Research and Application of Remote Sensing and GIS Technologies in Determining and Forecasting Land Use Changes by Markov Chain in Y Yen District - Nam Dinh Province

• Autorzy: Giang Le Thi , Thuan Duc Nguyen , Vinh Quoc Tran
• Języki publikacji: EN

Abstrakty

EN

The land's natural resources are invaluable and a requisite for the existence and development of humans and other organisms on Earth. In recent years, under the strong impact of new directions in economic and social development, the demand for land has been increasing. The percentage of land used for residential living, transportation, irrigation and infrastructure tends to increase, while the share of agricultural land is continuously decreasing. Consequently, the allocation and efficient use of land is one of the most important concerns in order to enable sustainable development, environmental protection and ecology. Therefore, research to determine the volatility and changing trends in land use is necessary. This study uses remote sensing and GIS technology, combined with the Markov Chain to determine variation and forecast the changes in land use in the Y Yen district of the Nam Dinh province of Vietnam. This will create a basis for helping land managers grasp the situation in local land use management. (original abstract)

Słowa kluczowe

EN

Geographic Information System (GIS); Markov chain; Land use; Spatial development

PL

System Informacji Przestrzennej; Łańcuch Markowa; Użytkowanie ziemi; Zagospodarowanie przestrzenne

• Czasopismo: Real Estate Management and Valuation
• Rocznik: 2016
• Numer: vol. 24, iss. 3
• Strony: 27--39

Twórcy

autor

Giang Le Thi

• Trau Quy, Gia Lam, Hanoi

autor

Thuan Duc Nguyen

• Trau Quy, Gia Lam, Hanoi

autor

Vinh Quoc Tran

• Trau Quy, Gia Lam, Hanoi

Bibliografia

• Assian Association on Remote Sensing, 2004, Asian Conference on Remote Sensing, 11-2004, Proceeding 1, 2.
• Chuvieco E., 2016, Fundamentals of Satellite Remote Sensing: An Environmental Approach, Second Edition, CRC Press, pp. 98-107.
• Crosetto M., Mroz M., 1998, Optical-Radar Data Fusion for Land Use Classification, Proceedings of the ISPRS - Comm, VII International Symposium ECO BP, Budapest, Hungary 1-4 sept, Int. Arch. Phot. and RS, Vol. XXXII., Part 7, pp. 698-705.
• john r.jensen, 1996, Introductory Digital Image Processing.
• kasper kok, manuel winograd 2002, Modelling Land-use Change for Central America, with Special Reference to the Impact of Hurricane Mitch, Ecological Modelling, 149, 53-69.
• Mróz M., 2002, Radiometric and Textural Fusion of Multiresolution Landsat 7 ETM+ Channels for Improvement of Visual Image Interpretation and Land Cover Classification, In the Proceedings of the 22nd Symposium of the European Association of Remote Sensing Laboratories (EARSeL), Prague, Czech Republic, 4-6 June 2002, "Geoinformation for European-wide Integration", Editor Thomas Benes, ISBN 90 77017712, pp. 251-258.
• Nguyen K.T., Pham V.T., Tran Q.V., Nguyen T.T.H., 2011, Curriculum on Remote Sensing, Hanoi University of Agriculture Publishing House.
• Norris, J.R., 1998, Markov Chains, Cambridge University Press.
• Richards, J.A, 1999, Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, p. 240.
• ROBERT GILMORE PONTIUS, SILVIA H.PETROVA, 2010, Assessing a Predictive Model of Land Change Using Incertain data, Environmental Modelling and Software, Vol. 25, pp. 299-309.
• DEEPARK K.RAY, BRYAN C.PIJANOWSKI, 2010, A Backcast Land Use Change Model to Generate Past Land Use Maps: Application and Validation At the Muskegon River Watershed of Michigan, USA, Journal of Land Use Science, 5(1), pp. 1-29.
• Sohl, T.L., Sayler, 2008, Using the FORE-SCEM Model to Project Land-Cover Change in the Southeastern United States, Ecological Modelling, Vol. 219, pp. 49-65.
• Sohl, T.L., Sayler, K.L., Drummond, M.A., Loveland, 2007, The FORE-SCE Model: A Practical Approach for Projecting Land Cover Change Using Scenario-Based Modeling, Journal of Land Use Science, 2(2), pp. 103-126.
• Vogelmann, J.E., Tolk, B., Zhu 2009, Monitoring Forest Changes in the Southwestern United States using Multitemporal Landsat data, Remote Sensing of Environment, Vol. 113, pp. 1739-1748.
• Wang, Y., Mitchell, B.R., Nugranad-Marzilli, J., Bonynge, Zhou, Y., Shriver, 2009, Remote Sensing of Land-Cover Change and Landscape Context of the National Parks: A CaseSstudy of the Northeast Temperate Network, Remote Sensing of Environment, Vol. 13, pp. 1453-1461.

Identyfikatory

DOI

10.1515/remav-2016-0019