Next Article in Journal
Burst Misalignment Evaluation for ALOS-2 PALSAR-2 ScanSAR-ScanSAR Interferometry
Previous Article in Journal
Estimation of Downwelling Surface Longwave Radiation under Heavy Dust Aerosol Sky
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(3), 215; doi:10.3390/rs9030215

Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data

1
Graduate School of Engineering, Kyoto University, C1-1-206, Kyotodaigaku-Katsura, Nishikyo-ku, Kyoto 615-8540, Japan
2
Graduate School of Engineering, Kyoto University, C1-1-209, Kyotodaigaku-Katsura, Nishikyo-ku, Kyoto 615-8540, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: James Campbell and Prasad S. Thenkabail
Received: 12 December 2016 / Revised: 17 February 2017 / Accepted: 23 February 2017 / Published: 27 February 2017
View Full-Text   |   Download PDF [8176 KB, uploaded 27 February 2017]   |  

Abstract

In this paper, we propose a method for using mobile laser-scanning data to estimate the green space ratio (GSR), a landscape index that represents the proportion of green area to the whole-view area. The proposed method first classifies and segments vegetation using voxel-based and shape-based approaches. Vertical planar-surface objects are excluded, and randomly distributed objects are extracted as vegetation via multi-spatial-scale analysis. Then, the method generates a map representing occlusion by vegetation, and estimates GSR at an arbitrary location. We applied the method to a data set collected in a residential area in Kyoto, Japan. We compared the results with the ground truth data and obtained a root mean squared error of approximately 4.1%. Although some non-vegetation with rough surfaces was falsely extracted as vegetation, our method seems to estimate GSR to an acceptable accuracy. View Full-Text
Keywords: green space ratio; mobile scanning data; urban landscape; multi-scale analysis green space ratio; mobile scanning data; urban landscape; multi-scale analysis
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Susaki, J.; Kubota, S. Automatic Assessment of Green Space Ratio in Urban Areas from Mobile Scanning Data. Remote Sens. 2017, 9, 215.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top