Mapping high-resolution mangrove forests in China using GF-2 imagery under the tide
- Vol. 27, Issue 6, Pages: 1320-1333(2023)
DOI： 10.11834/jrs.20221848
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摘要
遥感技术由于具有实时、准确、多尺度、可重复等优点广泛应用在红树林遥感制图研究中。目前，中国红树林遥感制图数据集中空间分辨率最高的产品是基于Sentinel 10 m数据生产的，此外，大多中国红树林遥感制图中忽略了潮汐的影响，导致红树林遥感制图结果不精准。本文利用国产高分二号数据源，顾及潮汐淹没的影响实现空间分辨率1 m的2020年中国红树林遥感制图。选取覆盖中国海岸线具有红树林分布的高分二号影像312景（24景高分一号影像补充缺失区域），对影像进行面向对象多尺度分割，采用红树林淹没指数作为顾及潮汐影响的表征参数，结合随机森林分类方法完成中国红树林高分遥感制图。研究结果表明：2020年中国红树林面积为29576.48 ha，95%的红树林主要分布在广西壮族自治区、广东省、海南省，其总体分类精度为92%，Kappa系数为0.89，尚未顾及潮汐影响的结果比顾及潮汐影响的结果少2531.24 ha。本文生产的红树林高分数据集可为中国红树林生态系统的监测、管理及评估提供高精度的数据支持，具有重要的实际应用价值。
Abstract
Remote sensing technology is widely used in mangrove forest mapping with the advantages of real-time mapping, accuracy, multiscalability, and repeatability. Until now, the mangrove forest dataset with the highest spatial resolution in China is produced by 10 m Sentinel data. In addition, most existing mangrove forest datasets in China ignore the influence of tide, leading to low spatial resolution and inaccurate mapping. On the basis of Chinese GF-2 images, this study aims to map Chinese mangrove forests in 2020 with a spatial resolution of 1 m under the tide. Specifically, 312 scenes of GF-2 image covering China's coastline (24 scenes of GF-1 image covers the areas without GF-2 images) were selected. First, the selected images were segmented using the object-based multiscale method, and the submerged mangrove recognition index was used as a tidal influence indicator. Finally, high-resolution mapping of mangrove forests in China was conducted using the random forest classifier. Results show that the mangrove area in China in 2020 was 29,576.48 ha, 95% of which was mainly distributed in the Guangxi, Guangdong, and Hainan Provinces. The overall classification accuracy was 92%, and the Kappa coefficient was 0.89. The mangrove area without tidal influence was 2,531.24 ha less than that with tidal influence. The high-resolution mangrove dataset generated in this study can provide high-precision data support for the monitoring, management, and evaluation of mangrove ecosystems in China, and it is valuable for practical application.
关键词
遥感高分二号红树林面向对象潮汐随机森林
Keywords
remote sensingGF-2mangrove forestsobject-orientedtiderandom forest classifier
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College of Computer Science and Engineering, Shandong University of Science and Technology
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Faculty of Geographical Science, Beijing Normal University
Southern Marine Science and Engineering Guangdong Laboratory
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