Land Cover Classification of the Yangtze River Delta Using MODIS Data

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  • 1. Sino-America Cooperative Remote Sensing Center, School of Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, China;
    2. Climate Center, Beijing Meteorological Bureau, Beijing 100089, China

Received date: 2006-11-17

  Revised date: 2007-01-17

  Online published: 2007-06-25

Supported by

National Natural Science Foundation of China, No.40333027; No.60674074; Scientific Research Foundation of Nanjing University of Information Science and Technology, No.Y653

Abstract

The Yangtze River Delta is one of the most economically developed regions in China. During the last decade, this area experienced rapid urban expansion, and accordingly, masses of cropland have been converted into human buildings. To analyze the influence of landscape change, it is important to provide up-to-date land cover information of this area. This paper describes the development of land cover map of the Yangtze River Delta using 250 m MODIS data, and the main satellite data used in this study were MODIS EVI data, MODIS reflectance data and DEM. A filter method based on time series was applied to eliminate EVI noise, and a PCA analysis was performed to reduce the volume of data. Besides, homogeneity was calculated to present spatial texture information. Therefore, a compositive classification matrix was generated. Considering the natural and artificial conditions of the Yangtze River Delta, a classification scheme was defined. ROIs (Region of Interest) were selected from Landsat ETM+ images by human interpretation consulting the 1: 1,000,000 Vegetation Atlas of China. Then the land cover map was generated using MLC method. After correction by buffering analysis, we got the final land cover classification of the Yangtze River Delta. The classification accuracy was assessed using fine-resolution Landsat images, with an overall accuracy of 95.98% . In addition, our classification result was compared with the MODIS-IGBP land cover production and showed better accuracy. The good result indicated the good behavior of the classification method and technical processing used in our research, and also suggested the advantage of 250 m MODIS data in regional land cover mapping.

Cite this article

XU Yongming, LIU Yonghong, WEI Ming, LU Jingjing . Land Cover Classification of the Yangtze River Delta Using MODIS Data[J]. Acta Geographica Sinica, 2007 , 62(6) : 640 -648 . DOI: 10.11821/xb200706009

References


[1] Shi Peijun, Gong Peng, Li Xiaobing et al. The Method and Practice of Land Use/Land Cover Change. Beijing: Science Press, 2000. 1-3.
[史培军, 宫鹏, 李晓兵等. 土地利用/ 覆盖变化研究的方法与实践. 北京: 科学出版社, 2000. 1-3.]

[2] Sellers P J, Dickinson R E, Randall D A et al. Modeling the exchanges of energy, water, and carbon between the continents and the atmosphere. Science, 1997, 275(5299): 502-509.

[3] DeFries R S, Townshed J G R. NDVI derived land cover classification at a globe scale. International Journal of Remote Sensing, 1994, 15(17): 3567-3586.

[4] Matthews E. Global vegetation and land use: New high resolution data bases for limited studies. Journal of Climatology and Applied Meteorology, 1983, 22(3): 474-487.

[5] Wilson M F, Henderson-Sellers A. A global archive of land cover and soils data for use in general circulation models. Journal of Climatology, 1985, 5: 119-143.

[6] Hansen M C, Reed B. A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products. International Journal of Remote Sensing, 2000, 21(6/7): 1365-1373.

[7] Price J C. Comparing MODIS and ETM+ data for regional and global land classification. Remote Sensing of Environment, 2003, 86(4): 1835-1852.

[8] Jia Ling, Du Jinkang, Zhao Ping. Land use dynamic monitoring in Hainan by TM data. Remote Sensing Information, 2003, (1): 22-25.
[ 贾凌, 都金康, 赵萍. 基于TM 的海南省土地利用/ 覆盖动态变化的遥感监测和分析. 遥感信息, 2003, (1): 22-25.]

[9] Chen Siqing, Liu Jiyuan, Zhuang Dafang et al. Quantifying land use and land cover change in Xilin River Basin using multi-temporal Landsat TM/ETM sensor data. Acta Geographica Sinica, 2003, 58(1): 45-52.
[陈四清, 刘纪远, 庄大方 等. 基于Landsat TM/ETM 数据的锡林河流域土地覆盖变化. 地理学报, 2003, 58(1): 45-52.]

[10] Tucker C J, Townshend J R G, Goff T E. African land cover classification using satellite data. Science, 1985, 227 (4685): 369-375.

[11] 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. International Journal of Remote Sensing, 2000, 21(6/7): 1303-1330.

[12] 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. International Journal of Remote Sensing, 2000, 21(6-7): 1331-1364.

[13] Pan Yaozhong, Li Xiaobing, He Chunyang. Research on comprehensive land cover classification in China: Based on NOAA/AVHRR and Holdridge PE index. Quaternary Sciences, 2000, 20(3): 270-281.
[潘耀忠, 李晓兵, 何春阳. 中国 土地覆盖综合分类研究: 基于NOAA/AVHRR 和Holdridge PE. 第四纪研究, 2000, 20(3): 270-281.]

[14] Friedl M A, McIver D K, Hodges J C F et al. Global land cover mapping from MODIS: Algorithms and early results. Remote Sensing of Environment, 2002, 83(1/2): 287-302.

[15] Muchoney D, Borak J, Chi H et al. Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America. International Journal of Remote Sensing, 2000, 21(6/7): 1115-1138.

[16] Wang Quanfang, Li Jiayong, Chen Baiming. Land cover classification system based on spectrum in Poyang Lake Basin. Acta Geographica Sinica, 2006, 61(4): 359-368.
[汪权方, 李家永, 陈百明. 基于地表覆盖物光谱特征的土地覆被分 类系统: 以鄱阳湖流域为例. 地理学报, 2006, 61(4): 359-368.]

[17] Liu Yonghong, Niu Zheng, Xu Yongming et al. Design of land cover classification system for China and its application research based on MODIS data. Transactions of the Chinese Society of Agricultural Engineering, 2006, 22(5): 99-104.
[刘勇洪, 牛铮, 徐永明等. 基于MODIS 数据设计的中国土地覆盖分类系统与应用研究. 农业工程学报, 2006, 22 (5): 99-104.]

[18] Huete A, Justice C, Leeuwen W V. Modis Vegetation Index (MOD13) Version 3. Algorithm Theoretical Basis Document, http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf, 1999.

[19] Editorial Board of the Vegetation Map of China, CAS. The Vegetation Atlas of China 1:1000000. Beijing: Science Press, 2001.
[中国科学院中国植被图编辑委员会. 1:100 万中国植被图集. 北京: 科学出版社, 2001.]

[20] DeFries R S, Hansen M, Townshend J R G et al. Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers. International Journal of Remote Sensing, 1998, 19(16): 3141-3168.

[21] Liu Yonghong, Niu Zheng. Regional land cover image classification and accuracy evaluation using MODIS data. Remote Sensing Technology and Application, 2004, 19(4): 217-224.
[刘勇洪, 牛铮. 基于MODIS 遥感数据的宏观土 地覆盖特征分类方法与精度分析研究. 遥感技术与应用, 2004, 19(4): 217-224.]

[22] DeFries, Chang J C. Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment, 2000, 74(3): 503-515.

[23] Kloditz C, van Boxtel A, Carfagna E et al. Estimating the accuracy of coarse scale classification using high scale Information. Photogrammetric Engineering Remote Sensing, 1998, 64(2): 127-133.

[24] Boles S H, Xiao X, Liu J et al. Land cover characterization of temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sensing of Environment, 2004, 90(4): 477-489.

[25] Wang Changyao, Luo Chengfeng, Qi Shuhua et al. A method of land cover classification for China based on NDVI-Ts space. Journal of Remote Sensing, 2005, 9(1): 93-99.
[王长耀, 骆成凤, 齐述华等. NDVI-Ts 空间全国土地覆盖分类 方法研究. 遥感学报, 2005, 9(1): 93-99.]

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