. Or, ces deux étapes cruciales du processus sont fortement liées. Dans cette thèse, nous proposons deux approches différentes basées sur la qualité des données et des connaissances, pour initialiser, guider et évaluer un processus collaboratif de manière objective: 1. Une première approche basée sur une stratégie d'extraction mono-classe qui permet de se focaliser sur les propriétés particulières d'une classe donnée afin de mieux labelliser les objets de cette classe par rapport au reste. 2. Une deuxième approche multi-classe offrant deux stratégies différentes d'agrégation d'extracteurs mono-classes qui permet l'obtention d'une image entièrement labellisée de manière automatiqueAutomatic interpretation of very high spatial resolution remotely sensed images is a complex but necessary task. Object-based image analysis approaches are commonly used to deal with this kind of images. They consist in applying an image segmentation algorithm in order to construct the abjects of interest, and then classifying them using data-mining methods. Most of the existing work in this domain consider the segmentation and the classification independently. However, these two crucial steps are closely related. ln this thesis, we propose two different approaches which are based on data and knowledge quality in order to initialize, guide, and evaluate a segmentation and classification collaborative process. 1. The first approach is based on a mono-class extraction strategy allowing us to focus on the particular properties of a given thematic class in order to accurately label the abjects of this class. 2. The second approach deals with multi-class extraction and offers two strategies to aggregate several mono-class extractors to get a final and completely labelled image
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Bouraoui, Seyfallah. "Time series analysis of SAR images using persistent scatterer (PS), small baseline (SB) and merged approaches in regions with small surface deformation." Phd thesis, Université de Strasbourg, 2013. http://tel.archives-ouvertes.fr/tel-01019429.
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This thesis aims at the study of small to large surface deformation that can be detected using the remote sensing interferometric synthetic aperture radar (InSAR) methods. The new developments of InSAR processing techniques allow the monitoring of surface deformation with millimeter surface change accuracy. Conventional InSAR use a pair of SAR images ("Master" and "Slave" images) in order to measure the phase difference between the two images taken at different times. The uncertainties in measurements using the conventional InSAR due to the atmospheric delay, the topographic changes and the orbital artifacts are the handicaps of this method. The idea of InSAR method is to measure the phase difference between tow SAR acquisitions. These measure refere to the ground movment according to the satellite position. In interferogram the red to blue colors refere to the pixel movement to or far from the satellite position in Line-Of-Sight (LOS) direction. In 2000's, Radar spacecraft have seen a large number of launching mission, SAR quisitions and InSAR applicability have seen explosion in differents geophysical studies due to the important SAR datas and facility of data accessibity. This SAR-mining needs other type and generation of InSAR processing.In 2001, Ferretti and others introduce a new method called Permanent Scatterer InSAR (PS) that is based on the use of more than one Slave image in InSAR processing with the same Master image. This method allows enhancing the LOS signal for each pixel (PS) by using the best time and/or space-correlated signal (from amplitude and/or from phase) for each pixel over the acquisitions. A large number of algorithms were developed for this purpose using thesame principle (variantes). In 2002, Berardino et al developed new algorithm for monitoring surface deformation based on the combination of stack of InSAR results from SAR couples respecting small baseline (SB) distance. Nowadays, these two methods represent the existing time series (TS) analysis of SAR images approaches. In addition, StaMPS software introduced by Hooper and others, in 2008 is able to combine these two methods in order to take advantages from both of this TS approaches in term of best signal correlation and reducing the signal noise errors. In this thesis, the time series studies of surface changes associate to differents geophysical phenomena will have two interest: the first is to highlight the PS and SBAS results and discuss the fiability of obtained InSAR signal with comparation with the previous studies of the same geophysical case or observations in the field and in the second time, the combined method will also validate the results obtained separately with differents TS techniques. The validation of obtained signal is assured by these two steeps: Both of PS and SBAS methods should give relatively the same interferograms and LOSdisplacement signal (in term of sign and values), in addition these results will be compared with the previous studies results or with observations on the field.In this thesis, the InSAR techniques are applied to different case-studies of small surface deformation [...]
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Usman, Muhammad [Verfasser], Rudolf [Akademischer Betreuer] [Gutachter] Liedl, Niels [Gutachter] Schütze, and Martin [Gutachter] Sauter. "Performance Assessment and Management of Groundwater in an Irrigation Scheme by Coupling Remote Sensing Data and Numerical Modeling Approaches / Muhammad Usman. Betreuer: Rudolf Liedl. Gutachter: Rudolf Liedl ; Niels Schütze ; Martin Sauter." Dresden : Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://d-nb.info/1105876535/34.
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Pivovarník, Marek. "New Approaches in Airborne Thermal Image Processing for Landscape Assessment." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-263356.
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Letecká termální hyperspektrální data přinášejí řadu informací o teplotě a emisivitě zemského povrchu. Při odhadování těchto parametrů z dálkového snímání tepelného záření je třeba řešit nedourčený systém rovnic. Bylo navrhnuto několik přístupů jak tento problém vyřešit, přičemž nejrozšířenější je algoritmus označovaný jako Temperature and Emissivity Separation (TES). Tato práce má dva hlavní cíle: 1) zlepšení algoritmu TES a 2) jeho implementaci do procesingového řetězce pro zpracování obrazových dat získaných senzorem TASI. Zlepšení algoritmu TES je možné dosáhnout nahrazením používaného modulu normalizování emisivity (tzv. Normalized Emissivity Module) částí, která je založena na vyhlazení spektrálních charakteristik nasnímané radiance. Nový modul je pak označen jako Optimized Smoothing for Temperature Emissivity Separation (OSTES). Algoritmus OSTES je připojen k procesingovému řetězci pro zpracování obrazových dat ze senzoru TASI. Testování na simulovaných datech ukázalo, že použití algoritmu OSTES vede k přesnějším odhadům teploty a emisivity. OSTES byl dále testován na datech získaných ze senzorů ASTER a TASI. V těchto případech však není možné pozorovat výrazné zlepšení z důvodu nedokonalých atmosférických korekcí. Nicméně hodnoty emisivity získané algoritmem OSTES vykazují více homogenní vlastnosti než hodnoty ze standardního produktu senzoru ASTER.
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Moura, Maíra Martim de, and Maíra Martim de Moura. "Influência de diferentes fontes e escalas de informação do relevo sobre a estimativa de cheias a partir do Hidrograma Unitário Instantâneo de Nash." Universidade Federal de Pelotas, 2018. http://guaiaca.ufpel.edu.br:8080/handle/prefix/3982.
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Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq
A água é um recurso natural indispensável à vida e primordial ao desenvolvimento econômico de uma região. Devido às mudanças climáticas, associadas às ações antrópicas e ao crescimento populacional, a ocorrência de problemas relacionados a cheias em bacias hidrográficas tem aumentado. O estudo de cheias em bacias hidrográficas permite a quantificação da magnitude das vazões de pico e do hidrograma de escoamento superficial direto oriundos de um ou mais eventos de chuva. No entanto, tais estimativas dependem de dados de séries históricas, o que pode ser problemático nos países em desenvolvimento devido a existência de um número insuficiente de seções com monitoramento fluviométrico, tornando a modelagem hidrológica de cheias uma ferramenta imprescindível. Diferentes métodos para estimativa de cheias vêm sendo apresentadas e utilizadas na literatura, com destaque para a do Hidrograma Unitário (HU), a do Hidrograma Unitário Instantâneo (HUI) e a do Hidrograma Unitário Instantâneo Geomorfológico (HUIG). Um modelo de HUI amplamente utilizados é o de Nash (HUIN), para o qual diversas propostas geomorfológicas vêm sendo desenvolvidas, estabelecendo relações para seus parâmetros a partir da caracterização física da bacia hidrográfica e da rede de drenagem. Durante a caracterização de bacias hidrográficas em softwares de geoprocessamento, a principal informação é a do relevo, obtida a partir de um Modelo Digital de Elevação (MDE), que pode ser obtido a partir de cartas topográficas, ou de imagens de radar (ex. SRTM) e sensor (ex. ASTER). O principal objetivo deste estudo foi avaliar a aplicabilidade e confiabilidade de diferentes fontes e escalas de informação do relevo visando à modelagem de cheias através do modelo de HUIN fundamentado em parâmetros geomorfológicos, tomando como base cinco bacias hidrográficas experimentais de diferentes características fisiográficas e dotadas de monitoramento hidrológico. Os MDEs utilizados foram obtidos de cartas topográficas na escala 1:50.000, de imagens SRTM com 30m e 90m, de imagens do banco de dados TOPODATA, de imagens ASTER, e somente para a menor bacia, de dados de um levantamento planialtimétrico. Foram selecionadas quatro propostas geomorfológicas para o HUIN, sendo duas delas baseadas na teoria do HUIG, e as outras duas em estudos empíricos realizados em diferentes bacias hidrográficas. Com base nos resultados obtidos para as bacias analisadas, as principais conclusões deste estudo foram: a) os parâmetros mais impactados pela fonte e escala da informação do relevo são a declividade do curso d’água principal e as razões de Horton e de Schumm; b) as bacias planas são mais suscetíveis a erros altimétricos e estes aumentam conforme a área da bacia diminui; c) não é possível observar uma combinação de proposta-MDE que descreva melhor ou pior o conjunto de bacias analisadas, nem levando em consideração a declividade, nem o tamanho da área das bacias; d) as propostas geomorfológicas que não dependem de informação da velocidade do escoamento apresentaram bons resultados em relação as baseadas na teoria do HUIG; e) a combinação de diferentes propostas permite estimar de forma satisfatória o comportamento do hidrograma de escoamento superficial direto e o tempo e a vazão de pico nas bacias estudadas.
Water is a natural resource indispensible to life and essential to regional economic development. Due to climate change, anthropic interferences, and rapid population growth, the occurrence of flood-related natural hazards in watersheds has increased. Watershed flood-related studies allow the estimation of peak streamflow and direct surface runoff hydrograph resulting from single or multiple rainfall events. However, such estimations are directly dependent on existing streamflow historical series, which might be troublesome in developing countries due to the lack of streamflow gauging stations. In this context, indirect flood estimation methods stand out. Among the different flood estimation methods presented in the literature, the Unit Hydrograph (UH), Instantaneous Unit Hydrograph (IUH), and Geomorphological Instantaneous Unit Hydrograph (GIUH) have caught researchers’ attention. The Nash’s IUH (NIUH) is one of the most widely used IUH models. Several geomorphological approaches have been developed for NIUH, thus relating its parameters to watershed and drainage network physical characteristics. During the characterization of watersheds in geoprocessing softwares, the main information is that of relief obtained from a Digital Elevation Model (MDE) which can be obtained from topographic maps or from radar images (e.g. SRTM) and sensor images (e.g. ASTER). The main objective of this study was to evaluate the applicability and reliability of different relief data sources and scales for determination of geomorphological parameters required to estimate floods from NIUH. This study took into account data sets from five experimental watersheds with different physiographical characteristics, which have hydrological monitoring. The DEMs analysed in this study were obtained from topographical maps in the 1:50,000 scale, SRTM images with 30 and 90-meter spatial resolution, TOPODATA database, ASTER images. For the smallest watershed, an in situ topographic survey was also carried out for DEM derivation. Four geomorphological approaches for the NIUH were selected; two of them were based on the GIUH theory, whereas, the others were adjusted from empirical studies conducted in different watersheds. Based on the results obtained for the analysed watersheds, the main conclusions were: a) the main watercourse’s slope and Horton’s and Schumm’s ratios are the most sensitive parameters to relief data sources and scales; b) flat watersheds are the most susceptible to altimetry errors, which increase as the watershed area decreases; c) it is not possible to identify any combinations of geomorphological approach-DEM that better or worse describe all the analyzed watersheds when assessing watershed slope or drainage area independently; d) the geomorphological approaches which do not depend on streamflow speed information presented satisfactory results when compared to those based on GIUH theory; and e) the combination of different approaches enables to satisfactorily estimate the behavior of direct surface runoff hydrographs and their peak streamflow and time in all the considered watersheds.
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Tyoda, Zipho. "Landslide susceptibility mapping : remote sensing and GIS approach." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79856.
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Thesis (MSc)--Stellenbosch University, 2013.Landslide susceptibility maps are important for development planning and disaster management. The current synthesis of landslide susceptibility maps largely applies GIS and remote sensing techniques. One of the most critical stages on landslide susceptibility mapping is the selection of landslide causative factors and weighting of the selected causative factors, in accordance to their influence to slope instability. GIS is ideal when deriving static factors i.e. slope and aspect and most importantly in the synthesis of landslide susceptibility maps. The integration of landslide causative thematic maps requires the selection of the weighting method; in order to weight the causative thematic maps in accordance to their influence to slope instability. Landslide susceptibility mapping is based on the assumption that future landslides will occur under similar circumstances as historic landslides. The weight of evidence method is ideal for landslide susceptibility mapping, as it calculates the weights of the causative thematic maps using known landslides points. This method was applied in an area within the Western Cape province of South Africa, the area is known to be highly susceptible to landslide occurrences. A prediction rate of 80.37% was achieved. The map combination approach was also applied and achieved a prediction rate of 50.98%. Satellite remote sensing techniques can be used to derive the thematic information needed to synthesize landslide susceptibility maps and to monitor the variable parameters influencing landslide susceptibility. Satellite remote sensing techniques can contribute to landslide investigation at three distinct phases namely: (1) detection and classification of landslides (2) monitoring landslide movement and identification of conditions leading up to an event (3) analysis and prediction of slope failures. Various sources of remote sensing data can contribute to these phases. Although the detection and classification of landslides through the remote sensing techniques is important to define landslide controlling parameters, the ideal is to use remote sensing data for monitoring of areas susceptible to landslide occurrence in an effort to provide an early warning. In this regard, optical remote sensing data was used successfully to monitor the variable conditions (vegetation health and productivity) that make an area susceptible to landslide occurrence.
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Inanlou, Farzad Michael-David. "Innovative transceiver approaches for low-power near-field and far-field applications." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52245.
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Wireless operation, near-field or far-field, is a core functionality of any mobile or autonomous system. These systems are battery operated or most often utilize energy scavenging as a means of power generation. Limited access to power, expected long and uninterrupted operation, and constrained physical parameters (e.g. weight and size), which limit overall power harvesting capabilities, are factors that outline the importance for innovative low-power approaches and designs in advanced low-power wireless applications. Low-power approaches become especially important for the wireless transceiver, the block in charge of wireless/remote functionality of the system, as this block is usually the most power hungry component in an integrated system-on-chip (SoC). Three such advanced applications with stringent power requirements are examined including space-based exploratory remote sensing probes and their associated radiation effects, millimeter-wave phased-array radar for high-altitude tactical and geological imaging, and implantable biomedical devices (IMDs), leading to the proposal and implementation of low-power wireless solutions for these applications in SiGe BiCMOS and CMOS and platforms.
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Meola, Joseph. "A model-based approach to hyperspectral change detection." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1320847592.
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Nesbit, Paul R. "Uninhabited Aerial Vehicles and Structure from Motion| A fresh approach to photogrammetry." Thesis, California State University, Long Beach, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1526938.
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Three-dimensional mapping and modeling can contribute to knowledge about the real world. Techniques are largely driven by available technology and typically involve expensive equipment and expert skill. Recent advances have led to low-cost remotely sensed data collection and generation of 3D terrain models using Uninhabited Aerial Vehicles (UAV) and Structure from Motion (SfM) processing software. This research presents a low-cost alternative to 3D mapping by pairing UAV collection methods with three SfM processing techniques. Surface models are generated from the same image set captured from a low-cost UAV coupled with a digital camera. Accuracy of resulting models identifies strengths and weaknesses of each technique. Analysis of different slope ranges investigates the divide at which surfaces generated become less reliable. This research provides a deeper understanding of the strengths and limitations of emerging technologies used together in a fresh approach to photogrammetry.
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Pyke, Benjamin, and Benjamin Pyke. "Practical Approach To Building A Mid-Wave Remote Sensing System." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/626377.
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The purpose of this project, Laser Active Transmitter & Receiver (LATR), was to build a mobile ground based remote sensing system that can detect, identify and quantify a specific gaseous species using Differential Absorption LIDAR (DIAL). This thesis project is concerned with the development and field testing of a mid-wave infrared active remote sensing system, capable of identifying and quantifying emissions in the 3.2 – 3.5 micron range. The goal is to give a brief description of what remote sensing is about and the specific technique used to analyze the collected data. The thesis will discuss the transmitter and the associated subsystems used to create the required wavelength, and the receiver used to collect the returns. And finally, the thesis will discuss the process of collecting the data and some of the results from field and lab collections.
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Bhang, Kon Joon. "Remote Sensing Approach for Hydrologic Assessments of Complex Lake Systems." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1212787335.
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Nielsen, Michael Meinild. "Inferring Land Use from Remote Sensing Imagery : A context-based approach." Doctoral thesis, Stockholms universitet, Kulturgeografiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-103082.
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This doctoral thesis investigates the potential of classification methods based on spatial context to infer specific forms of land use from remote sensing data. The problem is that some types of land use are characterized by a complex configuration of land covers that traditional per-pixel based methods have problems classifying due to spectral heterogeneity. The problem of spectral heterogeneity is also present in classification of high resolution imagery. Two novel methods based on contextual information are evaluated, Spatial Relational Post-Classification (SRPC) and Window Independent Context Segmentation (WICS). The thesis includes six case studies in rural and urban areas focusing on the classification of: agricultural systems, urban characteristics, and dead wood areas. In the rural case studies specific types of agricultural systems associated with different household strategies are mapped by inferring the physical expression of land use using the SRPC method. The urban remote sensing studies demonstrate how the WICS method is able to extract information corresponding to different phases of development. Additionally, different urban classes are shown to correspond to different socioeconomic profiles, demonstrating how urban remote sensing can be used to make a connection between the physical environment and the social lives of residents. Finally, in one study the WICS method is used to successfully classify dead trees from high resolution imagery. Taken together these studies demonstrate how approaches based on spatial context can be used to extract information on land use in rural and urban environments where land use manifests itself in the form of complex spectral class and land cover patterns. The thesis, thus, contributes to the research field by showing that contextual methods can capture multifaceted patterns that can be linked to land use. This, in turn, enables an increased use of remote sensing data, particularly in the social sciences.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript. Paper 5: Manuscript. Paper 6: Manuscript.
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Alahmadi, Mohammed. "A Recursive Approach for Adaptive Parameters Selection in AMultifunction Radar." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1448981863.
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Jetter, Joshua. "Analysis of a Systems Engineering Based Approach to the University Rover Challenge." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579709.
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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NVThe University Rover Challenge is a competition to build a scaled down version of a next-generation Mars rover. This paper describes the comprehensive systems engineering based approached used by the Missouri S&T Mars Rover Design Team. This student run, interdisciplinary team of approximately 50 students followed a comprehensive systems-engineering based approach to the conceptualization, design, implementation, test and evaluation of the project. This has allowed students to leverage their discipline specific expertise, while simultaneously facilitating the cross-disciplinary communication which is essential to the successful completion of the project. The team's performance in the competition will provide metrics to analyze the efficacy of this organization and approach.
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Munyati, Christopher. "Wetland change assessment on the Kafue Flats, Zambia : a remote sensing approach." Thesis, University of Stirling, 1997. http://hdl.handle.net/1893/21424.
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The Kafue Flats floodplain wetland system in southern Zambia is under increasing climate and human pressures. Firstly, drought episodes appear more prevalent in recent years in the region and secondly, two dams were built on the lower and upper ends of the wetland in 1972 and 1978, respectively, across the Kafue River which flows through the wetland. The study uses multi-temporal remote sensing to assess change in extent and vigour of green vegetation, and extent of water bodies and dry land cover on the Kafue Flats. The change detection's management value is assessed. Four normalised, co-registered digital Landsat images from 24 September 1984, 3 September 1988, 12 September 1991 and 20 September 1994 were used. The main change detection method used was comparison of classifications, supplemented by Normalised Difference Vegetation Index (NDVI) and Principal Component Analysis (PCA) change detection. Ancillary land use and environmental data were used in interpreting the change in the context of cause and effect. The results indicate inconsistent trends in the changes of most land cover classes, as a result of manipulation of the wetland by man through annual variations in the timing and magnitude of regulated flows into the wetland, as well as burning. However, the results also show spatial reduction in the wetland's dry season dense green reed-grass vegetation in upstream sections which are not affected by the water backing-up above of the lower dam. Sparse green vegetation is replacing the dense green vegetation in these upstream areas. It is inferred that this dry season degradation of the wetland threatens bird species which may use the reeds for dry season nesting. It is proposed that ground surveying and monitoring work at the micro-habitat level is necessary to ascertain the implications of the losses. It is concluded that, in spite of difficulties, multi-temporal remote sensing has a potential role in wetland change assessment on the Kafue Flats at the community level, but that it needs to be supplemented by targeted, micro-habitat level ground surveys.
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COSTA, GILSON ALEXANDRE OSTWALD PEDRO DA. "A KNOWLEDGE-BASED APPROACH FOR AUTOMATIC INTERPRETATION OF MULTIDATE REMOTE SENSING DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14130@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
O objetivo genérico desta Tese foi o desenvolvimento de técnicas computacionais baseadas em conhecimento para apoiar a interpretação automática de dados de sensoriamento remoto multi-temporais, com ênfase na investigação da aquisição e representação explícita de conhecimento temporal, bem como na sua integração com outros tipos de conhecimento dentro do processo de interpretação. Dois objetivos específicos, inter-relacionados, foram perseguidos: (i) o desenvolvimento de um novo método de classificação baseado no conceito de cadeias nebulosas de Markov (CNM), que provê meios para a estimação de seus parâmetros temporais e para a utilização de conhecimento temporal no processo de classificação; e (ii) a modelagem e implementação de um ambiente baseado em conhecimento, de código livre, para a interpretação de dados de sensoriamento remoto. Para validar o novo método de classificação multitemporal, foram realizados experimentos voltados à interpretação de uma seqüência de três imagens LANDSAT de uma área na Região Centro-Oeste do Brasil, utilizando um método estocástico e outro analítico para a estimação das matrizes de transição de classes que compõem o modelo CNM. Enquanto os classificadores mono-temporais obtiveram uma acurácia média por classe de 55%, o esquema multi-temporal alcançou acurácias entre 63% e 94%. Resultados semelhantes em termos de acurácia global foram verificados. Além disso, quando comparado a abordagens multi-temporais correlatas, o método proposto obteve melhores resultados. De forma a validar o ambiente baseado em conhecimento aqui proposto, o método CNM foi implementado através de suas funcionalidades. Um conjunto de experimentos nos quais diferentes variações do método CNM, estruturadas no novo ambiente, foi executado satisfatoriamente.
The general objective of this research was the development of knowledgebased computational techniques to support the interpretation of multitemporal remote sensing data, focusing on the investigation of the explicit representation of temporal knowledge and its integration to other types of knowledge; and also on the processing and acquisition of temporal knowledge. Two interrelated, specific objectives were pursued: (i) the development of a novel multitemporal classification method based on the concept of fuzzy Markov chain (FMC) that provides for the automatic estimation of its temporal related parameters and for the exploration of temporal knowledge in the classification process; and (ii) the design and implementation of an open-source, knowledge-based framework for multitemporal interpretation of remote sensing data. In order to validate the new multitemporal classification method, experiments were carried out aiming at the interpretation of a sequence of three LANDSAT images from the central region of Brazil, using both a stochastic and an analytical technique to estimate the class transition possibilities that compose the FMC model. While the monotemporal classifiers used in the experiments attained an average class accuracy of approximately 55%, the multitemporal scheme reached accuracies between 65% and 94%. Similar results in terms of overall accuracy were also observed. Furthermore, when compared to two alternative multitemporal classification approaches, the devised method consistently showed better results. In order to validate the proposed multitemporal framework, the FCM-based method was implemented using its temporal functionalities, and a number of experiments in which different variants of the FCM-based method were structured through the framework were successfully carried out.
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Mahlayeye, Mbali. "Single and multi-temporal assessment approach of natural resources using remote sensing." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/65908.
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The study area of this project is located in Makhado Municipality, Limpopo, South Africa. The Limpopo Province is commonly known for being rich in the country?s natural resources. It has a number of villages that are characterized by rich natural resources and a well-known nature reserve, Soutpansberg Mountains. Natural resources such as water, plantations, woodlands and grasslands are commonly found in these villages and are commonly used for alleviating poverty. Rural communities in this municipality are still highly dependent on natural resources. The high dependence on these natural resources subsequently affects negatively the natural environment, e.g. processes such as land degradation. Villages in this region have limited infrastructure development that influence people?s livelihood. Infrastructure developments are commonly known for contributing to growing the economy and it will be no different if such developments are built in these villages. Therefore, it is imperative to find innovative and scientific techniques that provide information which can assist in finding ways of balancing the interaction between the environment and its people. In order to successfully do so, ways of managing and monitoring of natural resources in villages such as Makhado becomes a necessity. Land cover information is required to adequately understand the extent and status of the natural resources of the Makhado region. This information is required for effective monitoring of natural resources. With the aid of remote sensing applications, land cover studies are possible. The applications always aim to provide efficient methods using low cost or freely available data. The main objective of this study was to innovatively and accurately map the land cover classes of Makhado Municipality using Landsat imagery. The study investigated the performance of single and multi-temporal assessment approach. The study found that the results of the multi-temporal approach were more accurate compared to the single-date approach for both periods. The overall accuracy of single-date classifications were 78.1% with Kc of 0.74 and 54.3% with Kc of 0.46 respectively. The classification map results of the multi-temporal approach were 72.9% with Kc of 0.68 and 79.0% and a Kc of 0.76 respectively. The multi-temporal classification maps were used for post-classification change detection. The results of these methods illustrated the major decrease in grasslands from 2006-2009 and 2013-2015 respectively. These results assisted in making further inferences of how the drastic and severe drought that occurred in 2015 till recently had a significant impact on the land cover.Dissertation (MSc)--University of Pretoria, 2017.
Geography, Geoinformatics and Meteorology
MSc
Unrestricted
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Smith, H. Todd. "A systems engineering approach to designing a remote sensing satellite simulation system." Master's thesis, This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-02162010-020240/.
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Jing, Sun. "Semi-automated rapid damage assessment usinghigh-resolution satellite imagery: a case study of the 2008 Wenchuanearthquake, China." Thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-144338.
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Servello, John A. "Thermal Identification of Clandestine Burials: A Signature Analysis and Image Classification Approach." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc33201/.
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Clandestine burials, the interred human remains of forensic interest, are generally small features located in isolated environments. Typical ground searches can be both time-consuming and dangerous. Thermal remote sensing has been recognized for some time as a possible search strategy for such burials that are in relatively open areas; however, there is a paucity of published research with respect to this application. This project involved image manipulation, the analyses of signatures for "graves" of various depths when compared to an undisturbed background, and the use of image classification techniques to tease out these features. This research demonstrates a relationship between the depth of burial disturbance and the resultant signature. Further, image classification techniques, especially object-oriented algorithms, can be successfully applied to single band thermal imagery. These findings may ultimately decrease burial search times for law enforcement and increase the likelihood of locating clandestine graves.
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Spooner, William Hugo. "Simulating temperatures and chlorophyll variability in the western English channel : an integrated observation/numerical approach." Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250110.
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Sundlie, Paul. "An Integer-Based Approach for Back Projection of Wide Area Imagery." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1335540912.
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Dawson, Terence Peter. "A modelling approach to the biochemical assay of vegetation canopies from remote sensing." Thesis, University of Southampton, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242747.
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Sill, Paul E. (Paul Eric). "Assessing Regional Gully Erosion Risk: A Remote Sensing and Geographic Information Systems Approach." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc332453/.
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Gully erosion has been established as a major source of sediment pollution in the upper Trinity River watershed in north-central Texas. This fact, along with a lack of models appropriate for a large-area gully erosion analysis established a need for a gully erosion study in the upper Trinity basin. This thesis project attempted to address this need by deriving an index indicative of gully erosion risk using remote sensing and geographic information systems (GIS) methodology. In context of previous field studies, the coarse spatial resolution of the input GIS data layers presented a challenge to prediction of gully prone areas. However, the remote sensing/GIS approach was found to provide useful reconnaissance information on gully risk over large areas.
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Rabbil, M. (Mehedi). "Accuracy assessment of remote sensing altimetry:an integrated approach of lake water level response." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201910172982.
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Abstract. Declination of lake water levels are extensively influenced by anthropogenic activities along with basin size, topography, and lake size. Lake Urmia was one of the largest hypersaline lakes in north-west Iran. Main outflow from the lake are evaporation and possible groundwater flux, whereas major sources of inflow are surrounding rivers and several streams. Overall, annual evaporation is higher than precipitation. The lake Water Level Fluctuation (WLF) is a function of inflow, rainfall, evaporation and groundwater flux. The aim of this study is to assess the accuracy of different Remote Sensing (RS) data which influences on WLF. Variation of Lake Urmia WLF in several sections were estimated based on different satellite missions e.g. Jason-2 and Jason-3 and compared with observed data and DAHITI dataset. The outcome showed that the algorithm of estimated WLF indicates better outline in some sections in Lake Urmia while some cases are not in good harmony with observed and DAHITI data. In order to find RS data accuracy, different driving factors on WLF were integrated by applying Water Balance simulation with combination of different scenarios for 2003–2007. The results showed the scenario which combines all observed data, has best pattern with observed Water Level of the lake to explain the response of the lake.
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Soenen, Scott, and University of Lethbridge Faculty of Arts and Science. "Remote sensing of montane forest structure and biomass : a canopy relectance model inversion approach." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2006, 2006. http://hdl.handle.net/10133/281.
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The multiple-forward-mode (MFM) inversion procedure is a set of methods for indirect canopy relectance model inversion using look-up tables (LUT). This thesis refines the MFM technique with regard to: 1) model parameterization for the MFM canopy reflectance model executions and 2) methods for limiting or describing multiple solutions. Forest stand structure estimates from the inversion were evaluated using 40 field validation sites in the Canadian Rocky Mountains. Estimates of horizontal and vertical crown radius were within 0.5m and 0.9m RMSE for both conifer and deciduous species. Density estimates were within 590 stems/ha RMSE for conifer and 310 stems/ha RMSE for deciduous. The most effective inversion method used a variable spectral domain with constrained, fine increment LUTs. A biomass estimation method was also developed using empirical relationships with crown area. Biomass density estimates using the MFM method were similar to estimates produced using other multispectral analysis methods (RMSE=50t/ha).xvi, 156 leaves : ill. (some col.), maps ; 29 cm.
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Xu, Siyao. "THE RECONSTRUCTION OF CLOUD-FREE REMOTE SENSING IMAGES: AN ARTIFICIAL NEURAL NETWORKS (ANN) APPROACH." [Kent, Ohio] : Kent State University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1248112891.
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Thesis (M.A.)--Kent State University, 2009.Title from PDF t.p. (viewed Mar. 11, 2010). Advisor: Mandy Munro-Stasiuk. Keywords: Remote Sensing Image; Cloud-free; Artificial Neural Networks. Includes bibliographical references (p. 57-59).
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Pham, Minh Tân. "Pointwise approach for texture analysis and characterization from very high resolution remote sensing images." Thesis, Télécom Bretagne, 2016. http://www.theses.fr/2016TELB0403/document.
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Ce travail de thèse propose une nouvelle approche ponctuelle pour l'analyse de texture dans l'imagerie de télédétection à très haute résolution (THR). Cette approche ne prend en compte que des points caractéristiques, et non pas tous les pixels dans l'image, pour représenter et caractériser la texture. Avec l'augmentation de la résolution spatiale des capteurs satellitaires, les images THR ne vérifient que faiblement l'hypothèse de stationnarité. Une telle approche devient donc pertinente étant donné que seuls l'interaction et les caractéristiques des points-clés sont exploitées. De plus, puisque notre approche ne considère pas tous les pixels dans l'image comme le font la plupart des méthodes denses de la littérature, elle est plus à-même de traiter des images de grande taille acquises par des capteurs THR. Dans ce travail, la méthode ponctuelle est appliquée en utilisant des pixels de maxima locaux et minima locaux (en intensité) extraits à partir de l'image. Elle est intégrée dans plusieurs chaînes de traitement en se fondant sur différentes techniques existantes telles la théorie des graphes, la notion de covariance, la mesure de distance géométrique, etc. En conséquence, de nombreuses applications basées sur la texture sont abordées en utilisant des données de télédétection (images optiques et radar), telles l'indexation d'images, la segmentation, la classification et la détection de changement, etc. En effectuant des expériences dédiées à chaque application thématique, la pertinence et l'efficacité du cadre méthodologique proposé sont confirmées et validéesThis thesis work proposes a novel pointwise approach for texture analysis in the scope of very high resolution (VHR) remote sensing imagery. This approach takes into consideration only characteristic pixels, not all pixels of the image, to represent and characterize textural features. Due to the fact that increasing the spatial resolution of satellite sensors leads to the lack of stationarity hypothesis in the acquired images, such an approach becomes relevant since only the interaction and characteristics of keypoints are exploited. Moreover, as this technique does not need to consider all pixels inside the image like classical dense approaches, it is more capable to deal with large-size image data offered by VHR remote sensing acquisition systems. In this work, our pointwise strategy is performed by exploiting the local maximum and local minimum pixels (in terms of intensity) extracted from the image. It is integrated into several texture analysis frameworks with the help of different techniques and methods such as the graph theory, the covariance-based approach, the geometric distance measurement, etc. As a result, a variety of texture-based applications using remote sensing data (both VHR optical and radar images) are tackled such as image retrieval, segmentation, classification, and change detection, etc. By performing dedicated experiments to each thematic application, the effectiveness and relevance of the proposed approach are confirmed and validated
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Siemes, Kerstin. "Establishing a sea bottom model by applying a multi-sensor acoustic remote sensing approach." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209381.
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Detailed information about the oceanic environment is essential for many applications in the field of marine geology, marine biology, coastal engineering, and marine operations. Especially, knowledge of the properties of the sediment body is often required. Acoustic remote sensing techniques have become highly attractive for classifying the sea bottom and for mapping the sediment properties, due to their high coverage capabilities and low costs compared to common sampling methods. In the last decades, a number of different acoustic devices and related techniques for analyzing their signals have evolved. Each sensor has its specific application due to limitations in the frequency range and resolution. In practice, often a single acoustic tool is chosen based on the current application, supported by other non-acoustic data where required. However, different acoustic remote sensing techniques can supplement each other, as shown in this thesis. Even more, a combination of complementary approaches can contribute to the proper understanding of sound propagation, which is essential when using sound for environmental classification purposes. This includes the knowledge of the relation between acoustics and sediment properties, the focus of this thesis. Providing a detailed three dimensional picture of the sea bottom sediments that allows for gaining maximum insight into this relation is aimed at.Chapters 4 and 5 are adapted from published work, with permission:
DOI:10.1121/1.3569718 (link: http://asadl.org/jasa/resource/1/jasman/v129/i5/p2878_s1) and
DOI:10.1109/JOE.2010.2066711 (link: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=5618582&queryText%3Dsiemes)
In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of the Université libre de Bruxelles' products or services.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished
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Mumby, Peter J. "Coral reef and seagrass assessment using satellite and airborne remote sensing : an ecological approach." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267071.
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Pianalto, Frederick Scott. "Estimating Sources of Valley Fever Pathogen Propagation in Southern Arizona: A Remote Sensing Approach." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/311322.
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Coccidioidomycosis (Valley Fever) is an environmentally-mediated respiratory disease caused by the inhalation of airborne spores from the fungi Coccidioides spp. The fungi reside in arid and semi-arid soils of the Americas. The disease has increased epidemically in Arizona and other areas within the last two decades. Despite this increase, the ecology of the fungi remains obscure, and environmental antecedents of the disease are largely unstudied. Two sources of soil disturbance, hypothesized to affect soil ecology and initiate spore dissemination, are investigated. Nocturnal desert rodents interact substantially with the soil substrate. Rodents are hypothesized to act as a reservoir of coccidioidomycosis, a mediator of soil properties, and a disseminator of fungal spores. Rodent distributions are poorly mapped for the study area. We build automated multi-linear regression models and decision tree models for ten rodent species using rodent trapping data from the Organ Pipe Cactus National Monument (ORPI) in southwest Arizona with a combination of surface temperature, a vegetation index and its texture, and a suite of topographic rasters. Surface temperature, derived from Landsat TM thermal images, is the most widely selected predictive variable in both automated methods. Construction-related soil disturbance (e.g. road construction, trenching, land stripping, and earthmoving) is a significant source of fugitive dust, which decreases air quality and may carry soil pathogens. Annual differencing of Landsat Thematic Mapper (TM) mid-infrared images is used to create change images, and thresholded change areas are associated with coordinates of local dust inspections. The output metric identifies source areas of soil disturbance, and it estimates the annual amount of dust-producing surface area for eastern Pima County spanning 1994 through 2009. Spatially explicit construction-related soil disturbance and rodent abundance data are compared with coccidioidomycosis incidence data using rank order correlation and regression methods. Construction-related soil disturbance correlates strongly with annual county-wide incidence. It also correlates with Tucson periphery incidence aggregated to zip codes. Abundance values for the desert pocket mouse (Chaetodipus penicillatus), derived from a soil-adjusted vegetation index, aspect (northing) and thermal radiance, correlate with total study period incidence aggregated to zip code.
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Agutu, Nathan Okoth. "Remote Sensing Based Approach to Enhance Food Security in the Greater Horn of Africa." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/70551.
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Greater Horn of Africa (GHA), one of the most food-insecure regions in the world, faces acute food insecurity with catastrophic consequences whenever a drought occurs. With climate change impacts and high population growth rate projected to exacerbate the food insecurity situation of the region, this thesis examined agricultural drought and groundwater exploitation (irrigated) related issues to enhance food security. The thesis identified several suitable agricultural drought indicators and explored groundwater irrigation potential for several regions.