The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/19475705.2017.1370025 |
| _version_ | 1819161191516209152 |
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| author | Patcharin Insom Chunxiang Cao Pisit Boonsrimuang Peerapong Torteeka Sornkitja Boonprong Di Liu Wei Chen |
| author_facet | Patcharin Insom Chunxiang Cao Pisit Boonsrimuang Peerapong Torteeka Sornkitja Boonprong Di Liu Wei Chen |
| author_sort | Patcharin Insom |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T17:08:25Z |
| format | Article |
| id | doaj.art-bcde07bd7b7b4ffab2dec6f0ab24e0bc |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-12-22T17:08:25Z |
| publishDate | 2017-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-bcde07bd7b7b4ffab2dec6f0ab24e0bc2022-12-21T18:19:09ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132017-12-01821662167710.1080/19475705.2017.13700251370025The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imageryPatcharin Insom0Chunxiang Cao1Pisit Boonsrimuang2Peerapong Torteeka3Sornkitja Boonprong4Di Liu5Wei Chen6Chinese Academy of SciencesChinese Academy of SciencesKing MongKut's Institute of Technology LadkrabangUniversity of Chinese Academy of SciencesChinese Academy of SciencesChinese Academy of SciencesChinese Academy of SciencesMonitoring 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.http://dx.doi.org/10.1080/19475705.2017.1370025time-seriesndvi kalman filterextended kalman filterwetland monitoring |
| spellingShingle | Patcharin Insom Chunxiang Cao Pisit Boonsrimuang Peerapong Torteeka Sornkitja Boonprong Di Liu Wei Chen The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery Geomatics, Natural Hazards & Risk time-series ndvi kalman filter extended kalman filter wetland monitoring |
| title | The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery |
| title_full | The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery |
| title_fullStr | The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery |
| title_full_unstemmed | The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery |
| title_short | The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery |
| title_sort | dynamics of wetland cover change using a state estimation technique applied to time series remote sensing imagery |
| topic | time-series ndvi kalman filter extended kalman filter wetland monitoring |
| url | http://dx.doi.org/10.1080/19475705.2017.1370025 |
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