Estimating Peatland Water Table Depth and Net Ecosystem Exchange: A Comparison between Satellite and Airborne Imagery

Abstract

Peatlands play a fundamental role in climate regulation through their long-term accumulation of atmospheric carbon. Despite their resilience, peatlands are vulnerable to climate change. Remote sensing offers the opportunity to better understand these ecosystems at large spatial scales through time. In this study, we estimated water table depth from a 6-year time sequence of airborne shortwave infrared (SWIR) hyperspectral imagery. We found that the narrowband index NDWI 1240 is a strong predictor of water table position. However, we illustrate the importance of considering peatland anisotropy on SWIR imagery from the summer months when the vascular plants are in full foliage, as not all illumination conditions are suitable for retrieving water table position. We also model net ecosystem exchange (NEE) from 10 years of Landsat TM5 imagery and from 4 years of Landsat OLI 8 imagery. Our results show the transferability of the model between imagery from sensors with similar spectral and radiometric properties such as Landsat 8 and Sentinel-2. NEE modeled from airborne hyperspectral imagery more closely correlated to eddy covariance tower measurements than did models based on satellite images. With fine spectral, spatial and radiometric resolutions, new generation satellite imagery and airborne hyperspectral imagery allow for monitoring the response of peatlands to both allogenic and autogenic factors.

Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data

“…Adjacent hummocks and hollows can differ in absolute elevation by as much as 0.30 m and are separated by an approximate horizontal distance of 1-2 m [51][52][53]. Given that the overlying vegetation, and their associated reflective properties, covary with the patterns in microtopography [24,25,54], the Mer Bleue HSI data is likely characterized by a sinusoidal spatial correlation structure with a period on the scale of 2-4 m. There are very few large high contrast targets in the Mer Bleue Peatland. Grey and black calibration tarps were laid out and captured in the imagery to provide high contrast edges.…”

“…Assuming the atmospheric interactions (absorption and scattering) can be reasonably well modelled and removed from the signal of each pixel [7], the spectral information from hyperspectral remote sensing data can be used to identify and characterize materials over large spatial extents. Hyperspectral remote sensing is commonly known by its imaging modality term hyperspectral imaging (HSI) [5] and has prominent applications in fields such as geology [8][9][10], agriculture [11][12][13], forestry [14][15][16], oceanography [17][18][19], forensics [20][21][22], and ecology [23][24][25].…”

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

“…Aerial photography is used primarily for interpretive-based classification of landcover and wetland class (e.g., bog, fen, marsh, swamp, shallow open water) and form (graminoid, shrubby, treed), wetland species discrimination [8,11,230], and for tracking long-term wetland evolution [12,13]. Hyperspectral sensors dominate in applications that require detailed mapping of wetland class and form [103] (Hymap and CASI), species identification [69,127] (CASI, MIVIS, AVIRIS, Hyperion), productivity and foliar chemistry [162,166] (Hymap, CASI), water properties including extent, chemistry, and turbidity [191,231] (MIVIS), and mine spill detection [227] (fluorometry). Due to its long-term availability, passive multi-spectral remote sensing has the broadest demonstrated range of application development for wetland comparison, including general landcover classification and more detailed wetland class discrimination [78,161].…”

“…They had less success comparing vegetation indices with net ecosystem productivity (between 25% and 53% using NDVI). Additional methods including using hyperspectral remote sensing of discrete wavelengths (531 and 570 nm) show promise for estimating the efficiency with which vegetation uses light for photosynthesis within forested environments [322][323][324] and with some success for chlorophyll and nitrogen content in peatlands [162]. Further, Hopkinson et al [128] directly compared biomass accumulation (gross primary production) due to growth of jack pine trees by comparing multi-temporal lidar data with plot allometry and eddy covariance methods.…”

The Least Square Adjustment for Estimating the Tropical Peat Depth Using LiDAR Data

“…Topographic covariates consist of slope, aspect, curvature, flow accumulation, wetness index, stream power index, landform classification, vegetation index, and soil map [26][27][28]. Peatland visual conditions also have a specific correlation to the existing peat depth, i.e., length and slope condition, land cover, vegetation, and the groundwater surface [14,29,30].…”