检索结果-南通市图书馆

A generalized supervised classification scheme to produce provincial wetland inventory maps:an application of Google Earth Engine for big geo data processing

引用

big Earth data 2019年第4期3卷 378-394页

作者： Meisam Amani Brian Brisco Majid Afshar S.Mohammad Mirmazloumi Sahel Mahdavi Sayyed Mohammad Javad Mirzadeh Weimin Huang Jean Granger Wood Environment&Infrastructure Solutions St.John’sNLCanada The Canada Center for Mapping and Earth Observation OttawaOntarioCanada Department of Computer Science Memorial University of NewfoundlandSt.John’sNLCanada Remote Sensing Research Center Faculty of Geodesy and Geomatics EngineeringK.N.Toosi University of TechnologyTehranIran Shanghai Astronomical Observatory University of Chinese Academy of ScienceShanghaiChina Department of Electrical Engineering Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandSt.John’sNLCanada C-CORE St.John’sNLCanada

Wetlands are important natural resources due to their numerous ecological ***,identifying their locations and extents is *** stability,repeatability,cost-effectiveness,multi-scale coverage,and proper spatial resolution imagery of satellites provide a valuable opportunity for their use in various large-scale applications,such as provincial wetland *** do so,it is required to(1)process and classify big geo data(i.e.a large amount of satellite datasets)in a time-and computationally-efficient approach and(2)collect a large amount of field *** this study,Google Earth Engine(GEE)and machine learning algorithms were utilized to process thousands of remote sensing images and produce provincial wetland inventory maps of the three Canadian provinces of Manitoba,Quebec,and Newfoundland and Labrador(NL).Additionally,using GEE,a generalized supervised classification method is proposed to produce a regional wetland map from a large area(e.g.,a province)when lacking field *** fact,using the field data from only Manitoba and assuming that all wetlands in Canada have similar characteristics,the wetland maps were generated for the other two *** overall classification accuracies for Manitoba,Quebec,and NL were 84%,78%,and 82%,respectively,indicating the high potential of the proposed method for aiding provincial wetland inventory systems.

关键词： Wetlands remote sensing Google Earth Engine big geo data image classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：