Classification Method for Dongting Lake Wetland Based on Geographic Information  

结合地理信息的洞庭湖湿地分类方法研究(英文)

作　　者：朱晓荣[1] 张怀清[1] 

机构地区：[1]中国林业科学院资源信息研究所,北京10091

出　　处：《Agricultural Science & Technology》2012年第10期2175-2179,2196,共6页农业科学与技术（英文版）

摘　　要：[Objective] This study aimed to improve the accuracy of remote sensing classification for Dongting Lake Wetland.[Method] Based on the TM data and ground GIS information of Donting Lake,the decision tree classification method was established through the expert classification knowledge base.The images of Dongting Lake wetland were classified into water area,mudflat,protection forest beach,Carem spp beach,Phragmites beach,Carex beach and other water body according to decision tree layers.[Result] The accuracy of decision tree classification reached 80.29%,which was much higher than the traditional method,and the total Kappa coefficient was 0.883 9,indicating that the data accuracy of this method could fulfill the requirements of actual practice.In addition,the image classification results based on knowledge could solve some classification mistakes.[Conclusion] Compared with the traditional method,the decision tree classification based on rules could classify the images by using various conditions,which reduced the data processing time and improved the classification accuracy.[目的]提高洞庭湖区湿地遥感分类的准确性。[方法]利用洞庭湖TM数据,并辅助地面GIS信息,通过专家分类知识库建立决策树分类方法,结合研究区的DEM进行洞庭湖湿地的影像分类,通过决策树层次实现了包括水体、泥沙滩地、防护林滩地、湖草、芦苇滩地和苔草滩地以及其他水体7种湿地类型的分类。[结果]决策树分类总体精度80.29%,总体Kappa系数为0.8839,分类精度相对于传统手段要高,证明基于该方法得到的数据准确度能够满足实际工作的需要;另外,基于知识分类的影像分类结果能够较好地解决一些错分的现象,针对湿地而言,混分现象最严重的泥滩地,在传统分类中大量的被分为了建筑用地,或者裸地,同时草滩地与林地的混分在基于知识分类的影像中边界也较明显。[结论]相比传统分类方法,决策树分类以规则为基础,可以同时利用多个条件进行分类,减少了数据处理时间,同时还提高了分类精度,最终得到试验区较为可靠的遥感分类图像。

关 键 词：Geographic information Decision tree classification 

分 类 号：X37[环境科学与工程—环境工程]