Data from: When can we detect lianas from space? Towards a mechanistic understanding of liana-infested forest optics
Data files
Feb 13, 2025 version files 1.07 GB
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Leaf_Angles.zip
1.05 GB
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PAI.zip
874.83 KB
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README.md
5.87 KB
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Spectral.zip
20.15 MB
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 could offer a powerful tool for quantifying the scale of liana infestation, provided the availability of robust detection methods. We analyze the consistency and global geographic specificity of spectral signals – i.e. reflectance across wavelengths - from liana-infested tree crowns and forest stands, examining the underlying mechanisms of these signals. We compiled a uniquely comprehensive 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 775 hectares of liana-infested forest stands. To unravel the mechanisms of the liana spectral signal, we applied mechanistic radiative transfer models across scales, establishing a synthesis of the relative importance of different mechanisms, which we then corroborated by field data on liana leaf chemistry and canopy structure. We find a consistent liana spectral signal at canopy and stand scales across globally distributed sites. This signature mainly arises at the canopy level due to direct effects of more horizontal leaf angles, resulting in a larger projected leaf area, and indirect effects from increased light scattering in the near and shortwave infrared 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 responsible for the liana canopy reflectance signal 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 in optical traits across environments, indicating possible convergent evolution due to shared constraints on leaf biochemical and structural traits.
https://doi.org/10.5061/dryad.xpnvx0krs
Description of the data and file structure
Overview
This README provides a high-level summary of multiple datasets used in the study “When can we detect lianas from space? Towards a mechanistic understanding of liana-infested forest optics.” These datasets encompass leaf angle measurements, Li-Cor canopy structure measurements (PAI), and reflectance data at both leaf and canopy levels, collectively aimed at validating models and understanding lianas optics.
Datasets included:
- Leaf angle data (2013 & 2019): Direct leaf angle measurements for 1540 of leaves from 18 tree and 19 liana species in Panama, obtained from canopy crane and telecommunication towers.
- Li-Cor measurements (2011-2016): Data on Plant Area Index (PAI), Leaf Area Index (LAI), and Wood Area Index (WAI) before and after liana removal at Gigante Peninsula, Panama.
- Reflectance data (leaf and canopy levels): Spectral data collected from leaves and canopies to study the optical properties of liana and tree leaves, or liana-infested and non-infested forest canopies. Details in the respective metadata files.
Further information:
For more detailed methodologies, or details on measurements, please refer to the individual README files associated with each dataset.
Files and variables
General information
- Data file formats: Data is organized in text files, CSVs, and HDF5 files depending on the dataset.
- Units of Measurement: specified partly below and in the readme files for each individual dataset
Dataset 1: Leaf Angles
File Structure
- /Leaf Angles/2013
- /Leaf Angles/2019
Variables and definitions
- lad (Leaf Angle Distribution): Leaf angle in degrees.
- pic: Picture number, connects to Pictures dataset.
- date: Date of measurement (YYYY-MM-DD).
- species: Genus and species name.
- code: 4-letter code for species, when identified.
- site: Site of collection.
- UTMX, UTMY: UTM coordinates (if available).
- type: Type of leaf (liana or tree).
- csv: Indicates if a CSV of points was made for calculating leaf angles (1 = yes, 0 = no).
- roi: Indicates if an ImageJ ROI file was created (1 = yes, 0 = no).
- rotated: Indicates if the original image appears rotated in ImageJ (1 = yes, 0 = no).
- comments: Any comments made during measurement or processing.
Missing Values
- Missing values are indicated by “NA” or blank cells where data is unavailable.
Dataset 2: PAI (Plant Area Index)
File Structure
- /PAI/1Data 2011 - PreCut/Plot# (where # is 1 to 16)
- /PAI/2Data 2011 - PostCut/Plot# (same structure for subsequent years up to 2015)
Variables and definitions
- year: The year of measurement.
- liana_cut: Treatment condition: ‘pre’ (before liana removal) or ‘post’ (after liana removal).
- plot: Plot number (1 to 16).
- data: Measurement subset (‘A’ or ‘B’).
- date: Date of measurement (YYYY-MM-DD).
- id_last_mment: Total number of measurements collected during the session.
- i_time, f_time: Start and end times of measurement (HH:MM:SS).
- comments: Notes or issues during measurement.
Missing Values
- Missing values are indicated by “NA” or specific notes in the
commentscolumn (e.g., file lost, breaks in data collection).
Dataset 3: Spectral Reflectance
File Structure
- /Spectral/Dataset_NAME.h5 (where NAME refers to one of the four datasets).
Variables and Definitions
- reflectance: Matrix with measured reflectance values (rows for samples, columns for wavelengths).
- wavelength: Vector of wavelengths (nm).
- meta: Metadata matrix or dataframe.
- meta_colnames: Column names for metadata.
Missing Values
- Missing metadata entries are indicated by “NA”.
Code/software
R code to extract leaf angles is supplied.
We refer to R-package ccrtm for model code developed in the study: https://cran.r-project.org/web/packages/ccrtm/index.html
Access information
Other publicly accessible locations of the data:
- none
Data was derived from the following sources:
Leaf Angles Dataset
- Detto, M., G. P. Asner, H. C. Muller-Landau, and O. Sonnentag (2015), “Spatial variability in tropical forest leaf area density from multireturn lidar and modeling,” J. Geophys. Res. Biogeosci., 120, doi:10.1002/2014JG002774.
- Visser et al. (2025), “When can we detect lianas from space? Towards a mechanistic understanding of liana-infested forest optics,” Ecology, in press.
PAI (Plant Area Index) Dataset
- Rodríguez-Ronderos, M. E., et al. (2016), “Effects of liana removal on the water relations of host trees in a Panamanian tropical forest,” Ecology, Vol. 97, No. 12, pp. 3271-3277.
Spectral Reflectance Dataset
- Visser et al. (2025), “When can we detect lianas from space? Towards a mechanistic understanding of liana-infested forest optics,” Ecology, in press.
- Sánchez-Azofeifa, G. A., et al. (2009), “Differences in leaf traits, leaf internal structure, and spectral reflectance between two communities of lianas and trees: Implications for remote sensing in tropical environments,” Remote Sensing of Environment, 113, pp. 2076–2088.
- Wu, J., et al. (2018), “Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest,” New Phytologist, 217, pp. 1507–1520.
- Marvin, D. C., G. P. Asner, and S. A. Schnitzer (2016), “Liana canopy cover mapped throughout a tropical forest with high-fidelity imaging spectroscopy,” Remote Sensing of Environment, 176, pp. 98–106.