基于Sentinel-2图像的混合神经网络红树林测绘

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Longjie Ye , Qihao Weng
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引用次数: 0

摘要

由于气候变化和人为干扰,作为生物多样性摇篮和蓝碳储存库的红树林正面临着生存挑战。因此,对红树林进行精确和快速的测绘变得非常重要,这可以为支持这种蓝碳资源的养护实践提供必要的信息。现有的红树林制图机器学习算法由于可移植性差,无法在动态潮汐条件下提供精确的制图解决方案。本研究开发了一种基于混合神经网络和视觉转换器的大面积红树林制图的通用方法,以有效捕获代表性特征。为了使红树林制图适应各种潮汐条件,通过编码融合Sentinel-2的三个波段:Green、NIR和SWIR,开发了一个视觉转换器架构。2021年的地面真实数据集是在解释谷歌地球和无人机图像后,由哨兵2号合成的图像创建的,其中包括88,645个训练样本(每个样本256 × 256像素)和24,969个测试样本。我们选择了中国30个沿海县作为测试数据集来评估所提出网络的有效性,并制作了一张10米的红树林地图,该地图报告了2021年中国红树林总面积为28,006.24公顷,总体精度(OA)为95.91%。与现有的数据产品Global Mangrove Watch 3.0、ESA WorldCover V200和HGMF相比,我们的方法在混合潮区优于第二好的产品HGMF, OA高出9.19%,平均F1得分高出9.36%。尽管Sentinel-2图像捕获的潮位有波动,但所提出的方法始终产生可靠的红树林制图结果,突出了潮汐信息的有效推导。与以往的制图方法相比,所提出的网络在区分和描绘混合潮区红树林生态系统方面具有明显的优势,为改善区域和全球尺度上的红树林监测提供了前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid neural network for mangrove mapping considering tide states using Sentinel-2 imagery
Mangroves, as cradles of biodiversity and blue carbon reservoirs, are facing survival challenges due to climate change and anthropogenic disturbance. Precise and rapid mapping of mangrove forests has thus become highly relevant, which can provide essential information to support the conservation practices of such blue carbon resources. Existing machine learning algorithms for mangrove mapping are incapable of delivering precise cartographic solutions under dynamic tidal conditions because of poor transferability. This study developed a generalized approach for large-area mangrove mapping using a hybrid neural network integrated with a vision transformer to effectively capture representative features. To adapt mangrove mapping to the variety of tidal conditions, a vision transformer architecture was developed by encoding the fusion of three Sentinel-2 bands: Green, NIR, and SWIR. The ground truth dataset for the year 2021 was created from the composited Sentinel-2 images after interpreting Google Earth and drone images, which comprised 88,645 training samples (256 × 256 pixels per sample) and 24,969 test samples. We selected 30 coastal counties as test dataset in China to evaluate the effectiveness of the proposed network and produced a 10 m mangrove map that reported a total mangrove area of 28,006.24 ha in China in 2021, yielding an overall accuracy (OA) of 95.91 %. Compared to existing data products, Global Mangrove Watch 3.0, ESA WorldCover V200 and HGMF, our method outperformed the second-best product HGMF in mixed tide regions, by a margin of 9.19 % in OA and by 9.36 % in mean F1 score. Despite fluctuations in tide levels captured by Sentinel-2 imagery, the proposed method consistently yielded robust mangrove mapping results, highlighting effective derivation of tidal information. In comparison with previous mapping methods, the superior efficacy of the proposed network is distinctly discernible in distinguishing and delineating mangrove ecosystems in mixed tide regions, presenting prospects for improved monitoring of mangroves at regional and global scales.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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