Synergistic use of multi- and hyperspectral remote sensing data and airborne LiDAR to retrieve forest floor reflectance
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023-08-01
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Language
en
Pages
15
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Remote Sensing of Environment, Volume 293
Abstract
Forest floor vegetation can account for a notable fraction of forest productivity and species diversity, and the composition of forest floor vegetation is an important indicator of site type. The signal from the forest floor influences the interpretation of optical remote sensing (RS) data. Retrieval of forest floor reflectance properties has commonly been investigated with multiangular RS data, which often have a coarse spatial resolution. We developed a method that utilizes a forest reflectance model based on photon recollision probability to retrieve forest floor reflectance from near-nadir data. The method was tested in boreal, hemiboreal, and temperate forests in Europe, with hemispherical photos and airborne LiDAR as alternative data sources to provide forest canopy structural information. These two data sources showed comparable performance, thus demonstrating the value of using airborne LiDAR as the structural reflectance model input to derive wall-to-wall maps of forest floor reflectance. We derived such maps from multispectral Sentinel-2 MSI and hyperspectral PRISMA satellite images for a boreal forest site. The validation against in situ measurements showed fairly good performance of the retrievals in sparse forests (that had effective plant area index less than 2). In dense forests, the retrievals were less accurate, due to the small contribution of forest floor to the RS signal. We also demonstrated the use of the method in monitoring the recovery of forest floor vegetation after a thinning disturbance. The reflectance model that we used is computationally efficient, making it well applicable also to data from new and forthcoming hyperspectral satellite missions.Description
| openaire: EC/H2020/771049/EU//FREEDLES Funding Information: We thank Juho Antikainen, Lukáš Fajmon, Petri Forsström, Karel Holouš, Mihkel Kaha, Bijay Karki, Lauri Korhonen, Andres Kuusk, Mait Lang, Titta Majasalmi, Jan Pisek, Ville Ranta, and the staff of the Hyytiälä forestry field station for their assistance in the field and airborne campaigns and data preprocessing. The study was carried out using PRISMA Products, © of the Italian Space Agency (ASI), delivered under an ASI License to use. This study has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [grant agreement No 771049]. The text reflects only the authors' view and the Agency is not responsible for any use that may be made of the information it contains. This study was also funded by the Academy of Finland grant DIMEBO [grant number 323004]. Funding Information: We thank Juho Antikainen, Lukáš Fajmon, Petri Forsström, Karel Holouš, Mihkel Kaha, Bijay Karki,Lauri Korhonen, Andres Kuusk, Mait Lang, Titta Majasalmi, Jan Pisek, Ville Ranta, and the staff of the Hyytiälä forestry field station for their assistance in the field and airborne campaigns and data preprocessing. The study was carried out using PRISMA Products, © of the Italian Space Agency (ASI), delivered under an ASI License to use. This study has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [grant agreement No 771049 ]. The text reflects only the authors' view and the Agency is not responsible for any use that may be made of the information it contains. This study was also funded by the Academy of Finland grant DIMEBO [grant number 323004 ]. Publisher Copyright: © 2023 The Authors
Keywords
Airborne laser scanning, Hyperspectral, PRISMA, Radiative transfer, Reflectance modeling, Sentinel-2, Spectroscopy, Understory
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Citation
Hovi, A, Schraik, D, Kuusinen, N, Fabiánek, T, Hanuš, J, Homolová, L, Juola, J, Lukeš, P & Rautiainen, M 2023, ' Synergistic use of multi- and hyperspectral remote sensing data and airborne LiDAR to retrieve forest floor reflectance ', Remote Sensing of Environment, vol. 293, 113610 . https://doi.org/10.1016/j.rse.2023.113610