Contributions of C-Band SAR Data and Polarimetric Decompositions to Subarctic Boreal Peatland Mapping

Merchant, Michael; Adams, Justin; Berg, Aaron; Baltzer, Jennifer L.; Quinton, William L.; Chasmer, L.

doi:10.1109/jstars.2016.2621043

Cited by 29 publications

(19 citation statements)

References 71 publications

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“…Sustainable use, conservation and restoration of peatlands, to preserve organic soils from further degradation, are presently key environmental priorities at European level [10] in regard to global importance in context of GHG emissions, habitat loss and water quality [5,[11][12][13]. To monitor peatlands, the remote sensing holds several advantages over in situ approaches in terms of cost, accessibility and spatial coverage [14,15], whereas Synthetic Aperture Radar (SAR), relative to optical and infrared remote sensors, is favourable because of the ability to penetrate vegetation canopies and the independence of observation of solar illumination or clouds [16][17][18]. Only SAR provides a large spatial coverage at a regular resampling interval which is weather-independent and cheap enough to be widely exploited [14,15].…”

Section: Introductionmentioning

confidence: 99%

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Long Term Interferometric Temporal Coherence and DInSAR Phase in Northern Peatlands

et al. 2020

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Peatlands of northern temperate and cold climates are significant pools of stored carbon. Understanding seasonal dynamics of peatland surface height and volume, often referred to as mire breathing or oscillation, is the key to improve spatial models of material flow and gas exchange. The monitoring of this type of dynamics over large areas is only feasible by remote sensing instruments. The objective of this study is to examine the applicability of Sentinel-1 synthetic aperture radar interferometry (InSAR) to characterize seasonal dynamics of peatland surface height and water table (WT) over open raised bog areas in Endla mire complex in central Estonia, characteristic for northern temperate bogs. Our results show that InSAR temporal coherence, sufficient for differential InSAR (DInSAR), is preserved in the open bog over more than six months of temporal baseline. Moreover, the coherence which is lost in a dry summer, make a recovery in autumn correlate with WT dynamics. The relationship between the coherence from a single master image and the corresponding WT difference is described by the second degree polynomial regression model (Root Mean Squared Error RMSE = 0.041 for coherence magnitude). It is also demonstrated that DInSAR phase is connected to bog surface dynamics and reveals differences between bogs and for ecotopes within a bog. These findings suggest that InSAR long term temporal coherence could be used to describe seasonal bog WT dynamics and differentiate between mire types and ecotopes within a bog. Moreover, DInSAR analysis has the potential to characterize seasonal mire surface oscillation which may be important for assessing the capacity of water storage or restoration success in northern temperate bogs.

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Section: Introductionmentioning

confidence: 99%

“…Northern peatlands are often small and heterogeneous, thus posing challenge for SAR in terms of spatial resolution [15,17,22], also due to loss of temporal coherence caused by vegetation [23][24][25]. However, SAR Interferometry (InSAR) has proven feasibility for monitoring long-term elevation changes in northern peatlands.…”

Section: Introductionmentioning

confidence: 99%

Long Term Interferometric Temporal Coherence and DInSAR Phase in Northern Peatlands

et al. 2020

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“…While wetland classes were accurately characterised, the accuracy of the extent of wetland classification was not identified in this study. Machine learning imputation and Support Vector Machine [272] supervised classification learning methods for land cover and wetland class, form, and type have average accuracies of 80% and 79% respectively, and range from 72%-99% (random forest) (e.g., References [35,36,38,52,75,113,155,203,271]) and 73%-90% (Support Vector Machine) (e.g., References [49,61,75,91,99,101,116]). These exceed the proposed minimum accuracy requirements in regions such as Alberta Canada but require that training data capture the full variability of each class identified by the classifier [273,274].…”

Section: Wetland Extent For Baseline Inventory and Long-term Monitoringmentioning

confidence: 99%

Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management

et al. 2020

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Wetlands have and continue to undergo rapid environmental and anthropogenic modification and change to their extent, condition, and therefore, ecosystem services. In this first part of a two-part review, we provide decision-makers with an overview on the use of remote sensing technologies for the ‘wise use of wetlands’, following Ramsar Convention protocols. The objectives of this review are to provide: (1) a synthesis of the history of remote sensing of wetlands, (2) a feasibility study to quantify the accuracy of remotely sensed data products when compared with field data based on 286 comparisons found in the literature from 209 articles, (3) recommendations for best approaches based on case studies, and (4) a decision tree to assist users and policymakers at numerous governmental levels and industrial agencies to identify optimal remote sensing approaches based on needs, feasibility, and cost. We argue that in order for remote sensing approaches to be adopted by wetland scientists, land-use managers, and policymakers, there is a need for greater understanding of the use of remote sensing for wetland inventory, condition, and underlying processes at scales relevant for management and policy decisions. The literature review focuses on boreal wetlands primarily from a Canadian perspective, but the results are broadly applicable to policymakers and wetland scientists globally, providing knowledge on how to best incorporate remotely sensed data into their monitoring and measurement procedures. This is the first review quantifying the accuracy and feasibility of remotely sensed data and data combinations needed for monitoring and assessment. These include, baseline classification for wetland inventory, monitoring through time, and prediction of ecosystem processes from individual wetlands to a national scale.

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“…Like all wetlands, peatlands have great economic, societal, and environmental value, including forming a habitat for various unique species and species at risk [1], playing a role in the hydrologic cycle [2] and in sequestering carbon [3]. Despite these benefits, globally, they are subject to degradation As SAR should be sensitive to variations in vegetation structure and wetness, several studies have assessed a variety of different SAR parameters, including polarimetric decompositions [10,[13][14][15][16]. In some studies, SAR has been shown to be useful for differentiating fen from bog based on differences in wetness and prevalence of grasses [13].…”

Section: Introductionmentioning

confidence: 99%

“…Most classifications of peatlands have been completed using a single image or a few intermittent images, often using different SAR beam-modes to capture different physical conditions that can be characterized at different incident angles [16], or using a few dates throughout the growing season to capture extremes in vegetation and wetness in spring and fall [12]. However, different peatland classes exhibit variability in temporal signatures in vegetation green-up and senescence, surface wetness, and water table depths.…”

Section: Introductionmentioning

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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Contributions of C-Band SAR Data and Polarimetric Decompositions to Subarctic Boreal Peatland Mapping

Cited by 29 publications

References 71 publications

Long Term Interferometric Temporal Coherence and DInSAR Phase in Northern Peatlands

Long Term Interferometric Temporal Coherence and DInSAR Phase in Northern Peatlands

Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management

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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