基于Landsat-8 OLI影像和指数法的红树林提取对比研究  

A Comparative Study of Mangrove Distribution Extraction based on Landsat-8 OLI Images and Spectral Index Methods

作　　者：刘凯[1,2] 王子予 曹晶晶 LIU Kai;WANG Ziyu;CAO Jingjing(School of Geography and Planning,Sun Yat-sen University,Provincial Engineering Research Center for Public Security and Disaster,Guangdong Key Laboratory for Urbanization and GeoSimulation,Guangzhou 510006,China;Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519000,China)

机构地区：[1]中山大学地理科学与规划学院广东省公共安全与灾害工程技术研究中心广东省城市化与地理环境空间模拟重点实验室,广东广州510006 [2]南方海洋科学与工程广东省实验室(珠海),深圳珠海519000

出　　处：《遥感技术与应用》2024年第1期55-66,共12页Remote Sensing Technology and Application

基　　金：广东省自然科学基金项目(2021A1515011462、2021A1515110157);国家自然科学基金项目(42201353);南方海洋科学与工程广东省实验室(珠海)创新团队建设项目(311021004)。

摘　　要：红树林是全球净初级生产力最高的生态系统之一,其在全球气候变化和海岸带地理环境演变研究中发挥着重要作用。快速且准确地获取大范围红树林空间分布,对于红树林资源的有效管理和开发利用具有重要意义。Landsat系列卫星影像已成为大范围、长周期红树林分布信息提取的重要数据源。选取华南沿海的英罗湾和珍珠港作为实验区,利用Landsat-8 OLI影像结合归一化差异红树林指数(Normalized Difference Mangrove Index,NDMI)、综合红树林识别指数(Combined Mangrove Recognition Index,CMRI)、模块化红树林识别指数(Modular Mangrove Recognition Index,MMRI)、红树林指数(Mangrove Index,MI)和红树林植被指数(Mangrove Vegetation Index,MVI)5种指数来提取红树林分布信息,并对比5种指数用于红树林提取的效果,筛选适用于Landsat-8 OLI影像的最佳红树林提取指数。提出了结合归一化差异水体指数(Normalized Difference Water Index,NDWI)优化的红树林分布信息提取方案,以提升红树林分布信息的遥感监测能力,并应用于广西沿海大范围红树林空间分布的提取。研究结果表明:(1)基于Landsat-8 OLI影像和指数法可以有效提取红树林分布信息;用于提取红树林的5种指数中MVI指数的提取效果最好,CMRI指数的提取效果最差。(2)结合NDWI指数可以进一步提高红树林提取精度,优化后的MVI指数应用于广西沿海红树林的提取结果最佳,总体精度达到97.10%。本文提出的研究方案和红树林指数阈值范围,可为大范围红树林分布提取提供参考和决策支持。Mangrove forests are among the ecosystems with the highest net primary productivity in the world,and they play an important role in the study of global climate change and the evolution of coastal zone geogra-phy.Rapid and accurate acquisition of the spatial distribution of mangroves on a large scale is vital for effectively managing and exploiting mangrove resources.Landsat satellite images have become an important data source for extracting large-scale and long-period mangrove distribution information.Yingluo Bay and Pearl Bay along the coast of Guangxi,China are selected as the study sites in this study.Landsat-8 OLI images are used to con-struct five indices to extract the distribution of mangroves,including Normalized Difference Mangrove Index(NDMI),Combined Mangrove Recognition Index(CMRI),Modular Mangrove Recognition Index(MMRI),Mangrove Index(MI)and Mangrove Vegetation Index(MVI).This study compared the efficiency of different indices used for mangrove extraction to determine the optimal mangrove extraction index.Optimiz-ing the mangrove distribution information extraction is proposed by combining Normalized Difference Water In-dex(NDWI)index.The aim is administrator improve the remote sensing classification accuracy of mangroves.It is also applied to the extraction of coastal mangroves in Guangxi.The results showed that:Mangrove distribu-tion can be effectively extracted based on Landsat-8 OLI satellite images and index method.By comparing the extraction accuracy of five indices of mangroves,we found that the MVI has the best extraction effect and the CMRI has the worst extraction effect.The combination of NDWI can better optimize the extraction accuracy of mangroves,and the optimized MVI applied to Guangxi coastal mangroves showed the best extraction results with an overall accuracy of 97.10%.The research strategy and the range of mangrove index thresholds in this pa-per can provide reference and decision support for large-scale mangrove distribution extraction.

关 键 词：红树林 遥感 指数法 信息提取 Landsat 8 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] S771.8[自动化与计算机技术—控制科学与工程]