Preprints
https://doi.org/10.5194/bg-2021-95
https://doi.org/10.5194/bg-2021-95

  19 Apr 2021

19 Apr 2021

Review status: this preprint is currently under review for the journal BG.

Unveiling spatial and temporal heterogeneity of a tropical forest canopy using high-resolution NIRv, FCVI, and NIRvrad from UAS observations

Trina Merrick1,4, Stephanie Pau1, Matteo Detto2,3, Eben North Broadbent4, Stephanie Bohlman5, Christopher J. Still6, and Angelica M. Almeyda Zambrano7 Trina Merrick et al.
  • 1Department of Geography, Florida State University, 113 Collegiate Loop, Tallahassee, Florida 32306, USA
  • 2Smithsonian Tropical Research Institute, Apartado 0843–03092, Balboa, Anc´on, Panama
  • 3Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey 08544 USA
  • 4Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL, 32608 USA
  • 5School of Forest Resources and Conservation, University of Florida, Gainesville, FL, 32608 USA
  • 6Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon 97331 USA
  • 7Spatial Ecology and Conservation Lab, Center for Latin American Studies, University of Florida, Gainesville, Florida 32608 USA

Abstract. Presented here for the first time are emerging vegetation indicators: near-infrared reflectance (NIRv) of vegetation, the fluorescence correction vegetation index (FCVI), and radiance (NIRvrad) of vegetation, for a tropical forest canopy calculated using UAS-based hyperspectral data. Fine-scale tropical forest heterogeneity represented by NIRv, FCVI, and NIRvrad, is investigated using unmanned aerial vehicle data and eddy covariance-based gross primary productivity estimates. By exploiting near-infrared signals, emerging vegetation indicators captured the greatest spatiotemporal variability, followed by the enhanced vegetation index (EVI), then the normalized difference vegetation index (NDVI), which saturates. Wavelet analyses showed the dominant spatial variability of all indicators is driven by tree clusters and larger-than-tree-crown size gaps (not individual tree crowns or leaf clumps), but emerging indices and EVI captured structural information at smaller spatial scales (~50 m) than NDVI (~90 m) and lidar (~70 m). As predicted in previous studies, we confirm that NIRv and FCVI are virtually identical for a dense green canopy despite the differences in how these indices were derived. Furthermore, we show that NIRvrad, which does not require separate irradiance measurements, correlated most strongly with gross primary productivity and photosynthetically active radiation. These emerging indicators, which are related to canopy structure and the radiation regime of vegetation canopies are promising tools to improve understanding of tropical forest canopy structure and function.

Trina Merrick et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-95', Anonymous Referee #1, 04 May 2021
    • AC1: 'Reply on RC1', Trina Merrick, 06 Jul 2021
    • AC3: 'Revised Manuscript with Authors' Responses to Comments', Trina Merrick, 06 Jul 2021
  • RC2: 'Comment on bg-2021-95', Anonymous Referee #2, 13 May 2021
    • AC2: 'Reply on RC2', Trina Merrick, 06 Jul 2021
    • AC4: 'Revised Manuscript with Authors' Responses to Comments', Trina Merrick, 06 Jul 2021

Trina Merrick et al.

Data sets

GatorEye Hyperspectral and Lidar data Eben N. Broadbent and Trina Merrick http://GatorEye.org

Trina Merrick et al.

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Short summary
We used UAS data to measure new vegetation indicators of a tropical forest canopy and found the new, near-infrared-based indicators unveil more heterogeneity of the canopy than traditional indices. Also, the new indicators and one traditional index capture information at smaller scales than the other traditional index and Lidar. The near-infrared radiance of vegetation also tracked daily productivity changes. We show that UAS-based emerging indices can improve tropical forest monitoring.
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