Why can we detect lianas from space?
bioRxiv (Cold Spring Harbor Laboratory)
Abstract
Abstract Lianas, woody vines acting as structural parasites of trees, have profound effects on the composition and structure of tropical forests, impacting tree growth, mortality, and forest succession. Remote sensing offers a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global specificity of spectral signals from liana-infested tree crowns and forest stands, examining the underlying mechanisms. We compiled a database, including leaf reflectance spectra from 5424 leaves, fine-scale airborne reflectance data from 999 liana-infested canopies, and coarse-scale satellite reflectance data covering hectares of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, corroborated by field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signature at canopy and stand scales across sites. This signature mainly arises at the canopy level due to direct effects of leaf angles, resulting in a larger apparent leaf area, and indirect effects from increased light scattering in the NIR and SWIR regions, linked to lianas’ less costly leaf construction compared to trees. The existence of a consistent global spectral signal for lianas suggests that large-scale quantification of liana infestation is feasible. However, because the traits identified are not exclusive to lianas, accurate large-scale detection requires rigorously validated remote sensing methods. Our models highlight challenges in automated detection, such as potential misidentification due to leaf phenology, tree life-history, topography, and climate, especially where the scale of liana infestation is less than a single remote sensing pixel. The observed cross-site patterns also prompt ecological questions about lianas’ adaptive similarities across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits. Open data statement Of the 17 datasets used, 10 are published and publicly accessible, with links provided in this submission (Appendix S1: Section S1). Upon acceptance, remaining seven datasets will be provided via Smithsonian’s Dspace. The open-source model code is available as R-package ccrtm ( https://cran.r-project.org/web/packages/ccrtm/index.html ) and on github ( https://github.com/MarcoDVisser/ccrtm ). Code will be archived in Zenodo should the manuscript be accepted for publication
Authors 25
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Leiden University · Princeton University
Affiliation as printed
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ
Institute of Environmental Sciences, Leiden University, Einsteinweg 2, 2333 CC Leiden, The Netherlands
Institute of Environmental Sciences, Leiden University, Einsteinweg , 333 CC Leiden, The Netherlands
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Affiliation as printed
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ
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Boston University · Ghent University
Affiliation as printed
Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Ghent, Belgium
Department of Earth and Environment, Boston University, Boston, USA
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Affiliation as printed
University of Hong Kong, Hong Kong
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Affiliation as printed
Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA
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Affiliation as printed
Salo Sciences, Inc., San Francisco, CA USA
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Affiliation as printed
Department of Plant Sciences, Forest Ecology and Conservation group, University of Cambridge, Cambridge, UK
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University of Helsinki · University of Cambridge
Affiliation as printed
Department of Geosciences and Geography, University of Helsinki, Helsinki, 00014, Finland
Department of Plant Sciences, Forest Ecology and Conservation group, University of Cambridge, Cambridge, UK
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Affiliation as printed
Department of Plant Sciences, Forest Ecology and Conservation group, University of Cambridge, Cambridge, UK
Department of Plant Sciences, The Conservation Research Institute, University of Cambridge, Cambridge CB2 3QZ, UK
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Affiliation as printed
Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Ghent, Belgium
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Affiliation as printed
Centre for Earth Observation Sciences (CEOS), Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, Alberta, Canada T6M 2R7
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Affiliation as printed
Centre for Earth Observation Sciences (CEOS), Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, Alberta, Canada T6M 2R7
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Affiliation as printed
School of Geography, University of Nottingham, University Park, Nottingham, United Kingdom
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Affiliation as printed
School of Geography, University of Nottingham, University Park, Nottingham, United Kingdom
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Affiliation as printed
School of Geography, University of Nottingham, University Park, Nottingham, United Kingdom
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Affiliation as printed
School of Geography, University of Nottingham, University Park, Nottingham, United Kingdom
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Affiliation as printed
School of Social Sciences, University of Dundee, Dundee, United Kingdom
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Affiliation as printed
School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, USA
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Brookhaven National Laboratory
Affiliation as printed
Terrestrial Ecosystem Science & Technology Group, Environmental Sciences Department, Brookhaven National Laboratory, Upton, NY 11973-5000, USA
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Affiliation as printed
Department of Biological Sciences, Marquette University, Milwaukee, WI 53201, USA
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National University of Singapore
Affiliation as printed
Department of Biological Sciences, National University of Singapore, Singapore
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Affiliation as printed
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ
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