Next Article in Journal
MODIS Time Series to Detect Anthropogenic Interventions and Degradation Processes in Tropical Pasture
Next Article in Special Issue
Urban Expansion and Its Impact on the Land Use Pattern in Xishuangbanna since the Reform and Opening up of China
Previous Article in Journal
IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data
Previous Article in Special Issue
Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(1), 71; doi:10.3390/rs9010071

Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record

1,†
,
1,2,†,* , 3
and
1
1
School of Forestry and Natural Resources, University of Arkansas at Monticello, Monticello, AR 71656, USA
2
Arkansas Forest Resources Center, University of Arkansas Division of Agriculture, Monticello, AR 71656, USA
3
Department of Geological & Atmospheric Science, Iowa State University, Ames, IA 50014, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Qihao Weng, Yuhong He and Prasad S. Thenkabail
Received: 6 November 2016 / Revised: 31 December 2016 / Accepted: 9 January 2017 / Published: 13 January 2017
View Full-Text   |   Download PDF [9422 KB, uploaded 13 January 2017]   |  

Abstract

Northwest Arkansas has undergone a significant urban transformation in the past several decades and is considered to be one of the fastest growing regions in the United States. The urban area expansion and the associated demographic increases bring unprecedented pressure to the environment and natural resources. To better understand the consequences of urbanization, accurate and long-term depiction on urban dynamics is critical. Although urban mapping activities using remote sensing have been widely conducted, long-term urban growth mapping at an annual pace is rare and the low accuracy of change detection remains a challenge. In this study, a time series Landsat stack covering the period from 1995 to 2015 was employed to detect the urban dynamics in Northwest Arkansas via a two-stage classification approach. A set of spectral indices that have been proven to be useful in urban area extraction together with the original Landsat spectral bands were used in the maximum likelihood classifier and random forest classifier to distinguish urban from non-urban pixels for each year. A temporal trajectory polishing method, involving temporal filtering and heuristic reasoning, was then applied to the sequence of classified urban maps for further improvement. Based on a set of validation samples selected for five distinct years, the average overall accuracy of the final polished maps was 91%, which improved the preliminary classifications by over 10%. Moreover, results from this study also indicated that the temporal trajectory polishing method was most effective with initial low accuracy classifications. The resulting urban dynamic map is expected to provide unprecedented details about the area, spatial configuration, and growing trends of urban land-cover in Northwest Arkansas. View Full-Text
Keywords: time series; change detection; temporal filtering; remote sensing time series; change detection; temporal filtering; remote sensing
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).

Supplementary material

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

Reynolds, R.; Liang, L.; Li, X.; Dennis, J. Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record. Remote Sens. 2017, 9, 71.

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