摘要
南美洲湿地面积广且类型多样,但湿地制图相关研究匮乏,通过遥感手段可为南美洲全域湿地制图提供科学技术支撑。本研究依托GEE (Google Earth Engine)平台面向南美洲湿地提出一种多源多特征集成的湿地制图方法。研究选取南美洲典型湿地地区为研究区,首先利用已有土地覆盖数据集提出一种有效的湿地样本采集流程以保证样本质量,其次结合哨兵1号、哨兵2号和SRTM数据构建多源特征集合,并基于随机森林的递归特征消除算法(RF_RFE)进行特征优选,构建不同特征组合方案对比多源特征对湿地分类结果的影响,最后采用随机森林算法对研究区湿地进行分类提取。研究结果表明,设计样本采集方案可有效提高样本质量,多源特征集合能够提升湿地分类精度,特征优选能够减少特征冗余并提升分类精度。研究区分类总体精度为85.62%,Kappa系数为0.8333,其中湿地类别的精度最低为69.85%,最高为95.18%。
Wetlands play an important role in maintaining ecological balance,conserving water resources,recharging groundwater,and controlling soil erosion.They are often called the“kidneys of the earth”because they help purify water by filtering out pollutants and sediments.South America has a vast area of wetlands,as well as a variety of wetlands types.While most of these wetlands were conserved in a relatively good condition until a few decades ago,pressures brought about by land use and climate change have threatened their integrity in recent years.However,no complete and uniform wetland map has provided adequate information on the location,distribution,size,and changing status of wetlands in South America.Remote sensing has been an effective tool for characterizing,mapping,and monitoring the complexity and dynamics of large areas of wetlands.Although fine wetland mapping may be done by combining data from many sources,the following two issues persist.On the one hand,given the complicated temporal dynamics and spectral heterogeneity of wetlands,large-scale wetland mapping remains a challenging task.On the other hand,supervised classification is a widely used technique for multi-category wetland mapping.However,selecting training samples is time consuming and labor intensive.Moreover,finer and more precise wetland information is currently unavailable for reference.In the study,we selected four study areas of typical wetlands in South America.First,an effective wetland sample collection process was proposed by using the existing land cover dataset to ensure the sample quality.Second,a multi-source feature set was constructed by combining Sentinel-1,Sentinel-2,and SRTM data.Then,feature selection is carried out on the basis of the random forest recursive feature elimination method(RF_RFE).We constructed a multi-feature combination scheme to compare the influence of multi-source features on wetlands classification.Finally,the random forest algorithm is used to classify wetlands in the study area.The research results sho
作者
黄玉玲
杨刚
孙伟伟
朱琳
黄可
孟祥超
HUANG Yuling;YANG Gang;SUN Weiwei;ZHU Lin;HUANG Ke;MENG Xiangchao(Department of Geography and Spatial Information Techniques,Ningbo University,Ningbo 315211,China;Faculty of Electrical Engineering and Computer Science,Ningbo University,Ningbo 315211,China)
出处
《遥感学报》
EI
CSCD
北大核心
2023年第6期1300-1319,共20页
NATIONAL REMOTE SENSING BULLETIN
基金
国家自然科学基金(编号:42122009,41971296)
宁波市科技创新2025重大专项(编号:2021Z107)
宁波市公益项目(编号:2021S089)
中国博士后科学基金(编号:2020M670440)
浙江省省属高校基本科研业务费专项资金资助(编号:SJLZ2022002)
浙江省自然科学基金(编号:LR19D010001)。
