Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record Page: 1
17 p.View a full description of this article.
Extracted Text
The following text was automatically extracted from the image on this page using optical character recognition software:
remote sensingMD P
Article
Monitoring Annual Urban Changes in a Rapidly
Growing Portion of Northwest Arkansas with a
20-Year Landsat Record
Ryan Reynolds 1, Lu Liang 1,2,*,t, XueCao Li 3 and John Dennis 1
1 School of Forestry and Natural Resources, University of Arkansas at Monticello, Monticello, AR 71656, USA;
reynoldsrhine@gmail.com (R.R.); dennis@uamont.edu (J.D.)
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;
lixuecaosysu@gmail.com
* Correspondence: liang@uamont.edu; Tel.: +1-870-460-1248
t These authors contributed equally to this work.
Academic Editors: Qihao Weng, Yuhong He and Prasad S. Thenkabail
Received: 6 November 2016; Accepted: 9 January 2017; Published: 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.
Keywords: time series; change detection; temporal filtering; remote sensing
1. Introduction
More than half of the world's population currently resides in urban areas, and the coming
decades are predicted to bring further profound changes to the size and spatial distribution of the
global population [1]. From their earliest beginnings, city landscapes have left an indelible markon the Earth [2]. Even though they are considered the engines of economic development and social
transformations, rapid urbanization is creating tremendous stresses on the environment, natural
resources, and public health not only within the city boundaries, but also in areas extending well
beyond them [3-5]. Urban growth is a major indicator of environmental quality and ecosystemRemote Sens. 2017, 9, 71; doi:10.3390/rs9010071
www.mdpi.com/journal/remotesensing
Upcoming Pages
Here’s what’s next.
Search Inside
This article can be searched. Note: Results may vary based on the legibility of text within the document.
Tools / Downloads
Get a copy of this page or view the extracted text.
Citing and Sharing
Basic information for referencing this web page. We also provide extended guidance on usage rights, references, copying or embedding.
Reference the current page of this Article.
Reynolds, Ryan; Liang, Lu; Li, XueCao & Dennis, John. Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record, article, January 13, 2017; Basel, Switzerland. (https://digital.library.unt.edu/ark:/67531/metadc1234380/m1/1/: accessed July 17, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Arts and Sciences.