基于无人机与卫星图像的大型绿藻孔石莼(Ulva pertusa)遥感估算研究

doi:10.11978/2021088

热带海洋学报 ›› 2022, Vol. 41 ›› Issue (3): 46-53.doi: 10.11978/2021088CSTR: 32234.14.2021088

基于无人机与卫星图像的大型绿藻孔石莼(Ulva pertusa)遥感估算研究

孟苗苗^1,^2,³(), 郑向阳^1,^2,³, 邢前国^1,^2,³(), 刘海龙^1,^2,³

1.中国科学院海岸带环境过程与生态修复重点实验室(烟台海岸带研究所), 山东烟台 264003
2.中国科学院海洋大科学研究中心, 山东青岛 266071
3.中国科学院大学, 北京 100049

收稿日期:2021-07-09 修回日期:2021-09-02 发布日期:2021-09-19
通讯作者: 邢前国
作者简介:孟苗苗(1997—)女, 山东省菏泽市人, 硕士研究生, 主要研究方向为海洋遥感。email: mmeng@yic.ac.cn
基金资助:
国家自然科学基金(42076188);国家自然科学基金(41676171);中国科学院先导专项(XDA1906000);中国科学院先导专项(XDA19060203);中国科学院先导专项(XDA19060501);中国科学院仪器研发重点项目(YJKYYQ20170048)

Remote sensing estimation of green macroalgae Ulva pertusa based on unmanned aerial vehicle and satellite image

MENG Miaomiao^1,^2,³(), ZHENG Xiangyang^1,^2,³, XING Qianguo^1,^2,³(), LIU Hailong^1,^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. Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2021-07-09 Revised:2021-09-02 Published:2021-09-19
Contact: XING Qianguo
Supported by:
National Natural Science Foundation of China(42076188);National Natural Science Foundation of China(41676171);The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA1906000);The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19060203);The Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19060501);The Instrument Developing Project of the Chinese Academy of Sciences(YJKYYQ20170048)

摘要/Abstract

摘要：

卫星影像是监测海面漂浮绿藻的重要数据源, 但是混合像元的存在使得绿藻提取存在一定的误差。想要实现近海区域底栖绿藻的精细监测, 需要解决绿藻亚像素覆盖度的问题。本文以厘米级分辨率无人机数据的绿藻提取结果为基准, 通过分析Landsat卫星影像绿藻光谱, 建立绿藻亚像素覆盖度与多种植被指数和多个特征波段反射率的反演模型。结果表明, 蓝、绿、红波段反射率与绿藻亚像素覆盖度呈现较好的线性关系, 随着绿藻亚像素覆盖度递增, 蓝、绿、红波段反射率的值均递减。将蓝、绿、红波段的三种绿藻亚像素覆盖模型进行验证, 发现绿波段反射率所建立的反演模型具有更高的准确性, 决定系数、均方根误差、平均相对误差分别为0.92%、0.07%、10.85%。本文所建立的模型可以估算大型绿藻亚像素覆盖度, 实现Landsat卫星影像对大型绿藻的精细监测。

关键词: 无人机, 卫星影像, 大型绿藻, 亚像素覆盖度

Abstract:

Satellite images are valuable data sources for monitoring floating green macroalgae on the sea surface. However, there are large errors in green macroalgae coverage derived on mixed pixels. It is thus important to solve the problem of sub-pixel coverage of green macroalgae, for precise monitoring of benthic green macroalgae in coastal area. In this paper, retrieval models were established to link sub-pixel coverage of green macroalgae with vegetation indexes and reflectance of characteristic bands by analyzing spectral characteristics of green macroalgae from the Landsat images, based on the results of green macroalgae coverage derived from unmanned aerial vehicle (UAV). The results show excellent linear relationships between the reflectance of blue, green, and red bands and the sub-pixel coverage of green macroalgae, and the reflectance decreases monotonically with increasing sub-pixel coverage. These three models were verified, and the results show that the model based on the reflectance of green band was more accuracy than the other indexes or index combination, with the highest coefficient of determination (R²), root mean square error (RMSE), and mean relative error (MRE) values of 0.92, 0.07, and 10.85%, respectively. Hence, we provide a model that can estimate the sub-pixel coverage of green macroalgae, and realize the precise monitoring of the coverage of green macroalgae extracted from Landsat images.

Key words: unmanned aerial vehicle (UAV), satellite image, green macroalgae, sub-pixel coverage

中图分类号:

孟苗苗, 郑向阳, 邢前国, 刘海龙. 基于无人机与卫星图像的大型绿藻孔石莼(Ulva pertusa)遥感估算研究[J]. 热带海洋学报, 2022, 41(3): 46-53.

MENG Miaomiao, ZHENG Xiangyang, XING Qianguo, LIU Hailong. Remote sensing estimation of green macroalgae Ulva pertusa based on unmanned aerial vehicle and satellite image[J]. Journal of Tropical Oceanography, 2022, 41(3): 46-53.

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参考文献 29

[1]	安德玉, 邢前国, 魏振宁, 等, 2018. 黄海典型漂浮大型藻类光谱特征分析[J]. 海洋与湖沼, 49(5): 1054-1060.
	AN DEYU, XING QIANGUO, WEI ZHENNING, et al, 2018. Spectral features and analysis of typical floating macroalgae in the yellow sea[J]. Oceanologia et Limnologia Sinica, 49(5): 1054-1060. (in Chinese with English abstract)
[2]	丁一, 黄娟, 崔廷伟, 等, 2015. 基于NDVI与丰度关系的MODIS影像浒苔混合像元分解方法[J]. 海洋学报, 37(7): 123-131. doi: 10.1007/s13131-018-1255-8
	DING YI, HUANG JUAN, CUI TINGWEI, et al, 2015. The decomposition method of MODIS images Enteromorpha mixed pixels based on the relation of NDVI to abundance[J]. Acta Oceanologica Sinica, 37(7): 123-131. (in Chinese with English abstract) doi: 10.1007/s13131-018-1255-8
[3]	樊彦国, 白羽, 陈潘潘, 等, 2015. 青岛近海浒苔光谱特征研究[J]. 海洋科学, 39(4): 87-91.
	FAN YANGUO, BAI YU, CHEN PANPAN, et al, 2015. Research of spectrum characteristics of enteromorpha in Qingdao offshore[J]. Marine Sciences, 39(4): 87-91. (in Chinese with English abstract)
[4]	蒋兴伟, 刘建强, 邹斌, 等, 2009. 浒苔灾害卫星遥感应急监视监测系统及其应用[J]. 海洋学报, 31(1): 52-64.
	JIANG XINGWEI, LIU JIANQIANG, ZOU BIN, et al, 2009. The satellite remote sensing system used in emergency response monitoring for Entermorpha prolifera disaster and its application[J]. Acta Oceanologica Sinica, 31(1): 52-64. (in Chinese with English abstract)
[5]	刘岩, 刘涛, 于丹, 等, 2010. 常见石莼科绿藻的生物学特征及分子系统学分析[J]. 中国海洋大学学报, 40(12): 71-80.
	LIU YAN, LIU TAO, YU DAN, et al, 2010. Biological characteristics and molecular systematics studies on common green algae of Ulvaceae[J]. Periodical Of Ocean University Of China, 40(12): 71-80. (in Chinese with English abstract)
[6]	邢前国, 郑向阳, 施平, 等, 2011. 基于多源、多时相遥感影像的黄、东海绿潮影响区检测[J]. 光谱学与光谱分析, 31(6): 1644-1647.
	XING QIANGUO, ZHENG XIANGYANG, SHI PING, et al, 2011. Monitoring “green tide” in the Yellow Sea and the East China Sea using multi-temporal and multi-source remote sensing images[J]. Spectroscopy and Spectral Analysis, 31(6): 1644-1647. (in Chinese with English abstract)
[7]	CUI TINGWEI, ZHANG JIE, SUN LIE, et al, 2012. Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation[J]. International Journal of Remote Sensing, 33(17): 5513-5527. doi: 10.1080/01431161.2012.663112
[8]	GOWER J, KING S, 2019. Seaweed, seaweed everywhere[J]. Science, 365(6448): 27. doi: 10.1126/science.aay0989
[9]	HAN HONGBIN, SONG WEI, WANG ZONGLING, et al, 2019. Distribution of green algae micro-propagules and their function in the formation of the green tides in the coast of Qinhuangdao, the Bohai Sea, China[J]. Acta Oceanologica Sinica, 38(8): 72-77.
[10]	HU CHUANMIN, 2009. A novel ocean color index to detect floating algae in the global oceans[J]. Remote Sensing of Environment, 113(10): 2118-2129. doi: 10.1016/j.rse.2009.05.012
[11]	LI LIN, XING QIANGUO, LI XUERONG, et al, 2018a. Assessment of the impacts from the world's largest floating macroalgae blooms on the water clarity at the West Yellow Sea using MODIS data (2002-2016)[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5): 1397-1402. doi: 10.1109/JSTARS.2018.2806626
[12]	LI LIN, ZHENG XIANGYANG, WEI ZHENNING, et al, 2018b. A spectral-mixing model for estimating sub-pixel coverage of sea-surface floating macroalgae[J]. Atmosphere-Ocean, 56(4): 296-302. doi: 10.1080/07055900.2018.1509834
[13]	LIU DONGYAN, KEESING J K, XING QIANGUO, et al, 2009. World's largest macroalgal bloom caused by expansion of seaweed aquaculture in China[J]. Marine Pollution Bulletin, 58(6): 888-895. doi: 10.1016/j.marpolbul.2009.01.013
[14]	MORAND P, MERCERON M, 2005. Macroalgal population and sustainability[J]. Journal of Coastal Research, 21(5): 1009-1020.
[15]	QI LIN, HU CHUANMIN, XING QIANGUO, et al, 2016. Long-term trend of Ulva prolifera blooms in the western Yellow Sea[J]. Harmful Algae, 58: 35-44. doi: 10.1016/j.hal.2016.07.004
[16]	QI LIN, HU CHUANMIN, WANG MENGQIU, et al, 2017. Floating algae blooms in the East China Sea[J]. Geophysical Research Letters, 44(22): 11501-11509. doi: 10.1002/2017GL075525
[17]	SCHREYERS L, VAN EMMERIK T, BIERMANN L, et al, 2021. Spotting green tides over Brittany from space: three decades of monitoring with Landsat imagery[J]. Remote Sensing, 13(8): 1408. doi: 10.3390/rs13081408
[18]	SHEN HUI, PERRIE W, LIU QINGRONG, et al, 2014. Detection of macroalgae blooms by complex SAR imagery[J]. Marine Pollution Bulletin, 78(1-2): 190-195. doi: 10.1016/j.marpolbul.2013.10.044
[19]	SMETACEK V, ZINGONE A, 2013. Green and golden seaweed tides on the rise[J]. Nature, 504(7478): 84-88. doi: 10.1038/nature12860
[20]	VALIELA I, MCCLELLAND J, HAUXWELL J, et al, 1997. Macroalgal blooms in shallow estuaries: controls and ecophysiological and ecosystem consequences[J]. Limnology and Oceanography, 42(5): 1105-1118. doi: 10.4319/lo.1997.42.5_part_2.1105
[21]	WANG MENGQIU, HU CHUANMIN, 2016. Mapping and quantifying Sargassum distribution and coverage in the Central West Atlantic using MODIS observations[J]. Remote Sensing of Environment, 183: 350-367. doi: 10.1016/j.rse.2016.04.019
[22]	WANG MENGQIU, HU CHUANMIN, BARNES B B, et al, 2019. The great Atlantic Sargassum belt[J]. Science, 365(6448): 83-87. doi: 10.1126/science.aaw7912
[23]	XING QIANGUO, TOSI L G, BRAGA F, et al, 2015. Interpreting the progressive eutrophication behind the world's largest macroalgal blooms with water quality and ocean color data[J]. Natural Hazards, 78(1): 7-21. doi: 10.1007/s11069-015-1694-x
[24]	XING QIANGUO, HU CHUANMIN, 2016a. Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: application of a virtual baseline reflectance height technique[J]. Remote Sensing of Environment, 178: 113-126. doi: 10.1016/j.rse.2016.02.065
[25]	XING QIANGUO, BRAGA F, TOSI L, et al, 2016b. Detection of low salinity groundwater seeping into the eastern Laizhou Bay (China) with the aid of Landsat thermal data[J]. Journal of Coastal Research, 74(S1): 149-156. doi: 10.2112/SI74-014.1
[26]	XING QIANGUO, GUO RUIHONG, WU LINGLING, et al, 2017. High-resolution satellite observations of a new hazard of golden tides caused by floating sargassum in winter in the Yellow Sea[J]. IEEE Geoscience and Remote Sensing Letters, 14(10): 1815-1819. doi: 10.1109/LGRS.2017.2737079
[27]	XING QIANGUO, WU LINGLING, TIAN LIQIAO, et al, 2018. Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign[J]. Marine Pollution Bulletin, 133: 150-156. doi: 10.1016/j.marpolbul.2018.05.035
[28]	XING QIANGUO, AN DEYU, ZHENG XIANGYANG, et al, 2019. Monitoring seaweed aquaculture in the Yellow Sea with multiple sensors for managing the disaster of macroalgal blooms[J]. Remote Sensing of Environment, 231: 111279. doi: 10.1016/j.rse.2019.111279
[29]	YE NAIHAO, ZHANG XIAOWEN, MAO YUZE, et al, 2011. ‘Green tides' are overwhelming the coastline of our blue planet: taking the world's largest example[J]. Ecological Research, 26(3): 477-485. doi: 10.1007/s11284-011-0821-8

动态阈值法	提取一致性	Kappa	F1 score
NGRDI	89%	0.72	0.83
NGBDI	83%	0.68	0.79
RGRI	79%	0.62	0.74
ExG	77%	0.38	0.65
红波段DN值	94%	0.88	0.92

基于无人机与卫星图像的大型绿藻孔石莼(Ulva pertusa)遥感估算研究

Remote sensing estimation of green macroalgae Ulva pertusa based on unmanned aerial vehicle and satellite image

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