2015—2020年辽东湾海冰冰情时空特征及其影响因素

doi:10.12082/dqxxkx.2021.210225

摘要/Abstract

摘要：

受冬季强寒潮侵袭,辽东湾会出现大范围结冰现象。为了分析2015—2020年辽东湾海冰冰情的变化规律与影响因素,本文选取Sentinel-1A/B数据开展辽东湾海冰监测。首先,采用巴氏距离选择最优纹理特征组合,再利用最大似然方法实现海冰分类;然后,根据上述海冰分类结果,分析海冰冰情等级、海冰外缘线、海冰面积、海冰类型和海冰结冰概率等冰情特征的变化规律;最后,研究海水深度、海温、气温和风速与海冰冰情的关系。主要结论如下：① 采用不同纹理特征组合方法和本文方法对2020年2月1日Sentinel-1B影像进行实验,结果表明本文方法的总体分类精度和Kappa系数分别为93.16%和0.85,分类精度最高。② 11月末到12月海冰类型以初生冰为主,间有灰冰;1月到2月中上旬以灰冰为主,间有初生冰和白冰;2月下旬到3月上旬的海冰类型以灰冰和初生冰为主。辽东湾内部结冰概率存在差异,北部沿岸结冰概率高于南部,东部结冰概率高于西部。辽东湾海冰冰情受海水深度、海温和气温影响明显,受风速影响较小。

关键词: 辽东湾海冰, 灰度共生矩阵, 巴氏距离, 海冰外缘线, 海冰面积, 海冰结冰概率, 海冰分类, 影响因素

Abstract:

Under the attack of strong cold wave in winter, a large-scale freezing phenomenon appears in Liaodong Bay. In order to analyze the change rules of sea ice condition and the environmental influencing factors in Liaodong Bay from 2015 to 2020, Sentinel-1A/B data are selected to carry out sea ice monitoring in Liaodong Bay. First, the Gray Level Co-occurrence Matrix is used to count the texture features. Then the optimal feature combination is selected based on the Bhattacharyya Distance, and the Maximum Likelihood method is adopted to classify sea ice. Then, according to the classification results of sea ice, the sea ice condition levels of Liaodong Bay in recent 5 years are determined. The change rules of ice condition characters such as the outer edge of sea ice, area, type, and freezing probability are analyzed. Finally, the influence of sea water depth on sea ice condition is discussed, the relationship between sea ice condition and sea temperature, air temperature, and wind speed is studied through correlation analysis. The main conclusions are as follows. Firstly, the Sentinel-1B image on February 1, 2020 is tested using the proposed method and different texture feature combination methods. The results show that the overall classification accuracy and Kappa coefficient of the proposed method are 93.16% and 0.85, respectively, which is the highest classification accuracy among all methods. The overall classification accuracy and Kappa coefficient of all images from 2015 to 2020 are above 85% and 0.80, respectively, which meet the accuracy requirements for sea ice monitoring. Secondly, in last November and December, the sea ice types are mainly primary ice, with gray ice in between. Gray ice is the main ice in January and early February, with primary and white ice in between. Gray and primary ice is dominant in late February and early March. There are differences in the sea ice freezing probability in Liaodong Bay. The sea ice freezing probability in the north coast is higher than that in the south, and the probability in the east is higher than that in the west. Sea depth in Liaodong Bay has different effects on sea ice development in different ranges of [-10, 0], [-20, -10], and [-30, -20]. The Pearson correlation coefficients of sea ice condition with sea temperature, air temperature, and wind speed in Liaodong Bay are -0.55 (P<0.01)、-0.59 (P<0.01)、and -0.22 (P=0.19), indicating that the sea ice condition is negatively correlated with sea temperature and air temperature, and has a low correlation with wind speed.

Key words: sea ice in Liaodong Bay, Gray Level Co-occurrence Matrix, Bhattacharyya Distance, sea ice edge line, sea ice area, sea ice freezing probability, sea ice classification, influence factor

赵泉华, 王肖, 王雪峰, 李玉. 2015—2020年辽东湾海冰冰情时空特征及其影响因素[J]. 地球信息科学学报, 2021, 23(11): 2025-2041.DOI:10.12082/dqxxkx.2021.210225

ZHAO Quanhua, WANG Xiao, WANG Xuefeng, LI Yu. Temporal and Spatial Characteristics of Sea Ice Condition and Its Influencing Factors in Liaodong Bay from 2015 to 2020[J]. Journal of Geo-information Science, 2021, 23(11): 2025-2041.DOI:10.12082/dqxxkx.2021.210225

图/表 16

表1

表2

灰度共生矩阵纹理特征统计量表

特征统计量	表达式	变量说明	物理含义	编号
mean_VV	$mea n VV = 1 M × N ∑ σ VV i = 0 M - 1 ∑ σ VV j = 0 N - 1 P (σ VV i, σ VV j, d, θ)$	M、N分别表示灰度共生矩阵的行列数	该区域内的平均亮度的情况	（4）
var_VV	$va r VV = ∑ σ VV i = 0 M - 1 ∑ σ VV j = 0 N - 1 (σ VV i - μ) 2 P (σ VV i, σ VV j, d, θ)$	$σ VV i$ 和 $σ VV j$ 分别像素i的VV和VH后向散射系数	该区域内灰度值的波动情况	（5）
con_VV	$co n VV = ∑ σ VV i = 0 M - 1 ∑ σ VV j = 0 N - 1 (σ VV i - σ VV j) 2 P (σ VV i, σ VV j, d, θ)$	VH后向散射系数;θ为2个像素连线向量的角度	像素灰度值的分布情况和局部变化情况	（6）
hom_VV	$ho m VV = ∑ σ VV i = 0 M - 1 ∑ σ VV j = 0 N - 1 P (σ VV i, σ VV j, d, θ) 1 + (σ VV i - σ VV j) 2$	d为2个像素的距离;	图像中纹理的同质性,可用于衡量纹理的局部变化情况	（7）
dis_VV	$di s VV = ∑ σ VV i = 0 M - 1 ∑ σ VV j = 0 N - 1 \| σ VV i - σ VV j \| P (σ VV i, σ VV j, d, θ)$	$P (σ VV i, σ VV j, d, θ)$ 表示灰度为从 $σ VV j$ 的像元出发,统计与其灰度为 $σ VV j$ 、距离为d的像元对出现的概率	设定大小窗口内像素灰度值分布的不规则程度	（8）
ent_VV	$en t VV = ∑ σ VV i = 0 M - 1 ∑ σ VV j = 0 N - 1 P (σ VV i, σ VV j) lg P (σ VV i, σ VV j, d, θ)$	同上	图像的信息量	（9）
asw_VV	$as m VV = ∑ σ VV i = 0 M - 1 ∑ σ VV j = 0 N - 1 P (σ VV i, σ VV j, d, θ) 2$	同上	图像像素灰度值分布的均匀程度和纹理的粗细程度	（10）
cor_VV	$co r VV = ∑ σ VV i = 0 M - 1 ∑ σ VV j = 0 N - 1 (σ VV i - μ) (σ VV j - μ) P (σ VV i, σ VV j, d, θ) σ 2$	同上	图像的局部相关性和纹理的细致程度	（11）

表2

图1

图2

表3

表4

表5

Tab. 6

图3

图4

图5

图6

图7

图8

图9

表7

参考文献 29

[1]	焦艳, 黄菲, 高松, 等. 基于长短时记忆神经网络的辽东湾海冰延伸期预报方法研究[J]. 中国海洋大学学报(自然科学版), 2020, 50(6):1-11.
	[ Jiao Y, Huang F, Gao S, et al. Research on extended-range forecast model of sea ice in the Liaodong bay based on long short term memory network[J]. Periodical of Ocean University of China, 2020, 50(6):1-11. ]
[2]	颜钰, 徐粒, 许映军, 等. 基于MODIS的渤海海冰资源量估算[J]. 资源科学, 2017, 39(11):2166-2175.
	[ Yan Y, Xu L, Xu Y J, et al. The estimate of sea ice resources in the Bohai Sea based on MODIS data[J]. Resources Science, 2017, 39(11):2166-2175. ]
[3]	刘眉洁, 戴永寿, 张杰, 等. 高分辨率全极化合成孔径雷达数据海冰二次分类方法研究[J]. 海洋学报(中文版), 2013, 35(4):80-87.
	[ Liu M J, Dai Y S, Zhang J, et al. The research on the object-based method of sea ice classification of high-resolution quad-polarization SAR data[J]. Acta Oceanologica Sinica, 2013, 35(4):80-87. ] doi: 10.1007/s13131-016-0936-4
[4]	邹斌, 林明森, 石立坚, 等. 遥感技术在海洋灾害监测中的应用[J]. 城市与减灾, 2018(6):61-65.
	[ Zou B, Lin M S, Shi L J, et al. Application of remote sensing technology in ocean disaster[J]. City and Disaster Reduction, 2018(6):61-65. ]
[5]	郑小莉, 杨晓梅, 刘兴权. 卫星遥感影像对海洋监测能力的评估分析——以渤海海区为例[J]. 地球信息科学学报, 2009, 11(6):860-866.
	[ Zheng X L, Yang X M, Liu X Q. Evaluation of satellite remote sensing for ocean monitoring: A case study of the Bohai sea[J]. Journal of Geo-information Science, 2009, 11(6):860-866. ] doi: 10.3724/SP.J.1047.2009.00860
[6]	王志勇, 王丽华, 刘健, 等. 基于多源中高分辨率遥感数据提取渤海辽东湾海冰要素信息[J]. 自然灾害学报, 2021, 30(1):174-182.
	[ Wang Z Y, Wang L H, Liu J, et al. Extraction of sea ice element information in Liaodong Bay of the Bohai sea based on multi-source medium and high resolution remote sensing data[J]. Journal of Natural Disasters, 2021, 30(1):174-182. ]
[7]	郭衍游, 谢宏全, 杨光. 基于MODIS影像渤海2012年海冰变化分析[J]. 海洋科学, 2014, 38(6):60-64.
	[ Guo Y Y, Xie H Q, Yang G. Variation analysis of sea ice in the Bohai Sea in 2012 based on MODIS[J]. Marine Sciences, 2014, 38(6):60-64. ]
[8]	张树德, 兰志刚. 基于卫星遥感图像的辽东湾海冰参数反演及空间分布特征分析[J]. 中国海上油气, 2019, 31(4):147-153.
	[ Zhang S D, Lan Z G. Satellite remote sensing images-based parameters inversion and spatial distribution characteristics analysis of sea ice in the Liaodong Bay[J]. China Offshore Oil and Gas, 2019, 31(4):147-153. ]
[9]	Yan Y, Huang K Y, Shao D D, et al. Monitoring the characteristics of the Bohai sea ice using high-resolution geostationary ocean color imager (GOCI) data[J]. Sustainability, 2019, 11(3):777. doi: 10.3390/su11030777
[10]	崔艳荣, 邹斌, 韩震, 等. 卷积神经网络在SAR遥感海冰分类中的应用可行性分析[J]. 海洋预报, 2019, 36(5):77-85.
	[ Cui Y R, Zou B, Han Z, et al. Feasibility analysis of convolutional neural networks in remote sensing sea ice classification[J]. Marine Forecasts, 2019, 36(5):77-85. ]
[11]	Liu H Y, Guo H D, Zhang L. SVM-based sea ice classification using textural features and concentration from RADARSAT-2 dual-pol ScanSAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(4):1601-1613. doi: 10.1109/JSTARS.4609443
[12]	赵泉华, 郭世波, 李晓丽, 等. 利用目标分解特征的全极化SAR海冰分类[J]. 测绘学报, 2018, 47(12):1609-1620.
	[ Zhao Q H, Guo S B, Li X L, et al. Polarimetric SAR sea ice classification based on target decompositional features[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(12):1609-1620. ]
[13]	Kozlov L E, Krek E V, Kostianoy A G, et al. Remote sensing of ice conditions in the Southeastern Baltic Sea and in the curonian lagoon and validation of SAR-Based ice thickness products[J]. Remote Sensing, 2020, 12(22):3754-3774. doi: 10.3390/rs12223754
[14]	Yu M, Lu P, Li Z Y, et al. Sea ice conditions and navigability through the Northeast Passage in the past 40 years based on remote-sensing data[J]. International Journal of Digital Earth, 2021, 14(5):555-574. doi: 10.1080/17538947.2020.1860144
[15]	刘成, 车达升, 李晓东. 黄渤海海冰分布特征及其影响因子[J]. 资源科学, 2019, 41(6):1167-1175.
	[ Liu C, Che D S, Li X D. Sea ice distribution and influencing factors in the Yellow Sea and the Bohai Sea during winter 2018[J]. Resources Science, 2019, 41(6):1167-1175. ]
[16]	郭东琳, 赵鹏, 王彬, 等. 基于卫星观测的辽东湾冰情统计分析[J]. 海洋与湖沼, 2019, 50(1):31-40.
	[ Guo D L, Zhao P, Wang B, et al. Sea ice in the Liaodong bay: Statistical analysis based on satellite observations[J]. Oceanologia et Limnologia Sinica, 2019, 50(1):31-40. ]
[17]	王焕松, 雷坤, 李子成, 等. 辽东湾海域水体富营养化的模糊综合评价[J]. 环境科学研究, 2010, 23(4):413-419.
	[ Wang H S, Lei K, Li Z C, et al. Fuzzy comprehensive evaluation of water eutrophication in Liaodong bay[J]. Research of Environmental Sciences, 2010, 23(4):413-419. ]
[18]	GB/T 14914.2-2019,海洋观测规范第2部分:海滨观测B/T 14914.2-2019,海洋观测规范第2部分:海滨观测[S]. 北京:国家市场监督管理总局;中国国家标准化管理委员会, 2019.
	[GB/T 14914.2-2019, The specification for marine observation-Part 2: Offshore observationB/T 14914.2-2019, The specification for marine observation-Part 2: Offshore observation[S]. Beijing: State Administration of Market Supervision and Administration; China National Standardization Administration Committee, 2019.]
[19]	刘眉洁, 戴永寿, 张杰, 等. 高分辨率全极化合成孔径雷达数据海冰二次分类方法研究[J]. 海洋学报(中文版), 2013, 35(4):80-87.
	[ Liu M J, Dai Y S, Zhang J, et al. The research on the object-based method of sea ice classification of high-resolution quad-polarization SAR data[J]. Acta Oceanologica Sinica, 2013, 35(4):80-87. ] doi: 10.1007/s13131-016-0936-4
[20]	Caballero G R, Platzeck G, Pezzola A, et al. Assessment of multi-date sentinel-1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach[J]. Agronomy, 2020, 10(6):845. doi: 10.3390/agronomy10060845
[21]	邓鸿儒, 崔宸洋, 单文龙, 等. 基于高分三号卫星SAR影像的城市建筑区提取[J]. 地理信息世界, 2018, 25(6):79-84.
	[ Deng H R, Cui C Y, Shan W L, et al. Urban building area extraction based on GF-3 satellite SAR images[J]. Geomatics World, 2018, 25(6):79-84. ]
[22]	张晰, 张杰, 纪永刚. 基于纹理特征分析的辽东湾SAR影像海冰检测[J]. 海洋科学进展, 2008, 26(3):386-393.
	[ Zhang X, Zhang J, Ji Y G. Sea ice detection from SAR images of the Liaodong bay based on texture analysis[J]. Advances in Marine Science, 2008, 26(3):386-393. ]
[23]	宣国荣, 柴佩琪. 基于巴氏距离的特征选择[J]. 模式识别与人工智能, 1996, 9(4):324-329.
	[ Xuan G R, Chai P Q. Feature selection based on bhattacharyya distance[J]. Pattern Recognition and Artificial Intelligence, 1996, 9(4):324-329. ]
[24]	自然资源部, 中国海洋灾害公报[EB/OL]. { HYPERLINK “ http://www.mnr.gov.cn/sj/sjfw/hy/gbgg/zghyzhgb/” } 2021-04-26.
	[ Ministry of Natural Resources of the People’s Republic of China, Bulletin of China Marine Disaster[EB/OL]. {HYPERLINK “ } 2021-04-26.]
[25]	刘成, 曹祥村, 尹朝晖, 等. 2007—2018年黄渤海海冰气候特征及其对气象因子的响应[J]. 海洋通报, 2019, 38(2):173-178.
	[ Liu C, Cao X C, Yin Z H, et al. Climatic characteristics of sea ice extent and its response to meteorological factors in the Yellow Sea and the Bohai Sea during 2007-2018[J]. Marine Science Bulletin, 2019, 38(2):173-178. ]
[26]	武浩, 夏芸, 许映军, 等. 2004年以来中国渤海海冰灾害时空特征分析[J]. 自然灾害学报, 2016, 25(5):81-87.
	[ Wu H, Xia Y, Xu Y J, et al. Analysis of spatiotemporal characteristics of sea ice disaster in the Bohai Sea since 2004[J]. Journal of Natural Disasters, 2016, 25(5):81-87. ]
[27]	许宁, 袁帅, 闫启仑. 渤海海冰及其对渤海生态环境的影响[J]. 环境保护, 2019, 47(7):20-25.
	[ Xu N, Yuan S, Yan Q L. Sea ice in the Bohai sea and its impact on marine ecological environment[J]. Environmental Protection, 2019, 47(7):20-25. ]
[28]	庞海洋. 近50年来渤海海冰时空变化与气候因子关系研究[D]. 烟台:鲁东大学, 2018.
	[ Pang H Y. Study on spatial-temporal variation and the relationship of climatic factors in recent 50 years in Bohai[D]. Yantai: Ludong University, 2018. ]
[29]	de’ Donato F, Michelozzi P. Climate change, extreme weather events and health effects[M]// The Mediterranean Sea. Dordrecht: Springer Netherlands, 2013:617-624.

卫星传感器	时段	获取日期		轨道号	成像模式	分辨率/m	极化方式/波段
Sentinel-1A	2015—2016	2015-11-29	2015-12-23	3-458	IW	10×10	VV/VH
		2016-01-04	2016-01-16
		2016-03-04
Sentinel-1B	2016—2017	2016-12-11	2016-12-23	3-455、 3-460	IW	10×10	VV/VH
		2017-01-04	2017-01-16
		2017-01-28	2017-02-21
Sentinel-1A	2017—2018	2017-11-25	2017-12-07	98-124、 98-129	IW	10×10	VV/VH
		2017-12-19	2017-12-31
		2018-01-12	2018-01-24
		2018-02-05	2018-02-17
		2018-03-01
Sentinel-1A	2018—2019	2018-12-02	2018-12-14	98-124、 98-129	IW	10×10	VV/VH
		2018-12-26	2019-01-07
		2019-01-19	2019-01-31
		2019-02-12	2019-02-24
Sentinel-1B	2019—2020	2019-12-03	2019-12-15	98-127	IW	10×10	VV/VH
		2019-12-27	2020-01-08
		2020-01-20	2020-02-01
		2020-02-13	2020-02-25
Landsat-8	2015—2016	2015-12-07	2016-01-24	120-32	-	30×30	1-5, 7
Landsat-8	2015—2016	2016-02-09	2016-02-25	120-32	-	30×30	1-5, 7
Landsat-8	2016—2017	2017-02-11		120-32	-	30×30	1-5, 7

不同特征组合编号	特征组合	总体分类精度/%	Kappa系数
1	var_VV, asw_VV, cor_VV, con_VV	90.47	0.82
2	var_VV, asw_VV, cor_VV, dis_VV	88.26	0.79
3	所有特征	84.75	0.77
本文方法	var_VV, asw_VV, cor_VV	93.16	0.85

时段	初冰日	终冰日	冰期/d	最大海冰外缘线/n mile	冰情
2015—2016	11-23	3-12	111	65.55	常冰年(3级)
2016—2017	11-22	3-30	102	49.35	偏轻冰年(2级)
2017—2018	11-25	3-14	110	77.50	常冰年(3级)
2018—2019	12-2	3-70	96	53.13	偏轻冰年(2级)
2019—2020	12-3	2-27	87	47.46	偏轻冰年(2级)

年度	海冰面积最大日		最大海冰面积/km²		海冰外缘线最大日		最大海冰外缘线/n mile
年度	本文	《中国海洋灾害公报》	本文	《中国海洋灾害公报》	本文	《中国海洋灾害公报》	本文	《中国海洋灾害公报》
2015—2016	2016-01-24	2016-02-01	11 212.80	21 594.00	2016-01-24	2016-02-01	65.55	79.00
2016—2017	2017-02-11	2017-01-24	7682.56	10 515.00	2017-02-11	2017-01-24	49.35	49.00
2017—2018	2018-02-05	2018-02-06	12 738.70	18 041.00	2018-02-05	2018-01-28	77.50	74.00
2018—2019	2019-02-12	2019-02-13	8958.80	12 058.00	2019-02-12	2019-02-14	53.13	52.00
2019—2020	2020-02-01	2020-02-06	7428.120	9165.00	2020-02-01	2020-02-06	47.46	45.00