Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring
Peatlands in Southeast Asia have been undergoing extensive and rapid degradation in recent years. Interferometric Synthetic Aperture Radar (InSAR) technology has shown excellent performance in monitoring surface deformation. However, due to the characteristics of high vegetation cover and large dyna...
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/18/4461 |
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| author | Xiaohan Zheng Chao Wang Yixian Tang Hong Zhang Tianyang Li Lichuan Zou Shaoyang Guan |
| author_facet | Xiaohan Zheng Chao Wang Yixian Tang Hong Zhang Tianyang Li Lichuan Zou Shaoyang Guan |
| author_sort | Xiaohan Zheng |
| collection | DOAJ |
| description | Peatlands in Southeast Asia have been undergoing extensive and rapid degradation in recent years. Interferometric Synthetic Aperture Radar (InSAR) technology has shown excellent performance in monitoring surface deformation. However, due to the characteristics of high vegetation cover and large dynamic changes in peatlands, it is difficult for classical InSAR technology to achieve satisfactory results. Therefore, an adaptive high coherence temporal subsets (HCTSs) small baseline subset (SBAS)-InSAR method is proposed in this paper, which captures the high coherence time range of pixels to establish adaptive temporal subsets and calculates the deformation results in corresponding time intervals, combining with the time-weighted strategy. Ninety Sentinel-1 SAR images (2019–2022) in South Sumatra province were processed based on the proposed method. The results showed that the average deformation rate of peatlands ranged from approximately −567 to 347 mm/year and was affected by fires and the changes in land cover. Besides, the dynamic changes of peatlands’ deformation rate a long time after fires were revealed, and the causes of changes were analyzed. Furthermore, the deformation results of the proposed method observed 2 to 127 times as many measurement points as the SBAS-InSAR method. Pearson’s r (ranged from 0.44 to 0.75) and Root Mean Square Error (ranged from 50 to 75 mm/year) were calculated to verify the reliability of the proposed method. Adaptive HCTSs SBAS-InSAR can be considered an efficient method for peatland degradation monitoring, which provides the foundation for investigating the mechanisms of peatland degradation and monitoring it in broader regions. |
| first_indexed | 2024-03-10T22:06:29Z |
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| id | doaj.art-917c41703d344959b2f9de2ecf6c3eef |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T22:06:29Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-917c41703d344959b2f9de2ecf6c3eef2023-11-19T12:48:01ZengMDPI AGRemote Sensing2072-42922023-09-011518446110.3390/rs15184461Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation MonitoringXiaohan Zheng0Chao Wang1Yixian Tang2Hong Zhang3Tianyang Li4Lichuan Zou5Shaoyang Guan6International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaInternational Research Center of Big Data for Sustainable Development Goals, Beijing 100094, ChinaPeatlands in Southeast Asia have been undergoing extensive and rapid degradation in recent years. Interferometric Synthetic Aperture Radar (InSAR) technology has shown excellent performance in monitoring surface deformation. However, due to the characteristics of high vegetation cover and large dynamic changes in peatlands, it is difficult for classical InSAR technology to achieve satisfactory results. Therefore, an adaptive high coherence temporal subsets (HCTSs) small baseline subset (SBAS)-InSAR method is proposed in this paper, which captures the high coherence time range of pixels to establish adaptive temporal subsets and calculates the deformation results in corresponding time intervals, combining with the time-weighted strategy. Ninety Sentinel-1 SAR images (2019–2022) in South Sumatra province were processed based on the proposed method. The results showed that the average deformation rate of peatlands ranged from approximately −567 to 347 mm/year and was affected by fires and the changes in land cover. Besides, the dynamic changes of peatlands’ deformation rate a long time after fires were revealed, and the causes of changes were analyzed. Furthermore, the deformation results of the proposed method observed 2 to 127 times as many measurement points as the SBAS-InSAR method. Pearson’s r (ranged from 0.44 to 0.75) and Root Mean Square Error (ranged from 50 to 75 mm/year) were calculated to verify the reliability of the proposed method. Adaptive HCTSs SBAS-InSAR can be considered an efficient method for peatland degradation monitoring, which provides the foundation for investigating the mechanisms of peatland degradation and monitoring it in broader regions.https://www.mdpi.com/2072-4292/15/18/4461tropical peatlandsadaptive HCTSsSBAS-InSARSentinel-1peatland degradation |
| spellingShingle | Xiaohan Zheng Chao Wang Yixian Tang Hong Zhang Tianyang Li Lichuan Zou Shaoyang Guan Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring Remote Sensing tropical peatlands adaptive HCTSs SBAS-InSAR Sentinel-1 peatland degradation |
| title | Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring |
| title_full | Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring |
| title_fullStr | Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring |
| title_full_unstemmed | Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring |
| title_short | Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring |
| title_sort | adaptive high coherence temporal subsets sbas insar in tropical peatlands degradation monitoring |
| topic | tropical peatlands adaptive HCTSs SBAS-InSAR Sentinel-1 peatland degradation |
| url | https://www.mdpi.com/2072-4292/15/18/4461 |
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