Insom Patcharin等:The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery
被阅读 171 次
2018-02-27
The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery
作者:Insom, P (Insom, Patcharin)[ 1,2 ] ; Cao, CX (Cao, Chunxiang)[ 1 ] ; Boonsrimuang, P (Boonsrimuang, Pisit)[ 3 ] ; Torteeka, P (Torteeka, Peerapong)[ 2,4 ] ; Boonprong, S (Boonprong, Sornkitja)[ 1,2 ] ; Liu, D (Liu, Di)[ 1,2 ] ; Chen, W (Chen, Wei)[ 1 ]
GEOMATICS NATURAL HAZARDS & RISK
卷: 8  期: 2  页: 1662-1677
DOI: 10.1080/19475705.2017.1370025
出版年: 2017
 
摘要
Monitoring the dynamics of inundation areas in wetlands over contiguous years is important because it influences wetland ecosystem monitoring. However, because the variable nature of wetlands tends to hamper monitoring change analyses, the potential for misinterpretation increases. The Kalman filter (KF) or extended Kalman filter (EKF), which uses recursive processing based on the former information, can be applied to time-series remote sensing imagery. In the experiment, a periodic triangle function of two modulated parameters is treated as the system model, and Normalized Difference Vegetation Index (NDVI) time-series data are used for the measurement model in the correction processes of the state estimation. A decision metric is computed from the mean and amplitude sequence, which results from the state estimation filter. Consequently, an optimal threshold is calculated using a minimum error thresholding algorithm based on a pre-labelled sample. NDVI time-series data from Poyang Lake, China-derived from 250-m Moderate Resolution Imaging Spectroradiometer satellite data obtained from January 2009 to December 2013-are applied to monitor the dynamics of inundation changes. The results show that the EKF achieves satisfactory results, with 85.52% accuracy in the year 2009, while the KF has an accuracy of 84.16% during that same time.
 
通讯作者地址: Cao, CX (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
[ 2 ] Univ Chinese Acad Sci, Beijing, Peoples R China
[ 3 ] King MongKuts Inst Technol Ladkrabang, Fac Engn, Telecommun Engn Dept, Bangkok, Thailand
[ 4 ] Chinese Acad Sci, Natl Astron Observ, Ctr Res & Applicat Space Debris, Beijing, Peoples R China