Lina Ke , Yao Lu , Pan Zhang , Quanming Wang , Zhenqi Cui , Qingli Jiang
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The results showed that: (1) The EMA-UNet proposed in this study achieved higher accuracy than comparative models, with an overall accuracy of 95.9 %, an F1 score of 94.3 %, and an Intersection over Union (IoU) of 89.4 %, with improved extraction effects. (2) China’s mariculture was estimated to cover 2,941.7 km<sup>2</sup>, with northern provinces accounting for 56.46 % of the total. Fujian, Shandong, and Liaoning were the three provinces with the largest mariculture areas, contributing 27.36 %, 25.85 %, and 20.87 % respectively. (3) China’s mariculture density showed a “dense in the east and sparse in the west”, “dense in the north and sparse in the south” pattern, with high-intensity aggregations in Fujian and central-southern Guangdong, and low-intensity aggregations in Bohai Bay and nearshore Guangxi, reflecting the influence of natural-socioeconomic factors. This study provides a fine-grained map of China’s mariculture distribution, serving as a data foundation for relevant studies. It also investigated the effects of extraction methods, data sources, and band features, offering insights for optimizing mariculture extraction methods.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104802"},"PeriodicalIF":8.6000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"National mariculture mapping in China at 10-m spatial resolution based on EMA-UNet and Sentinel-1/2 imagery\",\"authors\":\"Lina Ke , Yao Lu , Pan Zhang , Quanming Wang , Zhenqi Cui , Qingli Jiang\",\"doi\":\"10.1016/j.jag.2025.104802\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurately measuring mariculture’s area and spatial distribution is vital for the sustainable development of marine resources. However, existing research primarily focuses on the regional scale, while national mariculture studies remain limited, with significant differences in extracted areas. This study utilized time-series Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multi-Spectral Instrument (MSI) imagery and developed the Efficient Multi-scale Attention U-Net (EMA-UNet) to generate a fine-grained map of China’s mariculture. The spatial distribution characteristics of mariculture areas were revealed through spatial analysis methods. The results showed that: (1) The EMA-UNet proposed in this study achieved higher accuracy than comparative models, with an overall accuracy of 95.9 %, an F1 score of 94.3 %, and an Intersection over Union (IoU) of 89.4 %, with improved extraction effects. (2) China’s mariculture was estimated to cover 2,941.7 km<sup>2</sup>, with northern provinces accounting for 56.46 % of the total. Fujian, Shandong, and Liaoning were the three provinces with the largest mariculture areas, contributing 27.36 %, 25.85 %, and 20.87 % respectively. (3) China’s mariculture density showed a “dense in the east and sparse in the west”, “dense in the north and sparse in the south” pattern, with high-intensity aggregations in Fujian and central-southern Guangdong, and low-intensity aggregations in Bohai Bay and nearshore Guangxi, reflecting the influence of natural-socioeconomic factors. This study provides a fine-grained map of China’s mariculture distribution, serving as a data foundation for relevant studies. 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引用次数: 0
摘要
准确测量海水养殖的面积和空间分布对海洋资源的可持续发展至关重要。然而,现有的研究主要集中在区域尺度上,而国家海水养殖研究仍然有限,在提取地区存在显著差异。本研究利用Sentinel-1合成孔径雷达(SAR)和Sentinel-2多光谱仪器(MSI)的时间序列图像,开发了高效多尺度关注U-Net (EMA-UNet),生成了中国海鱼养殖的细粒度地图。通过空间分析方法揭示了海水养殖区的空间分布特征。结果表明:(1)本文提出的EMA-UNet比比较模型具有更高的准确率,总体准确率为95.9%,F1得分为94.3%,IoU (Intersection over Union)为89.4%,提取效果有所提高。(2)全国海水养殖面积2941.7 km2,其中北方省份占56.46%。福建、山东和辽宁是养殖面积最大的三个省份,分别占27.36%、25.85%和20.87%。(3)中国海鱼养殖密度呈现“东密西疏”、“北密南疏”的格局,福建和广东中南部为高强度聚集区,渤海湾和广西近岸为低强度聚集区,反映了自然-社会经济因素的影响。本研究提供了一幅精细的中国海鱼养殖分布图,为相关研究提供数据基础。研究了提取方法、数据来源和波段特征的影响,为优化海水养殖提取方法提供了见解。
National mariculture mapping in China at 10-m spatial resolution based on EMA-UNet and Sentinel-1/2 imagery
Accurately measuring mariculture’s area and spatial distribution is vital for the sustainable development of marine resources. However, existing research primarily focuses on the regional scale, while national mariculture studies remain limited, with significant differences in extracted areas. This study utilized time-series Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multi-Spectral Instrument (MSI) imagery and developed the Efficient Multi-scale Attention U-Net (EMA-UNet) to generate a fine-grained map of China’s mariculture. The spatial distribution characteristics of mariculture areas were revealed through spatial analysis methods. The results showed that: (1) The EMA-UNet proposed in this study achieved higher accuracy than comparative models, with an overall accuracy of 95.9 %, an F1 score of 94.3 %, and an Intersection over Union (IoU) of 89.4 %, with improved extraction effects. (2) China’s mariculture was estimated to cover 2,941.7 km2, with northern provinces accounting for 56.46 % of the total. Fujian, Shandong, and Liaoning were the three provinces with the largest mariculture areas, contributing 27.36 %, 25.85 %, and 20.87 % respectively. (3) China’s mariculture density showed a “dense in the east and sparse in the west”, “dense in the north and sparse in the south” pattern, with high-intensity aggregations in Fujian and central-southern Guangdong, and low-intensity aggregations in Bohai Bay and nearshore Guangxi, reflecting the influence of natural-socioeconomic factors. This study provides a fine-grained map of China’s mariculture distribution, serving as a data foundation for relevant studies. It also investigated the effects of extraction methods, data sources, and band features, offering insights for optimizing mariculture extraction methods.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.