基于多时相Landsat8影像的海南岛热带天然林类型遥感分类  

Classification of tropical natural forests in Hainan Island based on multi-temporal Landsat8 remote sensing images

作　　者：朱琦 郭华东 张露 梁栋 刘栩婷 万祥星 ZHU Qi;GUO Huadong;ZHANG Lu;LIANG Dong;LIU Xuting;WAN Xiangxing(Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;University of Chinese Academy of Sciences,Beijing 100049,China;Sanya Institute of Remote Sensing,Sanya 572029,China;Institute of Forest Resources Information Technique,Chinese Academy of Forestry,Beijing 100091,China)

机构地区：[1]中国科学院空天信息创新研究院数字地球重点实验室,北京100094 [2]中国科学院大学,北京100049 [3]三亚中科遥感研究所,三亚572029 [4]中国林业科学研究院资源信息研究所,北京100091

出　　处：《自然资源遥感》2022年第2期215-223,共9页Remote Sensing for Natural Resources

基　　金：海南省自然科学基金面上项目“基于多源多时相遥感数据的海南天然林分布提取及其典型林型识别研究”(编号:418MS112);国家自然科学基金面上项目“基于时序SAR数据的极地冰盖冻融状态和融化丰度探测方法研究”(编号:41876226)共同资助

摘　　要：热带森林在生物多样性保护以及全球气候变化研究中起着至关重要的作用,其植被类型的复杂性和多样性给遥感热带森林精细分类工作带来挑战。该文依托Google Earth Engine(GEE)平台多时相Landsat8数据,对我国海南省尖峰岭地区热带天然林进行分类探究,在分析多时相数据数量、组合方式对分类精度的影响基础上,针对典型热带雨林、热带季雨林、常绿阔叶林等的热带天然林植被型组类型,提出了一种基于多时相Landsat8影像的分类方法。结果表明:①随着多时相数据数量的增加,分类精度得到显著提升,海南岛天然林植被型组类型分类精度可以提高到91%;②当多时相数据达到一定数量后,分类精度趋于稳定;不同时相数据的组合方式都能提升热带森林分类精度,尤其是在参与分类的数据单独分类精度较低时,其多时相组合对分类精度的提升更加明显,体现了参与分类数据时相选择的宽泛性。所提方法发挥了遥感数据时相变化优势,为海南岛热带天然林类型遥感分类提供有效的参考。Tropical forests play a vital role in biodiversity conservation and research on global climate change.However,the complexity and diversity of vegetation types pose challenges to the fine remote sensing-based classification of tropical forests.The classification of tropical forests in the Jianfengling area,Hainan Province was analyzed using the multi-temporal Landsat8 data of the Google Earth Engine(GEE)platform.Based on the analysis of the impacts of the size and combination of multi-temporal data on the classification accuracy,this study proposed a classification method based on multi-temporal Landsat8 images for the vegetation type groups of tropical natural forests,such as typical tropical rain forest,tropical monsoon forest,and evergreen broad-leaved forests.The results are as follows.①The classification accuracy of tropical natural forests was significantly improved as the size of multi-temporal data increased.The classification accuracy of the vegetation type groups of natural forests in Hainan Island reached 91%.②When the multi-temporal data reached a certain size,the classification accuracy tended to be stable.Different combinations of multi-temporal data can improve the classification accuracy of tropical forests,especially when the classification accuracy of individual data involved was low.This finding reflects the broadness of the selection of temporal data.The proposed method,taking advantage of the temporal changes in remote sensing data,provides an effective reference for the remote sensing-based classification of tropical natural forests in Hainan Island.

关 键 词：热带森林分类 Google Earth Engine 支持向量机 时相数据 地球大数据 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]