Next Article in Journal
Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires
Next Article in Special Issue
The Impact of Lidar Elevation Uncertainty on Mapping Intertidal Habitats on Barrier Islands
Previous Article in Journal
Comparison of Retrieved L2 Products from Four Successive Versions of L1B Spectra in the Thermal Infrared Band of TANSO-FTS over the Arctic Ocean
Previous Article in Special Issue
Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(11), 1148; https://doi.org/10.3390/rs9111148

Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016

1
Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
2
The University of Chinese Academy of Sciences, Beijing 100049, China
3
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4
School of Geography, University of Nottingham, University Park, Nottingham NG7 2RD, UK
*
Author to whom correspondence should be addressed.
Received: 19 September 2017 / Revised: 31 October 2017 / Accepted: 3 November 2017 / Published: 14 November 2017
(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)
View Full-Text   |   Download PDF [4303 KB, uploaded 14 November 2017]   |  

Abstract

Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery. View Full-Text
Keywords: Landsat; support vector machine (SVM); impervious surface; classification uncertainty; uncertainty-based spatial-temporal consistency (USTC) model; temporal consistency (TC) model Landsat; support vector machine (SVM); impervious surface; classification uncertainty; uncertainty-based spatial-temporal consistency (USTC) model; temporal consistency (TC) model
Figures

Graphical abstract

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

Share & Cite This Article

MDPI and ACS Style

Shi, L.; Ling, F.; Ge, Y.; Foody, G.M.; Li, X.; Wang, L.; Zhang, Y.; Du, Y. Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016. Remote Sens. 2017, 9, 1148.

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