Abstract
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.
Highlights
Leaf-area index (LAI) and biomass are among the most important vegetation properties linked to biogeochem ical processes (Wilson et al 2007; van der Wal and Stien 2014)
We evaluated the final model perfor mance with root mean squared error (RMSE), normalized RMSE, and adjusted coefficient of determination
We evaluated how well-peatland leaf-area index (LAI) and biomass patterns can be detected with multi-source and multitemporal ultra-high-resolution remote sensing data and examined the benefit of hyperspectral drone data
Summary
Leaf-area index (LAI) and biomass are among the most important vegetation properties linked to biogeochem ical processes (Wilson et al 2007; van der Wal and Stien 2014). Green LAI quantifies the photosynthesizing leaf area per unit ground area and characterizes the interface between vegetation and atmosphere. It is a key parameter when the eddy covariance or chamber-based measurements of CO2 and CH4 are upscaled or modeled (Tuovinen et al 2019; Wilson et al 2007; Metzger et al 2015). PFTs form a way to divide plant species into groups based on their growth forms and environmen tal responses (Duckworth, Kent, and Ramsay 2000; Ustin and Gamon 2010; Chapin et al 1996). Users and their needs define the PFT grouping, but a common approach is to use groups such as evergreen and
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