DOI: 10.5061/dryad.ttdz08m5n
title: Satellite-derived trait data slightly improves tropical forest biomass, NPP, and GPP predictions

description: Improving tropical forest biomass predictions can accurately value
 tropical forests for their ecosystem services. Recently, the Global
 Ecosystem Dynamics Investigation (GEDI) lidar was activated on the
 international space station (ISS) to improve biomass predictions by
 providing detailed 3D forest structure and height data. However, there is
 still debate on how best to predict tropical forest biomass using GEDI
 data. Here we compare GEDI predicted biomass to 2,102 tropical forest
 biomass plots and find that adding a remotely sensed (RS) trait map of LMA
 (Leaf Mass per Area) significantly (P&lt;0.001) improves field biomass
 predictions, but by only a small amount (r2=0.01). However, it may also
 help reduce the bias of the residuals because, for instance, there was a
 negative relationship between both LMA (r2 of 0.34) and %P (r2=0.31) and
 residuals. This improvement in predictability corresponds with
 measurements from 523 individual trees where LMA predicts Diameter at
 Breast height (DBH) (the critical measurement underlying plot biomass)
 with an r2=0.04, and spectroscopy (400-1075 nm) predicts DBH with an
 r2=0.01. Adding environmental datasets may offer further improvements and
 max temperature (Tmax) predicts Amazonian biomass residuals with an r2 of
 0.76 (N=66). Finally, for a network of net primary production (NPP) and
 gross primary production (GPP) plots (N=21), RS traits are better at
 predicting fluxes than structure variables like tree height or Height Of
 Median Energy (HOME). Overall, trait maps, especially future improved ones
 produced by surface biology geology (SBG), may improve biomass and carbon
 flux predictions by a small but significant amount.
description: <strong>Field leaf trait and spectroscopy data –
 </strong>We used leaf trait and spectral data from an extensive
 field campaign along an elevation gradient (from 3500 m to 220 m
 elevation) in the Peruvian Amazon where<strong> </strong>leaf
 traits for<strong> </strong>60-80% of basal area of trees
 &gt;10cm DBH were measured within a well-studied 1 ha plot network
 from April – November 2013 (Enquist et al., 2017). In each one ha plot
 (N=10 plots), we sampled the most abundant species as determined through
 basal area weighting (enough species generally to cover ~80% of the plot’s
 basal area). For each species, we sampled the five (three in the lowlands)
 largest trees (based on diameter at breast height (DBH)) and sampled one
 sun and one shade branch. On each of these branches, leaf chemistry and
 leaf mass area (LMA) were measured with the methodology detailed in Asner
 et al. (2014). On five randomly selected leaves for each branch, we
 measured<strong> </strong>hemispherical reflectance with an
 ASD Fieldspec Handheld 2 with fiber optic cable, a contact probe that has
 its own calibrated light source, and a leaf clip (Analytical Spectral
 Devices High-Intensity Contact Probe and Leaf Clip, Boulder, Colorado,
 USA) following (Doughty et al., 2017). We measured leaf spectroscopy
 (400-1075 nm) on the same branches where the leaf traits were collected.
 Both LMA and Chlorophyll A had previously been shown with this dataset to
 have a correlation with leaf spectroscopy (Doughty et al., 2017). However,
 we had not previously tried to compare leaf spectral data with DBH
 directly.  <strong>Plot data</strong>
 – <em>Aboveground biomass</em> - We used
 2,102 of 19,160 total AGB field plots between +30° and -30° latitude
 classified as broadleaf evergreen trees by MODIS PFT using public data
 from Duncanson et al 2022 that was organized and publicly available
 through ORNL DAAC as an RDS (R data serialization) file. Distribution
 plots are shown in Fig S1 (AGB) and S2 (residuals).
 <em>NPP and GPP -</em> We also used 21, 1 ha plots
 where NPP and sometimes GPP were measured following the GEM protocol
 (Malhi et al., 2021). We focused on two regions: a Peruvian elevation
 transect with both NPP + GPP (n= 10, RAINFOR plot codes are ALP11, ALP30,
 SPD02, SPD01, TRU03, TRU08, TRU07, ESP01, WAY01, ACJ01(Malhi et al.,
 2017)) and a Bornean logging transect with only NPP (n= 11 RAINFOR plot
 codes are DAN-04, DAN-05, LAM-01, LAM-02, MLA-01, MLA-02, SAF-01, SAF-02,
 SAF-03, SAF-04, SAF-05 (Riutta et al., 2018). These plots were chosen
 because there are large changes in NPP/GPP across the elevation or logging
 gradient. <strong>GEDI data</strong> – We
 used the vertical forest structure (L2A and L2B, Version 2) and biomass
 (L4a) products from the GEDI instrument (R. Dubayah et al., 2020) between
 April 2019 to December 2022 for tropical forest regions (R. O. Dubayah et
 al., 2023). We used a quality filtering recipe developed in collaboration
 with GEDI Science Team members from the University of Maryland and NASA
 Goddard to identify the highest quality GEDI vegetation shots (R. Dubayah
 et al., 2022). A data layer that this iterative local outlier detection
 algorithm uses to exclude data is publicly available at R. O. Dubayah et
 al., (2023). For instance, some of the key data filters we applied were:
 included degrade flags of
 0,3,8,10,13,18,20,23,28,30,33,38,40,43,48,60,63,68, L2A and L2B quality
 flags = 1 (only use highest quality data), sensitivity &gt;= 0.98.
 With the GEDI data, we used canopy height, the height of median energy
 (HOME), and the number of canopy layers following Doughty et al 2023
 (Doughty et al., 2023).  Across all tropical forests,
 we created 300 by 300 m pixels containing all averaged (mean) GEDI data
 between 2019 and 2022. Using the centroid coordinates from each of the
 2,102 plots, we found the 300 by 300 m averaged GEDI pixel that
 encompassed the plot. If the plot was not encompassed by the GEDI data, we
 searched a wider area by incrementally averaging a gradually increasing
 area of 1, 3, 5, and 10 pixels. In other words, if no 300 by 300 m pixel
 encompassed the plot, then we averaged all GEDI data an area one pixel out
 (4 by 4 = 1200 by 1200 m, 6 by 6 = 1800 by 1800m, 11 by 11 = 3300m by
 3300m), gradually increasing the square until it encompassed an area with
 GEDI data. To compare with the NPP/GPP plots we compared RS trait and GEDI
 data for individual footprints within a 0.03 km radius of the plot
 coordinates. <strong>Remotely sensed leaf trait
 data</strong> – Based on a broader set of field campaigns,
 Aguirre-Gutiérrez et al., (2021) used Sentinel-2, climatic, topography,
 and soil data to create remotely sensed canopy trait maps for P=phosphorus
 % leaf concentration, WD = wood density g.cm<sup>-3</sup>, and
 LMA=Leaf mass area g m<sup>-2</sup>. 
 <strong>Other data layers – </strong>We
 compared<strong> </strong>% one peak to several other
 climates, soils, leaf traits, and ecoregion maps listed below for the
 Amazon basin. Each dataset had its own resolution, which we standardized
 to 0.1 by 0.1 degrees. We used total cation exchange capacity (CEC) from
 soil grids (Batjes et al., 2020) from 0-5cm in units of mmol(c)/kg. We
 averaged TerraClimate (Abatzoglou et al., 2018) data between 2000 and 2018
 for Vapor Pressure Deficit (VPD in kPa), Mean Monthly Precipitation (MMP)
 (mm/month), potential evapotranspiration (PET) and maximum and minimum
 temperature (°C).  <strong>Statistical
 analysis</strong> – We used the Matlab (Matlab, MathWorks Inc.,
 Natick, MA, USA) function “fitlm” to fit linear models to compare
 variables such as soil data, environmental data, leaf trait data (at 0.1°
 resolution) and GEDI structure data (300m and bigger resolution) to field
 biomass and NPP/GPP estimates. The P values listed are for
 the <em>t</em>-statistic of the two-sided hypothesis test. We
 used R to create a linear model to predict the best model ranked by AIC
 and parsimony using the dredge function from the MuMIn library (Bartoń,
 2009).  We also used the CAR package (Fox J & S, 2019) and the VIF
 command to test for multi-collinearity between variables. To account for
 spatial autocorrelation, we used Simultaneous Auto-Regressive
 (SAR<sup>err</sup>) models (F. Dormann et al., 2007) using the
 R library ‘spdep’ (Bivand, Hauke, & Kossowski, 2013). We tested
 different neighborhood distances from 10 km to 300 km and found that AIC
 was minimized at 80 km (Fig S3) and the corresponding correlogram showed
 reduced spatial autocorrelation (Fig S4). To predict leaf traits with the
 spectral information, we used the Partial Least Squares Regression (PLSR)
 (Geladi & Kowalski, 1986) using the PLSregress command in Matlab
 (Matlab, MathWorks Inc., Natick, MA, USA). To avoid over-fitting the
 number of latent factors, we minimized the mean square error with K-fold
 cross-validation. We use 70% of our data to calibrate our model and then
 the remaining 30% to test the accuracy of our model using
 r<sup>2</sup>. We use adjusted r<sup>2 </sup>which
 penalizes for small sample sizes throughout the manuscript.
description: # Satellite-derived trait data slightly improves tropical forest biomass,
 NPP, and GPP predictions
 [https://doi.org/10.5061/dryad.ttdz08m5n](https://doi.org/10.5061/dryad.ttdz08m5n) The dataset contains leaf trait and spectral data to create Figures 1 and 2. It contains plot biomass data and satellite-derived leaf trait and structure data to create Figures 3-6. It contains plot NPP, GPP, and satellite-derived leaf trait and structure data to create Figures 7-8. ## Description of the data and file structure, including the associated Code/Software The Matlab code Finalcode_GEDIbiomass_Doughty2024.m contains all the code and data to create all the figures in the paper. The code has several sections that can be run independently. The first section starting on line 1 uses the dataset traitcompare.mat to create Figure 1. This dataset contains two tables of leaf trait data called carnegiechem and merged. Units and column names are contained within the table. The second section starting on line 62 uses the dataset traitgedidat.mat to create Figures 3-5 and Figures S1 and S2. This dataset contains a table called agball with plot biomass and coordinates. It also has the trait and GEDI data for these plots in nested structures. Units and descriptions are given in the code. The third section starting on line 443 uses the datasets traitgedidat.mat and soilclimdata.mat to create Figure 6. The dataset traitgedidat.mat is the same as described above and soilclimdata.mat contains 0.1 by 0.1 degree gridded data for climate variables like Tmax (C) or VPD (Pa) or soil chemistry like CEC. Units and description are given in the code. The forth section starting on line 536 uses the dataset Tamtreeheight.mat to create Figures 7 and 8. The dataset gedivsplot.mat contains table data with plot data, and nearby trait and GEDI data for several GEM plots. Units and description are given in the code and the tables. The fifth section starting on line 791 uses the dataset CombspecDBH.mat to create Figure 2. The dataset has variables specallz which is the leaf spectral data from 350-1075 nm for each leaf and dbhz1 with is the corresponding tree dbh (cm). It also has LMAz which is the LMA data with datz as the corresponding spectral data. To estimate spatial autocorrelation and the best model by AIC to create Table 1 and Figures S3 and 4, we used the R code processgedidata.r and the dataset biomass_trait_GEDI.xlsx. This dataset contains a table with latitude, longitude, field biomass and remote sensed biomass (Mg Ha-1), and traits LMA (g m2), Phosphorus (%), tree height (m), HOME (m) and % one peak (unitless). ## Sharing/Access information Original GEDI data are available from the USGS.