Predicting Carbon Dynamics in Forests Using Remote

Predicting Carbon Dynamics in Forests Using Remote Measurements of Canopy Structure

K. C. Cushman

A dissertation submitted to the Graduate School of Brown University in fulfillment of the

requirements for the degree of Doctor of Philosophy in the Department of Ecology and

Evolutionary Biology

Providence, RI

February 2020

© Copyright 2020 by K. C. Cushman

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KC (Katherine Corin) Cushman Department of Ecology and Evolutionary Biology y Brown University y Providence, RI 02912 EDUCATION Brown University, Providence, RI 2014- present PhD Candidate, Department of Ecology and Evolutionary Biology Graduate Affiliate, Institute at Brown for Environment and Society Thesis committee: James Kellner (advisor), Erika Edwards, John Mustard, Stephen Porder Swarthmore College, Swarthmore, PA 2012 Bachelor of Arts, Highest Honors, Phi Betta Kappa Major in Biology, Minors in Engineering and Mathematics PROFESSRIONAL EXPERIENCE Intern, Smithsonian Tropical Research Institute, Panama 2012 – 2014 Advisor: Dr. Helene Muller-Landau Intern, Oak Ridge National Laboratory, Oak Ridge, TN Summers 2009, ‘10, ‘12 Biosciences Division Advisor: Dr. Timothy Tschaplinski AWARDS AND FELLOWSHIPS Graduate Research Fellow 2014- 2019 National Science Foundation Recognition and support for outstanding graduate students in STEM fields. Presidential Fellow 2014-2017 Brown University Recognition of academic promise across all disciplines. Sidney Frank Fellow 2014-2015 Brown University, Division of Biology and Medicine Recognition of meritorious pre-doctoral training. Leo M. Leva Memorial Prize in Biology 2012 Swarthmore College Department of Biology Awarded to a graduating senior in biology whose work shows unusual promise. NSF Research Experience for Undergraduates 2011 Friday Harbor Laboratories Supports active research participation by undergraduate students. Morris Monsky Prize in Mathematics 2009 Swarthmore College Department of Mathematics Awarded to first-year students who have demonstrated outstanding promise and enthusiasm.

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UT-Battelle National Merit Scholar 2008-2012 Recognition and honor for academically talented students. PUBLICATIONS Published: Cushman, K. C. and J. R. Kellner. Prediction of forest aboveground net primary production from

high-resolution vertical leaf-area profiles. (2019). Ecology Letters. 22 (3), 538-546. Kellner, J., Albert, L.A., Burley, J.T., and K. C. Cushman. The case for remote sensing of

individual plants. (2019). American Journal of Botany. 106 (9): 1-4. Kellner, J., Armston, J.D., Birrer, M., Cushman, K. C., Duncanson, L.I., Eck, C., Falleger, C.,

Imbach, B., Král, K., Krůček, M., Trochta, J., Vrška, T., and C. Zgraggen. New opportunities for forest remote sensing through ultra-high-density drone lidar. (2019). Surveys in Geophysics. 40(4): 959-977.

Albert, L., Cushman, K. C., Allen, D. W., Zong, Y., Alonso, L., and J. R. Kellner. Stray light

characterization in a high-resolution imaging spectrometer designed for solar-induced fluorescence. (2019). Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV. 10986: 109860G.

Mateo-Vega, J., Potvin, C., Monteza, J., Bacorizo, J,, Barrigón, J., Barrigón, R., López, N., Omi,

L., Opúa, M., Serrano, J., Cushman, K. C., and C. Meyer. (2017). Full and effective participation of indigenous peoples in forest monitoring for REDD+: Trial in Panama’s Darien. Ecosphere, 8(2).

Augspurger, C., Franson, S., and K.C. Cushman. Tree, Not Diaspore, Traits Predict Interspecific

Variation in Wind Dispersal of Neotropical Trees. (2016). Functional Ecology, 31(4): 808-820.

Augspurger, C. K., Franson, S. E., Cushman, K. C., and H. C. Muller‐Landau. (2016).

Intraspecific variation in seed dispersal of a Neotropical tree and its relationship to fruit and tree traits. Ecology and Evolution, 6(4), 1128-1142.

Cushman, K. C., Muller‐Landau, H. C., Condit, R. S., and S. P. Hubbell. (2014). Improving

estimates of biomass change in buttressed trees using tree taper models. Methods in Ecology and Evolution 5(6): 573-582.

Tschaplinski, T. J., Plett, J. M., Engle, N., Deveau, A., Cushman, K. C., Martin, M. Z., Doktycz,

M. J., Tuskan, G. A., Brun, A., Kohler, A., and F. Martin. (2014). Populus trichocarpa and Populus deltoides Exhibit Different Metabolomic Responses to Colonization by the Symbiotic fungus Laccaria bicolor. Molecular Plant-Microbe Interactions 27(6): 546-556.

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Forthcoming: Cushman, K. C. and J. R. Kellner. Inverse relationship between leaf area and net productivity in

a tropical forest landscape. (In review) VanValkenburgh, P., Cushman, K. C., Castillo Butters, L. J., Rojas Vega, C., Roberts, C.,

Kepler, C., and J. R. Kellner. Lasers without Lost Cities: Using drone-mounted LiDAR to Capture Architectural Complexity at Kuelap, Amazonas, Peru. (In revision, Journal of Field Archeology)

GRANTS Graduate Research, Training, and Travel Grant 2018 Institute at Brown for Environment and Society, Brown University $3,488 in research support for project “Developing new technological capacity for individual-based forest monitoring across landscapes.” Dissertation Development Grant 2017 Department of Ecology and Evolutionary Biology, Brown University Drollinger Family Charitable Foundation $12,849 in research support for project “Canopy cover predicts forest productivity.” Graduate Research Internship Program 2015 National Science Foundation, Smithsonian Institution $5,000 in research support for project “Genetic and environmental controls of tropical tree phenology.” Graduate Research, Training, and Travel Grant 2015 Institute at Brown for Environment and Society, Brown University $4,665 in research support for project “Is active leaf movement a determinant of canopy photosynthesis in tropical forests?” Grants-in-Aid of Research 2014 Sigma Xi $700 in research support for project for project “Fine root production in tropical trees: completing the picture of carbon allocation.” Center for Tropical Forest Science Research Grants Program 2013 Smithsonian Tropical Research Institute $7,655 in research support for project “Improving estimates of biomass change in buttressed trees using site-specific tree taper models.” LEAD AUTHOR PRESENTATIONS Invited: Cushman, K. C. and J. R. Kellner. “Vertical leaf-area profiles explain novel variation in tropical

forest aboveground net primary production.” Oral Presentation, Ecological Society of America Meeting; August 5-10, 2018; New Orleans, Louisiana, USA

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Contributed:

Cushman, K. C. and J. R. Kellner. “Landscape-scale relationship between tropical forest ANPP and leaf area.” Oral Presentation, Ecological Society of America Meeting; August 12-16, 2019; Louisville, Kentucky, USA.

Cushman, K. C., Hancock, S., Silva, C., and J. R. Kellner. “Quantifying diurnal leaf movement

and radiative transfer in tropical forests.” Oral Presentation, Association for Tropical Biology and Conservation Meeting; June 19-23, 2016; Montpellier, France

Cushman, K. C., Muller-Landau, H. C., Kellner, J .R., Wright, S. J., Condit, R., Detto, M., and C. M. Tribble. “Seasonal and Inter-annual Variation in Wood Production in Tropical Trees on Barro Colorado Island, Panama, is Related to Local Climate and Species Functional Traits.” Poster Presentation, American Geophysical Union Fall Meeting; December 14-18, 2015; San Francisco, CA, USA

Cushman, K. C., Muller-Landau, H. C., and S. P. Hubbell. “Improving Estimates of Biomass and

Biomass Change in Buttressed Trees Using Tree Taper Models.” Poster Presentation, Association for Tropical Biology and Conservation Meeting; June 23-27, 2013; San Jose, Costa Rica

Cushman, K. C., and R. A. Merz. “Maximizing feeding in minimal flow: behavioral and

morphological plasticity of Balanus glandula.” Oral Presentation, Society for Integrative and Comparative Biology Meeting; January 3-7, 2012; Charleston, SC, USA

TEACHING Certifications: Teaching Certificate I, Sheridan Center, Brown University 2015-2016 Experience: Instructor, Biodiversity and Ecology of Tropical Forests 2019 Pre-College Program, Brown University Teaching Assistant, Terrestrial Biogeochemistry, Brown University 2016 Teaching Assistant, Diversity of Life, Brown University 2015 Teaching Assistant, Evolutionary Biology, Brown University 2014 Peer Tutor, Linear Algebra, Calculus II, Swarthmore College 2009-2012 SERVICE Manuscript peer reviewer: Methods in Ecology and Evolution • New Phytologist • PLOS ONE •

iForest – Biogeosciences and Forestry • African Journal of Ecology Diversity and inclusion: 500 Women in Science, Providence Pod 2017-2019

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Diversity and inclusion in academic hiring, Workshop organizer 2018 Department of Ecology and Evolutionary Biology, Brown University Womxn in STEM Symposium, Participant 2019 Office of Institutional Equity and Diversity, Brown University Diversity and inclusion workshops and seminars, Participant 2018-2019 Sheridan Center for Teaching and Learning, Brown University “Using a Transparent Framework with Your Students and Mentors” “Designing a Student-Centered, Inclusive Pre-College Course” “Hidden Gender Inequities in Undergraduate Science” Volunteer positions: EEB Graduate Student Association President, Brown University 2018 Climate Action Plan Committee, Swarthmore College 2011-2012 Alumni Collegiate Representative, Technology Student Association 2010-2012 National President, Technology Student Association 2007-2008 SKILLS Computer and Programming Experience: R • MATLAB • Python • ENVI Quick Terrain Modeler • ArcGIS • CloudCompare Languages: Spanish (proficient) • French (proficient) PROFESSIONAL ASSOCIATIONS Associate for Tropical Biology and Conservation • American Geophysical Union • Ecological Society of America • Sigma Xi

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PREFACE

Carbon dynamics in forests are one of the largest sources of uncertainty in

projections of Earth’s future climate (Clark et al. 2017; Zhang et al. 2018). This

uncertainty is caused by two properties of forests. First, forests contain a huge quantity of

carbon—the amount of carbon stored in living trees is over 20 times greater than recent

annual emissions from fossil fuels (Saatchi et al. 2011; Quéré et al. 2018). Second, forests

are a highly dynamic carbon stock and potentially have positive and negative feedback

mechanisms with global climate change—on the one hand, increasing drought and storm

events may increase tree respiration and mortality, exacerbating the increase of global

atmospheric carbon dioxide (McDowell et al. 2018); on the other hand, rising

concentrations of atmospheric carbon dioxide may stimulate forest growth and carbon

uptake, mitigating the effects of climate change (Clark 2004). These feedback mechanisms

are organismal biological processes, making the study of forest ecology crucial for

predicting forests’ role in the global carbon cycle.

Current research suggests that global forests are, on average, accumulating carbon

and slowing the pace of climate change (Baker et al. 2004; Lewis et al. 2009; Pan et al.

2011). However, there is much uncertainty regarding where and when carbon accumulates

in forests, and whether forests will continue to mitigate human carbon emissions in the

future (Zhang et al. 2018). A critical cause of the uncertainty surrounding forests’ role in

the global carbon cycle is the fact that it is exceedingly difficult to measure carbon in

trees—the only direct measurement of carbon in trees is to harvest and weigh a tree (Clark

& Kellner 2012). Harvesting trees is infeasible over large scales not only because it is

logistically difficult, but also because it would destroy the forest we wish to measure.

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Instead, forest ecologists largely rely on two kinds of indirect measurements to inform our

understanding of forest carbon dynamics: small-scale field plots and large-scale remote

sensing.

In field plots, researchers map, identify, and measure the diameter of all trees to

estimate aboveground biomass from allometric equations (Condit 1998; Chave et al.

2014). Field plot methodology has remained basically unchanged for 150 years, and these

long-term measurements have yielded valuable insights into ecological processes such as

species distributions and tree demography. However, field plots are labor intensive, cover

a small and unrepresentative fraction of all forests, and usually lack information about

belowground dynamics and carbon allocated to pools and processes other than tree stems

(Fisher et al. 2008; Clark & Kellner 2012; Marvin et al. 2014). So diameter measurements

in forest plots alone are insufficient to quantify forests’ role in the global carbon cycle.

Remote sensing instruments, or sensors that measure objects from a distance, can

measure characteristics of forests over large areas. Remotely-sensed properties can relate

to a number of ecological processes—measurements of forest height relates to

aboveground biomass and leaf area (Tang et al. 2012; Asner & Mascaro 2014), and forest

reflectance at various wavelengths of light indicates productivity, leaf phenology, and

foliar nutrients (Kokaly et al. 2009; Saleska et al. 2016; Wu et al. 2016). Indeed, airplane

and satellite-based remote sensing platforms have been instrumental in characterizing the

extent and structure of Earth’s biomes (Hansen et al. 2010). Unfortunately, the spatial

resolution of traditional remote sensing products is generally coarse—the widely-used

Landsat and MODIS satellites operated by NASA have pixels that are hundreds and

thousands of square meters in size, respectively (Nelson et al. 1994; Fang et al. 2012).

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This limits the utility of traditional remote sensing products for measuring and

understanding organismal biological processes underlying forests’ role in the global

carbon cycle.

In my dissertation, I develop and use methods that fill gaps between the scales of

inference that are possible using field measurement methods and large-scale remote

sensing tools. I accomplish this using near-surface remote sensing, the deployment of

remote sensors at relatively close range, such as from towers or low-flying aircraft instead

of more distant sensors. Near-surface remote sensing allows measurements that are of a

small enough spatial resolution to study organismal processes—such as tree growth and

mortality—but can still be collected over areas large enough to understand how these

organismal processes contribute to ecosystem-level carbon dynamics (Kellner & Hubbell

2018; Zhao et al. 2018).

My dissertation research focuses on the relationships between forest structure and

forest carbon dynamics. Forest structure is an excellent tool for understanding forest

carbon dynamics for two reasons. First, the physical structure of forests reflects a number

of biological processes—species differences, competition, climatic limitations, and

disturbance regimes are all reflected in the functional traits of tree height, canopy shape,

and leaf area. Second, forest structure can be measured non-destructively and with great

precision.

I use lidar (light detection and ranging) technology to measure forest structure.

Lidar sensors use lasers to create three-dimensional models of forests (Dubayah & Drake

2000). In essence, lidar instruments emit a laser beam at a known angle and record the

time it takes for the laser energy to reflect from a surface and return to the sensor. The

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speed of light is known, so reflecting surfaces (i.e. trees and the ground) can be located in

space. My dissertation uses two forms of lidar data, discrete-return lidar data and full

waveform lidar data (Fig. I). In discrete-return lidar data, the diameter of the laser beam is

small compared to the objects being measured, and each reflection of a laser beam from a

surface is represented as a discrete point in space (Fig. IA). Discrete-return lidar data over

forests typically have many measurements per tree and create an image of the forest that is

visually intuitive to interpret (i.e. a tree typically looks like a tree in discrete-return lidar

data). In full-waveform lidar, the diameter of the laser beam is large compared to the

object being measured, and the sensor records the distribution of return time for reflected

energy (Fig. IB). Full waveform lidar data describe in great detail how laser light moves

vertically through a forest area—a process controlled by forest structure—but do not

resolve the horizontal distribution of material within the laser footprint. My dissertation

chapters use lidar data to characterize relationships between forest structure and carbon

dynamics, shedding light on processes important for the global carbon cycle.

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Fig. I. Example data from discrete-return lidar (A) and full waveform lidar (B). For each

type of lidar, the laser beam (orange) is shown measuring a tree on the right, and the

data are shown in black on the left.

In Chapter One, I established a connection between the vertical distribution of leaf

area and aboveground net primary production (ANPP) in tropical forests (Cushman &

Kellner 2019). Using radiative transfer theory and discrete-return lidar data, I quantified

vertical leaf-area profiles (i.e. the vertical distribution of leaf area) over each of 18 0.5 ha

forest plots at La Selva Biological Station, Costa Rica. I developed a partial least squares

regression model to predict field-measured ANPP using the lidar-derived vertical leaf-area

profiles. I found that vertical leaf-area profiles predicted ~40% of the variation in ANPP

among plots, and outperformed predictions from total leaf area (without considering the

vertical distribution of leaf area) and lidar discrete relative height (a lidar metric with

limited biological meaning). This research demonstrates that interpreting lidar data using a

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biologically-relevant framework increases the utility of lidar for predicting forest carbon

fluxes, and that maintaining the three-dimensional information contained in lidar is also

important for using lidar to predict carbon fluxes.

In Chapter Two, I explored drivers of spatial variation in ANPP within tropical

forest landscapes. Globally, spatial variation in ANPP is positively correlated with total

leaf area. This correlation is driven by large gradients among biomes in temperature and

resource availability. I explored whether the mechanisms driving the global trend are

important within regions, where temperature and precipitation are less variable. Using the

model I developed in Chapter One, I quantified vertical leaf-area profiles and predicted

ANPP within a lowland Neotropical rain forest landscape. I found that, within a tropical

forest, the relationship between ANPP and total leaf area is inverted. I used this analysis to

develop a new hypothesis that explains the discrepancy between the ANPP-leaf area

relationship within a tropical forest (negative) and the ANPP-leaf area relationship across

global biomes (positive)—within a tropical forest, carbon use efficiency is relatively more

important for controlling spatial variation in ANPP. This hypothesis is consistent with

previous leaf-level measurements finding that leaves high in the canopy (characteristic of

local areas of high total leaf area) have higher respiratory costs, and lower overall carbon

use efficiency, compared to leaves lower in the canopy.

In Chapter Three, I characterized how discrete but severe disturbances affect

canopy and carbon dynamics in tropical forests. Blowdowns, downdrafts that occur with

convective storms, are strong enough to cause widespread tree mortality in tropical

forests. However, because blowdown events are very infrequent over vast tropical forests,

studies of blowdowns rarely have detailed “before” data which with to quantify structural

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changes and carbon loss. I used four different lidar datasets to describe the effect of a

blowdown event that serendipitously struck a well-studied forest reserve in 2018,

including drone-based lidar collected ~ 1 year after the blowdown event. I showed that

this event decreased biomass, increased gap area, and caused a departure from the

landscape’s usual canopy dynamics. Further, I demonstrated that this blowdown is

undetectable using satellite-based approaches that underlie most literature on blowdowns

in tropical forests. Therefore, I suggest that previous satellite-based research has

underestimated the importance of blowdowns in tropical forest carbon dynamics.

In Chapter Four, I quantified the sensitivity of Global Ecosystem Dynamics

Investigation (GEDI) lidar data to change in canopy leaf area. GEDI is a waveform lidar

instrument on the International Space Station that is being used to quantify aboveground

biomass density (AGBD) in the world’s temperate and tropical forests (Dubayah et al. in

review). A critical component of the GEDI mission is the development of models that

predict AGBD using height metrics from GEDI’s waveform lidar data. One challenge to

developing AGBD models for GEDI is that GEDI will measure forests during leaf-off and

leaf-on conditions—the presence of leaves alters how lidar laser energy is reflected by a

forest, and thus affects measured lidar waveforms. In this chapter, I simulated leaf-off and

leaf-on GEDI data using discrete-return lidar data collected just before and just after leaf-

out in a temperate forest. I found that upper canopy waveform lidar metrics are relatively

insensitive to changes in leaf area, while lower canopy waveform lidar metrics are more

sensitive to changes in leaf area. As a result, I demonstrated that models predicting forest

biomass from GEDI waveform data must be limited to upper canopy metrics to

consistently predict forest biomass across leaf area conditions.

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Together, my dissertation chapters contribute to a growing understanding of

carbon dynamics in forests. I developed novel methods that are informed by biological

mechanisms to measure forest productivity (Chapter One) and biomass (Chapter Four) at

unprecedented spatial scales, and I used remote measurements of forest structure to

explain that carbon use efficiency drives spatial variation in ANPP across tropical forest

landscapes (Chapter Two) and that blowdown events are likely more important for

tropical forest carbon dynamics than previously thought (Chapter Three).

References

Asner, G.P. & Mascaro, J. (2014). Mapping tropical forest carbon: Calibrating plot

estimates to a simple LiDAR metric. Remote Sens. Environ., 140, 614–624.

Baker, T.R., Phillips, O.L., Malhi, Y., Almeida, S., Arroyo, L., Di Fiore, A., et al. (2004).

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Reviews and syntheses: Field data to benchmark the carbon-cycle models for

tropical forests. Biogeosciences, 14, 4663–4690.

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Clark, D.B. & Kellner, J.R. (2012). Tropical forest biomass estimation and the fallacy of

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Condit, R. (1998). Tropical forest census plots: methods and results from Barro

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Acknowledgements

I would like to thank my advisor, Jim Kellner, for providing the opportunity to

develop my skills and confidence as a researcher. I appreciate your continued support of

my ideas, even when our research took us in entirely unexpected directions. I am grateful

to my other committee members—Erika Edwards, Jack Mustard, and Stephen Porder—for

their feedback and perspectives during my time at Brown. Thanks to members of the

Porder and Kellner labs who provided valuable feedback on my work (Brooke Osborne,

Joy Winbourne, Justin Becknell, Joe Kendrick, Audrey Massmann, and Dafeng Zhang),

and particular thanks to Carlos Silva, Lindsay McCulloch, Loren Albert, and John Burley

for being pleasant and helpful company in the field.

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I have been extremely fortunate to have had the support of both EEB and IBES

staff—I am grateful to Shannon Silva, Lianne Mendonca, and Jesse Marsh in EEB, and to

Bonnie Horta, Paula Francis, Matt Margetta, Mariella Da Silva, and Crystal Caesar in

IBES. Thanks to Henry Johnson for his technical advice, without which my PhD would

have taken another year, at least.

This would work not have been possible without the help of many collaborators

outside of Brown. I want to thank Aeroscout (Benedikt Imbach, Carlo Zgraggen,

Christoph Eck, and Markus Birrer) for their efforts during five field campaigns, hospitality

in Lucerne, and patience answering my questions about the drone. I am grateful to the

Blue Cat Group (Kamil Král, Martin Krůček, Jan Trochta, and Tomáš Vrška) for their

collaboration in Zofin—I couldn’t ask for more thoughtful or kind colleagues. Thanks also

to members of the GEDI science team (John D. Armston, Ralph O. Dubayah, Laura I.

Duncanson, and Steven Hancock) who provided guidance on my final chapter.

I want to thank my previous research mentors who have, in fact, continued to

mentor me during graduate school. I feel lucky that Rachel Merz advised my

undergraduate thesis—my research has moved away from invertebrate biomechanics but I

hope my work always includes the curiosity, rigor, and fun that Rachel models in science.

Thanks to Jose-Luis Machado for sparking my interest in ecology, and for helping me find

a niche where I could use my interest in math and engineering. I am grateful to Helene

Muller-Landau for welcoming me to the community at STRI, for treating me like a

colleague since I was fresh from college, and for thoughtful feedback on research and

navigating my career in science. Finally, I want to thank a whole team of Oak Ridgers

who encouraged me in STEM from an early age, and still: Benita Albert, Tom D’Apolito,

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Nancy Engle, Heather Henderlight, Dan Kuban, Pam Leavy, Tom Livesay, Joanna

Mcfarlane, Rich Norby, and Tim Tschaplinski.

I am grateful to my robust support network in life. First, I want to thank my

parents, Janet Cushman and Bob Cushman, and my brother, Michael Cushman, for their

unconditional confidence in me, and for sharing the joy I’ve found in science. I am so

thankful to have gone through this process with the company and support of my partner,

David Sleboda. I also want to acknowledge Arden, our perfect and fluffy cat, who brings

me endless joy and comfort. Thanks to my Oak Ridge friends (especially Lauren Irby,

Whitney Irby, Sarah Ellen Johnston, Erin Luther, and Bernadette Riemer), my

Swarthmore friends (especially Mark Chin, Hilary Hamilton, Nolan Gear, Amelia

Possanza, and Johnny Taeschler), and my STRI friends (especially May Dixon, Jenalle

Eck, Sergio Estrada, Emily Francis, Dana Frederick, Katie Heineman, Peter Marting,

Pablo Ramos, Maria Del Carmen Ruiz, Carolina Sarmiento, Ummat Somjee, Erin Welsh,

and Camilo Zalamea) for keeping me in good spirits and in good company, even from far

away. Finally, thanks to my graduate student friends from Brown EEB (especially Nikole

Bonacorsi, Jess Boerma, Bianca Brown, Emily Hollenbeck, Lindsay McCulloch, Morgan

Moeglein, Cat Munro, Priya Nakka, Laura Nunes, and Brooke Osborne) who have

commiserated with me when I needed it—but more importantly helped me maintain a

fulfilling social life of reading, traveling, cooking, soccer, and watching The Bachelor.

xxii

TABLE OF CONTENTS

PREFACE .......................................................................................................................... ix

LIST OF TABLES ......................................................................................................... xxiii

LIST OF ILLUSTRATIONS ......................................................................................... xxiv

CHAPTER 1: Prediction of forest aboveground net primary production from high-

resolution vertical leaf-area profiles ................................................................................... 1

CHAPTER 2: Inverted relationship between leaf area index and forest aboveground net

productivity ....................................................................................................................... 57

CHAPTER 3: Canopy dynamics and detectability in a moderate tropical forest

blowdown: consequences for forest carbon balance ........................................................ 84

CHAPTER 4: Sensitivity of simulated GEDI waveforms to forest leaf area and

implications for footprint aboveground biomass models ............................................... 119

xxiii

LIST OF TABLES


resolution vertical leaf-area profiles

Table 1 ........................................................................................................................... 27


productivity

Table S1......................................................................................................................... 77


blowdown: consequences for forest carbon balance

Table 1 ......................................................................................................................... 107

Table S1....................................................................................................................... 112


implications for footprint aboveground biomass models

Table 1 ......................................................................................................................... 138

xxiv

LIST OF ILLUSTRATIONS

PREFACE

Fig. I ............................................................................................................................. xiii


resolution vertical leaf-area profiles

Fig. 1.............................................................................................................................. 28

Fig. 2.............................................................................................................................. 29

Fig. 3.............................................................................................................................. 31

Fig. S1 ........................................................................................................................... 38

Fig. S2 ........................................................................................................................... 39

Fig. S3 ........................................................................................................................... 41

Fig. S4 ........................................................................................................................... 43

Fig. S5 ........................................................................................................................... 45

Fig. S6 ........................................................................................................................... 47

Fig. S7 ........................................................................................................................... 49

Fig. S8 ........................................................................................................................... 50

Fig. S9 ........................................................................................................................... 52

Fig. S10 ......................................................................................................................... 53

Fig. S11 ......................................................................................................................... 54

xxv

Fig. S12 ......................................................................................................................... 55

Fig. S13 ......................................................................................................................... 56


productivity

Fig. 1.............................................................................................................................. 75

Fig. 2.............................................................................................................................. 76

Fig. S1 ........................................................................................................................... 78

Fig. S2 ........................................................................................................................... 79

Fig. S3 ........................................................................................................................... 80

Fig. S4 ........................................................................................................................... 81

Fig. S5 ........................................................................................................................... 82

Fig. S6 ........................................................................................................................... 83


blowdown: consequences for forest carbon balance

Fig. 1............................................................................................................................ 108

Fig. 2............................................................................................................................ 110

Fig. 3............................................................................................................................ 111

Fig. S1 ......................................................................................................................... 113

Fig. S2 ......................................................................................................................... 114

Fig. S3 ......................................................................................................................... 115

xxvi

Fig. S4 ......................................................................................................................... 116

Fig. S5 ......................................................................................................................... 117

Fig. S6 ......................................................................................................................... 118


implications for footprint aboveground biomass models

Fig. 1............................................................................................................................ 139

Fig. 2............................................................................................................................ 140

Fig. 3 ……………………………………………………………………………...... 141

Fig. 4............................................................................................................................ 142

Fig. 5............................................................................................................................ 143

Fig. 6............................................................................................................................ 144

Fig. 7............................................................................................................................ 145

1

CHAPTER 1:

Prediction of forest aboveground net primary production from high-resolution

vertical leaf-area profiles

K.C. Cushman1,2 and James R. Kellner1,2

1 Institute at Brown for Environment and Society, Brown University, 85 Waterman

Street, Providence, RI 02912

2 Department of Ecology and Evolutionary Biology, Brown University, 80 Waterman

Street, Providence RI, 02912

Modified from publication in Ecology Letters (2019), 22(3): 538-546.

2

Abstract

Temperature and precipitation explain about half the variation in aboveground net

primary production (ANPP) among tropical forest sites, but determinants of remaining

variation are poorly understood. Here we test the hypothesis that the amount of leaf area,

and its vertical arrangement, predicts ANPP when other variables are held constant.

Using measurements from airborne lidar in a lowland Neotropical rain forest, we quantify

vertical leaf-area profiles and develop models of ANPP driven by leaf area and other

measurements of forest structure. Vertical leaf-area profiles predict 39% of the variation

among plots. This number is 4.5 times greater than models using total leaf area

(disregarding vertical arrangement) and 2.1 times greater than models using canopy

height alone. Further, ANPP predictions from vertical leaf-area profiles were less biased

than alternate metrics. Variation in ANPP not attributable to temperature or precipitation

can be predicted by the vertical distribution of leaf area in this system.

Introduction

Analyses of aboveground net primary production (ANPP) in tropical forests have

focused on the importance of abiotic determinants of ANPP, including mean annual

precipitation and temperature (Vitousek 1984; Beer et al. 2010; Cleveland et al. 2011,

2015; Taylor et al. 2017). Together these variables explain up to half the variation in

ANPP among sites in global analyses of tropical field plots (Vitousek 1984; Schuur 2003;

Cleveland et al. 2011; Taylor et al. 2017). ANPP increases under warmer and wetter

conditions (Vitousek 1984; Cleveland et al. 2011), although wet environments can be

3

associated with lower ANPP under cooler temperatures (Schuur 2003; Taylor et al.

2017). The determinants of the remaining variation in ANPP are poorly understood. This

knowledge gap limits our ability to develop a biological understanding of carbon and

water fluxes at ecosystem and larger scales (Schimel et al. 2014; Clark et al. 2017).

Here we test the hypothesis that the amount of leaf area, and how it is arranged

vertically within canopies, can predict ANPP when precipitation and temperature are held

constant. There are three reasons why the vertical distribution of leaf area is likely to

predict ANPP. First, measurements of the net exchange of carbon dioxide (CO2) between

tropical forests and the atmosphere using eddy covariance indicate that the quantity of

absorbed photosynthetically-active radiation predicts net ecosystem exchange (Loescher

et al. 2003; Restrepo-Coupe et al. 2013), and that some forests are light limited (Saleska

et al. 2007, 2016, Morton et al. 2014, 2016; Guan et al. 2015; Wu et al. 2017a). Total

leaf area and how it is arranged spatially partly determines light absorption. Second, leaf

nutrient concentrations per unit area, area-based carbon fluxes, and leaf mass per unit

area (LMA) change predictably with height and respond more strongly to height than to

light environments in tropical forests (Meir et al. 2001; Domingues et al. 2005; Cavaleri

et al. 2008, 2010). This indicates that the efficiency with which absorbed light drives

photosynthesis may depend on the three-dimensional arrangement of leaves and suggests

that the vertical distribution of leaf area may be a stronger predictor of ANPP than total

leaf area without information about its vertical arrangement. Third, vertical distributions

of leaf area are also correlated with life-history variation and changes in forest

composition during succession (Westoby et al. 2002, Kellner et al. 2011; Stark et al.

2015, Becknell et al. 2018), when local disturbances such as tree-falls influence light

4

availability, soil nutrient availability, and tree growth rates (Vitousek & Denslow 1986;

Chandrashekara & Ramakrishnan 1994; Denslow et al. 1998; Feeley et al. 2007).

Although numerous studies have quantified the dependence between total leaf

area and productivity (Waring 1983; Kitayama & Aiba 2002; Doughty & Goulden 2008),

determining whether there is a relationship between the vertical arrangement of leaf area

and ANPP that is independent of precipitation and temperature has been much more

challenging. This is because until the availability of airborne light detection and ranging

(lidar) it has been exceedingly difficult to quantify vertical distributions of leaf area in

forests (Clark et al. 2008).

In this analysis, we use data from airborne lidar and stochastic radiative transfer

theory to quantify the relationship between vertical leaf-area profiles and ANPP in an

old-growth Neotropical rain forest landscape in the Atlantic lowlands of Costa Rica.

Lidar data have been used to generate digital terrain and surface elevation models

(Kellner et al. 2009a; Duncanson et al. 2010; Simard et al. 2011), and to quantify

distributions of aboveground structure and carbon density (Lefsky et al. 2002; Dubayah

et al. 2010; Asner et al. 2012; Baccini & Asner 2013; Detto et al. 2013; Taylor et al.

2015). These data can also be used to trace emitted laser pulses through the canopy

volume to compute the probability of intercepting leaf area at a given canopy depth, and

the vertical distribution of leaf area and light environments (Fig. 1) (Morsdorf et al. 2006;

Stark et al. 2012, 2015; Tang et al. 2012; Detto et al. 2015). We compared estimates of

wood and litter production to vertical leaf-area profiles and other measurements from

lidar data in 18 0.5 ha plots that experience the same precipitation and temperature (Clark

& Clark 2000). We compared the performance of models that predict ANPP using (i)

5

vertical leaf-area profiles, (ii) total leaf area lacking information about its vertical

arrangement, and (iii) the vertical distribution of point measurements from lidar,

expressed as discrete relative height (DRH) percentiles. Comparing the predictive power

of models using vertical leaf-area profiles (i) to those using total leaf area (ii) tests the

hypothesis that information in the vertical distribution of leaf area improves our ability to

predict ANPP in comparison to total leaf area without knowledge of its vertical

distribution. Comparing the performance of models using vertical leaf-area profiles (i) to

those using DRH percentiles (iii) tests the hypothesis that the vertical distribution of leaf

area, as opposed to the vertical distribution of point height measurements from lidar

(without considering uneven lidar sampling throughout the canopy) improves the

predictive power of canopy structure. We demonstrate that models driven by vertical

leaf-area profiles predict 2 – 5 times more variation in ANPP than models lacking either

vertical information or an ecologically-driven interpretation of lidar measurements.

Materials and methods

Study site

The study site is old-growth forest at La Selva Biological Station in Costa Rica

(10q 26c N, 83q 59c W). The site receives 4 m of rain annually and the mean annual

temperature is 26 C (Clark & Clark 2000). Although precipitation is less during the

January – April dry season, the mean monthly precipitation is > 100 mm in every month.

Ground elevation varies from 10-140 m with undulating topography. Within the old-

6

growth forest, the mean canopy height is 20.3 m ± 6.9 m SD (Kellner et al. 2009b). More

information about the study site is provided by McDade and Hartshorn (1994).

Lidar data collection

Lidar data were collected in September and October 2009 using the Optech

3100EA sensor, yielding an average point density of 3 returns/m2, a maximum of 2

returns per laser pulse (Neumann et al. 2012). Lidar data were projected relative to the

WGS 1984 ellipsoid. To compute height above ground, we used a digital terrain model

(DTM) developed by Kellner et al. (2009b). The accuracy of the DTM was demonstrated

using 4,184 ground-surveyed control points within old growth forest (Kellner et al.

2009a).

Ground-based ANPP estimates

We quantified ANPP using 18 0.5 ha plots that were randomly placed within old-

growth forest and stratified by three edaphic and topographic classes (the CARBONO

project; Clark & Clark 2000). The mean number of lidar returns in these plots was

17,770, ranging from 12,747 to 28,656. The three edaphic classes are flat alluvial terraces

of relatively high fertility, flat plateaus on relatively infertile Oxisols, and steep slopes on

relatively infertile Oxisols (Clark et al. 2013). Within each 0.5 ha plot, we quantified the

two major components of ANPP, which we analyze independently and in aggregate (total

ANPP = wood + litter production). Wood production was estimated from annual diameter

measurements of trees > 10 cm in diameter at breast height or above basal irregularities.

We quantified wood production using the change in estimated aboveground biomass of

living stems present at the beginning and end of the census interval (i.e. not incorporating

recruitment or mortality). To quantify aboveground biomass, we used Model 7 of Chave

7

et al. (2014). This model uses stem diameter and wood density, and incorporates a

regional diameter-height allometric model (E coefficient in Model 7 of Chave et al.

(2014), as tree height was not measured in the field). Species-level wood density was

used when possible. When species-level wood density was not known, we used the

genus, family, or site-level mean, in order of decreasing priority. We estimated litter

production using biweekly collections of leaf, reproductive, and twig litterfall in 9 traps

within each 0.5 ha plot (2.25 m2 per plot in total). Large leaves were collected from 9

ground-level traps while small leaves, twigs, and reproductive litter were collected from 9

standing traps 0.8 m above ground. We computed one value per plot for each ANPP

component (wood production, litter production, and total ANPP) by taking the mean of

annual estimates for the two years during and after the collection of lidar data (2008-2009

and 2009-2010). Detailed methods descriptions are publicly available (Clark & Clark

2017).

Calculation of leaf-area profiles from discrete-return lidar

We calculated vertical leaf-area profiles using discrete-return lidar data for each

plot using an algorithm derived from stochastic radiative transfer theory (Detto et al.

2015). This algorithm uses the height of each return, beam angle, and return number to

quantify absolute (not projected) leaf area (Box 1, Fig.1). For each plot, we quantified

total leaf area in 1-m vertical intervals from the top of the canopy (60 m) to 1 m in height.

The resulting vertical leaf-area profiles estimate leaf area density (m2 leaf area/m2

ground) in each 1-m vertical interval averaged over the 50 m × 100 m horizontal extent of

the plot. We assumed a spherical distribution of leaf angles (Detto et al. 2015). To

implement the leaf area algorithm, we translated the MATLAB function in Detto et al.

8

(2015) into R syntax. The average total leaf-area values estimated using this method

(3.59 ± 0.48 SE) are similar to independent estimates from hemispherical photography

published for the plots at the same time (3.76 ± 0.11 SE) (Loescher et al. 2003). There is

no correlation between the number of lidar returns in a plot and the total leaf area

estimated using this method (Pearson r = -0.23, P = 0.35).

Calculation of total leaf area and discrete relative height (DRH) percentiles

We also calculated total leaf area and DRH percentiles for each plot. We

estimated total leaf area in each plot by integrating all leaf-area values in the vertical leaf-

area profile. DRH percentiles are the height below which a given percentage of lidar

point measurements were recorded (i.e. 10% of the point measurements in a given area

are below the 10% DRH percentile height, and the 100% DRH percentile is the

maximum canopy height from all lidar returns). DRH percentiles are similar to relative

height metrics computed using waveform lidar (Drake et al. 2002; Anderson et al. 2008;

Dubayah et al. 2010), except that RH metrics are the cumulative height of waveform

energy and can therefore be negative. For each plot, we calculated 60 DRH percentiles in

even increments from 0% to 100% of lidar point measurements to ensure that the

resolution of DRH percentiles was equivalent to that of vertical leaf-area profiles.

Prediction of ANPP from canopy structure in plots

We predicted ANPP from vertical leaf-area profiles using a partial least squares

(PLS) regression model. PLS is similar to principal component analysis (PCA). Like

PCA, PLS models compute a linear transformation of input variables (here, vertical leaf-

area profiles) into new, orthogonal component variables. Unlike PCA, where input data

are transformed to maximize the variance of newly transformed component variables,

9

PLS iteratively transforms input variables to maximize the covariance of transformed

component variables and a response variable (Mevik & Wehrens 2007). We randomly

selected half of the 18 0.5 ha plots for model training and used the remaining half for

model validation, and we repeated the training and validation procedure 1,000 times.

Models were fit and predictions were derived using three PLS components. We used a

Deming regression to compare predicted and measured productivity in the validation

subset; we chose to use a Deming regression because it incorporates error in both

predicted and estimated productivity. In each of these 1,000 training and validation

analyses, plots were randomly chosen without respect to edaphic class. To test whether

representation of plot edaphic classes influenced results, we repeated this analysis

restricting the random sampling to ensure equal representation of each edaphic class in

training data and validation. Using independent training and validation subsets enables a

rigorous analysis of model performance, because it reduces the likelihood of overfitting.

Overfitting occurs when models perform well on training data but cannot be transferred

to data outside the training set.

For each random sample, we also predicted ANPP using the alternate structural

metrics of total leaf area and DRH percentiles. We used a linear model to predict ANPP

from total leaf area (ignoring the vertical distribution of leaf area), and we used PLS to

predict ANPP from DRH percentiles. We quantified the relationship between predicted

and observed values for each metric of canopy structure (vertical leaf-area profiles, total

leaf area, and DRH percentiles) and each component of ANPP (litter production, wood

production, and total ANPP). In a supplemental analysis, we compared the performance

of models using vertical leaf-area profiles to those driven by profiles of light

10

transmittance and absorption (Supporting Information (SI) 1). We tested for significant

differences in model performance between canopy-structure metrics using Kolmogorov-

Smirnov tests. We performed separate Kolmogorov-Smirnov tests for r2 values and

slopes by comparing the distributions 1,000 values for each canopy-structure metric

between predicted and observed productivity.

Results

Vertical leaf-area profile models predicted over a third of the variation in ANPP

among plots in this forest (Table 1, Fig. 2). The median r2 between observed ANPP and

predicted ANPP using vertical leaf-area profile models was 0.39 (median values and 95%

CIs are given in Table 1). The PLS model for litter production performed comparably,

with a median r2 of 0.40. The wood-production model had a lower median r2 of 0.03.

Models using vertical leaf-area profiles predicted significantly more variation in

ANPP than models using total leaf area in the absence of vertical information (Fig. 2, D =

0.552, P < 0.001) or models from DRH percentiles (D = 0.414, P < 0.001). Linear models

predicting productivity from total leaf area had a median r2 of 0.09 for ANPP using the

same 1,000 random samples, a median r2 of 0.15 for litter production, and a median r2 of

0.08 for wood production. PLS models predicting productivity from DRH percentiles had

a median r2 of 0.09 for ANPP, a median r2 of 0.15 for litter production, and a median r2 of

0.08 for wood production. When predicting ANPP from PLS models with only a single

component, vertical leaf-area profiles still significantly outperformed total leaf area

models (D = 0.317, P < 0.001) and DRH percentile models also using a single PLS

component (D = 0.435, P < 0.001) (Fig. S12).

11

The slope of the relationship between predicted and observed ANPP was

significantly closer to one when ANPP was predicted from vertical leaf-area profiles than

using models with total leaf area (D = 0.61, P < 0.001) or DRH percentiles (D = 0.191, P

< 0.001) (Table 1, Fig. 2). Using vertical leaf-area profiles, the median slope between

predicted and observed ANPP was 1.43. In contrast, the median slope using total leaf

area to predict ANPP was 10.15. Using DRH percentiles, the median slope was 1.89.

Loadings of the PLS components in models that predict ANPP from vertical leaf-

area profiles indicate that ANPP increases with leaf area between 10 and 20 m in height

and decreases with leaf area between 20 and 30 m in height. The model indicates that leaf

area at other heights has a negligible impact on ANPP (Fig. 3).

Using constrained random sampling to ensure equal representation of edaphic

classes did not significantly affect the amount of ANPP variation predicted by models

using vertical leaf-area profiles (median r2 was 0.39 in the original analysis and 0.37 in

the alternate analysis; P = 0.536; Figs. 1, S1), total leaf area (median r2 was 0.09 in the

original analysis and 0.08 in the alternate analysis, P = 0.121; Figs. 1, S1), or DRH

percentiles (median r2 was 0.18 in the original analysis and 0.16 in the alternate analysis;

P = 0.148; Figs. 1, S1).

Discussion

Vertical leaf-area profiles increased the power to predict ANPP by a factor of 2-5

compared to alternate canopy-structure metrics derived from airborne lidar. The

percentage of ANPP variation predicted by vertical leaf-area profiles in the Neotropical

rain forest examined here is comparable to the variation among sites explained by mean

12

annual precipitation and temperature in pan-tropical studies of forest plots (Vitousek

1984; Cleveland et al. 2011; Hofhansl et al. 2015; Taylor et al. 2017). Importantly, our

analysis predicts variation that is not caused by differences in precipitation or

temperature. These results highlight the importance of biotic factors in driving ANPP,

because leaf-area profiles are influenced by species composition (Asner et al. 2008) and

succession within a single landscape where precipitation and temperature are invariant

(Kellner et al. 2011; Becknell et al. 2018).

Soil fertility is also an important determinant of ANPP in tropical forests and can

explain 7-18% of ANPP variation not explained by mean annual precipitation and

temperature (Vitousek 1984; Cleveland et al. 2011; Hofhansl et al. 2015). However,

previous research indicates that differences in soil fertility are unlikely to be an important

source of the variation in ANPP in our analysis. The plots in this analysis are divided

among three soil and topography classes on Oxisol soils (relatively fertile floodplains,

relatively infertile upland plateaus, and slopes). There are differences in soil phosphorus

availability among plots, but even upland soils are fertile compared to more heavily-

weathered and typical tropical soils, as erosion maintains a supply of rock-derived

nutrients on upland soils (Vitousek & Denslow 1987; Porder et al. 2006). Taken together,

soils and topography explain 2% of the variation in ANPP among the plots in this study,

and the relationship is not statistically significant (one-way ANOVA: F = 1.13, P = 0.35).

Vertical leaf-area profiles are better predictors of litter production, which includes

leaves and small branches, than of wood production, which is defined as stems > 10 cm

diameter at breast height or above basal irregularities. Litter production in this landscape

is 1.5 times greater than wood production in terms of aboveground C (mean litter

13

production = 8.5 Mg C ha-1 yr-1, mean wood production = 5.5 Mg C ha-1 yr-1), and over

70% of litter production is leaf material (Clark et al. 2013). The strong relationship

between leaf-area profiles and ANPP observed in our data was driven by litter

production, as vertical leaf-area profile models predicted > 15 times more variation in

litter production than in wood production in our data (40.1% versus 2.6%). Leaf

production is a function of standing leaf area, how often leaves turn over (leaf lifespan),

and LMA. Given that LMA varies systematically with height in this landscape (Cavaleri

et al. 2010), and that LMA correlates with leaf lifespan in tropical trees (Reich et al.

1991; Santiago & Wright 2007), the result that vertical leaf-area profiles predict litter

production is not surprising.

However, the fact that only 2.6% of the variation in wood production can be

predicted by vertical leaf-area profiles stands in contrast to work in the Amazon (Stark et

al. 2012) and in a temperate mixed hardwood forest (Hardiman et al. 2011), which found

that vertical canopy structure explained 27% and 48%, respectively, of the variation in

wood production among plots. It is not possible to know with certainty why we do not

find a relationship between vertical leaf-area profiles and wood production. Here, we

highlight three differences in the studies that may, along with fundamental differences

among sites, contribute to the discrepancy between our findings and previous results.

First, the relationship between canopy structure and wood production may depend on plot

sizes and the duration of the study. Our study used 2-year wood production in 0.5 ha

plots. Stark et al. (2012) used 4-year wood production in 1 ha plots, and Hardiman et al.

(2011) used 10-year wood production in 0.08 ha plots. Second, the study of Hardiman et

al. (2011) was in a previously managed successional forest, where plot age varied from

14

55-88 years after harvesting and burning. Finally, our analysis used a statistical

framework (PLS regression) that included more fitted variables than the analyses of Stark

et al. (2012) and Hardiman et al. (2011). We employed a conservative cross-validation

framework to ensure that overfitting did not occur. While PLS models explain a median

of 33%, 74%, or 90% of the variation in wood production training data using 1, 2, or 3

components, respectively, the predictive power for independent wood production

validation data was low (2.6%).

Our findings identify relative contributions of leaf area to predicted ANPP as a

function of canopy height (Fig. 3). Leaf area between 20 and 30 m in height was

associated with lower total ANPP compared to leaf area between 10 and 20 m in height

(Fig. 3). The mean canopy height in this landscape is 20.3 m, with occasional emergent

trees that attain heights as large as 60 m (Kellner et al. 2009b; Thomas et al. 2013). Thus,

our results are consistent with decreasing ANPP in closed-canopy forest patches that are

in later stages of gap-phase regeneration (Chandrashekara & Ramakrishnan 1994), and

are consistent with associations between vertical leaf-area profiles and life-history

variation or successional status of tree species (Stark et al. 2015). To further test whether

decreasing productivity late in gap-phase regeneration is the mechanism responsible for

the observed relationship between productivity and vertical leaf-area profiles, we

compared ANPP to plant functional traits and gap-phase regeneration stage (SI 2).

Specifically, we chose wood density as a relevant functional trait because higher wood

density is expected in later-successional tree species with lower mortality rates, and

because wood density correlates with a number of other functional traits, including leaf

size and water potential (Chave et al. 2009). We quantified gap-phase regeneration stage

15

using annual canopy height measurements on a 5 x 5 m grid over each plot,

characterizing the 10-year trend in the proportion of the plot with low canopy height (<

15 m). The biomass-weighted mean plot wood density and 10-year trend in the frequency

of low-canopy sites predicted 10% and 39% of the variation in ANPP among plots,

respectively (Fig. S4). This is consistent with late-stage gap-phase regeneration as one

explanation for the observed relationship between vertical leaf-area profiles and ANPP.

The slope of the relationship between predicted and observed ANPP using

vertical leaf-area profiles was closer to 1 than for models based on total leaf area or DRH

percentiles (Fig. 2). Demonstrating that the model has minimal bias is important because

when the slope between predicted and observed values deviates greatly from one,

predictions systematically over or underestimate ANPP. Unbiased models using lidar-

derived vertical leaf-area profiles could be used to quantify ANPP for samples large

enough to characterize entire landscapes (Marvin et al. 2014), a task that is prohibitively

time-consuming using ground-based plots. Extending field-based predictions of ANPP

from small plots to larger areas is critically needed to benchmark Earth-system models,

because there is currently a mismatch in scale between Earth-system models, which are

designed to represent regions or plant functional types (Bonan et al. 2002), and the field

data used to inform them, which are collected within small plots that may not be

representative of these areas (Clark et al. 2017).

There are three caveats to our interpretation of the relationship between lidar-

derived vertical leaf-area profiles and ANPP. First, we assume that the contribution of

non-photosynthetic material to lidar returns is negligible. A separate study at the same

site found that leaves were responsible for 93% of reflected energy for a lidar instrument

16

of the same wavelength (1064 nm), while non-leaf plant material contributed only 7% of

reflected energy (Tang et al. 2012). Importantly, all other indirect methods of estimating

leaf area, including methods based on radiation extinction, hemispherical photographs, or

canopy analyzers, similarly cannot directly distinguish between leaf and non-leaf plant

material (Bréda 2003). Second, it is possible that other metrics correlated with vertical-

leaf area profiles underlie the relationship between vertical-leaf area profiles and

productivity. In particular, vertical profiles of light transmittance and absorption explain

variation in wood production (Stark et al. 2012; Stark et al. 2015); we found that these

metrics performed similarly (for total ANPP and wood production) or worse (for litter

production) than vertical leaf-area profiles, and that combining leaf-area and light profiles

did not further improve predictions (SI 1, Fig. S10). Our finding that leaf height is a

better predictor than leaf light environment is consistent with previous literature for this

site (Cavaleri et al. 2010). Third, we calculated vertical leaf-area profiles with lidar

height above ground, assuming that topographic relief does not greatly influence the

transmission of light through the canopy at the scale of our analysis. Our findings that

plot edaphic and topographic classes did not explain a significant amount of ANPP

variation, and that sampling with and without respect to edaphic and topographic classes

had a negligible effect on results (Figs. S1-S3), indicate that the relationship between

vertical leaf-area profiles and productivity is not qualitatively different in sloped and flat

plots.

Recent work has demonstrated that seasonal variation in aboveground

productivity is influenced by canopy structure (Morton et al. 2016; Cavaleri et al. 2017;

Wu et al. 2017b). Our analysis builds on this body of evidence by demonstrating that

17

vertical canopy structure is also predictive of spatial variation in ANPP. Here, vertical

leaf area profiles predict 39% of the variation in ANPP among plots in our analysis, a

quantity on-par with the predictive power of ANPP-climate relationships. Our analysis

suggests that this relationship between vertical canopy structure and ANPP is driven by

changes in productivity during gap-phase regeneration. Further characterizing the

relationship between forest structure and ANPP at a globally representative sample of

sites will permit investigation of regional drivers of ANPP using terrestrial and airborne

lidar, in addition to future spaceborne lidar missions (Dubayah et al. 2014).

Acknowledgements

We thank L. Albert, D. A. Clark, D. B. Clark, M. Detto, L. McCulloch, B.

Osborne, S. Porder, J. Winbourne, and two anonymous reviewers. The CARBONO

Project was supported by grants from the NSF, most recently DEB-0841872, DEB-

1357097. KCC was supported by an NSF Graduate Research Fellowship, the Brown

Presidential Fellowship, and the Institute at Brown for Environment and Society at

Brown University.

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Tables

Table 1. Predicting components of ANPP using vertical leaf-area profiles, total leaf area,

and discrete relative height (DRH) percentiles. ANPP = litter production + wood

production. Values for r2 and slope are from Deming regression models relating field

estimates of ANPP components to predicted ANPP components from 1,000 random

validation samples of 18 0.5 ha plots. Values in parentheses are the 95% range from the

1,000 random samples.

Structural metric Total ANPP Litter production Wood production r2 slope r2 slope r2 slope

Vertical leaf-area profiles

median 0.39 1.43 0.40 1.49 0.03 -0.11

95% CI (0.04,0.73) (0.36,7.98) (0.02,0.75) (0.2,6.41) (0.00,0.34) (-63.4,61.1)

Total leaf area

median 0.09 10.14 0.15 4.68 0.08 -5.84 95% CI (0.00,0.67) (-90.9,137) (0.00,0.77) (-36.7,49.4) (0.00,0.42) (-338,711)

DRH percentiles

median 0.18 1.88 0.13 1.40 0.04 -0.15 95% CI (0.00,0.59) (-11.0,20.9) (0.00,0.58) (-25.3,33.2) (0.00,0.40) (-34.7,35.4)

28

Figures

Fig. 1. Three-dimensional point cloud of airborne lidar data over one 0.5 ha plot (A), and

the associated vertical leaf-area profile (B) and DRH percentiles (C). Lidar data are

colored by height. The vertical leaf-area profile was calculated in 1 m vertical bins using

the algorithm of Detto et al. (2015), and total leaf area is the integral of the vertical

profile. DRH percentiles are the height below which a given percentage of lidar point

measurements were recorded.

29

Fig. 2. Relationships between observed and predicted ANPP using vertical leaf-area

profiles (A-C), total leaf area (D-F), and lidar discrete relative height (DRH) percentiles

(G-I). Histograms show distributions of r2 and slopes of Deming regression models

relating field estimates of ANPP to predicted ANPP for 1,000 random samples. Scatter

plots show data from all 1,000 samples, and the black line is the 1:1 relationship. On

30

histograms, green lines show the median (solid) and 95% confidence intervals (dashed).

Due to the high variance of values, x-axis limits for the Deming regression slope

histograms do not contain all values (C,F,I).

31

Fig. 3. Combined loading weight for the three PLS components of the model predicting

ANPP from vertical leaf-area profiles (A) and the average estimated leaf area for each

canopy height (B). PLS component weights are the loading multiplier for each leaf-area

layer to calculate the new component, and the combined loading weight was calculated

by adding the three component loading scores, scaled by the amount of variation

explained by each component. Loadings for each of the three PLS components are shown

individually in Fig. S13. Points show the mean and bars show 95% CI from 1,000

random samples (A) or the SD among 18 plots (B).

32

Box 1: Calculation of vertical leaf-area profiles from lidar data

We used the algorithm from Detto et al. (2015) to quantify vertical leaf-area

profiles from discrete-return lidar data. This model describes the amount of light I that

reaches depth z (where z = 0 is the top of the canopy) in direction Ω as a function of the

radiation at the top of the canopy in direction Ω, I0(Ω), minus the light intercepted before

height z:

𝐼(𝑧, Ω, 𝑘) = 𝐼0(Ω) − |𝜇(Ω)|−1 ∫ 𝑢(𝑧′)𝐺(𝑧′, Ω)𝑈(𝑧′, Ω, 𝑘)𝑑𝑧′𝑧0 (1)

where the light intercepted before height z is the product of total leaf area, u, the

projection of leaf area in direction Ω, G(Ω), and the light absorbed per leaf area in

direction Ω, U(Ω,k), integrated over all heights from the top of the canopy until z and

multiplied by the cosine of the angle in direction Ω, |µ(Ω)|-1. For multiple-return lidar

data, Equation (1) also depends on return number, k.

The leaf area algorithm from Detto et al. (2015) characterizes light penetration

through the canopy by assuming that lidar returns are numbered point samples along

paths of negligible cross-sectional area. Using the heights, return numbers, and incident

angles of lidar measurements, the probability that a lidar beam of a given incident angle,

θ, and return number, r, intercepts fewer than k leaves before a given canopy depth, z, is

calculated as:

𝐼𝑖,𝑠,𝑘𝐼0,𝑠

= 1 − 𝑃(𝑧<𝑧𝑖,𝜃=𝑠,𝑟=𝑘)𝑃(𝜃=𝑠,𝑟=1) = 1 −

∑ 𝑛𝑗,𝑠,𝑘𝑖𝑗=1

∑ 𝑛𝑗,𝑠,1𝑚𝑗=1

(2)

where m is the total canopy height and ni,s,k gives the number of kth lidar returns of

incident angle s in the ith discrete height interval. The discretized Equation (2) is used in a

33

numerical solution to Equation (1) that further accounts for the maximum number of

returns. For the complete numerical solution, see Detto et al. (2015).

SI 1. Predicting ANPP using light environment metrics.

Motivation and methods

Previous research found that vertical profiles of light transmittance and absorption

explain tree size distributions, wood production, and mortality in two Amazonian forests

(Stark et al. 2012, 2015). To address whether light environments are stronger predictors

of productivity than leaf area per se, we compared our results to vertical profiles of light

transmittance and absorption. We calculated light transmittance and light absorption for

each vertical layer following the approach described in Stark et al. (2012). That is, we

assumed that light transmittance (I) decreases from top-of-canopy irradiance (I0)

exponentially with leaf area (LAI):

𝐼 = 𝐼0𝑒−𝑘∙𝐿𝐴𝐼

For any discrete layer, i, of the canopy, the light transmittance through that layer, Ii, is

then given by:

𝐼𝑖 = 𝐼0𝑒−𝑘 ∑ 𝐿𝐴𝐼𝑗𝑁𝑗=𝑖

where N is the uppermost canopy layer, LAIj is the leaf area index (m2 m-2) of layer j, and

k is a constant describing how quickly light decreases with leaf area. We do not have site-

specific measurements for light at the bottom of the canopy at La Selva, so we used

k=0.88, a value reported for a tropical broadleaf forest in Paracou, French Guiana

(Cournac et al. 2002). We calculated light absorbance for each layer as the difference

34

between light transmitted from above to the top of the layer, and light transmitted to the

bottom of the layer. Light profiles were scaled (i.e. the value of I0 was chosen) such that

the total sum of all vertical leaf-area profile values equals the total sum of all vertical

light-transmittance profiles and all vertical light-absorption profiles.

Using vertical profiles of light transmittance and absorbance with the same vertical

resolution as our vertical leaf-area profiles, we performed four additional PLS analyses

(predicting litter, wood, and total ANPP in each analysis):

1. Using vertical light-transmittance profiles

2. Using vertical light-absorption profiles

3. Using combined vertical leaf-area and light-transmittance profiles

4. Using combined vertical leaf-area and light-absorption profiles

In the case of combined leaf-area and light profile analyses (analyses 3 and 4), vertical

leaf-area profiles and vertical light profiles were concatenated as a single vector and used

as input to the PLS model. For each of these four analyses, we predicted litter, wood, and

ANPP following the method of 1,000 random training and validation subsets used in the

original analysis.

Results and conclusions

We found that including models with light environment metrics do not

qualitatively outperform models with only vertical leaf-area profiles (Fig. S8). While

vertical leaf-area profiles and vertical light-absorption profiles performed equally for

predicting total ANPP (D = 0.06, P = 0.054), vertical leaf-area profiles predicted more

variation in litter production (median 40.2%) than vertical light-absorption profiles

(23.7%) or vertical light-transmission profiles (15.8%). This improvement in predictive

35

power for litter production was significant comparing vertical leaf-area profiles to

vertical light-absorption profiles (D = 0.30, P < 0.001) and vertical light-transmission

profiles (D = 0.46, P < 0.001). All models had similarly low predictive power for wood

production, predicting only 3-7% of variation.

Leaf area and light environments are related, but not redundant. In our study,

vertical profiles of leaf area and light absorption are correlated with a mean correlation

coefficient of 0.42 (min = 0.05, max = 0.69). Light absorbed by leaves, as opposed to leaf

area per se, is implicated in some mechanisms that we hypothesize are important for a

relationship between canopy structure and productivity: total light absorption, and

changes in leaf photosynthetic properties with height. However, the leaf trait of LMA,

which correlates with leaf lifespan in tropical trees (Reich et al. 1991; Santiago & Wright

2007), was found to vary more strongly with height than light in La Selva (Cavaleri et al.

2010). Leaf area, LMA, and leaf lifespan jointly determine leaf litter production.

SI 2. Predicting ANPP using alternate metrics reflecting gap-phase dynamics.


We performed two additional analyses to further explore whether decreasing ANPP

in later stages of gap-phase dynamics is the mechanism responsible for the observed

relationship between ANPP and vertical leaf-area profiles. These analyses were chosen to

test whether patterns of ANPP are predicted by: 1. species’ functional traits, or 2. past

canopy height dynamics. The methods and results are explained in greater detail below:

1. We calculated the biomass-weighted average wood density for each plot at the time

of lidar data collection. We chose wood density as a relevant functional trait

36

because higher wood density is expected in later successional tree species

with lower mortality rates, and because wood density correlates with a number

of other functional traits, including leaf size and water potential (Chave et al.

2009). We used plot-level wood density to predict ANPP using a Deming

regression model, and repeated for the same 1,000 random training and

validation subsets used in all other analyses.

2. We calculated the change in low-canopy area for each plot for the decade

preceding lidar data collection. From the year 2000, every CARBONO plot

has annual canopy height measurements on a 5 x 5 m grid over the entire plot.

For each measurement, the maximum canopy height (up to 15 m) was

measured from the ground (Kellner et al. 2009; Silva et al. 2013). For every

plot and year, we calculated the proportion of low-canopy area as the

proportion of grid cells with measured height below 15 m. Next, we fit a

linear model of low-canopy area as a function of year to estimate the decadal

trend in low-canopy area between 2000 and 2009 for each plot. We used this

plot-level change in low-canopy area to predict ANPP using a Deming

regression model, and repeated for the same 1,000 random training and

validation subsets used in all other analyses.


On average, biomass-weighted plot-level wood density predicted 5% of variation

in ANPP among plots (Fig. S7). On average, the plot-level change in low-canopy area

predicted 39% of variation in ANPP among plots (Fig. S7). This is consistent with

37

increasing canopy closure as one explanation for the observed relationship between

vertical leaf-area profiles and ANPP.

SI 3. Predicting ANPP using separate models for litter and wood production.


The relative contribution of leaf area to predicted litter production as a function of

height was similar to the pattern for ANPP, but the pattern for wood production was not

(Fig. S10, S11). We tested whether contrasting PLS loadings for litter and wood

production decreased our ability to predict ANPP, by predicting ANPP using independent

models for litter and wood production.


The performance of these models was nearly identical to the original analysis

(median variance predicted was 37.4% from independent models, and 38.4% in the

original analysis, Fig. S12).

38

Fig. S1. Relationships between observed and predicted ANPP using vertical leaf-area profiles

(A), total leaf area (B), or DRH percentiles (C), where ANPP predictions from vertical leaf-

area profiles and DRH percentiles were derived from PLS models with only one component (as

opposed to three components in the main text). Histograms show distributions of r2 and

slopes of Deming regression models fit to observed versus predicted ANPP for 1,000

random samples. On histograms, green lines show the median (solid) and 95%

confidence intervals (dashed).

39

Fig. S2. Relationships between observed and predicted ANPP using an alternate sampling

method, where the calibration subset for each of 1,000 bootstrapped samples includes an

equal number of plots from each soil edaphic class (results in the main text were

produced using calibration subsets chosen without respect to edaphic class). Results are

shown for observed and predicted ANPP using vertical leaf-area profiles (top row), total

leaf area (middle row), and discrete relative height (DRH) profiles (bottom row).

40

Histograms show distributions of r2 and slopes of Deming regression models fit to

observed versus predicted ANPP for 1,000 random samples. Scatter plots show data from

all 1,000 samples, and the black line is the 1:1 line. On histograms, green lines show the

median (solid) and 95% confidence intervals (dashed). Due to the high variance of

values, x-axis limits for the Deming regression slope histograms do not contain all

values.

41

Fig. S3. Relationships between observed and predicted litter productivity using vertical

leaf-area profiles (top row), total leaf area (middle row), and discrete relative height

(DRH) percentiles (bottom row). Histograms show distributions of r2 and slopes of

Deming regression models fit to observed versus predicted ANPP for 1,000 random

42

samples. Scatter plots show data from all 1,000 samples, and the black line is the 1:1 line.

On histograms, green lines show the median (solid) and 95% confidence intervals

(dashed). Due to the high variance of values, x-axis limits for the Deming regression

slope histograms do not contain all values.

43

Fig. S4. Relationships between observed and predicted litter productivity using an

alternate sampling method, where the calibration subset for each of 1,000 bootstrapped

samples includes an equal number of plots from each soil edaphic class (results in the

main text were produced using calibration subsets chosen without respect to edaphic

class). Results are shown for observed and predicted litter productivity using vertical


44

(DRH) profiles (bottom row). Histograms show distributions of r2 and slopes of Deming

regression models fit to observed versus predicted ANPP for 1,000 random samples.

Scatter plots show data from all 1,000 samples, and the black line is the 1:1 line. On



histograms do not contain all values.

45

Fig. S5. Relationships between observed and predicted wood productivity using vertical


(DRH) percentiles (bottom row). Histograms show distributions of r2 and slopes of

Deming regression models fit to observed versus predicted ANPP for 1,000 random

samples. Scatter plots show data from all 1,000 samples, and the black line is the 1:1 line.

46

On histograms, green lines show the median (solid) and 95% confidence intervals

(dashed). Due to the high variance of values, x-axis limits for the Deming regression

slope histograms do not contain all values.

47

Fig. S6. Relationships between observed and predicted wood productivity using an

alternate sampling method, where the calibration subset for each of 1,000 bootstrapped

samples includes an equal number of plots from each soil edaphic class (results in the

main text were produced using calibration subsets chosen without respect to edaphic

class). Results are shown for observed and predicted wood productivity using vertical


48

(DRH) profiles (bottom row). Histograms show distributions of r2 and slopes of Deming

regression models fit to observed versus predicted ANPP for 1,000 random samples.

Scatter plots show data from all 1,000 samples, and the black line is the 1:1 line. On



histograms do not contain all values.

49

Fig. S7. Relationships between observed and predicted ANPP using the biomass-

weighted wood density of each plot (A), or using the change in gap frequency between

2000 and 2009 (B). Details of these alternate analyses are provided above in SI 1.

Histograms show distributions of r2 and slopes of Deming regression models fit to

observed versus predicted ANPP for 1,000 random samples. Green vertical lines show

the median (solid) and 95% confidence intervals (dashed).

50

Fig. S8. Relationships between observed and predicted ANPP using vertical leaf-area profiles

(A-C), vertical light-transmittance profiles (D-F), vertical light-absorption profiles (G-I),

combined vertical leaf-area and light-transmittance profiles (J-L), or combined vertical

leaf-area and light-absorption profiles (M-O). Histograms show distributions of r2 and

51

slopes of Deming regression models fit to observed versus predicted ANPP for 1,000

random samples. On histograms, green lines show the median (solid) and 95%


52

Fig. S9. Component loading scores for the three PLS components of the model predicting

ANPP from vertical leaf-area profiles. Points show the mean and bars show 95% CI from

1,000 random samples. Each plots shows the loading multiplier for each leaf-area layer to

calculate the new component.

53

Fig. S10. Component loading scores for the three PLS components of the model

predicting litter productivity from vertical leaf-area profiles. Points show the mean and

bars show 95% CI from 1,000 random samples. Each plot shows the loading multiplier

for each leaf area vertical layer to calculate the new component. The total weight was

calculated by adding the three component loading scores times the amount of variation

explained by each component.

54

Fig. S11. Component loading scores for the three PLS components of the model

predicting wood productivity from vertical leaf-area profiles. Points show the mean and

bars show 95% CI from 1,000 random samples. Each plot shows the loading multiplier

for each leaf area vertical layer to calculate the new component. The total weight was

calculated by adding the three component loading scores times the amount of variation

explained by each component.

55

Fig. S12. Relationships between observed and predicted ANPP using separate PLS

models to predict wood and litter production contributions to ANPP. Histograms show

distributions of r2 and slopes of Deming regression models fit to observed versus

predicted ANPP for 1,000 random samples. Scatter plots show data from all 1,000

samples, and the black line is the 1:1 line. Green lines show the median (solid) and 95%


56

Fig. S13. The relationship between wood production and litter production (left) and the

relationship between total aboveground net primary production (ANPP, wood + litter

production) and total leaf area (right) for 18 0.5 ha CARBONO plots at La Selva

Biological Reserve in 2009. Points are colored by the soil edaphic class of each plot. The

Pearson correlation coefficient (r) is shown for each relationship.

57

CHAPTER 2:

Inverted relationship between leaf area index and forest aboveground net

productivity

K.C. Cushman1,2 and James R. Kellner1,2





Modified from submission to Nature Ecology and Evolution (in review, 2019).

58

Abstract

Aboveground net primary production (ANPP) is a critical component of carbon

fluxes between the land surface and the atmosphere (Pan et al. 2011; Clark et al. 2017).

There is a global positive relationship between leaf area index (LAI) and ANPP that

encompasses regions across large gradients in temperature and precipitation (Asner et al.

2003). This relationship is driven by the quantity of absorbed photosynthetically active

radiation (APAR). However, it is unclear whether mechanisms driving the global trend

are important within regions, where temperature and precipitation are less variable. Here

we quantify the relationship between LAI and ANPP in a lowland Neotropical rain forest.

We show that the relationship between LAI and ANPP is nearly identical in magnitude

but opposite in sign to the global relationship. Inversion of the relationship between LAI

and ANPP in the absence of gradients in temperature and precipitation is likely to be

driven by carbon use efficiency. Locations with high LAI experience elevated foliar

respiration because they support more leaf area and also because the rate of respiration

per unit leaf area is greater (Cavaleri et al. 2008). Our analysis establishes a link between

vertical distributions of leaf area and ANPP likely due to autotrophic respiratory costs.

Main text

Leaf area index (LAI) is an important constraint on coupled land-atmosphere

carbon-cycle models (Clark et al. 2017). This quantity, the one sided leaf area per unit

ground area, controls APAR that drives photochemical energy conversion (Clark et al.

2017). In global analyses that encompass large gradients in climate the relationship

between LAI and aboveground net primary production (ANPP) is positive (Asner et al.

59

2003). However, ANPP is determined not only by light absorption, but also by the

efficiency with which carbon and light are converted into aboveground plant tissue

(Chambers et al. 2004).

Regional relationships may differ from the global trend because mechanisms that

drive relationships between LAI and ANPP are scale dependent. Regions of the land

surface with low LAI are dry deserts, grasslands and savannas, and regions with high LAI

are wet needle leaf and broadleaf forests (Asner et al. 2003). Across broad gradients in

climate, differences in temperature and precipitation drive gradients in LAI and ANPP.

Within regions, plant functional types and biomes occupy subset of climates that drive

the global positive relationship between LAI and ANPP. When there is no variation in

climate, the relationship between LAI and ANPP must be driven by other mechanisms.

Here we examine the relationship between LAI and ANPP in an evergreen

broadleaf forest in the Atlantic lowlands of Costa Rica using vertical leaf-area profiles

from airborne lidar (Cushman & Kellner 2019). By focusing our investigation on a single

landscape, we eliminate spatial variation in temperature and precipitation, isolating the

importance of other mechanisms.

LAI explains more than a third of the variation in ANPP in this forest (Fig. 1a; r2

= 0.37, DF = 1537, P < 0.001). This relationship is nearly identical in magnitude, but

opposite in direction, to the reported global relationship between ANPP and LAI (Fig. 1b;

r2 = 0.33, DF = 706, P < 0.001). The ranges in LAI and ANPP in our study encompass

21% and 53% of the global ranges in reported LAI and ANPP, respectively (Fig. 1b). Our

data show that locations with both high and low LAI can each be associated with a range

60

in ANPP (Fig. S1). The negative relationship between ANPP and LAI remains when 35

ANPP values outside the range of calibration data are excluded from the analysis (r2 =

0.33, DF = 1502, P < 0.001).

One mechanism that explains the inverted relationship between LAI and ANPP is

the impact of leaf respiration on carbon use efficiency. There is a positive relationship

between LAI and canopy height (Fig. 2; r2 = 0.52, DF = 1537, P < 0.001). In stands with

high LAI, most light absorption occurs at the top of the canopy and leaf layers beneath

the canopy are shaded. In contrast, low LAI environments are short canopies with more

even illumination of leaf layers. This pattern reduces carbon use efficiency in high LAI

stands for two reasons. First, shaded leaves incur a respiratory cost that is not

compensated by light absorption. Second, independent of the light environment, taller

leaves experience larger leaf mass per unit area and a larger respiratory burden than

shorter leaves do (Cavaleri et al. 2008, 2010). This elevated respiratory burden for taller

leaves is not offset by higher photosynthetic capacity (Cavaleri et al. 2008). Taken

together, this indicates that high LAI stands experience elevated foliar respiration not

only because they support more leaf area, but also because the rate of respiration per unit

leaf area is greater. Foliar respiration is the largest single component of total autotrophic

respiration in this forest (Cavaleri et al. 2008). Previous work indicates that nighttime

respiration determines interannual variation in ANPP (Clark et al. 2003, 2013).

Our results show how carbon use efficiency can mediate the relationships

between canopy height, LAI, and ANPP. Because the size frequency and spatial

properties of disturbance events in forests can influence mean canopy height, our analysis

61

predicts a relationship between the frequency of disturbance and regional carbon use

efficiency.

This analysis shows that processes driving the relationship between LAI and

ANPP are scale-dependent. The global positive relationship is driven by gradients in

resource availability and APAR (Asner et al. 2003). When temperature and precipitation

are held constant, the relationship is inverted and driven by carbon use efficiency. The

relationship that emerges at intermediate scales is influenced by both APAR and carbon

use efficiency. We disaggregated the global relationship between LAI and ANPP within

evergreen broadleaf tropical forests(Scurlock et al. 2001). The relationship was not

significantly different from zero (r2 = 0.00, DF = 36, P = 0.75), an intermediate pattern

between the globally positive relationship and the locally negative one (Fig. 1b).

Our understanding of carbon use efficiency has important implications for

quantifying ANPP using global land remote sensing. Radiation-based estimates of net

primary production are estimated using the light use efficiency equation (Cleveland et al.

2015):

𝑁𝑃𝑃 = 𝐴𝑃𝐴𝑅 × 𝐿𝑈𝐸 − 𝑅𝐴 (Eq.1)

where LUE is light-use efficiency and RA is autotrophic respiration. APAR is derived

from measurements of satellite vegetation indices, and LUE is assumed to be constant

within biomes (Cleveland et al. 2015). RA determines carbon-use efficiency and is

currently assumed to be constant, or dependent on temperature and total LAI (Gifford

2003; Zhao & Running 2010; Cleveland et al. 2015). Because RA depends on LAI and

canopy height, our analysis indicates that three dimensional variation in LAI determines

62

carbon use efficiency and ANPP. Other recent research indicates that three dimensional

forest structure is also important for seasonal and interannual variation of tropical forest

productivity (Morton et al. 2014; Tang & Dubayah 2017; Smith et al. 2019). New high-

resolution measurements of vertical forest structure from space are producing vertical

leaf-area profiles throughout the world’s temperate and tropical forests (Dubayah et al. in

review). The analysis presented here demonstrates the potential for structural

measurements to improve not only satellite-based estimates of carbon stocks (Fan et al.

2019), but also radiation-based estimates of forest productivity.


Study site

The landscape-scale study included all 770 ha of old-growth forest at La Selva

Biological Station, Costa Rica. La Selva is located in the Atlantic lowland forest (10°260

N, 83°590 W) and has mean annual temperature of 26 C and mean annual precipitation of

4 m, with no pronounced dry season (McDade & Hartshorn 1994).

Lidar collection

We quantified vertical leaf-area profiles using discrete-return lidar data collected

in September and October 2009. Data were collected over the entire extent of old-growth

forest using an Optech 3100EA sensor (Neumann et al. 2012). Average lidar density was

3 returns/m2 with up to 2 returns per laser pulse. Lidar data were projected in UTM 16N,

WGS 1984 ellipsoidal format. For each lidar return, we converted absolute elevation to

63

height above ground height using a digital terrain model (DTM). A validation of this

DTM using 4,184 independent measurements within old-growth forest demonstrated a

strong and statistically significant linear relationship (intercept = -1.406, slope = 0.999, r2

= 0.994, RMSE = 1.85 m) (Kellner et al. 2009a).

ANPP measurements from forest plots

ANPP was estimated using data collected by Clark et al. from 18 0.5 plots located

throughout the old-growth forest at La Selva (the CARBONO project (Clark et al. 2013))

as in Cushman and Kellner (2019), taking the mean of two annual intervals during and

after the collection of lidar data. Plot locations were chosen to represent three edaphic

and topographic classes – relatively fertile flat alluvial terraces, relatively infertile flat

plateaus, and relatively infertile steep slopes (Clark & Clark 2000). ANPP was estimated

from litterfall and wood production, the largest components of ANPP in this forest (Clark

et al. 2013). Leaf, reproductive, and twig litterfall were collected using litterfall traps

collected every other week. Each plot contains 9 0.25 m2 litterfall traps at ground level

(large leaves) or 0.8 m above ground (small leaves, twigs, and reproductive litter). Wood

production was estimated from annual diameter growth at 1.3 m height of all stems

greater than 10 cm in diameter present at the beginning and end of the census interval.

We used an allometric model to estimate woody biomass from diameter measurements

(Model 7 in Chave et al. 2014), incorporating a regional diameter-height relationship and

wood density. Species-level mean wood density was used when known, but genus,

family, or site-level mean wood density values were used as needed to calculate a value

64

for each stem. Data and detailed methods for plot measurements are publicly available

(Clark & Clark 2019).

Landscape-scale ANPP, LAI, and ACD estimation

We used lidar-derived models to estimate LAI, ANPP, and ACD over the entire

extent of old-growth forest at La Selva. We divided the landscape into 0.5 ha square

pixels, with pixel area chosen to match plot-based ANPP estimates (50 × 100 m). We

quantified vertical leaf-area profiles using the algorithm described in Detto et al. (2015),

which treats lidar pulses as indicators of leaf presence or absence in discrete vertical

layers (Fig. S2). This algorithm is based on radiative transfer theory (Titov 1990;

Shabanov et al. 2000), and estimates LAI for each vertical layer based on the proportion

of lidar beams that intercept leaves. The method of Detto et al. incorporates a leaf angle

distribution function that, along with lidar pulse angle, allows estimation of total leaf

area, as opposed to projected leaf area (2015). This method also incorporates information

from multiple returns per lidar beam. We estimated LAI in 1-m vertical bins from 1 m to

60 m height, assuming a spherical leaf angle distribution (Detto et al. 2015). Total LAI

was calculated as the sum of leaf area across all vertical layers. For the 18 0.5 ha field

plots, this procedure yields a similar mean LAI (3.65 ± 0.11 SE) to published mean LAI

from hemispherical photography (3.76 ± 0.11 SE) from the same time (Loescher et al.

2003). As this method does not differentiate between lidar returns from leaf material

versus other plant material (e.g. wood), this quantity is sometimes referred to as “plant

area index”, or PAI, instead of LAI. However, non-leaf material is estimated to contribute

only 7% of reflected light at the wavelength of our lidar sensor in this landscape(Tang et

65

al. 2012), so we use the term LAI for consistency with other indirect measurements of

leaf area including hemispherical photographs and canopy analyzers (Bréda 2003).

For each 0.5 ha pixel, we predicted ANPP from vertical leaf-area profiles using

the partial least squares (PLS) model developed in Cushman and Kellner (2019), which

predicted 39% of ANPP variation among the 18 0.5 ha field plots at La Selva. This model

predicted more ANPP variation and was less biased than a model using total LAI

(without information about its vertical distribution) or a model using lidar height alone.

As in Cushman and Kellner (2019), we generated 1,000 separate PLS models using

random samples of 9 field plots at a time for model parametrization; the predicted ANPP

for each pixel is the median prediction from these 1,000 models.

We also calculated aboveground carbon density (ACD) for each pixel using a

model relating top-of-canopy height (TCH) to ACD parameterized using field plot data.

Top-of-canopy height was calculated using a canopy surface grid (2 m × 2 m grid cells)

of maximum lidar return heights; top-of-canopy height was taken as the average height of

all surface grid cells. For each 0.5 ha field plot, we calculated ACD using an allometric

model to estimate aboveground biomass for all stems greater than 10 cm in diameter, as

described above. We parameterized the local model relating TCH to ACD using a power

relationship (Asner & Mascaro 2014):

𝐴𝐶𝐷 = 𝑎𝑇𝐶𝐻𝑏 (Eq.2)

where a and b are parameters fit using non-linear maximum likelihood analysis. This

model explains 73% of variation among 18 field plots and the residuals of this model

showed no heteroscedasticity, so we did not fit an additional geometric error term as in

66

Asner and Mascaro (2014). We applied this model to lidar-derived TCH to estimate ACD

over the entire landscape.

Relationship of topography to ANPP, leaf area, and ACD variation

We evaluated whether topographic metrics (slope, elevation, catchment area, and

aspect), explain variation in LAI, ANPP, or ACD over the landscape. For topographic

metrics, we chose to include slope and elevation because they have been found to predict

aboveground biomass in tropical forests (Mascaro et al. 2011; Taylor et al. 2015). We

also included catchment area because drainage networks have been found to predict

canopy height in tropical forests (Detto et al. 2013) and erosion is important for

supplying nutrients to the soil at La Selva (Porder et al. 2006). Together, elevation and

catchment size correspond to soil types in the old-growth forest at La Selva (Clark et al.

1998) (Fig. S4). Aspect was considered because it relates to solar radiation and was found

to predict tree stature in forests (Marshall et al. 2012). Topographic analyses were

performed using the DTM described above with the ‘raster’ package in R (Hijmans

2018).

We used a Random Forest machine learning algorithm to quantify the relationship

between topographic metrics and ANPP, LAI, and ACD (Breiman 2001). Random Forest

creates a large ensemble of decision trees using random samples from training data, and

predicts the response variable (in this case ANPP, LAI, or ACD) for new data using an

average of all regression trees. We chose a Random Forest model because Random Forest

models are well-suited for ecological problems with interactions among predictor

variables (Fig. S5), incorporating both categorical and continuous predictors (Prasad et

67

al. 2006; Cutler et al. 2007). Random Forest models calculate the relative importance of

predictor variables by omitting one predictor and quantifying the loss of model predictive

power (Liaw & Wiener 2002). There are two user-defined variables that affect Random

Forest algorithm performance, mtry (the number of predictor variables considered at each

node) and ntree (the number of regression trees to build). We considered all combinations

of mtry = 1 – 3 and ntree = 500 – 1500 (in increments of 100) and selected the values of

mtry and ntree that had the highest predictive power. The ranges of mtry and ntree were

chosen to include the default values recommended by Brieman (2001), and values used

are reported Table S1. We quantified the predictive power of each Random Forest model

using a stratified 10-fold cross-validation procedure (Kohavi 1995). That is, all data were

divided into 10 mutually-exclusive subsets with approximately equal size; subsets were

assigned randomly with the constraint that each subset had the same number of samples

in the 0-10th, 10-20th, 20-30th (and so on) percentiles for ANPP. Separate Random Forest

models were fit omitting one subset at a time, then we fit a linear model to lidar-derived

vs. Random Forest-predicted values to evaluate predictive power; we report the mean

predictive power for the 10 subsets. We tested for significant differences in predictive

power between Random Forest models for ANPP, total leaf area, and ACD using

Kolmogorov-Smirnov tests. The random forest model was implemented in R using the

‘randomForest’ package (R Development Core Team 2011; Liaw & Wiener 2002). To

evaluate the sensitivity of our results to extrapolated ANPP and ACD values (i.e.,

predicted values outside the range of field plots used to parameterize models), we

repeated the Random Forest analyses using only data with ANPP and ACD values within

the range of field plots (Fig. S6; Table S1).

68

Global-scale analysis

We used a global database of LAI and ANPP observations to test whether the

relationship between leaf area and ANPP is different for individual forested biomes

compared to the global trend (Scurlock et al. 2001). We quantified the global trend using

all observations with LAI less than 12 m2 m-2 and total ANPP less than 4,000 g m-2 yr-1,

resulting in 708 observations from 177 sites and 17 biome cover classes. LAI and ANPP

thresholds were chosen to be consistent with previous analyses published alongside this

dataset, which removed inflated LAI measurements from methodological differences and

unrealistically large ANPP values (Scurlock et al. 2001; Asner et al. 2003). We used a

linear regression model to compare LAI with ANPP; unlike Asner et al. (2003) and

Scurlock et al. (2001) we did not force the intercept of this model to be zero.

Additionally, we evaluated whether the tropical evergreen broadleaf biome had a

different linear relationship between leaf area and ANPP than the global average (38

observations).

Data availability

All field plot data and detailed field plot methods are currently publicly available

(https://datadryad.org/resource/doi:10.5061/dryad.1v72g43, www.ots.ac.cr/carbonoproject).

Landscape-scale data products used in this analysis will be made publicly available upon

publication through https://github.com/kccushman/LandscapeLeafAreaProfiles. Global LAI

data are publicly available through the Oak Ridge National Laboratory Distributed Active

Archive Center for biogeochemical dynamics (Scurlock et al. 2001).

https://datadryad.org/resource/doi:10.5061/dryad.1v72g43

http://www.ots.ac.cr/carbonoproject

https://github.com/kccushman/LandscapeLeafAreaProfiles

69

Code availability

R code (version 3.5.1(R Development Core Team 2011)) used in this analysis will

be made publicly available upon publication through

https://github.com/kccushman/LandscapeLeafAreaProfiles.

Acknowledgments

We thank S. Saatchi for providing access to lidar data. The CARBONO Project

was supported by grants from the NSF, most recently DEB-0841872, DEB-1357097.

KCC was supported by an NSF Graduate Research Fellowship, the Brown Presidential

Fellowship, and the Institute at Brown for Environment and Society at Brown University.

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Figures

Fig. 1. Relationships between ANPP and LAI across 1539 0.5 ha samples of old-growth

forest at La Selva, Costa Rica (a), and predicted values from La Selva compared to global

variation in ANPP and LAI from a global database(Scurlock et al. 2001) (b). Field plots

used to parameterize the partial least squares model to predict ANPP from vertical leaf-

area profiles are shown in black. Samples where either ANPP or ACD values were

outside the range of field plots (i.e. extrapolated, 35 samples) are denoted in a different

color. The relationship between ANPP and LAI was significant within the landscape of

La Selva (r2 = 0.37, DF = 1537, P < 0.001, slope = -102.9 ± 3.4 SE, intercept = 1734.6 ±

13.1 SE) and in the global database (r2 = 0.33, DF = 706, P < 0.001, slope = 133.7 ± 7.1

SE, intercept = 291.0 ± 36.9 SE), but not within tropical evergreen forest data in the

global database (r2 = 0.00, DF = 36, P = 0.75, slope = 14.4 ± 44.5 SE, intercept = 1089.3

± 226.9 SE).

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Fig. 2. Relationship between lidar-derived top-of-canopy height and LAI across 1539 0.5

ha samples of old-growth forest at La Selva, Costa Rica. A linear relationship between

top-of-canopy height and LAI is shown in red and explains more than half the variation

in total leaf area (r2 = 0.52, DF = 1537, P < 0.001).

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Supporting information

Table S1. Random Forest analysis parameters and results. Parameters were chosen to

have the highest predictive power for 10 stratified cross-validation subsets. Predictive

power is quantified as the R2 and p value for a linear model related predicted versus

observed values for each of the 10 cross-validation subsets. The relative importance for

each topographic metric (catchment area, slope, aspect, and elevation) was scaled such

that the sum of relative importance values equals the total percent of variation explained

by the model; the mean and standard deviation of importance values across all 10 cross-

validation subsets is reported.

Metric Include

extrapolated

values?

Random Forest

parameters

Model predictive power R2 p

ntree mtry mean SD mean SD ANPP Yes 1400 1 0.091 0.040 0.001 0.001

ACD Yes 1200 1 0.125 0.065 0.000 0.000

LAI Yes 1400 1 0.103 0.046 0.001 0.002

ANPP No 1200 1 0.093 0.052 0.005 0.011

ACD No 1000 1 0.137 0.085 0.006 0.017

LAI No 1400 1 0.090 0.040 0.008 0.023

Metric Include

extrapolated

values?

Relative Importance Catchment area Slope Aspect Elevation

mean SD mean SD mean SD mean SD ANPP Yes 2.556 0.048 2.136 0.028 2.140 0.026 2.257 0.028

ACD Yes 3.376 0.059 3.173 0.033 2.845 0.038 3.138 0.044

LAI Yes 2.747 0.045 2.582 0.029 2.555 0.034 2.463 0.041

ANPP No 2.581 0.049 2.184 0.020 2.187 0.023 2.311 0.021

ACD No 3.691 0.075 3.453 0.055 3.076 0.043 3.440 0.049

LAI No 2.401 0.048 2.231 0.034 2.192 0.041 2.174 0.044

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Fig. S1. Examples of 4 observed vertical leaf-area profiles from 0.5 ha samples of old-

growth forest at La Selva, Costa Rica. Examples were chosen to have similar low or high

ANPP values (left and right columns, respectively) and to have similar low or high LAI

values (top and bottom rows, respectively).

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Fig. S2. High-resolution lidar point cloud (a) and the corresponding vertical leaf-area

profile (b). For visualization, the point cloud depicts a smaller area (22 m diameter) than

the plots analyzed in this study, and with ~ 100 times higher point density.

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Fig. S3. Power relationship between top-of-canopy height (TCH) and aboveground

carbon density (ACD) in 18 0.5 ha field plots at La Selva, Costa Rica, used to

parameterize the landscape scale lidar-derived ACD model. The power relationship

𝐴𝐶𝐷 = 𝑎𝑇𝐶𝐻𝑏, with a = 7.596 and b = 1.053 fit using non-linear maximum likelihood

analysis, explained 73% of the variation in field plot data (a) and the absolute value of

model residuals showed no significant linear relationship with plot measured ACD (b; r2

= 0.00, DF = 16, P = 0.59).

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Fig. S4. Correspondence between topography (a-c) and soil type (d) across 1539 0.5 ha

samples of old-growth forest at La Selva, Costa Rica. Note the soils are unclassified for

the southernmost corner of the reserve.

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Fig. S5. Pearson correlation coefficients among ANPP, LAI, ACD, and topographic

metrics (catchment area, slope, aspect, and elevation). Correlations were calculated using

1539 0.5 ha samples of old-growth forest at La Selva, Costa Rica. Significant correlations

are colored according to the correlation coefficient, while all insignificant correlations

(using a Bonferroni-corrected significance threshold of P < 0.05/21, for 21 separate

comparisons of 7 metrics) are white.

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Fig. S6. Performance of Random Forest models for predicting variation in ANPP (a,d),

ACD (b,e), and LAI (c,f) across old-growth forest at La Selva, Costa Rica. Analyses were

performed with (a-c, 1539 0.5 ha samples) and without (d-f, 1510 0.5 ha samples)

including 30 samples with extrapolated values for either ANPP or ACD. Bars heights

indicate the mean predictive power of Random Forest models for 10 stratified cross-

validation subsets, where the height of all bars sums to the total predictive power and the

height for each metric (elevation, catchment area, slope, or aspect) indicates the relative

importance of that metric. Error bars show ± 1 standard deviation in the relative

importance of each metric across the 10 cross-validation subsets.

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CHAPTER 3:

Canopy dynamics and detectability in a moderate tropical forest blowdown:

consequences for forest carbon balance

K. C. Cushman1,2, John T. Burley1,2, Benedikt Imbach3, Orlando Vargas4,

Carlo Zgraggen3, and James R. Kellner1,2





3 Aeroscout GmbH, Hochdorf, Switzerland

4 Organization for Tropical Studies, La Selva Biological Station, San Pedro, Costa Rica

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Abstract

Blowdowns—downdrafts associated with convective storms—can cause

widespread tree damage and mortality in tropical forests. However, the extent to which

blowdown disturbances and subsequent recovery govern tropical forest carbon dynamics

remains uncertain. Here, we characterize the effect of a blowdown event that struck the

well-studied forest at La Selva Biological Station, an unprecedented disturbance in the

50-year history of La Selva. This blowdown event was undetectable using common

methods to detect blowdowns based on satellite data. However, using a multi-decadal

record of forest structure from lidar data, we can show that the blowdown decreased

aboveground biomass density (AGBD), increased gap area, and caused a departure from

previous canopy dynamics. Consequently, we conclude that previous studies likely miss

moderate, but still consequential, blowdown events, underestimating the importance of

blowdowns for tropical forest carbon balance.

Introduction

Estimates of carbon dynamics in tropical forests indicate that tropical forests have

been acting as a carbon sink in recent decades, mitigating the pace of climate change

(Grace et al. 1995; Baker et al. 2004; Lewis et al. 2009; Espírito-Santo et al. 2014).

However, the cause of this increase is unclear—forest growth may be stimulated by

increased concentrations of atmospheric carbon dioxide (Cernusak et al. 2013), and/or

observed forest growth may be an artifact of measurement methods which underestimate

carbon loss from large but infrequent mortality events (Fisher et al. 2008; Chambers et al.

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2013). One major source of uncertainty in the cause of tropical forest carbon gain is poor

knowledge of the drivers of tree mortality (McDowell et al. 2018).

Tropical forest mortality events are unevenly distributed in space and time. Most

mortality events are small, but the presence of large-scale disturbances from blowdowns,

i.e. downdrafts associated with convective storms, has been recognized for decades

(Nelson et al. 1994; Everham & Brokaw 1996; Garstang et al. 1998). Remote sensing

studies increasingly indicate the importance of rare but large blowdowns for tropical

forest carbon dynamics—a single squall line storm producing blowdowns can affect

thousands of km2 of forest (Garstang et al. 1994), kill hundreds of thousands of trees

(Negrón-Juárez et al. 2010), and cause mortality rates > 50%, an order of magnitude

greater than small mortality rates under typical disturbance regimes (median 1-2%)

(Condit et al. 2006; Magnabosco Marra et al. 2018).

The extent, frequency, and consequences of blowdowns in tropical forests remain

uncertain. Because blowdowns are infrequent, it is rare to have detailed information

about forest condition over large areas before a blowdown event. Instead, blowdowns are

often identified using satellite imagery and mortality is subsequently assessed in the field

(Rifai et al. 2016; Schwartz et al. 2017; Negrón-Juárez et al. 2018). Without detailed

measurements of forest structure before blowdowns, it is difficult to precisely quantify

carbon loss from a storm. Additionally, these methods require that blowdowns are visible

in remotely-sensed imagery, which is a challenge because the ability to detect blowdowns

decreases quickly (< 2 yrs) and tropical forest landscapes have frequent cloud cover

(Nelson et al. 1994; Asner 2001). To our knowledge, one prior study reports the impact

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of a blowdown that coincidentally affected a large-scale well-studied forest site, finding

that most new blowdown gaps are, in fact, small (Silvério et al. 2019).

In May 2018, a blowdown event caused widespread tree mortality within the forest

reserve of La Selva Biological Station, Costa Rica. The 2018 blowdown event was

known from the daily field monitoring at this site, and was visible from space using ultra

high-resolution satellite imaging (Figure 1). Over 750 ha of old growth forest at La Selva

has been formally protected for over 50 years and various secondary forests have been

added to the reserve during that time (McDade & Hartshorn 1994). The carbon dynamics

of this system have been well-characterized by long-term research projects (Clark et al.

2003, 2013, 2017). In particular, La Selva has a wealth of existing information about

forest canopy structure and structural dynamics over time (Kellner et al. 2009b; Kellner

& Asner 2009; Dubayah et al. 2010; Silva et al. 2013). This makes La Selva an ideal

system for understanding how a transient blowdown event disrupts long-term tropical

forest dynamics.

Taking advantage of this opportunity, we deployed a drone-based platform to collect

lidar data at La Selva during May 2019. We focused our data collection around 91 ha of

forest most affected by the storm to maximize inference around blowdown-related

dynamics (Fig. 1); this area included both old growth (44%) and secondary forests (56%).

We used four lidar datasets to compare biomass and canopy dynamics during two

intervals of comparable length, one without (1997-2006) and one with (2009-2019) a

blowdown event. Here, we ask:

1. How did the blowdown affect aboveground biomass density (AGBD), gap area,

and gap size frequency across the mixed-age forest at La Selva?

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2. Do canopy dynamics during the blowdown, i.e. the gain and loss of forest height,

depart from long-term patterns at La Selva?

3. Was this blowdown detectable using typical Landsat satellite image analysis?

Results

AGBD

Overall AGBD increased by 13% during 1997-2006 (without the blowdown),

then decreased by 17% during 2009-2019 (with the blowdown, Table 1). The initial

increase in AGBD was driven by a 19% increase in secondary forests (compared to 6%

increase in old growth forests), while the subsequent decrease was larger in old growth

forests (20%) compared to secondary forests (14%).

Gap area

The amount of forest in gaps was < 4% of the landscape in all years (Table 1).

The proportion of forest in gaps decreased by 35% between 1997 and 2006, then almost

doubled (86% increase) between 2009 and 2019. The decrease in gap area during 1997-

2006 is greater in secondary (49%) than old growth forests (12%), but the increase in gap

area during 2009-2019 is greater in old-growth (86%) than in secondary forests (83%).

Gap size-frequency distribution

For all years, most gaps in the forest were small in size (Fig. S1). The power-law

scaling exponent (λ) describing the gap size-frequency distribution varied from 1.58 –

1.66; however, the 95% confidence intervals on the estimate of λ overlapped for all years

(Figure S2). Similarly, there was no discernable difference in λ between old growth

forests and secondary forests for any year (Fig. S3).

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Canopy height change

The average canopy height change from 1997-2006 was positive, while the

average canopy height change from 2009-2019 was negative (Fig. S4). The distribution

of canopy height change was more symmetric from 1997-2006 than from 2009-2019,

with large decreases more likely than large increases in the latter period. The differences

between periods were consistent for both old growth and secondary forests (Fig. S5).

Projected equilibrium canopy height distribution

The projected canopy height distribution represents the expected forest structure if

the observed canopy dynamics continued with similar frequency. We used observed

canopy height changes from each period to project the equilibrium distribution of canopy

height. From 1997-2006 dynamics, the projected mean canopy height was 24.7 m [95%

CI 24.6 – 24.9], with a roughly symmetrical distribution about that mean (Fig. 2). The

projected mean canopy height from 2009-2019 dynamics was 15.8 m [95% CI 15.7 –

16.1], with a highly asymmetric distribution of canopy heights where values below the

mean are more common than values above the mean.

Landsat imagery

We characterized blowdown detectability in Landsat imagery using the change in

per-pixel proportion of non-photosynthetic vegetation (ΔNPV = NPVfinal – NPVinitial), a

metric commonly used to detect blowdowns because it correlates with tree mortality

(Nelson et al. 1994; Rifai et al. 2016; Schwartz et al. 2017; Negrón-Juárez et al. 2018).

The mean ΔNPV was 0.022 (-0.054 min, 0.095 max), and the standard deviation in ΔNPV

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was 0.019. There was no significant linear relationship between ΔNPV and the change in

AGBD between 2009 and 2019 (DF = 213, F = 0.80, P = 0.37; Fig. 3).

Discussion

Blowdown effects on biomass and structure

The 2018 blowdown event decreased AGBD and increased gap area at La Selva.

Decreases in AGBD and increases in gap area were more prevalent within old growth

forest than in secondary forests (Table 1). Old growth forests were taller than secondary

forests prior to the blowdown, indicating individual trees had larger diameters and

crowns, but the blowdown event served to homogenize this landscape with mixed land-

use history. Thus, this result is consistent with previous research finding that larger trees

had higher mortality rates in blowdowns (Rifai et al. 2016; Silvério et al. 2019). This

result stands in stark contrast, however, with typical patterns of mortality at La Selva,

where mortality rates are lower for larger individuals in the canopy (Clark & Clark 1991;

Clark et al. 2004; Thomas et al. 2013).

Our interpretation assumes that changes in AGBD and gap area during the 2009-

2019 interval are driven by the 2018 blowdown event. We acknowledge that census

interval length can bias estimates of biomass change and demographic rates from forest

censuses (Muller-Landau et al. 2014; Kohyama et al. 2018). However, by using long

time intervals (~ 10 years) we likely underestimate AGBD loss attributable to the

blowdown for two reasons. First, we expect that the landscape accumulated AGBD

between the 2009 lidar observation and the 2018 blowdown event. We expect that this is

true because our study area contains growing secondary forests, and because field-based

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measurements indicate that old growth forests in this area steadily increased in biomass

between 1998 and 2016 due to decreasing rates of mortality and recruitment (i.e. trees

lived longer and got bigger; Clark et al. in press). Second, we likely underestimate

AGBD loss from the blowdown because post-blowdown lidar data were collected a year

after the disturbance, and initial recovery after blowdown can be rapid (Nelson et al.

1994; Schwartz et al. 2017; Silvério et al. 2019). Consequently, we consider our

conclusion regarding AGBD loss and gap area gain to be conservative.

Transient canopy dynamics from blowdowns

The 2018 blowdown event caused a departure from previous canopy dynamics for

La Selva. During 1997-2006, which lacked a severe disturbance event, canopy dynamics

project a symmetrical steady-state distribution of canopy height with a small distribution

of the landscape in tradition gaps (Fig. 2). The overall distribution of canopy height

followed this predicted shape for all years; previous analyses of the first time interval also

found that canopy dynamics in the old-growth forest were in a steady state (Kellner et al.

2009b; Dubayah et al. 2010). The observed mean canopy heights were somewhat shorter

than the projected equilibrium height, but this was expected because the study area

included secondary forests. In contrast, canopy dynamics during 2009-2019, the period

including the blowdown event, project a steady-state forest with a drastically different

forest structure, where a larger proportion of the forest contains low-canopy forest (Fig.

2). This steady-state projection of canopy height represents our expectation for this forest

if severe events continue to happen with the frequency of input lidar data (i.e. once per

decade). The 2018 blowdown event was a novel disturbance for La Selva, but extreme

precipitation events are expected to become more frequent with climate change

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(Orlowsky & Seneviratne 2012). Our results suggest that increased blowdown frequency

could lower the mean canopy height of tropical forest landscapes, and therefore the

carbon stored in tropical forest aboveground biomass.

Cryptic blowdowns in tropical forests

Despite the clear blowdown effects on AGBD, gap area, and canopy dynamics,

this event was not apparent from either the size-frequency distribution of forest gaps or

the change in the proportion of NPV in Landsat imagery (Table 1, Fig. 3).

Although gap area more than doubled during the period of the blowdown, the gap

size-frequency distribution did not indicate an increased dominance of large gaps. Instead

we found no significant difference in the gap size-frequency in time. As a result, we

suggest that although the gap size-frequency distribution can be useful for distinguishing

among large differences in forest disturbance regimes and for detecting very large

blowdown events (Kellner & Asner 2009; Negrón-Juárez et al. 2010; Chambers et al.

2013), this metric may not be sensitive enough to detect more moderate blowdown events

that are, nevertheless, a departure from typical disturbance regimes. This is possible

because large canopy gaps occupy a sizable area of forest but have equal weight as a

small canopy gap when fitting the gap size-frequency scaling exponent. Therefore, if a

blowdown event creates large gaps and peripheral small gaps, then the overall gap-size

frequency can remain unchanged.

Current knowledge of blowdown events in tropical forests largely comes from

studies that identify blowdown events from increases in NPV in Landsat images (Nelson

et al. 1994; Rifai et al. 2016; Schwartz et al. 2017; Negrón-Juárez et al. 2018). From the

size, frequency, and severity of Landsat-detected blowdown events, previous studies have

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found that small forest monitoring plots likely underestimate carbon loss from

blowdowns and misattribute subsequent forest regrowth to other causes, like carbon

fertilization (Fisher et al. 2008; Chambers et al. 2013). However, other studies challenge

this assertion, using observed power-law relationships between disturbance size and

frequency to demonstrate that large disturbance events are so infrequent that they do not

dominate large-scale carbon dynamics (Gloor et al. 2009; Espírito-Santo et al. 2014). Our

results indicate that Landsat-based studies underestimate total blowdown mortality

because smaller and less severe blowdowns are unlikely to be detected (Fig. 3). Our

observed ΔNPV values were significantly positive (i.e. the fraction of NPV increased; t =

16.7, DF = 214, P < 0.001), but the average ΔNPV value (0.02) is up to 40-fold smaller

than ΔNPV values reported in other studies (Negrón-Juárez et al. 2018), and is within the

95% confidence interval for no damage in a previous relationships between ΔNPV and

storm damage (Schwartz et al. 2017). We attribute this cryptic nature of moderate

blowdowns to the low spatial and temporal resolution of Landsat data, high cloud cover

in many tropical forest landscapes, and fast recovery of forests. Further, the cloudiest

parts of the Amazon are also the regions with the highest blowdown frequency, likely

exacerbating this underestimation (Asner 2001; Negrón-Juárez et al. 2018).

Conclusions

In 2018, a blowdown at La Selva Biological Station decreased AGBD, increased

gap area, and caused a departure from canopy structural dynamics. However, this event

was not detectable by changes in the size-frequency of canopy gaps or from changes in

surface reflectance measured by Landsat. Our results highlight that local, moderate

blowdowns can alter forest carbon stocks and structure but are likely not included in

94

current analyses of blowdown dynamics in tropical forests. Therefore, we suggest that

sampling bias in small forest blots, which causes underestimation of carbon loss from

blowdowns, is likely underappreciated. This study was facilitated by the unique historical

data at La Selva, but advances in the temporal and spatial resolution of satellite remote

sensing like—like Planet’s constellation of cube satellites and NASA’s Global

Ecosystems Dynamics Investigation—provide new opportunities to thoroughly

characterize how blowdowns contribute to carbon cycling in global tropical forests

(Dubayah et al. in review.; Michael et al. 2018).


Study site

This study was conducted at La Selva Biological Station, located in the lowland

Atlantic forest of Costa Rica (10°26´ N, 83°59´ W). The mean annual temperature at La

Selva is 26 C and the mean annual precipitation is 4 m, and all months have mean

precipitation > 100 mm (McDade et al. 1994). La Selva has undulating topography, with

elevation varying between 10 and 140 m above sea level. La Selva Biological Station

includes multiple land uses; our analysis includes 91.4 hectares of forest, comprised of

39.9 ha of old-growth forest and 51.5 ha of forests with past human disturbance

(secondary forests, abandoned agroforestry, abandoned plantation, selectively-logged

forests). Forests with past human disturbance have been naturally regenerating for a

range of time (since 1955-1995); we excluded secondary forests regenerating since 1996

or later.

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Lidar data

We use four separate airborne lidar datasets to quantify dynamics in canopy

structure and aboveground biomass. Data were collected in 1997, 2006, 2009, and 2019

(Table S1). Data from 1997, 2006, and 2009 were collected by airplane over the entire

reserve; data from 2019 were collected by drone for this study, focusing on an area ~ 1

km2 in size that includes the most severe damage from the blowdown (Fig. 1). All lidar

sensors were discrete-return systems. To minimize variation in lidar height estimates

from variable laser beam divergence and detector characteristics, we only used data from

first returns. For 2019 drone-based lidar with much higher native point density and scan

angle range (Kellner et al. 2019), we limited our analysis to lidar returns with scan angle

±15 degrees and randomly subsampled data to a homogenous resolution of 10 pts m-2.

Previous research demonstrates that lidar data collected above densities of 1 pts m-2 have

similar predictive power for determining many forest properties (including tree height,

tree density, and basal area) (Jakubowski et al. 2013); all lidar data in this study are

above this density threshold. Lidar data were projected in UTM 16N, WGS 1984

ellipsoidal format.

For all lidar data, we calculated height above ground using a digital terrain model

(DTM) created from the 2006 lidar and validated using 4,184 independent measurements

within the old-growth forest (intercept = -1.406, slope = 0.999, r2 = 0.994, RMSE = 1.85

m; Kellner et al. 2009a). We verified that there were no major inconsistencies in

geolocation among lidar datasets by comparing lidar returns from building roofs; we also

used roof lidar data to adjust for systematic height differences among datasets (< 1 m for

all lidar datasets; Table S1).

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AGBD

We estimated AGBD for each lidar dataset using a model parameterized with 18

0.5 ha field plots established for the CARBONO project (Clark et al. 2013). Tree

diameters are measured annually in CARBONO plots; field data from 1997, 2006, and

2009 were used to parametrize the lidar-derived AGBD model. For each field plot,

aboveground biomass was estimated for each tree greater than 10 cm diameter at 1.3 m

height using an allometric model including a regional diameter-height relationship and

wood density (Chave et al. 2014). We used wood density values at the most specific level

possible (species, genus, family, or site-level mean). Data and detailed methods for plot

measurements are publicly available (Clark & Clark 2019).

We used a model relating top-of-canopy height (TCH) to AGBD using a power

relationship:

𝐴𝐺𝐵𝐷 = 𝑎𝑇𝐶𝐻𝑏 (Eq. 2)

where a and b are parameters fit using non-linear maximum likelihood analysis (Asner &

Mascaro 2014). TCH was calculated using mean value of pixels in the 5 m × 5 m canopy

height raster that fell within the boundaries of a single plot. This model explained 71% of

variation among field plots, with 9.8% RMSE (Fig. S6A). The distributions of model

residuals showed no heteroscedasticity (Fig. S6B). The distributions of model residuals

were not significantly different among years (Fig. S6B), so we applied a single model to

all lidar datasets, using a 0.5 ha raster resolution corresponding with the field plot size.

Gap size-frequency distribution

To characterize the effect of disturbance on forest structure, we quantified the canopy

gap size-frequency distribution for each lidar dataset. We quantified canopy gaps by

97

creating a canopy height model (CHM) with 1.25 m pixels (Kellner & Asner 2009). To

ensure that every pixel had a height value (in the occasional case where a pixel has no

lidar returns), we created the canopy height model by using a Delaunay triangulation of

first returns, gridded to 1.25 m resolution (Roussel & Auty 2019). We defined gaps

according to Brokaw’s classic definition: any contiguous area ≤ 2 m in height (Brokaw

1982). We included diagonal pixels in our calculation of contiguous area.

We characterized the gap size-frequency distribution using the Zeta distribution,

which is a discrete probability distribution, defined for integers 𝑘 ≥ 1, giving the

probability that a gap contains k pixels:

𝑓(𝑘) = 𝑘−𝜆

𝜁(𝜆) (Eq. 1)

where 𝜁(𝜆) is the Riemann zeta function. The parameter 𝜆 is a power-law exponent

describing the distribution of gaps in the landscape—small values of 𝜆 indicate that large

gaps are more frequent, and larger values of 𝜆 indicate that small gaps are relatively more

important. Previous research indicates that the power-law Zeta distribution is appropriate

for comparing gap sizes in tropical forests with diverse predominant disturbance regimes

(Kellner & Asner 2009).

We estimated 𝜆 using a Markov chain Monte Carlo (MCMC) approach, the

Metropolis-Hastings algorithm. Using a Bayesian MCMC approach, we obtain a

posterior distribution for 𝜆, quantifying our confidence in the power-law exponent. We

used an uninformative prior function (uniform distribution between 1.01 and 5); this

conservative range for 𝜆 was chosen based on results from Kellner and Asner (2009). We

used a random normal proposal function, with mean equal to the previous iteration of 𝜆

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and standard deviation equal to 0.1. We used a chain of length 100,000 steps, discarded

the first 5,000 steps as the burn-in period, and thinned the chain by using every 25th

value. We used the remaining 3,800 vales of to determine the 95% confidence intervals

for the power-law exponent, λ.

Canopy height change

We quantified forest canopy dynamics for both approximately decadal time intervals

(1997-2006 and 2009-2019) by calculating the distribution of canopy height changes, and

the projected steady-state canopy height distribution. To reduce errors from registration

uncertainty, we used 5 m pixels in this analysis. Canopy height was estimated from the

average height of all lidar returns in a pixel; at this resolution, there were no pixels with

no lidar returns.

The distribution of canopy height change indicates the dominant forest dynamics

across the spatial and temporal scale of measurements. A forest in steady-state is

expected to have mean canopy height change of approximately zero, a forest recovering

from past disturbance is expected to have a mean canopy height change greater than zero,

and a forest that experiences large disturbance is expected to have a mean canopy height

change less than zero (Kellner et al. 2009b). For each time interval, we calculated the

distribution of canopy height change by subtracting the initial height of a pixel from the

final height of a pixel.

The steady-state canopy height distribution of a forest is the expected canopy height

if observed canopy dynamics continue in perpetuity. To calculate the projected steady-

state canopy height distributions for each time interval, we created a canopy height

transition matrix, 𝐴, with 53 rows and 53 columns. Each row and column of 𝐴

99

corresponds to a single 1-m height class, and the maximum value (53 m) was selected

from the maximum height of any pixel. An entry from row i and column j in 𝐴, aij,

represents the number of pixels that were in height class j at the beginning of the time

interval and in height class i at the end of the time interval. The projected steady-state

height distribution is then obtained using an eigenvector decomposition:

𝐴𝐱 = 𝛌𝐱 (Eq. 3)

where 𝐱 is the right-hand eigenvector, and 𝛌 is the eigenvalue. Here, 𝐱 gives the

distribution of canopy heights for which applying the canopy height transition matrix

results in no overall change in the distribution of heights. We used a Bayesian framework

to quantify uncertainty in our projections of steady-state canopy heights. Specifically, we

assume that forest area from an initial height transitions to a distribution of final heights

(i.e. the columns of 𝐴) following a multinomial distribution. The multinomial distribution

has a conjugate prior distribution, the Dirichlet distribution, which allows a numerical

solution of the posterior distribution (Clark 2007). We sampled from the posterior

distribution of each height class transition 10,000 times to determine the 95% confidence

intervals of the projected steady-state canopy height distribution.

Blowdown detection from Landsat imagery

To assess whether this blowdown event was detectible from Landsat imagery, we

compared Landsat 8 images before and after the blowdown event. We chose Landsat

images that were closest in time to the event without cloud cover over our area of

interest. These images came from November 10, 2017 (~ 6 months before the blowdown)

and December 31, 2018 (~ 7 months after the blowdown). Images were downloaded

using Google Earth Engine—we used the Landsat 8 Surface Reflectance Tier 1 data

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product, which is atmospherically correcting using United States Geological Survey Land

Surface Reflectance Code. We then performed a Spectral Mixture Analysis (SMA) with

endmembers for photosynthetic vegetation, non-photosynthetic vegetation (NPV), and

shade; previous studies have shown that the change in proportion of NPV per pixel

correlates with blowdown mortality and tree damage (Negrón-Juárez et al. 2010, 2018;

Rifai et al. 2016; Schwartz et al. 2017). Because no pure pixels of NPV were apparent in

our image, we used the tropical forest Landsat endmembers published by Schwartz et al.

(2017). SMA was performed using ENVI’s linear spectral unmixing tool, using the

constraint that endmembers must sum to one (Exelis Visual Information Solutions 2019).

Acknowledgments

We thank S. Saatchi for providing access to lidar data. Lidar collection in 2019

was supported by the National Science Foundation (DEB 1852710). The CARBONO

Project was also supported by grants from the NSF, most recently DEB-0841872, DEB-

1357097. KCC was supported by an NSF Graduate Research Fellowship, the Brown

Presidential Fellowship, and the Institute at Brown for Environment and Society at

Brown University.

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Tables

Table 1. Characteristics of forest structure and AGBD over 91.4 ha of forest from four

different lidar collections between 1997 and 2019 at La Selva Biological Station

Year 1997 2006

Forest type All Old growth Secondary All Old

growth Secondary

Mean AGBD (Mg/ha) 180 205 162 203 218 192

Total gap area (ha) 3.07 1.21 1.86 2.01 1.07 0.94

Total gap area (%) 3.35 3.03 3.61 2.20 2.68 1.82

λ (median [95% CI])

1.64 [1.61,1.67]

1.60 [1.55,1.65]

1.66 [1.62,1.70]

1.62 [1.58,1.66]

1.62 [1.56,1.68]

1.63 [1.58,1.68]

Mean canopy height (m) 18.8 21.6 16.6 21.5 23.1 20.2

Year 2009 2019

Forest type All Old growth Secondary All Old

growth Secondary

Mean AGBD (Mg/ha) 208 220 199 173 175 171

Total gap area (ha) 1.48 0.77 0.72 2.75 1.43 1.32

Total gap area (%) 1.62 1.92 1.39 3.01 3.58 2.56

λ (median [95% CI])

1.63 [1.58,1.68]

1.62 [1.55,1.69]

1.64 [1.58,1.70]

1.58 [1.55,1.61]

1.58 [1.54,1.62]

1.59 [1.55,1.63]

Mean canopy height (m) 22.1 23.4 21.2 18.5 19.0 18.0

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Figures

Fig. 1. Magnitude of May 2018 blowdown at La Selva Biological Station, as seen from

Planet Labs imagery on May 11 (A, before blowdown) and May 26 (B, after blowdown).

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Imagery is shown in a false color composite (red is the near-infrared measured band,

green is the blue measured band, and blue is the green measured band) such that

photosynthetic vegetation is bright red and non-photosynthetic vegetation is brown. The

boundaries of old growth and secondary forest areas used in this study are shown in A. A

survey of all trails (white) conducted immediately following the blowdown identified

trees that fell across regularly-maintained trails (B). Drone lidar data from 2019 are

shown for a subset of the forest (C), and the location of that subset is shown in the blue

rectangle in panel B.

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Fig. 2. Distributions of canopy heights (lines) measured by lidar in four years at La Selva

Biological Station, Costa Rica. Projected steady state canopy height distributions, from

canopy height dynamics, are shown for the interval with (2009-2019) and without (1997-

2006) a blowdown event (shaded bands show 95% confidence intervals).

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Fig. 3. Distribution of the change in proportion of non-photosynthetic vegetation (NPV)

in Landsat data over our study area at La Selva Biological Station, Costa Rica, before

(November 2017) and after (December 2018) the blowdown event (May 2018). Landsat

data (30 m resolution) were sampled to the resolution of AGBD data (0.5 ha) using a

bilinear interpolation.

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Supporting information Table S1. Characteristics of lidar data from four years at La Selva Biological Station, Costa Rica.

Collection date Sensor Height adjustment (m) Reference

Sep., 1997 FLI-MAP + 0.4 (Kellner et al. 2009b) Mar., 2006 Leica ALS50 - 0.5 (Kellner et al. 2009b) Sep.-Oct., 2009 Optech 3100 EA + 0.7 (Neumann et al. 2012) May, 2019 Riegl VUX-1 + 0.0 (Kellner et al. 2019)

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Fig. S1. Gap size-frequencies in La Selva Biological Station, quantified using airborne

lidar in four different years. Gaps were defined as areas with mean canopy height < 2 m.

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Fig. S2. Posterior probability density for the Zeta distribution power-law scaling

parameter, λ, describing the size-frequency of canopy gaps for four years at La Selva

Biological Station, Costa Rica. Here, old-growth and secondary forests are combined.

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Fig. S3. Posterior probability density for the Zeta distribution power-law scaling

parameter, λ, describing the size-frequency of canopy gaps for four years at La Selva

Biological Station, Costa Rica. Here, old-growth and secondary forests are separated for

each year.

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Fig. S4. Distribution of canopy height change across La Selva Biological Station, Costa

Rica, for two intervals with (2009-2019) and without (1997-2006) a blowdown event.

The vertical dashed lines denote the mean value for each interval. Here, old growth and

secondary forests are combined.

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Fig. S5. Distribution of canopy height change across La Selva Biological Station, Costa

Rica, for two intervals with (2009-2019) and without (1997-2006) a blowdown event.

The vertical dashed lines denote the mean value for each interval. Here, old growth and

secondary forests are separate for each year.

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Fig. S6. Relationship between lidar top-of-canopy height (TCH) and AGBD (A), and the

relationship between predicted AGBD and model residuals (B). TCH and AGBD were

related using a power relationship (𝐴𝐺𝐵𝐷 = 𝑎𝑇𝐶𝐻𝑏) that explained 71% of the variation

among plots, where a = 12.249 and b = 0.9193. For the 1997 data, 3 plots were excluded

that were not completely covered by the lidar data. We tested for significant differences

in model residuals among years using a one-way ANOVA, and found there were no

significant differences (F(2,48) = 1.69, P = 0.20). Additionally, there was no significant

linear relationship between predicted AGBD and residual values (DF = 49, F = 0.003, P

= 0.95).

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CHAPTER 4:

Sensitivity of simulated GEDI waveforms to forest leaf area and implications for

footprint aboveground biomass models

K.C. Cushman1,2, John D. Armston3, Ralph O. Dubayah3, Laura I. Duncanson3, Steven

Hancock4, Michelle Hofton3, Kamil Král5, Hao Tang3, James R. Kellner1,2





3 University of Maryland, College Park, College Park, MD

4 University of Edinburgh, United Kingdom

5 Silva Tarouca Research Institute, Brno, Czech Republic

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Abstract

The Global Ecosystems Dynamics Investigation (GEDI) measures vertical forest

structure (lidar waveforms) across the world’s temperate and tropical forests, providing

estimates of aboveground carbon stocks. Changes in leaf area due to plant phenology

during GEDI’s continuous two-year mission will influence waveform data. Here, we

evaluate the sensitivity of waveform data and predicted footprint aboveground biomass

density (AGBD) to changes in leaf area. We collected high-density discrete-return drone

lidar during leaf-off and leaf-on conditions in a temperate sub-montane forest in the

southern Czech Republic. Field campaigns were 51 days apart, immediately before

beginning and after completion of leaf flushing, isolating the effect of leaf area from

longer-term changes in woody vegetation structure. We used the GEDI waveform

simulator to produce simulated GEDI waveforms from the leaf-off and leaf-on field

campaigns. We aligned simulated waveforms with forest inventory data from a 25 ha

plot, and quantified changes in waveform relative height (RH) metrics, and the ability of

RH metrics to explain variation in AGBD during the time of data collection and when

transferred to the opposite leaf condition. We found that lower canopy waveform metrics

were sensitive to changes in leaf area, while upper canopy waveform metrics were robust

to leaf area changes. When lower-canopy lidar metrics are excluded from AGBD models,

both leaf-off and leaf-on data can be used to predict AGBD variation, and models are

transferable across leaf conditions.

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Introduction

Forests play a substantial role in the global carbon cycle—carbon sequestration in

forested ecosystems mitigates emissions from fossil fuel combustion, while deforestation

and forest degradation contribute to global carbon emissions (Pan et al. 2011; Quéré et al.

2018). Understanding the spatial distribution of forest aboveground biomass density

(AGBD) is important for measuring and predicting changes in the carbon balance of

forests. Measurements of forest AGBD from field plots or airborne platforms are

generally limited in scale, but current and upcoming satellite missions promise to

increase understanding of forest AGBD at the global scale (Exbrayat et al. 2019). One

such mission is NASA’s Global Ecosystem Dynamics Investigation (GEDI), which has

produced waveform lidar measurements from the International Space Station since

December, 2018 (Dubayah et al. 2014; Hancock et al. 2019). GEDI will use lidar

measurements to create data products including footprint-level AGBD (AGBD of

individual laser measurements, each 19-25 m in diameter) and gridded AGBD (AGBD at

1 km2 resolution derived from multiple footprints).

The presence or absence of leaves influences how lidar laser energy moves

through, and is reflected by, forests. GEDI measurements of forest structure will be

affected by changes in leaf area during the mission’s continuous two-year operational

period. During this time, temperate deciduous forests will be leafless during winter, and

deciduous or semi-deciduous tropical forests will be leafless during dry conditions

(Condit et al. 2000). GEDI measurements have no planned temporal repeat due to the

sampling pattern of GEDI, where footprints fall along 8 tracks with 60 m along track

spacing and 600 m between track spacing (Dubayah et al. in review). Therefore, any

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individual location will only have data for a single leaf condition. Additionally, data used

for GEDI AGBD model calibration and validation come from a single point in time, and

therefore do not encompass temporal variation in leaf area. In order to maximize the

utility of GEDI data, it is necessary to create models to predict AGBD from GEDI

waveforms that are insensitive to changes in leaf area.

Previous research comparing leaf-off and leaf-on lidar data finds that discrete-

return lidar data collected in either leaf-off or leaf-on conditions can be used to develop

models to predict AGBD and related forest metrics (Næsset 2005; Villikka et al. 2012;

Anderson & Bolstad 2013; Bouvier et al. 2015; White et al. 2015). The best calibration

data (leaf-off or leaf-on lidar) to predict AGBD varied by forest type, but absolute

differences in predictive power were generally small (Anderson & Bolstad 2013; Bouvier

et al. 2015). Fewer studies have examined the effect of transferring models calibrated

with leaf-off data to leaf-on conditions, and vice versa. These studies find that

transferring lidar-based models across leaf area conditions increases model error (root

mean square error, RMSE) by up to > 30% for AGBD and tree volume (Villikka et al.

2012; White et al. 2015). However, although these studies found that different lidar

metrics had variable sensitivity to changes in leaf area, analyses of transferability did not

include models limited to less sensitive metrics. Further, these studies were limited to

discrete-return, rather than waveform, lidar metrics.

In this study, we explore whether models can be developed to predict AGBD from

waveform lidar metrics that are transferrable across temporal changes in leaf area. We

simulated GEDI waveforms using discrete-return airborne lidar collected 51 days apart in

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a temperate sub-montane forest, during leaf-off and leaf-on conditions (Fig. 1). We use

these data to ask:

1. How sensitive are waveform metrics to changes in leaf area?

2. Are model explanatory power and model transferability across leaf area

conditions affected by parametrizing models with leaf-off or leaf-on data?

3. Are model explanatory power and model transferability across leaf area

conditions related to the waveform metrics included as predictive variables?


Study site

This study took place within the Žofín National Nature Reserve in the

Novohradské Hory Mountains of the southern Czech Republic, which has been formally

protected since 1838. The sub-montane forest of Žofín is dominated by European beech

(Fagus sylvatica, 75% of biomass); Norway spruce (Picea abies, 19% of biomass) is the

second most common species. At this site, mean annual temperature is 6.2 C and mean

annual precipitation is 866 mm (Janík et al. 2016).

Plot data

AGBD observations were taken from the 25 ha Žofín Forest Dynamics Plot

(ZFDP), where all trees with diameter at breast height (DBH) ≥ 1 cm are mapped,

identified to species, and measured every 5 years according to the Smithsonian Global

Forest Earth Observatory network protocol (Condit 1998). We used data from the 2017

ZFDP census. Aboveground biomass (AGB) was predicted for each individual tree using

the DBH-AGB allometries of Mukkonnen (2007), which has separate parametrizations

124

for European beech and Norway spruce. For any species not represented (6.5% of

biomass), the beech allometry was used for broadleaf trees and the spruce allometry was

used for needleleaf trees.

Lidar data collection

Lidar data were collected 51 days apart during leaf-off (April 16-17) and leaf-on

conditions (June 6-7) in 2018. The Brown Platform for Autonomous Remote Sensing

(BPAR) was used to collect lidar data. BPAR includes an Oxford Technical Solutions

(OXTS) Survey +2 GPS-IMU and a RIEGL VUX-1 laser scanner carried by a heavy-lift

helicopter designed and operated by Aeroscout GmbH (Kellner et al. 2019b). BPAR

collected data for > 1.5 km2 of forest using 90 total flight lines arranged in 2 orthogonal

sets of 45 lines each. Six flights were required to collect data in each campaign, with a

combined flight time of approximately five hours per campaign. Flights were carried out

with a speed of 6 m s-1 and an altitude of 110 m above ground, producing average point

densities of 2801 returns m-2 during leaf-off conditions and 2166 returns m-2 during leaf-

on conditions, with residual standard error of point location of 4.5 cm and 2.1 cm in leaf-

off and leaf-on conditions, respectively (Kellner et al. 2019b).

GEDI waveform simulation

The GEDI Simulator was used to simulate GEDI waveform lidar data from BPAR

discrete-return lidar data (Hancock et al. 2019). To be consistent with the GEDI laser,

only BPAR data with scan angles ≤ 6 degrees were used to simulate GEDI waveforms.

Waveforms centers were located on a 20 m by 20 m grid over the ZFPD. The same

waveform center coordinates were used to simulate leaf-off and leaf-on waveforms,

resulting in 570 simulated waveforms for each leaf condition.

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The discrete-return airborne lidar sensor used in this campaign has a different

wavelength (1,550 nm) than the GEDI laser (1,064 nm) (Hancock et al. 2019; Kellner et

al. 2019b). We do not expect that differences in wavelength affect our conclusions

because previous validation of the GEDI waveform simulator found that discrete-return

lidar at 1,550 nm and 1,064 nm produced similar simulated waveforms (Hancock et al.

2019).

We calculated the observed waveform AGBD by adding the AGB of all trees

whose location was within 11 m of each waveform center. For each waveform, we also

calculated the proportion of AGB contained in broadleaf trees. We used the “real relative

height” (RH) waveform metrics for all analyses with simulated waveforms. RH metrics

describe the heights below which a given proportion of waveform energy is reflected; for

example; 10% of waveform energy is reflected by vegetation below the height of RH 10,

and RH 100 is maximum canopy height.

Waveform sensitivity to leaf area

We evaluated the sensitivity of simulated waveforms to leaf area by comparing

RH metrics between leaf-off and leaf-on conditions. We compared the height difference

between leaf-off and leaf-on conditions for RH metrics of all waveforms, broadleaf

waveforms (all AGB in broadleaf trees), and needleleaf waveforms (< 10% of AGB in

broadleaf trees). We tested for significant differences for each RH metric between leaf-

off and leaf-on conditions using a paired Wilcoxon test, and we calculated the effect size

(Cohen’s d) of changing leaf area for each metric.

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Fitting footprint biomass models

We used linear regression models to describe the relationship between waveform

RH metrics and field AGB for the 570 footprints within the Zofin Forest Dynamics Plot.

We fit separate suites of models for leaf-off and leaf-on waveforms—models were fit

using only leaf-off data or only leaf-on data. We considered the RH metrics 10, 20, 30,

40, 50, 60, 70, 80, 90, and 98, and all their possible interactions, as candidate predictive

variables. We considered models with all possible combinations of two to four predictive

variables, and we considered four different data transformation options:

1. Field AGBD and all predictive variables are log-transformed

2. Field AGBD is log-transformed

3. Field AGBD and all predictive variables are square-root-transformed

4. Field AGBD is square-root-transformed

For log-transformed models, we considered two back-transformation options:

1. Baskerville method

2. Snowdon method

This results in 2,320,650 total candidate models for each leaf condition.

Evaluating footprint AGBD models

To evaluate candidate footprint biomass models, first we discarded models based

on the following criteria:

1. We discarded any candidate models where the maximum Pearson correlation

coefficient among any two predictive variables was ≥ 0.9.

2. We discarded any candidate models with any “variable inflation factor” ≥ 10. The

variation inflation factor is calculated for each predictive variable as 1/(1-R2),

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where R2 is the explanatory power of a linear model explaining variance in the

variable of interest using all other predictive variables for that candidate model.

3. We discarded any candidate models where the residuals of the fitted linear

regression model exhibited bias. We determined bias in the residuals by dividing

all 570 footprint observations into quintiles based on the predicted AGBD value.

We discarded models where the residuals from any quintile were significantly

different than zero using a t-test.

All models not discarded based on the criteria described were further evaluated based on

model explanatory power and model transferability across leaf area conditions.

Footprint AGBD model explanatory power

We evaluated model explanatory power using the relative RMSE of a linear

model comparing observed (field) and predicted AGBD, model, expressed as a

percentage of the average observed AGBD across all footprints. In this case, the same

dataset (leaf-off or leaf-on) was used to parameterize models and predict AGB. We call

this “Ordinary RMSE”.

Footprint AGBD model transferability

We evaluated model transferability across leaf area conditions by using different

datasets to parameterize models and predict AGBD—models were parameterized with

leaf-off data and applied to leaf-on data, and models were parameterized with leaf-on

data and applied to leaf-off data. We quantified model transferability using relative

RMSE of a linear model comparing observed and predicted AGBD, again expressed as a

percentage of the average observed AGBD across all footprints. We call this

“Transferability RMSE”.

128

RH metric effects on AGBD models

We evaluated the effect of including particular RH metrics on footprint AGBD

performance. To accomplish this, we examined Ordinary RMSE and Transferability

RMSE as a function of the minimum RH metric included as a model predictive variable

(either alone, or as an interaction with another RH metric).

We also quantified the importance of RH metrics when they appear as predictive

variables (not in interactions) using the relative coefficient magnitude:

(Eqn. 1)

where there are n predictive variables for that model.

Plot-level AGBD error

For each model, we added the predicted AGBD of all footprints to obtain the plot-

level predicted total AGBD. We then calculated the plot-level AGBD error as:

(Eqn. 2)

“Ordinary AGBD error” refers error where a model is calibrated and applied to a single

data set, while “Transferability AGBD error” refers to error where a model is calibrated

with data from on leaf condition, then applied to data from the opposite leaf condition.

Results

Sensitivity of simulated waveform metrics to leaf area

All waveform metrics changed significantly between leaf-off and leaf-on

conditions (Table 1). Lower-canopy RH metrics were more sensitive to changes in leaf

𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑚𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒 = 𝑎𝑏𝑠(𝑅𝐻 𝑚𝑒𝑡𝑟𝑖𝑐 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡)

∑ 𝑎𝑏𝑠(𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑛)𝑛𝑖=1

𝐴𝐺𝐵𝐷 𝑒𝑟𝑟𝑜𝑟 (%) = 𝑃𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑 𝑡𝑜𝑡𝑎𝑙 𝐴𝐺𝐵𝐷 − 𝑃𝑙𝑜𝑡 𝑡𝑜𝑡𝑎𝑙 𝐴𝐺𝐵𝐷

𝑃𝑙𝑜𝑡 𝑡𝑜𝑡𝑎𝑙 𝐴𝐺𝐵𝐷 × 100

129

area than upper-canopy RH metrics (Fig. 2). For RH 30 and above, the effect size of leaf

area changes decreases with increasing RH values (Table 1). In particular, during leaf-off

conditions RH 10-30 were close to 0 m in height with little variation, but during leaf-on

conditions RH 10-30 were both higher and more variable (Fig. 2a). Additionally,

simulated waveforms over areas with all broadleaf trees had higher sensitivity to leaf area

then simulated waveforms over areas with largely needleleaf trees (Fig. 2b).

Implications for footprint AGBD models

Out of 2,320,650 candidate biomass models for each leaf condition, < 1% of

models remained after models were excluded for criteria of predictive variable

correlation, variable inflation factor, or ordinary model residual bias. More candidate

models remained for models parameterized with leaf-off condition data (13,344 models

remaining) than for models parameterized with leaf-on condition data (1,612 models

remaining). For these remaining models, we evaluated model explanatory power

(Ordinary RMSE), model transferability (Transferability RMSE), and the influence of

included RH metrics on model performance.

Leaf area effects on model explanatory power

We evaluated footprint AGBD model explanatory power using Ordinary RMSE:

the RMSE of a linear model comparing observed and predicted AGBD, where a single

dataset (i.e. only leaf-off or only leaf-on data) was used to parametrize the model and

predict AGBD. The best model (lowest Ordinary RMSE) achieved with leaf-off data

(36.7%) was similar to that of leaf-on data (36.2%) (Fig. 3). Across all remaining models,

models parameterized with leaf-on data had a small but significant improvement in

Ordinary RMSE (36.4 ± 0.17%, mean ± standard deviation) compared to models

130

parameterized with leaf-off data (36.9 ± 0.16), compared using a Welch two sample t-test

to accommodate unequal sample sizes and variances (Fig. 4; t = 102.0, D.F. = 1920.5, P

< 0.001).

Leaf area effects on model transferability

We evaluated footprint AGBD model transferability across leaf area conditions

using Transferability RMSE: the RMSE of a linear model comparing observed and

predicted AGBD, where one leaf condition dataset was used to parametrize the model,

and the other leaf condition dataset was used predict AGBD. For both leaf-off and leaf-on

models, the single model with the lowest Ordinary RMSE did not also have the lowest

Transferability RMSE (Fig. 3). The leaf-off model with the lowest Transferability RMSE

(36.3%) had a lower Transferability RMSE than the best leaf-on model value (37.0%).

However, across all remaining models, models parametrized with leaf-on data had lower

and less variable Transferability RMSE (39.0 ± 0.66%) than models parameterized with

leaf-off data (136.2 ± 126.4%) (Fig. 4; t = 88.8, D.F. = 13349, P < 0.001).

Waveform metric effects on model performance

Model explanatory power (Ordinary RMSE) for leaf-off and leaf-on models is

relatively insensitive to the minimum waveform RH metric included (Fig. 5). For leaf-off

models, model transferability across leaf area conditions (Transferability RMSE)

improves when only metrics ≥ RH 40 are included. The average Transferability RMSE

for these 214 models is 38.2 % (± 1.11% standard deviation), a significant improvement

in transferability compared to all remaining leaf-off models (t = 89.3, D.F. = 13453, P <

0.001). Transferability for leaf-on models also improves slightly but significantly when

131

only metrics ≥ RH 40 are included (38.6 ± 0.57%; t = 4.04, D.F. = 1663, P < 0.001).

Plot-level AGBD error

At the plot-level, Ordinary AGBD error is similar for models with leaf-off (0.029

± 0.04%) and leaf-on (0.031 ± 0.11%) calibration. Transferability AGBD error is larger

and more variable for models with leaf-off calibration (87.6 ± 87.7%) than for models

with leaf-on calibration (-12.1 ± 2.4%). AGBD is overestimated when leaf-off calibrated

models are applied to leaf-on data, and AGBD is underestimated when leaf-on calibrated

models are applied to leaf-off data. Transferability AGBD error is reduced when only

metrics ≥ RH 40 are included for both models parametrized with leaf-off data (10.3 ±

4.8%) and models parametrized with leaf-on data (-11.1 ± 2.3 %).

Discussion

Prediction of forest AGBD from waveform lidar requires appropriate models

relating waveform metrics to estimated biomass. To fully utilize waveform lidar data

from the GEDI mission, it is necessary to build models that predict AGBD not only for

data collected under a single leaf area condition, but also for data collected across other

leaf conditions. Models that are transferable across leaf condition are necessary because

forests do not exist in clear and binary leaf-off or leaf-on states—rather, the degree and

timing of deciduousness varies at the level of individuals, species, and communities

(Condit et al. 2000; Augspurger & Bartlett 2003; Smith et al. 2019). Our results show

that either leaf-off or leaf-on waveform lidar data can be used to build models predicting

AGBD from waveform metrics (Fig. 4). In addition to explaining AGBD variation in data

used to calibrate models, some models calibrated with both leaf-off or leaf-on data were

132

also highly transferable across changes in leaf area (Fig. 4). Consequently, GEDI data

collected across variable leaf area conditions can inform estimates of global variation in

forest AGBD.

Simulated waveform metrics near the ground were more sensitive to changes in

leaf area than top-of-canopy metrics (Table 1; Fig. 2). We believe this discrepancy occurs

because fine twigs and branches reflect lidar beam energy even in leaf-off conditions, but

less lidar energy penetrates to the forest floor and reflects to the sensor during leaf-on

conditions (Fig. 1). These results are consistent with a study comparing leaf-off and leaf-

on discrete-return lidar; that study reported last return metrics were more affected by leaf

conditions than first return metrics (Næsset 2005).

Variability in model performance was related to the waveform metrics included in

each model (Fig. 5). Among models considered in this study, we found relatively little

variation in model explanatory power—all candidate models were within a range of 3%

Ordinary RMSE. In contrast, we found a wide range of model transferability across

candidate models, with Transferability RMSE varying from 37% to > 1000%,

particularly in models calibrated with leaf-off data. All models with minimum RH metric

≥ 40 had equally good Ordinary and Transferability RMSE, while models with minimum

RH metric < 40 were sometimes much less transferable. Low transferability in models

including RH metrics < 40 occurred because those metrics were most sensitive to

changes in leaf area (Table 1; Fig. 2).

Some models including RH metrics < 40 had high transferability, including all

models calibrated with leaf-on data. There are two reasons that explain this phenomenon.

First, leaf-off values for RH metrics < 40 have small ranges that fall within the much

133

larger ranges for leaf-on data (Fig. 2). As a result, applying models calibrated with leaf-

on data to leaf-off RH metrics, which are comparable to some leaf-on data, results in

reasonable AGBD predictions. The opposite is not true because leaf-on RH metrics < 40

can be much larger than leaf-off values, so applying leaf-off models to leaf-on data can

lead to gross overestimation of AGBD (Fig. 3). Second, RH metrics can be included in

models but relatively unimportant. This occurs if the model coefficient associated with

that RH metrics is small in magnitude compared to other coefficients. The relative

coefficient magnitudes of RH 10, 20, and 30 are generally large for models calibrated

with leaf-off data, but more often are small for models calibrated with leaf-on data (Fig.

6).

Plot-level AGBD error is much lower than the footprint-level RMSE because

models were selected to have low bias (See section 2.7). Ordinary AGBD error for

biomass prediction was < 1% at the plot scale for both leaf-off and leaf-on models (Fig.

7). Transferability AGBD error at the plot scale was larger for both leaf-off and leaf-on

models, but with opposite signs: models calibrated with leaf-on data underestimated

biomass when applied to leaf-off data, while models calibrated with leaf-off data

overestimated biomass when applied to leaf-on data (Fig. 7). This is expected because all

RH metrics were, on average, taller in leaf-on conditions than in leaf-off conditions (Fig.

2).

Conclusions

Here, we demonstrate that footprint-level models can be made such that they

accurately predict AGBD from GEDI data across conditions of changing leaf area. This

result is important because the GEDI mission will collect waveform lidar data in forests

134

with variable leaf area. It is particularly important that either leaf-on or leaf-off data can

be used to parametrize transferable models because most sites in the GEDI

calibration/validation dataset only include lidar data at a single time point, during leaf-on

conditions.

Based on these results, we recommend limiting AGBD models to RH metrics ≥

40. While some models including lower RH metrics still had low Ordinary and

Transferability RMSE, we believe this strategy is conservative and does not reduce

potential model performance. When only RH metrics ≥ 40 were used in models, plot-

level Transferability AGBD error was ~ 10% for models calibration with either leaf-on or

leaf-off data.

Our results highlight an area of research that may further improve AGBD

prediction across changing leaf conditions—combining RH metrics with leaf area data.

This could be achieved using vertical leaf-area profiles from GEDI data themselves, or

high-resolution data from other satellite platforms (e.g. Planet Labs) that may allow the

leaf status of individual canopy trees to be assessed (Kellner et al. 2019a). Quantifying

leaf area and deciduousness could provide a way to correct for the remaining

Transferability AGBD error in these results.

Acknowledgments

This work was supported by Brown University, the National Aeronautics and

Space Administration of the United States of America, and funds provided to Brown

University by Peggy and Henry D. Sharpe Jr. and Peter S. Voss. We thank Markus Birrer,

135

Christoph Eck, Cristoph Falleger, Benedikt Imbach, Martin Krůček, Henry Johnson, Jan

Trochta, Tomáš Vrška, and Carlo Zgraggen.

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Tables

Table 1. Sensitivity of waveform metrics to changes in leaf area. Significant differences

between leaf-off and leaf-on data for 570 footprints were tested using a paired Wilcoxon

test, and the effect size of changing leaf area was quantified using Cohen’s d.

Waveform

metric

Paired Wilcoxon Cohen's d

V P

RH 10 162723 < 0.001 1.69

RH 20 161561 < 0.001 1.92

RH 30 162730 < 0.001 1.93

RH 40 162140 < 0.001 1.25

RH 50 162122 < 0.001 0.85

RH 60 162122 < 0.001 0.61

RH 70 162701 < 0.001 0.48

RH 80 162169 < 0.001 0.39

RH 90 161855 < 0.001 0.31

RH 98 162477 < 0.001 0.21

139

Figures

Fig. 1. Conceptual figure showing discrete-return lidar returns in two footprints during

leaf-off and leaf-on conditions (a, b, e, h). Footprints were chosen to include either

broadleaf (a-d) or needleleaf (e-h) trees. The corresponding raw waveforms (c, g) and RH

metrics (d, h) are shown for each footprint and leaf condition. The vertical lines between

the leaf-off and leaf-on RH metrics denote the difference between leaf-off and leaf-on

metrics (d, h).

140

Fig. 2. Waveform sensitivity to leaf area. The distributions of RH metrics are shown for

all leaf-off and leaf-on footprints (a), and the leaf on-off differences are shown for

broadleaf and needleleaf footprints (b). Broadleaf footprints are all footprints with 100%

of AGB in broadleaf trees, and needleleaf footprints are all footprints with < 10% of

AGB in broadleaf trees. The leaf on-off difference is the height difference between RH

waveform metrics in leaf-off conditions and leaf-on conditions (see Fig. 1). For each

boxplot, the black line denotes the median value, the box denotes the interquartile range,

and the whisker length is 1.5 × the interquartile range.

141

Fig. 3. Illustration of model explanatory power (Ordinary RMSE) (a, b) and model

transferability under leaf phenology (Transferability RMSE) (c, d). Two models are

shown—the model with the lowest Ordinary RMSE among models parameterized with

leaf-off data (a, c) and the model with the lowest Ordinary RMSE among models

parameterized with leaf-on data (b, d). Vertical red lines separate quintiles determined by

predicted AGB values.

142

Fig. 4. Summary of model explanatory power (Ordinary RMSE) and model

transferability under leaf phenology (Transferability RMSE) for all candidate models that

were not excluded for predictive variable correlation, variable inflation factor, or model

residual bias (n = 13344 and n = 1612 models for leaf-off and leaf-on parameterization,

respectively).

143

Fig. 5. Model explanatory power (Ordinary RMSE) and model transferability under leaf

phenology (Transferability RMSE) as a function of minimum RH waveform metric

included in model predictive variables. Models shown were not excluded for predictive

variable correlation, variable inflation factor, or model residual bias (n = 13344 and n =

1612 models for leaf-off and leaf-on parameterization, respectively).

144

Fig. 6. Relative coefficient magnitudes for RH metrics ≤ 40 for models parameterized

with leaf-off and leaf-on data. Relative coefficient magnitudes were calculated each time

an RH metric appeared as a predictive variable (not as part of an interaction among two

RH metrics).

145

Fig. 7. Histograms of average AGB error for all footprints in the ZFPD for models

parameterized with and applied to leaf-off data (a), models parameterized with and

applied to leaf-on data (b), models parameterized with leaf-off data but applied to leaf-on

data (c), and models parameterized with leaf-on data but applied to leaf-off data (d). AGB

error was calculated by adding AGB of all footprints, and calculating the percent

difference compared to field-measured AGB of all footprints. Models shown were not

excluded for predictive variable correlation, variable inflation factor, or model residual

bias (n = 13344 and n = 1612 models for leaf-off and leaf-on parameterization,

respectively).

Documents

Predicting Carbon Dynamics in Forests Using Remote