http://coastaldata.ecnu.edu.cn/zh-hans zh-hans http://coastaldata.ecnu.edu.cn/zh-hans/quanqiuhaishangfengdianleidayaoganjingzhunshibie <span>全球海上风电雷达遥感精准识别</span> <span><span lang="" about="/zh-hans/user/28" typeof="schema:Person" property="schema:name" datatype="">contentmananger</span></span> <span>周二, 07/27/2021 - 00:00</span> <div class="field field--name-body field--type-text-with-summary field--label-hidden field__item"><p>— <em>Scientific Data</em>,  Vol8, 191, July 2021</p> <pre> <strong>张婷, 田波^*, Dhritiraj Sengupta, 张磊, 司亚丽</strong></pre> <pre> <strong>关键词：全球，风电，海岸带，时序分析，SAR</strong></pre> <p align="left" style="text-align:left; text-indent:21.0pt"><span style="font-size:10.5pt"><span style="font-family:等线"><span style="background:white"><span style="color:black">风能是重要的清洁能源，全球已有多个国家在海岸带地区安装了风电机组。全面、准确地掌握海上风电场的空间分布和数量对风能的有效利用，风电场的科学选址，生态环境效应评价以及实现“碳达峰、碳中和”联合国可持续发展目标等至关重要。受云和水汽影响，目前利用光学影像绘制沿海地区风电数据库具有很大的局限性。合成孔径雷达能够穿透云层，在高后向散射特性的海面目标识别上具有优势。</span></span></span></span></p> <p align="left" style="text-align:left; text-indent:21.0pt"><span style="font-size:10.5pt"><span style="font-family:等线"><span style="background:white"><span style="color:black">本研究利用时序雷达影像对全球海上风电机组进行识别，通过动态阈值分割及空间滤波算法，对</span></span><span lang="EN-US" style="background:white" xml:lang="EN-US"><span style="font-family:&quot;Roboto&quot;,serif"><span style="color:black">2015</span></span></span><span style="background:white"><span style="color:black">年至</span></span><span lang="EN-US" style="background:white" xml:lang="EN-US"><span style="font-family:&quot;Roboto&quot;,serif"><span style="color:black">2020</span></span></span><span style="background:white"><span style="color:black">年的</span></span><span lang="EN-US" style="background:white" xml:lang="EN-US"><span style="font-family:&quot;Roboto&quot;,serif"><span style="color:black">Sentinel-1</span></span></span><span style="background:white"><span style="color:black">时序影像进行处理分析，改善了单一固定阈值在不同地区、不同环境下容易遗漏识别对象或识别过多噪声的情况，以年度尺度影像融合技术判别目标出现的频次去除移动目标（如船只等）的影响，实现了对全球海上风电的高精度快速提取。通过构建时序数据变化检测算法，逐一获取单个风电机组的建造年份。</span></span></span></span></p> <p align="left" style="text-align:left; text-indent:21.0pt"><span style="font-size:10.5pt"><span style="font-family:等线"><span style="background:white"><span style="color:black">截至</span></span><span lang="EN-US" style="background:white" xml:lang="EN-US"><span style="font-family:&quot;Roboto&quot;,serif"><span style="color:black">2019</span></span></span><span style="background:white"><span style="color:black">年，全球共有</span></span><span lang="EN-US" style="background:white" xml:lang="EN-US"><span style="font-family:&quot;Roboto&quot;,serif"><span style="color:black">14</span></span></span><span style="background:white"><span style="color:black">个沿海国家建设了总计</span></span><span lang="EN-US" style="background:white" xml:lang="EN-US"><span style="font-family:&quot;Roboto&quot;,serif"><span style="color:black">6924</span></span></span><span style="background:white"><span style="color:black">台海上风电涡轮机组，遥感识别和提取综合准确度达到</span></span><span lang="EN-US" style="background:white" xml:lang="EN-US"><span style="font-family:&quot;Roboto&quot;,serif"><span style="color:black">95%</span></span></span><span style="background:white"><span style="color:black">。该风电数据集可以促进对全球和局部海上风电场空间分布格局和模式的理解，对保障海上航道安全，评估海洋环境以及实施海洋空间规划和可持续性管理提供基础性数据支撑。该全球海上风电场数据集</span></span><span lang="EN-US" style="background:white" xml:lang="EN-US"><span style="font-family:&quot;Roboto&quot;,serif"><span style="color:black">(GOWF)</span></span></span><span style="background:white"><span style="color:black">可在</span></span><span lang="EN-US" style="background:white" xml:lang="EN-US"><span style="font-family:&quot;Roboto&quot;,serif"><span style="color:black">ArcGIS Online</span></span></span><span style="background:white"><span style="color:black">公开查看与下载，并将持续更新。</span></span></span></span></p> </div> <div class="field field--name-field-portfolio-tags field--type-entity-reference field--label-hidden field__item"><a href="/zh-hans/taxonomy/term/36" hreflang="zh-hans">全球变化遥感</a></div> <div> <div class="item"> <div class="item-image"> <a href="/zh-hans/quanqiuhaishangfengdianleidayaoganjingzhunshibie"><img src="/sites/default/files/portfolio-images/3.png" alt="" loading="lazy" typeof="foaf:Image" /> </a> </div> </div></div> <div class="field field--name-field-zhuyaojieguo field--type-text-long field--label-above"> <div class="field__label">主要结果</div> <div class="field__item"><img alt="1" data-entity-type="file" data-entity-uuid="b8d8e8c6-b3d0-4692-9e77-56e035a86676" src="/sites/default/files/inline-images/11.png" class="align-center" width="1112" height="729" loading="lazy" /><img alt="2" data-entity-type="file" data-entity-uuid="fb66af3b-b5be-4750-8a6a-bc876155fbd5" src="/sites/default/files/inline-images/22.png" class="align-center" width="1065" height="862" loading="lazy" /> <hr /> <p><strong>      DOI - </strong><span lang="EN-US" style="font-size:10.5pt;mso-bidi-font-size:&#10;11.0pt;font-family:等线;mso-ascii-theme-font:minor-latin;mso-fareast-theme-font:&#10;minor-fareast;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:&quot;Times New Roman&quot;;&#10;mso-bidi-theme-font:minor-bidi;mso-ansi-language:EN-US;mso-fareast-language:&#10;ZH-CN;mso-bidi-language:AR-SA" xml:lang="EN-US" xml:lang="EN-US"><span style="font-family:&#10;&quot;Segoe UI&quot;,sans-serif;color:#006699;background:#E1E6D7;text-decoration:none;&#10;text-underline:none"><a href="https://doi.org/10.1038/s41597-021-00982-z">https://doi.org/10.1038/s41597-021-00982-z</a></span></span></p> </div> </div> Mon, 26 Jul 2021 22:00:00 +0000 contentmananger 152 at http://coastaldata.ecnu.edu.cn