Investigating the Potential Use of RADARSAT-2 and UAS imagery for Monitoring the Restoration of Peatlands

Abstract

The restoration of peatlands is critical to help reduce the effects of climate change and further prevent the loss of habitat for many species of flora and fauna. The objective of this research was to evaluate RADARSAT-2 satellite imagery and high-resolution Unmanned Aerial Systems (UASs) to determine if they could be used as surrogates for monitoring the success of peatland restoration. Areas of peatland that were being actively harvested, had been restored from past years (1994–2003), and natural shrub bog in Lac St. Jean, Quebec were used as a test case. We compared the Freeman–Durden and Touzi decompositions by applying the Bhattacharyya Distance (BD) statistic to see if the spectral signatures of restored peatland could be separated from harvested peat and natural shrub bog. We flew Unmanned Aerial Surveys (UASs) over the study site to identify Sphagnum and Polytrichum strictum, two indicator species of early peatland restoration success. Results showed that the Touzi decomposition was better able to separate the spectral signatures of harvested, restored, and natural shrub bog (BD values closer to 9). Symmetric scattering type αs1, Helicity |τ1,2,3|, a steep incidence angle, and peak growing season appear to be important for separating the spectral signatures. We had moderate success in detecting Sphagnum and Polytrichum strictum visually by using texture and pattern but were unable to use colour due to differences in sun angle and clouds during the UAS flights. Results suggest that RADARSAT-2 data using the Touzi decomposition and UAS imagery show potential for monitoring peatland restoration success over time.

A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

“…UAV-based restoration monitoring involves a variety of objectives, including biomass estimates [120], hydrological mapping [8,9,11,123], morphological evaluations [43,57], and classification of plant communities in peatlands [10,56,119,122] and coastal regions [44,117,118,121]. While many researchers leverage RGB aerial imagery for restoration monitoring [8,9,120,122,123], multispectral imagery, and specifically the near-infrared wavelength, which can detect species and topography in shallow wetland environments [155].…”

“…Only 55 studies (~46% of the pool) reported some form of survey and image mosaicking error. Since implications of mosaicking and positioning errors are likely to substantially vary among management goals (e.g., [28,119,128,133]), the lack of clear accuracy benchmarks as well as standards for accuracy and error reporting in UAV applications presents an important action item for the wetland UAV community in the future.…”

“…Several important aspects of UAV data processing can also be challenged by wetland environments and thus need to be considered early in application workflows. For instance, three-dimensional spatial accuracy in UAV-derived products is dictated both by the field operational factors (e.g., positioning system in the instrument, precision of GCPs, and other reference locations) and data post-processing (e.g., additional geo-rectification to correct for spatial shifts in image mosaics [119]). Positional inaccuracies may accumulate and propagate across workflows, eroding the quality and reliability of the final products [162].…”

“…From the data processing perspective, it is important to evaluate the potential implications of uncertainties in spatial resolution and positional accuracy for project goals in order to decide on the optimal minimum mapping units (MMUs) and the appropriate strategies in navigating such uncertainties. An MMU choice should ideally balance the benefits of high spatial resolution with the needs to improve signal-to-noise ratio and avoid mixed pixels, both of which may increase with greater pixel size and also vary with electromagnetic aspects of data and instruments [13,109,119]. This means that data analyses, especially computer-based routines, may not always require the highest "achievable" spatial resolution and may, in fact, benefit from some degree of data aggregation and filtering to reduce noise [14,96].…”

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

“…Several UAS methods have also been applied in peatland restoration monitoring. Many methods [28][29][30][31][32] are restricted to two-dimensional products, such as orthomosaic pictures for the classification of vegetation coverage. However, there are also examples of UAS-SfM-based topographical analysis [21,33,34].…”