eprintid: 13945
rev_number: 9
eprint_status: archive
userid: 5
dir: disk0/00/01/39/45
datestamp: 2016-11-15 13:59:31
lastmod: 2021-08-27 17:28:00
status_changed: 2016-11-15 13:59:31
type: article
metadata_visibility: show
item_issues_count: 1
creators_name: Bechtel, B.
creators_name: See, L.
creators_name: Mills, G.
creators_name: Foley, M.
creators_id: 8571
creators_orcid: 0000-0002-2665-7065
title: Classification of Local Climate Zones Using SAR and Multispectral Data in an Arid Environment
ispublished: pub
divisions: prog_esm
keywords: urban areas, Multisensor systems, remote sensing, satellite applications, synthetic aperture radar
abstract: 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 km2) 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.
date: 2016-03-29
date_type: published
publisher: IEEE
id_number: 10.1109/JSTARS.2016.2531420
creators_browse_id: 276
full_text_status: none
publication: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
volume: 9
number: 7
pagerange: 3097-3105
refereed: TRUE
issn: 1939-1404
coversheets_dirty: FALSE
fp7_project: no
fp7_type: info:eu-repo/semantics/article
citation:   Bechtel, B., See, L. <https://pure.iiasa.ac.at/view/iiasa/276.html> ORCID: https://orcid.org/0000-0002-2665-7065 <https://orcid.org/0000-0002-2665-7065>, Mills, G., & Foley, M.  (2016).  Classification of Local Climate Zones Using SAR and Multispectral Data in an Arid Environment.   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (7) 3097-3105. 10.1109/JSTARS.2016.2531420 <https://doi.org/10.1109/JSTARS.2016.2531420>.