The value of hyperspectral UAV imagery in characterizing tundra vegetation

• Published in: Remote Sensing of Environment
• Main Authors: Putkiranta, Pauli, Räsänen, Aleksi, Korpelainen, Pasi, Erlandsson, Rasmus, Kolari, Tiina M. H., Pang, Yuwen, Villoslada, Miguel, Wolff, Franziska, Kumpula, Timo, Virtanen, Tarmo
• Other Authors: orcid:0000-0002-3629-1837, 4100311110, Luonnonvarakeskus
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier
• Subjects: tundra; plant communities; multispectral imaging; hyperspectral imaging; drone; biodiversity; Arctic; Northern Finland; Tundra
• Online Access: https://jukuri.luke.fi/handle/10024/554950; https://doi.org/10.1016/j.rse.2024.114175

The fine-scale spatial heterogeneity of low-growth Arctic tundra landscapes necessitates the use of high-spatial-resolution remote sensing data for accurate detection of vegetation patterns. While multispectral satellite and aerial imaging, including the use of uncrewed aerial vehicles (UAVs), are common approaches, hyperspectral UAV imaging has not been thoroughly explored in these ecosystems. Here, we assess the added value of hyperspectral UAV imaging relative to multispectral UAV imaging in modelling plant communities in low-growth oroarctic tundra heaths in Saariselkä, northern Finland. We compare three different spectral compositions: 4-channel broadband aerial images, 5-channel broadband UAV images and 112-channel narrowband UAV images. Based on field vegetation plot data, we estimate vascular plant aboveground biomass, leaf area index, species richness, Shannon's diversity index, and community composition. We use spectral and topographic information to compile 12 explanatory datasets for random forest regression and classification. For aboveground biomass and leaf area index, the highest R2 values were 0.60 and 0.65, respectively, and broadband variables were most important. In the best models for biodiversity metrics species richness and Shannon's index R2 values were 0.53 and 0.46, respectively, with hyperspectral, topographic, and multispectral variables having high importance. For 4 floristically determined community clusters, both random forest classifications and fuzzy cluster membership regressions were conducted. Overall accuracy (OA) for classification was 0.67 at best, while cluster membership was estimated with an R2 of 0.29–0.53. Variable importance was heavily dependent on community composition, but topographic, multispectral, and hyperspectral data were all selected for these community composition models. Hyperspectral models generally outperformed multispectral ones when topographic data were excluded. With topographic data, this difference was diminished, and performance improvements from ...