Articles

China Land Cover Classification at 1 km Spatial Resolution Based on a Multi-source Data Fusion Approach

  • RAN Youhua ,
  • LU Ling ,
  • LI Xin
Expand
  • Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China

Received date: 2008-09-16

  Revised date: 2008-10-20

  Online published: 2009-02-10

Abstract

Land cover plays a significant role in the earth system science, which reflects the influence of human activities and environmental changes. In China, many land use/cover maps can be used in recent years. How to combine these date effectively to better produce land cover map is a key question. This paper presents a method to map land cover classification using Dempster-Shafer evidential reasoning (DS). DS theory is a non-parametric classifier and can handle data from any number of sources at any scale of measurement and has an explicit mechanism for handling information uncertainty through the use of the concept of ignorance. The 1km land use data of China in 2000 from China landuse database, the 1∶1000000 vegetation map, the 1km glacier map from 1∶100000 glacier map, the 1∶1000000 wetland map and the MODIS land cover classification product (MOD12Q1) as multi-source of evidence to support each IGBP land cover class. These evidences are then combined using Dempster′s Rule of combination. Finally, a new land cover map with higher accuracy is generated by a decision rule based on maximum belief function and could be representative of the Chinese land-cover status in 2000. A simple compare validation is taken and result shows that the accuracy of each land cover class of new land cover map has be strongly promoted relative to IGBP DISCover and MOD12Q1 land cover map especially for the accuracy of the croplands, urban, glacier, wetland and water bodies class. The evidence theory is can be used for fusing multi classification information. The fusion result is sensitive for the basic probability assignment of input data. The results will be validated further and the basic probability for multi-source input data will be assigned more scientific.

 

Cite this article

RAN Youhua , LU Ling , LI Xin . China Land Cover Classification at 1 km Spatial Resolution Based on a Multi-source Data Fusion Approach[J]. Advances in Earth Science, 2009 , 24(2) : 192 -203 . DOI: 10.11867/j.issn.1001-8166.2009.02.0192

References

[1] Turner B L II, Skole D, Sanderson S, et al. Land-use and land-cover change, in Science/Research Plan[R]. HDP Report 7/IGBP Report 35, Stockholm and Geneva, 1995.
[2] Crutzen P J, Andreae M O. Biomass burning in the tropics-impact on atmospheric chemistry and biogeochemical cycles[J]. Science,1990, 250(4 988):  1 669-1 678.
[3] Keller M, Jacob D J, Wofsy S C, et al. Effects of tropical deforestation on global and regional atmospheric chemistry[J]. Climatic Change, 1991, 19: 139-158.
[4] Hederson-Sellers A, Wilson M F. Surface alhedo data for climatic modeling[J].Review of Geophysics and Space Physics, 1983, 21: 1 743-1 778.
[5] Li Xiaobing.International research on environmental consequence of land use/cover change[J].Advances in Earth Science, 1999, 14(4):395-400.[李晓兵. 国际土地利用—土地覆盖变化的环境影响研究[J]. 地球科学进展, 1999, 14(4):395-400.]
[6] Li Xiaobing, Chen Yunhao, Yu Feng. Global and regional cover mapping from remote sensing data: Status, strategies and trends[J].Advances in Earth Science, 2004,19(1):71-80.[李晓兵, 陈云浩,喻锋. 基于遥感数据的全球及区域土地覆盖制图——现状、战略和趋势[J]. 地球科学进展, 2004,19(1):71-80.]
[7] Yongjiu Dai, Xubin Zeng, Robert E, et al. Common Land Model (CLM) (Technical Documentation and User′s Guide)[Z]. 2001.
[8] Sellers P J, Randall D A, Collatz G J, et al. A revised land surface parameterization (SiB2) for atmospheric GCMs Part I: Model formulation[J].Journal of Climate,1996, 9(4): 676-705.
[9] Loveland T R, Reed B C, Brown J F, et al. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data[J].International Journal of Remote Sensing,2000, 21(6/7): 1 303-1 330.
[10] Hansen M C, Defries R S, Townshend J R G, et al. Global land cover classification at 1 km spatial resolution using a classification tree approach[J].International Journal of Remote Sensing, 2000, 21(6/7): 1 331-1 364.
[11] Bartholome E, Belward A S. GLC2000: A new approach to global land cover mapping sfrom Earth observation data[J]. International Journal of Remote Sensing,2005, 26(9):1 959-1 977.
[12] Friedl M A, McIver D K, Hodges J C F, et al. Global land cover mapping from MODIS: Algorithms and early results[J].Remote Sensing of Environment, 2002, 83(1): 287-302.
[13] Ran Y, Li X, Lu L. Evaluation of four remote sensing based land cover products over China[J].International Journal of Remote Sensing, 2008(in press).
[14] Liu J,Liu M, Deng X, et al. The land use and land cover change database and its relative studies in China[J].Journal of Geographical Sciences,2002, 12(3):275-282.
[15] Liu J, Liu M L, Tian H Q, et al. Spatial and temporal patterns of China's cropland during 1990-2000 An analysis based on Landsat TM data[J].Remote Sensing of Environment, 2005,98(4):442-456.
[16] Liu Jiyuan. MacroScale Survey and Dynamic Study of Natural Resources and Environment of Chinese by Remote Sensing[M]. Beijing: Chinese Science and Technology Press, 1996.[刘纪远. 中国资源环境遥感宏观调查与动态研究[M].北京:中国科学技术出版社, 1996.]
[17] Liu Jiyuan. Study on national resources and environment survey and dynamic monitoring using remote sensing[J].Journal of remote sensing,1997, 1(3): 225-230.[刘纪远. 国家资源环境遥感宏观调查与动态监测研究国家资源环境遥感宏观调查与动态监测研究[J]. 遥感学报,1997, 1(3): 225- 230.]
[18] Liu Jiyuan, Zhang Zengxiang, Zhuang Dafang, et al. A study on the spatial-temporal dynamic changes of land-use and driving forces analyses of China in the 1990s[J].Geographical Research,2003, 22(1):1-12.[刘纪远,张增祥,庄大方,等. 20世纪90年代中国土地利用变化时空特征及其成因分析[J].地理研究, 2003, 22(1): 1-12.]
[19] Hou Xueyu, ed. Vegetation Map (1∶1 000 000) in China[M]. Beijing:Science Press, 2001.[侯学煜主编,1∶1 000 000中国植被图集[M].北京:科学出版社,2001.]
[20] Wu Lizong, Li Xin. China Glacier Information System[M/CD].Beijing: Ocean Press, 2004.[吴立宗, 李新. 中国冰川信息系统(配光盘)[M/CD]. 北京:海洋出版社, 2004.]
[21] Zhang Shuqing. An introduction of wetland science database in China[J].Scientia Geographica Sinica, 2002,22(2): 188-189.[张树清. 中国湿地科学数据库简介[J]. 地理科学, 2002,22(2): 188-189.]
[22] Hansen M C, Reed B. A comparison of the IGBP DISCover and University of Maryland 1 km global land covers products[J]. International Journal of Remote Sensing,2000, 21(6&7): 1 365-1 373.
[23] Hodges J C F, Friedl M A, Strahler A H. The MODIS global land cover product: New data sets for global land surface parameterization[C]//Proceedings of the Global Change Open Science Conference, Amsterdam, 2001.
[24] Dempster A P. Upper and lower probabilities induced by multivalued mappings[J].Annals of Mathematical Statistics,1967, 38: 325-329.
[25] Shafer G. A Mathematical Theory of Evidence[M]. Princeton, NJ: Princeton University Press, 1976.
[26] Peddle D R. MERCURY: An evidential reasoning image classifier[J].Computers and Geosciences,1995, 21(10): 1 163-1 176.[27] Soh L, Tsatsoulis C, Gineris D. ARKTOS: An intelligent system for SAR sea ice image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2004, 42(1): 229-247.
[28] Comber A J, Law A N R, Lishman J R. A comparison of Bayes′, Dempster-Shafer and Endorsement theories for managing knowledge uncertainty in the context of land cover monitoring[J].Computers, Environment and Urban Systems, 2004, 28(4): 311-327.
[29] Sun W, Liang S, Xu G, et al. Mapping plant functional types from MODIS data using multisource evidential reasoning[J]. Remote Sensing of Environment,2008, 112(3): 1 010-1 024.
[30] Liu Chunping. Studies on the Methods and Application of Multi-Sources Information Fusion in Remote Sensing[D]. Nanjing: Nanjing University of Science and Technology, 2002.[刘纯平. 多源遥感信息融合方法及其应用研究[D].南京: 南京理工大学,2002.][31] Zhu Xiaokun. Application of Fusion Model of Dempster-Shafer Evidence Reasoning in Remote Sensing[D]. Wuhan:Wuhan University, 2005.[祝晓坤.Dempster-Shafer证据推理融合模型在遥感分类中的应用[D].武汉:武汉大学,2005.]
[32] Peng Tianqiang, Li Bicheng. Remote sensing image classification method based on evidence theory and neural networks[J].Journal of Data Acquisition & Processing,2003, 18(2): 170-174.[彭天强,李弼程.一种基于证据理论与神经网络的遥感影像分类方法[J].数据采集与处理,2003, 18(2): 170-174.]
[33] Wang Xuhong, Zhou Mingquan, Geng Guohua. Reseach of the application of the theory of dempster_Shafer in intelligent remote sense classification[J].Computer Applications and Software,2004, 21(9): 28-29.[王旭红,周明全,耿国华.“Dempster Shafer”证据理论在智能化遥感分类中应用研究[J].计算机应用与软件,2004,21(9): 28-29.]
[34] Wang Yang, Zheng Qinbo, Zhang Junping. Target classification of the data fusion in multichannel using Dempster-Shafer method[J].Journal of Infrared and Millimeter Waves, 2002, 21(3): 229-232.[汪洋,郑亲波,张钧屏. 用证据理论方法进行多波段数据融合的目标分类[J].红外与毫米波学报,2002, 21(3): 229-232.]
[35] Zadeh L A. Fuzzy sets as a basis for a theory of possibility[J].Fuzzy Sets and Systems,1978, 1(1): 3-28.
[36] Ronald Eastman J. IDRISI Kilimanjaro Guide to GIS and Image Processing[Z]. Clark University, 2003.

Outlines

/