Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing

Abstract

Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising temperatures, and elevated levels of atmospheric carbon dioxide have been shown to cause plant community compositional changes. Shifts in plant composition affect the productivity, species diversity, and carbon cycling of peatlands. We used hyperspectral remote sensing to characterize the response of boreal peatland plant composition and species diversity to warming, hydrologic change, and elevated CO2. Hyperspectral remote sensing techniques offer the ability to complete landscape-scale analyses of ecological responses to climate disturbance when paired with plot-level measurements that link ecosystem biophysical properties with spectral reflectance signatures. Working within two large ecosystem manipulation experiments, we examined climate controls on composition and diversity in two types of common boreal peatlands: a nutrient rich fen located at the Alaska Peatland Experiment (APEX) in central Alaska, and an ombrotrophic bog located in northern Minnesota at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found a strong effect of plant functional cover on spectral reflectance characteristics. We also found a positive relationship between species diversity and spectral variation at the APEX field site, which is consistent with other recently published findings. Based on the results of our field study, we performed a supervised land cover classification analysis on an aerial hyperspectral dataset to map peatland plant functional types (PFTs) across an area encompassing a range of different plant communities. Our results underscore recent advances in the application of remote sensing measurements to ecological research, particularly in far northern ecosystems.

Land cover classification of treeline ecotones along a 1100 km latitudinal transect using spectral‐ and three‐dimensional information from UAV‐based aerial imagery

“…A possible solution for increasing the classification accuracies could be to delineate the class into wetland sub‐classes found in the treeline ecotone such as open fen and bog (Halvorsen et al., 2020), or add other relevant features to the classification. The short‐wave infrared region (SWIR) has been found to be important for predicting wetlands (Mcpartland et al., 2019; Meingast et al., 2014). Various topographical features such as topographical wetness index (TWI) have been found to be important for predicting wetlands, but only for specific spatial resolutions (Lidberg et al., 2020; Rasanen et al., 2014) Thus, both high spatial and spectral resolution is necessary to map wetland plant communities (Du et al., 2021).…”

A Multiscale Productivity Assessment of High Andean Peatlands across the Chilean Altiplano Using 31 Years of Landsat Imagery

“…As a result, we believe that the combination of 30 m Landsat data and 10 m Sentinel 2 data are the most promising remote sensing sources for studying long-term high Andean peatland productivity (30+ years) at high spatiotemporal resolutions moving forward. These decadal studies can be further complemented with detailed assessments of peatland biomass [46,47], functional traits and plant composition [48,49], soil moisture [50,51] and water table measurements [52][53][54][55] using active radar or LiDAR systems, hyperspectral sensors, optical sensors on unmanned vehicles or multi-sensor approaches recently developed for other regions around the world.…”

Airborne HySpex Hyperspectral Versus Multitemporal Sentinel-2 Images for Mountain Plant Communities Mapping

“…For many years, plant communities have been successfully classified on the basis of airborne hyperspectral images [81,82], which is understandable as imaging spectroscopy offers high spectral, radiometric, and spatial resolutions, which allow the identification of specific morphological and anatomical features of individual species but require a proper data acquisition period. The experience of our team indicates that the best results are achieved in late summer and early autumn [10,16,28,48], because during this period, discoloration and morphological elements are typical for these species, e.g., plants have dry ears, which, despite small sizes densely cover their habitats, reflecting a specific set of electromagnetic waves.…”