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Wang, Mingliang. "Distributional modelling in forestry and remote sensing." Thesis, University of Greenwich, 2005. http://gala.gre.ac.uk/6337/.
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The use of distributional models in forestry is investigated, in terms of their capability of modelling distributions of forest mensurational attributes, for modelling and inventory purposes. Emphasis is put on: (i) the univariate and bivariate modelling of tree diameters and heights for stand-level modelling work, and (ii) heuristic methods for use and analysis of distributions which occur in multi-temporal EO imagery, (for the inventory-related tasks of land-use mapping, change detection and growth modelling). In univariate distribution modelling, a new parameterization of the widely-used Johnson’s SB distribution is given, and new Logit-Logistic, generalised Weibull and the Burr system (XII, III, IV) models are introduced into forest modelling. The Logit-Logistic distribution is found to be the best among those compared. The use of regression-based methods of parameter estimation is also investigated. In the domain of bivariate distribution modelling of tree diameters and heights the Plackett method (a particular form of copula) is used to construct Plackett-based bivariate Beta, SB and Logit-Logistic distributions, (the latter two are new), which are compared with each other and the SBB distribution. Other copula functions, including the normal copula, are further employed (for the first time in forest modelling) to construct bivariate distributional models. With the normal copula, the superiority of the Logit-Logistic in the univariate domain is extended into the bivariate domain. To use multi-temporal EO imagery, two pre-processing procedures are necessary: image to image co-registration, and radiometric correction. A spectral correlation-based pixel-matching method is developed to “refine” manually selected control points to achieve very accurate image co-registration. A robust non-parametric method of spectral-distribution standardization is used for relative radiometric correction between images. Finally, possibilities for further research are discussed.
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Phaladi, Shikoane Given. "Using GPS bistatic signal for land and ocean remote sensing in South Africa." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/4920.
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Includes bibliographical references (leaves 73-77).This project discusses the basic principles and theory of this new technology, and concentrates on reflection points and Fresnel zones. The CPS receivers are placed at different coastal regions within South Africa, and the simulation of the reflection points and Fresnel zones are observed as the CPS satellites pass over South Africa. The East London area was chosen as the location to place the receiver throughout my analysis.
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Wilkie, Craig John. "Nonparametric statistical downscaling for the fusion of in-lake and remote sensing data." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8626/.
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Lakes are vital components of the global biosphere, supporting complex ecosystems and playing important roles in the global biogeochemical cycle. However, they are vulnerable to the threat from climate change and their responses to climate forcing, eutrophication and other pressures, and their possibly confounding interactions, are not yet well understood. Monitoring lake health is therefore essential, in order to understand the changing patterns over space and time. Traditionally, in-situ data, which are collected directly from within lakes and analysed in laboratories, have been available for analysis. However, although these data are assumed to be accurate within measurement error, they are expensive to collect, so that few, if any, in-situ sampling locations are available for each lake, often with infrequent sampling at each location. On the other hand, remotely-sensed data, which are derived from reflectance measurements of the Earth's surface, obtained from satellites, have recently become widely available. These data have good spatial coverage of up to 300 metre resolution, covering entire lakes, often with a monthly-average time-scale, but they must firstly be calibrated with the in-situ data to ensure accuracy, before inferences are made. The data for this research were provided by the GloboLakes project (www.globolakes.ac.uk), which is a consortium research project that is investigating the state of lakes and their responses to environmental drivers on a global scale. The research primarily focusses on log(chlorophyll-a) data for Lake Balaton, in Hungary, and for the Great Lakes of North America. The key question of interest for this research is: ``How can data fusion be performed for in-situ and remotely-sensed lake water quality data, accounting for the spatiotemporal change of support between the point-location, point-time in-situ data and the grid-cell-scale, monthly-averaged remotely-sensed data, producing a fused dataset that takes accuracy from the in-situ data and spatial and temporal information from the remotely-sensed data?" In order to answer this question, this thesis presents the following work: An initial analysis of the data for Lake Balaton motivates the following work, by demonstrating the spatial and temporal patterns in the data, using mixed-effects models, generalised additive models, kriging and principal components analysis. Following the identification of statistical downscaling as an appropriate method for fusion of the data, statistical downscaling models are developed, specifically in the framework of Bayesian hierarchical models with spatially-varying coefficients, for the novel application to data for log(chlorophyll-a), producing fully calibrated maps of fused data across lake surfaces, with associated comprehensive uncertainty measures. Bivariate and multiple-lakes statistical downscaling models are developed and applied, motivated by the assumption that sharing information between variables and between lakes can improve the accuracy of model predictions. The statistically novel method of nonparametric statistical downscaling is developed, to account for both the spatial and temporal aspects of the change of support between the in-situ and remotely-sensed data. Using methodology from both functional data analysis and statistical downscaling, the model treats in-situ and remotely-sensed data at each location as observations of smooth functions over time, estimated using bases, with the basis coefficients related via a spatially-varying coefficient regression. This is computed within a Bayesian hierarchical model, enabling the calculation of comprehensive uncertainties. This thesis presents the background, motivation, model development and application of the novel method of nonparametric statistical downscaling, filling the gap in the literature of accounting for changing temporal support in statistical downscaling modelling. Results are presented throughout this thesis, to demonstrate the utility of the method for real lake water quality data.
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Li, Junhua 1970. "Scale analysis in remote sensing based on wavelet transform and multifractal modeling." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82916.
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With the development of Geographical Information System (GIS) and remote sensing techniques, a great deal of data has provided a set of continuous samples of the earth surface from local, regional to global scales. Several multi-scale, multi-resolution, pyramid or hierarchical methods and statistical methods have been developed and used to investigate the scaling property of remotely sensed data: local variance, texture method, scale variance, semivariogram, and fractal analysis. This research introduces the wavelet transform into the realm of scale study in remote sensing and answers three research questions. Three specific objectives corresponding to the three research questions are answered. They include: (1) exploration of wavelets for scale-dependent analysis of remotely sensed imagery; (2) examination of the relationships between wavelet coefficients and classification accuracy for different resolutions and their improvement of classification accuracy; and (3) multiscaling analysis and stochastic down-scaling of an image by using the wavelet transform and multifractals. The significant results obtained are: (1) Haar wavelets can be used to investigate the scale-dependent and spatial structure of an image and provides another method for selection of optimal sampling size; (2) there is a good relationship between classification accuracy and wavelet coefficients. High/low wavelet coefficient reflects low/high classification accuracy in each land cover type. (3) the maximum likelihood classifier with inclusion of wavelet coefficients can improve land cover classification accuracies. (4) the moment-scale analysis of wavelet coefficients can be used to investigate the multifractal properties of an image. Also the stochastic down-scaling model developed based on wavelet and multifractal generates good simulation results of the fine resolution image.
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Gong, Mengyi. "Statistical methods for sparse image time series of remote-sensing lake environmental measurements." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8608/.
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Remote-sensing technology is widely used in Earth observation, from everyday weather forecasting to long-term monitoring of the air, sea and land. The remarkable coverage and resolution of remote sensing data are extremely beneficial to the investigation of environmental problems, such as the state and function of lakes under climate change. However, the attractive features of remote-sensing data bring new challenges to statistical analysis. The wide coverage and high resolution means that data are usually of large volume. The orbit track of the satellite and the occasional obscuring of the instruments due to atmospheric factors could result in substantial missing observations. Applying conventional statistical methods to this type of data can be ineffective and computationally intensive due to its volume and dimensionality. Modifications to existing methods are often required in order to incorporate the missingness. There is a great need of novel statistical approaches to tackle these challenges. This thesis aims to investigate and develop statistical approaches that can be used in the analysis of the sparse remote-sensing image time series of environmental data. Specifically, three aspects of the data are considered, (a) the high dimensionality, which is associated with the volume and the dimension of data, (b) the sparsity, in the sense of high missing percentages and (c) the spatial/temporal structures, including the patterns and the correlations. Initially, methods for temporal and spatial modelling are explored and implemented with care, e.g. harmonic regression and bivariate spline regression with residual correlation structures. In recognizing the drawbacks of these methods, functional data analysis is employed as a general approach in this thesis. Specifically, functional principal component analysis (FPCA) is used to achieve the goal of dimension reduction. Bivariate basis functions are proposed to transform the satellite image data, which typically consists of thousands/millions of pixels, into functional data with low dimensional representations. This approach has the advantage of identifying spatial variation patterns through the principal component (PC) loadings, i.e. eigenfunctions. To overcome the high missing percentages that might invalidate the standard implementation of the FPCA, the mixed model FPCA (MM-FPCA) was investigated in Chapter 3. Through estimating the PCs using a mixed effect model, the influence of sparsity could be accounted for appropriately. Data imputation can be obtained from the fitted model using the (truncated) Karhunen-Loeve expansion. The method's applicability to sparse image series is examined through a simulation study. To incorporate the temporal dependence into the MM-FPCA, a novel spatio-temporal model consisting of a state space component and a FPCA component is proposed in Chapter 4. The model, referred to as SS-FPCA in the thesis, is developed based on the dynamic spatio-temporal model framework. The SS-FPCA exploits a flexible hierarchical design with (a) a data model consisting of a time varying mean function and random component for the common spatial variation patterns formulated as the FPCA, (b) a process model specifying the type of temporal dynamic of the mean function and (c) a parameter model ensuring the identifiability of the model components. A 2-cycle alternating expectation - conditional maximization (AECM) algorithm is proposed to estimate the SS-FPCA model. The AECM algorithm allows different data augmentations and parameter combinations in various cycles within an iteration, which in this case results in analytical solutions for all the MLEs of model parameters. The algorithm uses the Kalman filter/smoother to update the system states according to the data model and the process model. Model investigations are carried out in Chapter 5, including a simulation study on a 1-dimensional space to assess the performance of the model and the algorithm. This is accompanied by a brief summary of the asymptotic results of the EM-type algorithm, some of which can be used to approximate the standard errors of model estimates. Applications of the MM-FPCA and SS-FPCA to the remote-sensing lake surface water temperature and Chlorophyll data of Lake Victoria (obtained from the European Space Agency's Envisat mission) are presented at the end of Chapter 3 and 5. Remarks on the implications and limitations of these two methods are provided in Chapter 6, along with the potential future extensions of both methods. The Appendices provide some additional theorems, computation and derivation details of the methods investigated in the thesis.
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Demir, Metin A. "Perturbation theory of electromagnetic scattering from layered media with rough interfaces." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1174660001.
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劉文慶 and Wenqing Liu. "Fast tracking of evoked potentials variations by wavelet analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243411.
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Propastin, Pavel. "Remote sensing based study on vegetation dynamics in drylands of Kazakhstan." Doctoral thesis, Stuttgart Ibidem-Verl, 2007. http://hdl.handle.net/11858/00-1735-0000-0006-B26A-A.
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Liu, Wenqing. "Fast tracking of evoked potentials variations by wavelet analysis /." Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25205523.
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Osuna, Francisco. "Semi-automated frame transformations using FFT analysis on 2-D Images." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.
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Gapper, Justin J. "Bias Reduction in Machine Learning Classifiers for Spatiotemporal Analysis of Coral Reefs using Remote Sensing Images." Chapman University Digital Commons, 2019. https://digitalcommons.chapman.edu/cads_dissertations/2.
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This dissertation is an evaluation of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing images. Three scientific studies have been conducted as part of this research: 1) Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean 2) Coral Reef Change Detection in Remote Pacific Islands using Support Vector Machine Classifiers 3) A Generalized Machine Learning Classifier for Spatiotemporal Analysis of Coral Reefs in the Red Sea. The aim of this dissertation is to propose and evaluate a methodology for developing a robust machine learning classifier that can effectively be deployed to accurately detect coral reefs at scale. The hypothesis is that Landsat data can be used to train a classifier to detect coral reefs in remote sensing imagery and that this classifier can be trained to generalize across multiple sites. Another objective is to identify how well different classifiers perform under the generalized conditions and how unique the spectral signature of coral is as environmental conditions vary across observation sites. A methodology for validating the generalization performance of a classifier to unseen locations is proposed and implemented (Controlled Parameter Cross-Validation,). Analysis is performed using satellite imagery from nine different locations with known coral reefs (six Pacific Ocean sites and three Red Sea sites). Ground truth observations for four of the Pacific Ocean sites and two of the Red Sea sites were used to validate the proposed methodology. Within the Pacific Ocean sites, the consolidated classifier (trained on data from all sites) yielded an accuracy of 75.5% (0.778 AUC). Within the Red Sea sites, the consolidated classifier yielded an accuracy of 71.0% (0.7754 AUC). Finally, long-term change detection analysis is conducted for each of the sites evaluated. In total, over 16,700 km2 was analyzed for benthic cover type and cover change detection analysis. Within the Pacific Ocean sites, decreases in coral cover ranged from 25.3% reduction (Kingman Reef) to 42.7% reduction (Kiritimati Island). Within the Red Sea sites, decrease in coral cover ranged from 3.4% (Umluj) to 13.6% (Al Wajh).
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Li, Jiang. "Linear unmixing of hyperspectral signals via wavelet feature extraction." Diss., Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-11082002-213652.
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Ciminelli, Jennifer M. "A GIS and Remote Sensing Based Analysis of Impervious Surface Influences on Bald Eagle (Haliaeetus leucocephalus) Nest Presence in the Virginia Portion of the Chesapeake Bay." VCU Scholars Compass, 2006. http://hdl.handle.net/10156/1928.
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Fuller, Ryan Michael. "Adaptive Noise Reduction Techniques for Airborne Acoustic Sensors." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1355361066.
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Gurol, Selime. "Statistical Learning And Optimization Methods For Improving The Efficiency In Landscape Image Clustering And Classification Problems." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606595/index.pdf.
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Remote sensing techniques are vital for early detection of several problems such as natural disasters, ecological problems and collecting information necessary for
finding optimum solutions to those problems. Remotely sensed information has also important uses in predicting the future risks, urban planning, communication.Recent developments in remote sensing instrumentation offered a challenge to the mathematical and statistical methods to process the acquired information.
Classification of satellite images in the context of land cover classification is the main concern of this study. Land cover classification can be performed by statistical learning methods like additive models, decision trees, neural networks, k-means
methods which are already popular in unsupervised classification and clustering of image scene inverse problems.
Due to the degradation and corruption of satellite images, the classification performance is limited both by the accuracy of clustering and by the extent of the classification. In this study, we are concerned with understanding the performance of the available unsupervised methods with k-means, supervised methods with Gaussian maximum likelihood which are very popular methods in land cover classification.
A broader approach to the classification problem based on finding the optimal discriminants from a larger range of functions is considered also in this work. A novel
method based on threshold decomposition and Boolean discriminant functions is developed as an implementable application of this approach. All methods are applied
to BILSAT and Landsat satellite images using MATLAB software.
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Velasco-Forero, Santiago. "Contributions en morphologie mathématique pour l'analyse d'images multivariées." Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2012. http://pastel.archives-ouvertes.fr/pastel-00820581.
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Cette thèse contribue au domaine de la morphologie mathématique et illustre comment la statistique multivariée et les techniques d'apprentissage numérique peuvent être exploitées pour concevoir un ordre dans l'espace des vecteurs et pour inclure les résultats d'opérateurs morphologiques au processus d'analyse d'images multivariées. En particulier, nous utilisons l'apprentissage supervisé, les projections aléatoires, les représentations tensorielles et les transformations conditionnelles pour concevoir de nouveaux types d'ordres multivariés et de nouveaux filtres morphologiques pour les images multi/hyperspectrales. Nos contributions clés incluent les points suivants :* Exploration et analyse d'ordre supervisé, basé sur les méthodes à noyaux.* Proposition d'un ordre nonsupervisé, basé sur la fonction de profondeur statistique calculée par projections aléatoires. Nous commençons par explorer les propriétés nécessaires à une image pour assurer que l'ordre ainsi que les opérateurs morphologiques associés, puissent être interprétés de manière similaire au cas d'images en niveaux de gris. Cela nous amènera à la notion de décomposition en arrière plan. De plus, les propriétés d'invariance sont analysées et la convergence théorique est démontrée.* Analyse de l'ordre supervisé dans les problèmes de correspondance morphologique de patrons, qui correspond à l'extension de l'opérateur tout-ou-rien aux images multivariées grâce à l'utilisation de l'ordre supervisé.* Discussion sur différentes stratégies pour la décomposition morphologique d'images. Notamment, la décomposition morphologique additive est introduite comme alternative pour l'analyse d'images de télédétection, en particulier pour les tâches de réduction de dimension et de classification supervisée d'images hyperspectrales de télédétection.* Proposition d'un cadre unifié basé sur des opérateurs morphologiques, pour l'amélioration de contraste et pour le filtrage du bruit poivre-et-sel.* Introduction d'un nouveau cadre de modèles Booléens multivariés en utilisant une formulation en treillis complets. Cette contribution théorique est utile pour la caractérisation et la simulation de textures multivariées.
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Parshakov, Ilia. "Automatic class labeling of classified imagery using a hyperspectral library." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Geography, c2012, 2012. http://hdl.handle.net/10133/3372.
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Image classification is a fundamental information extraction procedure in remote sensing that is used in land-cover and land-use mapping. Despite being considered as a replacement for manual mapping, it still requires some degree of analyst intervention. This makes the process of image classification time consuming, subjective, and error prone. For example, in unsupervised classification, pixels are automatically grouped into classes, but the user has to manually label the classes as one land-cover type or another. As a general rule, the larger the number of classes, the more difficult it is to assign meaningful class labels. A fully automated post-classification procedure for class labeling was developed in an attempt to alleviate this problem. It labels spectral classes by matching their spectral characteristics with reference spectra. A Landsat TM image of an agricultural area was used for performance assessment. The algorithm was used to label a 20- and 100-class image generated by the ISODATA classifier. The 20-class image was used to compare the technique with the traditional manual labeling of classes, and the 100-class image was used to compare it with the Spectral Angle Mapper and Maximum Likelihood classifiers. The proposed technique produced a map that had an overall accuracy of 51%, outperforming the manual labeling (40% to 45% accuracy, depending on the analyst performing the labeling) and the Spectral Angle Mapper classifier (39%), but underperformed compared to the Maximum Likelihood technique (53% to 63%). The newly developed class-labeling algorithm provided better results for alfalfa, beans, corn, grass and sugar beet, whereas canola, corn, fallow, flax, potato, and wheat were identified with similar or lower accuracy, depending on the classifier it was compared with.vii, 93 leaves : ill., maps (some col.) ; 29 cm
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Preiss, Mark. "Detecting scene changes using synthetic aperture radar interferometry /." Title page, table of contents and abstract only, 2004. http://web4.library.adelaide.edu.au/theses/09PH/09php9242.pdf.
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Madron, Justin. "REFORESTATION OF RED SPRUCE (PICEA RUBENS) ON THE CHEAT MOUNTAIN RANGE, WEST VIRGINIA." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3113.
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The (Plethodon nettingi) Cheat Mountain Salamander is a rare and endangered species that relies heavily on (Picea rubens) Red Spruce for habitat. P. rubens communities on the Cheat Mountain range in West Virginia have been disturbed by fires and logging, and regeneration of P. rubens stands are central to the survival of the P. netting. A supervised and unsupervised landscape classification of three Landsat images over the past 26 years was conducted to analyze change in P. rubens communities on Cheat Mountain Range. Change detection results revealed that from 1986-2012 a 52% growth increase of P. rubens stands, 18% loss, and 29% stayed the same over the last 26 years. P. rubens stands are vital habitat to the rare and endangered P. netting and regrowth of P. rubens is vital in restoring the habitat of the salamander on the Cheat Mountain. The regrowth of P. rubens on the Cheat Mountain range is critical to the survival of the P. nettingi. Identifying critical forest as it relates to salamander habitat is essential for conservation efforts. Since not all P. rubens stands are of equal significance to the P. nettingi, it is important to identify and map those that adhere to their stringent habitat needs as defined by forest fragmentation, aspect, slope, and lithology. I used spatial analysis and remote sensing techniques to define critical forest characteristics by applying a forest fragmentation model utilizing morphological image analysis, northeast and southwest aspects, moderate slopes, and limestone lithology. Patches were ranked based on this quantitative model and key P. rubens stands identified using spatial statistics. The results could aid in prioritizing research areas as well as conservation planning in regards to P. rubens and the P. nettingi. In this study, the MaxEnt modeling framework was used to predict habitat suitability for P. rubens under current conditions and under two future climate change scenarios. P. rubens distribution data was acquired from the U.S Geological Survey. Both the IPCC A1B and A2 emission scenarios of the HadCM3 global circulation model were projected to years 2040-2069 and 2070-2099. Results showed that a substantial decline in the suitability of future P. rubens habitat on the Cheat Mountain is likely under both climate change scenarios, particularly at lower elevations. By the end of the century, P. rubens is likely to be extirpated from the Cheat Mountain Range. By the end of century, the A1B and A2 scenarios predict the average habitat suitability for P. rubens on Cheat Mountain will be 0.0002 and 0.00004 respectively. Conservation as well as species migration efforts for P. rubens should be focused on areas such as Cheat Mountain to preserve this vital habitat.
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Shah, Vijay Pravin. "A wavelet-based approach to primitive feature extraction, region-based segmentation, and identification for image information mining." Diss., Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-07062007-134150.
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EDWARDS, KARLA ROBERTA LISA. "Site-Specific Point Positioning and GPS Code Multipath Parameterization and Prediction." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1300860715.
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Crudge, Steven. "Quantification of rill erosion using field measurements and remote sensing techniques." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26196.
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This research examines the use of remote sensing techniques to quantify rill erosion in two agricultural fields in the Lower Fraser Valley. Soil erosion during the winter is particularly problematic in some of the sloping soils developed from loess over glacio-marine parent materials. New techniques are needed to quantify rill erosion on a timely basis, and this research focuses on measuring the extent and rate of rill erosion from field and aerial photograph measurements.
A model which used rill measurements as input, was used
to determine the rill plan areas, rill volumes, and thus rill
erosion rates in the test area. Using field rillometer
measurements of rills as input into the model resulted in a soil loss estimate of 49m³ /ha/yr or 38.4 t/ha/yr for the test site. This soil loss estimate is deemed to be more reliable than erosion plot and Universal Soil Loss Equation estimates of soil loss for the test area. The rill volume and plan area of three main rills, using three different rill measurement methods for input into the model, were compared. Using field measuring tape measurements of rills as input into the model, resulted in a soil loss estimate which was 16 % greater than the estimate from rillometer measurements. Using photo rill width measurements and an estimation of rill depths and bottom widths from field data as model input, resulted in a soil loss estimate which was 22 % less than the estimate from rillometer measurements.
Spectral reflection measurements made in rill, interrill and depositional areas were found to be significantly different, confirming that rill erosion could be assessed in a quantitative manner using digital image analysis techniques. The spectral separation was largely due to differences in organic matter, surface roughness and imaging geometry. The latter is of particular importance in creating darker shadowed rill sides opposite bright sun-facing rill sides within a single rill. A maximum likelihood classifier, used as part of the computer based image analysis, determined the rill plan area for a sample area to be 9 % less than the rill plan area obtained from the model using rillometer input. This indicates the potential of digital analysis to quickly determine the plan area of larger rills.
Digital elevation and moisture content data confirmed that the topographic shape of the field is important in determining the spatial pattern of rill formation. The combination of such data with image analysis and geographic information systems (GIS) have great potential in the timely quantification of erosion in the future.Land and Food Systems, Faculty of
Graduate
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Doster, Timothy J. "Mathematical methods for anomaly grouping in hyperspectral images /." Online version of thesis, 2009. http://hdl.handle.net/1850/11575.
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Zen, Simone. "Bio-morphodynamics of evolving river meander bends from remote sensing, field observations and mathematical modelling." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/9081.
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Interactions between fluvial processes and vegetation along the natural channel margins have been shown to be fundamental in determining meandering rivers development. By colonizing exposed sediments, riparian trees increase erosion resistance and stabilize fluvial sediment transport through their root systems, while during a flood event the above-ground biomass interacts with the water flow inducing sediment deposition and altering scour patterns. In turn river dynamics and hydrology influence vegetative biomass growth, affecting the spatial distribution of vegetation. These bio-morphological dynamics have been observed to direct control accretion and degradation rates of the meander bend. In particular, vegetation encroachments within the point bar (i.e. colonizing species and strand wood), initiate pioneeristic landforms that, when evolving, determine the lateral shifting of the margin that separates active channel from river floodplain and thus inner bank aggradation (bar push). This diminishes the portion of the morphologically active channel cross-section, influencing the erosion of the cutting bank and promoting channel widening (bank pull ). As a result of the cyclical occurrence of these erosional and depositional processes, meandering rivers floodplain show a typical ridge and swale pattern characterized by the presence of complex morphological structures, namely, benches, scrolls and chutes within the new-created floodplain. Moreover, difference in migration rate between the two banks have been observed to induce local temporal variations in channel width that affect river channel morphodynamics and its overall planform through their influence on the local flow field and channel bed morphology. Despite enormous advances in field and laboratory techniques and modelling development of the last decades, little is known about the relation between floodplain patterns and their controlling bio-morphological interactions that determine the bank accretion process. This knowledge gap has so far limited the development of physically-based models for the evolution of meandering rivers able to describe the lateral migration of banklines separately. Most existing meander migration models are indeed based on the hypothesis of constant channel width. Starting from this knowledge gap, the present doctoral research has aimed to provide more insight in the mutual interactions among flow, sediment transport and riparian vegetation dynamics in advancing banks of meandering rivers. In order to achieve its aims, the research has been designed as an integration of remote sensing and in-situ field observations with a mathematical modelling approach to i) provide a quantitative description of vegetation and floodplain channel topography patterns in advancing meanders bend and to ii) explore the key control factors and their role in generating the observed patterns. The structure of the present PhD work is based on four main elements. First, two types of airborne historical data (air photographs and Lidar survey) have been investigated, in order to quantify the effects of spatial-temporal evolution of vegetation pattern on meander morphology and to provide evidence for the influence of vegetation within the topography of the present floodplain. Such remote sensing analysis has highlighted a strong correspondence between riparian canopy structure and geomorphological patterns within the floodplain area: this has clearly shown the need to interpret the final river morphology as the result of a two-way interaction between riparian vegetation dynamics and river processes. Second, field measurements have been conducted on a dynamic meander bend of the lower reach of the Tagliamento River, Italy, with the initial aim of checking the outcomes of the remote sensing analysis through ground data. The outcomes of the field measurements have further supported the results, providing ground evidence on the relations between vegetation and topographic patterns within the transition zone that is intermediate between the active channel bed and the vegetated portion of the accreting floodplain. The influence of vegetation on inner bank morphology has also been interpreted in the light of the expected time scales of inundation and geomorphic dynamics that characterize the advancing process of the inner bank. The combined analysis of both remotely sensed data and field measurements associated with the historical hydrological dataset have allowed to quantitatively characterize the biophysical characteristics of the buffer zone, close to the river edge, where the accretion processes take place. The third research element has foreseen the development of a biophysically- based, simplified bio-morphodynamic model for the lateral migration of a meander bend that took advantage of the empirical knowledge gained in the analysis of field data. The model links a minimalist approach that includes biophysically-based relationships to describe the interaction between riparian vegetation and river hydro-morphodynamic processes, and employs a non linear mathematical model to describe the morphodynamics of meander channel bed. Model application has allowed to reproduce the spatial oscillations of vegetation biomass density and ground morphology observed in the previous analyses. Overall, the model allows to understand the role of the main controlling factors for the ground and vegetation patterns that characterize the advancing river bank and to investigate the temporal dynamics of the morphologically active channel width, providing insights into the bank pull and bar push phenomena. The fourth and concluding element of the present PhD research is an analytical investigation of the fundamental role of unsteadiness on the morphodynamic response of the river channel. Results obtained in the previous elements have clearly showed the tendency of a meander bend to develop temporal oscillations of the active channel width during its evolution, but no predictive analytical tool was previously available to investigate the channel bed response to such non-stationary planform dynamics. A non linear model has therefore been proposed to investigate the effect of active channel width unsteadiness on channel bed morphology. The basic case of free bar instability in a straight channel has been used in this first investigation, which has shown the tendency of channel widening to increase river bed instability compared to the steady case, in qualitative agreement with experimental observations. Overall, the research conducted within the present Doctoral Thesis represents a step forward in understanding the bio-morphodynamics of meandering rivers that can help the development of a complete bio-morphodynamic model for meandering rivers evolution, able to provide support for sustainable river management.
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Li, Feng Engineering & Information Technology Australian Defence Force Academy UNSW. "Development of super resolution techniques for finer scale remote sensing image mapping." Awarded by:University of New South Wales - Australian Defence Force Academy. Engineering & Information Technology, 2009. http://handle.unsw.edu.au/1959.4/44098.
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In this thesis, methods for achieving finer scale multi-spectral classification through the use of super resolution (SR) techniques are investigated. A new super resolution algorithm Maximum a Posteriori based on the universal hidden Markov tree model (MAP-uHMT) is developed which can be applied successfully to super-resolve each multi-spectral channel before classification by standard methods. It is believed that this is the first time that a true super resolution algorithm has been applied to multi-spectral classification, and results are shown to be excellent. Image registration is an important step for SR in which misalignment can be measured for each of many low resolution images; therefore, a new and computationally efficient image registration is developed for this particular application. This improved elastic image registration method can deal with a global affine warping and local shift translations based on coarse to fine pyramid levels. The experimental results show that it can provide good registration accuracy in less computational time than comparable methods. Maximum a posteriori (MAP) is adopted to deal with the ill-conditioned problem of super resolution, wherein a prior is constructed based on the universal hidden Markov tree (uHMT) model in the wavelet domain. In order to test this prior for MAP estimation, it is first tested in the simpler and typically ill-conditioned problem of image denoising. Experimental results illustrate that this new image denoising method achieves good performance for the test images. The new prior is then extended to SR. By combining with the new elastic image registration algorithm, MAP-uHMT can super resolve both some natural video frames and remote sensing images. Test results with both synthetic data and real data show that this method achieves super resolution both visually and quantitatively. In order to show that MAPuHMT is also applicable more widely, it is tested on a sequence of long-range surveillance images captured under conditions of atmospheric turbulence distortion. The results suggest that super resolution may have been achieved in this application also.
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Kim, Kangwook. "Numerical and experimental investigation of impulse-radiating antennas for use in sensing applications." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/14944.
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Gleitsmann, Anke. "Exploiting the spatial information in high resolution satellite data and utilising multi-source data for tropical mountain forest and land cover mapping." Doctoral thesis, Stuttgart Ibidem-Verl, 2005. http://deposit.d-nb.de/cgi-bin/dokserv?id=2852171&prov=M&dok_var=1&dok_ext=htm.
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Abeykoon, Mahinda. "Stepwise application of unconstrained linear mixture model for classification of urban land cover." Virtual Press, 2004. http://liblink.bsu.edu/uhtbin/catkey/1306381.
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This study involves stepwise application of Unconstrained Linear Mixer Model (ULMM) for sub-pixel classification of residential areas using Land sat 7 TM image. The image was geometrically and radiometrically corrected and spectral enhancement and classifications were done to determine the possible number of target classes. In the first step, five end-members were used as inputs and the pixels which were considered as well fit to ULMM were identified as outputs. The unidentified pixels were separated and taken to the second step with new end members. This method identified 52% of the mixed pixels were identified in the first phase and 6% in the second phase. 42% of the pixels were left as unidentified after the two steps. The pixels identified by ULMM were grouped into high and low density residential subclasses. The resulting image indicated very low RMS errors. However the percentages of pixels unidentified were high. The independent accuracy test carried out using census population density and the resulting image indicated a low relationship. A hyper-spectral imagery with finer spatial resolution may provide a better sub pixel classification.Department of Geography
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Aqdus, Syed Ali. "Airborne multispectral and hyperspectral remote sensing techniques in archaeology a comparative study /." Thesis, Thesis restricted. Connect to e-thesis to view abstract, 2009. http://theses.gla.ac.uk/812/.
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Thesis (Ph.D.) - University of Glasgow, 2009.Ph.D. thesis submitted to the Faculty of Physical Sciences, Department of Geographical and Earth Sciences and the Faculty of Arts, Department of Archaeology, University of Glasgow, 2009. Includes bibliographical references. Print version also available.
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Uno, Yoji. "Application of machine learning methods and airborne hyperspectral remote sensing for crop yield estimation." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80890.
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This study investigated the potential of developing in-season crop yield forecasting and mapping systems based on interpretation of airborne hyperspectral remote sensing imagery by machine learning algorithms. The data used for this study was obtained over a corn (Zea mays L.) field in eastern Canada.The experimental plots were set up at the Emile A. Lods Agronomy Research Center, Montreal, Quebec. Corn was grown under the twelve combinations of three nitrogen application rates (60, 120, and 250 kg N/ha), and four weed control strategies (Broad leaf weed, Grass weed, Broad leaf and grass weed control, and no weed control). The images of the experimental field were taken with a Compact Airborne Spectrographic Imager (CASI) at three times (June 30 for early growth stage, August 5 for tassel stage, and Aug 25 for mature stage) during the year 2000 growing season.
Two machine learning algorithms, Artificial Neural Networks (ANN) and Decision Tree (DT) were evaluated. The performance of ANNs was compared with four conventional modeling methods. For the DT algorithms, two different aspects, (i) DT as a classification method, and (ii) DT as a feature selection tool, were explored in this study.
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Thornhill, Kenneth L. II. "An investigation of the environment surrounding supercell thunderstorms using wind profiler data." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/26958.
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Licenciado, Jose Luis Alvarex-Perez. "Two novel studies of electromagnetic scattering in random media in the context of radar remote sensing." Thesis, University of Nottingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368345.
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Karimi-Zindashty, Yousef. "Application of hyperspectral remote sensing in stress detection and crop growth modeling in corn fields." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85560.
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This study used hyperspectral data to determine nitrogen, weed, and water stresses in a corn (Zea mays L.) field in southwestern Quebec, and incorporated these data in crop growth models for better crop growth simulation under stressful conditions.In 2000, aerial hyperspectral images (72 wavebands, ranging from 407 to 949 nm) were acquired, and analyzed using a stepwise approach to identify wavebands useful in detecting weed and nitrogen stresses. Discriminant analysis (DA) was used to classify different weed and nitrogen treatments and their combinations. This analysis showed greater classification accuracy (nearly 75%) than those obtained with artificial neural networks (58%) or decision tree algorithms (60%), at the initial growth stages, the time when remedial actions are most needed to alleviate weed and nitrogen stresses.
To explore the possibility of improving nitrogen stress detection in corn in the presence of a confounding water stress, ground-based 2151 narrow-waveband reflectance values (350 to 2500 nm), were collected in 2002. Using DA with the chosen subset of narrow-wavebands, a classification accuracy of greater than 95% was obtained.
For crop growth monitoring, the STICS model was evaluated for yield and biomass estimation in cornfields under different stressful growth conditions using the data collected from 2000 to 2002. Measured yield, biomass, and leaf area index (LAI) were used for both calibration and validation of the model. High correlation coefficients between the measured and estimated grain yield (0.96), biomass (0.98), and LAI (0.93) indicated that the model has good potential in the simulation of corn growth. The model was also linked with LAI values estimated from the hyperspectral observations using the Support Vector Machines technique. Coupling STICS with remote sensing resulted in an overall improvement in the simulation of corn yield (6.3%) and biomass (3.7%).
A new approach was developed to apply crop growth models for yield estimation in weedy areas. The proposed method first corrects the measured/estimated LAI values in weed infested fields for weed effect, and then uses the corrected LAI values as input to the crop growth model. The results showed that the crop yield and biomass predictions were correctly simulated by this method.*
*This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).
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Hardison, Tanya. "Applications of Remote Sensing and GIS to Modeling Fire for Vegetative Restoration in Northern Arizona." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4323/.
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An accurate fire model is a useful tool in predicting the behavior of a prescribed fire. Simulation of fire requires an extensive amount of data and can be accomplished best using GIS applications. This paper demonstrates integrative procedures of using of ArcGIS™, ERDAS Imagine™, GPS, and FARSITE© to predict prescribed fire behavior on the Kaibab-Paiute Reservation. ArcGIS was used to create a database incorporating all variables into a common spatial reference system and format for the FARSITE model. ArcGIS Spatial Analyst was then used to select optimal burn sites for simulation. Our predictions will be implemented in future interagency efforts towards vegetative restoration on the reservation.
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Hudson, Austin Scott. "Applications of Remote Sensing to the Study of Estuarine Physics: Suspended Sediment Dynamics in the Columbia River Estuary." PDXScholar, 2014. https://pdxscholar.library.pdx.edu/open_access_etds/2093.
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Estuarine circulation and its associated transport processes drive the environmental integrity of many near-shore habitats (the coastal ocean, rivers, estuaries and emergent wetlands). A thorough understanding and consideration of this circulation is, therefore, vital in the proper management of these habitats. The aim of this study is to bring together theory and new satellite observations in the Columbia River Estuary to increase our understanding of estuarine circulation and transport. Surface reflectance measurements gathered by the Moderate Imaging Spectroradiometer (MODIS) are first compared to in situ observations to develop an empirical model for remotely derived surface turbidity. Results indicate that MODIS data significantly correlate with in situ measurements of turbidity throughout the CRE (R2 = 0.96). Remote estimates of turbidity are then used to explore the physical processes that drive their spatial distribution. Although the response to different hydrodynamic conditions varies throughout the system, global levels of turbidity are most sensitive to fluvial and tidal inputs and increase during spring tides and high river flow. As a result, the turbidity field has temporal cycles that are consistent with the frequency of these processes. The location of the estuarine turbidity maximum (ETM) is highly dynamic and typically migrates downstream as the tidal velocity or river flow increases. The ETM becomes trapped near the Megler Bridge (river kilometer 20), however, and the presence of strong topography in this region suggests there exists an interaction between bottom topography and sediment transport.
A 2-D semi-analytical model, developed herein from the simplified Navier-Stokes equations, confirms that topographic features exhibit substantial influence on longitudinal turbidity distributions. The model considers the coupled, tidally-averaged velocity (composed of gravitational circulation, internal tidal asymmetry, and river flow) and salinity fields and assumes a condition of morphodynamic equilibrium to estimate the distribution of sediment for arbitrary channel configurations. Model simulations demonstrate that topographic highs tend to increase local seaward sediment fluxes, and that topographic lows increase local landward sediment fluxes. Sediment flux convergence near topographic highs compresses the local turbidity distribution, whereas flux divergence near topographic lows dilates the distribution and, under appropriate conditions, produces multiple ETMs.
In summary a combination of the model and satellite data has given valuable new insights into the sediment dynamics of estuarine environments; in particular, both show that turbidity distribution and ETM location vary considerably with tidal and river flow conditions, fluctuating on a variety of timescales, and are heavily influenced by bottom topography.
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Kintz, Andrew Lane. "Nullspace MUSIC and Improved Radio Frequency Emitter Geolocation from a Mobile Antenna Array." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1479896813925084.
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Cordova, Vicente D. "Regional-scale carbon flux estimation using MODIS imagery." Virtual Press, 2005. http://liblink.bsu.edu/uhtbin/catkey/1325989.
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The National Aeronautics and Space Agency NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) platform carried by Terra and Aqua satellites, is providing systematic measurements summarized in high quality, consistent and well-calibrated satellite images and datasets ranging from reflectance in the visible and near infrared bands to estimates of leaf area index, vegetation indices and biome productivity. The objective of this research was to relate the spectral responses and derived MODIS products of ecosystems, to biogeochemical processes and trends in their physiological variables. When different sources of data were compared, discrepancies between the MODIS variables and the corresponding ground measurements were evident. Uncertainties in the input variables of MODIS products algorithms, effects of cloud cover at the studied pixel, estimation algorithm, and local variation in land cover type are considered as the cause. A simple "continuous field" model based on a physiologically-driven spectral index using two ocean-color bands of MODIS satellite sensor showed great potential to track seasonally changing photosynthetic light use efficiency and stress-induced reduction in net primary productivity of terrestrial vegetation. The model explained 88% of the variability in Flux tower-based daily Net Primary Productivity. Also a high correlation between midday gross CO2 exchange with both daily and 8-day mean gross CO2 exchange, consistent across all the studied vegetation types, was found. Although it may not be possible to estimate 8-day mean Light Use Efficiency reliably from satellite data, Light Use Efficiency models may still be useful for estimation of midday values of gross CO2 exchange which could then be related to longer term means of CO2 exchange. In addition, the MODIS enhanced vegetation index shows a high potential for estimation of ecosystem gross primary production, using respiration values from MODIS surface temperature, providing truly per-pixel estimates.Department of Natural Resources and Environmental Management
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DeChant, Caleb Matthew. "Hydrologic Data Assimilation: State Estimation and Model Calibration." PDXScholar, 2010. https://pdxscholar.library.pdx.edu/open_access_etds/172.
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This thesis is a combination of two separate studies which examine hydrologic data assimilation techniques: 1) to determine the applicability of assimilation of remotely sensed data in operational models and 2) to compare the effectiveness of assimilation and other calibration techniques. The first study examines the ability of Data Assimilation of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF), is made using a coupled SNOW17 and the Microwave Emission Model for Layered Snowpack model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the Advanced Microwave Scanning Radiometer-Earth Observing System at the 36.5GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. The second study compares the effectiveness of different calibration techniques in hydrologic modeling. Currently, the most commonly used methods for hydrologic model calibration are global optimization techniques. While these techniques have become very efficient and effective in optimizing the complicated parameter space of hydrologic models, the uncertainty with respect to parameters is ignored. This has led to recent research looking into Bayesian Inference through Monte Carlo methods to analyze the ability to calibrate models and represent the uncertainty in relation to the parameters. Research has recently been performed in filtering and Markov Chain Monte Carlo (MCMC) techniques for optimization of hydrologic models. At this point, a comparison of the effectiveness of global optimization, filtering and MCMC techniques has yet to be reported in the hydrologic modeling community. This study compares global optimization, MCMC, the PF, the Particle Smoother, the EnKF and the Ensemble Kalman Smoother for the purpose of parameter estimation in both the HyMod and SAC-SMA hydrologic models.
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Manabe, Victor Danilo 1986. "Metodologia para mapeamento da expansão de cana-de-açúcar no Estado de Mato Grosso por meio de séries temporais de NDVI/MODIS." [s.n.], 2014. http://repositorio.unicamp.br/jspui/handle/REPOSIP/257105.
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Orientador: Jansle Vieira RochaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agrícola
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Resumo: O aumento na produção da cana-de-açúcar vem gerando grande discussão sobre a sustentabilidade da produção e a sua influência direta na mudança de uso da terra, principalmente em áreas de pastagem e cultura anual. O estudo da dinâmica da cana-de-açúcar tem influência direta em questões como a composição da produção agrícola, nos impactos sobre a biodiversidade, no desenvolvimento social e humano e na definição de políticas públicas. Índice de vegetação, através de séries temporais de imagens, tem sido utilizado para mapeamento de uso da terra de grandes áreas (estados, países ou regiões), através de produtos do sensor MODerate resolution Imaging Spectroradiometer (MODIS). Este trabalho avaliou o desempenho de diferentes técnicas de filtragem em séries temporais e também realizou detecção automatizada de áreas de cana-de-açúcar e principais usos da terra para os anos de 2005, 2008 e 2012, e consequente mudança de uso da terra, utilizando séries temporais NDVI/MODIS, no estado de Mato Grosso. Foi utilizado o NDVI dos produtos MOD13Q1 e MYD13Q1 do sensor MODIS para identificação das áreas de diferentes usos da terra. Primeiramente foram avaliados os filtros Savitz-Golay , HANTS e Flat Bottom de maneira individual e também com a combinação Flat Bottom + HANTS e Flat Bottom + Savitz-Golay, nas séries de dados somente referentes ao NDVI MODIS/Terra e em conjunto com NDVI MODIS/Aqua. Tendo o resultado, que a utilização MODIS/Terra e MODIS/Aqua trouxe melhora significativa no resultado da classificação, quando utilizado em conjunto a algum filtro de série temporal, sendo o Savitzky-Golay, o que apresentou melhor resultado na diferenciação dos alvos. Na identificação e mapeamento automatizado, de áreas de cana-de-açúcar e outros principais usos da terra para a região (cultura anual, pastagem, cerrado e mata), para os anos de 2005, 2008 e 2012, os valores de acertos para cana-de-açúcar foram de 83%, 82% e 85% nos anos 2005, 2008 e 2012, respectivamente, e o acerto total foram de 89%, 88% e 89%, também para os anos 2005, 2008 e 2012. Ao cruzar os mapeamentos, foi possível realizar a análise da mudança de uso da terra para cana-de-açúcar. A certeza na mudança de uso da terra, quando implementa em áreas anteriormente destinadas a agricultura anual foi de 80% e 82%, na comparação de 2005 para 2008 e 2008 para 2012, respectivamente. No uso anterior de pastagem e cerrado este valor apresentou valores de 69% e 30%, respectivamente, na mudança de 2005 para 2008, e 66% e 34%, respectivamente, na mudança de 2008 para 2012. O resultado na analise de mudança de usa da terra teve a predominância de áreas de pastagem como principal uso anterior a cana-de-açúcar, seguida pela agricultura e o cerrado como responsável pelo restante do uso anterior da terra. Assim, o método para identificação da mudança de uso da terra apresentou um erro a ser considero, porém a tendência de ocorrência se apresenta de maneira consistente
Abstract: The production increase of sugarcane has generated discussion about the sustainability of production and its direct impact on the land use change, especially in pasture and annual crops areas. The study of the dynamics of sugarcane has a direct impact on issues such as the composition of agricultural production, the impacts on biodiversity, social and human development and the definition of public policies. Vegetation index through time series images have been used to map land use of large areas (states, countries or regions) using sensor Moderate Resolution Imaging Spectroradiometer (MODIS). This study evaluated the performance of different time series smoothing techniques and also held automated detection of sugarcane areas and main land uses for the years 2005, 2008 and 2012, and the consequent land use change, using NDVI/MODIS time series in Mato Grosso state. It was used NDVI product of MOD13Q1 and MYD13Q1 to identify areas of different land uses. At first, Savitz-Golay, Hants and Flat Bottom individually and also the combination Flat Bottom + Hants and Flat Bottom + Savitz-Golay, it was applied on NDVI time series data only related to MODIS/Terra and in conjunction with MODIS/Aqua. The result was that the use MODIS/Terra and MODIS/Aqua brought significant improvement in the overall classification, when used in conjunction with any time series smoothing, and the Savitzky-Golay showed better results in the differentiation of targets. The mapping areas of sugarcane and other major land uses (annual crops, grassland, savanna and forest), for the years 2005, 2008 and 2012, the number of right answers for sugarcane were 83 %, 82 % and 85 % in the years 2005, 2008 and 2012, respectively, and total accuracy were 89 %, 88 % and 89 %, also for the years 2005, 2008 and 2012. When crossing the maps, it was possible to perform the analysis of the land use change to cane sugar. The certainty of change in land use, when deploy in areas previously designed to annual agriculture was 80 % and 82 % in 2005 compared to 2008 and 2008 compared to 2012 respectively. The past use of grassland and savannah, this value, showed values of 69 % and 30 %, respectively, in the change from 2005 to 2008, and 66 % and 34 %, respectively, in the change from 2008 to 2012. The result of the study of land use changing had the predominance of grazing areas as the former principal use sugarcane, followed by agriculture and savanna as responsible for the remainder of the previous land use. Thus, the method to identifying the change of land use has an error to consider, but the trend appears to occur consistently
Mestrado
Planejamento e Desenvolvimento Rural Sustentável
Mestre em Engenharia Agrícola
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Chiang, Yang-Sheng. "Estimating landscape level leaf area index and net primary productivity using field measurements, satellite imagery, and a 2-D ecophysiological model." Virtual Press, 2004. http://liblink.bsu.edu/uhtbin/catkey/1294241.
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This study has provided a landscape level estimate of leaf area index (LAI) and net primary productivity (NPP) for a temperate broadleaf forest ecosystem in south-central Indiana. The estimates were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products LAI and NPP from both spatial and temporal perspectives. The evidence suggests that field-based estimates were poorly correlated with global MODIS data due to the simplifying assumptions of the MODIS global applicability, saturation problems of the red reflectance in highly vegetated areas, homogeneous land cover types of the study area, and other design assumptions of the field-based estimates. To improve the localized applicability of MODIS product algorithms, an empirical and localized algorithm combining in-situ measurements, the buildup of a localized biophysical model, and remote sensing-derived data were suggested for each local-scaled ecosystem.Department of Natural Resources and Environmental Management
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Ekberg, Christopher. "Verification of the Incidence Angle Dependence within the Satellite Microwave Radiative Transfer Model, RadTb." Honors in the Major Thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/437.
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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.Bachelors
Engineering and Computer Science
Computer Engineering
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Baris, Yuksel. "Automated Building Detection From Satellite Images By Using Shadow Information As An Object Invariant." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614909/index.pdf.
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Apart from classical pattern recognition techniques applied for automated building detection in satellite images, a robust building detection methodology is proposed, where self-supervision data can be automatically extracted from the image by using shadow and its direction as an invariant for building object. In this methodologyfirst the vegetation, water and shadow regions are detected from a given satellite image and local directional fuzzy landscapes representing the existence of building are generated from the shadow regions using the direction of illumination obtained from image metadata. For each landscape, foreground (building) and background pixels are automatically determined and a bipartitioning is obtained using a graph-based algorithm, Grabcut. Finally, local results are merged to obtain the final building detection result. Considering performance evaluation results, this approach can be seen as a proof of concept that the shadow is an invariant for a building object and promising detection results can be obtained when even a single invariant for an object is used.
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Darmenov, Anton. "Developing and testing a coupled regional modeling system for establishing an integrated modeling and observational framework for dust aerosol." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28217.
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Thesis (M. S.)--Earth and Atmospheric Sciences, Georgia Institute of Technology, 2009.Committee Chair: Sokolik, Irina; Committee Member: Curry, Judith; Committee Member: Kalashnikova, Olga; Committee Member: Nenes, Athanasios; Committee Member: Stieglitz, Marc.
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López, Leonardo Ramírez. "Pedologia quantitativa: espectrometria VIS-NIR-SWIR e mapeamento digital de solos." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/11/11140/tde-23062009-140151/.
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Para a avaliação das características do solo relacionadas com o potencial uso dos solos, assim como para a avaliação da fertilidade, as análises químicas e físicas de rotina são os métodos convencionalmente usados. Estes são bastante custosos e demorados o que tem representado no Brasil uma dificuldade no seu uso por parte de pequenos agricultores, além da aplicabilidade da agricultura de precisão no manejo de solos. Atualmente a pedometria está fornecendo a possibilidade de incorporar em ciência do solo técnicas bastante sofisticadas que podem ajudar a diminuir o custo na obtenção da informação e compreender melhor o funcionamento dos processos do solo. Entre os tópicos mais recentes que estão incluídos na pesquisa relacionada com pedometria está a espectroscopia de reflectância. Embora se tenha demonstrado que uma grande quantidade de atributos podem ser estimados a partir da resposta espectral do solo via sensoriamento, ainda não se têm atingido níveis de acurácia ótimos em relação às metodologias convencionais, sobretudo no referente a atributos químicos. Para tanto, o presente trabalho foi desenvolvido com a finalidade de responder basicamente os seguintes questionamentos: a. Existem faixas espectrais específicas das bases trocáveis ou se estas podem mudar em função do argilomineral fornecedor da capacidade de troca de cátions?; b. A calibração de modelos usando unicamente algumas faixas espectrais específicas pode melhorar o desempenho destes?; c. Qual é a influência dos níveis de acurácia dos modelos espectrais sobre mapas construídos com atributos estimados a partir destes?; d. Como os tamanhos dos grupos de amostras de calibração influenciam a acurácia dos modelos?; e. Como a calibração de atributos relacionados com o intemperismo podem auxiliar no mapeamento de classes de solo?The routine soil analysis is traditionally used on the evaluation of soil attributes related to land use potential, and the assessment of fertility. It is costly and time consuming, making it inaccessible for small farmers, and hampering the applicability of precision agriculture on soil management in Brazil. Currently, pedometrics is providing the possibility of incorporating in soil science sophisticated techniques that can help to reduce the cost of obtaining information and improve the understanding about how several soil processes works. One of the more recent topics on pedometrics is the soil reflectance spectroscopy. Through the soil reflected energy is possible to infer several soil properties, although optimum accuracy levels in the spectral estimation of soil attributes have not yet reached. In this sense, the aim of this study was basically answer the following questions: a. The exchangeable bases have specific spectral bands or the spectral responses of theses depends on the clay mineral?; b. the calibration of models by using only some specific spectral bands may improve the prediction performance?; c. What is the influence of the accuracy of prediction models on maps constructed with predicted soil attributes?; D. How calibration set size affect the accuracy of the models?; e. How the calibration of models for prediction of soil attributes related to soil weathering may assist the digital soil mapping?
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Dupigny-Giroux, Lesley-Ann. "Techniques for rainfall estimation and surface characterization over northern Brazil." Thesis, McGill University, 1996. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40345.
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The sertao of northeast Brazil is a semiarid region characterized by recurring droughts. The vastness of the area (650,000 km$ sp2)$ poses a challenge to the effective monitoring of the impacts of drought at a scale that would be useful to the inhabitants of the sertao. Remote sensing data provide a viable way of assessing the extent and nature of drought across the landscape.The work present a more effective algorithm to estimate rainfall from both the cold and warm cloud types present. Using a decision-tree methodology, the analysis yields rainfall estimates over the 0-21 mm range. Because seasonal variations in rainfall produce differences in vegetation, soils and hydrologic responses, Principal Components Analysis was used to examine these land surface responses. Individual components and component pairings were useful in identifying variations in vegetation density, geobotanical differences and drainage characteristics. The presence of cloud cover was found to dampen the land surface information that could be extracted. Landsat Thematic Mapper (TM) imagery was then used to produce a moisture index which characterizes surface wetness in relation to other features present in a scene. The multispectral combination of TM bands 1, 4 and 6 allowed for the separation of the surface types present, in locational space. This space was defined by an open-ended triange made up of a vertical "water line", a horizontal line of equal vegetation density; and a negatively-slopping iso-moisture line. The stability of the moisture index was influenced by varying scale and seasonal conditions.
In the drought conditions that prevailed in 1991-1992, these methods provide important additions to existing drought monitoring approaches in the Brazilian northeast. Further calibration is required in order to extend their applicability to other geographical regions and time frames.
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Cardim, Guilherme Pina. "Proposição de plataforma co-design para processamento de imagens de sensoriamento remoto /." Presidente Prudente, 2019. http://hdl.handle.net/11449/191040.
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Orientador: Erivaldo Antonio da SilvaResumo: O processamento digital de imagens (PDI) consiste em uma área de grande interesse científico. Em Cartografia, o PDI é muito utilizado para extração de feições cartográficas de interesse presentes nas imagens de sensoriamento remoto (SR). Dentre as feições cartográficas, a detecção de malhas viárias é de grande interesse científico, pois proporciona a obtenção de informações atualizadas e acuradas para a realização de planejamentos urbanos. Devido à sua importância, a literatura científica possui diversos trabalhos propondo diferentes metodologias de extração de malhas viárias em imagens digitais. Dentre as metodologias, é possível encontrar metodologias propostas baseadas em lógica fuzzy, em detector de bordas e crescimento de regiões, por exemplo. Contudo, os estudos existentes focam na aplicação da metodologia de extração para determinadas áreas ou situações e utilizam recortes da imagem em seus estudos devido à grande quantidade de informações contidas nessas imagens. O avanço tecnológico proporcionou que imagens de SR sejam adquiridas com alta resolução espacial, espectral e temporal. Esse fato produz uma grande quantidade de dados a serem processados durante estudos desenvolvidos nessas imagens, o que acarreta um alto custo computacional e, consequentemente, um alto tempo de processamento. Na tentativa de reduzir o tempo de execução das metodologias de extração, os desenvolvedores dedicam esforços na redução da complexidade dos algoritmos e na utilização de outros recurs... (Resumo completo, clicar acesso eletrônico abaixo)
Resumen: El procesamiento digital de imágenes (PDI) consiste en un área de gran interés científico en diferentes áreas. En Cartografía, el PDI es muy utilizado en estudios de teledetección para extracción de los objetos cartográficos de interés presentes en las imágenes orbitales. Entre los objetos cartográficos de interés, la detección de redes viales se ha vuelto de gran interés científico proporcionando la obtención de informaciones actualizadas y precisas para la realización de planificaciones urbanas, por ejemplo. En este sentido, la literatura científica posee diversos trabajos proponiendo diferentes metodologías de extracción de redes viales en imágenes orbitales. Es posible encontrar metodologías propuestas basadas en lógica fuzzy, detector de bordes y crecimiento por región, por ejemplo. Sin embargo, los estudios existentes se centran en la aplicación de la metodología de extracción para determinadas áreas o situaciones y utilizan recortes de la imagen orbitales en sus estudios debido a la gran cantidad de informaciones contenidas en esas imágenes. Además, el avance tecnológico proporcionó que las imágenes de teledetección se adquieran con altas resoluciones espacial, espectral y temporal. Este hecho produce una gran cantidad de datos a ser procesados durante estudios desarrollados en esas imágenes, lo que acarrea en un alto costo computacional y, consecuentemente, un alto tiempo de procesamiento. En el intento de reducir el tiempo de respuesta de las metodologías de extracci... (Resumen completo clicar acceso eletrônico abajo)
Abstract: Digital image processing (DIP) consists of an area of great scientific interest in different areas. In Cartography, the DIP is widely used in remote sensing studies to extract cartographic features of interest present in orbital images. Among the cartographic features, the detection of road networks has become of great scientific interest, since it can provide accurate and updated information for urban planning, for example. In this sense, the scientific literature has several works proposing different methodologies of extraction of road networks in orbital images. It is possible to find proposed methodologies based on fuzzy logic, edge detector and growth by region, for example. However, the existing studies focus on the application of the extraction methodology to certain areas or situations and use orbital image cuts in their studies due to the large amount of information contained in these images. In addition, the technological advance has allowed the acquisition of remote sensing images with high spatial, spectral and temporal resolutions. This fact produces a large amount of data to be processed during studies developed in these images, which results in a high computational cost and, consequently, a high processing time. In an attempt to reduce the response time of the extraction methodologies, the developers dedicate efforts in reducing the complexity of the algorithms and in using some available hardware resources suggesting solutions that include software and hardwar... (Complete abstract click electronic access below)
Doutor
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Paiva, Yhasmin Gabriel. "Estimativa do índice de área foliar por métodos óticos e sensoriamento remoto para calibrar modelo ecofisiológico em plantios de eucalipto em áreas de relevo ondulado." Universidade Federal de Viçosa, 2009. http://locus.ufv.br/handle/123456789/5282.
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Previous issue date: 2009-07-24Conselho Nacional de Desenvolvimento Científico e Tecnológico
The purpose of this study was to estimate the leaf area index (LAI) in eucalyptus forest plantations, using optical instruments and satellite imagery to evaluate the response to the physical and climatic conditions of the study area, and based on these data, calibrate and simulate yields with an ecophysiological growth model. The study was conducted in four eucalyptus stands of two ages in the following two regions: Cocais, at a higher altitude and Rio Doce, at a lower altitude, in the Rio Doce basin, in eastern Minas Gerais. Nine plots were marked in each stand, according to the exposure of the relief surface. The experimental data were collected in August 2008 (dry season) and January 2009 (rainy season). The LAI was estimated by measures of the plant area index (PAI) by the equipment LAI-2000 (LI-COR) and hemispherical photographs. The expression PAI was adopted since these sensors do not measure only the leaf element. The method of destructive analysis was used to check the accuracy of the LAI estimates. Meteorological data were collected at two monitoring stations installed near the stands in each region of the study. The model 3PG (Physiological Principles in Predicting Growth) was run using the parameterization established in previous studies for the same regions. The 3PG was calibrated with vegetation indices (VIs) of the sensor Moderate Resolution Imaging Spectroradiometer (MODIS). The model simulation was performed based on the corrected global radiation according to the slope and exposure of the relief surface. It was found that the PAI derived from LAI2000 was the indirect method that correlated best with the observed LAI. Eucalyptus responded to climatic seasonality, with lower LAI values in the dry than in the rainy season. The LAI,in the stands at higher altitudes (Cocais) exceeded that of the lower plots (Rio Doce), probably due to the higher evapotranspiration demand in the Rio Doce basin. The incidence of solar radiation on the northern slope surfaces was higher, in agreement with the correction performed for inclined surfaces. On this face, again, higher LAI values were measured in the field and estimated by the model 3PG well as for other estimated variables that express the crop productivity. The results for the inclined areas were not conclusive. Studies should investigate whether the response pattern is repeated in analyses of the influence of the latitude of the site. The estimates of the model 3PG calibrated by the normalized difference vegetation index (NDVI) agreed well with the observed data and temporal data verified by MODIS-VI.
Este trabalho teve por objetivo estimar o índice de área foliar (IAF) em plantios florestais de eucalipto, utilizando instrumentos óticos e imagens de satélite para se avaliar a resposta frente às condições físico-climáticas presentes na área de estudo e, a partir destes dados, calibrar e simular a produtividade por meio de um modelo ecofisiológico de crescimento. O estudo foi realizado em quatro talhões de plantios de eucalipto em duas diferentes idades presentes em duas regiões: Cocais, de maior altitude e Rio Doce, mais baixa, localizadas na bacia do Rio Doce, leste de Minas Gerais. Foram alocadas nove parcelas em cada talhão, considerando as faces de exposição do relevo. Os dados experimentais foram coletados em agosto de 2008 (período seco) e janeiro de 2009 (período chuvoso). Realizaram-se as estimativas do IAF por meio de medidas de índice de área de planta (IAP) pelos equipamentos LAI- 2000 (LI-COR) e câmera com lentes hemisféricas. Adotou-se a expressão IAP visto que esses sensores não discernem unicamente o elemento foliar em suas medidas. Para verificar a exatidão das estimativas do IAF foi utilizado o método da análise destrutiva. Os dados meteorológicos foram adquiridos por meio de duas estações automáticas instaladas próximas às áreas dos talhões em cada região do estudo. Executou-se o modelo 3PG (Physiological Principles in Predicting Growth) com parametrização realizada em trabalhos anteriores para as mesmas regiões. Foi realizada a calibração do 3PG com índices de vegetação (IV s) do sensor Moderate Resolution Imaging Spectroradiometer (MODIS). A simulação do modelo foi realizada a partir da radiação global corrigida segundo a inclinação e a face de exposição do terreno. Verificou-se que o IAP obtido com LAI2000 foi o método indireto melhor correlacionado com o IAF observado. O eucalipto respondeu à sazonalidade climática, apresentando menores valores de IAF na época seca em relação à estação chuvosa. O rendimento dos talhões localizados nas maiores altitudes (Cocais) superou os talhões das altitudes mais baixas (Rio Doce) em IAF, provavelmente devido à maior demanda evapotranspirativa presente em Rio Doce. As faces de exposição norte apresentaram maior incidência de radiação solar de acordo com a correção realizada para superfícies inclinadas. Nesta face, também, foram verificados maiores valores de IAF medidos a campo e estimados pelo modelo 3PG bem como para outras variáveis estimadas que expressam a produtividade do plantio. Os resultados para as áreas de relevo inclinado não são conclusivos, devendo ser realizados estudos para verificar se o padrão de resposta obtido se repete analisando a influência da latitude do local. O modelo 3PG calibrado pelo NDVI apresentou boas estimativas pontuais com os dados observados e temporais verificada pelos dados IV s-MODIS.
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Yap, Han Lun. "Constrained measurement systems of low-dimensional signals." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47716.
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The object of this thesis is the study of constrained measurement systems of signals having low-dimensional structure using analytic tools from Compressed Sensing (CS). Realistic measurement systems usually have architectural constraints that make them differ from their idealized, well-studied counterparts. Nonetheless, these measurement systems can exploit structure in the signals that they measure. Signals considered in this research have low-dimensional structure and can be broken down into two types: static or dynamic. Static signals are either sparse in a specified basis or lying on a low-dimensional manifold (called manifold-modeled signals). Dynamic signals, exemplified as states of a dynamical system, either lie on a low-dimensional manifold or have converged onto a low-dimensional attractor.
In CS, the Restricted Isometry Property (RIP) of a measurement system ensures that distances between all signals of a certain sparsity are preserved.
This stable embedding ensures that sparse signals can be distinguished one from another by their measurements and therefore be robustly recovered. Moreover, signal-processing and data-inference algorithms can be performed directly on the measurements instead of requiring a prior signal recovery step. Taking inspiration from the RIP, this research analyzes conditions on realistic, constrained measurement systems (of the signals described above) such that they are stable embeddings of the signals that they measure.
Specifically, this thesis focuses on four different types of measurement systems. First, we study the concentration of measure and the RIP of random block diagonal matrices that represent measurement systems constrained to make local measurements. Second, we study the stable embedding of manifold-modeled signals by existing CS matrices. The third part of this thesis deals with measurement systems of dynamical systems that produce time series observations. While Takens' embedding result ensures that this time series output can be an embedding of the dynamical systems' states, our research establishes that a stronger stable embedding result is possible under certain conditions. The final part of this thesis is the application of CS ideas to the study of the short-term memory of neural networks. In particular, we show that the nodes of a recurrent neural network can be a stable embedding of sparse input sequences.
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Hood, Ben Andrew Ashcom. "Extrasolar planet search and characterisation." Thesis, St Andrews, 2007. http://hdl.handle.net/10023/359.
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Nepali, Anjeev. "County Level Population Estimation Using Knowledge-Based Image Classification and Regression Models." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc30498/.
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This paper presents methods and results of county-level population estimation using Landsat Thematic Mapper (TM) images of Denton County and Collin County in Texas. Landsat TM images acquired in March 2000 were classified into residential and non-residential classes using maximum likelihood classification and knowledge-based classification methods. Accuracy assessment results from the classified image produced using knowledge-based classification and traditional supervised classification (maximum likelihood classification) methods suggest that knowledge-based classification is more effective than traditional supervised classification methods. Furthermore, using randomly selected samples of census block groups, ordinary least squares (OLS) and geographically weighted regression (GWR) models were created for total population estimation. The overall accuracy of the models is over 96% at the county level. The results also suggest that underestimation normally occurs in block groups with high population density, whereas overestimation occurs in block groups with low population density.