| 2015—2020年辽东湾海冰冰情时空特征及其影响因素 |
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| 引用本文： | 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,2021,23(11):2025-2041. |
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| 作者姓名： | 赵泉华 王肖 王雪峰 李玉 |
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| 作者单位： | 辽宁工程技术大学测绘与地理科学学院, 阜新 123000 |
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| 基金项目： | 辽宁省教育厅科学技术研究一般项目(LR2019JL001) |
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| 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。
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| 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 |
| 收稿时间： | 2020-04-25 |
Temporal and Spatial Characteristics of Sea Ice Condition and Its Influencing Factors in Liaodong Bay from 2015 to 2020 |
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| 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].Geo-information Science,2021,23(11):2025-2041. |
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| Authors: | ZHAO Quanhua WANG Xiao WANG Xuefeng LI Yu |
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| Institution: | School of Geomatics, Liaoning Technical University, Fuxin 123000, China |
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| 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. |
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| Keywords: | 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 |
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