Next Article in Journal
Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST
Previous Article in Journal
Human Activity Influences on Vegetation Cover Changes in Beijing, China, from 2000 to 2015
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(3), 272; doi:10.3390/rs9030272

Mapping of Vegetation Using Multi-Temporal Downscaled Satellite Images of a Reclaimed Area in Saemangeum, Republic of Korea

1
Department of Landscape Architecture, Chonbuk National University, 567 Baekje-daero, Jeonju-si 54896, Korea
2
Earth Observation Research Team, Korea Aerospace Research Institute, 169-84, Gwahak-ro, Yuseong-Gu, Daejeon 34133, Korea
3
Dongyeong Forest, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu Metropolitan City 42601, Korea
4
Department of Geographic Information Systems Engineering, Namseoul University, 91 Daehak-ro Seonghwan-eup Sebuk-gu, Cheonan-si 31020, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Eileen H. Helmer, Sangram Ganguly and Prasad S. Thenkabail
Received: 12 January 2017 / Revised: 3 March 2017 / Accepted: 12 March 2017 / Published: 15 March 2017
View Full-Text   |   Download PDF [13082 KB, uploaded 20 March 2017]   |  

Abstract

The aim of this study is to adapt and evaluate the effectiveness of a multi-temporal downscaled images technique for classifying the typical vegetation types of a reclaimed area. The areas reclaimed from estuarine tidal flats show high spatial heterogeneity in soil salinity conditions. There are three typical vegetation types for which the distribution is restricted by the soil conditions. A halophyte-dominated vegetation is located in a high saline area, grass vegetation is found in a mid- or low saline area, and reed/small-reed vegetation is situated in a low saline area. Multi-temporal satellite images were used to classify the vegetation types. Landsat images were downscaled to take into account spatial heterogeneity using cokriging. A random forest classifier was used for the classification, with downscaled Landsat and RapidEye images. Classification with RapidEye images alone demonstrated a lower level of accuracy than when combined with multi-temporal downscaled images. The results demonstrate the usefulness of a downscaling technique for mapping. This approach can provide a framework which is able to maintain low costs whilst producing richer images for the monitoring of a large and heterogeneous ecosystem. View Full-Text
Keywords: vegetation classification; random forest; downscaling; multi-temporal image; cokriging; Saemangeum vegetation classification; random forest; downscaling; multi-temporal image; cokriging; Saemangeum
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

Beon, M.-S.; Cho, K.H.; Kim, H.O.; Oh, H.-K.; Jeong, J.-C. Mapping of Vegetation Using Multi-Temporal Downscaled Satellite Images of a Reclaimed Area in Saemangeum, Republic of Korea. Remote Sens. 2017, 9, 272.

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