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Journal articles on the topic 'GIS and Remote Sensing'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

TATEISHI, RYUTARO. "Combination of remote sensing and GIS." Journal of the Japan society of photogrammetry and remote sensing 24 (1985): 65–67. http://dx.doi.org/10.4287/jsprs.24.special2_65.

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12

Collier, P. "Uncertainty in Remote Sensing and GIS." Photogrammetric Record 19, no. 107 (September 2004): 251–52. http://dx.doi.org/10.1111/j.0031-868x.2004.282_2.x.

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13

Halounová, Lena. "Freeware for GIS and Remote Sensing." Geoinformatics FCE CTU 2 (December 19, 2007): 53–60. http://dx.doi.org/10.14311/gi.2.7.

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Education in remote sensing and GIS is based on software utilization. The software needs to be installed in computer rooms with a certain number of licenses. The commercial software equipment is therefore financially demanding and not only for universities, but especially for students. Internet research brings a long list of free software of various capabilities. The paper shows a present state of GIS, image processing and remote sensing free software.

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14

Hoffmann, Jörn, and Per Sander. "Remote sensing and GIS in hydrogeology." Hydrogeology Journal 15, no. 1 (December 12, 2006): 1–3. http://dx.doi.org/10.1007/s10040-006-0140-2.

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15

Allen, Thomas R. "Advances in remote sensing and GIS analysis." Geomorphology 36, no. 3-4 (February 2001): 257–58. http://dx.doi.org/10.1016/s0169-555x(00)00027-1.

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16

Nagasawa, Ryota, and Yoshiyuki Hioki. "Vegetation Mapping using Remote Sensing and GIS." Landscape Ecology and Management 11, no. 1 (2006): 1–2. http://dx.doi.org/10.5738/jale.11.1.

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17

Hioki, Yoshiyuki, and Ryota Nagasawa. "Vegetation mapping using remote sensing and GIS." Landscape Ecology and Management 11, no. 2 (2007): 105. http://dx.doi.org/10.5738/jale.11.105.

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18

Nandi, Debabrata, Satyendra Nathjee, and Tanmoy Chatterjee. "Microwatershed Management Using Remote Sensing and GIS." Advanced Science Letters 22, no. 2 (February 1, 2016): 305–10. http://dx.doi.org/10.1166/asl.2016.6866.

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19

Shi, W. Z., M. Ehlers, and M. Molenaar. "Uncertainties in integrated remote sensing and GIS." International Journal of Remote Sensing 26, no. 14 (July 2005): 2911–15. http://dx.doi.org/10.1080/01431160500197537.

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20

Turker, Mustafa, and Eugene Derenyi. "GIS Assisted Change Detection Using Remote Sensing." Geocarto International 15, no. 1 (March 2000): 51–56. http://dx.doi.org/10.1080/10106040008542139.

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21

MISSOTTEN, R., Q. HAN, and A. AURELI. "Remote sensing and GIS activities in UNESCO." International Journal of Remote Sensing 15, no. 15 (October 1994): 3111–18. http://dx.doi.org/10.1080/01431169408954311.

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22

Anonymous. "Standards for mapping, remote sensing, and GIS." Eos, Transactions American Geophysical Union 71, no. 51 (1990): 1872. http://dx.doi.org/10.1029/eo071i051p01872-02.

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23

McClean, Colin. "Remote sensing and GIS in ecosystem management." Land Use Policy 13, no. 3 (July 1996): 237–38. http://dx.doi.org/10.1016/0264-8377(96)84065-8.

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24

Richert, Scott E., and A. Alaric Sample. "Remote Sensing and GIS in Ecosystem Management." Journal of Wildlife Management 59, no. 3 (July 1995): 623. http://dx.doi.org/10.2307/3802473.

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25

Taloor, Ajay Kumar, Narsimha Adimalla, and Ajanta Goswami. "Remote Sensing and GIS applications in Geoscience." Applied Computing and Geosciences 11 (September 2021): 100065. http://dx.doi.org/10.1016/j.acags.2021.100065.

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26

Twumasi, Nana Yaw Danquah, Zhenfeng Shao, and Altan Orhan. "Remote Sensing and GIS Methods in Urban Disaster Monitoring and Management – An Overview." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 918–26. http://dx.doi.org/10.31142/ijtsrd23976.

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27

Nagarajan, N., and S. Poongothai. "Identification of Land use and Land cover Changes using Remote Sensing and GIS." International Journal of Engineering and Technology 3, no. 5 (2011): 570–76. http://dx.doi.org/10.7763/ijet.2011.v3.287.

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28

Kumari, Alpana, and Jitendra Kumar. "Application of GIS and Remote Sensing for Developing Watershed GIS." International Journal of Scientific and Research Publications (IJSRP) 10, no. 9 (September 6, 2020): 20–30. http://dx.doi.org/10.29322/ijsrp.10.09.2020.p10505.

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29

S.P.Mehta, S. P. Mehta. "Decision Support System for Sustainable Development of a Watersheds Using Remote Sensing and GIS." International Journal of Scientific Research 2, no. 11 (June 1, 2012): 185–88. http://dx.doi.org/10.15373/22778179/nov2013/59.

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30

Kumar Gautam, Pawan. "Spatio-Temporal Analysis of Sinuosity of Ghaghara River: A Remote Sensing and GIS Approach." International Journal of Science and Research (IJSR) 8, no. 9 (September 5, 2019): 1361–64. http://dx.doi.org/10.21275/art20201442.

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31

Sun, J., and H. Xiang. "RESEARCH ON KEY TECHNOLOGY OF MINING REMOTE SENSING DYNAMIC MONITORING INFORMATION SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 893–96. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-893-2017.

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Problems exist in remote sensing dynamic monitoring of mining are expounded, general idea of building remote sensing dynamic monitoring information system is presented, and timely release of service-oriented remote sensing monitoring results is established. Mobile device-based data verification subsystem is developed using mobile GIS, remote sensing dynamic monitoring information system of mining is constructed, and "timely release, fast handling and timely feedback" rapid response mechanism of remote sensing dynamic monitoring is implemented.

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32

Gao, Jay, and Yansui Liu. "Applications of remote sensing, GIS and GPS in glaciology: a review." Progress in Physical Geography: Earth and Environment 25, no. 4 (December 2001): 520–40. http://dx.doi.org/10.1177/030913330102500404.

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Remote sensing has served as an efficient method of gathering data about glaciers since its emergence. The recent advent of Geographic Information Systems (GIS) and Global Positioning Systems (GPS) has created an effective means by which the acquired data are analysed for the effective monitoring and mapping of temporal dynamics of glaciers. A large number of researchers have taken advantage of remote sensing, GIS and GPS in their studies of glaciers. These applications are comprehensively reviewed in this paper. This review shows that glacial features identifiable from aerial photographs and satellite imagery include spatial extent, transient snowline, equilibrium line elevation, accumulation and ablation zones, and differentiation of ice/snow. Digital image processing (e.g., image enhancement, spectral ratioing and automatic classification) improves the ease and accuracy of mapping these parameters. The traditional visible light/infrared remote sensing of two-dimensional glacier distribution has been extended to three-dimensional volume estimation and dynamic monitoring using radar imagery and GPS. Longitudinal variations in glacial extent have been detected from multi-temporal images in GIS. However, the detected variations have neither been explored nor modelled from environmental and topographic variables. GPS has been utilized independent of remote sensing and GIS to determine glacier ice velocity and to obtain information about glacier surfaces. Therefore, the potential afforded by the integration of nonconventional remote sensing (e.g., SAR interferometry) with GIS and GPS still remains to be realized in glaciology. The emergence of new satellite images will make remote sensing of glaciology more predictive, more global and towards longer terms.

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33

Luo, Jun, Danlin Yu, and Miao Xin. "Modeling Urban Growth Using GIS and Remote Sensing." GIScience & Remote Sensing 45, no. 4 (October 2008): 426–42. http://dx.doi.org/10.2747/1548-1603.45.4.426.

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34

Krishnaveni, K. S., and P. P. Anilkumar. "MANAGING URBAN SPRAWL USING REMOTE SENSING AND GIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W11 (February 14, 2020): 59–66. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w11-59-2020.

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Abstract. Indian cities, like several other developing cities around the world, are urbanizing at an alarming rate. This unprecedented and uncontrolled urbanization may result in urban sprawl, which is characterized by low-density impervious surfaces, often clumsy, extends along the fringes of metropolitan areas with unbelievable pace, disperse, auto-dependent with environmentally and socially impacting characteristics. The ill-effects of urban sprawl in developing countries scenario is a bit complicated compared to that of developed countries because of uncontrolled population growth and haphazard urbanization. This paper attempts to investigate the capabilities of remote sensing and GIS techniques in understanding the urban sprawl phenomenon in a better way compared to time- consuming conventional methods. An overview of the enormous potential of remote sensing and GIS techniques in mapping and monitoring the Spatio-temporal patterns urban sprawl is dealt with here. The spatial pattern and dynamics of the urban sprawl of Kozhikode Metropolitan Area (KMA, Kerala, India) during the period from 1991 to 2018 using the integrated approach of remote sensing and GIS are attempted here. Index derived Built-up Index (IDBI) which is a thematic index-based index (combination of Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI) and Soil Adjusted Vegetation Index (SAVI)) is used for the rapid and automated extraction of built-up features from the time series satellite imageries. The extracted built-up areas of each year are then used for Shannon’s entropy calculations, which is a method for the quantification of urban sprawl. The results of IDBI and Shannon’s entropy analysis highlight the fact that there occurs an alarming increase in the built-up areal extent from 1991 to 2018. The urban planning authorities can make use of these techniques of built-up area extraction and urban sprawl analysis for effective city planning and sprawl control.

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35

Liu, Ru, Yan Jun Liu, and Xiao Qing Dong. "Tsunami Warning Based on Remote Sensing and GIS." Advanced Materials Research 1030-1032 (September 2014): 1855–58. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1855.

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Tsunami, one of the most horrible disasters in the earth, the killer wave contains incogitable powerful energy rushing to the shore and coastline. Nowadays the best way for avoiding the tragedy is to use Tsunami Warning System (TWS) to forecast. How does it work based on remote sensing and GIS? This question attracts us to find answer and make discovery. We believe with the development of human culture and technology, TWS will be consummate one day. And at that time, people will not get so much loss as today by tsunami.

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36

D. Nagaharshitha et al.,, D. Nagaharshitha et al ,. "Remote Sensing and GIS Applications in Fruit Industry." International Journal of Agricultural Science and Research 7, no. 5 (2017): 73–76. http://dx.doi.org/10.24247/ijasroct201710.

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37

Friedrich, Robert L., and Robert V. Blystone. "Internet Teaching Resources for Remote Sensing and GIS." BioScience 48, no. 3 (March 1998): 187–92. http://dx.doi.org/10.2307/1313264.

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38

Naidu, Dadi Sanyasi. "Soft Artificial Computing In GIS And Remote Sensing." International Journal of Advanced Multidisciplinary Scientific Research 1, no. 4 (April 15, 2018): 122–27. http://dx.doi.org/10.31426/ijamsr.2018.1.4.222.

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39

Goodchild, Michael F. "Metrics of scale in remote sensing and GIS." International Journal of Applied Earth Observation and Geoinformation 3, no. 2 (January 2001): 114–20. http://dx.doi.org/10.1016/s0303-2434(01)85002-9.

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40

S.M, Acharya, Pawar S.S, and Wable N.B. "Application of Remote Sensing & GIS in Agriculture." International Journal of Advanced Engineering Research and Science 5, no. 4 (2018): 63–65. http://dx.doi.org/10.22161/ijaers.5.4.10.

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41

Hinton, J. C. "GIS and remote sensing integration for environmental applications." International journal of geographical information systems 10, no. 7 (October 1996): 877–90. http://dx.doi.org/10.1080/02693799608902114.

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42

Hinton, J. C. "GIS and remote sensing integration for environmental applications." Geographical Information Systems 10, no. 7 (November 1, 1996): 877–90. http://dx.doi.org/10.1080/026937996137765.

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43

Wang, Le, and Changshan Wu. "Population estimation using remote sensing and GIS technologies." International Journal of Remote Sensing 31, no. 21 (November 8, 2010): 5569–70. http://dx.doi.org/10.1080/01431161.2010.496809.

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44

Bunting, Peter, Daniel Clewley, Richard M. Lucas, and Sam Gillingham. "The Remote Sensing and GIS Software Library (RSGISLib)." Computers & Geosciences 62 (January 2014): 216–26. http://dx.doi.org/10.1016/j.cageo.2013.08.007.

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45

Foody, G. M. "Editorial: Ecological applications of remote sensing and GIS." Ecological Informatics 2, no. 2 (June 2007): 71–72. http://dx.doi.org/10.1016/j.ecoinf.2007.06.001.

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46

Taloor, Ajay Kumar, GirishCh Kothyari, and Ajanta Goswami. "Remote sensing and GIS applications in Quaternary Science." Quaternary International 575-576 (February 2021): 1–4. http://dx.doi.org/10.1016/j.quaint.2021.02.001.

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47

JYOTI, JAIN THOLIYA. "LAND RESOURCE MANAGEMENT THROUGH GIS AND REMOTE SENSING." i-manager’s Journal on Pattern Recognition 6, no. 3 (2019): 18. http://dx.doi.org/10.26634/jpr.6.3.16865.

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48

He, Qing Song, and Fan Gui Zeng. "Survey and Evaluation of Coalfield Geological Exploration and Coal Resources Based on Remote Sensing Technology." Applied Mechanics and Materials 380-384 (August 2013): 3930–33. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3930.

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With the development of computer science and information technology, computer data mining technology is updated, which makes the image mosaic technology that can detail data processing for remote sensing image. On this basis, this paper uses advanced computer image processing technology to carry on the combination of wavelet decomposition, combined with the GIS remote sensing technology, the coal exploration and investigation evaluation technique are carried out in-depth discussion. The paper establishes the mathematical model of GIS remote sensing image processing, and through the wavelet decomposition method, the function of image processing is given. In the third part, combined with the MATLAB data processing software, coal GIS satellite remote sensing image is carried out resource evaluation by the programming operation and the size distribution of coal rock is drawn, the coal content evaluation data table of remote sensing area is finally obtained, in which the content of No. 1 coal seam is highest reached 9860 tons, the horizontal extension of four coal seams is between 200-500m, and the longitudinally is extending between 50-100m.

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49

Khan, Sikander Nawaz. "Integration of GIS with Remote Sensing and GPS for Disaster Mitigation." Journal of Disaster Research 10, no. 2 (April 1, 2015): 225–30. http://dx.doi.org/10.20965/jdr.2015.p0225.

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Natural disasters like flood, earthquake, cyclone, volcanic eruption and others are causing immense losses to the property and lives every year. Current status and actual loss information on natural hazards can be determined and also prediction for next probable disasters can be made using different remote sensing and mapping technologies. Global Positioning System (GPS) calculates the exact position of damage. It can also communicate with wireless sensor nodes embedded in potentially dangerous places. GPS provides precise and accurate locations and other related information like speed, track, direction and distance of target objects to emergency responders.Remote Sensing facilitates to map damages without having physical contact with target area. Now with the addition of more remote sensing satellites and other advancements, early warning system is used very efficiently. Remote sensing is being used both at local and global scale. High Resolution Satellite Imagery (HRSI), airborne remote sensing and space-borne remote sensing is playing a vital role in disaster management.Early in Geographic Information System (GIS) was used to collect, arrange, and map the spatial information, but now it has the capability to analyze spatial data. This analytical ability of GIS is the main cause of its adoption by different emergency service providers like the police and ambulance service.The full potential of these so called 3S technologies cannot be used alone. Integration of GPS and other remote sensing techniques with GIS has pointed new horizons in modeling of earth science activities. Many remote sensing cases, including Asian Ocean Tsunami in 2004, Mount Mangart landslides and Pakistan-India earthquake in 2005 are described in this paper.

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50

Gillespie, Thomas W. "Remote sensing of animals." Progress in Physical Geography: Earth and Environment 25, no. 3 (September 2001): 355–62. http://dx.doi.org/10.1177/030913330102500303.

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The improved accuracy and precision of animal tracking via satellites has made a significant impact on quantifying large-scale biogeographic patterns for a variety of taxa with important implications for conservation and natural resource management. This paper reviews research undertaken from 1995 to 1999 to provide an overview of advances in the remote sensing of animal movements in both terrestrial and marine environments and to identify promising trends for biogeographic research in the twenty-first century. Remote sensing of animals by satellite provides a new method to test a number of biogeographic hypotheses related to migration and can identify a number of environmental correlates associated with the distributions of species. Tracking of smaller species and increases in sample size are sure to occur as transmitter size and cost continue to decrease in the next decade. Geographers can significantly contribute to the understanding of species dispersal and distributional patterns by combining real-time and archived global and regional datasets with existing data from past studies and future research projects. Only four studies used GIS data or remote sensed imagery in this review, while the remaining studies cited used simple digital line graphs of countries, topography, land and sea boundaries.

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