结合淹没红树林指数特征与K-means聚类的滨海自然与人工湿地提取方法  

Extracting Coastal Natural and Constructed Wetlands by Combining the Inundated Mangrove Forest Index and the K-means Clustering Algorithm

作　　者：赵铜铁钢 吴迪熠 杨振华 ZHAO Tongtiegang;WU Diyi;YANG Zhenhua(Center of Water Resources and Environment,Sun Yat-Sen University,Guangzhou 510275,China;Eco-Environmental Monitoring and Scientific Research Center,Bureau of Eco-Environmental Supervision of South China Sea Water and Pearl River Basin,Ministry of Ecology and Environment,Guangzhou 510610,China)

机构地区：[1]中山大学水资源与环境研究中心,广州510275 [2]生态环境部珠江流域南海海域生态环境监督管理局生态环境监测与科学研究中心,广州510610

出　　处：《地球信息科学学报》2024年第12期2805-2817,共13页Journal of Geo-information Science

基　　金：广东省“珠江人才计划”青年创新团队项目(2019ZT08G090)。

摘　　要：滨海自然与人工湿地监测对水环境和自然资源保护具有重要意义。考虑水位变化等动态影响,湿地遥感监测涉及多时段影像合成、水位动态过程表征、不同区分度特征指数选择和聚类算法边界分割等技术难题。基于谷歌地球引擎(Google Earth Engine,GEE)遥感云平台提供的Landsat卫星遥感数据,增加淹没红树林指数(Inundated Mangrove Forest Index,IMFI)等指标作为随机森林(Random Forest,RF)算法的特征变量,通过K-means方法进行自动聚类,将非监督分类与监督分类方法结合起来开发一种自然与人工湿地提取方法。面向粤港澳大湾区,采用生产者精度、用户精度、总体精度和Kappa系数评价基于长时序影像的滨海自然与人工湿地识别。结果表明:(1)相比现有指数,IMFI能更有效区分水域、养殖坑塘和滩涂;(2)通过协同增加的K-means分类结果与IMFI,以分割人工湿地和将滩涂聚类,能增强湿地类间、湿地与其他地物的区分性,方法可以有效解决湿地类间、湿地与水域间的错分与漏分问题;(3)方法在粤港澳大湾区滨海区域分类的平均总体精度为89.23%,平均Kappa系数为0.8731,在时间上波动小。整体上,该方法为高精度滨海自然与人工湿地动态监测预警提供技术支撑。The monitoring of coastal natural and constructed wetlands is of great importance to the protection of coastal water environment and natural resources.In practice,dynamic ranges of coastal natural and constructed wetlands can be monitored by using satellite image synthesis to represent the processes of wetlands being affected by dynamic changes in tidal levels.They can also be achieved by developing remote sensing indexes that are effective in characterizing natural wetlands exhibiting certain spectrum characteristics and by using advanced numerical algorithms that are capable of segmenting constructed wetlands showing some distinct boundaries.Based on multi-source remote sensing and ground data,this paper has presented a novel method to extract natural and constructed wetlands by combining unsupervised and supervised classification methods.Specifically,based on the Landsat images on the Google Earth Engine(GEE)cloud platform,the Inundated Mangrove Forest Index(IMFI)and related indexes are derived as the characteristic variables for the Random Forest(RF)algorithm;the slope obtained from elevation is also used to reduce the misclassification of mangrove forests since the majority of mangrove forests tends to be distributed in areas with gentle topography;and furthermore the K-means clustering algorithm is used to automatically extract wetlands without morphological processing.Through the case study of the Guangdong-Hong Kong-Macao Greater Bay Area(GBA),the metrics of Producer's Accuracy(PA),User's Accuracy(UA),Overall Accuracy(OA)and Kappa coefficient are used to verify the effectiveness of the method through applications to long-term satellite images.The results show that:(1)Compared with other indexes,the IMFI can more effectively identify water,aquaculture ponds and tidal flats;(2)By combining the K-means clustering algorithm with the IMFI,the distinctions between wetland classes and between wetlands and other ground objects can be enhanced by segmenting constructed wetland and clustering tidal flats;(3)The ave

关 键 词：粤港澳大湾区 自然湿地 人工湿地 淹没红树林指数 随机森林 K-MEANS聚类 GEE云平台 

分 类 号：P237[天文地球—摄影测量与遥感]