Relevant bibliographies by topics / Landsat satellites Remote sensing

Academic literature on the topic 'Landsat satellites Remote sensing'

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Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Landsat satellites Remote sensing"

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Lodwick, G. D., and S. H. Paine. "SATELLITE REMOTE SENSING IN SURVEYING PRESENT OPPORTUNITIES, FUTURE POSSIBILITIES." Canadian Surveyor 40, no. 3 (September 1986): 315–26. http://dx.doi.org/10.1139/tcs-1986-0025.

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Of all the areas of the earth sciences affected by satellite remote sensing, the surveying profession has been one of the last to take advantage of its unique features. This is due in part to: resolution limitations of Landsat 1, 2 and 3, difficulties in registration and positioning of the imagery, technical constraints in handling vast quantities of digital data, and the excellent methods currently available for the production of cartographic products. Nevertheless, satellite remote sensing has now emerged as a complementary procedure to many existing techniques utilized in surveying and mapping. Already, Landsat is being used for topographic mapping, hydrographic surveying and resource mapping purposes. However, with the improved resolution of Landsat 4, the potential of stereoscopic coverage with the SPOT satellites and present developments in computer processing and data manipulation, satellite remote sensing in the next decade will emerge as an indispensable tool for mapping and cartography.
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Arifjanov, Aybek, Shamshodbek Akmalov, Shakhzod Shodiev, and Abdukarim Haitov. "Discussion of different Remote sensing satellite possibilities for scientifical Earth observations." E3S Web of Conferences 264 (2021): 04007. http://dx.doi.org/10.1051/e3sconf/202126404007.

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More than 1,000 satellites are launched into space, and they differ in their functions, rotation orbits, resolution, and other properties. Scientists divide the satellites into low-resolution, medium-resolution, high-resolution, and very high-resolution satellites by their properties. Now, the biggest challenge facing scientists is to use some of these different resolution images in their field. To get the expected result, it is very important to analyze the image that needs an which gives more accurate results. Therefore, the main attention of this article is aimed to find the answer to these problems. In this article 3 satellite images which have different resolution are analyzed. The possibility of middle-resolution images of MODIS, high-resolution images of Landsat, and very high-resolution images of WorldView-2 (WV-2) satellites using GIS are analyzed. A research area was the Syrdarya region, and downloaded different images of satellites of this area and compared with using e Cognition. According to the results, a more accurate satellite image for irrigation sets information is WorldView-2 images. In comparison analysis, it shows more accurate properties than other satellite images. As irrigation sets are small objects for the analysis, very high spatial resolution satellite images are important. Water discharge and surface change happen very fast; thus, it requires daily monitoring of the condition. And in this case, the temporal resolution of the MODIS and Landsat is 16 day, and it is a too long period.
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Ezzell, Carol. "Landsat remote sensing project short of funds for satellites." Nature 325, no. 6101 (January 1987): 187. http://dx.doi.org/10.1038/325187a0.

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Bedell, Richard. "Remote Sensing in Mineral Exploration." SEG Discovery, no. 58 (July 1, 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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Mycke-Dominko, Małgorzata. "The Remote Sensing Method of Forest Fire Danger Rating Categorization." Miscellanea Geographica 11, no. 1 (December 1, 2004): 359–62. http://dx.doi.org/10.2478/mgrsd-2004-0038.

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Abstract The article presents the use of satellite images in the determination of forest fire danger rating categories. The assessment was carried out based on images from the LANDSAT TM, IKONOS and NOAA satellites, with the finding that the LANDSAT TM images are the most useful. A new solution proposed is to make forest fire danger rating categories refer to forest ranger sub-districts, what gives the forest service greater control over forest fire prevention activities. Forest fire danger assessment was done taking into account remote sensing indices such as the NDVI, TNDVI, and IHT, as well by the analysis of the spatial distribution and the number of fires in the previous six years. In accordance with the Polish State Forest Classification System, three classes were specified: 1 – high fire danger, 2 – moderate fire danger, 3 – low fire danger.
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Orlov, P. Yu, M. A. Boyarchuk, I. G. Zhurkin, and V. V. Nekrasov. "Development of geo-information technique and experimental studies on cross-calibration of Kanopus-V spacecraft series’ RSE sensors." Geodesy and Cartography 966, no. 12 (January 20, 2021): 31–42. http://dx.doi.org/10.22389/0016-7126-2020-966-12-31-42.

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Cross-calibration of the Earth’s remote sensing payload is an addition to the traditionally used flight calibration. It consists of homogeneous terrain regions` image acquiring with a calibrated and reference apparatus and comparing the measured values of the spectral radiance. When selecting references for cross-calibration, the main requirements are the proximity of the spatial resolution and spectral channels of the satellite payload, as well as the observation conditions. Remote sensing spacecrafts Sentinel-2A / 2B and Landsat 8 were selected asreferences. An algorithm was developed to search for intersections of Earth remote sensing satellites ground tracks, which enables finding the parts of the Earth’s surface observed from satellites involved in calibration at a time difference not exceeding 30 minutes. Prediction of satellite paths is carried out using the analytical propagation model SGP4, and two-line element sets of orbital parameters (TLE) taken from open sources. Using the obtained intersection points of propagated ground tracks, the Kanopus-V grouping survey was planned and the corresponding materials by foreign systems were obtained. Basing on them, spectral radiance values obtained by calibrating satellites were compared showing the result of less than 10 % discrepancy.
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Laidler, Gita J., and Paul Treitz. "Biophysical remote sensing of arctic environments." Progress in Physical Geography: Earth and Environment 27, no. 1 (March 2003): 44–68. http://dx.doi.org/10.1191/0309133303pp358ra.

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Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical measures derived from local sites, to landscape and even regional scales. The most common investigations include mapping spatial vegetation patterns or assessing biophysical tundra characteristics, using medium resolution satellite data. For instance, Landsat TM data have been shown to be useful for broad vegetation mapping and analysis, but not accurately representative of smaller vegetation communities or local spatial variation. It is anticipated, that high spatial resolution remote sensing data, now available from commercial remote sensing satellites, will provide the necessary sampling scale to link field data to remotely sensed reflectance data. As a result, it is expected that these data will improve the representation of biophysical variables over sparsely vegetated regions of the Arctic.
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Silva, Isadora Taborda, Jéssica Rabito Chaves, Helen Rezende Figueiredo, Bruno Silva Ferreira, César Claudio Cáceres Encina, Dhonatan Diego Pessi, Normandes Matos Silva, Eliane Guaraldo, and Antonio Conceição Paranhos Filho. "Identification of Continental Wetlands Using Different Orbital Remote Sensors." Terr Plural 15 (2021): 1–25. http://dx.doi.org/10.5212/terraplural.v.15.2115518.001.

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This paper evaluates the potential of false-color composite images, from 3 different remote sensing satellites, for the identification of continental wetlands. Landsat 8, Sentinel-2 and CBERS-4 scenes from three different Ramsar sites (i.e., sites designated to be of international importance) two sites located within the Mato-Grossense Pantanal and one within the Sul-mato-grossense were used for analyses. For each site, images from both the dry and rainy seasons were analyzed using Near-Infrared (NIR), Shortwave Infrared (SWIR), and visible (VIS) bands. The results show that false-color composite images from both the Landsat 8 and the Sentinel-2 satellites, with both SWIR 2-NIR-BLUE and NIR-SWIR-RED spectral band combinations, allow the identification of wetlands.
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Rodrigues, Jose Carlos Verle, Michael H. Cosh, E. Raymond Hunt, Gilberto J. de Moraes, Geovanny Barroso, William A. White, and Ronald Ochoa. "Tracking Red Palm Mite Damage in the Western Hemisphere Invasion with Landsat Remote Sensing Data." Insects 11, no. 9 (September 11, 2020): 627. http://dx.doi.org/10.3390/insects11090627.

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Red palm mites (Raoiella indica Hirst, Acari: Tenuipalpidae) were first observed in the western hemisphere on the islands and countries surrounding the Caribbean Sea, infesting the coconut palm (Cocos nucifera L.). Detection of invasive pests usually relies upon changes in vegetation properties as result of the pest activity. These changes may be visible in time series of satellite data records, such as Landsat satellites, which have been available with a 16-day repeat cycle at a spatial resolution of 30 m since 1982. Typical red palm mite infestations result in the yellowing of the lower leaves of the palm crown; remote sensing model simulations have indicated that this feature may be better detected using the green normalized difference vegetation index (GNDVI). Using the Google Earth Engine programming environment, a time series of Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus and Landsat 8 Operational Land Imager data was generated for plantations in northern and northeast Brazil, El Salvador, and Trinidad-Tobago. Considering the available studied plantations, there were little or no differences of GNDVI before and after the dates when red palm mites were first revealed at each location. A discussion of possible alternative approaches are discussed related to the limitations of the current satellite platforms.
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Fichtelmann, B., E. Borg, and E. Schwarz. "The Influence of the Time Equation on Remote Sensing Data Interpretation." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 195–201. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-195-2015.

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The interpretation of optical Earth observation data (remote sensing data from satellites) requires knowledge of the exact geographic position of each pixel as well as the exact local acquisition time. But these parameters are not available in each case. If a satellite has a sun-synchronous orbit, equator crossing time (ECT) can be used to determine the local crossing time (LCT) and its corresponding solar zenith distance. Relation between local equator crossing time (LECT) and LCT is given by orbit geometry. The calculation is based on ECT of satellite. The method of actual ECT determination for different satellites on basis of the two-line-elements (TLE), available for their full lifetime period and with help of orbit prediction package is well known. For land surface temperature (LST) studies mean solar conditions are commonly used in the relation between ECT given in Coordinated Universal Time (UTC) and LECT given in hours, thus neglecting the difference between mean and real Sun time (MST, RST). Its difference is described by the equation of time (ET). Of particular importance is the variation of LECT during the year within about ±15 minutes. This is in each case the variation of LECT of a satellite, including satellites with stable orbit as LANDSAT (L8 around 10:05 a.m.) or ENVISAT (around 10:00 a.m.). In case of NOAA satellites the variation of LECT is overlaid by a long-term orbital drift. Ignatov et al. (2004) developed a method to describe the drift-based variation of LECT that can be viewed as a formal mathematical approximation of a periodic function with one or two Fourier terms. But, nevertheless, ET is not included in actual studies of LST. Our paper aims to demonstrate the possible influence of equation of time on simple examples of data interpretation, e.g. NDVI.
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Dissertations / Theses on the topic "Landsat satellites Remote sensing"

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Rauchmiller, Robert Frank 1959. "MEASUREMENT OF THE LANDSAT THEMATIC MAPPER MTF USING A TWO-DIMENSIONAL PHASED ARRAY OF POINT SOURCES (MODULATION TRANSFER, SATTELITE, POINT SPREAD FUNCTION)." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/276361.

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Padula, Francis P. "Historic thermal calibration of Landsat 5 TM through an improved physics based approach /." Online version of thesis, 2008. http://hdl.handle.net/1850/7833.

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KASTNER, CAROL JANE. "IN-FLIGHT ABSOLUTE RADIOMETRIC CALIBRATION OF THE LANDSAT THEMATIC MAPPER (WHITE SANDS, NEW MEXICO)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/188121.

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The in-flight absolute radiometric calibration of the Thematic Mapper (TM) is being conducted using the results of field measurements at White Sands, New Mexico. These measurements are made to characterize the ground and atmosphere at the time the TM is acquiring an image of White Sands. The data are used as input to a radiative transfer code that computes the radiance at the entrance pupil of the TM. The calibration is obtained by comparing the digital counts associated with the TM image of the measured ground site with the radiative transfer code result. The calibrations discussed here are for the first four visible and near-infrared bands of the TM. In this dissertation the data reduction for the first calibration attempts on January 3, 1983, and July 8, 1984, is discussed. Included are a review of radiative transfer theory and a discussion of model atmospheric parameters as defined for the White Sands area. These model parameters are used to assess the errors associated with the calibration procedure. Each input parameter to the radiative transfer code is varied from its model value in proportion to the uncertainty with which it can be determined. The effects of these uncertainties on the predicted radiances are determined. It is thought that the optical depth components τ(Ray), τ(Mie), τ(oz), and τ(H₂O) can be measured to within 10%, 2%, 10%, and 30%, respectively. For the white gypsum sand, surface reflectance uniformity is on the order of 1.5%, and the overall uncertainty in measured reflectance is about 2%. This is due to an uncertainty in the reflectance factor of the calibration plates. The greatest uncertainty in calibration is attributed to our uncertainty in the aerosol parameters, in particular the imaginary component of refractive index. The cumulative effect of these uncertainties is thought to produce an uncertainty in computed radiance of about 5%.
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Turner, Anthony Michael Carleton University Dissertation Geography. "Forest clearcut mapping in Northern Ontario using LANDSAT thematic mapper imagery: a user-oriented approach." Ottawa, 1988.

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Witman, Sandra Lynn 1958. "RADIOMETRIC CALIBRATION OF THE THEMATIC MAPPER 48-INCH DIAMETER SPHERICAL INTEGRATING SOURCE (48-SIS) USING TWO DIFFERENT CALIBRATION METHODS." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/275523.

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

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Benvenuti, Fernando Aparecido. "Relação de indices espectrais de vegetação com a produtividade da cana-de-açucar e atributos edaficos." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/257007.

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Orientador: Mara de Andrade Marinho Weill
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola
Made available in DSpace on 2018-08-06T21:36:04Z (GMT). No. of bitstreams: 1 Benvenuti_FernandoAparecido_M.pdf: 3756556 bytes, checksum: 1f05bbbc450fed0290775bc42c39fc0d (MD5) Previous issue date: 2005
Mestrado
Planejamento e Desenvolvimento Rural Sustentável
Mestre em Engenharia Agrícola
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Gallie, Elizabeth Ann. "Chromaticity analysis of LANDSAT Multispectral Scanner and Thematic Mapper imagery of Chilko Lake, British Columbia, using a theoretical optical water quality model." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/30572.

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Chromaticity analysis of LANDSAT Multispectral Scanner (MSS) imagery of Chilko Lake, B.C. reveals a. locus whose shape has not been previously reported. To investigate the cause of this and to come to a broader understanding of chromaticity analysis for MSS and Thematic Mapper (TM) data, an optical water quality model has been used. The model is composed of a four component reflectance model (R-model), an interface model and an atmospheric model. The R-model was calibrated for Chilko Lake by determining the specific absorption and backscattering spectra for suspended minerals (SM), chlorophyll-a uncorrected for phaeophytins (C) and yellow substance (YS). The fourth component is water. The model reproduces the observed locus shape and indicates that it is primarily a function of SM, with the unreported lower limb on MSS imagery caused by SM gradients with concentrations less than 1-2 mg/L. The effects of C, YS and SM cannot be separated on plots of chromaticity coordinates X and Y for either MSS or TM data. In addition, haze or wind gradients, if they occur over water with low levels of SM, would look similar to the lower limb on MSS XY plots. However, if brightness is used in combination with X, the model predicts that C and YS, though themselves inseparable, can be differentiated from SM at all but the lowest concentrations of SM. Furthermore, haze and wind gradients can be distinguished from the lower limb. Thus the addition of brightness to chromaticity analysis has the potential to significantly improve the technique. The model was tested by comparing simulated chromaticity results with results from actual images (one TM image and three MSS images) for which ground truth had been collected. Qualitative predictions regarding haze and water quality patchiness were confirmed. Correlation analysis with R² values from 0.81 to 0.95 also strongly confirmed predictions regarding SM, but showed that the model is systematically underestimating SM. Correlation tests for a combined C and YS factor (CYS) were inconclusive because of the systematic modeling error, but classification maps provide weak evidence that CYS is behaving qualitatively as predicted and that CYS can be differentiated from SM. The modeling error is thought to originate in atmospheric assumptions which are not met. The R-model which is fundamental to the study has been tested and is not a major source of error. The study concludes that the model is qualitatively correct and that the use of brightness improves chromaticity analysis by allowing separation of CYS and SM, though further work should be undertaken to verify these results. Maps of CYS and SM in Chilko Lake reveal that CYS tends to be higher along the western shore and where the hypolimnion is exposed. SM are highest near stream mouths. The distribution patterns are related to physical processes within the lake and provide a synoptic view of the connection between water quality parameters and circulation which would be difficult to achieve in any other way.
Forestry, Faculty of
Graduate
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Salvaggio, Carl. "Automated segmentation of urban features from Landsat-Thematic Mapper imagery for use in pseudovariant feature temporal image normalization /." Online version of thesis, 1987. http://hdl.handle.net/1850/11371.

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Bross, Lesley Crandell. "Using Landsat TM Imagery to Monitor Vegetation Change Following Flow Restoration to the Lower Owens River, California." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2635.

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Rehabilitating river corridors to restore valuable riparian habitat consumes significant resources from both governments and private companies. Given these considerable expenditures, it is important to monitor the progress of such projects. This study evaluated the utility of using Landsat Thematic Mapper remotely-sensed data from 2002 and 2009 to monitor vegetation change induced by instream flow restoration to the Lower Owens River in central California. This study compared the results of an unsupervised classification with an NDVI threshold classification to appraise the resources required and effectiveness of each analysis method. The results were inspected by creating standard remote sensing accuracy error matrices and by correlating landscape pattern metrics with bird indicator species. Both sets of classified maps show a noticeable increase in riparian vegetation in the study area following flow restoration in 2006, indicating an improvement of the quality of bird habitat. The study concluded that analyzing vegetation change using the unsupervised classification technique required more effort, expert knowledge, and supplementary data than using the NDVI threshold method. If these prerequisites are met, the output from the unsupervised classification process produces a more precise map of land cover change than the NDVI threshold method. However, if an analyst is lacking either resources or ground verification data, the NDVI threshold technique is capable of providing a generalized, but still valid evaluation of vegetation change. This conclusion is supported by higher correlations between indicator bird species under the unsupervised classification method than were found with the NDVI threshold method.
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Books on the topic "Landsat satellites Remote sensing"

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Quiel, Friedrich. Landnutzungskartierung mit LANDSAT-Daten. Bonn: Die Bundesforschungsanstalt, 1986.

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Pace, Scott. Remote sensing and global competitiveness. Santa Monica, CA: RAND, 1993.

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Schott, John R. Radiometric analysis of the longwave infrared channel of the thematic mapper on Landsat 4 and 5. [Washington, DC: National Aeronautics and Space Administration, 1986.

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Ager, Alan A. Characterizing meadow vegetation with multitemporal landsat thematic mapper remote sensing. Portland, OR]: U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 2004.

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McHale, Ryan K. Landsat and its valuable role in satellite imagery of earth. Hauppauge, NY: Nova Science Publisher's, Inc., 2011.

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United States. Congress. House. Committee on Science, Space, and Technology. Subcommittee on Natural Resources, Agriculture Research, and Environment. Long-term future of the Landsat system: Hearing before the Subcommittee on Natural Resources, Agriculture Research, and Environment and the Subcommittee on International Scientific Cooperation of the Committee on Science, Space, and Technology, U.S. House of Representatives, One Hundred First Congress, first session, May 9, 1989. Washington: U.S. G.P.O., 1989.

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United, States Congress House Committee on Science Space and Technology Subcommittee on Natural Resources Agriculture Research and Environment. Long-term future of the Landsat system: Hearing before the Subcommittee on Natural Resources, Agriculture Research, and Environment and the Subcommittee on International Scientific Cooperation of the Committee on Science, Space, and Technology, U.S. House of Representatives, One Hundred First Congress, first session, May 9, 1989. Washington: U.S. G.P.O., 1989.

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United States. Congress. House. Committee on Science, Space, and Technology. Subcommittee on Natural Resources, Agriculture Research, and Environment. Long-term future of the Landsat system: Hearing before the Subcommittee on Natural Resources, Agriculture Research, and Environment and the Subcommittee on International Scientific Cooperation of the Committee on Science, Space, and Technology, U.S. House of Representatives, One Hundred First Congress, first session, May 9, 1989. Washington: U.S. G.P.O., 1989.

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Tilton, James C. Landsat-4 and Landsat-5 multispectral scanner coherent noise characterization and removal. Greenbelt, Md: Goddard Space Flight Center, 1988.

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Martinuzzi, Sebastian. Creating cloud-free landsat ETM+ data sets in tropical landscapes: Cloud and cloud-shadow removal. Portland, OR: U.S. Dept. of Agriculture, Pacific Northwest Research Station, 2007.

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Book chapters on the topic "Landsat satellites Remote sensing"

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Wong, Man Sing, Xiaolin Zhu, Sawaid Abbas, Coco Yin Tung Kwok, and Meilian Wang. "Optical Remote Sensing." In Urban Informatics, 315–44. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_20.

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AbstractApplications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.
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Boufeniza, Redouane, Fouzia Bachari Houma, Mohammad Alsahli, and Nour el Islam Bachari. "Quantification of Phytoplanktonic Algae Density in Algiers Bay (Algeria) by Combining In Situ Measurements and Landsat Satellite Images." In Advances in Remote Sensing and Geo Informatics Applications, 329–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01440-7_73.

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Brazeau, Stéphanie, Cécile Vignolles, Ramesha S. Krishnamurthy, Juli Trtanj, John Haynes, Steven Ramage, Thibault Catry, et al. "Needs, challenges, and opportunities: a review by experts." In Earth observation, public health and one health: activities, challenges and opportunities, 93–103. Wallingford: CABI, 2022. http://dx.doi.org/10.1079/9781800621183.0003.

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Abstract This book chapter discusses all the information collected has been grouped together into eight categories: (i) aligning with and supporting UN Sustainable Development Goals; (ii) focusing on public health needs and key theme areas for further research; (iii) accessing and developing Earth Observation (EO) and geospatial evidence-based data and products leveraging public health capacities; (iv) developing a sustainable community of practice; (v) developing knowledge and know-how; (vi) developing solutions: methods, tools, and systems; (vii) implementing technical infrastructures and technologies; and (viii) participating in EO satellite mission development for monitoring disease risks. One such advancement attributable to Landsat data is the ability to monitor changing patterns in forest cover loss and human encroachment on previously wild areas that allows for better prediction of zoonotic disease emergence. For example, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the US Aqua and Terra satellites offer atmosphere, land, cryosphere, and ocean products that are used in several user communities. MODIS indicator data sets have been so successful that they do not require additional remote sensing analysis; they can be used directly in predictive models. Some EO satellite systems offer ARD (i.e. pre-processed images) and related information products derived from the raw data stream generated by the satellite instruments and the use of algorithms.
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Arvidson, Terry, Julia Barsi, Murzy Jhabvala, and Dennis Reuter. "Landsat and Thermal Infrared Imaging." In Thermal Infrared Remote Sensing, 177–96. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6639-6_9.

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Chandrashekar, S. "China’s Remote Sensing Satellites." In China's Space Programme, 133–67. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1504-8_9.

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Lindgren, David T. "The Landsat system." In Land use planning and remote sensing, 67–86. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-017-2035-9_6.

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Rao, P. Krishna, Susan J. Holmes, Ralph K. Anderson, Jay S. Winston, and Paul E. Lehr. "Remote Sensing Instrumentation." In Weather Satellites: Systems, Data, and Environmental Applications, 105–17. Boston, MA: American Meteorological Society, 1990. http://dx.doi.org/10.1007/978-1-944970-16-1_11.

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Yang, Xiaojun. "Use of Archival Landsat Imagery to Monitor Urban Spatial Growth." In Urban Remote Sensing, 15–33. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470979563.ch2.

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Friedl, Randall, and Stacey Boland. "Mission Costs of Earth-Observing Satellites." In Encyclopedia of Remote Sensing, 405–7. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_106.

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Camps-Valls, Gustavo, Devis Tuia, Luis Gómez-Chova, Sandra Jiménez, and Jesús Malo. "Remote Sensing from Earth Observation Satellites." In Remote Sensing Image Processing, 1–25. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-02247-0_1.

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Conference papers on the topic "Landsat satellites Remote sensing"

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Soares, Vicente P., and Roger M. Hoffer. "Eucalyptus forest change classification using multi-date Landsat TM data." In Satellite Remote Sensing, edited by Eric Mougin, K. Jon Ranson, and James A. Smith. SPIE, 1995. http://dx.doi.org/10.1117/12.200769.

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Tollefsen, Tore. "Distribution of cloud-free Landsat images in the Barents region." In Satellite Remote Sensing, edited by Anton Kohnle and Adam D. Devir. SPIE, 1994. http://dx.doi.org/10.1117/12.197376.

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Bartl, Renate, Maria Petrou, William J. Christmas, and P. L. Palmer. "Automatic registration of cadastral maps and Landsat TM images." In Satellite Remote Sensing III, edited by Jacky Desachy. SPIE, 1996. http://dx.doi.org/10.1117/12.262900.

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Shimabukuro, Yosio E., William T. Lawrence, and Francis J. Ahern. "Tropical vegetation analysis with Landsat thematic mapper and Canadian synthetic aperture radar data." In Satellite Remote Sensing, edited by Eric Mougin, K. Jon Ranson, and James A. Smith. SPIE, 1995. http://dx.doi.org/10.1117/12.200766.

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Walker, Charles L., and Maria T. Kalcic. "Gram-Schmidt orthogonalization technique for atmospheric and sun glint correction of Landsat imagery." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196778.

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D'Urso, Guido, and Massimo Menenti. "Mapping crop coefficients in irrigated areas from Landsat TM images." In Satellite Remote Sensing II, edited by Edwin T. Engman, Gerard Guyot, and Carlo M. Marino. SPIE, 1995. http://dx.doi.org/10.1117/12.227167.

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Bogliolo, M. P., Maria F. Buongiorno, and Stefano Salvi. "Integration of digital elevation model and spectral radiance for topographic normalization of Landsat TM imagery." In Satellite Remote Sensing, edited by Pat S. Chavez, Jr., Carlo M. Marino, and Robert A. Schowengerdt. SPIE, 1994. http://dx.doi.org/10.1117/12.197225.

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Pranzini, Enzo, and Gaia Righini. "Lithological discrimination using thermal inertia derived from Landsat TM data: a new algorithm proposed (TMTI)." In Satellite Remote Sensing, edited by Eugenio Zilioli. SPIE, 1994. http://dx.doi.org/10.1117/12.197297.

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Schneider, K., Heike Bach, and Wolfram Mauser. "Determination of water-quality parameters in Lake Constance using Landsat-TM images." In Satellite Remote Sensing III, edited by Giovanna Cecchi, Guido D'Urso, Edwin T. Engman, and Preben Gudmandsen. SPIE, 1997. http://dx.doi.org/10.1117/12.264264.

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Hoffer, Roger M., and Vicente P. Soares. "Combination of Landsat TM and ERS-1 satellite data to discriminate forest cover types in Brazil." In Satellite Remote Sensing, edited by Eric Mougin, K. Jon Ranson, and James A. Smith. SPIE, 1995. http://dx.doi.org/10.1117/12.200763.

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Reports on the topic "Landsat satellites Remote sensing"

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Salazar, Lina, Ana Claudia Palacios, Michael Selvaraj, and Frank Montenegro. Using Satellite Images to Measure Crop Productivity: Long-Term Impact Assessment of a Randomized Technology Adoption Program in the Dominican Republic. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003604.

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This study combines three rounds of surveys with remote sensing to measure long-term impacts of a randomized irrigation program in the Dominican Republic. Specifically, Landsat 7 and Landsat 8 satellite images are used to measure the causal effects of the program on agricultural productivity, measured through vegetation indices (NDVI and OSAVI). To this end, 377 plots were analyzed (129 treated and 248 controls) for the period from 2011 to 2019. Following a Differencein-Differences (DD) and Event study methodology, the results confirmed that program beneficiaries have higher vegetation indices, and therefore experienced a higher productivity throughout the post-treatment period. Also, there is some evidence of spillover effects to neighboring farmers. Furthermore, the Event Study model shows that productivity impacts are obtained in the third year after the adoption takes place. These findings suggest that adoption of irrigation technologies can be a long and complex process that requires time to generate productivity impacts. In a more general sense, this study reveals the great potential that exists in combining field data with remote sensing information to assess long-term impacts of agricultural programs on agricultural productivity.
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Johansen, Richard A., Christina L. Saltus, Molly K. Reif, and Kaytee L. Pokrzywinski. A Review of Empirical Algorithms for the Detection and Quantification of Harmful Algal Blooms Using Satellite-Borne Remote Sensing. U.S. Army Engineer Research and Development Center, June 2022. http://dx.doi.org/10.21079/11681/44523.

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Harmful Algal Blooms (HABs) continue to be a global concern, especially since predicting bloom events including the intensity, extent, and geographic location, remain difficult. However, remote sensing platforms are useful tools for monitoring HABs across space and time. The main objective of this review was to explore the scientific literature to develop a near-comprehensive list of spectrally derived empirical algorithms for satellite imagers commonly utilized for the detection and quantification HABs and water quality indicators. This review identified the 29 WorldView-2 MSI algorithms, 25 Sentinel-2 MSI algorithms, 32 Landsat-8 OLI algorithms, 9 MODIS algorithms, and 64 MERIS/Sentinel-3 OLCI algorithms. This review also revealed most empirical-based algorithms fell into one of the following general formulas: two-band difference algorithm (2BDA), three-band difference algorithm (3BDA), normalized-difference chlorophyll index (NDCI), or the cyanobacterial index (CI). New empirical algorithm development appears to be constrained, at least in part, due to the limited number of HAB-associated spectral features detectable in currently operational imagers. However, these algorithms provide a foundation for future algorithm development as new sensors, technologies, and platforms emerge.
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Hodul, M., H. P. White, and A. Knudby. A report on water quality monitoring in Quesnel Lake, British Columbia, subsequent to the Mount Polley tailings dam spill, using optical satellite imagery. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330556.

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In the early morning on the 4th of August 2014, a tailings dam near Quesnel, BC burst, spilling approximately 25 million m3 of runoff containing heavy metal elements into nearby Quesnel Lake (Byrne et al. 2018). The runoff slurry, which included lead, arsenic, selenium, and vanadium spilled through Hazeltine Creek, scouring its banks and picking up till and forest cover on the way, and ultimately ended up in Quesnel Lake, whose water level rose by 1.5 m as a result. While the introduction of heavy metals into Quesnel Lake was of environmental concern, the additional till and forest cover scoured from the banks of Hazeltine Creek added to the lake has also been of concern to salmon spawning grounds. Immediate repercussions of the spill involved the damage of sensitive environments along the banks and on the lake bed, the closing of the seasonal salmon fishery in the lake, and a change in the microbial composition of the lake bed (Hatam et al. 2019). In addition, there appears to be a seasonal resuspension of the tailings sediment due to thermal cycling of the water and surface winds (Hamilton et al. 2020). While the water quality of Quesnel Lake continues to be monitored for the tailings sediments, primarily by members at the Quesnel River Research Centre, the sample-and-test methods of water quality testing used, while highly accurate, are expensive to undertake, and not spatially exhaustive. The use of remote sensing techniques, though not as accurate as lab testing, allows for the relatively fast creation of expansive water quality maps using sensors mounted on boats, planes, and satellites (Ritchie et al. 2003). The most common method for the remote sensing of surface water quality is through the use of a physics-based semianalytical model which simulates light passing through a water column with a given set of Inherent Optical Properties (IOPs), developed by Lee et al. (1998) and commonly referred to as a Radiative Transfer Model (RTM). The RTM forward-models a wide range of water-leaving spectral signatures based on IOPs determined by a mix of water constituents, including natural materials and pollutants. Remote sensing imagery is then used to invert the model by finding the modelled water spectrum which most closely resembles that seen in the imagery (Brando et al 2009). This project set out to develop an RTM water quality model to monitor the water quality in Quesnel Lake, allowing for the entire surface of the lake to be mapped at once, in an effort to easily determine the timing and extent of resuspension events, as well as potentially investigate greening events reported by locals. The project intended to use a combination of multispectral imagery (Landsat-8 and Sentinel-2), as well as hyperspectral imagery (DESIS), combined with field calibration/validation of the resulting models. The project began in the Autumn before the COVID pandemic, with plans to undertake a comprehensive fieldwork campaign to gather model calibration data in the summer of 2020. Since a province-wide travel shutdown and social distancing procedures made it difficult to carry out water quality surveying in a small boat, an insufficient amount of fieldwork was conducted to suit the needs of the project. Thus, the project has been put on hold, and the primary researcher has moved to a different project. This document stands as a report on all of the work conducted up to April 2021, intended largely as an instructional document for researchers who may wish to continue the work once fieldwork may freely and safely resume. This research was undertaken at the University of Ottawa, with supporting funding provided by the Earth Observations for Cumulative Effects (EO4CE) Program Work Package 10b: Site Monitoring and Remediation, Canada Centre for Remote Sensing, through the Natural Resources Canada Research Affiliate Program (RAP).
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Ager, Alan A., and Karen E. Owens. Characterizing meadow vegetation with multitemporal Landsat thematic mapper remote sensing. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2004. http://dx.doi.org/10.2737/pnw-rn-544.

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Chen, Z., S. E. Grasby, C. Deblonde, and X. Liu. AI-enabled remote sensing data interpretation for geothermal resource evaluation as applied to the Mount Meager geothermal prospective area. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330008.

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The objective of this study is to search for features and indicators from the identified geothermal resource sweet spot in the south Mount Meager area that are applicable to other volcanic complexes in the Garibaldi Volcanic Belt. A Landsat 8 multi-spectral band dataset, for a total of 57 images ranging from visible through infrared to thermal infrared frequency channels and covering different years and seasons, were selected. Specific features that are indicative of high geothermal heat flux, fractured permeable zones, and groundwater circulation, the three key elements in exploring for geothermal resource, were extracted. The thermal infrared images from different seasons show occurrence of high temperature anomalies and their association with volcanic and intrusive bodies, and reveal the variation in location and intensity of the anomalies with time over four seasons, allowing inference of specific heat transform mechanisms. Automatically extracted linear features using AI/ML algorithms developed for computer vision from various frequency bands show various linear segment groups that are likely surface expression associated with local volcanic activities, regional deformation and slope failure. In conjunction with regional structural models and field observations, the anomalies and features from remotely sensed images were interpreted to provide new insights for improving our understanding of the Mount Meager geothermal system and its characteristics. After validation, the methods developed and indicators identified in this study can be applied to other volcanic complexes in the Garibaldi, or other volcanic belts for geothermal resource reconnaissance.
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Dudley, J. P., and S. V. Samsonov. Système de traitement automatisé du gouvernement canadien pour la détection des variations et l'analyse des déformations du sol à partir des données de radar à synthèse d'ouverture de RADARSAT-2 et de la mission de la Constellation RADARSAT : description et guide de l'utilisateur. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329134.

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Remote sensing using Synthetic Aperture Radar (SAR) offers powerful methods for monitoring ground deformation from both natural and anthropogenic sources. Advanced analysis techniques such as Differential Interferometric Synthetic Aperture Radar (DInSAR), change detection, and Speckle Offset Tracking (SPO) provide sensitive measures of ground movement. With both the RADARSAT-2 and RADARSAT Constellation Mission (RCM) SAR satellites, Canada has access to a significant catalogue of SAR data. To make use of this data, the Canada Centre for Mapping and Earth Observation (CCMEO) has developed an automated system for generating standard and advanced deformation products from SAR data using both DInSAR and SPO methods. This document provides a user guide for this automated processing system.
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Lasko, Kristofer, and Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42402.

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Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.
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