Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

Abstract

There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500-900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3-61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between −14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits.

Aboveground biomass patterns across treeless northern landscapes

“…Our results generally highlight the benefits of multi-source remote sensing Amani et al 2017;Greaves et al 2016) because regressions with all data performed the best in all other sites than Herschel. Surprisingly, only a few DEM features were considered important, which is in contrast to some earlier studies (Greaves et al 2016;Riihimäki, Heiskanen, and Luoto 2017;Räsänen et al 2020b;Harris and Baird 2019). This probably relates to multiple reasons.…”

“…The Sodankylä study site is an open northern boreal peatland surrounded by pine dominated forests in northern Finland. The vegetation pattern consists of strings with shrubs and a few birch trees and lawns and flarks with Sphagnum and wet brown mosses and sedges (Räsänen et al 2020b;Dinsmore et al 2017).…”

“…It has been reported that vegetation patterns and abundance in tundra and peatland landscapes are partly controlled by topography (Riihimäki, Heiskanen, and Luoto 2017;Räsänen et al 2020b;Greaves et al 2016;Mikola et al 2018;Harris and Baird 2019); therefore, we included ArcticDEM data. To remove noise from the data, we first filtered the DEM data with 3 × 3 pixel circular mean filter.…”

“…While plant height information is useful in shrub-dominated landscapes (Cunliffe et al 2020b;Greaves et al 2017Greaves et al , 2016, NDVI might be a better predictor if the landscape is characterized by relatively high herbaceous plant biomass Johansen and Tommervik 2014). However, in landscapes with high moss biomass, in particular Sphagnum, neither plant height nor NDVI is a sufficient proxy for biomass (Räsänen et al 2020b).…”

“…In Arctic tundra, the properties of the permafrost and associated periglacial land forms exert a strong control over vegetation distribution (Siewert et al 2021), and northern peatlands are characterized by considerable microtopographical and plant community-type variation in the form of peatland microforms (Laitinen et al 2007). In some landscapes, naturally non-vegetated areas alternate with high biomass shrub vegetation (Atkinson and Treitz 2013;Cunliffe et al 2020b), whereas other landscapes can be characterized by almost continuous moss or graminoid cover (Räsänen et al 2020b(Räsänen et al , 2020a. However, synthesized overviews of how biomass is divided into PFTs in different types of northern landscapes have been scarce.…”