Skip to main content
SpringerLink
Log in
Book cover

Chinese Conference on Image and Graphics Technologies

IGTA 2013: Advances in Image and Graphics Technologies pp 92–101Cite as

The Remote Sensing Monitoring Analysis Based on Object-Oriented Classification Method

  • Conference paper

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 363))

Abstract

In this paper, based on multi-temporal remote monitoring technology, using object-oriented classification method to monitor the change of vegetation of Zhangye oasis from TM/ETM data in 1989, 2000, 2011 years. The results show that: (1) the multi-resolution segmentation converted the single cell which had the similar texture, spectrum and shape to the object. Integrating nearest neighbor classifier and membership classifier to class the three data, and the overall accuracy of classification was 89.5%, Kappa coefficient was 0.9. The classification stability was 0.45 and 0.47 in 2000 and 2011 years. It showed that object-oriented classification method accuracy is higher than traditional classification method. (2)The three classification results indicate the area of bare land was larger than other, and it was reducing, with a percentage was 73.21%, 64.76%, 60.17%. The vegetation mainly distributed in both sides of Heihe River, the percentage of three data were 16.01%, 29.9%, 33.6%. The saline land was mainly distributed in the northwest of the oasis region, the percentage dropped to 2.33% from 4.89% during 1989 and 2011 years. (3) NDVI of the upstream was higher than the NDVI of the downstream on sides of river, the NDVI raised and the maximum value was 0.54. NDVI increased significantly from 1989 to 2011 years in Linze central region, and the maximum value reached to 0.58 in 2011 years, and it had the same characteristic in the southeast of Ganzhou district. The average NDVI of 2011 years was higher than in 2000 and 1989.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cao, X.M., Chen, X., Bao, A.M.: Response of vegetation to temperature and precipitation in Xinjiang during the period of 1998−2009. Journal of Arid Land 3(2), 94–103 (2011)

    Article  Google Scholar 

  2. Dai, S.P., Zhang, B., Wang, H.J.: Vegetation cover change and the driving factors over northwest China. Journal of Arid Land 3(1), 25–33 (2010)

    Google Scholar 

  3. Li, X.H., Shi, Q.D., Chang, S.L.: Change of NDVI based on NOAA image in northwest arid area of China in 1981−2001. Arid Land Geography 31(6), 940–945 (2008)

    Google Scholar 

  4. Tsuchiya, K., Igarshi, T., Qong, M.: Land cover change detection based on satellite data for an arid area to the south of Aksu in Taklimakan desert. Journal of Arid Land 2(1), 14–19 (2010)

    Google Scholar 

  5. Qian, Y.B., Zhang, H.Y., Wu, Z.N.: Vegetation composition and distribution on the northern slope of Karlik Mountain to Naomaohu basin, East Tianshan Mountains. Journal of Arid Land 3(1), 15–24 (2011)

    Article  Google Scholar 

  6. Zhu, W.B., Lv, A.F., Jia, S.F.: Spatial distribution of vegetation and the influencing factors in Qaidam Basin based on NDVI. Journal of Arid Land 3(2), 85–93 (2011)

    Article  Google Scholar 

  7. Xin, Z.B., Xu, J.X., Zheng, W.: Impact of climate change and human activities on the Loess Plateau in vegetation cover. Science in China Series D: Earth Sciences 37(11), 1504–1514 (2007)

    Google Scholar 

  8. IPCC (Intergovernmental Panel on Climate Change): Summary for policymakers of the synthesis report of the IPCC fourth assessment report. Cambridge University Press, New York (2007)

    Google Scholar 

  9. Shi, Y.F., Shen, Y.P., Li, D.L.: Discussion on the present climate change from warm-dry to warm-wet in Northwest China. Quaternary Sciences 23(2), 152–164 (2003)

    Google Scholar 

  10. Guo, Z.M., Miao, Q.L., Li, X.: Change characteristics of temperature in North China since recent 50 years. Arid Land Geography 28(2), 176–182 (2005)

    Google Scholar 

  11. Hu, R.J., Jiang, F.Q., Wang, Y.J.: A study on signals and effects of climatic pattern change from warm-dry to warm-wet in Xinjiang. Arid Land Geography 25(3), 194–200 (2002)

    Google Scholar 

  12. Zhang, J., Pan, X.L., Gao, Z.Q.: Estimation of net primary productivity of the oasis-desert ecosystems in arid west China on RS-based ecological process. Arid Land Geography 29(2), 255–261 (2006)

    Google Scholar 

  13. Méndez-Barroso, L.A., Vivonia, E.R., Watts, C.J.: Seasonal and inter annual relations between precipitation, surface soil moisture and vegetation dynamics in the North American monsoon region. Journal of Hydrology 377(1-2), 59–70 (2009)

    Article  Google Scholar 

  14. Foody, G.M., Boyd, D.S., Cutler, M.E.J.: Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing Environment 85, 463–474 (2003)

    Article  Google Scholar 

  15. Schowengerdt, R.A.: Remote sensing: models and methods for image processing. Academic Press (2006)

    Google Scholar 

  16. Blaschke, T., Lang, S., Lorup, E.: Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. In: Cremers, A., Greve, K. (eds.) Environmental Information for Planning, Politics and the Public, vol. 2, pp. 555–570. Metropolis, Marburg (2000)

    Google Scholar 

  17. Clinton, N., Holt, A., Scarborough, J.: Accuracy assessment measures for object-based image segmentation goodness. Photogrammetric Engineering and Remote Sensing 76(3), 289–299 (2010)

    Google Scholar 

  18. Tucker, C.J., Townshend, J.R.G.: African land-cover classification using satellite data. Science 227(4685), 369–375 (1985)

    Article  Google Scholar 

  19. McFeeters, S.K.: The use of the normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17(7), 1425–1432 (1996)

    Article  Google Scholar 

  20. Dronova, I., Gong, P., Wang, L.: Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China. Remote Sensing of Environment 115(12), 3220–3236 (2011)

    Article  Google Scholar 

  21. Schneevoigt, N.J., van der Linden, S., Kellenberger, T.: Object-oriented classification of alpine landforms from an ASTER scene and digital elevation data (Reintal, Bavarian Alps). Grazer Schriften der Geographie und Raumforschung 45, 53–62 (2010)

    Google Scholar 

  22. Yang, H.L., Peng, J.H.: Remote sensing classification based on Markov random field and fuzzy C-means clustering. Acta Geodaetica et Cartographica Sinica 41(2), 213–218 (2012)

    Google Scholar 

  23. Zhao, P., Fu, Y.F., Zheng, L.G.: Cart-based land use/cover classification of remote sensing images. Journal of Remote Sensing 9(6), 708–716 (2005)

    Google Scholar 

  24. Guo, L., Pei, Z.Y., Wu, Q.: Application of method and process of object-oriented land use-cover classification using remote sensing images. Transactions of the CSAE 26(7), 194–198 (2010)

    Google Scholar 

  25. Liu, Z.G., Shi, W.Z., Li, D.R.: Partially supervised classification of remotely sensed imagery using support vector machines. Journal of Remote Sensing 9(4), 36–372 (2005)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Engineering & Technical College, Chengdu University of Technology, 614007, Leshan, China

    HaiJun Wang & Xiao Bin Huang

  2. Institute of Scientific and Technical Information, Chinese Academy of Tropical Agricultural Sciences (CATAS), 571737, Danzhou, China

    ShengPei Dai

Authors
  1. HaiJun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. ShengPei Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao Bin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Institute of Information Science, Beijing Jiaotong University, 100044, Beijing, China

    Qiuqi Ruan

  3. Institute of Computing Technology, Chinese Academy of Science, 100080, Beijing, China

    Xilin Chen

  4. Tsinghua University, Beijing, China

    Huimin Ma

  5. Chinese Academy of Sciences, Beijing, China

    Liang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, H., Dai, S., Huang, X.B. (2013). The Remote Sensing Monitoring Analysis Based on Object-Oriented Classification Method. In: Tan, T., Ruan, Q., Chen, X., Ma, H., Wang, L. (eds) Advances in Image and Graphics Technologies. IGTA 2013. Communications in Computer and Information Science, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37149-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37149-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37148-6

  • Online ISBN: 978-3-642-37149-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation