Multi-Level Classification Based on Trajectory Features of Time Series for Monitoring Impervious Surface Expansions

Wang, Beibei1; Chen, Zhenjie1,2; Zhu, A-Xing3,4,5; Hao, Yuzhu1; Xu, Changqing1

2019-03-02

10.3390/rs11060640

Remote Sensing   影响因子和分区

2072-4292

2072-4292

As urbanization has profound effects on global environmental changes, quick and accurate monitoring of the dynamic changes in impervious surfaces is of great significance for environmental protection. The increased spatiotemporal resolution of imagery makes it possible to construct time series to obtain long-time-period and high-accuracy information about impervious surface expansion. In this study, a three-step monitoring method based on time series trajectory segmentation was developed to extract impervious surface expansion using Landsat time series and was applied to the Xinbei District, Changzhou, China, from 2005 to 2017. Firstly, the original time series was segmented and fitted to remove the noise caused by clouds, shadows, and interannual differences, leaving only the trend information. Secondly, the time series trajectory features of impervious surface expansion were described using three phases and four types with nine parameters by analyzing the trajectory characteristics. Thirdly, a multi-level classification method was used to determine the scope of impervious surface expansion, and the expansion time was superimposed to obtain a spatiotemporal distribution map. The proposed method yielded an overall accuracy of 90.58% and a Kappa coefficient of 0.90, demonstrating that Landsat time series remote sensing images could be used effectively in this approach to monitor the spatiotemporal expansion of impervious surfaces.

image time series LandTrendr decision tree support vector machine (SVM) impervious surface expansion Changzhou

SCI ; EI

英语

其他

National Natural Science Foundation of China[41571378]

Remote Sensing

Remote Sensing

WOS:000465615300039

MDPI

20213910955334

Remote sensing ; Support vector machines ; Time series ; Trajectories

Computer Software, Data Handling and Applications:723 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Mathematical Statistics:922.2 ; Systems Science:961

Web of Science

1.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing 210023, Jiangsu, Peoples R China
2.Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
4.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
5.Southern Univ Sci & Technol, Ctr Social Sci, Shenzhen 518055, Peoples R China

Wang, Beibei,Chen, Zhenjie,Zhu, A-Xing,et al. Multi-Level Classification Based on Trajectory Features of Time Series for Monitoring Impervious Surface Expansions[J]. Remote Sensing,2019,11(6).

Wang, Beibei,Chen, Zhenjie,Zhu, A-Xing,Hao, Yuzhu,&Xu, Changqing.(2019).Multi-Level Classification Based on Trajectory Features of Time Series for Monitoring Impervious Surface Expansions.Remote Sensing,11(6).

Wang, Beibei,et al."Multi-Level Classification Based on Trajectory Features of Time Series for Monitoring Impervious Surface Expansions".Remote Sensing 11.6(2019).