涨落潮下滨海湿地植被信息遥感识别方法

Remote Sensing Identification Method of Vegetation Information of Coastal Wetland Under Ebb and Flow

滨海湿地具有岸线保护、生物多样性保育、物质生产、能量交换以及提供休憩科研空间等多种生态服务功能。植被作为滨海湿地的重要组成部分,其分布状况、结构变化等景观信息在很大程度反映了滨海湿地的健康状况。为分析滨海湿地涨落潮下遥感识别植被种类的可行性,选用两期Landsat 8涨落潮影像,基于统计判别式、决策树监督分类和非监督分类方法对黄河三角洲滨海湿地进行了植被分类。结果显示,统计判别式监督分类的分类效果最佳,分类精度高达97%,能准确区分各植被类型,且涨落潮对分类结果影响不大。研究表明,基于遥感技术在涨落潮不同状态下对滨海湿地植被信息提取具有可行性,可为滨海湿地植被监测、生态恢复以及滨海地区蓝碳储量估算提供技术和数据支持。

Coastal wetland has many ecological service functions such as shoreline protection,biodiversity conservation,material production,energy exchange,and providing leisure and scientific research space.Vegetation is an important part of coastal wetland,and its distribution,structural changes and other landscape information reflect the health status of coastal wetlands to a large extent.In order to analyze the feasibility of identifying vegetation species under ebb and flow of coastal wetland by remote sensing,two periods of Landsat 8 ebb and flow images were selected to classify vegetation in coastal wetland of Yellow River Delta based on statistical discriminant,decision tree supervised classification and unsupervised classification methods.The results showed that the classification effect of statistical discriminant supervised classification was the best,the classification accuracy was up to 97%,and the vegetation types could be accurately distinguished,and the ebb and flow had little effect on the classification results.The research indicated that remote sensing technology is feasible to extract vegetation information of coastal wetland under different tidal conditions,which can provide technical and data support for vegetation monitoring,ecological restoration and blue carbon storage estimation of coastal wetland.