[1]YANG Li-jun,MA Ming-dong,TANG Li-jun.Research on Wetland Vegetation Classification of Chongming Eastern Tidal Flat Based on TM Image[J].Research of Soil and Water Conservation,2013,20(01):126-130.
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Research on Wetland Vegetation Classification of Chongming Eastern Tidal Flat Based on TM Image
Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume:
20
Number of periods:
2013 01
Page number:
126-130
Column:
Public date:
2013-02-28
- Title:
- Research on Wetland Vegetation Classification of Chongming Eastern Tidal Flat Based on TM Image
- Keywords:
- vegetation classification; knowledge discovery; vegetation index; water index; neural network
- CLC:
- TP751
- DOI:
- -
- Abstract:
- Remote sensing classification of wetland vegetation is of great importance to wetland ecosystem conservation and management. The classification of wetland vegetation are difficult because of the intricate environment and the similar vegetation spectrum. Taking Chongming eastern tidal flat wetland as the study site, the spectral curves of on-the-spot survey and remote sense of wetland vegetation was analyzed by spectrometers and Landsat 5 Image. Based on the spectral curves, wetland vegetation was classified by methods of knowledge discovery-based information extracting. Firstly, information of wetland vegetation was extracted from remote images; then vegetation index such as NDVI and DVI was calculated and evaluated about the distinguishability of typical vegetation; and finally, taking the best vegetation index as auxiliary information, wetland vegetation was classified by neural network supervised method. The classification accuracy of this method was high, and it reached 86.5%, indicating that this method was universality, and can provide theoretical basis and technical way of automatic or semi-automatic vegetation classification and identification.
- References:
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[1] 孟宪民.湿地与全球环境变化[J].地理科学,1999,19(5):385-391.
[2] 孙广友.中国湿地科学的进展与展望[J].地球科学进展,2000,15(6):666-669.
[3] 李晓兵,史培军.基于NOAA/AVHRR数据的中国主要植被类型NDVI变化规律研究[J].植物学报,1999,41(3):314-324.
[4] 刘瑜,韩震.基于遥感的长江口南汇潮滩植被群落时空动态变化[J].上海海洋大学学报,2009,18(5):579-585.
[5] 刘瑜,韩震,李睿.基于主成分分析和植被指数的潮滩植被信息提取研究[J].遥感应用,2010(4):44-50.
[6] Coops H, Hanganu J, Thdor M, et al. Classification of Danube Delta lakes based on aquatic vegetation and turbidity[J]. Hydrobiologia,1999,415:187-191.
[7] Dogan O K, Akyurek Z, Beklioglu M. Identification and mapping of submerged plants in a shallow lake using quickbird satellite data[J]. Environmental Magagement,2009,90(7):2138-2143.
[8] Lacapra V C, Melack J M, Gastil M, et al. Remote sensing of foliar chemistry of inundated rice with imaging spectrometry[J].Remote Sensing of Environment,1996,55(1):50-58.
[9] 张策,臧淑英,金竺,等.基于支持向量机的扎龙湿地遥感分类研究[J].湿地科学,2011,9(3):263-270.
[10] 阿也提古丽·斯迪克,赵书河,左平,等.基于知识工程师的崇明岛东滩自然保护区盐沼植被分类研究[J].国土资源遥感,2010(1):123-126.
[11] 刘瑜,韩震.植被指数在长江口潮滩湿地植被信息提取中的应用研究[J].遥感技术与应用,2009,24(6):777-783.
[12] 姚薇,李志军.Landsat卫星遥感影像的大气校正方法研究[J].大气科学学报,2011,34(2):251-256.
[13] 侯东,宋国宝,董燕生,等.TM传感器辐射定标参数精度分析[J].国土资源遥感,2010(4):14-18.
[14] 席晓燕,沈楠,李小娟.ETM+影像水体提取方法研究[J].计算机工程与应用,2009,30(4):993-996.
[15] 曹荣龙,李存军,刘良云,等.基于水体指数的密云水库面积提取及变化监测[J].测绘科学,2008,33(2):158-160.
[16] 李庆海,陶本藻.概率统计原理和在测量中的应用[M].北京:测绘出版社,1990.
[17] 李均力,盛永伟,骆剑承.喜马拉雅山地区冰湖信息的遥感自动化提取[J].遥感学报,2011,15(1):36-43.
[18] 田庆久,闵祥军.植被指数研究进展[J].地球科学进展,1998,13(4):327-333.
[19] Zhao Bin, Yan Yaner, Guo Haiqiang, et al. Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators:an application in the Yangtze River Delta Area[J]. Ecological Indicators,2009,9(2):346-356.
[20] Aronoff S, Goodfellow W. Effective analysis of image processing to regional geochemical data[J]. Advanced Technology for Monitoring and Processing Global Environmental Data,1985,7:220-234.
[21] 张友水,冯学智,阮仁宗,等.Kohonen神经网络在遥感影像分类中的应用[J].遥感学报,2004,8(2):178-184.
[22] 张金屯,杨洪晓.自组织特征人工神经网络在庞泉沟自然保护区植物群落分类中的应用[J].生态学报,2007,27(3):1005-1010.
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