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Siemon, Noel, and n/a. "Civil remote sensing policy in Australia : a case study concerning the commercialisation of a government-developed technology." University of Canberra. Administrative Studies, 1993. http://erl.canberra.edu.au./public/adt-AUC20061108.154949.
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Drayton, Robert S. "The application of remote sensing to water resources." Thesis, Aston University, 1989. http://publications.aston.ac.uk/14269/.
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Techniques are developed for the visual interpretation of drainage features from satellite imagery. The process of interpretation is formalised by the introduction of objective criteria. Problems of assessing the accuracy of maps are recognized, and a method is developed for quantifying the correctness of an interpretation, in which the more important features are given an appropriate weight. A study was made of imagery from a variety of landscapes in Britain and overseas, from which maps of drainage networks were drawn. The accuracy of the mapping was assessed in absolute terms, and also in relation to the geomorphic parameters used in hydrologic models. Results are presented relating the accuracy of interpretation to image quality, subjectivity and the effects of topography. It is concluded that the visual interpretation of satellite imagery gives maps of sufficient accuracy for the preliminary assessment of water resources, and for the estimation of geomorphic parameters. An examination is made of the use of remotely sensed data in hydrologic models. It is proposed that the spectral properties of a scene are holistic, and are therefore more efficient than conventional catchment characteristics. Key hydrologic parameters were identified, and were estimated from streamflow records. The correlation between hydrologic variables and spectral characteristics was examined, and regression models for streamflow were developed, based solely on spectral data. Regression models were also developed using conventional catchment characteristics, whose values were estimated using satellite imagery. It was concluded that models based primarily on variables derived from remotely sensed data give results which are as good as, or better than, models using conventional map data. The holistic properties of remotely sensed data are realised only in undeveloped areas. In developed areas an assessment of current land-use is a more useful indication of hydrologic response.
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Miller, S. T. "Remote sensing applications to flood hydrology in Belize." Thesis, Aston University, 1986. http://publications.aston.ac.uk/14242/.
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The research compares the usefullness of four remote sensing information sources, these being LANDSAT photographic prints, LANDSAT computer compatible tapes, Metric Camera and SIR-A photographic prints. These sources provide evaluations of the catchment characteristics of the Belize and Sibun river basins in Central America. Map evaluations at 1:250,000 scale are compared to the results of the same scale, remotely sensed information sources. The values of catchment characteristics for both maps and LANDSAT prints are used in multiple regression analysis, providing flood flow formulae, after investigations to provide a suitable dependent variable discharge series are made for short term records. The use of all remotely sensed information sources in providing evaluations of catchment characteristics IS discussed. LANDSAT prints and computer compatible tapes of a post flood scene are used to estimate flood distributions and volumes. These are compared to values obtained from unit hydrograph analysis, using the dependent discharge series and evaluate the probable losses from the Belize river to the floodplain, thereby assessing the accuracy of LANDSAT estimates. Information relating to flood behaviour is discussed in terms of basic image presentation as well as image processing. A cost analysis of the purchase and use of all materials is provided. Conclusions of the research indicate that LANDSAT print material may provide information suitable for regression analysis at levels of accuracy as great as those of topographic maps, that the differing information sources are uniquely applicable and that accurate estimates of flood volumes may be determined even by post flood imagery.
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Primus, Ida. "Scale-recursive estimation of precipitation using remote sensing data." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10852.
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Ahn, Gi-Choul. "Remote sensing and geophysical analysis of the Radian Lineament, Antarctica." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1413284938.
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Konings, Alexandra Georges. "Microwave remote sensing of water in the soil - plant system." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101833.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 171-191).<br>Remotely sensed measurements made by radars or radiometers in the low microwave frequency range are sensitive to soil moisture, soil roughness, and vegetation water content. Measurements made at multiple polarizations can be used to determine additional ancillary parameters alongside the primary variable of interest. However, if an attempt is made to retrieve too many parameters from too few measurements, the resulting retrievals will contain high levels of noise. In this thesis, I introduce a framework to determine an upper bound on the number of geophysical parameters that can be retrieved from remotely sensed measurements such as those made by microwave instruments. The principles behind this framework, as well as the framework itself, are then applied to derive two new ecohydrological variables: a) soil moisture profiles across much of the root-zone and b) vegetation optical depth, which is proportional to vegetation water content. For P-band observations, it is shown that soil moisture variations with depth must be accounted for to prevent large forward modeling - and thus retrieval - errors. A Tikhonov regularization approach is then introduced to allow retrieval of soil moisture in several profile layers by using statistics on the expected co-variation between soil moisture at different depths. The algorithm is tested using observations from the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) Mission over the Harvard Forest in Western Massachusetts. Additionally, at L-band, a multi-temporal algorithm is introduced to determine vegetation optical depth (VOD) alongside soil moisture. The multi-temporal approach used reduces the chance of compensating errors between the two retrieved parameters (soil moisture and vegetation optical depth), caused by small amounts of measurement noise. In several dry tropical ecosystems, the resulting VOD dataset is shown to have opposite temporal behavior to coincident cross-polarized backscattering coefficients, an active microwave indicator of vegetation water content and scattering. This possibly shows dry season bud-break or enduring litter presence in these regions. Lastly, cross-polarized backscattering coefficients are used to test the hypothesis that vegetation water refilling slows down under drought even at the ecosystem scale. Evidence for this hypothesis is only found in the driest location tested.<br>by Alexandra Georges Konings.<br>Ph. D.
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Albanwan, Hessah AMYM. "Remote Sensing Image Enhancement through Spatiotemporal Filtering." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492011122078055.
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Welle, Paul. "Remotely Sensed Data for High Resolution Agro-Environmental Policy Analysis." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1012.
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Policy analyses of agricultural and environmental systems are often limited due to data constraints. Measurement campaigns can be costly, especially when the area of interest includes oceans, forests, agricultural regions or other dispersed spatial domains. Satellite based remote sensing offers a way to increase the spatial and temporal resolution of policy analysis concerning these systems. However, there are key limitations to the implementation of satellite data. Uncertainty in data derived from remote-sensing can be significant, and traditional methods of policy analysis for managing uncertainty on large datasets can be computationally expensive. Moreover, while satellite data can increasingly offer estimates of some parameters such as weather or crop use, other information regarding demographic or economic data is unlikely to be estimated using these techniques. Managing these challenges in practical policy analysis remains a challenge. In this dissertation, I conduct five case studies which rely heavily on data sourced from orbital sensors. First, I assess the magnitude of climate and anthropogenic stress on coral reef ecosystems. Second, I conduct an impact assessment of soil salinity on California agriculture. Third, I measure the propensity of growers to adapt their cropping practices to soil salinization in agriculture. Fourth, I analyze whether small-scale desalination units could be applied on farms in California in order mitigate the effects of drought and salinization as well as prevent agricultural drainage from entering vulnerable ecosystems. And fifth, I assess the feasibility of satellite-based remote sensing for salinity measurement at global scale. Through these case studies, I confront both the challenges and benefits associated with implementing satellite based-remote sensing for improved policy analysis.
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Taherkia, Hassan. "Remote sensing applied to slope stability in mountainous roads in Iran." Thesis, Aston University, 1985. http://publications.aston.ac.uk/14233/.
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The Alborz Mountain range separates the northern part of Iran from the southern part. It also isolates a narrow coastal strip to the south of the Caspian Sea from the Central Iran plateau. Communication between the south and north until the 1950's was via two roads and one rail link. In 1963 work was completed on a major access road via the Haraz Valley (the most physically hostile area in the region). From the begining the road was plagued by accidents resulting from unstable slopes on either side of the valley. Heavy casualties persuaded the government to undertake major engineering works to eliminate ''black spots" and make the road safe. However, despite substantial and prolonged expenditure the problems were not solved and casualties increased steadily due to the increase in traffic using the road. Another road was built to bypass the Haraz road and opened to traffic in 1983. But closure of the Haraz road was still impossible because of the growth of settlements along the route and the need for access to other installations such as the Lar Dam. The aim of this research was to explore the possibility of applying Landsat MSS imagery to locating black spots along the road and the instability problems. Landsat data had not previously been applied to highway engineering problems in the study area. Aerial photographs are better in general than satellite images for detailed mapping, but Landsat images are superior for reconnaissance and adequate for mapping at the 1 :250,000 scale. The broad overview and lack of distortion in the Landsat imagery make the images ideal for structural interpretation. The results of Landsat digital image analysis showed that certain rock types and structural features can be delineated and mapped. The most unstable areas comprising steep slopes, free of vegetation cover can be identified using image processing techniques. Structural lineaments revealed from the image analysis led to improved results (delineation of unstable features). Damavand Quaternary volcanics were found to be the dominant rock type along a 40 km stretch of the road. These rock types are inherently unstable and partly responsible for the difficulties along the road. For more detailed geological and morphological interpretation a sample of small subscenes was selected and analysed. A special deve loped image analysis package was designed at Aston for use on a non specialized computing system. using this package a new and unique method for image classification was developed, allowing accurate delineation of the critical features of the study area.
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Unal, Alper. "MEASUREMENT, ANALYSIS, AND MODELING OF ON-ROAD VEHICLE EMISSIONS USING REMOTE SENSING." NCSU, 1999. http://www.lib.ncsu.edu/theses/available/etd-19990527-104246.
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<p>The main objectives of this research are; to develop on-road emission factor estimates for carbon monoxide (CO) and hydrocarbon (HC) emissions; to collect traffic and vehicle parameters that might be important in explaining variability in vehicle emissions; to develop an empirical traffic-based model that can predict vehicle emissions based upon observable traffic and vehicle parameters. Remote sensing technology were employed to collect exhaust emissions data. Traffic parameters were collected using an area-wide traffic detector, MOBILIZER. During the measurements, license plates were also recorded to obtain information on vehicle parameters. Data were collected at two sites, having different road grades and site geometries, over 10 days of field work at the Research Triangle area of North Carolina. A total of 11,830 triggered measurement attempts were recorded. After post-processing, 7,056 emissions were kept in the data base as valid measurements. After combining with the traffic and license vehicle parameters, a data base has been developed. Exploratory analysis has been conducted to find variables that are important to explain the variability of the emission estimates. Statistical methods were used to compare the mean of the emissions estimates for different sub-populations. For example, multi-comparison analysis has been conducted to compare the mean emissions estimates from vehicles having different model years. This analysis showed that the mean emissions from older vehicles were statistically different than the mean emissions estimates from the recent model year vehicles.One of the contributions of the research was developing an empirical traffic-based emission estimation model. For this purpose, data collected during the study were used to develop a novel model which combines the Hierarchical Tree-Based Regression method and Ordinary Least Squares regression. The key findings from this research include: (1) the measured mean CO emission estimate for Research Triangle park area of North Carolina is estimated as 340 grams/gallon, whereas the mean HC emissions estimate is found to be as 47 grams/gallon (2) inter-vehicle variability in vehicle emissions can be as high as two orders-of-magnitude; (3) intra-vehicle variability is lower compared to the inter-vehicle variability; (4) some vehicle variables such as vehicle model year and vehicle type are important factors in explaining the inter-vehicle variability in emissions estimates; (5) emission estimation model developed in this research can be applied to estimate the emissions from on-road vehicles. <P>
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Zhang, Li. "REMOTE SENSING OF WATER QUALITY IN LAKE ERIE USING MODIS IMAGERY DATA." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1357232811.
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Reichle, Rolf H. (Rolf Helmut) 1968. "Variational assimilation of remote sensing data for land surface hydrologic applications." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/28220.
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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2000.<br>Includes bibliographical references (p. 283-192).<br>Soil moisture plays a major role in the global hydrologic cycle. Most importantly, soil moisture controls the partitioning of available energy at the land surface into latent and sensible heat fluxes. We investigate the feasibility of estimating large-scale soil moisture profiles and related land surface variables from low-frequency (L-band) passive microwave remote sensing observations using weak-constraint variational data assimilation. We extend the iterated indirect representer method, which is based on the adjoint of the hydrologic model, to suit our application. The four-dimensional (space and time) data assimilation algorithm takes into account model and measurement uncertainties and provides optimal estimates by implicitly propagating the full error covariances. Explicit expressions for the posterior error covariances are also derived. We achieve a dynamically consistent interpolation and extrapolation of the remote sensing data in space and time, or equivalently, a continuous update of the model predictions from the data. Our hydrologic model of water and energy exchange at the land surface is expressly designed for data assimilation. It captures the key physical processes while remaining computationally efficient. The assimilation algorithm is tested with a series of experiments using synthetically generated system and measurement noise. In a realistic environment based on the Southern Great Plains 1997 (SGP97) hydrology experiment, we assess the performance of the algorithm under ideal and non ideal assimilation conditions. Specifically, we address five topics which are crucial to the design of an operational soil moisture assimilation system. (1) We show that soil moisture can be satisfactorily estimated at scales finer than the resolution of the brightness images (downscaling), provided sufficiently accurate fine-scale model inputs are available. (2) The satellite repeat cycle should be shorter than the average interstorm period. (3) The loss of optimality by using shorter assimilation intervals is offset by a substantial gain in computational efficiency. (4) Soil moisture can be satisfactorily estimated even if quantitative precipitation data are not available. (5) The assimilation algorithm is only weakly sensitive to inaccurate specification of the soil hydraulic properties. In summary, we demonstrate the feasibility of large-scale land surface data assimilation from passive microwave observations.<br>by Rolf H. Reichle.<br>Ph.D.
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Sivarajan, Saravanan. "Estimating Yield of Irrigated Potatoes Using Aerial and Satellite Remote Sensing." DigitalCommons@USU, 2011. https://digitalcommons.usu.edu/etd/1049.
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Multispectral aerial and satellite remote sensing plays a major role in crop yield prediction due to its ability to detect crop growth conditions on spatial and temporal scales in a cost effective manner. Many empirical relationships have been established in the past between spectral vegetation indices and leaf area index, fractional ground cover, and crop growth rates for different crops through ground sampling. Remote sensing-based vegetation index (VI) yield models using airborne and satellite data have been developed only for grain crops like barley, corn, wheat, and sorghum. So it becomes important to validate and extend the VI-based model for tuber crops like potato, taking into account the most significant parameters that affect the final crop yield of these crops.
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Diaz, Carlos Luis Perez. "Development of a Microwave - Remote Sensing Based Snow Depth Product." Thesis, The City College of New York, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10745516.
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<p> Snow is a key component of the Earth’s energy balance, climate, environment, and a major source of freshwater in many regions. Seasonal and perennial snow cover affect up to 50% of the Northern Hemisphere landmass, which accounts for vast regions of the Earth that influence climate, culture, and commerce significantly. Information on snow properties such as snow cover, depth, and wetness is important for making hydrological forecasts, monitoring climate change, weather prediction, and issuing snowmelt runoff, flash flood, and avalanche warnings. Hence, adequate knowledge of the areal extent of snow and its properties is essential for hydrologists, water resources managers, and decision-makers. </p><p> The use of infrared (IR) and microwave (MW) remote sensing (RS) has demonstrated the capability of estimating the presence of snow cover and snowpack properties with accuracy. However, there are few publicly accessible, operational RS-based snow depth products, and these only provide the depth of recently accumulated dry snow because retrievals lose accuracy drastically for wet snow (late winter - early spring). Furthermore, it is common practice to assume snow grain size and wetness to be constant to retrieve certain snow properties (e.g. snow depth). This approach is incorrect because these properties are space- and time- dependent, and largely impact the MW signal scattering. Moreover, the remaining operational snow depth products have not been validated against in-situ observations; which is detrimental to their performance and future calibrations. </p><p> This study is focused on the discovery of patterns in geospatial data sets using data mining techniques for mapping snow depth globally at 10 km spatial resolution. A methodology to develop a RS MW-based snow depth and water equivalent (SWE) product using regression tree algorithms is developed. The work divided into four main segments includes: (1) validation of RS-based IR and MW-retrieved Land Surface Temperature (LST) products, (2) studying snow wetness by developing, validating, and calibrating a Snow Wetness Profiler, (3) development of a regression tree algorithm capable of estimating snow depth based on radiative (MW observations) and physical snowpack properties, and (4) development of a global MW-RS-based snow depth product built on the regression tree algorithm. </p><p> A predictive model based on Regression Tree (RT) is developed in order to model snow depth and water equivalent at the Cooperative Remote Sensing Science and Technology Center – Snow Analysis and Field Experiment (CREST-SAFE). The RT performance analyzed based on contrasting training error, true prediction error, and variable importance estimates. The RT algorithm is then taken to a broader scale, and Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission – Water 1 (GCOM-W1) MW brightness temperature measurements were used to provide snow depth and SWE estimates. These SD and SWE estimates were evaluated against twelve (12) Snow Telemetry (SNOTEL) sites owned by the National Resources Conservation Service (NRCS) and JAXA’s own snow depth product. Results demonstrated that a RS MW-based RT algorithm is capable of providing snow depth and SWE estimates with acceptable accuracy for the continental United States, with some limitations. The major setback to the RT algorithm is that it will only provide estimates based on the data with which it was trained. Therefore, it is recommended that the work be expanded, and data from additional in-situ stations be used to re-train the RT algorithm. The CREST snow depth and water equivalent product, as it was named, is currently operational and publicly accessible at https://www.noaacrest.org//snow/products/. </p><p>
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Baines, Linda M. "The application of remote sensing to the management of urban wildlife habitats." Thesis, Aston University, 1988. http://publications.aston.ac.uk/14281/.
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The project set out with two main aims. The first aim was to determine whether large scale multispectral aerial photography could be used to successfully survey and monitor urban wildlife habitats. The second objective was to investigate whether this data source could be used to predict population numbers of selected species expected to be found in a particular habitat type. Panchromatic, colour and colour infra-red, 1:2500 scale aerial photographs, taken in 1981 and 1984, were used. For the orderly extraction of information from the imagery, an urban wildlife habitat classification was devised. This was based on classifications already in use in urban environments by the Nature Conservancy Council. Pilot tests identified that the colour infra-red imagery provided the most accurate results about urban wildlife habitats in the study area of the Blackbrook Valley, Dudley. Both the 1981 and 1984 colour infra-red photographs were analysed and information was obtained about the type, extent and distribution of habitats. In order to investigate whether large scale aerial photographs could be used to predict likely animal population numbers in urban environments, it was decided to limit the investigation to the possible prediction of bird population numbers in Saltwells Local Nature Reserve. A good deal of research has already been completed into the development of models to predict breeding bird population numbers in woodland habitats. These models were analysed to determine whether they could be used successfully with data extracted from the aerial photographs. The projects concluded that 1:2500 scale colour infra-red photographs can provide very useful and very detailed information about the wildlife habitats in an urban area. Such imagery can also provide habitat area data to be used with population predictive models of woodland breeding birds. Using the aerial photographs, further investigations into the relationship between area of habitat and the breeding of individual bird species were inconclusive and need further research.
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Fontanet, i. Ambròs Mireia. "Optimal irrigation scheduling combining water content sensors and remote sensing data." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/668901.
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By 2025, the Food and Agriculture Organization of the United Nations predicts that two-thirds of the world population will experience water stress conditions. In addition, it is expected that world population will increase significantly during the following years. Agriculture is the largest consumer of fresh water, estimated 75%. The optimization of its use is therefore crucial for future. The main objective of irrigation scheduling is to guarantee maximum crop yield while saving significant amounts of water. The main difficulty to determine a correct and optimal irrigation scheduling strategy is due to complexity and variability of the Soil-Plant-Atmosphere continuum. In each field site, this continuous SPA has different patterns as a consequence of environmental conditions, crop, soil hydraulic properties and soil tillage. Several tools and methodologies are employed nowadays to monitor an improve irrigation scheduling. However, they all have advantages and limitations, or, each one is used without considering the information provided by the others, simplifying the system and avoiding information.
This thesis aims to improve irrigation scheduling by combining different tools available at the present time. This combination aims to answer different kind of questions when irrigation scheduling in a field site must be defined. For this, each tool has been used and supported by others. In addition, this thesis will discuss when and how these tools might be employed.
Firstly, we compare different tools and methodologies used to measure water content in an agriculture field. Specifically, we compared gravimetric measurements and water content sensors measurements with DISPATCH algorithm data. This algorithm is one of several algorithms who estimates soil surface water content using remote sensing data. The main goal was determining if remote sensing data can improve water content data measured by sensors. We found that DISPATCH algorithm is no capable to improve water content sensors measurements.
Secondly, we present a methodology where a simulation-optimization problem is solved in order to find an optimal irrigation scheduling strategy. This strategy might guarantee the maximum economic net margin. This methodology has been applied in a real field. The optimal irrigation scheduling of this field is compared with the traditional irrigation scheduling method, based water requirements. Results show that even though the traditional method supplies the volume of water evapotranspired, this methodology of scheduling irrigation is not enough to avoid crop water stress, compromising final crop yield. In this case, optimal irrigation strategy improves the final net margin in comparison the traditional method. We demonstrate that depending on soil properties, optimal irrigation scheduling is different.
Thirdly, we improved the irrigation scheduling in a field site where irrigation was applied with the same criteria. In this part of the thesis we employed NDVI remote sensing data, water content sensors and simulation models to determine the optimal irrigation. The improvement is based on management zones delineation with NDVI data. In this case, water content sensors are used to define if each management zone presents different water content patterns and to verify that when the field is divided into different management zones there is a gain in terms of water content uniformity. Finally, an optimal irrigation scheduling calendar is proposed to allow the consultants to take within-season irrigation decisions. Results show that management zones are dynamic during the growing season and the optimal irrigation scheduling might also be dynamic. In addition, we found that the uniform irrigation applied in the entire field, without considering the possible differences in soil properties, produced waterlogging in two management zones, therefore, transpiration decreases in comparison to the others.<br>El 2025 la "Food and Agriculture Organization" de les Nacions Unides prediu que dues terceres parts de la població mundial patirà condicions d'estrés hídric. A més a més, s'espera que la població mundial augmenti els propers anys. L'agricultura és el consumidor principal d'aigua dolça, concretament un 75%. Considerant aquest context, existeix una necessitat important d'optimitzar l'aigua de reg en un futur proper. La programació del reg és l'encarregada de determinar el moment i la quantitat d'aigua que s'ha d'aplicar. El seu objectiu principal és garantir un rendiment màxim del cultiu i a la vegada estalviar aigua. La dificultat principal per determinar l'estratègia de programació de reg correcta i òptima és degut a la complexitat i variabilitat del continu Sòl-Planta-Atmòsfera. Diverses eines i metodologies són emprades avui en dia per monitoritzar i determinar la programació del reg. Malgrat l'àmplia variabilitat de possibilitats, totes elles tenen avantatges i limitacions, o sovint, cada una d'elles és fa servir sense considerar la informació que poden proporcionar les altres, simplificant el sistema i obviant informació. Aquesta tesis vol millorar la programació del reg combinant diferents eines i metodologies que estan disponibles avui en dia. La combinació té com a objectiu satisfer necessitats diferents en el moment que la programación del reg ha de ser definida, destacant les avantatges i minimitzant les limitacions de cada metodologia i eina, així com l'efecte de l'escala. Primerament, hem comparat diferents eines i metodologies per mesurar el contingut d'aigua al sòl en una parcel·la de cultiu. Concretament, s'han comparat mesures gravimètriques i de contingut d'aigua de sensors amb mesures de l'algorisme DISPATCH. Aquest algorisme és un de varis algorismes que estimen el contingut d¿aigua superficial del sòl emprant dades de teledetecció. L'objectiu principal era determinar si les dades de teledetecció poden millorar les dades de contingut d'aigua mesurades pels sensors. El resultats mostren que, ara per ara i considerant les condicions de camp, el DISPATCH no és capaç de millorar les mesures dels sensors de contingut d'aigua al sòl. Després, presentem una metodologia on es soluciona un problema de simulació-optimització per a determinar una estratègia de programació de reg òptim. Aquesta estratègia ha de garantir un rendiment econòmic net màxim. S'ha aplicat en una parcel·la de cultiu real. L'estratègia de programació de reg òptim ha estat comparat amb el mètode tradicional de programació de reg, que està basat amb el càlcul de les necessitats hídriques. Encara que el mètode tradicional reemplaça per complet l'aigua evapotranspirada, els resultats mostren que la manera de repartir l'aigua no evita l'estres hídric del cultiu, disminuint el rendiment. En aquest cas, la programació del reg òptim millora el rendiment econòmic net. Els resultats mostren que depenent el tipus de sol, la programació del reg ha de ser diferent. Finalment, s'ha millorat la programació del reg en una parcel·la de cultiu on el reg s'havia aplicat uniformement en tota la seva extensió. En aquesta part de la tesis, es fan servir dades de teledetecció de NDVI, dades de sensors de contingut d'aigua al sòl i models de simulació per a determinar el reg òptim. La millora està basada en la delineació de zones maneig amb les dades de NDVI. En aquest cas, els sensors s'han emprat per a determinar si casa zona de maneig representa diferents patrons de contingut d'aigua i per a validar si quan la parcel·la és dividida en zones de maneig, la variabilitat disminueix. Finalment, es proposa un calendari de programació de reg òptim per a poder prendre decisions en la campanya de reg. Els resultat mostren que les zones de maneig són dinàmiques així com el reg òptim. A més a més s'ha vist que el reg uniforme produeix asfixia radicular a dues de les zones de maneig, disminueix la transpiració en comparació amb els altres.
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Almamalachy, Yousif. "Utilization of Remote Sensing in Drought Monitoring Over Iraq." Thesis, Portland State University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10283891.
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<p> Agricultural drought is a creeping disaster that overshadows the vegetative cover in general and cropland specifically in Iraq, a country that was well known for its agricultural production and fertile soil. In the recent years, the arable lands in Iraq experienced increasing land degradation that led to desertification, economic losses, food insecurity, and deteriorating environment. Remote sensing is employed in this study and four different indices are utilized, each of which is derived from MODIS satellite mission products. Agricultural drought maps are produced from 2003 to 2015 after masking the vegetation cover. Year 2008 was found the most severe drought year during the study period, where drought covered 37% of the vegetated land. This part of the study demonstrated the capability of remote sensing in fulfilling the need of an early warning system for agricultural drought over such a data-scarce region.</p><p> This study also aims to monitor hydrological drought. The Gravity Recovery and Climate Experiment (GRACE) satellite-derived monthly Terrestrial Water Storage (TWS) is the hydrological drought indicator, that is used to calculate the deficit. Severity of drought events are calculated by integrating monthly water deficit over the drought period. In addition, drought recovery time is assessed depending on the estimated deficit. Major drought events are classified into several levels of severity by applying a drought monograph approach. The results demonstrated that GRACE TWS is a reliable indicator for drought assessment over Iraq, and provides useful information for decision makers which can be utilized in developing drought adaptation and mitigation strategies. </p><p>
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Oldfield, Robin B. "Lithological mapping of Northwest Argentina with remote sensing data using tonal, textural and contextual features." Thesis, Aston University, 1988. http://publications.aston.ac.uk/14287/.
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Tonal, textural and contextual properties are used in manual photointerpretation of remotely sensed data. This study has used these three attributes to produce a lithological map of semi arid northwest Argentina by semi automatic computer classification procedures of remotely sensed data. Three different types of satellite data were investigated, these were LANDSAT MSS, TM and SIR-A imagery. Supervised classification procedures using tonal features only produced poor classification results. LANDSAT MSS produced classification accuracies in the range of 40 to 60%, while accuracies of 50 to 70% were achieved using LANDSAT TM data. The addition of SIR-A data produced increases in the classification accuracy. The increased classification accuracy of TM over the MSS is because of the better discrimination of geological materials afforded by the middle infra red bands of the TM sensor. The maximum likelihood classifier consistently produced classification accuracies 10 to 15% higher than either the minimum distance to means or decision tree classifier, this improved accuracy was obtained at the cost of greatly increased processing time. A new type of classifier the spectral shape classifier, which is computationally as fast as a minimum distance to means classifier is described. However, the results for this classifier were disappointing, being lower in most cases than the minimum distance or decision tree procedures. The classification results using only tonal features were felt to be unacceptably poor, therefore textural attributes were investigated. Texture is an important attribute used by photogeologists to discriminate lithology. In the case of TM data, texture measures were found to increase the classification accuracy by up to 15%. However, in the case of the LANDSAT MSS data the use of texture measures did not provide any significant increase in the accuracy of classification. For TM data, it was found that second order texture, especially the SGLDM based measures, produced highest classification accuracy. Contextual post processing was found to increase classification accuracy and improve the visual appearance of classified output by removing isolated misclassified pixels which tend to clutter classified images. Simple contextual features, such as mode filters were found to out perform more complex features such as gravitational filter or minimal area replacement methods. Generally the larger the size of the filter, the greater the increase in the accuracy. Production rules were used to build a knowledge based system which used tonal and textural features to identify sedimentary lithologies in each of the two test sites. The knowledge based system was able to identify six out of ten lithologies correctly.
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Sharifi, Husham (Husham Shawn) 1972. "Remote information organization and decentralized education." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80185.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Technology and Policy Program, 1999.<br>Includes bibliographical references (p. 79-81).<br>by Husham Sharifi.<br>S.M.
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Ellis, R. J. "Evaluation of remote sensing for the detection of landfill gas and leachate in an urban environment." Thesis, Aston University, 1997. http://publications.aston.ac.uk/14150/.
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The technique of remote sensing provides a unique view of the earth's surface and considerable areas can be surveyed in a short amount of time. The aim of this project was to evaluate whether remote sensing, particularly using the Airborne Thematic Mapper (ATM) with its wide spectral range, was capable of monitoring landfill sites within an urban environment with the aid of image processing and Geographical Information Systems (GIS) methods. The regions under study were in the West Midlands conurbation and consisted of a large area in what is locally known as the Black Country containing heavy industry intermingled with residential areas, and a large single active landfill in north Birmingham. When waste is collected in large volumes it decays and gives off pollutants. These pollutants, landfill gas and leachate (a liquid effluent), are known to be injurious to vegetation and can cause stress and death. Vegetation under stress can exhibit a physiological change, detectable by the remote sensing systems used. The chemical and biological reactions that create the pollutants are exothermic and the gas and leachate, if they leave the waste, can be warmer than their surroundings. Thermal imagery from the ATM (daylight and dawn) and thermal video were obtained and used to find thermal anomalies on the area under study. The results showed that vegetation stress is not a reliable indicator of landfill gas migration, as sites within an urban environment have a cover too complex for the effects to be identified. Gas emissions from two sites were successfully detected by all the thermal imagery with the thermal ATM being the best. Although the results were somewhat disappointing, recent technical advancements in the remote sensing systems used in this project would allow geo-registration of ATM imagery taken on different occasions and the elimination of the effects of solar insolation.
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Jones, Helen K. "The investigation of vegetation change using remote sensing to detect and monitor migration of landfill gas." Thesis, Aston University, 1991. http://publications.aston.ac.uk/14298/.
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Decomposition of domestic wastes in an anaerobic environment results in the production of landfill gas. Public concern about landfill disposal and particularly the production of landfill gas has been heightened over the past decade. This has been due in large to the increased quantities of gas being generated as a result of modern disposal techniques, and also to their increasing effect on modern urban developments. In order to avert diasters, effective means of preventing gas migration are required. This, in turn requires accurate detection and monitoring of gas in the subsurface. Point sampling techniques have many drawbacks, and accurate measurement of gas is difficult. Some of the disadvantages of these techniques could be overcome by assessing the impact of gas on biological systems. This research explores the effects of landfill gas on plants, and hence on the spectral response of vegetation canopies. Examination of the landfill gas/vegetation relationship is covered, both by review of the literature and statistical analysis of field data. The work showed that, although vegetation health was related to landfill gas, it was not possible to define a simple correlation. In the landfill environment, contribution from other variables, such as soil characteristics, frequently confused the relationship. Two sites are investigated in detail, the sites contrasting in terms of the data available, site conditions, and the degree of damage to vegetation. Gas migration at the Panshanger site was dominantly upwards, affecting crops being grown on the landfill cap. The injury was expressed as an overall decline in plant health. Discriminant analysis was used to account for the variations in plant health, and hence the differences in spectral response of the crop canopy, using a combination of soil and gas variables. Damage to both woodland and crops at the Ware site was severe, and could be easily related to the presence of gas. Air photographs, aerial video, and airborne thematic mapper data were used to identify damage to vegetation, and relate this to soil type. The utility of different sensors for this type of application is assessed, and possible improvements that could lead to more widespread use are identified. The situations in which remote sensing data could be combined with ground survey are identified. In addition, a possible methodology for integrating the two approaches is suggested.
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Hyatt, Carly Adeline. "Development and Regional Application of Sub-Seasonal Remote- Sensing Chlorophyll Detection Models." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4390.
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Remote sensing has been used as an effective chlorophyll-a detection method in inland lakes and reservoirs. Concentration estimates of chlorophyll-a approximate the amounts of algae and phytoplankton in a body of water, can indicate the existence of large blooms and high nutrient loading, and can be used as an indicator of water quality. These biomasses pose potential threats to the quality of the water and the local environment by depleting oxygen, influencing the taste of the drinking water and detrimentally affecting aesthetics and recreation. Deer Creek Reservoir exhibited eutrophic tendencies in the early 1990's, caused by phosphorus pollution. This was made evident by accelerated algae growth. Following remediation efforts, Deer Creek Reservoir, as well as nearby Jordanelle Reservoir have been closely monitored with regular field sampling. These field data have been used to develop remote sensing methods using Landsat images to provide supplementary information for reservoir management. These remote sensing methods allow for mapping of the distribution of chlorophyll-a, which provides spatial distribution average, and maximum estimates of chlorophyll-a concentrations, data and information that are not feasible with in-field sampling. In this thesis, traditional methods for remote sensing models are discussed, and a novel sub-seasonal approach based on seasonal algal succession is proposed and demonstrated. Each seasonal model is created using a standard stepwise regression using historic field data and the associated Landsat images and is statistically tested for leverage to ensure unbiased model development. These sub-seasonal detection models are applied to 5 reservoirs in the central-Utah region to provide a more comprehensive description of reservoir behavior and water quality trends over the past 30 years. Historic trends of the average and maximum chlorophyll-a estimates are provided for each of the reservoirs. Example color maps are presented to demonstrate the ability of remote sensing to represent the spatial distribution of algae (using chlorophyll as an indicator). Limitations for this approach are discussed, as well as applications for remotely sensed water quality data on a regional scale.
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Cunha, Luciana Kindl da. "Exploring the benefits of satellite remote sensing for flood prediction across scales." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2848.
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Space-borne remote sensing datasets have the potential to allow us to progress towards global scale flood prediction systems. However, these datasets are limited in terms of space-time resolution and accuracy, and the best use of such data requires understanding how uncertainties propagate through hydrological models. An unbiased investigation of different datasets for hydrological modeling requires a parsimonious calibration-free model, since calibration masks uncertainties in the data and model structure. This study, which addresses these issues, consists of two parts: 1) the development and validation of a multi-scale distributed hydrological model whose parameters can be directly linked to physical properties of the watershed, thereby avoiding the need of calibration, and 2) application of the model to demonstrate how data uncertainties propagate through the model and affect flood simulation across scales.
I based the model development on an interactive approach for model building. I systematically added processes and evaluated their effects on flood prediction across multiple scales. To avoid the need for parameter calibration, the level of complexity in representing physical processes was limited by data availability. I applied the model to simulate flows for the Cedar River, Iowa River and Turkey River basins, located in Iowa. I chose this region because it is rich in high quality hydrological information that can be used to validate the model. Moreover, the area is frequently flooded and was the center of an extreme flood event during the summer of 2008. I demonstrated the model's skills by simulating medium to high-flow conditions; however the model's performance is relatively poor for dry (low flow) conditions. Poor model performance during low flows is attributed to highly nonlinear dynamics of soil and evapotranspiration not incorporated in the model. I applied the hydrological model to investigate the predictability skills of satellite-based datasets and to investigate the model's sensibility to certain hydro-meteorological variables such as initial soil moisture and bias in evapotranspiration. River network structure and rainfall are the main components shaping floods, and both variables are monitored from space. I evaluated different DEM sources and resolution DEMs as well as the effect of pruning small order channels to systematically decreasing drainage density. Results showed that pruning the network has a greater effect on simulated peak flow than the DEM resolution or source, which reveals the importance of correctly representing the river network. Errors on flood prediction depend on basin scale and rainfall intensity and decrease as the basin scale and rainfall intensity increases. In the case of precipitation, I showed that simulated peak flow uncertainties caused by random errors, correlated or not in space, and by coarse space-time data resolution are scale-dependent and that errors in hydrographs decrease as basin scale increases. This feature is significant because it reveals that there is a scale for which less accurate information can still be used to predict floods. However, the analyses of the real datasets reveal the existence of other types of error, such as major overall bias in total volumes and the failure to detect significant rainfall events that are critical for flood prediction.
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El-Dardiry, Hisham Abd El-Kareem. "The Use of Multi-Sensor Quantitative Precipitation Estimates for Deriving Extreme Precipitation Frequencies with Application in Louisiana." Thesis, University of Louisiana at Lafayette, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1585854.
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<p> The Radar-based Quantitative Precipitation Estimates (QPE) is one of the NEXRAD products that are available in a high temporal and spatial resolution compared with gauges. Radar-based QPEs have been widely used in many hydrological and meteorological applications; however, a few studies have focused on using radar QPE products in deriving of Precipitation Frequency Estimates (PFE). Accurate and regionally specific information on PFE is critically needed for various water resources engineering planning and design purposes. This study focused first on examining the data quality of two main radar products, the near real-time Stage IV QPE product, and the post real-time RFC/MPE product. Assessment of the Stage IV product showed some alarming data artifacts that contaminate the identification of rainfall maxima. Based on the inter-comparison analysis of the two products, Stage IV and RFC/MPE, the latter was selected for the frequency analysis carried out throughout the study. The precipitation frequency analysis approach used in this study is based on fitting Generalized Extreme Value (GEV) distribution as a statistical model for the hydrologic extreme rainfall data that based on Annual Maximum Series (AMS) extracted from 11 years (2002-2012) over a domain covering Louisiana. The parameters of the GEV model are estimated using method of linear moments (L-moments). Two different approaches are suggested for estimating the precipitation frequencies; Pixel-Based approach, in which PFEs are estimated at each individual pixel and Region-Based approach in which a synthetic sample is generated at each pixel by using observations from surrounding pixels. The region-based technique outperforms the pixel based estimation when compared with results obtained by NOAA Atlas 14; however, the availability of only short record of observations and the underestimation of radar QPE for some extremes causes considerable reduction in precipitation frequencies in pixel-based and region-based approaches. </p>
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Lee, I.-Chieh. "Instantaneous Shoreline Extraction Utilizing Integrated Spectrum and Shadow Analysis From LiDAR Data and High-resolution Satellite Imagery." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345174939.
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Geli, Hatim M. E. "Modeling Spatial Surface Energy Fluxes of Agricultural and Riparian Vegetation Using Remote Sensing." DigitalCommons@USU, 2012. https://digitalcommons.usu.edu/etd/1165.
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Modeling of surface energy fluxes and evapotranspiration (ET) requires the understanding of the interaction between land and atmosphere as well as the appropriate representation of the associated spatial and temporal variability and heterogeneity. This dissertation provides new methodology showing how to rationally and properly incorporate surface features characteristics/properties, including the leaf area index, fraction of cover, vegetation height, and temperature, using different representations as well as identify the related effects on energy balance flux estimates including ET.
The main research objectives were addressed in Chapters 2 through 4 with each presented in a separate paper format with Chapter 1 presenting an introduction and Chapter 5 providing summary and recommendations. Chapter 2 discusses a new approach of incorporating temporal and spatial variability of surface features. We coupled a remote sensing-based energy balance model with a traditional water balance method to provide improved estimates of ET. This approach was tested over rainfed agricultural fields ~ 10 km by 30 km in Ames, Iowa. Before coupling, we modified the water balance method by incorporating a remote sensing-based estimate for one of its parameters to ameliorate its performance on a spatial basis. Promising results were obtained with indications of improved estimates of ET and soil moisture in the root zone.
The effects of surface features heterogeneity on measurements of turbulence were investigated in Chapter 3. Scintillometer-based measurements/estimates of sensible heat flux (H) were obtained over the riparian zone of the Cibola National Wildlife Refuge (CNWR), California. Surface roughness including canopy height (hc), roughness length, and zero-plane displacement height were incorporated in different ways, to improve estimates of H. High resolution, 1-m maps of ground surface digital elevation model and canopy height, hc, were derived from airborne LiDAR sensor data to support the analysis.
The effects of using different pixel resolutions to account for surface feature variability on modeling energy fluxes, e.g., net radiation, soil, sensible, and latent heat, were studied in Chapter 4. Two different modeling approaches were applied to estimate energy fluxes and ET using high and low pixel resolution datasets obtained from airborne and Landsat sensors, respectively, provided over the riparian zone of the CNWR, California. Enhanced LiDAR-based hc maps were also used to support the modeling process. The related effects were described relative to leaf area index, fraction of cover, hc, soil moisture status at root zone, groundwater table level, and vegetation stress conditions.
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Nchia, David A. "International regulation of civilian remote sensing satellites, 1972-1991 : the role of domestic policy, marketplace, and technology /." The Ohio State University, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487688973684137.
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Yeu, Yeon. "FEATURE EXTRACTION FROM HYPERSPECTRAL IMAGERY FOR OBJECT RECOGNITION." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306848130.
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Yilmaz, Musa. "Active Microwave Remote Sensing Of Soil Moisture: A Case Study In Kurukavak Basin." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12610309/index.pdf.
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Soil moisture condition of a watershed plays a significant role in separation of rainfall into infiltration and surface runoff, and hence is a key parameter for the majority of physical hydrological models. Due to the large difference in dielectric constants of dry soil and water, microwave remote sensing and particularly the commonly available synthetic aperture radar is a potential tool for such studies.
The main aim of this study is to produce the distributed soil moisture maps of a catchment from active microwave imagery. For this purpose, nine field trips are performed within a small basin in western Anatolia and point surface soil moisture values are collected with a Time Domain Reflectometer. The field studies are planned to match radar image acquisitions and accomplished over the water year of 2004 - 2005.
In this context, first, the Dubois Model, a semi-empirical backscatter model is utilized in the reverse order to develop radar backscatter &<br>#8211<br>soil roughness relationship and soil roughness maps of the study area are obtained. Then another relationship is built between radar backscatter and the three governing surface parameters: local incidence angle, soil moisture and soil roughness, which is later used in the soil moisture estimation methods. Depending on land use and vegetation cover condition, surface soil moisture maps of the catchment are produced by Backscatter Correction Factors, Water Cloud Model and Basin Indexes methods.
In the last part of the study, the soil moisture maps of the basin are input to a semi-distributed hydrological model, HEC-HMS, as the initial soil moisture condition of a flood event simulation. In order to investigate the contribution of distributed initial soil moisture data on model outputs, simulation of the same flood event is also performed with the lumped initial soil moisture condition. Finally, a comparison between both the distributed and lumped model simulation outputs and with the observed data is carried out.
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Nim, Carl Johann IV. "THE NATIONAL SEA GRANT COLLEGE PROGRAM DEAN JOHN A. KNAUSS MARINE POLICY FELLOWSHIP: A PROFESSIONAL EXPERIENCE WITH NOAA'S CORAL REEF WATCH." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1304952219.
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Zeichen, Marta Manca. "Protection and management of marine areas in the Mediterranean Sea : applications of satellite remote sensing." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/195605/.
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Marine Protected Areas (MPAs) are recognised globally as effective tools for protecting valuable and vulnerable marine ecosystems (habitats, species and communities), maintaining the biological diversity, and safeguarding the associated historical and cultural resources. MPAs accommodate local communities and regulate the different uses of the sea, fostering more sustainable use of marine resources. Moreover, MPAs are increasingly being used as environmental laboratories, enabling a greater scientific understanding of marine systems. In the Mediterranean Sea about a hundred of MPAs have been designated during the last decades, all but one of which are in coastal areas. This study develops a new way of using RS techniques tailored for the monitoring and management of Mediterranean MPAs. The advance in satellite Remote Sensing (RS) technologies has made possible to look at the MPAs not only by means of discrete in situ surveys but rather on the basis of a “synoptic” and repeated view. The primary aim of this thesis was to establish how the satellite sensors can be successfully used and whether RS provides reliable tools for monitoring and managing Mediterranean MPAs. The study aimed specifically at describing and identifying, by means of passive remote sensors, the spatial and temporal scale of the bio-physical processes occurring in Mediterranean MPAs. Observations retrieved by ocean colour and thermal infra-red sensors, for a range of MPA study sites, were used to depict system functioning by the analysis of the prevailing spatial and temporal variations of the geophysical parameters and biophysical conditions. The seasonal variations of the ecological indicators (i.e. phytoplankton blooms and thermal trends) were analysed over various MPAs located in different regions of the Mediterranean basin, and different biooptical algorithms were tested in a coastal MPA. The short-term and long-term monitoring (interannual) of the ecological indicators is key to elucidating trends and modifications in the biogeochemical balance of the basin possibly caused by environmental changes which could potentially affect the MPA’s resilience. Consequently it is now possible to monitor MPAs easily and at low cost, by integrating RS with the traditional sampling methodologies to work towards safeguarding of valuable marine habitats and species. RS should be considered as key tool that fosters the ecosystem-based management.
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Hajigholizadeh, Mohammad. "Water Quality Modelling Using Multivariate Statistical Analysis and Remote Sensing in South Florida." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2992.
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The overall objective of this dissertation research is to understand the spatiotemporal dynamics of water quality parameters in different water bodies of South Florida. Two major approaches (multivariate statistical techniques and remote sensing) were used in this study. Multivariate statistical techniques include cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), discriminant analysis (DA), absolute principal component score-multiple linear regression (APCS-MLR) and PMF receptor modeling techniques were used to assess the water quality and identify and quantify the potential pollution sources affecting the water quality of three major rivers of South Florida. For this purpose, a 15-year (2000–2014) data set of 12 water quality variables, and about 35,000 observations were used. Agglomerative hierarchical CA grouped 16 monitoring sites into three groups (low pollution, moderate pollution, and high pollution) based on their similarity of water quality characteristics. DA, as an important data reduction method, was used to assess the water pollution status and analysis of its spatiotemporal variation. PCA/FA identified potential pollution sources in wet and dry seasons, respectively, and the effective mechanisms, rules, and causes were explained. The APCS-MLR and PMF models apportioned their contributions to each water quality variable.
Also, the bio-physical parameters associated with the water quality of the two important water bodies of Lake Okeechobee and Florida Bay were investigated based on remotely sensed data. The principal objective of this part of the study is to monitor and assess the spatial and temporal changes of water quality using the application of integrated remote sensing, GIS data, and statistical techniques. The optical bands in the region from blue to near infrared and all the possible band ratios were used to explore the relation between the reflectance of a waterbody and observed data. The developed MLR models appeared to be promising for monitoring and predicting the spatiotemporal dynamics of optically active and inactive water quality characteristics in Lake Okeechobee and Florida Bay. It is believed that the results of this study could be very useful to local authorities for the control and management of pollution and better protection of water quality in the most important water bodies of South Florida.
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Adjei, Zola Yaa. "Using Remote Sensing to Explore the Time History of Emergent Vegetation at Malheur Lake, Oregon." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5647.
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The growth patterns of emergent vegetation can be a useful indicator for factors affecting lake health. However, field data to characterize emergent vegetation at many reservoirs may not be available or may be limited to small, isolated areas. We present a case study using remotely sensed data from the Landsat satellite to generate data to represent emergent vegetation in the near-shoreline and tributary delta areas of Malheur Lake, Oregon. We selected late June images for this study as vegetation is relatively mature in late June and visible, but has not completely grown-in providing a better indication of vegetation coverage in satellite images. We investigated the correlation of vegetation coverage (an indicator of emergent vegetation) with lake area on the day of the satellite collection, average daily maximum temperatures for April, May, June, and July, and average daily precipitation in June, all parameters that could affect vegetation. To estimate historic emergent vegetation extent, we computed the Normalized Difference Vegetation Index (NDVI) for 30 years of Landsat satellite images from 1984 to 2013. Around Malheur Lake we identified eight regions-of-interest (ROI): three inlet areas, three wet-shore areas (swampy areas), and two dry-shore areas (less swampy areas). For each ROI we generated time-series data to quantify the emergent vegetation as determined by the percent of area covered by pixels with NDVI values greater than 0.2. We measured lake area by computing the Modified Normalized Difference Water Index (MNDWI) and computing the area by summing the pixels that indicated water. We compared NDVI time-series values with the time series for lake area, June precipitation, and maximum daily temperatures for April, May, June, and July to determine if these parameters were correlated. Correlation would imply that emergent vegetation was influenced by the parameter. We found that correlations of vegetative extent in any of the eight ROIs with the selected parameters were minimal, indicating that there are other factors besides the ones chosen that drive emergent vegetation levels in Malheur Lake. This study demonstrates that Landsat data have sufficient spatial and temporal detail for quantification and description of ecosystem changes and thus offer a good source of information to understand historic trends in reservoir health. We expect that future work will explore other potential drivers for emergent vegetation extent, such as carp populations in Malheur Lake which are known to affect emergent vegetation. Carp were not considered in this study as we did not have access to data that reflect carp numbers over this 30 year period.
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Tekeli, Ahmet Emre. "Operational Hydrological Forecasting Of Snowmelt Runoff By Remote Sensing And Geographic Information Systems Integration." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606081/index.pdf.
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Snow indicates the potential stored water volume that is an important source of water supply, which has been the most valuable and indispensable natural resource throughout the history of the world. Euphrates and Tigris, having the biggest dams of Turkey, are the two largest trans-boundary rivers that originate in Turkey and pass throughout the water deficit nations Syria, Iran, Iraq and Saudi Arabia bringing life as well as water all their way. Snowmelt runoff originating from the mountains of Eastern Turkey accounts for 60 to 70 % of total annual discharge observed in Euphrates and Tigris. For an optimum operation of the dams, maximizing energy production, mitigation of floods and satisfying water rights, hydrological models which can both simulate and forecast the river discharges of Euphrates and Tigris are needed.
In this study a hydrological model, snowmelt runoff model (SRM), is used in conjunction with remote sensing and geographic information systems to forecast the river discharges in the headwaters of Euphrates River, Upper Euphrates Basin.
NOAA and MODIS satellite images were used to derive the snow covered area (SCA) information required by SRM. Linear reduction methodologies based on accumulated air temperature, with constant or varying gradient, were developed to get the continuous daily SCA values from the discrete daily satellite images.
Temperature and precipitation forecasts were gathered from two different numerical weather prediction models, namely European Center for Medium Range Weather Forecasts (ECMWF) and Mesoscale Model Version 5 (MM5) from Turkish State Meteorological Services. These data sets provided t+24 hour forecasts of both temperature and precipitation.
Temperature, precipitation and SCA information are fed into SRM. Discharge forecasts obtained from the model outputs are compared with the observed values. The overall performance of the model was seen as promising. Possible reasons of the mismatches between the forecasted and observed values are searched. Experiences gained throughout the study are summarized and recommendations on further forecast studies are mentioned.
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Wang, Guiwei. "Automatic information extraction and prediction of karst rocky desertification in Puding using remote sensing data." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-23988.
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Karst rocky desertification (KRD) is one kind of severe environmental problem existing in southwest of China. Reveal KRD condition is vital to solve the problem. A way to address the problem is by identifying KRD areas, so that policy-makers and researchers may get a better view of the issue and know where the areas affected by the problem are located. The study area is called Puding which is a county located in the central part of Guizhou province. Based on Landsat data, by using GIS and RS techniques, KRD information of Puding was extracted. Furthermore, the study monitored decades of change of the environmental problem in Puding and predicted possible condition in the future. Other researchers and decision makers may get a better view of the issue from the study results. In addition to Landsat data, other used data includes: ASTER Global digital elevation model data, Modis data, Google Earth data and other thematic maps. In the study, expert classification system and spectral features based model two methods were applied to extract KRD information and compare with each other. Their classified rules were taken from previous studies separately. Necessary preprocessing procedures such as atmospheric correction and geometrical correction were performed before extraction. After extraction relevant results were evaluated and analyzed. Predictions were made by cellular automata Markov module. Based on extracted KRD results, the distribution, percentage, change, and prediction of KRD conditions in Puding were presented. The results of the accuracy evaluation showed that the spectral features based model had acceptable performance. However, the KRD results extracted by expert classification system method were poor. The extracted KRD results, including KRD maps and the prediction map, both indicated that KRD areas in Puding were decreased from 1993 (spring) to 2016 (spring) and suggested to pay more attention to KRD areas changes with the seasons
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Flores, Alejandro Nicolas. "Hillslope-scale soil moisture estimation with a physically-based ecohydrology model and L-band microwave remote sensing observations from space." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/47734.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.<br>Includes bibliographical references (p. 469-488).<br>Soil moisture is a critical hydrosphere state variable that links the global water, energy, and carbon cycles. Knowledge of soil moisture at scales of individual hillslopes (10's to 100's of meters) is critical to advancing applications such as landslide prediction, rainfall-runoff modeling, and wildland fire fuel load assessment. This thesis develops a data assimilation framework that employs the ensemble Kalman Filter (EnKF) to estimate the spatial distribution of soil moisture at hillslope scales by combining uncertain model estimates with noisy active and passive L-band microwave observations. Uncertainty in the modeled soil moisture state is estimated through Monte Carlo simulations with an existing spatially distributed ecohydrology model. Application of the EnKF to estimate hillslope-scale soil moisture in a watershed critically depends on: (1) identification of factors contributing to uncertainty in soil moisture, (2) adequate representation of the sources of uncertainty in soil moisture, and (3) formulation of an observing system to estimate the geophysically observable quantities based on the modeled soil moisture. Uncertainty in the modeled soil moisture distribution arises principally from uncertainty in the hydrometeorological forcings and imperfect knowledge of the soil parameters required as input to the model. Three stochastic models are used in combination to simulate uncertain hourly hydrometeorological forcings for the model. Soil parameter sets are generated using a stochastic approach that samples low probability but potentially high consequence parameter values and preserves correlation among the parameters. The observing system recognizes the role of the model in organizing the factors effecting emission and reflection of L-band microwave energy and emphasizes the role of topography in determining the satellite viewing geometry at hillslope scales.<br>(cont.) Experiments in which true soil moisture conditions were simulated by the model and used to produce synthetic observations at spatial scales significantly coarser than the model resolution reveal that sequential assimilation of observations improves the hillslope-scale near-surface moisture estimate. Results suggest that the data assimilation framework is an effective means of disaggregating coarse-scale observations according to the model physics represented by the ecohydrology model. The thesis concludes with a discussion of contributions, implications, and future directions of this work.<br>by Alejandro Nicolas Flores.<br>Ph.D.
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Anyintuo, Thomas Becket. "Seepage-Coupled Finite Element Analysis of Stress Driven Rock Slope Failures for BothNatural and Induced Failures." Scholar Commons, 2019. https://scholarcommons.usf.edu/etd/7731.
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Rock slope failures leading to rock falls and rock slides are caused by a multitude of factors, including seismic activity, weathering, frost wedging, groundwater and thermal stressing. Although these causes are generally attributed as separate causes, some of them will often act together to cause rock slope failures. In this work, two of the above factors, seepage of water through cracks and crack propagation due to the after effects of blasting are considered. Their combined impact on the development of rock falls and rock slides is modeled on ANSYS workbench using the Bingham Canyon mine slope failure of 2013 as a case study. Crack path modeling and slope stability analysis are used to show how a combination of crack propagation and seepage of water can lead to weakening of rock slopes and ultimate failure. Based on the work presented here, a simple approach for modeling the development of rock falls and rock slides due to crack propagation and seepage forces is proposed. It is shown how the information from remote sensing images can be used to develop crack propagation paths. The complete scope of this method involves demonstrating the combination of basic remote sensing techniques combined with numerical modeling on ANSYS workbench.
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Kaiser, Stefan. "Legal implications of satellite based communication navigation and surveillance systems for civil aviation." Thesis, McGill University, 1990. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=22385.
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This thesis deals with the legal problems arising from the introduction of satellite based communication, navigation and surveillance systems for civil aviation. The technical innovations are asking for an international institutional implementation, which has not yet started.<br>After a brief look at the technical aspects of the new systems (Chapter II), existing institutional arrangements of international satellite systems, air-navigation infrastructure and air traffic control are outlined (Chapter III). A legal analysis presents the obstacles and alternatives future institutional arrangements will be confronted with, and leads to a definition of the institutional problem (Chapter IV).<br>The core of the thesis is a proposal for regional intergovernmental organizations, which coordinate the operation of aeronautical satellite communications and air traffic control as an intermediary between the States and service providers (Chapter V). Among other problems financing, user charges, and liability are discussed. Legal problems of navigation systems are discussed on the base of the emerging global systems (Chapter VI).
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Ahring, Trevor S. "Phreatophytes in southwest Kansas used as a tool for predicting hydrologic properties." Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1657.
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van, Opstal Jonna D. "Analyzing Irrigation District Water Productivity by Benchmarking Current Operations Using Remote Sensing and Simulation of Alternative Water Delivery Scenarios." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/4920.
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Irrigation systems are designed to deliver water to crops, but their efficacy varies widely due to operational decisions, weather variability, and water availability. The operation of an irrigation system is studied in this dissertation to determine irrigation performance and potential for improvement.Satellite remote-sensing was used to determine inter-annual variability in crop evapotranspiration and link it with weather patterns and operational decisions. A decade was studied to include several dry, wet and average years of snowfall. It was found that the irrigation district has the capacity to buffer a dry year, but crop evapotranspiration patterns indicated that the buffer capacity of the irrigation district is limited in a second dry year.
Studying the current operations of an irrigation system also requires an analysis of the spatial variability within the system to identify potential areas for improvement. Achieving such information is challenging due to the spatial heterogeneity between farm fields. The Ador irrigation system simulation model is used in this study with satellite remote sensing data, which were combined in the calibration and validation process to ease the re-adjustment of management parameters. This approach provides a cost-effective and innovative method for model simulation when field observations are limited.
Alternative water delivery scenarios were simulated with the Ador irrigation system simulation model to quantify changes in the water balance, irrigation performance, and water productivity. Results for implementing a minimal irrigation time indicated that irrigation events occurred with a higher frequency and reduced crop water stress. Water productivity for the irrigation district increased substantially in this scenario, whilst district water savings were achieved by diverting less irrigation water. Advantages are only achieved if farmers collectively make the decision to change.
A water accounting analysis is required to examine if water savings are achieved at basin scale. There is a potential for the rebound effect to occur, which suggests that an increase of water efficiency causes the increase of water consumption. Simulation results indicated that if the efficiency is increased through improvements of the water delivery, the water consumption increased. Water savings achieved by reducing irrigation diversions did not compensate for the decrease in drainage that downstream users depend on.
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Li, Dongyue. "Exploration of the potential for hydrologic monitoring via passive microwave remote sensing with a new footprint-based algorithm." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306249556.
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Evans, David M. "A Spatiotemporal Analysis of Aspen Decline in Southern Utah’s Cedar Mountain, Using Remote Sensing and Geographic Information Systems." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/734.
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Widespread mortality of quaking aspen (Populus tremuloides Michx.) has occurred over large expanses of the Western US during the 20th century. While much of this decline was due to conifer encroachment into seral aspen, significant aspen losses also occurred in areas of persistent aspen and may have been exasperated by drought conditions. Aspen decline has been especially notable at Cedar Mountain, Utah, an area of mostly private land and extensive persistent aspen coverage. The objectives of this study were to create a time series of live and dead aspen cover on the Cedar Mountain landscape, using remotely sensed imagery, and to test whether water stress correlated to the decline therein. To accomplish these objectives, a decision tree classifier was used to classify the Cedar Mountain area into live and dead aspen cover classes for the years 1985, 1990, 1995, 2001, 2005, and 2008. Thereafter, post-classification change analysis was performed to determine areas and time periods of elevated decline. Regression analyses were performed to ascertain correlations between climatic data and percent change in aspen cover. A topographic analysis using zonal statistics was also performed to determine landscape positions where aspen decline is more prevalent. The time series models indicated that aspen decline followed a step-wise pattern with an overall decrease of 23.57 % in aspen cover during a 23-year period. Considerable aspen decline occurred early in the study time frame, with decreases of 1.38 and 1.36 -1 in 1990 and 1995, respectively. The middle period between 1995 and 2001 had no net change in aspen cover. However, the end of the time series showed the greatest decline with decreases of 1.56 and 1.99 % yr-1 in 2005 and 2008, respectively. There was a correlation between percent change in aspen cover and precipitation, suggesting that drought weakens aspen, making it susceptible to future decline. The topographic zonal statistics revealed that drier landscape positions had greater frequencies of dead aspen. The most significant predictor of aspen decline was elevation, which was significantly greater in the live aspen for three of the five years.
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Bachour, Roula. "Modeling and forecasting evapotranspiration for better management of irrigation command areas." DigitalCommons@USU, 2013. https://digitalcommons.usu.edu/etd/2077.
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It has become very crucial to manage water resources to meet the needs of the growing population. In irrigation command areas, and in order to build a better plan to manage service delivery from canals and reservoirs, it is important to build appropriate knowledge of water needs on a field basis. There is often a lag between the order and delivery of water to the field. Knowledge of the crop water requirement at the field level helps the decision maker to make the right choices leading to more efficient handling of the available water. The purpose of this study was to develop methodologies and tools that allow better management of irrigation water and water delivery systems, such as machine learning models that can be used as tools for decision support systems of water management. To achieve better modeling and prediction, wavelet decompositions were explored for their ability to give information about time and frequency changes in the data. Remote sensing approaches were also used for their ability to quantify water requirements at the spatial level. Therefore, this dissertation explored the use of the above-mentioned data tools and techniques to address water management problems. The framework of this dissertation consisted of three components that provide tools to support irrigation system operational decisions. In general, the results for each of the methods developed were satisfactory, relevant, and encouraging. They provided significant potential for improving decision making for real-time applications in irrigation command areas and better management of the water resources.
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Brammeier, John R. "On the performance of X-band dual-polarization radar-rainfall estimation algorithms during the SMAPVEX-16 field campaign." Thesis, University of Iowa, 2019. https://ir.uiowa.edu/etd/6915.
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Soil moisture estimates from space on a continuous spatial domain could afford researchers with insight about agricultural productivity, flood vulnerability, and biological processes. To evaluate satellite soil moisture estimates, the SMAPVEX-16 experiment was one of a suite of verification data collection campaigns for NASA’s Soil Moisture Active Passive satellite. Soil moisture and its role in rainfall partitioning are of great interest to researchers at the Iowa Flood Center [IFC], which was founded in Iowa City, Iowa after a devastating flood event in 2008. A network of two dual-pol capable X-band radar units owned by the IFC, as well as five tipping bucket rain gauges, complemented by 15 from the USDA’s Agricultural Research Service were deployed in Central Iowa from May to August 2016 to monitor precipitation on a fine spatiotemporal domain. The data from this particular experiment was analyzed.
Several radar-rainfall algorithms were assembled with a focus on assimilating multivariate radar data. Different variables allow researchers to overcome problems due to signal attenuation by X-band radars, and process radar observations into rainfall accumulations by several methods popular in the literature. Special techniques for accumulating instantaneous rainfall rates at discrete observation intervals were employed to account for the movement of storms. The rain totals between the observation points were estimated and the accumulations were compared to the rain gauge totals.
Methods of rain rate calculation that assimilate many sources of data, such as radar reflectivity, differential reflectivity, and specific differential phase shift yielded the best results.
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Bateman, Timothy M. "Exploring and Describing the Spatial and Temporal Dynamics of Medusahead in the Channeled Scablands of Eastern Washington Using Remote Sensing Techniques." DigitalCommons@USU, 2017. https://digitalcommons.usu.edu/etd/6896.
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Medusahead is a harmful weed that is invading public lands in the West. The invasion is a serious concern to the public because it can reduce forage for livestock and wildlife, increase fire frequency, alter important ecosystem cycles (like water), reduce recreational activities, and produce landscapes that are aesthetically unpleasing. Invasions can drive up costs that generally require taxpayer’s dollars. Medusahead seedlings typically spread to new areas by attaching itself to passing objects (e.g. vehicles, animals, clothing) where it can quickly begin to affect plants communities. To be effective, management plans need to be sustainable, informed, and considerate to invasion levels across large landscapes. Ecological remote sensing analysis is a method that uses airborne imagery, taken from drones, aircrafts, or satellites, to gather information about ecological systems. This Thesis strived to use remote sensing techniques to identify medusahead in the landscape and its changes through time. This was done for an extensive area of rangelands in the Channel Scabland region of eastern ashington. This Thesis provided results that would benefit land managers that include: 1) a dispersal map of medusahead, 2) a time line of medusahead cover through time, 3) “high risk’ dispersal areas, 4) climatic factors showing an influence on the time line of medusahead, 5) a strategy map that can be utilized by land managers to direct management needs. This Thesis shows how remote sensing applications can be used to detect medusahead in the landscape and understand its invasiveness through time. This information can help create sustainable and effective management plans so land managers can continue to protect and improve western public lands threatened by the invasion of medusahead.
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Reali, Andrea. "Potentialities of Unmanned Aerial Vehicles in Hydraulic Modelling : Drone remote sensing through photogrammetry for 1D flow numerical modelling." Thesis, KTH, Byggvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234306.
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In civil and environmental engineering numerous are the applications that require prior collection of data on the ground. When it comes to hydraulic modelling, valuable topographic and morphology features of the region are one of the most useful of them, yet often unavailable, expensive or difficult to obtain. In the last few years UAVs entered the scene of remote sensing tools used to deliver such information and their applications connected to various photo-analysis techniques have been tested in specific engineering fields, with promising results. The content of this thesis aims contribute to the growing literature on the topic, assessing the potentialities of UAV and SfM photogrammetry analysis in developing terrain elevation models to be used as input data for numerical flood modelling. This thesis covered all phases of the engineering process, from the survey to the implementation of a 1D hydraulic model based on the photogrammetry derived topography The area chosen for the study was the Limpopo river. The challenging environment of the Mozambican inland showed the great advantages of this technology, which allowed a precise and fast survey easily overcoming risks and difficulties. The test on the field was also useful to expose the current limits of the drone tool in its high susceptibility to weather conditions, wind and temperatures and the restricted battery capacity which did not allow flight longer than 20 minutes. The subsequent photogrammetry analysis showed a high degree of dependency on a number of ground control points and the need of laborious post-processing manipulations in order to obtain a reliable DEM and avoid the insurgence of dooming effects. It revealed, this way, the importance of understanding the drone and the photogrammetry software as a single instrument to deliver a quality DEM and consequently the importance of planning a survey photogrammetry-oriented by the adoption of specific precautions. Nevertheless, the DEM we produced presented a degree of spatial resolution comparable to the one high precision topography sources. Finally, considering four different topography sources (SRTM DEM 30 m, lidar DEM 1 m, drone DEM 0.6 m, total station&RTK bathymetric cross sections o.5 m) the relationship between spatial accuracy and water depth estimation was tested through 1D, steady flow models on HECRAS. The performances of each model were expressed in terms of mean absolute error (MAE) in water depth estimations of the considered model compared to the one based on the bathymetric cross-sections. The result confirmed the potentialities of the drone for hydraulic engineering applications, with MAE differences between lidar, bathymetry and drone included within 1 m. The calibration of SRTM, Lidar and Drone based models to the bathymetry one demonstrated the relationship between geometry detail and roughness of the cross-sections, with a global improvement in the MAE, but more pronounced for the coarse geometry of SRTM.
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Ahlmer, Anna-Klara. "Integrating remotely sensed hydrologic parameters into an index of sediment connectivity." Thesis, KTH, Hållbar utveckling, miljövetenskap och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235791.
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The expected increase in precipitation and temperature in Scandinavia, and especially short-time heavy precipitation, will increase the frequency of flooding. Urban areas are the most vulnerable, and specifically, the road infrastructure. The accumulation of large volumes of water and sediments on road-stream intersections gets severe consequences for the road drainage structures. The need for a tool to identify characteristics that impacts the occurrence of flooding, and to predict future event is thus essential. This study integrates the spatial and temporal soil moisture properties into the research about flood prediction methods. Soil moisture data is derived from remote sensing techniques, with focus on the soil moisture specific satellites ASCAT and SMOS. Furthermore, several physical catchments descriptors (PCDs) are used to identify catchment characteristics that are prone to flooding and an inventory of current road drainage facilities are conducted. Finally, the index of sediment connectivity (IC) by Cavalli, Trevisani, Comiti, and Marchi (2013) is implemented to assess the flow of water and sediment within the catchment. A case study of two areas in Sweden, Västra Götaland and Värmland, that was affected by severe flooding in August 2014 are included. The results show that the method with using soil moisture satellite data is promising for the inclusion of soil moisture data into estimations of flooding and the index of sediment connectivity.<br>De förväntade ökningarna i nederbörd och temperatur i Skandinavien, och speciellt extrem korttidsnederbörd, kommer att öka frekvensen av översvämningar. Urbana områden är de mest sårbara, och speciellt väginfrastrukturen. Ackumuleringen av stora volymer av vatten och sediment där väg och vattendrag möts leder till allvarliga konsekvenser för dräneringskonstruktionerna. Behovet av ett verktyg för att identifiera egenskaper som påverkar förekomsten av översvämningar, och för att förutsäga framtida händelser är väsentligt. Studien integrerar markfuktighet både rumsligt och tidsmässigt i forskningen om metoder för översvämningsrisker. Markfuktighetsdata är inkluderat från fjärranalysteknik, med fokus på de specifika satelliterna för markfuktighet, ASCAT och SMOS. Vidare är flertalet faktorer (PCDs) inkluderade för att identifiera egenskaper i avrinningsområden som är benägna till översvämning samt en inventering av nuvarande vägdräneringskonstruktioner. Slutligen är ett index med sediment connectivity (Cavalli et al., 2013) implementerat för att se flödet av vatten och sediment inom avrinningsområdet. En fallstudie med två områden i Sverige, Västra Götaland och Värmland, som drabbades av allvarliga översvämningar i augusti 2014 är inkluderat. Resultaten visar att metoden att använda markfuktighet från satellitdata är lovande för inkludering i uppskattningar av översvämningsrisk och i indexet med sediment connectivity.
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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 Agronomia<br>Computer 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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Johnson, Adam Bradford. "THE USE OF REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEMS TO CREATE LAND USE AND LAND COVER MAPS AND TO DETERMINE THE CHANGES IN THE LAND USE AND LAND COVER OVER A TEN YEAR PERIOD." MSSTATE, 2005. http://sun.library.msstate.edu/ETD-db/theses/available/etd-07072005-193332/.
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Construction of land use and land cover (LULC) maps was accomplished through the use of remote sensing and GIS. Remote sensing and GIS were used to classify 1990 Landsat 5 and 2000 Landsat 7 Mississippi Gulf Coast imagery into six LULC classes: urban, barren, forested vegetation, non-forested vegetation, marsh, and water. An accuracy assessment was performed on the 2000 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the ten year span of time. The paper concludes that remote sensing and GIS can be used to create LULC maps. It also notes that the maps generated can be used to delineate the changes that take place over time.
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Hansen, Christopher Felix. "Lidar Remote Sensing Of Forest Canopy Structure: An Assessment Of The Accuracy Of Lidar And Its Relationship To Higher Trophic Levels." ScholarWorks @ UVM, 2015. http://scholarworks.uvm.edu/graddis/356.
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Light detection and ranging (LiDAR) data can provide detailed information about three-dimensional forest horizontal and vertical structure that is important to forest productivity and wildlife habitat. Indeed, LiDAR data have been shown to provide accurate estimates to forest structural parameters and measures of higher trophic levels (e.g., avian abundance and diversity). However, links between forest structure and tree function have not been evaluated using LiDAR. This study was designed and scaled to assess the relationship of LiDAR to multiple aspects of forest structure and higher trophic levels (arthropod and bird populations), which included the ground-based collection of percent crown and understory closure, as well as arthropod and avian abundance and diversity data. Additional plot-based measures were added to assess the relationship of LiDAR to forest health and productivity. High-resolution discrete-return LiDAR data (flown summer of 2009) were acquired for the Hubbard Brook Experimental Forest (HBEF) in New Hampshire, USA. LiDAR data were classified into four canopy structural categories: 1) high crown and high understory closure, 2) high crown and low understory closure, 3) low crown and high understory closure, and 4) low crown and low understory closure. Nearby plots from each of the four LiDAR categories were grouped into "blocks" to assess the spatial consistency of data. Ground-based measures of forest canopy structure, site, stand and individual tree measures were collected on nine 50 m-plots from each LiDAR category (36 plots total), during summer of 2012. Analysis of variance was used to assess the relationships between LiDAR and a suite of tree function measures. Our results show the novel ability of LiDAR to assess forest health and productivity at the stand and individual tree level. We found significant correspondence between LiDAR categories and our ground-based measures of tree function, including xylem increment growth, foliar nutrition, crown health, and stand mortality. Furthermore, we found consistent reductions in xylem increment growth, decreases in foliar nutrition and crown health, and increases in stand mortality related to high understory closure. This suggests that LiDAR measures can reflect competitive interactions, not just among overstory trees for light, but also interactions between overstory trees and understory vegetation for resources other than light (e.g., nutrients). High-resolution LiDAR data show promise in the assessment of forest health and productivity related to tree function.