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作 者:索安宁[1,2] 王鹏[1,2] 袁道伟[1,2] 于永海[1,2] 张明慧[3]
机构地区:[1]国家海洋环境监测中心,辽宁大连116023 [2]国家海洋局海域管理技术重点实验室,辽宁大连116023 [3]大连海洋大学海洋与土木工程学院,辽宁大连116023
出 处:《海洋学报》2016年第9期54-63,共10页
基 金:国家自然科学基金面上项目(41376120);海洋公益性行业科研专项(201405025;201005011)
摘 要:为详细掌握围填海存量资源状态,提高海域空间资源监管精细化水平,本文提出围填海存量资源概念,并将围填海存量资源划分为围而未填区域、填而未建区域、低密度建设区域、低洼坑塘、低效盐田和低效养殖池塘等6种类型,以及工业区、城镇区等围填海存量资源的消耗类型。在此基础上,采用高空间分辨率卫星遥感影像,建立了面向对象的围填海存量资源遥感影像分类提取方法与技术流程,构建了围填海存量资源指数,并以营口市南部海岸区域为例进行了实证研究。结果表明:(1)面向对象的围填海存量资源遥感影像分类提取方法能够精细地提取围填海存量资源信息,准确率达到90%以上;(2)填而未建区域、低效盐田、低效养殖池塘占研究区围填海存量资源总面积的62.51%,研究区围填海存量资源指数为0.49,但内部差异明显,北部区域为0.66,中部区域为0.44,南部区域为0.37。Aimed to reveal status of existing sea reclamation resource and improve monitoring level of marine space,the conception of existing sea reclamation resource is defined and divided it into encircled and unfilled region, reclamation and non construction region, low density building region, low-lying ponds, low economic salt ponds and low economic fishing ponds, and consumption types such as industrial region, cities and town region. Then high resolution satellite remote sensing images was employed to obtain existing sea reclamation resource information. And the technique flow of remote sensing image classification for existing sea reclamation resource based on obiect- oriented method established, and existing sea reclamation resource index was created for existing sea reclamation resource monitoring and analysis. South coast of Yingkou was chose as study areas based on above. The results show that remote sensing image classification for existing sea reclamation resource based on object-oriented method established in this paper can extract existing sea reclamation resource information with accuracy rate more than 90%. Reclamation and non construction region, low economic salt ponds and low economic fishing ponds account for 62.51% areas of total existing sea reclamation resource. The existing sea reclamation resource index is 0.49 for total study areas and 0.66 for north part, 0.44 for middle part and 0. 37 for south part.
分 类 号:TP79[自动化与计算机技术—检测技术与自动化装置]
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