Ben DeVries


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Application of L-band SAR for mapping tundra shrub biomass, leaf area index, and rainfall interception
Qianyu Chang, Simon Zwieback, Ben DeVries, Aaron Berg
Remote Sensing of Environment, Volume 268

Rapid shrub expansion has been observed across the Arctic, driving a need for regional-scale estimates of shrub biomass and shrub-mediated ecosystem processes such as rainfall interception. Synthetic-Aperture Radar (SAR) data have been shown sensitive to vegetation canopy characteristics across many ecosystems, thereby potentially providing an accurate and cost-effective tool to quantify shrub canopy cover. This study evaluated the sensitivity of L-band Advanced Land Observing Satellite 2 (ALOS-2) data to the aboveground biomass and Leaf Area Index (LAI) of dwarf birch and alder in the Trail Valley Creek watershed, Northwest Territories, Canada. The σ° VH /σ° VV ratio showed strong sensitivity to both LAI (R 2 = 0.72 with respect to in-situ measurements) and wet aboveground biomass (R 2 = 0.63) of dwarf birch. Our ALOS-2-derived maps revealed high variability of birch shrub LAI and biomass across spatial scales. The LAI map was fed into the sparse Gash model to estimate shrub rainfall interception, an important but under-studied component of the Arctic water balance. Results suggest that on average across the watershed, 17 ± 3% of incoming rainfall was intercepted by dwarf birch (during summer 2018), highlighting the importance of shrub rainfall interception for the regional water balance. These findings demonstrate the unexploited potential of L-band SAR observations from satellites for quantifying the impact of shrub expansion on Arctic ecosystem processes. • L-band SAR is a skillful predictor for tundra shrub biomass and leaf area index. • High spatial variation in tundra shrub cover captured by L-band SAR. • Distributed rainfall interception by shrub mapped across the watershed. • Amount of interception closely linked to shrub leaf area index.

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Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions
Michael Merchant, Mayah Obadia, Brian Brisco, Ben DeVries, Aaron Berg
Remote Sensing, Volume 14, Issue 5

Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.