InSAR Time Series Analysis of L-Band Data for Understanding Tropical Peatland Degradation and Restoration

Zhou, Zhiwei; Li, Zhenhong; Waldron, Susan; Tanaka, Akiko

doi:10.3390/rs11212592

Cited by 21 publications

(23 citation statements)

References 48 publications

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“…We applied 17 Â 5 (range Â azimuth) and 22 Â 9 multilook for SENTINEL-1 and PALSAR-2, respectively, referring to previous studies (Alshammari et al, 2018;Marshall et al, 2018;Zhou et al, 2019).…”

Section: Study Areamentioning

confidence: 99%

Use of multifrequency (C‐band and L‐band) SAR data to monitor peat subsidence based on time‐series SBAS InSAR technique

et al. 2021

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Peatlands in tropical regions like Indonesia are undergoing irreversible subsidence due to changes in land use (e.g., deforestation) and land management practices (e.g., drainage alteration), resulting in massive amounts of soil carbon loss. Several satellite-borne synthetic aperture radar (SAR) sensors are operating concurrently at different frequencies, providing potentially useful data for monitoring surface motion over tropical peatlands. This study focused on the capability of C-band (SENTINEL-1) and L-band (PALSAR-2) SAR data to monitor the surface changes in the tropical peatlands area of Bengkalis Island, Indonesia, by applying time-series interferometric SAR (InSAR) with the small baseline subset (SBAS) technique. The average vertical velocity measured by SENTINEL-1 and PALSAR-2 for the period of 2018-2019 was À1.41 and À2.65 cm yr À1 , respectively. We also explored the potential of groundwater level (GWL) data converted to vertical displacement for validating SBAS InSAR.PALSAR-2 performed the best, exhibiting lower RMSE values for each land use compared to SENTINEL-1, with an overall RMSE of 1.383 and 1.988 cm yr À1 , respectively. Also, the subsidence rates of SENTINEL-1 were underestimated, showing a significantly lower mean subsidence difference (0.96 cm yr À1 ) than the reference.Our GWL-based subsidence data offered an alternative validation method for InSARbased subsidence estimation. Therefore, the integration of time-series InSAR and GWL data can provide crucial information for monitoring the degradation of tropical peatlands.

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Section: Study Areamentioning

confidence: 99%

Use of multifrequency (C‐band and L‐band) SAR data to monitor peat subsidence based on time‐series SBAS InSAR technique

et al. 2021

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“…While the aim of this paper is not to interpret the physical cause behind these patterns, we suspect that the steps in the time series are related to a combination of groundwater level changes within the peat, which is known to follow annual precipitation (e.g., [38,39]), and periodic gas injection and withdrawal from the sediments in this region. Disentangling these signals will require further investigation and field studies.…”

Section: Resultsmentioning

confidence: 99%

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

et al. 2021

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Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.

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“…They did not test these statistics of their coherence time series in classification. Additionally, InSAR has been used in peatlands in a few instances to monitor surface degradation and displacement [27].…”

Section: Interferometric Coherence Used In Classificationmentioning

confidence: 99%

Using Growing-Season Time Series Coherence for Improved Peatland Mapping: Comparing the Contributions of Sentinel-1 and RADARSAT-2 Coherence in Full and Partial Time Series

et al. 2020

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Differences in topographic structure, vegetation structure, and surface wetness exist between peatland classes, making active remote sensing techniques such as SAR and LiDAR promising for peatland mapping. As the timing of green-up, senescence, and hydrologic conditions vary differently in peatland classes, and in comparison with upland classes, full growing-season time series SAR imagery was expected to produce higher accuracy classification results than using only a few select SAR images. Both interferometric coherence, amplitude and difference in amplitude time series datasets were assessed, as it was hypothesized that these may be able to capture subtle changes in phenology and hydrology, which in turn differentiate classes throughout a growing season. Groups of variables were compared for their effectiveness in Random Forest classification for both Sentinel-1 and Radarsat-2. The Shapley value was used to determine the contribution of each group of variables in thirty scenarios, and Mean Decrease in Accuracy was compared to evaluate its ability to rank variables by relative importance. Despite being dual-pol, the results of classifications using Sentinel-1 coherence (12-day repeat) were significantly better than using fully polarimetric RADARSAT-2 coherence (24-day repeat), likely owing to the difference in baseline and specific acquisition dates of the data in this study. Overall, full growing season Sentinel-1 coherence time series produced higher accuracy results than fully polarimetric quad pol RADARSAT-2 coherence amplitude, difference in amplitude and polarimetric decomposition time series. Using a full growing season of time-series imagery in classification resulted in higher accuracy than using a few dates over a growing season. Using mean decrease in accuracy to rank and reduce variables resulted in a weaker classification than if the entire time series is used.

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InSAR Time Series Analysis of L-Band Data for Understanding Tropical Peatland Degradation and Restoration

Cited by 21 publications

References 48 publications

Use of multifrequency (C‐band and L‐band) SAR data to monitor peat subsidence based on time‐series SBAS InSAR technique

Use of multifrequency (C‐band and L‐band) SAR data to monitor peat subsidence based on time‐series SBAS InSAR technique

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

Using Growing-Season Time Series Coherence for Improved Peatland Mapping: Comparing the Contributions of Sentinel-1 and RADARSAT-2 Coherence in Full and Partial Time Series

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