Gotowe bibliografie tematyczne / Remote-sensing maps

Gotowa bibliografia na temat „Remote-sensing maps”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 19 lutego 2022

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Artykuły w czasopismach na temat "Remote-sensing maps"

Asylbekova, A., i A. Kikkarina. "Updating topographic maps using Remote Sensing". Journal of Geography and Environmental Management 42, nr 1 (2016): 168–74. http://dx.doi.org/10.26577/jgem.2016.1.296.

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Villmann, Thomas, Erzsébet Merényi i Barbara Hammer. "Neural maps in remote sensing image analysis". Neural Networks 16, nr 3-4 (kwiecień 2003): 389–403. http://dx.doi.org/10.1016/s0893-6080(03)00021-2.

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Zeug, Gunter, i Olaf Kranz. "Remote Sensing Based Population Maps for Crisis Response". Photogrammetrie - Fernerkundung - Geoinformation 2010, nr 1 (1.02.2010): 33–46. http://dx.doi.org/10.1127/1432-8364/2010/0038.

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King, Trude V. V., Raymond F. Kokaly, Todd M. Hoefen i Michaela R. Johnson. "Hyperspectral remote sensing data maps minerals in Afghanistan". Eos, Transactions American Geophysical Union 93, nr 34 (21.08.2012): 325–26. http://dx.doi.org/10.1029/2012eo340002.

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Babayev, A. G., i N. G. Kharin. "REMOTE SENSING METHODS FOR COMPILING MAPS OF DESERTIFICATION". Mapping Sciences and Remote Sensing 26, nr 4 (październik 1989): 325–30. http://dx.doi.org/10.1080/07493878.1989.10641780.

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Bedell, Richard. "Remote Sensing in Mineral Exploration". SEG Discovery, nr 58 (1.07.2004): 1–14. http://dx.doi.org/10.5382/segnews.2004-58.fea.

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ABSTRACT The proliferation of remote sensing platforms has resulted in unprecedented opportunities for ore deposit vectoring. Importantly, remote sensing technology is now beyond the vague identifıcation of alteration, and can accurately map specifıc minerals and directly contribute to the understanding of ore systems. Remote sensing is making discoveries of new alteration zones within classic and previously well mapped ore systems, as well as outlining their geometry and mineralogy. Confıning this review to the geologically important reflected-light remote sensing systems, there are four main categories of sensors readily available to economic geologists, including the following: (1) submeter resolution panchromatic satellites that offer little spectral information but provide base maps; (2) multispectral Landsat satellites that can map iron and clay alteration; (3) the new ASTER satellite that can map important alteration groups and some specifıc minerals; and (4) hyperspectral airborne scanners that can provide maps of specifıc mineral species important to detailed alteration mapping. At the core of comprehending this plethora of technology is the difference between spectral and spatial resolution. This review will provide an understanding of the more fundamental aspects of remote sensing systems that will help fıeld geologists to interact better with and leverage this rapidly evolving technology.

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Yakubov, Gayrat, Khamid Mubarakov, Ilkhomjon Abdullaev i Azizjon Ruziyev. "Creating large-scale maps for agriculture using remote sensing". E3S Web of Conferences 227 (2021): 03002. http://dx.doi.org/10.1051/e3sconf/202122703002.

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Reliable information on the real state of agricultural lands will be required to the development of appropriate measures for the rational use of agricultural lands. To obtain such information, it is necessary to keep permanent and systematic records and inventories of land resources. Large-scale special plans and maps will be required for accounting, inventory and classification of agricultural land. Currently in Uzbekistan such cartographic materials are being created on the scale 1: 10 000 and 1: 25 000 by administrative and territorial units, farms or individual land plots. The article considers the issues of creation of special maps of agricultural land in scale 1:10000 on the example of Sharof Rashidov district of Jizzakh region using remote sensing data with very high spatial resolution KOMPSAT-3.

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Zhu, Zhiqin, Yaqin Luo, Guanqiu Qi, Jun Meng, Yong Li i Neal Mazur. "Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution". Remote Sensing 13, nr 16 (6.08.2021): 3104. http://dx.doi.org/10.3390/rs13163104.

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Remote sensing images have been widely used in military, national defense, disaster emergency response, ecological environment monitoring, among other applications. However, fog always causes definition of remote sensing images to decrease. The performance of traditional image defogging methods relies on the fog-related prior knowledge, but they cannot always accurately obtain the scene depth information used in the defogging process. Existing deep learning-based image defogging methods often perform well, but they mainly focus on defogging ordinary outdoor foggy images rather than remote sensing images. Due to the different imaging mechanisms used in ordinary outdoor images and remote sensing images, fog residue may exist in the defogged remote sensing images obtained by existing deep learning-based image defogging methods. Therefore, this paper proposes remote sensing image defogging networks based on dual self-attention boost residual octave convolution (DOC). Residual octave convolution (residual OctConv) is used to decompose a source image into high- and low-frequency components. During the extraction of feature maps, high- and low-frequency components are processed by convolution operations, respectively. The entire network structure is mainly composed of encoding and decoding stages. The feature maps of each network layer in the encoding stage are passed to the corresponding network layer in the decoding stage. The dual self-attention module is applied to the feature enhancement of the output feature maps of the encoding stage, thereby obtaining the refined feature maps. The strengthen-operate-subtract (SOS) boosted module is used to fuse the refined feature maps of each network layer with the upsampling feature maps from the corresponding decoding stage. Compared with existing image defogging methods, comparative experimental results confirm the proposed method improves both visual effects and objective indicators to varying degrees and effectively enhances the definition of foggy remote sensing images.

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Huang, Liang, Qiuzhi Peng i Xueqin Yu. "Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering". Journal of Spectroscopy 2020 (23.03.2020): 1–9. http://dx.doi.org/10.1155/2020/2725186.

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In order to improve the change detection accuracy of multitemporal high spatial resolution remote-sensing (HSRRS) images, a change detection method of multitemporal remote-sensing images based on saliency detection and spatial intuitionistic fuzzy C-means (SIFCM) clustering is proposed. Firstly, the cluster-based saliency cue method is used to obtain the saliency maps of two temporal remote-sensing images; then, the saliency difference is obtained by subtracting the saliency maps of two temporal remote-sensing images; finally, the SIFCM clustering algorithm is used to classify the saliency difference image to obtain the change regions and unchange regions. Two data sets of multitemporal high spatial resolution remote-sensing images are selected as the experimental data. The detection accuracy of the proposed method is 96.17% and 97.89%. The results show that the proposed method is a feasible and better performance multitemporal remote-sensing image change detection method.

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Anugraha, A. S., i H. J. Chu. "LAND USE CLASSIFICATION FROM COMBINED USE OF REMOTE SENSING AND SOCIAL SENSING DATA". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (19.09.2018): 33–39. http://dx.doi.org/10.5194/isprs-archives-xlii-4-33-2018.

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<p><strong>Abstract.</strong> Large amounts of data can be sensed and analyzed to discover patterns of human behavior in cities for the benefit of urban authorities and citizens, especially in the areas of traffic forecasting, urban planning, and social science. In New York, USA, social sensing, remote sensing, and urban land use information support the discovery of patterns of human behavior. This research uses two types of openly accessible data, namely, social sensing data and remote sensing data. Bike and taxi data are examples of social sensing data, whereas sentinel remote sensed imagery is an example of remote sensing data. This research aims to sense and analyze the patterns of human behavior and to classify land use from the combination of remote sensing data and social sensing data. A decision tree is used for land use classification. Bike and taxi density maps are generated to show the locations of people around the city during the two peak times. On the basis of a geographic information system, the maps also reflect the residential and office areas in the city. The overall accuracy of land use classification after the consideration of social sensing data is 85.3%. The accuracy assessment shows that the combination of remote sensing data and social sensing data facilitates accurate urban land use classification.</p>

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Rozprawy doktorskie na temat "Remote-sensing maps"

Kannich, Rosene. "Automated selection of topographic base information for thematic maps". Thesis restricted. Connect to e-thesis to view abstract, 2007. http://theses.gla.ac.uk/544/.

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Thesis (MSc(R)) - University of Glasgow, 2007.
MSc(R) thesis submitted to the Faculty of Physical Sciences, Department of Geographical and Earth Sciences, University of Glasgow, 2007. Includes bibliographical references. Print version also available.

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Horstman, Kevin Charles. "Geological, remote sensing, and geophysical investigation of the greater Arivaca region, Pima and Santa Cruz counties, Arizona". Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/565552.

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Necsoiu, Dorel Marius. "A Data Fusion Framework for Floodplain Analysis using GIS and Remotely Sensed Data". Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2557/.

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Throughout history floods have been part of the human experience. They are recurring phenomena that form a necessary and enduring feature of all river basin and lowland coastal systems. In an average year, they benefit millions of people who depend on them. In the more developed countries, major floods can be the largest cause of economic losses from natural disasters, and are also a major cause of disaster-related deaths in the less developed countries. Flood disaster mitigation research was conducted to determine how remotely sensed data can effectively be used to produce accurate flood plain maps (FPMs), and to identify/quantify the sources of error associated with such data. Differences were analyzed between flood maps produced by an automated remote sensing analysis tailored to the available satellite remote sensing datasets (rFPM), the 100-year flooded areas "predicted" by the Flood Insurance Rate Maps, and FPMs based on DEM and hydrological data (aFPM). Landuse/landcover was also examined to determine its influence on rFPM errors. These errors were identified and the results were integrated in a GIS to minimize landuse / landcover effects. Two substantial flood events were analyzed. These events were selected because of their similar characteristics (i.e., the existence of FIRM or Q3 data; flood data which included flood peaks, rating curves, and flood profiles; and DEM and remote sensing imagery.) Automatic feature extraction was determined to be an important component for successful flood analysis. A process network, in conjunction with domain specific information, was used to map raw remotely sensed data onto a representation that is more compatible with a GIS data model. From a practical point of view, rFPM provides a way to automatically match existing data models to the type of remote sensing data available for each event under investigation. Overall, results showed how remote sensing could contribute to the complex problem of flood management by providing an efficient way to revise the National Flood Insurance Program maps.

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Umbert, Ceresuela Marta. "Exploiting the multiscale synergy among ocean variables : application to the improvement of remote sensing salinity maps". Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/321115.

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Les imatges de teledetecció de la superfície oceànica proporcionen una vista sinòptica de la complexa geometria de la circulació oceànica, dominada per la variabilitat de mesoescala. Estructures com filaments i vòrtex són presents en els diferents escalars advectats pel flux oceànic. L’origen més probable d’aquestes estructures és el caràcter turbulent dels corrents, aquestes estructures són persistents amb el temps i compatibles amb la dinàmica mesoscalar oceànica. A escales espacials de quilòmetres o més, la turbulència és principalment 2D, i una complexa geometria, plena de filaments i remolins de mides diferents, emergeix en les imatges superficials de teledetecció de concentració de clorofil·la-a, salinitat superficial, així com en altres escalars més coneguts com són la temperatura superficial i la topografia dinàmica. L’objectiu d’aquesta tesi és explorar i aplicar metodologies de mapatge que permeten millorar la qualitat de mapes de teledetecció oceànica en general, i en particular de la salinitat superficial del mar (SSS). Les diferents metodologies emprades en aquesta tesi han estat aplicades amb l’objectiu específic de millorar els mapes de teledetecció de salinitat superficial del mar proveïts per la missió SMOS de l’Agència Espaial Europea. SMOS és el primer satèl·lit capaç de mesurar la humitat del sol i salinitat oceànica des de l’espai a escala global. La primera part d’aquesta tesi se centra a analitzar les característiques dels productes de nivell 2 (L2) de salinitat de SMOS i produir mapes de nivell 3 (L3) de salinitat utilitzant aproximacions clàssiques: millora del filtratge, mitjana ponderada i Interpolació Òptima. En el curs de la nostra recerca obtenim un conjunt de recomanacions de com processar les dades de SMOS començant des del nivell L2. Aquesta tesi també presenta una nova tècnica de fusió de dades que permet explotar les estructures turbulentes comunes entre diferents variables oceàniques, representant un pas endavant en la cadena de processat per generar mapes de nivell 4 (L4). Aquesta tècnica de fusió es basa teòricament en les propietats geomètriques dels traçadors advectats per la dinàmica oceànica (Turiel et al., 2005a). Degut a l’efecte de forta cissalla als fluits turbulents, l’estructura espacial d’un traçador oceànic hereta algunes propietats del flux subjacent, i en particular el seu arranjament geomètric. Com a conseqüència, les diferents variables oceàniques mostren propietats d’escala similars a la cascada d’energia turbulenta (Seuront and Schmitt, 2005; Nieves et al., 2007; Nieves and Turiel, 2009; Isern-Fontanet et al., 2007). El mètode de fusió agafa un senyal de menor qualitat (afectat per soroll, forats de dades i/o de resolució més baixa) i en millora la seva qualitat. A més d’això, el mètode de fusió és capaç d’extrapolar les dades de forma geofísicament coherent. Aquesta millora del senyal s’aconsegueix utilitzant una altra variable oceànica adquirida amb major qualitat, cobertura espacial més gran i/o millor resolució. Un punt clau d’aquesta aproximació és la suposició de l’existència d’una estructura multifractal de les imatges de teledetecció oceànica (Lovejoy et al., 2001b), i que les línies de singularitat de les diferents variables de l’oceà coincideixen. Sota aquestes premises, els gradients de les dues variables a fusionar estan relacionats per una matriu suau. Com a primera i simple aproximació, s’assumeix que aquesta matriu és proporcional a la identitat; això porta a un esquema de regressió lineal local. Aquesta tesi mostra que aquesta aproximació senzilla permet reduir l’error i millorar la cobertura del producte de nivell 4 resultant. D’altra banda, s’obté informació sobre la relació estadística entre les dues variables fusionades, ja que la dependència funcional entre elles es determina per cada punt de la imatge.
Remote sensing imagery of the ocean surface provides a synoptic view of the complex geometry of ocean circulation, which is dominated by mesoscale variability. The signature of filaments and vortices is present in different ocean scalars advected by the oceanic flow. The most probable origin of the observed structures is the turbulent character of ocean currents, and those signatures are persistent over time scales compatible with ocean mesoscale dynamics. At spatial scales of kilometers or more, turbulence is mainly 2D, and a complex geometry, full of filaments and eddies of different sizes, emerges in remote sensing images of surface chlorophyll-a concentration and surface salinity, as well as in other scalars acquired with higher quality such as surface temperature and absolute dynamic topography. The aim of this thesis is to explore and apply mapping methodologies to improve the quality of remote sensing maps in general, but focusing in the case of remotely sensed sea surface salinity (SSS) data. The different methodologies studied in this thesis have been applied with the specific goal of improving surface salinity maps generated from data acquired by the European Space Agency's mission SMOS, the first satellite able to measure soil moisture and ocean salinity from space at a global scale. The first part of this thesis will introduce the characteristics of the operational SMOS Level 2 (L2) SSS products and the classical approaches to produce the best possible SSS maps at Level 3 (L3), namely data filtering, weighted average and Optimal Interpolation. In the course of our research we will obtain a set of recommendations about how to process SMOS data starting from L2 data. A fusion technique designed to exploit the common turbulent signatures between different ocean variables is also explored in this thesis, in what represents a step forward from L3 to Level 4 (L4). This fusion technique is theoretically based on the geometrical properties of advected tracers. Due to the effect of the strong shear in turbulent flows, the spatial structure of tracers inherit some properties of the underlying flow and, in particular, its geometrical arrangement. As a consequence, different ocean variables exhibit scaling properties, similar to the turbulent energy cascade. The fusion method takes a signal affected by noise, data gaps and/or low resolution, and improves it in a geophysically meaningful way. This signal improvement is achieved by using an appropriate data, which is another ocean variable acquired with higher quality, greater spatial coverage and/or finer resolution. A key point in this approach is the assumption of the existence of a multifractal structure in ocean images, and that singularity lines of the different ocean variables coincide. Under these assumptions, the horizontal gradients of both variables, signal and template, can be related by a smooth matrix. The first, simplest approach to exploit such an hypothesis assumes that the relating matrix is proportional to the identity, leading to a local regression scheme. As shown in the thesis, this simple approach allows reducing the error and improving the coverage of the resulting Level 4 product; Moreover, information about the statistical relationship between the two fields is obtained since the functional dependence between signal and template is determined at each point.

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Tyoda, Zipho. "Landslide susceptibility mapping : remote sensing and GIS approach". Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79856.

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Thesis (MSc)--Stellenbosch University, 2013.
Landslide susceptibility maps are important for development planning and disaster management. The current synthesis of landslide susceptibility maps largely applies GIS and remote sensing techniques. One of the most critical stages on landslide susceptibility mapping is the selection of landslide causative factors and weighting of the selected causative factors, in accordance to their influence to slope instability. GIS is ideal when deriving static factors i.e. slope and aspect and most importantly in the synthesis of landslide susceptibility maps. The integration of landslide causative thematic maps requires the selection of the weighting method; in order to weight the causative thematic maps in accordance to their influence to slope instability. Landslide susceptibility mapping is based on the assumption that future landslides will occur under similar circumstances as historic landslides. The weight of evidence method is ideal for landslide susceptibility mapping, as it calculates the weights of the causative thematic maps using known landslides points. This method was applied in an area within the Western Cape province of South Africa, the area is known to be highly susceptible to landslide occurrences. A prediction rate of 80.37% was achieved. The map combination approach was also applied and achieved a prediction rate of 50.98%. Satellite remote sensing techniques can be used to derive the thematic information needed to synthesize landslide susceptibility maps and to monitor the variable parameters influencing landslide susceptibility. Satellite remote sensing techniques can contribute to landslide investigation at three distinct phases namely: (1) detection and classification of landslides (2) monitoring landslide movement and identification of conditions leading up to an event (3) analysis and prediction of slope failures. Various sources of remote sensing data can contribute to these phases. Although the detection and classification of landslides through the remote sensing techniques is important to define landslide controlling parameters, the ideal is to use remote sensing data for monitoring of areas susceptible to landslide occurrence in an effort to provide an early warning. In this regard, optical remote sensing data was used successfully to monitor the variable conditions (vegetation health and productivity) that make an area susceptible to landslide occurrence.

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Aleong-Mackay, Kathryn. "Landsat imagery and small-scale vegetation maps : data supplementation and verification : a case study of the Maralal area, northern Kenya". Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66182.

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Camacho, Mark A. "Depth analysis of Midway Atoll using Quickbird multi-spectral imaging over variable substrates". Thesis, Monterey California. Naval Postgraduate School, 2006. http://hdl.handle.net/10945/2674.

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Shallow water bathymetry is important for both safe navigation and natural resource management purposes. Extracting depth information from spectral imagery allows identification of benthic features and characterization of coral reef habitats, especially in remote islands. Techniques have been developed to extract water depth from multispectral imagery (Lyzenga, 1978; Philpot, 1989). These techniques can be difficult to apply in optically shallow waters with heterogeneous bottom types and varying albedo, and require tuning of multiple parameters. An improved algorithm to extract water depth from multispectral satellite imagery was proposed by Stumpf et al. (2003) to generate bathymetric maps with limited a priori information. The algorithm is based on the ratios of transformed reflectance values in the visible bands, retrieving greater depths than previous algorithms and compensating for variable bottom type and albedo. This method requires fewer tunable parameters and can be applied to low-albedo features. Although Stumpf et al. (2003) conclude that the method is robust and works well over variable bottom types, recent studies have pointed out limitations, mostly attributable to varying albedo (Clark, 2005; Densham, 2005). This research attempts to quantify the contribution of variable benthic substrates to the algorithmâ s accuracy by classifying the scene into its main bottom types and tuning the coefficients separately. The algorithm is evaluated using a QuickBird high resolution multispectral image of the remote Midway Atoll, in the Northwestern Hawaiian Islands. Classifying the image into two main bottom types and tuning the coefficients separately produced a small improvement in the accuracy of the bathymetric estimates when bottom reflectance is included as a factor. This result indicates that Stumpf et al. (2003)â s ratio method is not insensitive to variable bottom type, and that knowledge of the distribution and extent of different benthic substrates in optically shallow waters has the potential to improve bathymetric derivation in remote coastal areas such as coral reef environments in the Pacific.

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Park, Kyoung Jin. "Generating Thematic Maps from Hyperspectral Imagery Using a Bag-of-Materials Model". The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366296426.

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Baxter, Katrina. "Linking seafloor mapping and ecological models to improve classification of marine habitats : opportunities and lessons learnt in the Recherche Archipelago, Western Australia". University of Western Australia. School of Plant Biology, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0181.

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[Truncated abstract] Spatially explicit marine habitat data is required for effective resource planning and management across large areas, although mapped boundaries typically lack rigour in explaining what factors influence habitat distributions. Accurate, quantitative methods are needed. In this thesis I aimed to assess the utility of ecological models to determine what factors limit the spatial extent of marine habitats. I assessed what types of modeling methods were able to produce the most accurate predictions and what influenced model results. To achieve this, initially a broad scale marine habitat survey was undertaken in the Recherche Archipelago, on the south coast of Western Australia using video and sidescan sonar. Broad and more detailed functional habitats types were mapped for 1054km2 of the Archipelago. Broad habitats included high and low profile reefs, sand, seagrass and extensive rhodolith beds, although considerable variation could be identified from video within these broad types. Different densities of seagrass were identified and reefs were dominated by macroalgae, filter feeder communities, or a combination of both. Geophysical characteristics (depth, substrate, relief) and dominant benthic biota were recorded and then modelled using decision trees and a combination of generalised additive models (GAMs) and generalised linear models (GLMs) to determine the factors influencing broad and functional habitat variation. Models were developed for the entire Archipelago (n=2769) and a subset of data in Esperance Bay (n=797), which included exposure to wave conditions (mean maximum wave height and mean maximum shear stress) calculated from oceanographic models. Additional distance variables from the mainland and islands were also derived and used as model inputs for both datasets. Model performance varied across habitats, with no one method better than the other in terms of overall model accuracy for each habitat type, although prevalent classes (>20%) such as high profile reefs with macroalgae and dense seagrass were the most reliable (Area Under the Curve >0.7). ... This highlighted not only issues of data prevalence, but also how ecological models can be used to test the reliability of classification schemes. Care should be taken when mapping predicted habitat occurrence with broad habitat models. It should not be assumed that all habitats within the type will be defined spatially, as this may result in the distribution of distinctive and unique habitats such as filterfeeders being underestimated or not identified at all. More data is needed to improve prediction of these habitats. Despite the limitations identified, the results provide direction for future field sampling to ensure appropriate variables are sampled and classification schemes are carefully designed to improve descriptions of habitat distributions. Reliable habitat models that make ecological sense will assist future assessments of biodiversity within habitats as well as provide improved data on the probability of habitat occurrence. This data and the methods developed will be a valuable resource for reserve selection models that prioritise sites for management and planning of marine protected areas.

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Carmody, James Daniel Physical Environmental &amp Mathematical Sciences Australian Defence Force Academy UNSW. "Deriving bathymetry from multispectral and hyperspectral imagery". Awarded by:University of New South Wales - Australian Defence Force Academy. School of Physical, Environmental and Mathematical Sciences, 2007. http://handle.unsw.edu.au/1959.4/38654.

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Knowledge of water depth is a crucial for planning military amphibious operations. Bathymetry from remote sensing with multispectral or hyperspectral imagery provides an opportunity to acquire water depth data faster than traditional hydrographic survey methods without the need to deploy a hydrographic survey vessel. It also provides a means of collecting bathymetric data covertly. This research explores two techniques for deriving bathymetry and assesses them for use by those involved in providing support to military operations. To support this aim a fieldwork campaign was undertaken in May, 2000, in northern Queensland. The fieldwork collected various inherent and apparent water optical properties and was concurrent with airborne hyperspectral imagery collection, space-based multispectral imagery collection and a hydrographic survey. The water optical properties were used to characterise the water and to understand how they affect deriving bathymetry from imagery. The hydrographic data was used to assess the performance of the bathymetric techniques. Two methods for deriving bathymetry were trialled. One uses a ratio of subsurface irradiance reflectance at two wavelengths and then tunes the result with known water depths. The other inverts the radiative transfer equation utilising the optical properties of the water to derive water depth. Both techniques derived water depth down to approximately six to seven metres. At that point the Cowley Beach waters became optically deep. Sensitivity analysis of the inversion method found that it was most sensitive to errors in vertical attenuation Kd and to errors in transforming the imagery into subsurface irradiance reflectance, R(0-) units. Both techniques require a priori knowledge to derive depth and a more sophisticated approach would be required to determine water depth without prior knowledge of the area of interest. This research demonstrates that water depth can be accurately mapped with optical techniques in less than ideal optical conditions. It also demonstrates that the collection of inherent and apparent optical properties is important for validating remotely sensed imagery.

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Książki na temat "Remote-sensing maps"

Yao gan zhuan ti fen xi yu di xue tu pu. Beijing: Ke ue chu ban she, 2002.

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Nelson, Elizabeth. BOREAS hardcopy maps. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 2000.

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Beckel, Lothar. Österreich: Satelliten-Bild-Atlas. Salzburg: Druckhaus Nonntal-Bücherdienst, 1988.

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SanFilipo, John R. Satellite image maps of Pakistan. [Reston, VA: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Taiwan gang wan ji hai an shu wei tu xiang zi liao ku jian li zhi yan jiu. Taibei Shi: Jiao tong bu yun shu yan jiu suo, 2002.

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Gao fen bian lü yao gan ying xiang zhong dao lu ti qu fang fa de yan jiu. Beijing: Ke xue chu ban she, 2012.

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Survey, United States Geological. Controlled photomosaic of the MTM 20197 quadrangle, Orcus Patera region of Mars. Reston, VA: U.S. Geological Survey, 1995.

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United States Geological Survey. Controlled photomosaic of the MTM 45002 quadrangle, Acidalia Planitia region of Mars. Reston, VA: U.S. Geological Survey, 1995.

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Pohn, Howard A. Radar and landsat lineament maps of the Glens Falls 1 x 2 quadrangle, New York, Vermont, and New Hampshire. [Denver, Colo.?]: Dept. of the Interior, U.S. Geological Survey, 1986.

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Pohn, Howard A. Radar and landsat lineament maps of the Glens Falls 1p0s x 2p0s quadrangle, New York, Vermont, and New Hampshire. [Denver, Colo.?]: Dept. of the Interior, U.S. Geological Survey, 1986.

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Części książek na temat "Remote-sensing maps"

Czaplewski, Raymond L. "Accuracy Assessment of Maps of Forest Condition". W Remote Sensing of Forest Environments, 115–40. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0306-4_5.

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Popelka, Stanislav. "Eye-Tracking Evaluation of Non-Photorealistic Maps of Cities and Photo-Realistic Visualization of an Extinct Village". W Remote Sensing and Cognition, 87–109. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351040464-5.

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Stefanidis, C. N., i A. P. Cracknell. "Self Organised Maps: the Combined Utilisation of Feature and Novelty Detectors". W Neurocomputation in Remote Sensing Data Analysis, 242–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59041-2_27.

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Kinne, Stefan. "Remote sensing data combinations: superior global maps for aerosol optical depth". W Satellite Aerosol Remote Sensing over Land, 361–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-69397-0_12.

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Stolte, Nilo. "Visualizing Remotely Sensed Depth Maps using Voxels". W Machine Vision and Advanced Image Processing in Remote Sensing, 170–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60105-7_15.

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Boekaerts, P., E. Nyssen i J. Cornelis. "A Comparative Study of Topological Feature Maps Versus Conventional Clustering for (Multi-Spectral) Scene Identification in METEOSAT Imagery". W Neurocomputation in Remote Sensing Data Analysis, 232–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59041-2_26.

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Millhauser, John K., i Christopher T. Morehart. "The Ambivalence of Maps: A Historical Perspective on Sensing and Representing Space in Mesoamerica". W Digital Methods and Remote Sensing in Archaeology, 247–68. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40658-9_11.

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Babonis, Gregory S., i Matthew W. Becker. "Use of Multifrequency Synthetic Aperture Radar (SAR) to Support Regional-Scale Groundwater Potential Maps". W Remote Sensing of the Terrestrial Water Cycle, 383–95. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118872086.ch23.

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Erener, Arzu, Gulcan Sarp i Muhammet İbrahim Karaca. "Building Hights and Floor Estimation Using 3D Maps, Central Part of Kucukcekmece, Istanbul, Turkey". W Advances in Remote Sensing and Geo Informatics Applications, 159–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01440-7_37.

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Marques, Nuno C., i Ning Chen. "Border Detection on Remote Sensing Satellite Data Using Self-Organizing Maps". W Progress in Artificial Intelligence, 294–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24580-3_35.

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Streszczenia konferencji na temat "Remote-sensing maps"

Parkes, Stephen D., Matthew F. McCabe, Samir K. Al-Mashhawari i Jorge Rosas. "Reproducibility of crop surface maps extracted from Unmanned Aerial Vehicle (UAV) derived digital surface maps". W SPIE Remote Sensing, redaktorzy Christopher M. U. Neale i Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2241280.

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Marangi, Carmela, Licinio Angelini, F. De Carlo, Giuseppe Nardulli, M. Pellicoro i Sebastiano Stramaglia. "Clustering by inhomogeneous chaotic maps in landmine detection". W Europto Remote Sensing, redaktor Sebastiano B. Serpico. SPIE, 2001. http://dx.doi.org/10.1117/12.413888.

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Alonso-Benito, Alfonso, Pedro A. Hernandez-Leal, Manuel Arbelo, Alejandro Gonzalez-Calvo, Jose A. Moreno-Ruiz i Jose R. Garcia-Lazaro. "Satellite image based methods for fuels maps updating". W SPIE Remote Sensing, redaktorzy Christopher M. U. Neale i Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2241990.

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Kressler, Florian, Klaus Steinnocher, Andreas Busch, André Streilein i Michael Franzen. "Supporting the update of maps by object-oriented classification of orthophotos". W Remote Sensing, redaktorzy Manfred Ehlers i Ulrich Michel. SPIE, 2006. http://dx.doi.org/10.1117/12.688843.

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Villa, Alberto, Jocelyn Chanussot, Jon Atli Benediktsson i Christian Jutten. "Supervised super-resolution to improve the resolution of hyperspectral images classification maps". W Remote Sensing, redaktor Lorenzo Bruzzone. SPIE, 2010. http://dx.doi.org/10.1117/12.864938.

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Nasr, Ayman H. "Comparative study for the DEM generation from RADARSAT stereoscopic data and topographic maps". W Remote Sensing, redaktor Francesco Posa. SPIE, 2005. http://dx.doi.org/10.1117/12.620609.

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Binaghi, Elisabetta, Pietro A. Brivio, Pier P. Ghezzi i Anna Rampini. "Assessing the accuracy of soft thematic maps using fuzzy set-based error matrices". W Remote Sensing, redaktor Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373256.

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Ibarrola-Ulzurrun, Edurne, Consuelo Gonzalo-Martin i Javier Marcello-Ruiz. "Influence of pansharpening techniques in obtaining accurate vegetation thematic maps". W SPIE Remote Sensing, redaktorzy Ulrich Michel, Karsten Schulz, Manfred Ehlers, Konstantinos G. Nikolakopoulos i Daniel Civco. SPIE, 2016. http://dx.doi.org/10.1117/12.2241501.

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Soares, Fernando, i Giovanni Nico. "Waterline extraction in optical images and InSAR coherence maps based on the geodesic time concept". W Remote Sensing, redaktor Lorenzo Bruzzone. SPIE, 2010. http://dx.doi.org/10.1117/12.864687.

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Nikolakopoulos, Konstantinos, Dimitris Vaiopoulos i Georgios Skianis. "SRTM DTM versus one created from 1/50.000 topographic maps: the case of Kos Island". W Remote Sensing, redaktor Francesco Posa. SPIE, 2005. http://dx.doi.org/10.1117/12.626794.

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Raporty organizacyjne na temat "Remote-sensing maps"

Toutin, Th. Map Making with Remote Sensing Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219719.

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Dysart, Mary D. Remote Sensing and Mass Migration Policy Development. Fort Belvoir, VA: Defense Technical Information Center, luty 2011. http://dx.doi.org/10.21236/ada562466.

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Dysart, Mary D. Remote Sensing and Mass Migration Policy Development. Fort Belvoir, VA: Defense Technical Information Center, październik 2012. http://dx.doi.org/10.21236/ada567838.

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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot i in. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, grudzień 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.

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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.

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Graham, D. F., i A. Ciesielski. Combining field observations and remote sensing to map the Grenville Front along the Pascagama River, Quebec. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/193842.

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Suir, Glenn, Christina Saltus i Sam Jackson. Remote Assessment of Swamp and Bottomland Hardwood Habitat Condition in the Maurepas Diversion Project Area. Engineer Research and Development Center (U.S.), sierpień 2021. http://dx.doi.org/10.21079/11681/41563.

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This study used high spatial resolution satellite imagery to identify and map Bottomland Hardwood (BLH) BLH and swamp within the Maurepas Diversion Project area and use Light Detection and Ranging (Lidar) elevation data, vegetation indices, and established stand-level thresholds to evaluate the condition of forested habitat. The Forest Condition methods and data developed as part of this study provide a remote sensing-based supplement to the field-based methods used in previous studies. Furthermore, several advantages are realized over traditional methods including higher resolution products, repeatability, improved coverage, and reduced effort and cost. This study advances previous methods and provides products useful for informing ecosystem decision making related to environmental assessments.

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Suhartono, Suhartono, Agoes Soegianto i Achmad Amzeri. Mapping of land potentially for maize plant in Madura Island-Indonesia using remote sensing data and geographic information systems (GIS). EM International, listopad 2020. http://dx.doi.org/10.21107/amzeri.2020.1.

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Maize productivity in Indonesia was still low (5.241 tons/ha) compared to the average of the ten largest maize producing countries in the world (6.179 tons/ha). The potential for maize on the island of Madura is approximately 360,000 hectares. The potential for maize cultivation in Madura continues to decrease in land quality due to improper land clearing and land-use change. The purpose of this research was to make a map of land suitability for maize using Remote Sensing Data and Geographic Information System (GIS). The land suitability method for maize plants used satellite imagery as a data source, supported by fieldwork and secondary data. Data analysis using Geographic Information Systems (GIS). The results of the analysis of land suitability modeling based on agroecosystem potential found that most of the Madura area was suitable for maize cultivation. Madura island had a land area of 456,622.3ha for maize cultivation, where 170.379.5 (15.4%) was very appropriate, 211.412.3 ha (46.3%) was appropriate, 160,098.6 (35.1%) was less appropriate, and 14,732.0 ha (3.2%) was not appropriate.

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Douglas, Thomas A., Christopher A. Hiemstra, Stephanie P. Saari, Kevin L. Bjella, Seth W. Campbell, M. Torre Jorgenson, Dana R. N. Brown i Anna K. Liljedahl. Degrading Permafrost Mapped with Electrical Resistivity Tomography, Airborne Imagery and LiDAR, and Seasonal Thaw Measurements. U.S. Army Engineer Research and Development Center, lipiec 2021. http://dx.doi.org/10.21079/11681/41185.

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Accurate identification of the relationships between permafrost extent and landscape patterns helps develop airborne geophysical or remote sensing tools to map permafrost in remote locations or across large areas. These tools are particularly applicable in discontinuous permafrost where climate warming or disturbances such as human development or fire can lead to rapid permafrost degradation. We linked field-based geophysical, point-scale, and imagery surveying measurements to map permafrost at five fire scars on the Tanana Flats in central Alaska. Ground-based elevation surveys, seasonal thaw-depth profiles, and electrical resistivity tomography (ERT) measurements were combined with airborne imagery and light detection and ranging (LiDAR) to identify relationships between permafrost geomorphology and elapsed time since fire disturbance. ERT was a robust technique for mapping the presence or absence of permafrost because of the marked difference in resistivity values for frozen versus unfrozen material. There was no clear relationship between elapsed time since fire and permafrost extent at our sites. The transition zone boundaries between permafrost soils and unfrozen soils in the collapse-scar bogs at our sites had complex and unpredictable morphologies, suggesting attempts to quantify the presence or absence of permafrost using aerial measurements alone could lead to incomplete results. The results from our study indicated limitations in being able to apply airborne surveying measurements at the landscape scale toward accurately estimating permafrost extent.

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Wilkinson, L., P. Budkewitsch, D. F. Graham, J. Henderson i M. D'Iorio. Alternative methods of base map generation using remote sensing and GIS: a pilot study in the western Churchill Province, Northwest Territories. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1997. http://dx.doi.org/10.4095/208634.

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Pokrzywinski, Kaytee, Kaitlin Volk, Taylor Rycroft, Susie Wood, Tim Davis i Jim Lazorchak. Aligning research and monitoring priorities for benthic cyanobacteria and cyanotoxins : a workshop summary. Engineer Research and Development Center (U.S.), sierpień 2021. http://dx.doi.org/10.21079/11681/41680.

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In 2018, the US Army Engineer Research and Development Center partnered with the US Army Corps of Engineers–Buffalo District, the US Environmental Protection Agency, Bowling Green State University, and the Cawthron Institute to host a workshop focused on benthic and sediment-associated cyanobacteria and cyanotoxins, particularly in the context of harmful algal blooms (HAB). Technical sessions on the ecology of benthic cyanobacteria in lakes and rivers; monitoring of cyanobacteria and cyanotoxins; detection of benthic and sediment-bound cyanotoxins; and the fate, transport, and health risks of cyanobacteria and their associated toxins were presented. Research summaries included the buoyancy and dispersal of benthic freshwater cyanobacteria mats, the fate and quantification of cyanotoxins in lake sediments, and spatial and temporal variation of toxins in streams. In addition, summaries of remote sensing methods, omic techniques, and field sampling techniques were presented. Critical research gaps identified from this workshop include (1) ecology of benthic cyanobacteria, (2) identity, fate, transport, and risk of cyanotoxins produced by benthic cyanobacteria, (3) standardized sampling and analysis protocols, and (4) increased technical cooperation between government, academia, industry, nonprofit organizations, and other stakeholders. Conclusions from this workshop can inform monitoring and management efforts for benthic cyanobacteria and their associated toxins.

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