Classification of Local Climate Zones Using SAR and Multispectral Data in an Arid Environment

被引：94

作者：

Bechtel, Benjamin ^{[1
]}

See, Linda ^{[2
]}

Mills, Gerald ^{[3
]}

Foley, Micheal ^{[3
]}

机构：

[1] Univ Hamburg, Inst Geog, D-20146 Hamburg, Germany

[2] Int Inst Appl Syst Anal, A-2361 Laxenburg, Austria

[3] Univ Coll Dublin, Sch Geog, Dublin 4, Ireland

来源：

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | 2016年 / 9卷 / 07期

关键词：

Multisensor systems; remote sensing; satellite applications; synthetic aperture radar; urban areas; METROPOLITAN REGION; HYPERSPECTRAL DATA; URBAN; PATTERN; LAYER;

D O I：

10.1109/JSTARS.2016.2531420

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

There is an urgent need for more detailed spatial information on cities globally that has been acquired using a standard method to facilitate comparison and the transfer of scientific and practical knowledge between places. As part of the world urban database and access portal tools (WUDAPT) initiative, a simple workflow has been developed to perform this task. Using freely available satellite imagery (Landsat) and software (SAGA), WUDAPT characterizes settlements using the local climate zone (LCZ) scheme, which decomposes the city into distinctive neighborhoods (>1 km(2)) based on typical properties (e.g., green proportion and built fraction). In this paper, the methodology is extended to examine the effect of adding synthetic aperture radar (SAR) data, which is now freely available from Sentinel 1, for generating LCZs. Using the city of Khartoum as a case study, the results show that combining multispectral and SAR data improves the overall performance of several classifiers, with random forest (RF) performing the best overall.

引用

页码：3097 / 3105

页数：9

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