Original article

VEGETATION CLASSIFICATION OF EAST CHINA USING MULTI-TEMPORAL NOAA-AVHRR DATA

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  • Department of Environmental Science, East China Normal University, Shanghai 200062, China

Received date: 2004-02-26

  Accepted date: 2004-10-19

  Online published: 2005-05-30

Abstract

East China lies in the subtropical monsoon climatic zone and is dominated by subtropical evergreen broad-leaved forests, a unique vegetation type on earth mainly distributed in East Asia with the largest and most concentrated distribution in China. It is important to be able to monitor and estimate forest biomass and production, regional carbon storage, and global climate change impacts of these important vegetation types. In this paper, we used coarse resolution remote sensing data to identify the vegetation types in East China and develop a map of the spatial distribution of vegetation types in this region. Nineteen maximum normalized difference vegetation index (NDVI) composite images (Acquisition time span 7 months from February through August), which were derived from 10-days of National Oceanographic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) channel 1 and channel 2 observations, and an unsupervised classification method, the ISODATA algorithm, was employed to identify the vegetation types. The image was processed using principal component analysis (PCA) to reduce the dimensions of the dataset resulting in a total of 28 spectral clusters of land cover of which 2 clusters were urban/bare soil and water. The 26 remaining spectral clusters were merged into 6 vegetation types: evergreen broad-leaved forest, coniferous forest, bamboo forest, shrub-grass, aquatic vegetation and agricultural vegetation using the Chinese vegetation taxonomy system. The spatial distribution and areal extent calculated for the coniferous forests, shrub-grass, evergreen broad-leaved forests and agricultural vegetation compared well with the Vegetation Atlas of China at a 1∶1 000 000 scale. The spatial accuracy for coniferous forests, shrub-grass, evergreen broad-leaved forests and agricultural vegetation was 79.2%, 91.3%, 68.2% and 95.9%, respectively, and the area accuracy was 92.1%, 95.9%, 63.8% and 90.5%, respectively. The spatial and area accuracy of the bamboo forest was 28.7% and 96.5%, the spatial accuracy of aquatic vegetation was 69.6%, but there is great error in its area accuracy because image acquisition did not cover the full year. Our research demonstrated the feasibility of using NOAA-AVHRR to identify the different vegetation types in the subtropical evergreen broad-leaved forest zone in East China. The spatial location of the 6 identified vegetation types coincided with the actual geographical distribution of the actual vegetation types in East China.

Cite this article

LI Jun-Xiang, DA Liang-Jun, WANG Yu-Jie, SONG Yong-Chang . VEGETATION CLASSIFICATION OF EAST CHINA USING MULTI-TEMPORAL NOAA-AVHRR DATA[J]. Chinese Journal of Plant Ecology, 2005 , 29(3) : 436 -443 . DOI: 10.17521/cjpe.2005.0058

References

[1] Achard F, Estreguil C (1995). ForestclassificationofSoutheastA siausingNOAAAVHRRdata. RemoteSensingofEnvironment, 54,198-208.
[2] Achard F, Eva H, Mayaux P (2001). Tropicalforestmappingfromcoarsespatialresolutionsatellitedata:productionandaccuracyassessmentissues. InternationalJournalofRemoteSensing, 22,2741-2762.
[3] Boyd DS, Ripple WJ (1997). Potentialvegetationindicesfordeter miningglobalforestcover. InternationalJournalofRemoteSens ing, 18,1395-1401.
[4] Cihlar J, Ly H, Xiao Q (1996). LandcoverclassificationwithAVHRRmultichannelcompositioninNortherenvironments. RemoteSensingofEnvironment, 58,36-51.
[5] DeFries R, Townshend JRG (1994). NDVI_derivedlandcoverclassificationsonaglobalscale. InternationalJournalofRemoteSensing, 15,3567-3586.
[6] Editorial Board of The Forest of Jiangxi Province (江西森林编辑委员会) (1986). TheForestofJiangxiProvince (江西森林). JiangxiScience&TechnologyPress, Nanchang. (inChinese).
[7] Editorial Board of Vegetation of Anhui Province (安徽植被编辑委员会) (1983). VegetationofAnhuiProvince (安徽植被). An huiScienceandTechnologyPress, Hefei. (inChinese).
[8] Ehrlich D, Estes JE, Singh A (1994). ApplicationofNOAA_AVHRR 1kmdataforenvironmentalmonitoring. InternationalJournalofRemoteSensing, 15,145-161.
[9] Eidenshink JC, Faundeen JL (1994). The1kmAVHRRgloballanddataset:firststagesinimplementation. InternationalJour nalofRemoteSensing, 15,3443-3462.
[10] Goward SN, Huemmrich KF (1992). VegetationcanopyPARab sorbanceandthenormalizeddifferencevegetationindex:anas sessmentusingtheSAILmodel. RemoteSensingofEnvironment, 39,119-140.
[11] Hou XY (侯学煜) (2000). VegetationAtlasofChina (中国植被图集). SciencePress, Beijing. (inChinese).
[12] Li XB (李晓兵), Shi PJ (史培军) (1999). Researchonregula tionofNDVIChangeofChineseprimaryvegetationtypesbasedonNOAA/AVHRRdata. ActaBotanicaSinica (植物学报), 41,314-324. (inChinesewithEnglishabstract).
[13] Lin P (林鹏) (1990). VegetationofFujianProvince (福建植被). FujianScience&TechnologyPress, Fuzhou. (inChinese).
[14] Mayaux P, Gond V, Bartholome E (2000). Anear_realtimeforestcovermapofMadagascarderivedfromSPOT -4VEGETATION (VGT) data. InternationalJournalofRemoteSensing, 21,3139-3144.
[15] Moody A, Strahler AH (1994). CharacteristicsofcompositeAVHRRdataandproblemsintheirclassification. InternationalJournalofRemoteSensing, 15,3473-3491.
[16] Running SW, Hunt ER, Nemani R, Glassy J (1994). MODISLAI (leafareaindex) andFPAR (fractionphotosyntheticallyactivera diation). MODISalgorithmdocument.NASA, 19.
[17] Scepan J (1999). Thematicvalidationofhigh_resolutiongloballand_coverdatasets. PhotogrammetricEngineering&RemoteSensing, 65,1051-1060.
[18] Sellers PJ (1987). Canopyreflectance, photosynthesisandtranspi rationⅡ.Theroleofbiophysicsinthelinearityoftheirinterde pendence. RemoteSensingofEnvironment, 21,143-183.
[19] Sellers PJ, Turker CJ, Collatz GJ, Los SO, Justice CO, Dazlich DA, Randall DA (1994). Aglobal1°by1°NDVIdatasetforclimatestudies.Part2, Thegenerationofglobalfieldsofterres trialbiophysicalparametersfromtheNDVI. InternationalJournalRemoteSensing, 15,3519-3545.
[20] Sheng YW (盛永伟), Chen WY (陈维英), Xiao QG (肖乾广) GuoL (郭亮) (1995). Chinesevegetationclassificationwithme teorologicalsatellitevegetationindexdata. ChineseScienceBul letin (科学通报), 40,68-71. (inChinese).
[21] Song YC (宋永昌) (1999). PerspectiveofthevegetationzonationofforestregionineasternChina. ActaBotanicaSinica (植物学报), 41,541-552. (inChinesewithEnglishabstract).
[22] The Forest of Zhejiang Province Compilation Committee Edition (浙江森林编辑委员会) (1993). TheForestofZhejiangProvince (浙江森林). ChinaForestryPublishingHouse, Beijing. (inChinese).
[23] Townshend JRG (1994). GlobaldatasetsforlandapplicationfromAdvancedVeryHighResolutionRadiometer:anintroduction. InternationalJournalofRemoteSensing, 15,3319-3332.
[24] Townshend JRG, Justice CO, Skole D, Malingreau JP, Cihlar J, Teiliet P, Sadowski F, Ruttenberg S (1994). The1kmresolu tionglobaldataset:needsofTheInternationalGeosphereBio sphereProgramme. InternationalJournalofRemoteSensing, 15,3417-3441.
[25] Tucker CJ, Townshend JRG, Goff TE (1985a). Africaland_coverclassificationusingsatellitedata. Science, 227,369-375.
[26] Tucker CJ, Vanpraet CL, Sharman MJ, vanIttersum G (1985b). SatelliteremotesensingoftotalherbaceousbiomassproductionintheSenegaleseSahel:1980-1984. RemoteSensingofEnviron ment, 17,233-249.
[27] USGS (2004). Globallandcovercharacteristicsdataset. Http://edcdaac.usgs.gov/glcc/globdoc2_0.html.Cited21October2004.
[28] Wu ZY (吴征镒) (1980). VegetationofChina (中国植被). SciencePress, Beijing. (inChinese).
[29] Xiao X, Boles S, Liu J, Zhuang D, Liu M (2002). Characteriza tionofforesttypesinNortheasternChina, usingmulti_temporalSPOT_4VEGETATIONsensordata. RemoteSensingofEnviron ment, 82,335-348.
[30] Zhu Z, Evans DL (1994). U.S.foresttypesandpredictedpercentforestcoverfromAVHRRdata. PhotogrammetricEngineeringandRemoteSensing, 60,525-531.
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