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Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India Cover

Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

Open Access
|Jun 2018

References

  1. Alrababah, M. A., & Alhamad, M. N. (2006). Land use/cover classification of arid and semi-arid Mediterranean landscapes using Landsat ETM. International Journal of Remote Sensing, 27, 2703.10.1080/01431160500522700
  2. Asrar, G., M. Fuchs, E. T. Kanemasu, and J. H. Hatfield (1984), Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat, Agron J., 76, 300 – 306.
  3. Campbell, J. B. (1987). Introduction to Remote Sensing. The Guilford Press, New York, USA, 551 pp.10.1080/10106048709354126
  4. Chavula, G., Brezonik, P. & Bauer, M. (2011). “Land Use and Land Cover Change (LULC) in the Lake Malawi Drainage Basin, 1982-2005”, International Journal of Geosciences, vol. 2, no. 2, pp. 172-178.
  5. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B. & Lambin, E. (2004). Review article digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing 25:1565–1596.
  6. Fan, F., Weng, Q. and Wang, Y., (2007). Land Use and Land Cover Change in Guangzhou, China, from 1998 to 2003, Based on Landsat TM /ETM+ Imagery, Sensors,7, 1323-1342.10.3390/s7071323
  7. Fluor C.P., (2014). Absorptivity Module & NDVI Measurement Instruction Manual, product PSI, spol. s r. o., Drásov 470, 664 24 Drásov, Czech Republic
  8. Im, J. & Jensen, J.R., (2005). A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sensing of Environment 99: 326–340.
  9. Huete, A. R., (1988), A soil-adjusted vegetation index (SAVI), Remote Sens. Environ. 25:295-309.
  10. Jovanović, D., Govedarica, M., Pržulj, Đ. (2007). The tracking use change area of Vojvodine Using shooting by passing LANDSAT ETM + and TM satellite images Vodoprivreda 0350-0519, 39, 229-230, 337-343.
  11. Jovanović, D., Govedarica, M., Badnjarević, M., (2011). Presenting And Comparing the Object Based Image Analysis and Standard Image Analysis For Change Detection of Forest Areas, Using Low-Resolution Satellite Imagery. SGEM, 2, 11, 329-336.10.5593/sgem2011/s08.103
  12. Lillesand, T.M., Kiefer, R.W. and Chipman, J.W. (2008). Remote Sensing and Image Interpretation. 6th Edition, John Wiley & Sons, Hoboken.
  13. Lunetta R., (2004). Land-cover change detection using multi-temporal modis NDVI data, Remote Sensing of Environment, 105, 2006, 142–154.10.1016/j.rse.2006.06.018
  14. Liu J.G. & Mason, P.J, (2009). Essential Image Processing and GIS for Remote Sensing. John Wiley & Sons, Inc.
  15. Junfeng, L., Zhibao, D., Guangyin, H., Changzhen, Y., Zhenhai, W., Xiang, S., (2011). Land use and land cover change and its driving forces in the source region of the Yangtze River during 1990–2005, International Symposium on Water Resource and Environmental Protection, IEEE, Xi’an, China
  16. Manandhar, R., Odeh, I. O. A., Ancev, T., (2009). Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data using Post-classification Enhancement. Remote Sensing 1, 3, 330-344.10.3390/rs1030330
  17. Matinfar H.R., Sarmadian F., Alavi Panah S.K., and Heck R.J., (2007). Comparisons of Object-Oriented and Pixel-Based Classification of Land Use/Land Cover Types Based on Lansadsat7, Etm+ Spectral Bands (Case Study: Arid Region of Iran)”, American-Eurasian J. Agric. & Environ. Sci., vol 2 (4), p.p. 448-456, 2007. ISSN 1818-6769, 2007
  18. Mishra, V.N., Rai, P.K., Mohan, K. (2014). Prediction of land use changes based on land change modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute Jovan Cvijic SASA, 64: 111–127.
  19. Mishra, V.N., and Rai, P.K. (2016). A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences, 9 (4):1-18. Doi: 10.1007/s12517-015-2138-3.10.1007/s12517-015-2138-3
  20. Mishra, V.N., Rai, P.K., Kumar, P. and Prashad, R., (2016). Evaluation of Land Use/Land Covers Classification Accuracy Using Multi-Temporal Remote Sensing Images, Forum Geographic (Romania), 15 (1), 45-53.10.5775/fg.2016.137.i
  21. Macleod, R.D., & Congalon, R.G., (1998). A quantitative comparison of change detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering & Remote Sensing 64:207–216.
  22. McNairn, H., Protz, R., (1993). Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using Thematic Mapper. Canadian Journal of Remote Sensing 19, 2, 152-159.10.1080/07038992.1993.10874543
  23. Pielke Sr., R. A., Marland, G., Betts, R. A., Chase, T. N., Eastman, J. L., Niles, J. O., Niyogi, Devdutta S., and Running, S. W., (2002). “The influence of land-use change and landscape dynamics on the climate system: relevance to climatechange policy beyond the radiative effect of greenhouse gases.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 360 (1797):1705–1719. doi:10.1098/rsta.2002.1027.10.1098/rsta.2002.102712460493
  24. Prajapati, A., (2016). Land and Forest Management by Land Use/Land Cover Analysis & Change Detection Technique Using Remote Sensing & GIS, Journal of Landscape Ecology, Vol. 9 (3), 63-77. DOI: https://doi.org/10.1515/jlecol-2016-0005.10.1515/jlecol-2016-0005
  25. Qi, J., Moran, M. S., Huete, A. R., Jackson, R. D., Chehbouni, A., (1991). View atmosphere soil effect on vegetation indices derived from SPOT images. Proceedings of the 5th International Symposium Physical Measurements and Signatures in Remote Sensing, Courchevel, France 2, 785-790.
  26. Rai, P.K., Sweta and Mishra, A. Onagh, M., (2011). Multi Seasonal IRS-IC LISS III data for Change Detection Analysis and Accuracy Assessment: A Case Study, Journal of GIS Trend, 2(2):13-19.
  27. Rai, P.K., Singh, S. and Mohan, K., (2015). Land Use Change Detection Using Multi-Temporal Satellite Data: A Case Study of Haridwar District, Uttrakhand, Journal of Scientific Research, (Banaras Hindu University), 59 (1 & 2), 1-16.
  28. Rozenstein, O. and Karnieli, A., (2011). Comparison of methods for land-use classification incorporating remote sensing and GIS inputs, Applied Geography, 31, pp. 533–544.
  29. Singh, A., (1989). Digital Change Detection Techniques Using Remotely Sensed Data, International Journal of Remote Sensing 10, 6, 989-1003.10.1080/01431168908903939
  30. Van Lynden, G. W. J., and Mantel, S., (2001). “The role of GIS and remote sensing in land degradation assessment and conservation mapping: some user experiences and expectations.” International Journal of Applied Earth Observation and Geoinformation, 3 (1):61–68. doi:10.1016/S0303-2434(01)85022-4.10.1016/S0303-2434(01)85022-4
  31. Vishwakarma, C.S., Thakur, S., Rai, P.K., Kamal, V. and Mukharjee, S., (2016). Changing Land Trajectories: A Case Study from India Using Remote Sensing, European Journal of Geography. Vol. 7 (2), 63-73.
  32. Williams, D. L., Goward, S., & Arvidson, T., (2006). Landsat: yesterday, today, and tomorrow. Photogrammetric Engineering & Remote Sensing, 72(10), 1171–1178.10.14358/PERS.72.10.1171
  33. Zaki R., Zaki A. and Ahmed S., (2011). “Land Use and Land Cover Changes in Arid Region: The Case New Urbanized Zone, Northeast Cairo, Egypt,” Journal of Geographic Information System, Vol. 3 No. 3, pp. 173-194. doi: 10.4236/jgis.2011.33015.10.4236/jgis.2011.33015
DOI: https://doi.org/10.1515/jlecol-2017-0017 | Journal eISSN: 1805-4196 | Journal ISSN: 1803-2427
Language: English
Page range: 5 - 18
Submitted on: May 8, 2017
Accepted on: Jul 14, 2017
Published on: Jun 21, 2018
Published by: Czech Society for Landscape Ecology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 Sunita Singh, Praveen Kumar Rai, published by Czech Society for Landscape Ecology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.