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自然资源遥感  2023, Vol. 35 Issue (2): 34-41    DOI: 10.6046/zrzyyg.2022296
  海岸带空间资源及生态健康遥感监测专栏 本期目录 | 过刊浏览 | 高级检索 |
卫星遥感辅助的大型海藻养殖动态对比监测——以威海市为例
侯英卓1,2,3(), 纪灵4, 邢前国1,2,3(), 盛德志1,2,3
1.中国科学院烟台海岸带研究所,中国科学院海岸带环境过程与生态修复重点实验室,烟台 264003
2.山东省海岸带环境过程重点实验室,烟台 264003
3.中国科学院大学,北京 100049
4.国家海洋局烟台海洋环境监测中心站,烟台 264006
Satellite remote sensing-assisted comparative monitoring of dynamic characteristics of macroalgae aquaculture in Weihai City, Shandong Province, China
HOU Yingzhuo1,2,3(), JI Ling4, XING Qianguo1,2,3(), SHENG Dezhi1,2,3
1. CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
2. Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. Yantai Marine Environment Monitoring Center, Yantai 264006, China
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摘要 

大型海藻养殖的时空动态变化监测对其环境管理至关重要,目前关于不同品种的大型海藻养殖的对比监测鲜有研究报道。文章基于Sentinel-2卫星影像,利用归一化植被指数(normalized difference vegetation index,NDVI)与支持向量机(support vector machine,SVM),对山东省威海市文登区南部海域紫菜养殖区与荣成市南部海域海带养殖区的动态特征进行了监测。研究结果表明: ①威海市文登区的紫菜养殖在2016年遥感影像上首次出现,与该市历史上首次出现紫菜养殖的年份相符; 基于文章方法提取的紫菜养殖区与海带养殖区的整体提取效果较好,总体精度可达84%以上; ②2017—2021年度紫菜养殖区的遥感监测面积整体呈逐年增加趋势,空间上养殖区呈现远离岸边的分布趋势; ③紫菜与海带养殖区监测面积总体呈冬高、夏低的冷水型藻类养殖季节变化特征,但紫菜养殖区监测面积最小值与最大值出现时间较海带养殖区早1~2个月。卫星遥感较统计年鉴能提供更精确的时间与空间信息,研究可为中国北方海岸带大型海藻养殖管理提供监测技术与数据上的参考与借鉴。

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侯英卓
纪灵
邢前国
盛德志
关键词 Sentinel-2大型海藻紫菜海带山东半岛黄海    
Abstract

Monitoring the spatio-temporal dynamic changes in macroalgae aquaculture is crucial to its environmental management. However, few studies have been reported on the comparative monitoring of different macroalgae species. Based on images of the Sentinel-2 satellite and using the normalized difference vegetation index (NDVI) and the support vector machine (SVM), this study monitored the dynamic characteristics of both the Porphyra aquaculture area in the sea area of southern Wendeng District, Weihai City, Shandong Province and the kelp aquaculture area in the sea area of southern Rongcheng City, Weihai City. The results show that: ① The Porphyra aquaculture in Wendeng District was first captured in the satellite images of 2016, which is the same as the first year of Porphyra aquaculture in this city; the extraction method used in this study performed well in extracting the information about both the Porphyra and the kelp aquaculture areas overall, with the overall accuracy of 84% and above; ② During 2017—2021, the Porphyra aquaculture area monitored through remote sensing increased year by year and showed a trend far away from the shore; ③ The Porphyra and kelp aquaculture areas monitored both showed seasonal variations (high in winter and low in summer) of cold-water macroalgae aquaculture, but the minimum and maximum values of the Porphyra aquaculture area appeared 1~2 months earlier than those of the kelp aquaculture area. Compared with statistical yearbooks, satellite remote sensing can provide more accurate spatio-temporal information on macroalgae aquaculture. This study can be used as a reference in terms of monitoring technology and data for the management of macroalgae aquaculture in coastal areas of northern China.

Key wordsSentinel-2    macroalgae    Porphyra    kelp    Shandong Peninsula    Yellow Sea
收稿日期: 2022-07-27      出版日期: 2023-07-07
ZTFLH:  TP79  
基金资助:中国科学院A类战略性先导科技专项“地球大数据科学工程”(XDA19060203);国家自然科学基金项目“海表漂浮大型藻空间分布特征及其在覆盖面积卫星遥感估算中的应用研究”(42076188);“面向冬季大型藻类高分遥感的海表耀光消减与利用研究”(41676171);中国科学院仪器设备研制重点项目“沿海水色环境污染和资源机载高光谱成像探测仪”(YJKYYQ20170048)
通讯作者: 邢前国(1975-),男,博士,研究员,研究方向为海岸带-海洋环境遥感、数值模拟与评估。Email: qgxing@yic.ac.cn
作者简介: 侯英卓(1999-),男,硕士研究生,研究方向为海洋遥感。Email: yingzhuohou@yic.ac.cn
引用本文:   
侯英卓, 纪灵, 邢前国, 盛德志. 卫星遥感辅助的大型海藻养殖动态对比监测——以威海市为例[J]. 自然资源遥感, 2023, 35(2): 34-41.
HOU Yingzhuo, JI Ling, XING Qianguo, SHENG Dezhi. Satellite remote sensing-assisted comparative monitoring of dynamic characteristics of macroalgae aquaculture in Weihai City, Shandong Province, China. Remote Sensing for Natural Resources, 2023, 35(2): 34-41.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022296      或      https://www.gtzyyg.com/CN/Y2023/V35/I2/34
Fig.1  研究区地理位置及大型藻类养殖区分布
年份 影像日期
2016年 01-25,03-25,04-24,05-04,07-13,08-22,
09-11,11-20,12-10
2017年 01-09,03-10,04-19,05-19,08-02,09-11,
09-21,10-21,11-15,12-05
2018年 01-09,02-13,03-25,04-19,05-04,06-03,
08-02,10-21,11-10,11-30
2019年 01-24,02-23,03-25,04-14,06-23,08-17,
09-26,10-31,11-20,12-20
2020年 01-14,02-23,03-24,04-03,06-22,07-02,
09-20,10-25,11-09,12-19
2021年 01-18,02-02,03-24,04-18,05-18,07-22
Tab.1  所用影像介绍
Fig.2  技术路线
Fig.3  样本分布及相应的光谱曲线与NDVI频率分布
Fig.4  2017—2021年度紫菜养殖区的遥感监测结果
Fig.5  2020年2月23日海带养殖区的遥感监测结果
Fig.6  遥感监测的紫菜养殖区面积与养殖方个数变化
指标 紫菜养殖区 海带养殖区
2019-01-24 2019-04-14 2019-06-23 2019-10-31 2019-01-24 2019-04-14 2019-06-23 2019-10-31
OA/% 99 99 92 95 94 94 95 84
Kappa 0.99 0.97 0.84 0.89 0.87 0.88 0.90 0.68
Tab.2  精度评价结果
Fig.7  紫菜养殖遥感监测面积的季节变化
Fig.8  2019—2020年紫菜与海带养殖的监测面积变化
Fig.9  2015—2020年统计年鉴中紫菜与海带的养殖面积变化
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