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

• Published in: GIScience & Remote Sensing
• Main Authors: Aleksi Räsänen, Sari Juutinen, Margaret Kalacska, Mika Aurela, Pauli Heikkinen, Kari Mäenpää, Aleksi Rimali, Tarmo Virtanen
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis Group 2020
• Subjects: biomass; hyperspectral imaging; leaf-area index; ultra-high spatial resolution; unmanned aerial systems (uas); Mathematical geography. Cartography; GA1-1776; Environmental sciences; GE1-350; Northern Finland
• Online Access: https://doi.org/10.1080/15481603.2020.1829377; https://doaj.org/article/cdf4d7c4ff434599831577551aaff4ca

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 ...