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2019 | 28 | 3 |

Tytuł artykułu

Using the RS method to analyse construction land changes in Tongren during 2002 and 2016

Autorzy

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
Timely and accurately obtained regional construction land information for environmental protection, ecological construction, and regional planning is important. In this study, we used Landsat7 ETM+ images acquired in 2002 and Landsat8 images acquired in 2016 as data sources, and performed maximum likelihood classification and class masking in order to extract construction land information in Tongren. The experimental results showed that the construction land of the study area was 306.07 km² in 2002 and 675.15 km² in 2016. This shows an increase of 369.08 km², which marks an increase by 120.59%, while the expansion ratio was quite larger. Construction land was mainly transferred from cultivated land (338.65 km²), and shows that large areas of cultivated land were replaced by construction land. The main reason for the construction land increase was urban expansion, and the county expansion ratio was between 3.72 and 7.92. Road construction, mining destruction, and residential expansion are additional reasons, but these have caused smaller changes to the area of construction land, with a change ratio of only 0.34. In mountainous Karst areas, urban expansion may cause ecological problems; therefore, we should protect the natural environment and maintain the landscape in its original state so as not to cause excessive disturbance to land resources.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

28

Numer

3

Opis fizyczny

p.1277-1286,fig.,ref.

Twórcy

autor
  • School of Land and Tourism, Luoyang Normal University, Luoyang, Henan Province, China
autor
  • School of Land and Tourism, Luoyang Normal University, Luoyang, Henan Province, China
autor
  • School of Land and Tourism, Luoyang Normal University, Luoyang, Henan Province, China

Bibliografia

  • 1. HU W., YANG Y.H., DUAN Z.L. On Intelligent Extraction of Construction Land Based On High-Resolution Satellite Image. Geology of Anhui, 3, 210, 2014.
  • 2. SUN F., XU S.W., WU X.C., XU S.H. Application of high reliability and automatic construction land change detection to land supervision. Remote Sensing for Land and Resources, 2, 131, 2015.
  • 3. KONG W., GUO J., HAO O.M. Study on Land Intensive Use Response on Economic Development and Regional Differentiated Control of Constructed Land. China Population, Resources And Environment, 24, 100, 2014.
  • 4. LIU Y., LI X. Study on sustainable development strategy of construction land in China. Economic Review, 9, 24, 2013.
  • 5. RAO P., WANG J.L., WANG Y. Extraction of information on construction land based on multi-feature decision tree classification. Transactions of the Chinese Society of Agricultural Engineering, 30, 233, 2014.
  • 6. TOWNSHEND J.R.G., CHRISTOPHER O.J. Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing. Remote Sensing of Environment, 83, 351, 2002.
  • 7. LUNETTA R.S., LYON J.G (EDS.). Geospatial data accuracy assessment. Las Vegas: Environmental Protection Agency, 87, 2003.
  • 8. JENSEN, J.R. Introductory Digital Image Processing: A Remote Sensing Perspective: Third Edition. New Jersey: Prentice Hall, Inc, 2005.
  • 9. GAO Z.Q., NING J.C., GAO W. Response of land surface temperature to coastal land use/cover change by remote sensing. Transactions of the CSAE, 25, 274, 2009.
  • 10. SCHNEIDER A. Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment, 12, 689, 2012.
  • 11. HU S.G., WANG L. Automated urban land-use classification with remote sensing. International Journal of Remote Sensing, 34, 790, 2013.
  • 12. YANG C.J., ZHOU C.H. Extracting Residential Areas on the TM Imagery. Journal of Remote Sensing, 4, 146, 2000.
  • 13. CHA Y., NI S.X., YANG S. An Effective Approach to Automatically Extract Urban Land-use from TM Imagery. Journal of Remote Sensing, 7, 37, 2003.
  • 14. ZHA Y., GAO J., NI S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24, 583, 2003.
  • 15. YANG Z.X., HE X.F. Automatic extraction of urban land-use information from remote sensing images based on improved NDBI method. Journal of Hohai University(Natural Sciences), 38, 181, 2010.
  • 16. POWELL R.L., ROBERTS D.A., DENNISON P.E., HESS L.L. Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: manaus, brazil. Remote Sensing of Environment, 106 (2), 253, 2007.
  • 17. WENG F.Q., PU R.L. Mapping and assessing of urban impervious areas using multiple endmember spectral mixture analysis: A case study in the city of Tampa, Florida. Geocarto International, 28, 594, 2013.
  • 18. XU H.Q. Fast information extraction urban built-up land based on the annalysis of spectral signature and normalized difference index. Geographical Research, 24, 311, 2005.
  • 19. YANG Y.L., TANG Y., HE Z.W., FENG J., WANG L. Identification and extraction of urban construction land information by index image - a case study of Yueyang City. Science of Surveying and Mapping, 36, 208, 2011.
  • 20. RAO P., WANG J., WANG Y. Extraction of information on construction land based on multi-feature decision tree classification. Transactions of the Chinese Society of Agricultural Engineering, 30, 233, 2014.
  • 21. http://www.trs.gov.cn/news/2014414/n22.html
  • 22. ZHANG Y.S., ODEH I.O.A., RAMADAN E. Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data. International Journal for Remote Sensing, 34, 168, 2013.
  • 23. CHEN Y.C. Study on the Reasons and Policies Towards the Rapid Expansion of Large Cities. City Planning Review, 27, 33, 2003.
  • 24. DENG X.H., BI K. Analysison the Karst Topographic Distributionin Guizhou Province. Guizhou Geology, 21, 191, 2004.
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  • 26. https://wallstreetcn.com/articles/228401

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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