事务(进程 ID 797)与另一个进程被死锁在 锁 资源上，并且已被选作死锁牺牲品。请重新运行该事务。 基于TM影像的崇明东滩湿地植被分类研究-《水土保持研究》

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基于TM影像的崇明东滩湿地植被分类研究

《水土保持研究》[ISSN:1005-3409/CN:61-1272/P] 卷: 20 期数: 2013年01期 页码: 126-130 栏目: 出版日期: 2013-02-28
Title:
Research on Wetland Vegetation Classification of Chongming Eastern Tidal Flat Based on TM Image
作者:
杨立君1,2, 马明栋2, 唐立军3
1. 河海大学 水文水资源学院, 南京 210098;
2. 南京邮电大学 地理与生物信息学院, 南京 210003;
3. 上海祥阳水利勘测有限公司, 上海 202156
Author(s):
YANG Li-jun1,2, MA Ming-dong2, TANG Li-jun3
1. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China;
2. College of Geographical and Biological Information, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
3. Shanghai Xiangyang Water Conservancy Survey and Design Co., Ltd., Shanghai, 202156, China
关键词:
Keywords:
分类号:
TP751
摘要:
湿地植被遥感分类对于湿地生态环境的保护与管理具有重要意义，遥感湿地植被分类的难点是湿地环境复杂，植被光谱相似。以崇明东滩湿地为研究对象，利用FieldSpec 3 Hi-Res光谱仪和Landsat 5卫星影像，分析了湿地植被实测和遥感影像反射光谱曲线。在分析的基础上，采用基于知识发现的信息提取方法对湿地植被进行分类。首先将潮滩植被从遥感影像中提取出来；然后计算NDVI、DVI等植被指数，并进行典型植被可区分性植被指数评价；最后将最优植被指数（KT1，TRVI和DVI）作为辅助信息，对潮滩植被进行神经网络监督分类。研究结果显示，该方法的分类总精度较高达86.5%，具有一定的适用性。研究结果可为实现自动、半自动化植被分类与识别提供理论依据和技术支持。
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.

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更新日期/Last Update: 1900-01-01