Relevant bibliographies by topics / GIS and Remote Sensing

Academic literature on the topic 'GIS and Remote Sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "GIS and Remote Sensing"

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YAVARI, GHOLAMREZA, and M. MEHDI FAZELBEYGI. "Development of Small Urban Center, Using Remote Sensing and Gis." Indian Journal of Applied Research 4, no. 8 (October 1, 2011): 275–79. http://dx.doi.org/10.15373/2249555x/august2014/70.

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Mayani, Kaushikkumar R., and V. M. Patel V. M. Patel. "Relevance of Remote Sensing and GIS in Water Resoureces Engineering." Indian Journal of Applied Research 1, no. 11 (October 1, 2011): 50–51. http://dx.doi.org/10.15373/2249555x/aug2012/17.

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Castaño, Santiago, David Sanz, and Juan José Gómez Alday. "Remote Sensing and GIS Tools for the Groundwater Withdrawals Quantification." Journal of Agricultural Science and Applications 01, no. 01 (March 30, 2012): 33–36. http://dx.doi.org/10.14511/jasa.2012.010106.

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Sikder, Shuvra, and M. Abrar Rubaiyat Islam. "Aerosol Optical Thickness (AOT) Assessment Using GIS & Remote Sensing." International Journal of Innovative Research in Computer Science & Technology 6, no. 4 (July 2018): 69–72. http://dx.doi.org/10.21276/ijircst.2018.6.4.5.

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Li, Jing Zhong, Hui Yan, and Meng Meng Yang. "The Application of Agricultural Remote Sensing in Remote Sensing Teaching Practice." Advanced Materials Research 926-930 (May 2014): 4626–29. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.4626.

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This paper has analyzed the current application status of agricultural Remote Sensing and the situation of teaching practice of Remote Sensing in Geographic Information Systems (GIS) at length; and practically looked for their breakthrough point. On this basis, the paper constructs the frame structure of their combination preliminarily, making the teaching practice combine with the practical application, which allows for the finishing of the process of teaching practice as well as serving the society. This provides a new idea for the combination of production, studying and researching in GIS and broadens the combination mode of teaching and application. Simultaneously, it promotes the students practical ability of using remote sensing and GIS to process and analyze data in actual projects, inspires the students capability of initiative and creative thinking, and improves the students interests of Autonomous learning.
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Cole, Daniel G. "Remote Sensing for GIS Managers." Cartographic Perspectives, no. 56 (March 1, 2007): 54–56. http://dx.doi.org/10.14714/cp56.308.

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SAITO, Genya. "Remote Sensing and GIS for Agricultural Information." Geoinformatics 8, no. 2 (1997): 107–16. http://dx.doi.org/10.6010/geoinformatics1990.8.2_107.

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Archibald, Peter D. "GIS and remote sensing data integration." Geocarto International 2, no. 3 (September 1987): 67–73. http://dx.doi.org/10.1080/10106048709354110.

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Gábor, Remetey-Fülöpp. "Remote sensing and GIS in Hungary." Geocarto International 28, no. 1 (February 2013): 1–2. http://dx.doi.org/10.1080/10106049.2013.766427.

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Flasse, S. "Remote Sensing and GIS Accuracy Assessment." Photogrammetric Record 20, no. 111 (September 2005): 306–7. http://dx.doi.org/10.1111/j.1477-9730.2005.00333_3.x.

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Dissertations / Theses on the topic "GIS and Remote Sensing"

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Dambe, Natalia. "Riverine flooding using GIS and remote sensing." Master's thesis, Faculty of Engineering and the Built Environment, 2020. https://hdl.handle.net/11427/31738.

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Floods are caused by extreme meteorological and hydrological changes that are influenced directly or indirectly by human activities within the environment. The flood trends show that floods will reoccur and shall continue to affect the livelihoods, property, agriculture and the surrounding environment. This research has analyzed the riverine flood by integrating remote sensing, Geographical Information Systems (GIS), and hydraulic and/or hydrological modeling, to develop informed flood mapping for flood risk management. The application of Hydrological Engineering Center River Analysis System (HEC RAS) and HEC HMS models, developed by the USA Hydrologic Engineering Center of the Army Corps of Engineers in a data-poor environment of a developing country were successful, as a flood modeling tools in early warning systems and land use planning. The methodology involved data collection, preparation, and model simulation using 30m Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) as a critical data input of HEC RAS model. The findings showed that modeling using HEC-RAS and HEC HMS models in a data-poor environment requires intensive data enhancements and adjustments; multiple utilization of open sources data; carrying out multiple model computation iterations and calibration; multiple field observation, which may be constrained with time and resources to get reasonable output.
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Gustavsson, Andreas, and Selberg Martin. "Delineation of Ditches in Wetlandsby Remote Sensing." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-354612.

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Wetlands have been heavily affected by human-alteration, this is done to drain thewetland so that the ground can be used for other purposes such as forestry oragriculture. With high accuracy data available now, it is possible to use differentmethods automatically to identify these ditches in wetlands or other areas. Fourdifferent methods were applied to two areas to delineate ditches. To compare the different methods, the results were quantified by comparing with amanually created map of the ditches. Drainage density and an agreement index wereused. The results indicate that the methods of impoundment index and map gullydepth are best able to distinguish the ditches in wetlands. The former gave a betterresult on areas inside wetlands while the latter gave a better result with non-wetlandareas. The other two methods make mistakes and misjudgements that givemisleading results, they ignore ditches partially or completely, or finds ditches inareas without them. Even so, all methods are at least a clear improvement over thecurrently available property map's water flows, but not in the same class as the timeconsumingmanual method.
Våtmarker har påverkats mycket av människan genom dikning, detta görs för attdränera våtmarken så att marken kan användas för andra syften som skogsbruk ellerjordbruk. Med höjddata i hög noggrannhet som nu finns tillgänglig är det möjligt attanvända olika metoder för att urskilja dessa diken i våtmarker. Fyra olika metodermed applicerades på två olika områden för att undersöka möjligheten att urskiljadiken. För att jämföra de olika metoderna kvantifieras resultaten genom att jämföras meden manuellt skapad karta över diken. Dränerings densitet och ettöverensstämmelseindex användes. Resultaten pekar på att metoderna fördämningsindex (impoundment index) och ravindjupskartering (map gully depth) klarar bäst avatt urskilja diken i våtmarker. Där den föregående ger ett gav ett bättre resultat inomvårmarker och den senare gav ett bättre resultat generellt över området. De tvåandra metoderna gör missar och felbedömningar som ger missvisande resultat.Oavsett det, så är karteringen av dikena i nuläget en klar förbättring överFastighetskartans vattenflöden, men inte i samma klass som den tidskrävandemanuella metoden.
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Al, Sghair Fathi Goma. "Remote sensing and GIS for wetland vegetation study." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4581/.

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Remote Sensing (RS) and Geographic Information System (GIS) approaches, combined with ground truthing, are providing new tools for advanced ecosystem management, by providing the ability to monitor change over time at local, regional, and global scales. In this study, remote sensing (Landsat TM and aerial photographs) and GIS, combined with ground truthing work, were used to assess wetland vegetation change over time at two contrasting wetland sites in the UK: freshwater wetland at Wicken Fen between 1984 and 2009, and saltmarsh between 1988 and 2009 in Caerlaverock Reserve. Ground truthing studies were carried out in Wicken Fen (UK National Grid Reference TL 5570) during 14th - 18th June 2010: forty 1 m2 quadrats were taken in total, placed randomly along six transects in different vegetation types. The survey in the second Study Area Caerlaverock Reserve (UK National Grid Reference NY0464) was conducted on 5th - 9th July 2011, with a total of forty-eight 1 m2 quadrats placed randomly along seven transects in different vegetation types within the study area. Two-way indicator species (TWINSPAN) was used for classification the ground truth samples, taking separation on eigenvalues with high value (>0.500), to define end-groups of samples. The samples were classified into four sample-groups based on data from 40 quadrats in Wicken Fen, while the data were from 48 quadrats divided into five sample-groups in Caerlaverock Reserve. The primary analysis was conducted by interpreting vegetation cover from aerial photographs, using GIS combined with ground truth data. Unsupervised and supervised classifications with the same technique for aerial photography interpretation were used to interpret the vegetation cover in the Landsat TM images. In Wicken Fen, Landsat TM images were used from 18th August 1984 and 23rd August 2009; for Caerlaverock Reserve Landsat TM imagery used was taken from 14th May 1988 and 11th July 2009. Aerial photograph imagery for Wicken Fen was from 1985 and 2009; and for Caerlaverock Reserve, from 1988 and 2009. Both the results from analysis of aerial photographs and Landsat TM imagery showed a substantial temporal change in vegetation during the period of study at Wicken Fen, most likely primarily produced by the management programme, rather than being due to natural change. In Cearlaverock Reserve, results from aerial photography interpretation indicated a slight change in the cover of shrubs during the period 1988 to 2009, but little other change over the study period. The results show that the classification accuracy using aerial photography was higher than that of Landsat TM data. The difference of classification accuracy between aerial photography and Landsat TM, especially in Caerlaverock Reserve, was due to the low resolution of Landsat TM images, and the fact that some vegetation classes occupied an area less than that of the pixel size of the TM image. Based on the mapping exercise, the aerial photographs produced better vegetation classes (when compared with ground truthing data) than Landsat TM images, because aerial photos have a higher spatial resolution than the Landsat TM images. Perhaps the most important conclusion of this study is that it provides evidence that the RS/GIS approach can provide useful baseline data about wetland vegetation change over time, and across quite expansive areas, which can therefore provide valuable information to aid the management and conservation of wetland habitats.
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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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Ahmadzadeh, M. R. "Reasoning with uncertainty in remote sensing." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/804/.

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Almond, Simon John. "Remote sensing within GIS for woodland inventory and monitoring." Thesis, University of Portsmouth, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386832.

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Firoozi, Nejad Behnam. "Population mapping using census data, GIS and remote sensing." Thesis, Queen's University Belfast, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.705917.

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This thesis assesses approaches to population surface modeling by pulling together the benefits of reference gridded population data with local regression procedures and geographically weighted regression. This study provides a more detailed assessment of surface modelling accuracy than was achieved in any previous studies to assess factors which explain errors in the predictions. The primary aim of this thesis is to evaluate Martin’s (1989) population surface modeling approach and also design and implement a method using secondary data, suitable for application in England and Wales. This research is based on the idea that population data presented for a single zone could be redistributed in the zone using local parameters such as housing density. A weighted sum performs the spatial redistribution. The thesis also aims to make use of remote sensing (RS) data and image processing techniques such as maximum likelihood classification and normalised difference vegetation index to identify (un) populated cells. The potential of Landsat images and RS data analysis is assessed particularly for countries where high quality land use data are not readily obtainable, and their generation is not feasible in the near future. This thesis focuses on the identification of unpopulated cells, rather than populated units, using RS data. Case studies make use of data from Northern Ireland (NI), and Jonkoping in southern Sweden. The outcomes indicate the impact of population density, population variance, and resolution of source zones on the accuracy of population allocation to grid cells using Martin’s (1989) model. The results show significant accuracy in prediction to 100m cells using an alternative approach based on settlement data for NI and this is recommended as an alternative method for England and Wales. It also concluded that there the potential to generate population surfaces using Landsat data for areas where local residential data are not easily accessible.
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Mason, Philippa Jane. "Landslide hazard assessment using remote sensing and GIS techniques." Thesis, Imperial College London, 1999. http://hdl.handle.net/10044/1/8899.

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Blackburn, George Alan. "Remote sensing of deciduous woodlands : a tool for ecological investigations." Thesis, University of Southampton, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239872.

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Berberoglu, Suha. "Optimising the remote sensing of Mediterranean land cover." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285646.

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Books on the topic "GIS and Remote Sensing"

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Bhatta, Basudeb. Remote sensing and GIS. Delhi: Oxford University Press, 2008.

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Remote sensing and GIS. 2nd ed. New Delhi, India: Oxford University Press, 2011.

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Remote sensing for GIS managers. Redlands, Ca: ESRI Press, 2005.

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Foody, Giles M., and Peter M. Atkinson, eds. Uncertainty in Remote Sensing and GIS. Chichester, UK: John Wiley & Sons, Ltd, 2002. http://dx.doi.org/10.1002/0470035269.

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Nayak, Shailesh, K. Gopal Reddy, and P. Rajendra Prasad. Coastal oceanography: Remote sensing and GIS applications. Hyderabad: Indian National Centre for Ocean Information Services, 2007.

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Liu, Jian Guo, and Philippa J. Mason. Image Processing and GIS for Remote Sensing. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118724194.

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Al-Quraishi, Ayad M. Fadhil, and Abdelazim M. Negm, eds. Environmental Remote Sensing and GIS in Iraq. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-21344-2.

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Quattrochi, Dale A., Elizabeth Wentz, Nina Siu-Ngan Lam, and Charles W. Emerson, eds. Integrating Scale in Remote Sensing and GIS. London : Routledge, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315373720.

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Khebour Allouche, Faiza, and Abdelazim M. Negm, eds. Environmental Remote Sensing and GIS in Tunisia. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63668-5.

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Weng, Qihao. Remote sensing and GIS integration: Theories, methods, and applications. New York: McGraw-Hill, 2010.

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Book chapters on the topic "GIS and Remote Sensing"

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Shekhar, Shashi, and Hui Xiong. "Remote Sensing." In Encyclopedia of GIS, 957. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1114.

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Shekhar, Shashi, and Hui Xiong. "Remote Sensing, Aerial." In Encyclopedia of GIS, 957. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1115.

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Shekhar, Shashi, and Hui Xiong. "Remote Sensing Specifications." In Encyclopedia of GIS, 957. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1117.

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Shekhar, Shashi, and Hui Xiong. "Remote Sensing Standards." In Encyclopedia of GIS, 957. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1118.

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Weir, Michael J. C. "GIS Education and Training." In Eurocourses: Remote Sensing, 491–97. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_25.

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Shekhar, Shashi, and Hui Xiong. "Remote Sensing, Satellite-Based." In Encyclopedia of GIS, 957. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1116.

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Wani, Mushtaq A. "Remote Sensing and GIS." In Agricultural Impacts of Climate Change, 263–83. Boca Raton : CRC Press, 2019-: CRC Press, 2019. http://dx.doi.org/10.1201/9780429326349-15.

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Tang, Junmei. "GIS Fundamentals for Agriculture." In Springer Remote Sensing/Photogrammetry, 25–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66387-2_3.

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Woodcock, Curtis E. "Uncertainty in Remote Sensing." In Uncertainty in Remote Sensing and GIS, 19–24. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470035269.ch2.

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Gupta, Ravi Prakash. "Integrating Remote Sensing Data with Other Geodata (GIS Approach)." In Remote Sensing Geology, 393–428. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05283-9_15.

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Conference papers on the topic "GIS and Remote Sensing"

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Dyras, Izabela. "Satellite derived precipitation mapping using GIS technology." In Remote Sensing, edited by Manfred Ehlers and Ulrich Michel. SPIE, 2005. http://dx.doi.org/10.1117/12.627728.

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Ryerson, Charles C., and Thomas S. Anderson. "Logic tree-based GIS inference of geologic structure." In Remote Sensing, edited by Manfred Ehlers and Ulrich Michel. SPIE, 2007. http://dx.doi.org/10.1117/12.738004.

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Duarte, L., O. Moutinho, and A. Teodoro. "MicMac GIS application: free open source." In SPIE Remote Sensing, edited by Ulrich Michel, Karsten Schulz, Manfred Ehlers, Konstantinos G. Nikolakopoulos, and Daniel Civco. SPIE, 2016. http://dx.doi.org/10.1117/12.2240196.

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Smits, Paul C., Alessandro Annoni, and Silvana G. Dellepiane. "Integration of GIS and remote sensing image analysis techniques." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373265.

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Nikolakopoulos, Konstantinos G., P. I. Tsombos, and A. D. Vaiopoulos. "Monitoring a quarry using high resolution data and GIS techniques." In Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2010. http://dx.doi.org/10.1117/12.864527.

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Wagtendonk, Alfred, and Richard A. M. De Jeu. "Mobile GIS and optimizing data collection methods in hydrological fieldwork." In Remote Sensing, edited by Manfred Owe and Guido D'Urso. SPIE, 2005. http://dx.doi.org/10.1117/12.627937.

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Demharter, Timo, and Ulrich Michel. "GIS4schools: a new approach in GIS education." In SPIE Remote Sensing, edited by Ulrich Michel, Daniel L. Civco, Manfred Ehlers, Karsten Schulz, Konstantinos G. Nikolakopoulos, Shahid Habib, David Messinger, and Antonino Maltese. SPIE, 2012. http://dx.doi.org/10.1117/12.979706.

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Slob, Siefko, Max Fernandez-Alonso, Francois Kervyn, and Mariton Bornas. "Volcanic hazard mapping in the Philippines using remote sensing and GIS." In Remote Sensing, edited by Giovanna Cecchi and Eugenio Zilioli. SPIE, 1998. http://dx.doi.org/10.1117/12.332734.

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Guan, Fulai, Ruijiang Wei, Chunqiang Li, and Wenzong Zhang. "The application of GIS in the regionalization of plastic sunlight greenhouse." In Remote Sensing, edited by Manfred Ehlers and Ulrich Michel. SPIE, 2007. http://dx.doi.org/10.1117/12.737531.

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Muthu, Kavitha, and Maria Petrou. "Landslide hazard mapping using a GIS and a fuzzy neural network." In Remote Sensing, edited by Manfred Ehlers, Francesco Posa, Hermann J. Kaufmann, Ulrich Michel, and Giacomo De Carolis. SPIE, 2004. http://dx.doi.org/10.1117/12.564986.

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Reports on the topic "GIS and Remote Sensing"

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Robert Paul Breckenridge. Improving Rangeland Monitoring and Assessment: Integrating Remote Sensing, GIS, and Unmanned Aerial Vehicle Systems. Office of Scientific and Technical Information (OSTI), May 2007. http://dx.doi.org/10.2172/978362.

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Cuesta, Rosa, Ignacio Fiz Fernández, and Eva Subías Pascual. Hydraulic and urban management during Roman times based on GIS and remote sensing analysis (Clunia, Spain). Edicions i Publicacions de la UdL, 2019. http://dx.doi.org/10.21001/rap.2019.29.4.

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Suhartono, Suhartono, Agoes Soegianto, and Achmad Amzeri. Mapping of land potentially for maize plant in Madura Island-Indonesia using remote sensing data and geographic information systems (GIS). EM International, November 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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Wilkinson, L., P. Budkewitsch, D. F. Graham, J. Henderson, and 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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Fung, K. B., D. W. Fraser, and R. P. Gauthier. Application of Remote Sensing Data for Monitoring of Gas Pipeline Right-of-Way. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1998. http://dx.doi.org/10.4095/219196.

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Jerry Myers. AIRBORNE, OPTICAL REMOTE SENSING OF METHANE AND ETHANE FOR NATURAL GAS PIPELINE LEAK DETECTION. Office of Scientific and Technical Information (OSTI), May 2003. http://dx.doi.org/10.2172/825563.

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7

Jerry Myers. AIRBORNE, OPTICAL REMOTE SENSING OF METHANE AND ETHANE FOR NATURAL GAS PIPELINE LEAK DETECTION. Office of Scientific and Technical Information (OSTI), November 2003. http://dx.doi.org/10.2172/827485.

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Jerry Myers. AIRBORNE, OPTICAL REMOTE SENSING OF METHANE AND ETHANE FOR NATURAL GAS PIPLINE LEAK DETECTION. Office of Scientific and Technical Information (OSTI), May 2004. http://dx.doi.org/10.2172/825677.

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9

Saltzman, Eric S., Mark Donelan, and Warren De Bruyn. Laboratory Investigation of Air-Sea Interfacial Properties in Relation to Gas Exchange and Remote Sensing. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada631381.

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Carroll, Herbert B., and Genliang Guo. A New Methodology for Oil and Gas Exploration Using Remote Sensing Data and Surface Fracture Analysis. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/3244.

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