结合多源专题数据和目视解译的大区域密集湿地样本数据生产  

Dense wetland sample production at large scale by combining multi-source thematic datasets and visual interpretation

作　　者：彭凯锋 蒋卫国 侯鹏 凌子燕 牛振国 毛德华[6] 黄卓 PENG Kaifeng;JIANG Weiguo;HOU Peng;LING Ziyan;NIU Zhenguo;MAO Dehua;HUANG Zhuo(State Key Laboratory of Remote Sensing Science,Faculty of Geographical Science,Beijing Normal University,Beijing 100875,China;School of Geographical and Environmental Science,Tianjin Normal University,Tianjin 300387,China;Satellite Environment Center,Ministry of Ecology and Environment,Beijing 100094,China;School of Geography and Planning,Nanning Normal University,Nanning 530001,China;Aerospace Information Research Institute,China Academy of Sciences,Beijing 100094,China;Key Laboratory of Wetland Ecology and Environment,Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102,China)

机构地区：[1]北京师范大学地理科学学部遥感科学国家重点实验室,北京100875 [2]天津师范大学地理与环境科学学院,天津300387 [3]生态环境部卫星环境应用中心,北京100094 [4]南宁师范大学地理科学与规划学院,南宁530001 [5]中国科学院空天信息创新研究院,北京100094 [6]中国科学院东北地理与农业生态研究所湿地生态与环境重点实验室,长春130102

出　　处：《遥感学报》2024年第2期334-345,共12页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:42071393,42301413,41571077);国家自然科学基金联合基金重点项目(编号:U1901219)。

摘　　要：样本数据是开展湿地制图的研究基础之一,对于数据生产和精度验证具有重要作用。针对湿地生态系统类型多样,大区域的全湿地类型样本生产困难的问题,本研究提出了一种准确、高效的大区域密集湿地样本生产框架。该框架主要包括两部分:首先,基于已有的湿地数据产品,使用规则筛选的方法直接生产稳定的湿地样本点,能够得到河流、湖泊、水库、滨海木本沼泽(红树林)、滩涂的5种湿地类型样本点;其次,基于多源专题数据进行规则筛选,生产潜在湿地样本点,并利用Google Earth Engine大数据云平台、Google Earth平台和Collect Earth平台进行目视解译,以确定潜在湿地样本点的类型属性。本文开展大洲尺度的全湿地类型样本生产,结果表明:本研究共生产了150688个湿地样本点,其中内陆湿地样本点为121412个,滨海湿地样本点为11563个,人工湿地样本点为17693个。13种湿地类型中,湖泊样本点占比最大(39.22%),潟湖样本点占比最小(0.19%)。本文生产了稳定、高质量的湿地样本点,样本数量充足,空间分布合理,能够为湿地分类器的训练和分类结果精度验证提供可靠的数据基础。Sample collection is one of key research foundations for wetland mapping.It plays an important role in classifier training and accuracy validation.Generally,wetland samples are produced by visual interpretation based on high-spatial-resolution images or automatic generation based on multi-source existing dataset.The visual interpretation is time and labor consuming and cannot meet the demand for large-scale wetland classification.The automatic sample-generation method is unsuitable to detailed-type wetland mapping due to the diversity of wetlands and classification-scheme inconsistency of existing wetland datasets.Thus,an efficient and accurate sampling method is in demand for large-scale and detailed-type wetland mapping.In our study,we collected a series of auxiliary datasets and developed an efficient solution for continental-scale wetland sample generation by combining automatic sampling method and visual interpretation.In the first part,the samples of five wetland types can be automatically generated by rule filtering based on multi-source existing datasets.River,lake,and reservoir samples were created using the JRC Global Surface Water,Global River Widths from Landsat and HydroLAKES datasets.Coastal swamp(mangrove)samples were produced by using Global Mangrove Watch dataset.Tidal flat samples were generated using the Global Intertidal Change dataset.In the second part,by combining time series of MODIS NDVI images and existing auxiliary datasets,we first produced potential wetland samples for coarse wetland types(i.e.,vegetated wetland samples and inundated wetland samples).Then,we identified them by visual interpretation based on the Google Earth Engine platform,Google Earth software,and Collect Earth software.We applied our sample method in our study area,and produced continental-scale and detailed-type wetland samples.Results indicated that the total wetland samples in our study area was 150688,among which 141412 points were inland wetland samples,11563 were coastal wetland samples,and 17693 were human-ma

关 键 词：遥感 湿地 样本生产 多源专题数据 目视解译 Google Earth Engine Google Earth Collect Earth 

分 类 号：P2[天文地球—测绘科学与技术]