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Journal of Applied Remote Sensing

Detecting spatio-temporal and typological changes in land use from Landsat image time series
Author(s): Wenxiang Wang; Zhenjie Chen; Xiang Li; Haoqing Tang; Qiuhao Huang; Lean Qu
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Paper Abstract

With the development of earth observation technology, the spatial and temporal resolution of remote sensing images have improved dramatically, providing abundant data for detecting land use change in detail. However, extracting spatio-temporal and typological changes in land use from remote sensing image time series is still challenging. Landsat image time series and land survey data are combined to develop a method to detect in detail the time and types of land use change. From the Landsat images, the time series of normalized difference vegetation index variation slope (TSNVS) on each pixel was constructed. The average TSNVS of sample pixels was used as the reference series. Based on TSNVS, the change in land use at six time points (S1 to S6) was detected by measuring the similarity of the time series on undetermined pixels and reference series. Land survey data were used to identify detailed types of land use change. The 16 different types of land use change were identified. The results indicate that the proposed method is effective at detecting spatio-temporal and typological changes in land use from Landsat image time series. After verification, the overall accuracy was 92%, and the kappa coefficient was 0.9.

Paper Details

Date Published: 25 July 2017
PDF: 15 pages
J. Appl. Rem. Sens. 11(3) 035006 doi: 10.1117/1.JRS.11.035006
Published in: Journal of Applied Remote Sensing Volume 11, Issue 3
Show Author Affiliations
Wenxiang Wang, Nanjing Univ. (China)
Zhenjie Chen, Nanjing Univ. (China)
Xiang Li, Nanjing Univ. (China)
Haoqing Tang, Nanjing Univ. (China)
Qiuhao Huang, Nanjing Univ. (China)
Lean Qu, Nanjing Univ. (China)
Anhui Normal Univ. (China)


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