| 摘要: |
| [目的]海州湾紫菜养殖作为连云港重要的农业产业，其养殖规模的动态监测和产业发展内在驱动力分析对紫菜产业的发展规划以及整片海域的生态环境评估意义重大。[方法]以2004-2019年海州湾冬春两季Landsat系列遥感影像为基础数据源，通过Google Earth Engine的随机森林算法提取紫菜养殖区的时空动态变化状况和近岸人工开发信息，结合紫菜产业近期的统计资料和促进保障政策，进行了紫菜产业发展的空间格局变化以及驱动因子分析。[结果]结果表明：（1）在空间上，海州湾紫菜养殖区总体呈近岸向外海拓展变化趋势，由沿海岸线带状分布变成片状分布在整个海州湾；（2）时间上，2004年紫菜养殖面积最少为1551hm2，约占江苏省紫菜养殖面积1/13，2015—2019年养殖规模迅速增长至22700 hm2，约占江苏省紫菜养殖面积的1/2；（3）针对15年的紫菜养殖面积、价格、政策、海岸工程建设（龙桥）等数据实施量化并进行整体相关性分析。证实了价格与紫菜养殖面积之间存在极显著相关（相关系数r=0.79，p<0.01），龙桥等基础设施建设与养殖面积也存在极强相关性（r=0.84，p<0.01），政策与养殖面积显著性不强（r=0.34，p>0.05），[结论]揭示了海州湾紫菜养殖产业空间分布格局由沿海向外海扩展的分布规律主要受紫菜受市场价格机制和海岸工程建设（龙桥）的影响。 |
| 关键词: 海州湾 紫菜养殖区 空间分异 驱动因子 相关性分析 |
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| Correlation analysis of spatial distribution change and driving factors of laver cultivation in Haizhou bay |
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sujingli, fanwei, WangFei
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East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences
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| Abstract: |
| Dynamic monitoring and internal driving factors analysis of laver cultivation is necessary for sustainable development and planning to this important agriculture variety in Haizhou bay. Dynamic monitoring of laver cultivation could be analyzed with the Landsat series remote sensing data in the winter and spring period during 2004-2019 based on the random forest classify method in Google Earth Engine (GEE). Internal driving factors analysis would be combined with not only traditional social and economic data but also the cultivation area dynamic change and coastal construction development data generated by remote sensing data. The result has shown that， spatial distribution of laver culture area in Haizhou bay is overall expanding from the nearshore to the open sea， and shaping from a strip distribution along the coastline to a flaky distribution in the whole Haizhou Bay. Laver culture area has increased from 1551 hectares in 2004 to 23，400 hectares in 2018. And cultivation area of laver accounting for in Jiangsu Province has rapidly increased from 1/13 to 1/2 during 2004-2018. laver cultivation area dynamic change， market price index， policy and coastal engineering construction (longqiao) in the past 15 years were quantified and overall correlation analyzed. It is confirmed that there is a very significant correlation between the price and the cultivation area of laver (correlation coefficient r=0.79， p<0.01)， and there is also a strong correlation between the construction of infrastructure such as Longqiao and the cultivation area (r=0.84， p<0.01). The policy and cultivation area are not significant (r=0.34， p>0.05). Spatial distribution of the laver cultivation in Haizhou bay is from the coastline to open sea which is mainly affected by the market price mechanism and coastal engineering construction (Longqiao). |
| Key words: Haizhou bay laver cultivation area spatial differentiation driving factors correlation analysis |