Dissertations / Theses on the topic 'Landsat satellites Remote sensing'
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Rauchmiller, Robert Frank 1959. "MEASUREMENT OF THE LANDSAT THEMATIC MAPPER MTF USING A TWO-DIMENSIONAL PHASED ARRAY OF POINT SOURCES (MODULATION TRANSFER, SATTELITE, POINT SPREAD FUNCTION)." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/276361.
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Padula, Francis P. "Historic thermal calibration of Landsat 5 TM through an improved physics based approach /." Online version of thesis, 2008. http://hdl.handle.net/1850/7833.
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KASTNER, CAROL JANE. "IN-FLIGHT ABSOLUTE RADIOMETRIC CALIBRATION OF THE LANDSAT THEMATIC MAPPER (WHITE SANDS, NEW MEXICO)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/188121.
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The in-flight absolute radiometric calibration of the Thematic Mapper (TM) is being conducted using the results of field measurements at White Sands, New Mexico. These measurements are made to characterize the ground and atmosphere at the time the TM is acquiring an image of White Sands. The data are used as input to a radiative transfer code that computes the radiance at the entrance pupil of the TM. The calibration is obtained by comparing the digital counts associated with the TM image of the measured ground site with the radiative transfer code result. The calibrations discussed here are for the first four visible and near-infrared bands of the TM. In this dissertation the data reduction for the first calibration attempts on January 3, 1983, and July 8, 1984, is discussed. Included are a review of radiative transfer theory and a discussion of model atmospheric parameters as defined for the White Sands area. These model parameters are used to assess the errors associated with the calibration procedure. Each input parameter to the radiative transfer code is varied from its model value in proportion to the uncertainty with which it can be determined. The effects of these uncertainties on the predicted radiances are determined. It is thought that the optical depth components τ(Ray), τ(Mie), τ(oz), and τ(H₂O) can be measured to within 10%, 2%, 10%, and 30%, respectively. For the white gypsum sand, surface reflectance uniformity is on the order of 1.5%, and the overall uncertainty in measured reflectance is about 2%. This is due to an uncertainty in the reflectance factor of the calibration plates. The greatest uncertainty in calibration is attributed to our uncertainty in the aerosol parameters, in particular the imaginary component of refractive index. The cumulative effect of these uncertainties is thought to produce an uncertainty in computed radiance of about 5%.
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Turner, Anthony Michael Carleton University Dissertation Geography. "Forest clearcut mapping in Northern Ontario using LANDSAT thematic mapper imagery: a user-oriented approach." Ottawa, 1988.
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Witman, Sandra Lynn 1958. "RADIOMETRIC CALIBRATION OF THE THEMATIC MAPPER 48-INCH DIAMETER SPHERICAL INTEGRATING SOURCE (48-SIS) USING TWO DIFFERENT CALIBRATION METHODS." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/275523.
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Aleong-Mackay, Kathryn. "Landsat imagery and small-scale vegetation maps : data supplementation and verification : a case study of the Maralal area, northern Kenya." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66182.
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Benvenuti, Fernando Aparecido. "Relação de indices espectrais de vegetação com a produtividade da cana-de-açucar e atributos edaficos." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/257007.
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Orientador: Mara de Andrade Marinho WeillDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola
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Mestrado
Planejamento e Desenvolvimento Rural Sustentável
Mestre em Engenharia Agrícola
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Gallie, Elizabeth Ann. "Chromaticity analysis of LANDSAT Multispectral Scanner and Thematic Mapper imagery of Chilko Lake, British Columbia, using a theoretical optical water quality model." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/30572.
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Chromaticity analysis of LANDSAT Multispectral Scanner (MSS) imagery of Chilko Lake, B.C. reveals a. locus whose shape has not been previously reported. To investigate the cause of this and to come to a broader understanding of chromaticity analysis for MSS and Thematic Mapper (TM) data, an optical water quality model has been used. The model is composed of a four component reflectance model (R-model), an interface model and an atmospheric model. The R-model was calibrated for Chilko Lake by determining the specific absorption and backscattering spectra for suspended minerals (SM), chlorophyll-a uncorrected for phaeophytins (C) and yellow substance (YS). The fourth component is water.
The model reproduces the observed locus shape and indicates that it is primarily a function of SM, with the unreported lower limb on MSS imagery caused by SM gradients with concentrations less than 1-2 mg/L. The effects of C, YS and SM cannot be separated on plots of chromaticity coordinates X and Y for either MSS or TM data. In addition, haze or wind gradients, if they occur over water with low levels of SM, would look similar to the lower limb on MSS XY plots. However, if brightness is used in combination with X, the model predicts that C and YS, though themselves inseparable, can be differentiated from SM at all but the lowest concentrations of SM. Furthermore, haze and wind gradients can be distinguished from the lower limb. Thus the addition of brightness to chromaticity analysis has the potential to significantly improve the technique.
The model was tested by comparing simulated chromaticity results with results from actual images (one TM image and three MSS images) for which ground truth had been collected. Qualitative predictions regarding haze and water quality patchiness were confirmed. Correlation analysis with R² values from 0.81 to 0.95 also strongly confirmed predictions regarding SM, but showed that the model is systematically underestimating SM. Correlation tests for a combined C and YS factor (CYS) were inconclusive because of the systematic modeling error, but classification maps provide weak evidence that CYS is behaving qualitatively as predicted and that CYS can be differentiated from SM. The modeling error is thought to originate in atmospheric assumptions
which are not met. The R-model which is fundamental to the study has been tested and is not a major source of error.
The study concludes that the model is qualitatively correct and that the use of brightness improves chromaticity analysis by allowing separation of CYS and SM, though further work should be undertaken to verify these results. Maps of CYS and SM in Chilko Lake reveal that CYS tends to be higher along the western shore and where the hypolimnion is exposed. SM are highest near stream mouths. The distribution patterns are related to physical processes within the lake and provide a synoptic view of the connection between water quality parameters and circulation which would be difficult to achieve in any other way.Forestry, Faculty of
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Salvaggio, Carl. "Automated segmentation of urban features from Landsat-Thematic Mapper imagery for use in pseudovariant feature temporal image normalization /." Online version of thesis, 1987. http://hdl.handle.net/1850/11371.
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Bross, Lesley Crandell. "Using Landsat TM Imagery to Monitor Vegetation Change Following Flow Restoration to the Lower Owens River, California." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2635.
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Rehabilitating river corridors to restore valuable riparian habitat consumes significant resources from both governments and private companies. Given these considerable expenditures, it is important to monitor the progress of such projects. This study evaluated the utility of using Landsat Thematic Mapper remotely-sensed data from 2002 and 2009 to monitor vegetation change induced by instream flow restoration to the Lower Owens River in central California. This study compared the results of an unsupervised classification with an NDVI threshold classification to appraise the resources required and effectiveness of each analysis method. The results were inspected by creating standard remote sensing accuracy error matrices and by correlating landscape pattern metrics with bird indicator species. Both sets of classified maps show a noticeable increase in riparian vegetation in the study area following flow restoration in 2006, indicating an improvement of the quality of bird habitat. The study concluded that analyzing vegetation change using the unsupervised classification technique required more effort, expert knowledge, and supplementary data than using the NDVI threshold method. If these prerequisites are met, the output from the unsupervised classification process produces a more precise map of land cover change than the NDVI threshold method. However, if an analyst is lacking either resources or ground verification data, the NDVI threshold technique is capable of providing a generalized, but still valid evaluation of vegetation change. This conclusion is supported by higher correlations between indicator bird species under the unsupervised classification method than were found with the NDVI threshold method.
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Cobbing, Benedict Louis. "The use of Landsat ETM imagery as a suitable data capture source for alien acacia species for the WFW programme." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1005532.
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Geographic Information System technology today allows for the rapid analysis of vast amounts of spatial and non-spatial data. The power of a GIS can only be effected with the rapid collection of accurate input data. This is particularly true in the case of the South African National Working for Water (WFW) Programme where large volumes of spatial data on alien vegetation infestations are captured throughout the country. Alien vegetation clearing contracts cannot be generated, for WFW, without this data, so that the accurate capture of such data is crucial to the success of the programme. Mapping Invasive Alien Plant (IAP) data within WFW is a perennial problem (Coetzee, pers com, 2002), because not enough mapping is being done to meet the annual requirements of the programme in the various provinces. This is re-iterated by Richardson, 2004, who states that there is a shortage of accurate data on IAP abundance in South Africa. Therefore there is a need to investigate alternate methods of data capture; such as remote sensing, whilst working within the existing WFW data capture standards. The aim of this research was to investigate the use of Landsat ETM imagery as a data capture source for mapping alien vegetation for the WFW Programme in terms of their approved mapping methods, for both automated and manual classification techniques. The automated and manual classification results were compared to control data captured by differential Global Positioning Systems (DGPS). The research tested the various methods of data capture using Landsat ETM images over a range of study sites of varying complexity: a simple grassland area, a medium complexity grassy fynbos site and a complicated indigenous forest site. An important component of the research was to develop a mapping (classification) Ranking System based upon variables identified by WFW as fundamental in data capture decision making: spatial and positional accuracy, time constraints and cost constraints for three typical alien invaded areas. The mapping Ranking System compared the results of the various mapping methods for each factor for the study sites against each other. This provided an indication of which mapping method is the most efficient or suitable for a particular area.
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Fried, Samantha Jo. "Landsat in Contexts: Deconstructing and Reconstructing the Data-to-Action Paradigm in Earth Remote Sensing." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/89431.
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There is a common theme at play in our talk of data generally, of digital earth data more specifically, and of environmental monitoring most specifically: more data leads to more action and, ultimately, to societal good. This data-to-action framework is troubled. Its taken-for-grantedness prevents us from attending to the processes between data and action. It also dampens our drive to investigate the contexts of that data, that action, and that envisioned societal good. In this dissertation, I deconstruct this data-to-action model in the context of Landsat, the United States' first natural resource management satellite. First, I talk about the ways in which Landsat's data and instrumentation hold conflicting narratives and values within them. Therefore, Landsat data does not automatically or easily yield action toward environmental preservation, or toward any unified societal good. Furthermore, I point out a parallel dynamic in STS, where critique is somewhat analogous to data. We want our critiques to yield action, and to guide us toward a more just technoscience. However, critiques—like data—require intentional, reconstructive interventions toward change. Here is an opportunity for a diffractive intervention: one in which we read STS and remote sensing through each other, to create space for interdisciplinary dialogue around environmental preservation. A focus on this shared goal, I argue, is imperative. At stake are issues of environmental degradation, dwindling resources, and climate change. I conclude with beginnings rather than endings: with suggestions for how we might begin to create infrastructure that attends to that forgotten space between data, critique, action, and change.Doctor of Philosophy
I have identified a problem I call the data-to-action paradigm. When we scroll around on Facebook and find articles –– citing pages and pages of statistics –– on our rapidly melting glaciers and increasingly unpredictable weather patterns, we are existing within this paradigm. We have been offered evidence of looming, catastrophic change, but no suggestions on what to do about it. This is not only happening with climatological data and large-scale environmental systems modelling. Rather, this is a general problem across the field of Earth Remote Sensing. The origins of this data-to-action paradigm, I argue, can be found in old and new rhetoric about Landsat, the United States’ first natural resource management satellite. This rhetoric often says that Landsat — and other natural resource management satellites’ — data is a way toward societal good. The more data we have, the more good will proliferate in the world. However, we haven’t been specific about what that good might look like, and what kinds of actions we might take toward that good using this data. This is because, I argue, Earth systems science is politically complicated, with many different conceptions of societal good. In order to be more specific about how we might use this data toward some kind of good we must (1) explore the history of environmental data, and figure out where this rhetoric comes from (which I I do in this dissertation), and (2) encourage interdisciplinary collaborations between Earth Remote Sensing scientists, social scientists, and humanists, to more specifically flesh out connections between digital Earth data, its analyses, and subsequent civic action on such data.
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Das, Sujata. "Automatic detection of roads in spot satellite images." Thesis, Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/80011.
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The improved spatial resolution of the data from the SPOT satellite provides a substantially better basis for monitoring urban land use and growth with remote sensing than Landsat data. The purpose of this study is to delineate the road network in 20-m resolution SPOT-images of urban areas automatically. The roads appear as linear features. However, most edge and line detectors are not effective in detecting roads in these images because of the low signal to noise ratio, low contrast and blur in the imagery. For the automatic recognition of roads, a new line detector based on surface modelling is developed. A line can be approximated by a piecewise straight curve composed of short linear line-elements, called linels, each characterized by a direction, a length and a position. The approach to linel detection is to fit a directional surface that models the ideal local intensity profile of a linel in the least square sense. A Gaussian surface with a direction of invariance forms an adequate basis for modelling the ideal local intensity profile of the roads. The residual of the least squares fit as well as the parameters of the fit surface characterize the linel detected. The reliable performance of this line operator makes the problems of linking linels more manageable.Master of Science
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McCloy, K. R. "Development and evaluation of a remote sensing algorithm suitable for mapping environments containing significant spatial variability : with particular reference to pastures /." Title page and table of contents only, 1987. http://web4.library.adelaide.edu.au/theses/09PH/09phm127.pdf.
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Davis, Tiana. "Quantifying Chlorophyll a Content Through Remote Sensing: A Pilot Study of Utah Lake." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1261.pdf.
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Walker, Jessica. "Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/39403.
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This dissertation investigated the practicality and expediency of applying remote sensing data fusion products to the analysis of dryland vegetation phenology. The objective of the first study was to verify the quality of the output products of the spatial and temporal adaptive reflectance fusion method (STARFM) over the dryland Arizona study site. Synthetic 30 m resolution images were generated from Landsat-5 Thematic Mapper (TM) data and a range of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance datasets and assessed via correlation analysis with temporally coincident Landsat-5 imagery. The accuracy of the results (0.61 < R2 < 0.94) justified subsequent use of STARFM data in this environment, particularly when the imagery were generated from Nadir Bi-directional Reflectance Factor (BRDF)-Adjusted Reflectance (NBAR) MODIS datasets. The primary objective of the second study was to assess whether synthetic Landsat data could contribute meaningful information to the phenological analyses of a range of dryland vegetation classes. Start-of-season (SOS) and date of peak greenness phenology metrics were calculated for each STARFM and MODIS pixel on the basis of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) time series over a single growing season. The variability of each metric was calculated for all STARFM pixels within 500 m MODIS extents. Colorado Plateau Pinyon Juniper displayed high amounts of temporal and spatial variability that justified the use of STARFM data, while the benefit to the remaining classes depended on the specific vegetation index and phenology metric. The third study expanded the STARFM time series to five years (2005-2009) to examine the influence of site characteristics and climatic conditions on dryland ponderosa pine (Pinus ponderosa) forest phenological patterns. The results showed that elevation and slope controlled the variability of peak timing across years, with lower elevations and shallower slopes linked to higher levels of variability. During drought conditions, the number of site variables that controlled the timing and variability of vegetation peak increased.Ph. D.
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Wang, Jingjing. "Satellite Mapping of Past Biosolids (Sewage Sludge) and Animal Manure Application to Agriculture Fields in Wood County, Ohio." Bowling Green State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1245276797.
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Morton, David Dean. "Land Cover of Virginia From Landsat Thematic Mapper Imagery." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36851.
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Knowledge of land cover is important in a variety of natural resources applications. This knowledge becomes more powerful within the spatial analysis capabilities of a geographic information system (GIS). This thesis presents a digital land cover map of Virginia, produced through interpretation of 14 Landsat Thematic Mapper (TM) scenes, circa 1991-1993.
The land cover map, which has a 30m pixel size, was produced entirely with personal computers. Hypercluster aggregation, an unsupervised classification method, was used when hazy and mountainous conditions were not present. A haze correction procedure by Lavreau (1991) was used, followed by a supervised classification on coastal areas. An enhanced supervised classification, focusing on topographic shading, was performed in the mountains. Color infrared photographs, digital maplets, expert knowledge, and other maps were used as training data. Aerial videography transects were flown to acquire reference data.
Due to the spatial inaccuracies inherent in the videography reference data, only homogeneous land cover areas were used in the accuracy assessment. The results of the overall accuracy for each scene determined the ordering of scenes within the statewide land cover mosaic (i.e., scenes with higher accuracy had a higher proportion of area represented). An accuracy assessment was then performed on the statewide land cover mosaic. An overall accuracy of 81.8% and a Kappa statistic of 0.81 resulted. A discussion of potential reasons for land cover class confusion and suggestions for classification improvements are presented.
Overall deciduous forest was the most common land cover in Virginia. Herbaceous areas accounted for 20% of the land area, which was the second largest. Mixed forest and coastal wetlands were the cover types with the least area, each under 3%.Master of Science
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Hayes, Ladson. "Techniques for facilitating the registration and rectification of satellite data with examples using data from the advanced very high resolution radiometer and the Landsat multispectral scanner." Thesis, University of Dundee, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303169.
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Masamvu, K. S. "Satellite remote sensing for the monitoring of environmental hazards and assessment of disasters in Southern Africa." Thesis, University of Bristol, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376614.
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Theel, Ryan T. "A 15-year evaluation of the Mississippi and Alabama coastline barrier islands, using Landsat satellite imagery." Master's thesis, Mississippi State : Mississippi State University, 2007. http://library.msstate.edu/etd/show.asp?etd=etd-06282007-120152.
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Wilfong, Bryan N. "Detecting an invasive shrub in deciduous forest understories using remote sensing." Oxford, Ohio : Miami University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1217288997.
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Van, der Merwe Hendrik Naude. "Remote sensing driven lithological discrimination within nappes of the Naukluft Nappe Complex, Namibia." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/97147.
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Thesis (MSc)--Stellenbosch University, 2015.ENGLISH ABSTRACT: Geological remote sensing is a powerful tool for lithological discrimination, especially in arid regions with minimal vegetative cover to obscure rock exposures. Commercial multispectral imaging satellites provide a broad spectral range with which to target specific rock types. Landsat ETM+ (7), ASTER, and SPOT 5 multispectral images were acquired and digitally processed: band ratioing, principle components analysis, and maximum likelihood supervised classification. The sensors were evaluated on the ability to discriminate between sedimentary rocks in a structurally complex setting. The study focusses on the formations of the Naukluft Nappe Complex, Namibia. Previous work of the area had to be consulted in order to identify the main target rock types. Dolomite, limestone, quartzite, and shale were determined to make up the majority of rock types in the area. Landsat, ASTER, and SPOT 5 imagery were acquired and pre-processed. Each was subjected to transform techniques: band ratios and PCA. Band ratios were tailored to highlighted target rock types as well as a number of control ratios to ensure the integrity of important ratios. PCA components were inspected to find the most useful ones which were combined into FCCs. Transform results, expert knowledge, and a geological map were consulted to identify training and accuracy samples for the supervised classifications. All three classifications made use of the same set of training and accuracy samples to facilitate useful comparisons. Transform results were promising for Landsat and ASTER images, while SPOT 5 struggled. The limited spectral resolution of SPOT 5 limited its use for identifying target rock types, with the superior spatial resolution contributing very little. Landsat benefitted from good spectral resolution. This allowed for good performance with highlighting limestone and dolomite, while being less successful with shale. Quartzite was a real problem as the spectral resolution of Landsat could not cover this range as well. ASTER, having the highest spectral resolution, could distinguish between all four target rock types. Landsat and ASTER results suffered in areas where formations were relatively thin (smaller than sensor spatial resolution). The supervised classification results were similar to the transforms in that both Landsat and ASTER provided useful results, while SPOT 5 failed to yield definitive results. Accuracy assessment determined that ASTER performed the best at 98.72%. Landsat produced an accuracy of 93.29% while SPOT 5 was 80.17% accuracy. Landsat completely overestimated the amount of quartzite present, while all results classified significant proportions Quaternary sediments as shale. Limestone was well represented in even the poorest results, while dolomite usually struggled in areas where it was in close association with quartzite. Silica yields relatively strong responses in the TIR spectrum which could lead to misclassification of dolomite, which also has strong TIR signatures.
AFRIKAANSE OPSOMMING: Geologiese afstandswaarneming is 'n kragtige tegniek vir litologiese diskriminasie, veral in droë streke met minimale plantbedekking om dagsome te verduister. Kommersiële multispektrale satelliete beelde bied 'n breë spektrale reeks waarmee spesifieke gesteentetipes geteiken kan word. Landsat ETM + (7), ASTER, en SPOT 5 multispektrale beelde was bekom en digitaal verwerk: bandverhoudings, hoofkomponente-ontleding, en maksimum waarskynlikheid klassifikasie. Die sensors is geëvalueer op hul vermoë om te onderskei tussen sedimentêre gesteentes in 'n struktureel komplekse omgewing. Die studie fokus op die formasies van die Naukluft Dekblad Kompleks, Namibië. Vorige werk van die area was geraadpleeg om die hoofgesteentetipes te identifiseer. Dit was bepaal dat dolomiet, kalksteen, kwartsiet, en skalie die oorgrote meerderheid van kliptipes in area opgemaak het. Landsat, ASTER, en SPOT 5 beelde is verkry en voorverwerk. Elke beeld was onderwerp aan transformasietegnieke: bandverhoudings en hoofkomponente-ontleding. Bandverhoudings is aangepas om teiken rotstipes uit te lig asook 'n aantal kontrole bandverhoudings om die integriteit van belangrike verhoudings te verseker. Hoofkomponente-ontleding komponente is ondersoek om die mees bruikbares te vind en dié was gekombineer in valse kleur samestellings. Transformasie resultate, deskundige kennis, en 'n geologiese kaart was geraadpleeg om opleidings- en verwysingsmonsters was verkry vanaf die beelde vir die klassifikasies. Al drie klassifikasies gebruik gemaak van dieselfde stel van die opleiding- en akkuraatheidsmonsters om sodoende betekenisvolle vergelykings te verseker. Transformasie resultate is belowend vir Landsat en ASTER beelde, terwyl SPOT 5 minder bruikbaar was. Die noue spektrale resolusie van SPOT 5 beperk die gebruik daarvan vir die identifisering van teiken gesteentetipes terwyl die hoë ruimtelike resolusie baie min bydra. Landsat het voordeel getrek uit goeie spektrale resolusie. Dit goeie resultate opgelwer met die klem op kalksteen en dolomiet, terwyl skalie aansienlik swakker resultate opgelewer het. Kwartsiet was 'n werklike probleem omdat die spektrale resolusie van Landsat nie breed genoeg was om hierdie kliptipe te onderskei nie. ASTER, met die hoogste spektrale resolusie, kon onderskei tussen al vier teiken rotstipes. Landsat en ASTER resultate was baie negatief beïnvloed in gebiede waar formasies relatief dun was (kleiner as sensor ruimtelike resolusie). Die klassifikasie resultate was soortgelyk aan die transformasies in dat beide Landsat en ASTER nuttige resultate opgelewer het, terwyl SPOT 5 misluk het. Akkuraatheids assessering het bepaal dat ASTER die beste gevaar het met 98,72%. Landsat het 'n akkuraatheid van 93,29% opgelewer, terwyl SPOT 5 80,17% akkuraat was. Landsat het die hoeveelheid kwartsiet heeltemal oorskat, terwyl al die resultate groot hoeveelhede Kwaternêre sedimente as skalie geklassifiseer het. Kalksteen is goed verteenwoordig in tot die armste resultate, terwyl resultate gewoonlik afgeneem het waar dolomiet in noue verband met kwartsiet was. Dit is moontlik asgevolg van silika se relatiewe sterk reaksies in die termiese infra-rooi spektrum wat kan lei tot die foutiewe klassifisering met dolomiet (wat ook sterk reageer in die TIR spektrum).
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Gonzalez, Sanpedro Maria del Carmen. "Optical and radar remote sensing applied to agricultural areas in Europe." Toulouse 3, 2008. http://www.theses.fr/2008TOU30228.
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L'objectif de la thèse est de développer des méthodes de cartographie et de suivi des cultures basées sur des données de télédétection, radar et optique. Les résultats pourront être combinés avec d'autres techniques, notamment avec des modèles de croissance des cultures, pour améliorer la prévision des récoltes. Quatre instruments différents, 3 sur satellite (LANDSAT-TM, ENVISAT-MERIS, ENVISAT-ASAR) et 1 aéroporté (AIRSAR) sont utilisés dans trois régions d'étude en Europe (Barrax, Toulouse et Flevoland). Les travaux sont présentés en deux parties, optique et radar. Dans la première partie, les données LANDSAT sont utilisées pour l'inversion du LAI à Barrax (Castilla-La Mancha) à l'aide du modèle de transfert radiatif PROSPECT+SAIL. Les résultats sont validés avec des mesures expérimentales acquises au cours de la campagne sur le terrain ESA SPARC-2003, montrant une bonne corrélation. Une méthode est ensuite proposée pour inverser le LAI et la chlorophylle à partir de données MERIS. La méthode implique une inversion du modèle, PROSPECT+SAIL avec une contrainte temporelle (une courbe pour l'ensemble du cycle de culture est inversée). Les résultats montrent que cette méthode fonctionne mieux que les inversions date par date. Toutefois, l'inversion de la chlorophylle nécessite encore une étude plus approfondie. Dans la partie radar, une méthode de classification basée sur les connaissances des mécanismes de rétrodifusion est proposée. Elle utilise des données polarimétriques en bande C de l'instrument AIRSAR. La méthode est appliquée à des images dans le Flevoland (Pays-Bas). Les résultats indiquent que ces méthodes peuvent être plus robustes que les méthodes statistiques usuelles. .El aumento de la población mundial, así como la importancia social y económica que el sector agrícola tiene en muchas regiones del mundo, hace que sea muy importante desarrollar métodos que permitan hacer un seguimiento del estado de los cultivos, mejorar la gestión de los mismos, así como poder realizar una estimación temprana de la producción. La principal causa de incertidumbre en la producción de las cosechas es debida a las condiciones meteorológicas, por ejemplo, en las regiones áridas y semiáridas del mundo los períodos de sequía generan grandes pérdidas en la producción agrícola, la cuales se traducen en hambrunas. Así, la FAO, durante su cumbre de Junio 2008, insistió en la necesidad de aumentar a producción agrícola como una medida para reforzar la seguridad alimentaria y reducir la desnutrición en el mundo. La preocupación por aumentar la producción de cultivos, ha generado, durante las últimas décadas, importantes cambios en las técnicas agrícolas. Por ejemplo, se ha producido un uso generalizado de productos fitosanirios, de cultivos modificados genéticamente, así como un aumento de la agricultura intensiva. A su vez, la rotación de cultivos está cada vez más influenciada por el mercado, siendo los cambios en la distribución espacial de los cultivos muy frecuentes. Por lo tanto, para poder hacer estimaciones de la producción agrícola, es necesario producir eriódicamente mapas de cultivos, así como cartografiar su estado de desarrollo. La presente tesis doctoral tiene como objetivo desarrollar métodos basados en datos de teledetección, en la región del óptico y en la región del radar, que permitan realizar un seguimiento de los cultivos, así como una cartografía de los mismos. Los resultados de esta tesis pueden combinarse con otras técnicas, especialmente con los modelos de crecimiento de cultivo, para mejorar la predicción de las cosechas. Los métodos de teledetección para la clasificación y la cartografía de cultivos utilizando datos en la región del óptico están bien establecidos y pueden considerarse casi operacionales. La desventaja de estos estudios basados en datos ópticos es que no pueden aplicarse a regiones donde la cobertura nubosa es frecuente. En esos casos, la utilización de datos radar es más recomendable. Sin embargo, los métodos de clasificación utilizando datos radar no están tan bien establecidos y es necesario realizar más estudios científicos en este campo. Es por ello, que esta tesis se centra en la clasificación de cultivos mediante datos radar, concretamente datos aerotransportados AIRSAR y datos ASAR del satélite ENVISAT. El seguimiento de los cultivos mediante teledetección se basa en la estimación de parámetros biofísicos y su evolución en el tiempo. Estos parámetros son, entre otros, LAI (índice de área foliar), clorofila y biomasa. En esta tesis se han utilizado datos del satélite LANSAT-TM para la inversión de LAI, y datos ENVISAT-MERIS para la estimación de LAI y clorofila
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Bartholomew, Paul J. "Mapping and Modeling Chlorophyll-a Concentrations in the Lake Manassas Reservoir Using Landsat Thematic Mapper Satellite Imagery." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/32691.
Full textAbstract:
Carried out in collaboration with the Occoquan Water Monitoring Lab, this thesis presents the results of research that sought to ascertain the spatial distribution of chlorophyll-a concentrations in the Lake Manassas Reservoir using a combination of Landsat TM satellite imagery and ground based field measurements. Images acquired on May 14, 1998 and March 8, 2000 were analyzed with chlorophyll-a measurements taken on 13, 1998 and March 7, 2000. A ratio of Landsat TM band 3: Landsat Band 4 was used in a regression with data collected at eight water quality monitoring stations run by the Occoquan Watershed Monitoring Lab. Correlation coefficients of 0.76 for the 1998 data and 0.73 for the 2000 data were achieved. Cross validation statistical analysis was used to check the accuracy of the two models. The standard error and error of the estimate were reasonable for the models from both years. In each instance, the ground data was retrieved approximately 24 hours before the Landsat Image acquisition and was a potential source of error. Other sources of error were the small sample size of chlorophyll-a concentration measurements, and the uncertainty involved in the location of the water quality sampling stations.Master of Science
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Parizoto, Nathalia Maria Salvadeo Fernandes [UNESP]. "Estudo de ilhas de calor no município de Piratininga/SP, por meio de dados orbitais do landsat 5 sensor tm." Universidade Estadual Paulista (UNESP), 2013. http://hdl.handle.net/11449/90638.
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000757050.pdf: 1211527 bytes, checksum: e7589291899907f7a2c5bb86c3de1113 (MD5)Com a ocupação massiva da malha urbana e as atividades decorrentes do crescimento desordenado dessas áreas, ocorre uma alteração do clima urbano, tornando-o insalubre. Um dos fatores que contribui para a má qualidade de vida em decorrência deste fato são as ilhas de calor que consiste no acúmulo de calor na superfície e eleva a temperatura nas cidades. A vegetação urbana constituída de arborização urbana, bosques e áreas verdes, têm como função minimizar este efeito. Para conciliar esses fatores o trabalho tem como objetivo levantar os dados de temperatura de superfície através de imagens de satélite, LANDSAT 5 captada no dia 18/12/2012 dos pontos 75 e 76, órbita 221, banda 6 e levantamento in loco com termo-higrômetro, com posse dos dados, cruzar com o levantamento vegetativo realizado e proporcionar uma visão de gestão para melhorar o microclima do município em estudo. O sensoriamento remoto e o sistema de informação geográfica permitem a avaliação de diferentes temperaturas da superfície terrestre. A área reduzida do Município e a baixa resolução do sensor utilizado dificultaram a análise de temperatura da área urbana sendo necessário o uso de outros programas para auxiliar na interpretação de dados. A cobertura de vegetação na área urbana interfere diretamente na diminuição da temperatura melhorando o micro-clima urbano. As diferentes coberturas da superfície analisada também interferem na temperatura aparente. Entre as classes estudas a classe cultivo de eucalipto e mata nativa apresentaram temperatura aparente amena em torno de 22.0°C e o solo exposto a maior temperatura aparente entre 37.0°C. Os setores 4,5 e 7 apresentam uma média de temperatura de 27,0°C sendo os setores com temperatura mais amena devido a área de cobertura vegetal. A diferença de temperatura nos setores 9, 10, 12 e 13 de 32.0°C para 27.0°C é devido a existência de...
With the massive occupation of the urban and activities arising from the uncontrolled growth of these areas, a change occurs in the urban climate, making it unhealthy. One factor that contributes to poor quality of life due to this fact are the islands of heat which consists in the accumulation of surface heat and raises the temperature in the cities. The vegetation consists of urban greening, urban forests and green areas, have the function to minimize this effect. To reconcile these factors work aims to collect data on surface temperature by satellite images, LANDSAT 5 captured on 18/12/2012 point 75 e 76, orbits 221, band 6 and on-site survey with thermo-hygrometer, with possession of the data, crossing to the survey conducted vegetative and provide a management vision to improve the microclimate of the city under study. The remote sensing and the geographic information system allow the evaluation of different earth surface temperatures. The municipality reduced area and the low resolution detector used makes the temperature analyzes at the urban area more difficult requiring the use of other programs to assist in the data interpretation. The vegetation coverage at urban area directly affects in the temperature decreasing improving the urban micro-climate. The different analyzed surface coverage also affects the apparent temperature. Among the studied ranks, the eucalyptus cultivation and native forest rank show mild apparent temperature around 22.0°C and the solo exposed to the higher apparent temperature around 37.0°C. The 4,5 and 7 sectors present a 27,0°C temperature average, and these are the sectors with the milder temperature due to the vegetation coverage area. The temperatures difference at the 9, 10, 12 and 13 sectors from 32.0°C to 27.0°C is due to the existing green areas, such as squares. The site survey high temperatures measured by thermo-hygrometer are around 35.0°C due to...
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Parizoto, Nathalia Maria Salvadeo Fernandes 1985. "Estudo de ilhas de calor no município de Piratininga/SP, por meio de dados orbitais do landsat 5 sensor tm /." Botucatu, 2013. http://hdl.handle.net/11449/90638.
Full textAbstract:
Orientador: Sérgio CamposBanca: Ellen Fitipaldi B. Carrega
Banca: Fernanda Leite Ribeiro
Resumo: Com a ocupação massiva da malha urbana e as atividades decorrentes do crescimento desordenado dessas áreas, ocorre uma alteração do clima urbano, tornando-o insalubre. Um dos fatores que contribui para a má qualidade de vida em decorrência deste fato são as ilhas de calor que consiste no acúmulo de calor na superfície e eleva a temperatura nas cidades. A vegetação urbana constituída de arborização urbana, bosques e áreas verdes, têm como função minimizar este efeito. Para conciliar esses fatores o trabalho tem como objetivo levantar os dados de temperatura de superfície através de imagens de satélite, LANDSAT 5 captada no dia 18/12/2012 dos pontos 75 e 76, órbita 221, banda 6 e levantamento in loco com termo-higrômetro, com posse dos dados, cruzar com o levantamento vegetativo realizado e proporcionar uma visão de gestão para melhorar o microclima do município em estudo. O sensoriamento remoto e o sistema de informação geográfica permitem a avaliação de diferentes temperaturas da superfície terrestre. A área reduzida do Município e a baixa resolução do sensor utilizado dificultaram a análise de temperatura da área urbana sendo necessário o uso de outros programas para auxiliar na interpretação de dados. A cobertura de vegetação na área urbana interfere diretamente na diminuição da temperatura melhorando o micro-clima urbano. As diferentes coberturas da superfície analisada também interferem na temperatura aparente. Entre as classes estudas a classe cultivo de eucalipto e mata nativa apresentaram temperatura aparente amena em torno de 22.0°C e o solo exposto a maior temperatura aparente entre 37.0°C. Os setores 4,5 e 7 apresentam uma média de temperatura de 27,0°C sendo os setores com temperatura mais amena devido a área de cobertura vegetal. A diferença de temperatura nos setores 9, 10, 12 e 13 de 32.0°C para 27.0°C é devido a existência de ...
Abstract: With the massive occupation of the urban and activities arising from the uncontrolled growth of these areas, a change occurs in the urban climate, making it unhealthy. One factor that contributes to poor quality of life due to this fact are the islands of heat which consists in the accumulation of surface heat and raises the temperature in the cities. The vegetation consists of urban greening, urban forests and green areas, have the function to minimize this effect. To reconcile these factors work aims to collect data on surface temperature by satellite images, LANDSAT 5 captured on 18/12/2012 point 75 e 76, orbits 221, band 6 and on-site survey with thermo-hygrometer, with possession of the data, crossing to the survey conducted vegetative and provide a management vision to improve the microclimate of the city under study. The remote sensing and the geographic information system allow the evaluation of different earth surface temperatures. The municipality reduced area and the low resolution detector used makes the temperature analyzes at the urban area more difficult requiring the use of other programs to assist in the data interpretation. The vegetation coverage at urban area directly affects in the temperature decreasing improving the urban micro-climate. The different analyzed surface coverage also affects the apparent temperature. Among the studied ranks, the eucalyptus cultivation and native forest rank show mild apparent temperature around 22.0°C and the solo exposed to the higher apparent temperature around 37.0°C. The 4,5 and 7 sectors present a 27,0°C temperature average, and these are the sectors with the milder temperature due to the vegetation coverage area. The temperatures difference at the 9, 10, 12 and 13 sectors from 32.0°C to 27.0°C is due to the existing green areas, such as squares. The site survey high temperatures measured by thermo-hygrometer are around 35.0°C due to...
Mestre
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Blackmore, Debra Sue. "Use of Water Indices Derived from Landsat OLI Imagery and GIS to Estimate the Hydrologic Connectivity of Wetlands in the Tualatin River National Wildlife Refuge." Thesis, Portland State University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10191067.
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This study compared two remote sensing water indices: the Normalized Difference Water Index (NDWI) and the Modified NDWI (MNDWI). Both indices were calculated using publically-available data from the Landsat 8 Operational Land Imager (OLI). The research goal was to determine whether the indices are effective in locating open water and measuring surface soil moisture. To demonstrate the application of water indices, analysis was conducted for freshwater wetlands in the Tualatin River Basin in northwestern Oregon to estimate hydrologic connectivity and hydrological permanence between these wetlands and nearby water bodies. Remote sensing techniques have been used to study wetlands in recent decades; however, scientific studies have rarely addressed hydrologic connectivity and hydrologic permanence, in spite of the documented importance of these properties. Research steps were designed to be straightforward for easy repeatability: 1) locate sample sites, 2) predict wetness with water indices, 3) estimate wetness with soil samples from the field, 4) validate the index predictions against the soil samples from the field, and 5) in the demonstration step, estimate hydrologic connectivity and hydrological permanence. Results indicate that both indices predicted the presence of large, open water features with clarity; that dry conditions were predicted by MNDWI with more subtle differentiation; and that NDWI results seem more sensitive to sites with vegetation. Use of this low-cost method to discover patterns of surface moisture in the landscape could directly improve the ability to manage wetland environments.
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Motswaledi, Mokhine. "Using remote sensing indices to evaluate habitat intactness in the Bushbuckridge area : a key to effective planning." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96798.
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Thesis (MSc)--Stellenbosch University, 2015.ENGLISH ABSTRACT: Anthropological influences are threatening the state of many savanna ecosystems in most rural landscapes around the world. Effective monitoring and management of these landscapes requires up to date maps and data on the state of the environment. Degradation data over a range of scales is often not readily available due to a lack of financial resources, time and technical capabilities. The aim of this research was to use a medium resolution multispectral SPOT 5 image from 2010 and Landsat 8 images from 2014 to map habitat intactness in the Bushbuckridge and Kruger National Park (KNP) region. The images were pre-processed and segmented into meaningful image objects using an object based image analysis (OBIA) approach. Five image derivatives namely: brightness, compactness, NIR standard deviation, area and the normalised difference vegetation index (NDVI) were evaluated for their capability to model habitat intactness. A habitat intactness index was generated by combining the five derivatives and rescaling them to a data range of 0 to 10, with 0 representing completely transformed areas, 10 being undisturbed natural vegetation. Field data were collected in October 2014 using a field assessment form consisting of 10 questions related to ecosystem state, in order to facilitate comparisons with the remote sensing habitat intactness index. Both satellite data sets yielded low overall accuracies below 30%. The results were improved by applying a correction factor to the reference data. The results significantly improved with SPOT 5 producing the highest overall accuracy of 62.6%. The Landsat 8 image for May 2014 achieved an improved accuracy of 60.2%. The SPOT 5 results showed to be a better predictor of habitat intactness as it assigned natural vegetation with better accuracy, while Landsat 8 correctly assigned mostly degraded areas. These findings suggest that the method was not easily transferable between the different satellite sensors in this savanna landscape, with a high occurrence of forest plantations and rural settlements too. These areas caused high omission errors in the reference data, resulting in the moderate overall accuracies obtained. It is recommended that these sites be clipped out of the analysis in order to obtain acceptable accuracies for non-transformed areas. The study nevertheless demonstrated that the habitat intactness index maps derived can be a useful data source for mapping general patterns of degradation especially on a regional scale. Therefore, the methods tested in this study can be integrated in habitat mapping projects for effective conservation planning.
AFRIKAANSE OPSOMMING: Antropologiese invloede bedreig die toestand van savanna-ekostelsels in die meeste landelike landskappe regoor die wêreld. Doeltreffende monitering en bestuur van hierdie landskappe vereis op datum kaarte en inligting oor die toestand van die omgewing. Agteruitgangsdata van verskillende skale is dikwels nie geredelik beskikbaar nie weens 'n gebrek aan finansiële hulpbronne, tyd en tegniese vermoëns. Die doel van hierdie navorsing was om ‘n hoë resolusie multispektrale SPOT 5 beeld van 2010 en Landsat 8 beelde van 2014 te gebruik om die habitatongeskondenheid in die Bushbuckridge en Kruger Nasionale Park (KNP) streek te karteer. Die beelde is voorverwerk en gesegmenteer om sinvolle beeldvoorwerpe te skep deur die gebruik van ‘n voorwerp gebaseerde beeldanalise (OBIA) benadering. Vyf beeldafgeleides naamlik: helderheid, kompaktheid, NIR standaardafwyking, area en die genormaliseerde verskil plantegroei-indeks (NDVI) is geëvalueer vir hul vermoë om habitat ongeskondenheid te modelleer. ‘n Habitatongeskondenheidsindeks is gegenereer deur die kombinasie van die vyf afgeleides wat herskaal is na 'n datareeks van 0 tot 10, met 0 om totaal getransformeerde gebiede te verteenwoordig en 10 om ongestoorde natuurlike plantegroei voor te stel. Velddata is versamel in Oktober 2014 met gebruik van 'n veldassesseringsvorm, bestaande uit 10 vrae wat verband hou met die toestand van die ekostelsel, om vergelykings met die afstandswaarneming habitatongeskondenheidsindeks te fasiliteer. Beide satellietdatastelle het lae algehele akkuraatheid onder 30% opgelewer. Die resultate is deur die toepassing van 'n regstellingsfaktor tot die verwysing data verbeter. Die resultate het aansienlik verbeter met SPOT 5 wat die hoogste algehele akkuraatheid van 62.6% gelewer het. Die Landsat 8 beeld vir Mei 2014 bereik 'n verbeterde akkuraatheid van 60.2%. Die SPOT 5 resultate het geblyk om ‘n beter voorspeller van habitatongeskondenheid te wees as gevolg van ‘n beter akkuraatheid vir natuurlike plantegroei, terwyl Landsat meestal gedegradeerde gebiede kon voorspel. Hierdie bevindinge dui daarop dat die metode nie maklik oordraagbaar was tussen die verskillende satelliet sensors in hierdie savanna landskap nie, veral as gevolg van ‘n hoë voorkoms van bosbouplantasies en landelike nedersettings. Hierdie gebiede veroorsaak hoë weglatingsfoute in die verwysing data, wat lei tot gematigde algehele akkuraatheid. Dit word aanbeveel dat hierdie areas gemasker word tydens die ontleding om aanvaarbare akkuraatheid te verkry vir nie-getransformeerde gebiede. Nogtans het die studie getoon dat die afgeleide habitatongeskondenheidsindekskaarte ‘n nuttige bron van data kan wees vir die kartering van algemene patrone van agteruitgang, veral op 'n plaaslike skaal. Daarom kan die getoetsde metodes in die studie in habitatkarteringsprojekte vir doeltreffende bewaring beplanning geïntegreer word. Stellenbosch University https://scholar.sun.ac.za
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Pope, Allen J. "Multispectral classification and reflectance of glaciers : in situ data collection, satellite data algorithm development, and application in Iceland & Svalbard." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/245061.
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Glaciers and ice caps (GIC) are central parts of the hydrological cycle, are key to understanding regional and global climate change, and are important contributors to global sea level rise, regional water resources and local biodiversity. Multispectral (visible and near-infrared) remote sensing has been used for studying GIC and their changing characteristics for several decades. Glacier surfaces can be classified into a range of facies, or zones, which can be used as proxies for annual mass balance and also play a significant role in understanding glacier energy balance. However, multispectral sensors were not designed explicitly for snow and ice observation, so it is not self-evident that they should be optimal for remote sensing of glaciers. There are no universal techniques for glacier surface classification which have been optimized with in situ reflectance spectra. Therefore, the roles that the various spectral, spatial, and radiometric properties of each sensor play in the success and output of resulting classifications remain largely unknown. Therefore, this study approaches the problem from an inverse perspective. Starting with in situ reflectance spectra from the full range of surfaces measured on two glaciers at the end of the melt season in order to capture the largest range of facies (Midtre Lovénbreen, Svalbard & Langjökull, Iceland), optimal wavelengths for glacier facies identification are investigated with principal component analysis. Two linear combinations are produced which capture the vast majority of variance in the data; the first highlights broadband albedo while the second emphasizes the difference in reflectance between blue and near-infrared wavelengths for glacier surface classification. The results confirm previous work which limited distinction to snow, slush, and ice facies. Based on these in situ data, a simple, and more importantly completely transferrable, classification scheme for glacier surfaces is presented for a range of satellite multispectral sensors. Again starting with in situ data, application of relative response functions, scaling factors, and calibration coefficients shows that almost all simulated multispectral sensors (at certain gain settings) are qualified to classify glacier accumulation and ablation areas but confuse classification of partly ash-covered glacier surfaces. In order to consider the spatial as well as the spectral properties of multispectral sensors, airborne data are spatially degraded to emulate satellite imagery; while medium-resolution sensors (~20-60 m) successfully reproduce high-resolution (2 m) observations, low-resolution sensors (i.e. 250 m+) are unable to do so. These results give confidence in results from current sensors such as ASTER and Landsat ETM+ as well as ESA’s upcoming Sentinel-2 and NASA’s recently launched LDCM. In addition, images from the Landsat data archive are used to classify glacier facies and calculate the albedo of glaciers on the Brøgger Peninsula, Svalbard. The time series is used to observe seasonal and interannual trends and investigate the role of melt-albedo feedback in thinning of Svalbard glaciers. The dissertation concludes with recommendations for glacier surface classification over a range of current and future multispectral sensors. Application of the classification schemes suggested should help to improve the understanding of recent and continuing change to GIC around the world.
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Metzler, Jacob W. "Use of Multi-temporal IKONOS and LANDSAT ETM+ Satellite Imagery to Determine Forest Stand Conditions in Northern Maine." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/MetzlerJW2004.pdf.
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Guo, Qi. "Bangladesh Shoreline Changes During the Last Four Decades Using Satellite Remote Sensing Data." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503258115717912.
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Lee, Steven. "Detecting Wetland Change through Supervised Classification of Landsat Satellite Imagery within the Tunkwa Watershed of British Columbia, Canada." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-15910.
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Wetlands are considered to be one of the most valuable natural occurring forms of land cover in the world. Hydrologic regulation, carbon sequestration, and habitat provision for a wide assortment of flora and fauna are just a few of the benefits associated with wetlands. The implementation of satellite remote sensing has been demonstrated to be a reliable approach to monitoring wetlands over time. Unfortunately, a national wetland inventory does not exist for Canada at this time. This study employs a supervised classification method of Landsat satellite imagery between 1976 and 2008 within the Tunkwa watershed, southwest of Kamloops, British Columbia, Canada. Images from 2005 and 2008 were repaired using a gap-filling technique due to do the failure of the scan-line corrector on the Landsat 7 satellite in 2003. Percentage pixel counts for wetlands were compared, and a diminishing trend was identified; approximately 4.8% of wetland coverage loss was recognized. The influence of the expansion of Highland Valley Copper and the forestry industry in the area may be the leading causes of wetland desiccation. This study expresses the feasibility of wetland monitoring using remote sensing and emphasizes the need for future work to compile a Canadian wetland inventory.
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CUNHA, John Elton de Brito Leite. "Monitoramento ambiental por sensoriamento remoto: avaliação, automação e aplicação ao bioma Caatinga utilizando séries históricas Landsat." Universidade Federal de Campina Grande, 2018. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1555.
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O baixo monitoramento e altas pressões climáticas e antrópicas fazem do bioma Caatinga, semiárido brasileiro, um dos mais vulneráveis do mundo. Séries temporais de sensoriamento remoto são valiosas para analisar as LCC em áreas com alta sazonalidade, mas demandam muitos recursos computacionais. Estudos anteriores utilizam séries temporais superiores a 30 anos de índices de vegetação com baixa resolução espacial (1 a 8 km). No entanto, esta resolução espacial geralmente não permite identificar ações humanas (impactos) no meio ambiente. Nos últimos anos, houve melhorias na qualidade da imagem do Landsat (radiométrica e geométrica) e agora estão prontas para suportar o monitoramento e análise dos processos na superfície terrestre. O objetivo deste estudo é analisar, a partir de sensores de média resolução espadai, as alterações na cobertura do solo de origem antrópica numa área do bioma Caatinga. Para este fim, utilizou-se algoritmos para gerar índices de vegetação, albedo de superfície e evapotranspiração a partir de dados dos sensores a bordo dos satélites da família Landsat. Para aumentar a eficiência na geração dessas informações, os algoritmos foram conduzidos para operar com baixa demanda por dados de estações meteorológicas e sem intervenção humana durante o processamento. Além disso, um serviço de alto desempenho para processamento de dados orbitais é proposto. Os dados gerados por estes algoritmos foram testados com a informações de campo, demonstrando a possibilidade de utilizar os algoritmos em processos automáticos. As técnicas de computação em nuvem e paralelização utilizadas neste estudo foram eficientes na produção de séries temporais superiores a 30 anos de variáveis em média resolução espacial. A principal aplicação desenvolvida neste trabalho utilizou séries temporais do Landsat por um período de 31 anos em resolução temporal mensal, a fim de investigar os padrões espaciais e temporais da mudança de cobertura do solo em uma área de Caatinga, semiárido do estado da Paraíba, no Brasil. Um novo índice espectral - índice Surface Albedo (SAI) - é proposto para melhorar a observação da condição biofísica da vegetação. Os índices NDVI, EVI e SAI foram utilizados para avaliar o monitoramento das LCC impulsionadas por ações humanas em contraste a alteração induzida pelo clima. Séries temporais dos índices foram aplicados ao método TSS RESTREND para monitoramento das LCC. O método é empregado para remover as influências a curto prazo da precipitação na fisionomia da cobertura do solo, permitindo assim avaliar a capacidade dos índices utilizados para discriminar alterações nas regiões semiáridas. Google Earth, imagens RapidEye e observações in situ (a partir de outubro de 2017) foram usadas para observar condições de preservação/degradação ao longo do tempo. Os resultados mostram que o índice SAI é capaz de distinguir entre cobertura do solo "alterada" e "inalterada" com uma alta acurácia, 87%, para detectar corretamente o ano da LCC. Quando utilizado o índice SAI, o TSS RESTREND demonstrou-se adequado para detectar LCC na Caatinga, e seu melhor desempenho foi alcançado quando o evento de mudança ocorre na região central da série temporal (1990-2010), com algumas imprecisões em anos secos. O menor desempenho dos índices EVI e NDVI na detecção das LCC no bioma da Caatinga é explicado pela sua alta sensibilidade às variações da cobertura de folhas, como resultado de condições sazonais ou extremas de seca. O LCC afeta todo o sistema soloplanta-atmosfera, como remoção de biomassa e mudanças nas propriedades do solo, bem como no microclima, devido à exposição direta à radiação, precipitação e vento. A este respeito, a SAI é suposto ser mais sensível às alterações artificiais na superfície terrestre, devido à sua capacidade de capturar uma maior quantidade de feedback ambiental.
Low monitoring plus high human and climate pressures make the Caatinga biome one of the most vulnerabte biomes in the world. Time series of remote sensing are vafuable for analyzing LCC in áreas with high seasonalrty, but they require a lot of computationai resources. Earlier studíes mostly use > 30- years time series of vegetatíon indexes at low spatial resolution (1 to 8 km). However, this spatial resolution usually does not allow to identify human actions (impacts) on the environment. Landsat imagery quality (radiometricalfy as weli as geometrically) and availability has improved in recent years and is now ready to support high temporal resolution monitoring and analysis of land surface processes. The objective of this study is to analyze, from sensors of médium spatial resolution, the changes in land cover of anthropic origin in an area of the Caatinga biome. For this purpose, algorithms were used to generate vegetation índices, surface albedo and evapotranspiration from sensor data on the satellites of the Landsat family. To increase the efficiency in generating this information, the algorithms were conducted to operate with low demand for meteorological station data and without human intervention during processing. In addition, a high performance service for orbitai data processing is proposed. The data generated by these algorithms were tested to field observations, demonstrating the possibility of using these algorithms in automatic processes. The techniques of cloud computing and parallelization used in this study were efficient in producing long time series (over 30 years) of these variables in average spatial resolution. The main application developed in this work, used Landsat time series for a period of 31 years at monthly resolution in order to investigate spatial and temporal pattems of hotspots of land cover change in a Caatinga area of the semi-arid region of the Paraiba state, Brazil. A new spectral index - Surface Albedo Index (SAI) - is proposed to improve the observation of vegetation biophysica!condition and change. SAI, NDV1 and EVI are compared in order to evaluate the suitability of monitoring LCC driven by human actions in contrast to climate induced (drought) alteration. The TSS RESTREND method was successfully applied to Landsat time series for LCC monitoring. It is employed in order to remove the short-term influences of precípitation on land cover physiognomy, thus allowing to assess the ability of the index time series to discriminate LCC in drylands. Google Earth, Rapid Eye images and in situ observations (from October 2017) were used to observe preservation / degradation conditions along the time. Results showthat SAI is able to distinguish between "changed" and "unchanged" land cover with a high accuracy (87%) to detect the year of change. When using the SAI index, TSS RESTREND is suitable to detect LCC in the Caatinga, and its best performance was achieved when the change event occurred in the middle of the time series (1990-2010), with some inaccuracies in dry years. The lower ability of EVI and NDVI in the detection of LCC in the Caatinga biome is explained by their high sensitivity to leaf cover variations (as a result of seasonal or extreme drought conditions). LCC impacts the whole soilplant-atmosphere system, such as biomass remova!and changes in soil properties as weil as mícroclimate, due to the direct exposure to radiation, precípitation, and wind. In this regard, SAI is supposed to be more sensitive to man-made alterations of the land surface, due to its ability to capture a higher number of environmental feedbacks.
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Fröjse, Linda. "Multitemporal Satellite Images for Urban Change Detection." Thesis, KTH, Geoinformatik och Geodesi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-38539.
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The objective of this research is to detect change in urban areas using two satellite images (from 2001 and 2010) covering the city of Shanghai, China. These satellite images were acquired by Landsat-7 and HJ-1B, two satellites with different sensors. Two change detection algorithms were tested: image differencing and post-classification comparison. For image differencing the difference image was classified using unsupervised k-means classification, the classes were then aggregated into change and no change by visual inspection. For post-classification comparison the images were classified using supervised maximum likelihood classification and then the difference image of the two classifications were classified into change and no change also by visual inspection. Image differencing produced result with poor overall accuracy (band 2: 24.07%, band 3: 25.96%, band 4: 46.93%), while post-classification comparison produced result with better overall accuracy (90.96%). Post-classification comparison works well with images from different sensors, but it relies heavily on the accuracy of the classification. The major downside of the methodology of both algorithms was the large amount of visual inspection.
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Wilson, Natalie R. "A Comparison of Remote Sensing Indices and a Temporal Study of Cienegas at Cienega Creek from 1984 to 2011 using Multispectral Satellite Imagery." The University of Arizona, 2014. http://hdl.handle.net/10150/337200.
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Desert wetlands, in particular those slow moving bodies of water known as cienegas, are important sites for biodiversity in arid landscapes and serve as indicators of hydrological functioning on the landscape-level. One of the most extensive systems of cienegas, historical or extant, in southeastern Arizona lies along Cienega Creek, located southeast of Tucson, Arizona. Satellite imagery analysis is heavily utilized to determine landscape-level trends, but cienegas present a challenge to traditional analysis methods. The Normalized Difference Vegetation Index (NDVI), the classic measure of vegetation greenness, reacts counter-intuitively to open water and is affected by open ground, both common occurrences in cienega habitats. Additional remote sensing indices have been developed that balance sensitivity to these environmental elements. This research explores these remote sensing indices at Cienega Creek applying one topographic index to current elevation data and five spectral indices to Thematic Mapper imagery from 1984 to 2011. Temporal trends were identified for all spectral indices and all indices were compared for suitability in cienega habitats. Temporal trends were analyzed for spatial clustering and spatial trends identified. The Normalized Difference Infrared Index utilizing Landsat Thematic Mapper band 5 outperformed other indices at differentiating between cienega, riparian, and upland habitats and is more suitable than NDVI for analyzing cienega habitats in such circumstances.
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Thompson, Kenneth Parker. "A Political History of U.S. Commercial Remote Sensing, 1984-2007: Conflict, Collaboration, and the Role of Knowledge in the High-Tech World of Earth Observation Satellites." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/30235.
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The political history of U.S. commercial remote sensing began in 1984 when the U.S. government first attempted to commercialize its civil earth observation satellite system " Landsat. Since then, the high technology of earth imaging satellite systems has generated intense debates and policy conflicts, primarily centered on U.S. government concerns over the national security and foreign policy implications of high-resolution commercial satellite systems. Conversely, proponents of commercial observation satellites have urged U.S. policymakers to recognize the scientific and socio-economic utility of commercial remote sensing and thus craft and implement regulatory regimes that allow for a greater degree of information openness and transparency in using earth observation satellite imagery. This dissertation traces and analyzes that tumultuous political history and examines the policy issues and social construction of commercial remote sensing to determine the role of knowledge in the effective crafting and execution of commercial remote sensing laws and policies.
Although individual and organizational perspectives, interests, missions, and cultures play a significant role in the social construction of commercial observation satellite systems and programs, the problem of insufficient knowledge of the myriad dimensions and complex nature of commercial remote sensing is a little studied but important component of this social construction process. Knowledge gaps concerning commercial remote sensing extend to various dimensions of the subject matter, such as the global, economic, technical, and legal/policy aspects.
Numerous examples of knowledge voids are examined to suggest a connection between deficient knowledge and divergent policy perceptions as they relate to commercial remote sensing. Relevant knowledge voids are then structurally categorized to demonstrate the vastness and complexity of commercial remote sensing policy issues and to offer recommendations on how to fill such knowledge gaps to effect increased collaboration between the US government and the U.S. commercial remote sensing industry. Finally, the dissertation offers suggestions for future STS studies on policy issues, particularly those that focus on the global dimensions of commercial remote sensing or on applying the knowledge gap concept advanced by this dissertation to other areas of science and technology policymaking.Ph. D.
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Palandro, David A. "Coral reef habitat change and water clarity assessment (1984-2002) for the Florida Keys national marine sanctuary using landsat satellite data." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001778.
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Pham-Duc, Binh. "Satellite remote sensing of the variability of the continental hydrology cycle in the lower Mekong basin over the last two decades." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS024/document.
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Les eaux superficielles sont nécessaires à toute forme de vie en tant que parties intégrantes de tout processus de vie sur Terre. Quantifier les eaux de surface et suivre leurs variations est primordial en raison du lien direct qui existe entre les variables hydrologiques et le changement climatique. La télédétection par satellite, de l’hydrologie continental offre l’opportunité unique d’étudier, depuis l’espace, les processus hydrologiques à différentes échelles (régionale et globale). Dans cette thèse, différentes techniques ont été développées afin d’étudier les variations des eaux superficielles ainsi que d’autres variables hydrologiques, au niveau du bassin inférieur du Mékong (entre le Vietnam et le Cambodge) et ce en utilisant plusieurs estimations satellitaires différentes. Cette thèse s’articule autour de quatre points principaux. Premièrement, l’utilisation d’observations satellitaires dans le visible et dans l’infrarouge (MODIS) est étudiée et comparée afin d’évaluer les eaux de surface au niveau du bassin inférieur du Mékong. Quatre méthodes de classification ont été utilisées afin de différencier les types de surface (inondés ou pas) dans le bassin. Les différentes méthodes ont donné des cartes d’eaux de surface aux résultats semblables en terme de dynamique saisonnière. La classification la plus adaptée aux régions tropicales a été ensuite choisie pour produire une carte des eaux de surface à la résolution de 500 m entre janvier 2001 et aujourd’hui. La comparaison des séries temporelles issues de cette carte et de celles issues du produit de référence MODIS donne une forte corrélation temporelle (> 95%) pour la période 2001-2007. Deuxièmement, l’utilisation des observations issues du satellite SAR Sentinel-1 est examinée à des fins identiques. L’imagerie satellitaire optique est ici remplacée i par les images SAR qui grâce aux longueurs d’ondes utilisées dans le micro-ondes, permettent de « voir » à travers les nuages. Un jeu d’images Landsat-8-sans-nuage est alors utilisé pour entraîner un Réseau de Neurones (RN) afin de restituer des cartes d’eaux de surface par l’utilisation d’un seuillage sur les sorties du modèle RN. Les cartes sont à la résolution spatiale de 30 m et disponibles depuis janvier 2015. Comparées aux cartes de référence Landsat-8-sans-nuage, les sorties de modèles RN montre une très grande corrélation (90%) ainsi qu’une détection "vraie" à 90%. Les cartes restituées d’eaux de surface utilisant la technologie SAR sont enfin comparées aux cartes d’inondation issues de données topographiques. Les résultats montrent une fois encore une très grande consistance entres les deux cartes avec 98% des pixels considérés comme inondés dans cartes SAR se trouvant dans les régions de très grande probabilité d’inondation selon la topographie (>60%). Troisièmement, la variation volumique des eaux de surface est calculée comme le produit de l’étendue de la surface avec la hauteur d’eau. Ces deux variables sont validées à l’aide d’autres produits hydrologiques et montrent de bons résultats. La hauteur d’eau superficielle est linéairement interpolée aux régions non inondées afin de produire des cartes mensuelles à la résolution spatiale de 500 m. La hauteur d’eau est ensuite analysée pour estimer les variations volumiques. Ces résultats montrent une très bonne corrélation avec la variation volumique induite par la mesure du contenu en eau du satellite GRACE (95%) ainsi qu’avec la variation des mesures in situ de débit des rivières. Finalement, deux produits globaux et multi-satellites d’eaux superficielles sont comparés à l’échelle régionale et globale sur la période 1993-2007: GIEMS et SWAMPS. Lorsqu’elles existent, les données auxiliaires sont utilisées afin de renforcer l’analyse. Les deux produits montrent une dynamique similaire, mais 50% des pixels inondés dans SWAMPS se trouvent le long des côtesSurface water is essential for all forms of life since it is involved in almost all processes of life on Earth. Quantifying and monitoring surface water and its variations are important because of the strong connections between surface water, other hydrological components (groundwater and soil moisture, for example), and the changing climate system. Satellite remote sensing of land surface hydrology has shown great potential in studying hydrology from space at regional and global scales. In this thesis, different techniques using several types of satellite estimates have been made to study the variation of surface water, as well as other hydrological components in the lower Mekong basin (located in Vietnam and Cambodia) over the last two decades. This thesis focuses on four aspects. First, the use of visible/infrared MODIS/Terra satellite observations to monitor surface water in the lower Mekong basin is investigated. Four different classification methods are applied, and their results of surface water maps show similar seasonality and dynamics. The most suitable classification method, that is specially designed for tropical regions, is chosen to produce regular surface water maps of the region at 500 m spatial resolution, from January 2001 to present time. Compared to reference data, the MODIS-derived surface water time series show the same amplitude, and very high temporal correlation for the 2001-2007 period (> 95%). Second, the use of SAR Sentinel-1 satellite observations for the same objective is studied. Optical satellite data are replaced by SAR satellite data to benefit the ability of their microwave wavelengths to pass through clouds. Free-cloud Landsat-8 satellite imagery are set as targets to train and optimize a Neural Network (NN). Predicted surface water maps (30 m spatial resolution) are built for the studied region from January 2015 to present time, by applying a threshold (0.85) to the output of the NN. Compared to reference free-cloud Landsat-8 surface water maps, results derived from the NN show high spatial correlation (_90%), as well as true positive detection of water pixels (_90%). Predicted SAR surface water maps are also compared to floodability maps derived from topography data, and results show high consistency between the two independent maps with 98% of SAR-derived water pixels located in areas with a high probability of inundation (>60%). Third, the surface water volume variation is calculated as the product of the surface water extent and the surface water height. The two components are validated with other hydrological products, and results show good consistencies. The surface water height are linearly interpolated over inundated areas to build monthly maps at 500 m spatial resolution, then are used to calculate changes in the surface water volume. Results show high correlations when compared to variation of the total land surface water volume derived from GRACE data (95%), and variation of the in situ discharge estimates (96%). Fourth, two monthly global multi-satellite surface water products (GIEMS & SWAMPS) are compared together over the 1993-2007 period at regional and global scales. Ancillary data are used to support the analyses when available. Similar temporal dynamics of global surface water are observed when compared GIEMS and SWAMPS, but _50% of the SWAMPS inundated surfaces are located along the coast line. Over the Amazon and Orinoco basins, GIEMS and SWAMPS have very high water surface time series correlations (95% and 99%, respectively), but SWAMPS maximum water extent is just a half of what observed from GIEMS and SAR estimates. SWAMPS fails to capture surface water dynamics over the Niger basin since its surface water seasonality is out of phase with both GIEMS- and MODIS-derived water extent estimates, as well as with in situ river discharge data
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Loew, Teagan K. "Improvement to Total Maximum Daily Load (TMDL) Measurements and Monitoring by Satellite Remote Sensing Applications." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1333388592.
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Hutchings, James Forrest. "Monitoring Property Boundaries for the Appalachian National Scenic Trail Using Satellite Images." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/32103.
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The Appalachian National Scenic Trail is a unit of the National Park System created by the National Trails Act of 1968. Commonly referred to as the Appalachian Trail, or the AT, this National Park has some of the longest boundaries of any park. The AT is routed more than 2000 miles along the mountains of the eastern United States. The land purchased for the protection of the AT creates a separate boundary on each side of the trail. Monitoring these boundaries for intrusions or encroachments is a difficult and time-consuming task when done totally by field methods. This thesis presents a more efficient and consistent monitoring process using remote sensing data and change detection algorithms. Using Landsat TM images, Normalized Difference Vegetation Index (NDVI), and image difference change detection, this research shows that major boundary encroachments can be detected. Detection of sub-pixel vegetation index decreases identifies specific locations for field inspection. Assuming low cost multispectral Landsat imagery is available, simple NDVI difference calculation allows this technique to be applied to the entire AT one or more times per year. This procedure would improve the response time for encroachment mediation. The producerâ s accuracy for finding possible encroachments was 100 percent and the consumerâ s accuracy for possible encroachments indicated was 78.3 percent. Due to limited image availability, this study only examines change between one pair of Landsat images. Further refinement of these techniques should investigate other Landsat images at other times. Use of other remote sensing systems and change detection algorithms could be the focus of further research.Master of Science
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DiGirolamo, Paul Alrik. "A Comparison of Change Detection Methods in an Urban Environment Using LANDSAT TM and ETM+ Satellite Imagery: A Multi-Temporal, Multi-Spectral Analysis of Gwinnett County, GA 1991-2000." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/anthro_theses/18.
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Land cover change detection in urban areas provides valuable data on loss of forest and agricultural land to residential and commercial development. Using Landsat 5 Thematic Mapper (1991) and Landsat 7 ETM+ (2000) imagery of Gwinnett County, GA, change images were obtained using image differencing of Normalized Difference Vegetation Index (NDVI), principal components analysis (PCA), and Tasseled Cap-transformed images. Ground truthing and accuracy assessment determined that land cover change detection using the NDVI and Tasseled Cap image transformation methods performed best in the study area, while PCA performed the worst of the three methods assessed. Analyses on vegetative and vegetation changes from 1991- 2000 revealed that these methods perform well for detecting changes in vegetation and/or vegetative characteristics but do not always correspond with changes in land use. Gwinnett County lost an estimated 13,500 hectares of vegetation cover during the study period to urban sprawl, with the majority of the loss coming from forested areas.
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Schmedtmann, Jonas. "Automatizing photo interpretation of satellite imagery in the context of the Common Agriculture Policy subsidy control." Master's thesis, ISA/UL, 2014. http://hdl.handle.net/10400.5/8294.
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Mestrado em Engenharia do Ambiente - Instituto Superior de AgronomiaComputer Assisted Photo-Interpretation (CAPI) uses remotely sensed imagery to control farmers’ subsidy applications in the context of the EU’s Common Agriculture Policy. A simple and reproducible method to automatize CAPI in an operational context with the overreaching goal to reduce control costs and completion time was developed in this study. Validated control data provided by the Portuguese Control and Paying Agency for Agriculture (IFAP) and a multispectral atmospherically corrected Landsat ETM+ time series were used to calibrate and test the method. Taking advantage of the nature of subsidy declarations, object-based land cover classification for the 12 most controlled classes was carried out in the region of Ribatejo. The main feature of the presented method is that it allows choosing a confidence level on the automatic classification of farmers’ parcels. While higher confidence levels reduce the risk of misclassifications, lower levels increase the number of automatic control decisions. A confidence level of 80% is a good compromise. This confidence level leads to over 55% of automatically taken control decisions with an overall accuracy of 84%. Furthermore, over 85% of all parcels classified as maize, rice, wheat or vineyard can be controlled by the method with the optimal confidence level.
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Tamstorf, Mikkel P. "Satellitbaseret vegetationskortlægning i Vestgrønland." [København] : Miljø- og Energiministeriet, Danmarks Miljøundersøgelser, 2001. http://www.dmu.dk/1_viden/2_Publikationer/3_Ovrige/rapporter/PHD_mpt.pdf.
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Sayão, Veridiana Maria. "Land surface temperature and reflectance spectra integration obtained from Landsat on the soil attributes quantification." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/11/11140/tde-20032018-112133/.
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Soil attributes directly influence on its surface temperature. Although there are several studies using soil spectra obtained from satellites, soil evaluation through Land Surface Temperature (LST) is still scarce. The broad availability of satellite thermal data and the development of algorithms to retrieve LST facilitated its use in soil studies. The objective of this study was to evaluate soil LST variations due to its composition and verify the potential of using LST on soil attributes quantification, also integrated with reflectance spectra and elevation data. The study area (198 ha) is located in Sao Paulo state, Brazil, and had plowed bare soil during the satellite image acquisition date. Soil samples were collected in a regular grid of 100 x 100 m (depths: 0-0.2 m and 0.8- 1.0 m); soil granulometry, organic matter (OM) and iron oxides were determined by wet chemistry analysis. In this study, an image of Landsat 5 was used for extracting LST using the inversion of Planck\'s function in band 6 (10,400 - 12,500 nm), and land surface emissivity was estimated using Normalized Difference Vegetation Index threshold method. Reflectance values were extracted from bands 1, 2, 3, 4, 5 and 7. Models for soil attributes quantification were performed using Linear Regression (LR), with samples from 62 auger points distributed in 14 toposequences. Simple LR was applied for generating prediction models based on LST and on elevation data (extracted from a Digital Elevation Model). Multiple LR was applied in order to generate prediction models using atmospherically corrected spectral reflectance from Visible, Near-Infrared and Shortwave infrared (Vis-NIR-SWIR) bands as predictors, and also for the prediction of soil attributes using simultaneously Vis-NIR-SWIR, LST and elevation data, and only significant variables identified by T-tests were used. Predictive performance of models was assessed based on adjusted coefficient of determination (R2adj), Root Mean Squared Error (RMSE, g kg-1) and Ratio of Performance to Interquartile Range (RPIQ) obtained in validation. Ordinary kriging was also performed and the resulted interpolated surfaces were compared to the maps obtained from the best LR model. There was significant correlation between soil attributes and reflectance, LST and elevation data, and soils with clay texture were differentiated from sandy soils based on LST mean values. For all soil attributes, models using only elevation presented the worst performance; models using only LST, moderate performance; and using Vis-NIR-SWIR bands, good predictive performance. For clay, the best model obtained had bands 4-7, LST and elevation as predictors; for sand and iron oxides, the best model had bands 4-7 and LST; for OM, band 4, band 7 and LST. The use of LST for estimating soil attributes increases the predictive performance of multiple LR models when associated with other variables obtained through remote sensing, particularly surface reflectance data, improving the validation of models reaching high R2adj, high RPIQ and low RMSE values. Maps for sand, OM and iron oxides obtained through ordinary kriging outperformed those obtained for the same attributes using LR models based on RS co-variables, and for clay, both approaches reached the same accuracy level. Mapping of soil clay, sand, OM and iron oxides contents through multiple LR models using Landsat 5 products is a simple and easy to reproduce technique, appropriate for soil attributes mapping in bare soil agricultural areas.Os atributos do solo influenciam diretamente na sua temperatura de superfície. Apesar de existir vários estudos utilizando espectros de solos obtidos de satélite, a avaliação do solo por meio da Temperatura de Superfície Terrestre (em inglês Land Surface Temperature, LST) ainda é escassa. A ampla disponibilidade de dados termais de satélite e o desenvolvimento de algoritmos para derivar a LST facilitou o seu uso em estudos de solos. O objetivo desse trabalho foi avaliar variações da LST do solo devidas à sua composição e verificar o potencial de uso da LST na quantificação de atributos do solo, também integrada com dados de espectros de reflectância e elevação. A área de estudo (198 ha) está localizada no estado de São Paulo, Brasil, e estava com solo exposto e arado na data de aquisição da imagem de satélite. Amostras de solo foram coletadas em um grid regular de 100 x 100 m (profundidades: 0.02 m e 0.8-1.0 m); a granulometria do solo, matéria orgânica (MO) e óxidos de ferro foram determinados via análises físicas e químicas laboratoriais. Neste estudo, uma imagem do Landsat 5 foi utilizada para extrair a temperatura de superfície usando a inversão da função da Lei de Planck na banda 6 (10.400 - 12.500 nm), e a emissividade de superfície foi estimada utilizando o método do limiar do Índice de Vegetação da Diferença Normalizada. Valores de reflectância das bandas 1, 2, 3, 4, 5 e 7 foram extraídos. Modelos para quantificação de atributos do solo foram feitos usando Regressão Linear (RL), com amostras de 62 pontos de tradagem distribuídos em 14 topossequências. A RL simples foi aplicada para gerar modelos de predição baseados na LST e também na elevação (extraída de um modelo digital de elevação). A RL múltipla foi aplicada para gerar modelos de predição usando os espectros de reflectância com correção atmosférica das bandas do Visível, Infravermelho próximo e Infravermelho de ondas curtas (Vis-NIR-SWIR) como preditores; também foi aplicada para predição de atributos do solo usando simultaneamente dados do Vis-NIR-SWIR, LST e elevação, e apenas variáveis significativas identificadas por teste T foram usadas. A performance preditiva dos modelos foi avaliada baseada no coeficiente de determinação ajustado (R2adj), raiz do erro quadrático médio (RMSE, g kg-1) e razão de desempenho do intervalo interquartil (RPIQ) obtidos na validação. A krigagem ordinária também foi feita e as superfícies interpoladas resultantes foram comparadas com o melhor modelo de RL. Houve correlação significativa entre os atributos do solo e dados de reflectância, LST e elevação, e solos com textura argilosa foram diferenciados de solos arenosos com base em valores médios de LST. Para todos os atributos do solo, os modelos usando apenas elevação apresentaram a pior performance, modelos usando somente LST, performance moderada, e usando as bandas do Vis-NIR-SWIR, boa performance preditiva. Para argila, o melhor modelo obtido teve as bandas 4-7, LST e elevação como preditores; para areia e óxidos de ferro, o melhor modelo teve as bandas 4-7 e LST; para MO, banda 4, banda 7 e LST. O uso da LST para estimar atributos do solo aumenta a performance preditiva de modelos de RL múltipla quando associada a outras variáveis obtidas via sensoriamento remoto (SR), particularmente dados de reflectância de superfície, melhorando a validação dos modelos atingindo altos valores de R2adj e RPIQ e baixos valores de RMSE. Os mapas para areia, MO e óxidos de ferro obtidos via krigagem ordinária superaram aqueles obtidos para os mesmos atributos usando modelos de RL baseados em co-variáveis obtidas via SR, e para argila, ambas abordagens atingiram o mesmo nível de acurácia. O mapeamento dos conteúdos de argila, areia, matéria orgânica e óxidos de ferro do solo via modelos de RL múltipla utilizando produtos do Landsat 5 é uma técnica simples e fácil de reproduzir, apropriada para o mapeamento de atributos do solo em áreas de agricultura com solo exposto.
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Rydberg, Anna. "Multispectral image analysis for extraction of remotely sensed features in agricultural fields /." Uppsala : Centre for Image Analysis, Swedish Univ. of Agricultural Sciences (Centrum för bildanalys, Sveriges lantbruksuniv.), 2001. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=009768972&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Hasan, Ali Fadhil. "Évaluation de la dégradation des forêts primaires par télédétection dans un espace de front pionnier consolidé d’Amazonie orientale (Paragominas)." Thesis, Le Mans, 2019. http://www.theses.fr/2019LEMA3002/document.
Full textAbstract:
La dégradation de la forêt est un changement de sa structure et de la composition floristique et faunistique, ce qui conduit à une perte de biodiversité, de production de biens et de services et à un accroissement de la vulnérabilité aux aléas climatiques et aux incendies. Elle concerne de vastes espaces en zone tropicale particulièrement dans les régions de fronts pionniers plus ou moins consolidés où la forêt primaire est soumise à l’extraction de bois, aux incendies et à la fragmentation. Pour évaluer son ampleur et son intensité, il est nécessaire de recourir à la télédétection. Mais les méthodologies disponibles restent encore insuffisantes.L’enjeu scientifique est de développer des méthodes adaptées à de grandes surfaces afin d’analyser l’effet de différentes perturbations sur les trajectoires suivies par le couvert forestier. Il s’agit également de distinguer différentes intensités de dégradation suite à l’accumulation de perturbations. C’est un préalable indispensable pour définir et mettre en œuvre des plans de gestion adaptés. Le premier axe de ce travail a pour objectif de cartographier annuellement l’ampleur des perturbations, d’identifier les principaux types de perturbations et de caractériser la trajectoire de restauration de l’activité photosynthétique. Il est réalisé à partir de séries temporelles d’images Landsat traitées au moyen du progiciel CLASlite. L’agrégation des couvertures annuelles résultant des traitements avec CLASlite a également permis de constituer un indicateur de dégradation résultant du cumul de processus de perturbations sur plusieurs annéesThe forest degradation is a change of the structure and the composition of flora and fauna, which leads to a loss of biodiversity, of production of goods and services and an increased vulnerability to weather hazards and fires. This process concerns large areas in the tropics, particularly in agricultural frontier where primary forest is subject to timber extraction, fire and fragmentation. Remote sensing is used to assess the magnitude and the extent of forest degradation. However, the methodologies available are still insufficient. The scientific challenge is to develop methods adapted to large areas to analyze the effect of different disturbances on the trajectories followed by the forest cover. It is also to identify different intensities of degradation following disturbances events. This is a prerequisite for defining and implementing appropriate management plans. The first axis of this work aims to map annually the extent of the disturbances, to identify the main types of disturbances and to characterize the restoration trajectory of the photosynthetic activity. This work is based on time series of Landsat images processed using CLASlite software. The aggregation of the annual coverages resulting from treatments with CLASlite also made it possible to constitute an indicator of degradation resulting from the accumulation of disturbance processes over several years. The second axis aims to evaluate the evolution of the forest sensitivity to drought as a function of its degradation and to build a degradation indicator. The approach uses MODIS images and TRMM precipitation data. This work is implemented in the municipality of Paragominas (state of Pará, Brazil)
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Hardy, Robert F. "Assessments of Surface-Pelagic Drift Communities and Behavior of Early Juvenile Sea Turtles in the Northern Gulf of Mexico." Thesis, University of South Florida, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1569947.
Full textAbstract:
Knowledge of species distribution and habitat associations are essential for conservation measures. Such information is lacking for many marine species due to their occupancy of broad and ephemeral habitats that are difficult to access for study. Sea turtles, specifically the surface−pelagic juvenile stage of some species, are a group for which significant knowledge gaps remain surrounding their distribution and habitat use. Recent research has confirmed the long−standing hypothesis that the surface−pelagic juvenile stage occurs within surface−pelagic drift communities (SPDC). Within the North Atlantic and surrounding basins, the holopelagic macroalgae Sargassum spp. dominates SPDC and serves as a remotely−detectable indicator of SPDC. The present study focuses on surface−pelagic habitats of four sea turtle species and addresses knowledge gaps using two approaches: habitat mapping and behavioral examination. Remote sensing techniques were used to identify SPDC, and satellite telemetry to examine behavior. This work was conducted in three parts and is presented in three chapters.
Imagery collected from the Landsat satellites (5 and 7) was used to quantify the area of SPDC (km2). Approximately 1,800 Landsat images collected from 2003–2011 were examined for SPDC. The first chapter discusses the abundance, seasonality, and distribution of SPDC within the eastern Gulf of Mexico waters where surface−pelagic green, hawksbill, Kemp’s ridley, and loggerhead turtles are known to occur. SPDC was found year−round within the eastern Gulf of Mexico, and the amount of habitat peaked during summer months. The amount of SPDC within the eastern Gulf of Mexico varied annually with peaks in 2005, 2009, and 2011. High concentrations of SPDC were discovered within offshore waters of the northeastern Gulf of Mexico and southern West Florida Shelf.
Within the second chapter, the behavior of 10 surface−pelagic juvenile Kemp’s ridleys was examined using satellite telemetry. Using remotely−sensed imagery, the sea surface habitats used by tracked turtles were examined. Surface−pelagic juveniles are hypothesized to be principally passive drifters. The behavior of tracked turtles was examined to determine if they exhibited periods of active and passive behavior, which may indicate periods of swim and drift. The proximity of tracked turtles to remotely−detected SPDC was examined when coincident Landsat imagery was available (within one day of the turtle’s position). Turtles were tracked for 36.5 days (mean) and exhibited primarily passive behavior during the tracking period. The satellite transmitters messaged frequently and reported temperatures significantly higher than sea surface temperatures. Landsat imagery was available coincident to the tracks of nine individuals. SPDC was present within 74% of images, and the mean distance between tracked turtles and SPDC was 54 km. Close associations between tracked turtles and SPDC were documented for four individuals. Results suggest that the tracked turtles spent a majority of the time drifting within SPDC.
The final chapter discusses the density of SPDC within northern and western Gulf of Mexico waters from 2009–2011. Seasonal abundance peaks occurred throughout the study area, but the timing varied. SPDC peaked earlier (late spring) within the northwestern Gulf of Mexico. Moving eastward, the timing of seasonal peaks shifted progressively later during the year. Within the western portions of the study area, SPDC was found to be significantly higher than in the eastern Gulf of Mexico.
The eastern Gulf of Mexico may provide critical developmental habitats for several North Atlantic sea turtle species. Additional study is necessary to determine if portions of the western Gulf of Mexico could serve in a similar capacity. SPDC is extremely vulnerable to anthropogenic impacts, specifically oil spills and the occurrence of persistent marine debris. Conservation of SPDC may be challenged by its ephemeral nature; however, the results presented herein could advise conservation efforts (e.g., delineation of critical habitat). The present study described spatial patterns of SPDC occurrence, regions of high abundance, and seasonality. The description of the behavior surface−pelagic sea turtles offers refinements to the spatial distribution of this life stage. These results, coupled with information on circulation patterns and the distribution of sea turtle nesting beaches, can be used to better predict when and where sea turtles and SPDC may be found. For example, the year−round persistence of SPDC within the eastern Gulf of Mexico and the location of major nesting beaches located upstream support the area’s designation as critical habitat for surface−pelagic green, hawksbill, Kemp’s ridley, and loggerhead turtles.
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Teixeira, Karla dos Santos. "Uma proposta metodológica de integração de técnicas de análise espectral e de inteligência computacional, baseadas em conhecimento, para o reconhecimento de padrões em imagens multiespectrais." Universidade do Estado do Rio de Janeiro, 2012. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=5779.
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Somente no ano de 2011 foram adquiridos mais de 1.000TB de novos registros digitais
de imagem advindos de Sensoriamento Remoto orbital. Tal gama de registros, que possui
uma progressão geométrica crescente, é adicionada, anualmente, a incrível e extraordinária
massa de dados de imagens orbitais já existentes da superfície da Terra (adquiridos desde a
década de 70 do século passado). Esta quantidade maciça de registros, onde a grande maioria
sequer foi processada, requer ferramentas computacionais que permitam o reconhecimento
automático de padrões de imagem desejados, de modo a permitir a extração dos objetos
geográficos e de alvos de interesse, de forma mais rápida e concisa. A proposta de tal
reconhecimento ser realizado automaticamente por meio da integração de técnicas de Análise
Espectral e de Inteligência Computacional com base no Conhecimento adquirido por
especialista em imagem foi implementada na forma de um integrador com base nas técnicas
de Redes Neurais Computacionais (ou Artificiais) (através do Mapa de Características Auto-
Organizáveis de Kohonen SOFM) e de Lógica Difusa ou Fuzzy (através de Mamdani).
Estas foram aplicadas às assinaturas espectrais de cada padrão de interesse, formadas pelos
níveis de quantização ou níveis de cinza do respectivo padrão em cada uma das bandas
espectrais, de forma que a classificação dos padrões irá depender, de forma indissociável, da
correlação das assinaturas espectrais nas seis bandas do sensor, tal qual o trabalho dos
especialistas em imagens. Foram utilizadas as bandas 1 a 5 e 7 do satélite LANDSAT-5 para a
determinação de cinco classes/alvos de interesse da cobertura e ocupação terrestre em três
recortes da área-teste, situados no Estado do Rio de Janeiro (Guaratiba, Mangaratiba e Magé)
nesta integração, com confrontação dos resultados obtidos com aqueles derivados da
interpretação da especialista em imagens, a qual foi corroborada através de verificação da
verdade terrestre. Houve também a comparação dos resultados obtidos no integrador com
dois sistemas computacionais comerciais (IDRISI Taiga e ENVI 4.8), no que tange a
qualidade da classificação (índice Kappa) e tempo de resposta. O integrador, com
classificações híbridas (supervisionadas e não supervisionadas) em sua implementação,
provou ser eficaz no reconhecimento automático (não supervisionado) de padrões
multiespectrais e no aprendizado destes padrões, pois para cada uma das entradas dos recortes
da área-teste, menor foi o aprendizado necessário para sua classificação alcançar um acerto
médio final de 87%, frente às classificações da especialista em imagem. A sua eficácia
também foi comprovada frente aos sistemas computacionais testados, com índice Kappa
médio de 0,86.Only in 2011 were acquired over 1.000TB of new digital image registers arising from orbital remote sensing. This range of data, which has a geometric progression increasing, is added annually to an extraordinary and incredible mass of data from existing satellite images of Earth's surface (acquired since the 70s of last century). This massive amount of raw data requires computational tools which allow the automatic recognition of image patterns desired to allow the extraction of geographical objects and targets of interest more quickly and concisely. The proposal for such recognition to be performed automatically through Spectral Analysis and Computational Intelligence integration, based on knowledge acquired by image experts, was implemented as an integrator based on Computational Neural Networks (via Kohonens Self-Organizing Feature Maps - SOM) and Fuzzy Logic (through Mamdani) techniques. These techniques were applied to the spectral signatures pattern formed by the quantization levels or gray levels of the corresponding pattern in each spectral band of each pattern of interest, so that the pattern classification will depend, in an inseparable manner, of the spectral signatures correlation of the six bands of the sensor, like the work of image experts. Bands 1 to 5 and 7 of the Landsat-5 satellite were used for the determination of five classes / targets of interest in cover and land occupation, in three test areas located in the State of Rio de Janeiro (Guaratiba, Mangaratiba and Magé) in this integration with comparison of results with those derived from the interpretation of the imaging expert, which was corroborated by checking the ground truth. There was also a results comparison obtained with two commercial computer systems (IDRISI Taiga and ENVI 4.8) with the integrator, regarding the quality of classification (Kappa) and response time. The integrator, with hybrid classifications (supervised and unsupervised) in its implementation, proved to be effective in multispectral automatic (unsupervised) pattern recognition and in learning of these patterns, because as the input of a new test area occurs, the lower became the process of learning, which achieve a final average accuracy o f 87%, compared to the experts classifications. Its efficacy was also demonstrated compared to systems tested, with average Kappa of 0.86.
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Frey, Elizabeth G. "An examination of distributional assumptions in LANDSAT TM imagery /." Online version of thesis, 1995. http://hdl.handle.net/1850/12253.
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