Environmental Research Letters
LETTER • OPEN ACCESS
Exploring the relation between remotely sensed vertical canopy structure
and tree species diversity in Gabon
To cite this article: Suzanne Mariëlle Marselis et al 2019 Environ. Res. Lett. 14 094013
 
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Environ. Res. Lett. 14 (2019) 094013 https://doi.org/10.1088/1748-9326/ab2dcd
LETTER
Exploring the relation between remotely sensed vertical canopy
structure and tree species diversity in Gabon
SuzanneMariëlleMarselis1,13, HaoTang1 , JohnArmston1, KatharineAbernethy2,10, AlfonsoAlonso4,
Nicolas Barbier5, Pulchérie Bissiengou6, Kathryn Jeffery2, DavidKenfack7, Nicolas Labrière3,
Seung-Kuk Lee1, SimonLLewis8,9, HervéMemiaghe10, JohnRPoulsen11, LeeWhite2,9,12 andRalphDubayah1
1 Department ofGeographical Sciences, University ofMaryland, College Park,MD,United States of America
2 Division of Biological andEnvironmental Sciences Tropical Ecology andConservation, University of Stirling, FK9 4LA,United Kingdom
3 Laboratoire Evolution et Diversité Biologique, Toulouse, France
4 Center for Conservation and Sustainability, SmithsonianConservation Biology Institute,Washington,DC,United States of America
5 AMAP, IRD, CNRS, INRA,UnivMontpellier, CIRAD,Montpellier, France
6 Institut de pharmacopée et demédecine traditionnelle (HerbierNational duGabon), CENAREST, Libreville, Gabon
7 Center for Tropical Forest Science—Forest Global EarthObservatory, Smithsonian Tropical Research Institute, Smithsonian Institution,
WashingtonDC,United States of America
8 Department ofGeography, University College London, London, UnitedKingdom
9 School of Geography, University of Leeds, Leeds, UnitedKingdom
10 Institut de Recherche en Ecologie Tropicale (IRET), CENAREST, Libreville, Gabon
11 Nicolas School of the Environment, DukeUniversity, Durham,NC,United States of America
12 AgenceNationale des ParcsNationaux (ANPN), Gabon
13 Author towhomany correspondence should be addressed.
E-mail:marselis@umd.edu
Keywords: biodiversity, radar, lidar, LVIS, ICESat, GEDI, TanDEM-X
Supplementarymaterial for this article is available online
Abstract
Mapping tree species diversity is increasingly important in the face of environmental change and
biodiversity conservation.We explore a potential way ofmapping this diversity by relating forest
structure to tree species diversity inGabon. First, we test the relation between canopy height, as a
proxy for niche volume, and tree species diversity. Then, we test the relation between vertical canopy
structure, as a proxy for vertical niche occupation, and tree species diversity.We use large footprint
full-waveform airborne lidar data collected across four study sites inGabon (Lopé,Mabounié,
Mondah, andRabi) in combinationwith in situ estimates of species richness (S) and Shannon diversity
(H′). Linearmodels using canopy height explained 44% and 43%of the variation in S andH′ at the
0.25 ha resolution. Linearmodels using canopy height and the plant area volume density profile
explained 71%of this variation.Wedemonstrate applications of thesemodels bymapping S andH′ in
Mondah using a simulatedGEDI-TanDEM-X fusion height product, across the four sites usingwall-
to-wall airborne lidar data products, and across and between the study sites using ICESat lidar
waveforms. Themodeling results are encouraging in the context of developing pan-tropical structure-
diversitymodels applicable to data from current and upcoming spaceborne remote sensingmissions.
1. Introduction
Spatial information on tree species diversity is impor-
tant to enable effective conservation and biodiversity
management (Turner et al 2003) and allow for a better
understanding of scale dependent relationships
between forest composition and productivity (Luo
et al 2019). Information on the local presence, absence,
and diversity of tree species has traditionally been
collected through in situ forest inventories (Rios-
Saldaña et al 2018). Yet, those are time-consuming and
expensive, which often prevents extensive and spatially
representative coverage. Using such inventories in
combination with remotely sensed data is one
approach to overcome some of these limitations and
relationships between remotely sensed environmental
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data (e.g. mean annual temperature, annual precipita-
tion, dry season length, etc) and vegetation species
have been developed to map Amazonian and Global
vascular plant diversity (Ter Steege et al 2003, Mutke
and Barthlott 2005). However, these approaches
incorporate little information on the vegetation itself
and the data products are provided at spatial resolu-
tions (typically ∼100 km grid cells) not optimized for
conservationmanagement.
Huston (1979) pointed out that there is a large
body of literature regarding the relation between forest
structure and the diversity of different taxa, while only
few studies explore the relation between forest struc-
ture and tree species diversity itself. It has since been
argued on several occasions that vegetation structure
could be related to tree species diversity via the occu-
pation of vertical niche space. This is the basis of a
potential structure-diversity relationship, and three
hypotheses have been proposed to explain it
(Kohyama 1996, Sheil and Burslem 2003, Marks et al
2016, Gatti et al 2017). First, the forest architecture
hypothesis asserts that tree species in the tropics have a
higher variance in adult tree height, corresponding to
higher species diversity, than in the temperate zone
where short-statured stands with trees of uniform
adulthood-height demonstrate less diversity
(Kohyama 1993, 1996). Second, the gap-size fre-
quency hypothesis, proposed by Sheil and Burslem
(2003), asserts that canopy gaps provide a range of
light conditions to which different species can bemore
or less specialized. A changing gap-size frequency dis-
tribution would allow different light regimes and thus
different species to grow (Denslow 1980). Third, the
height-diversity hypothesis asserts that a higher forest
volume provides greater niche space and would thus
likely result in a higher species diversity. A significant
positive relation between tree species diversity and tree
height, as a proxy for forest volume, was found,
explaining a limited amount of the variance in tree
species diversity within the USA and on a global scale
(Marks et al 2016, Gatti et al 2017). However, Givnish
(2017) objects that the use of just forest height leads to
deceptive results since it only provides information on
the potential trait space (volume below canopy) that
could be occupied, but does not measure the occupa-
tion of this trait space. In this study we address this
caveat by exploring the relation between tree species
diversity and the vertical vegetation structure; here-
after referred to as the structure-diversity relationship.
Active remote sensing systems, such as lidar and
radar provide information on the vertical arrange-
ment of the canopy (Bergen et al 2009). Specifically,
full-waveform lidar data provide detailed and more
direct measurements of the vertical profile of canopy
elements, which is available from the previously earth-
orbiting GLAS instrument onboard of the ICESat
satellite (Tang and Dubayah 2017), NASA’s airborne
Land Vegetation and Ice Sensor (LVIS) (Blair et al
1999, Tang et al 2012) and the recently launched
Global Ecosystem Dynamics Investigation (GEDI).
Such vertical canopy profiles providing information
on the amount of plant material (leaves and branches)
along the vertical axis have previously been used to
distinguish successional vegetation types in a tropical
savanna-forest mosaic in Gabon (Marselis et al 2018).
We expect that these profiles also provide information
on the occupation of available niche space between the
forest floor and the top of the canopy and can be rela-
ted to tree species diversity. The implication is that
mapping tree species diversity in the tropics may then
be feasible due to the launch of GEDI, which is collect-
ing billions of lidar waveforms between 51.6° latitude
north and south of the equator, in December 2018.
Data from other spaceborne missions, such as the
TanDEM-X radar and the ICESat-2 lidar missions,
may aid in the mapping process by gap-filling GEDI
data products, and increasing the spatial resolution
and extent of these data (Qi and Dubayah 2016, Lee
et al 2018).
The overall goal of this study is to explore the
structure-diversity relationship in a tropical forest and
savanna landscape in Gabon. In particular, we seek to
understand whether vertical canopy structure explains
more of the variance in tropical tree species diversity
than canopy height alone and whether we can use this
relationship to predict tree species diversity across
Gabon using different remote sensing datasets.
The remainder of the study is organized as follows:
(i) we first create linear models to relate canopy height
from lidar waveforms to tree species diversity from
field data; (ii) we then use two sets of metrics describ-
ing the vertical canopy profile and relate these to the
tree species diversity; (iii)we then compare the perfor-
mance of these models; (iv) and use the height model
to predict tree species diversity from LVIS canopy
height and fromGEDI-TanDEM-X fusion height pro-
ducts in Mondah; (v) and we apply the vertical struc-
ture model to gridded LVIS products and vertical
profiles derived from ICESat to predict tree species
diversity across and between four study sites.
2.Method
In this section, we introduce the four study sites and
the datasets used. We explain the processing of the
field and lidar data, followed by the model develop-
ment for testing the structure-diversity relationship
and the application of these models to create spatial
predictions of tree species diversity from different
active remote sensing data products.
2.1. Field data processing
Gabon was chosen as case-study location because of
the large availability of field and remote sensing
datasets collected during the AfriSAR campaign
(Fatoyinbo et al 2017). The four study sites in Gabon
are referred to as Lopé, Mondah, Mabounié and Rabi
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Environ. Res. Lett. 14 (2019) 094013
(figure 1) and each site includes multiple sampling
plots. All sites fall within the general classification of
tropical terra firme broadleaf forest, but have different
species compositions and assemblies, disturbance
history, and management regimes. The plots in this
analysis are distributed between savanna, successional,
degraded and old-growth tropical forest and are
situated in climate zones with different annual pre-
cipitation and temperature (table 1, see SI1 is available
online at stacks.iop.org/ERL/14/094013/mmedia for
detailed information). In Lopé twelve stem mapped
field plots (nine 1 ha, three 0.5 ha) were established in
2016 as part of the AfriSAR campaign. These data are
available through ForestPlots.net (Lopez-Gonzalez
et al 2009, 2011, Labrière et al 2018). TheMondah data
were also collected during the AfriSAR campaign in
2016 and are publicly available through theNASAOak
Ridge National Laboratory Distributed Active Archive
Center (ORNLDAAC) (Fatoyinbo et al 2018). The site
comprises fifteen 1 ha stem mapped plots, thirteen of
which are coincident with the LVIS lidar data. We
excluded three of the thirteen plots from the Mondah
site that contained a high percentage of unidentified
trees (SI1). Twelve 1 ha field plots were established in
Mabounié in 2012 (Labrière et al 2018), of which 10
are coincident with the LVIS lidar data and suitable for
this study. A 25 ha plot comprises the Rabi site. These
data were collected by the Smithsonian Conservation
Biology Institute, National Zoological Park, and the
Forest Global EarthObservatory (ForestGEO), and are
available on request through the ForestGeo website14.
From the Rabi plot, we selected the thirteen non-
adjacent hectares and considered them as separate
plots for the analysis. Subsampling kept this study site
fromdominating themodels, as the Rabi site consisted
of a total sampled area twice as large as the other sites
(figure 2(a)). Field data collection at the four sites was
performed by different people and organizations,
which led to datasets with varying characteristics (in
terms of e.g. plot layout and minimum DBH mea-
sured). Details about the study sites and field datasets
are described in SI1. For consistency among the sites
we only included trees with DBH…10 cm in our
analysis. The availability of stem maps (in Lopé,
Mondah, and Rabi) and subplots (in Mabounié)
enabled testing of the structure-diversity relation at
different resolutions. This was necessary because
species diversity and plot size are not linearly related
(species-area curve, described by MacArthur and
Wilson 1967) and no optimal resolution has been
identified for the structure-diversity relation. Smaller
plots were created by subdividing each original plot
into smaller squares or rectangles to create 5 spatial
resolutions: 1, 0.5, 0.25, 0.0625 and 0.04 ha
(figure 2(b)). Mabounié data were only used at the 1 ha
and 0.04 ha resolution, due to the absence of stem
maps. At each resolution only plots with at least one
identified tree were included in the analysis. This
resulted in respectively 41, 64, 128, 481 and 935 plots
for the resolutions 1.0 ha through 0.04 ha. Tree species
diversity was then quantified for each of the plots at all
resolutions using two variables: the Shannon diversity
(H′) and tree species richness (S) expressed as the total
number of tree species per area (Morris et al 2014).
2.2. Lidar data processing
Full waveform lidar data were collected using the LVIS
instrument with a 1 km swath width, a 1064 nm
wavelength and a nominal footprint diameter of
∼20 m across all study sites as part of the AfriSAR
campaign in February andMarch 2016. The following
metrics were derived from all lidar waveforms using
mature, validated and published algorithms: Relative
Height 100 (RH100), Plant Area Index (PAI) at 5 m
Figure 1. Field and lidar datasets from four regions (Mondah,Mabounié, Rabi and Lopé) in central andwestGabon are used in this
study. LVIS lidar acquisitions are displayed as gridded canopy height (m). Insets showdistribution of field plots across each study site,
markers are not to scale offield plot size.
14
https://forestgeo.si.edu/sites/africa/rabi
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Environ. Res. Lett. 14 (2019) 094013
Table 1. Information for each of the study sites regarding the total sampled area, temperature and precipitation, the tree density, species richness and Shannon diversity calculated including only trees withDBH…10 cm, at the 1 ha
resolution.
Tree density (trees/ha) Total no. of
species
Species richness (S) (No.
species/ha)
Shannon diversity (H′)
(H′/ha)
Name
Total
Area (ha)
Mean annual temper-
ature (°C)
Mean annual precipitation
(mm/y)
Total no. of
trees Range Mean std Range Mean Std Range Mean Std
Lope 10.5 26–33a 1500a 3140 9–501 308 174 118 2–54 32 21.6 0.64–3.08 2.15 0.90
Mondah 10 25b 3000–3500b 2368 26–453 237 173 139 7–75 32 21.3 1.46–3.58 2.60 0.69
Mabounié 10 26b 2030b 3537 222–444 354 61 183 44–68 54 7.7 2.90–3.61 3.27 0.23
Rabi 13 24–28c 2300c 6056 231–689 466 119 211 55–101 82 13.5 3.35–4.04 3.70 0.18
a White (2001).
b Labrière et al (2018).
c Lee et al (2006).
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nviron.R
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(2019)094013
vertical resolution forming the Plant Area Volume
Density (PAVD) profile and cumulative PAI at 5 m
vertical resolution (Blair et al 1999, Tang et al 2012,
Marselis et al 2018). These footprint-level metrics are
publicly available as part of the AfriSAR deliverables
through the NASA ORNL DAAC (Tang et al 2018).
RH100 was used to represent canopy top height. We
chose the PAVD and cumulative PAI profile because
they describe the vertical vegetation structure as a
biophysical property that can also be derived fromdata
from other instruments such as ICESat and GEDI,
which enables transferability of the models. Addition-
ally, they have previously been used to understand
structural and functional differences between vegeta-
tion communities (Cuni-Sanchez et al 2016, Decuyper
et al 2018, Marselis et al 2018). For each plot, the
average of all waveform metrics and the standard
deviation of Canopy Height (RH100_sd) were calcu-
lated using all waveforms with the footprint center
locatedwithin the plot boundary.
2.3.Data analysis
The field and lidar data processing resulted in five
datasets, one for each spatial resolution, comprised of
n samples, with n equivalent to the number of plots at
the specific resolution. Each sample had one value for
S, H′ and each of the lidar metrics. Four sets of lidar
metrics were created to test the structure-diversity
relationship (table 2). We built linear models using
metric sets 1 through 4 at each of the five spatial
resolutions. Variable importance could not be rigor-
ously assessed for the individual metrics due to
collinearity between themetrics describing the vertical
profile, since the existence of plant material higher in
the canopy is dependent on the existence of material
lower in the canopy. For each model, we performed
leave-one-plot-out cross-validation. All plots within a
1 ha plot were kept aside in the cross-validation
approach for models built at the 0.5, 0.25, 0.0625 and
0.04 ha resolutions, to avoid overestimation of model
performance by the model being trained towards the
local variationwithin the 1 ha plot. For eachmodel, we
calculated the R-squared (R2) and the root mean
squared difference, as a percentage (RMSD%) of the
mean observed S and H′, from the cross-validated
predictions to evaluate model performance (Piñeiro
et al 2008). Bias was evaluated as the intercept of the
linear relation between predicted (x-axis) and
observed (y-axis) S and H′. An intercept not signifi-
cantly different from 0 indicated an unbiased
Figure 2. (a)Thirteen 1 ha plots (dark grey)were selected from the 25 haRabi plot and included in this study. (b) Subdivision of 1 ha
plots to allow for analyzing the structure-diversity relationship at 5 spatial resolutions led to plots of sizes I. (100×100 m), II.
(100×50 m), III. (50×50 m), IV. (25×25 m) andV. (20×20 m).
Table 2. Sets ofmetrics used in linearmodels predictingH′ and S.
Metric set Model name Explanatory variables
1 Height RH100
2 Height and standard deviation of height RH100,RH100_sd
3 PAVDprofile+height P5a, P10, P15, P20, P25, P30, P35, P40, P45, P50b, RH100
4 Cumulative PAI profile+height CP5c, CP10, CP15, CP20, CP25, CP30, CP35, CP40, CP45, CP50, RH100
a P5=PAI between 0 and 5m aboveground, subsequently P10= the PAI between 5 and 10metc.
b P50=PAI between 45 and 50mabove ground, in case vegetation is higher than 50m this value indicates the PAI above 45m.
c CP5=Cumulative PAI between 0 and 5m aboveground, subsequently CP10= the PAI between 0 and 10m, etc.
5
Environ. Res. Lett. 14 (2019) 094013
prediction. The slope of the linear relation indicated
the consistency of the predicted and observed vari-
ables. A slope significantly different from 1 indicated
no consistency (Piñeiro et al 2008). We used 95%
confidence intervals around theR2 to evaluate whether
metric sets 3 and 4 (canopy structure) led to signifi-
cantly better diversity predictions than sets 1 and 2
(canopy height).
We used the canopy height and standard deviation
of canopy height model at the 0.25 ha resolution to
predict S andH′ forMondah from two datasets: (1) the
canopy height product from LVIS gridded at 50 m and
(2) a simulated GEDI-TanDEM-X fusion height pro-
duct at 20 m resolution (Lee et al 2018). TanDEM-X is
a twin X-band SAR satellite mission from which the
HH polarization was used for the fusion product.
GEDI simulations were created from the LVIS wave-
forms using the GEDI simulator, considering 50%
cloud cover (Lee et al 2018, Hancock et al 2019). We
resampled the GEDI-TanDEM-X product to 50 m
resolution and computed mean and standard devia-
tion of forest height for each cell.
We used the PAVD (starting at P10) and canopy
height model at 0.25 ha resolution to predict S and H′
for all four regions. We applied these models to (1)
LVIS data products gridded at 50 m resolution and (2)
ICESat waveforms collected within and between the
four study sites. We used all ICESat waveforms col-
lected between 2004 and 2006. P5 was omitted as it has
a low accuracy due to the interference of slope with
vegetation close to the surface within the nominal
footprint of 50 m.
3. Results
We first provide the results of the linear models using
canopy height alone to predict S and H′. Then we
provide the results of the models using the vertical
canopy profile metrics to predict S and H′. Last, we
show the spatial predictions of S and H′ created using
the calibrated and validatedmodels.
3.1.Modeling results using canopy height
The cross-validated linear models using canopy height
(set 1) explains a limited percentage of the variance in
H′ and S. At the 1.0 ha resolution, 28% and 35% of the
variation in H′ and S is explained (R2=0.28 and
R2=0.35). At the 0.25 ha resolution R2=0.43 and
0.44 (figure 3 and SI2). Incorporating information on
canopy height variation (set 2) increases R2 to 0.58 and
0.52 at the 0.25 ha resolution. However, the R2
confidence intervals comparing the model perfor-
mance using metric set 1 and 2 are overlapping at all
spatial scales, suggesting that adding information on
the variance in canopy height within a plot does not
necessarily lead to better predictive models
(figure 4, SI2).
3.2.Modeling results using canopy structure
Including information on the vertical canopy profile
(set 3) increases the explained variance in both H′ and
S to 71% at the 0.25 ha resolution, a significant
improvement in model performance (figure 4 and
SI2). Metric sets 3 and 4, including two different
expressions of the vertical canopy profile, generally
lead to similar results and only the results of set 3 are
discussed in the remainder of the paper (SI2).
Our results show the best model performances for
the structure-diversity relation (highest R2, lowest
RMSD% and unbiased predictions) at the 0.25 ha
resolution, for both H′ and S models (figures 4 and 5
and and SI2).
Figure 3.Canopy height explains up to 44%of the variation in tree species richness and 43%of the variation in Shannon diversity
across the study sites inGabon (cross-validated results).
6
Environ. Res. Lett. 14 (2019) 094013
3.3. Predictivemodeling results
The 0.25 ha linear models using metric set 2 (model
performance for H′:R2=0.58, RMSD=24%; and S;
R2=0.52, RMSD=40%)were used tomapH′ and S
in Mondah from (1) LVIS gridded products and (2)
GEDI-TanDEM-X gridded products (figure 6). GEDI-
TanDEM-X derived predictions for both species rich-
ness and Shannon Diversity are consistent with the
LVIS predictions (R2=0.68 andR2=0.66) but over-
all biased slightly low (figure 6).
Spatial predictions of H′ and S for all study sites
were created from the LVIS wall-to-wall data products
and ICESat waveforms using the 0.25 ha linear models
with the PAVD profile starting at P10 to P50 and
canopy height (model performance for H′: R2=0.71,
RMSD=20%; and S; R2=0.71, RMSD=31%)
(figure 7). The ICESat footprints overlapping with the
wall-to-wall lidar show ICESat predictions of S and H′
are slightly higher than predictions from LVIS. Shan-
non diversity predictions (with R2=0.66 and
RMSD=26%) are more accurate than the species
richness predictions (with R2=0.62 and
RMSD=38%).
4.Discussion
Our study presents regional results relating canopy
height and vertical canopy structure, derived from
active remote sensing data, to tree species diversity in
African tropical forest. We first discuss the modeling
results and the relationship between canopy structure
and tree species diversity. Then we discuss some
limitations to our approach and close with an outlook
on the applications of the structure-diversitymodels.
4.1.Model performance
Canopy height has been hypothesized to be related to
tree species diversity by assuming canopy height is a
proxy for canopy volume, representing the available
niche space. Our study shows a significant relationship
between canopy height and the Shannon index and
species richness; affirming that canopy height explains
a limited percentage of the variation in tree species
diversity. These results are in line with previous results
found in the USA and globally by Marks et al (2016)
and Gatti et al (2017). We expected that including
information on the variance in canopy height within a
Figure 4 Linearmodels using height alone as a predictive variable generally show the lowestR2 and highest error (RMSD%). Adding
variation in canopy height (RH100_sd) to themodel leads to slightly better, but not statistically different,model performance (see
overlapping error bars on the R2 plots). Incorporating information on canopy structure significantly improves the S andHmodels at
the 0.04–0.25 ha resolutions, when compared to using height alone. Error bars are larger at larger plot size because of smaller sample
sizes.
7
Environ. Res. Lett. 14 (2019) 094013
plot might lead to better predictive models as the
combination of metrics provides a more accurate
proxy for forest volume than canopy height alone. Our
models show an increase in R2 across all resolutions,
but the improvement is not statistically significant.We
then hypothesized that including information about
the occupancy of vertical space, implemented by
including the PAVD profile in the canopy height
model, would lead to better predictions of tree species
diversity. The PAVD profile is a direct measure of the
degree of occupation of vertical niche space (divided in
5 m height bins) by plant material (PAI index) and
thus resembles occupied niche space instead of avail-
able niche space. Our results support the hypothesis as
we find a statistically significant improvement in
model performance at the 0.04–0.25 ha resolutions
when including the PAVD profile. At larger plot sizes
(0.5 and 1.0 ha) the R2 values increased but not
Figure 5.Predicted versus observed species richness and Shannon diversity resulting from themodels usingmetric set 3 (PAVD
profiles and height), points are colored by study site. 1.0 ha resolutionmodels for S are significantly biased, 0.25 hamodels show
highest accuracy and statistically unbiased predictions for both species richness and Shannon diversity.
8
Environ. Res. Lett. 14 (2019) 094013
Figure 6. Shannon diversity and species richness predicted inMondah using canopy height (RH100) and standard deviation of canopy
height (RH100_sd). Predictions created fromgridded LVIS data products and a simulatedGEDI-TanDEM-X fusion product. Points
in center panel are colored by density.
Figure 7. ICESat data enabled predictions ofH′ and Swithin and between the study sites. ICESat richness predictions were biased
slightly high compared to LVIS predictions. Points in center panel are colored by density.
9
Environ. Res. Lett. 14 (2019) 094013
significantly.We tested the structure-diversity relation
at different resolutions because neither species rich-
ness nor Shannon diversity is linearly related to plot
size (MacArthur and Wilson 1967, Hill 1973). Our
models performed best at the 0.25 ha resolution (a
decrease in performance was found for both smaller
and larger plot sizes). We consider two potential
explanations for this phenomenon. The first one is
related to the quality of measurement: large plots tend
to have greater sampling errors caused by a smaller
sample size, while smaller plots are less precise due to a
higher influence of geolocation error (though mini-
mized by Labrière et al (2018), a small misalignment
between plot and lidar data may still result in poorer
model performance). The second potential explana-
tion is the maximum natural variation in the struc-
ture-diversity relation at an intermediate resolution.
In a small plot only a limited number of trees could
possibly grow due to space constraints, reducing the
total variability or upper boundary of tree species
diversity. By contrast, the amount of different tree
species may get close to a certain maximum in a large
plot (as species-area curves suggest) while similar
canopy structure may be found as in smaller plots. In
our study, it is at an intermediate resolution that the
largest variation in tree species/plot was found, as
indicated by the highest coefficient of variation in tree
species richness at the 0.25 ha resolution (SI3). This
trend is consistent with the trend of canopy structure
with its maximum coefficient of variation at the
0.25 ha resolution. Yet, it is difficult to decouple the
two factors due to the limited data sets in this study,
and additional research would be needed to better
identify the ecological mechanisms behind this
phenomenon. Nonetheless, the modeling results for
predicting Shannon diversity and species richness
follow the same trend—for both diversity variables the
PAVD profile with height provides a significantly
better model than using height alone at the 0.25 ha
resolution—strengthening our confidence in the exis-
tence of a structure-diversity relationship across these
study sites inGabon.
4.2. Limitations
Time lag between field and lidar data collection was
minimized, but we had to accept a maximum time-lag
of five years (Rabi) which may have affected the
strength of the structure-diversity relationship. The
new census of the Rabi field data, which is under
collection at the time of writing, may provide more
insight on tree species diversity change over time and
the effect of time-lag in the structure-diversity
relation.
The diversity metrics used to establish the struc-
ture-diversity relation rely heavily on the accuracy of
species identification in the field. Tree species identifi-
cation in the tropics can be very complex and highly
trained local botanists are needed to provide the most
accurate identification. In most of our study sites
(Rabi, Mabounié and Lopé) the percentage of trees of
unknown species was low, but in Mondah this
percentage was much higher because the species iden-
tification was carried out by botanists with knowledge
of vascular plant taxonomy in Gabon, but not specifi-
cally for the Mondah region (SI1). Slik et al (2015)
showed that higher percentages of unidentified trees
can affect model outcomes, which should be kept in
mind when evaluating our results and future research
could assess the specific impact of unidentified trees
on our models, for example by re-inventory of the
trees inMondah.
In our study, we were not able to include small
trees because only two field inventory datasets inclu-
ded trees with DBH…1 cm, thus we included only
trees with DBH…10 cm to allow for consistency
across all study sites. However, Fricker et al (2015)
showed that the inclusion of small trees in Barro Col-
orado Island (Panama) improved their model perfor-
mance (relating species richness to various metrics
describing terrain, hydrology and canopy structure)
from R2=0.25 when using trees with DBH…10 cm
to R2=0.35 when using trees with DBH…1 cm.
More information on small trees will be needed to
assess their specific impacts on the models created in
this study.
The plots included in the analysis were distributed
across different vegetation types: savanna, forest with
different degrees of degradation, successional forest
and low-disturbance old-growth tropical forest. It is
likely that the encountered structure-diversity rela-
tionship is driven partly by this gradient in forest types.
More undisturbed old-growth tropical forest plots
would be needed to study the structure-diversity rela-
tionship within old-growth forest. Additionally, it is
yet unclear how the structure-diversity relationship
holds or changes across different biogeographic
regions, with changing limitations to tree growth, and
different climatic niches that may change the canopy
structure and the use of vertical niche space. Future
research should focus on the transferability of the
structure-diversity models to other regions and con-
tinents to establish the potential of using this method
pan-tropically or potentially across multiple biomes.
Lastly, studying how the structure-diversity relation-
ship changes between the local-scale, such as in a large
plot like BCI (Fricker et al 2015; Wolf et al 2012), and
at a regional scale, such as developed here and in Mao
et al (2018) and Robinson et al (2018), will be impor-
tant to ultimately evaluate the structure-diversity rela-
tionship pan-tropically.
4.3. Applications and future research
The commissioning of new satellite missions, provid-
ing information on vertical canopy structure, is an
important incentive to explore the potential applica-
tions of the structure-diversity relationship. Here we
10
Environ. Res. Lett. 14 (2019) 094013
demonstrated the application of the developedmodels
to wall-to-wall LVIS data products to create predictive
maps of diversity and richness in the four study sites.
The spatial area covered by these wall-to-wall products
is limited due to the airborne nature of the data and
data gaps occur as the laser energy does not penetrate
clouds, which are often prevalent over Gabon. But, the
real strength of our modeling approach lies in its
potential to be applied to data from other instruments
such as ICESat, GEDI, and TanDEM-X. The ICESat
predictions overlapping with the LVIS wall-to-wall
products showed S and H′ predictions around the 1:1
line. The scatter around the 1:1 line is partly caused by
the model accuracy, the time-lag between these two
data products (collected 10–12 years before the LVIS
data) and the accuracy of the ICESat data products.
But, the major drawback for using ICESat is the sparse
sampling, leaving tens of kilometers between tracks
over Gabon. On the contrary, the recently launched
GEDI lidar mission will provide much denser lidar
sampling with between-track spacing of 600 m and
along-track spacing of 60 m. However, the nominal
footprint size (∼22 m diameter) is close to the smallest
resolution tested here and our modelling results
showed a higher predictive error compared to the
models that can be applied directly to ICESat data. On
the other hand, fusion of the GEDI waveforms with
other active remote sensing data, such as from
TanDEM-X radar, has the potential to change this
resolution, fill in data gaps and provide wall-to-wall
data products allowing for the application of models
developed at different resolutions (Qi and
Dubayah 2016, Lee et al 2018). It may then also be
possible to build structure-diversity models directly
on fused GEDI-TanDEM-X data products when these
data become available more widely after GEDI’s data
collection. So far, it has been shown that canopy height
can be retrieved using GEDI-TanDEM-X fusion, but,
if we would be able to extract more information on the
vertical canopy profile using GEDI and TanDEM-X
data fusion, or fusion with another interferometric
sensor such as on the BIOMASS mission, it may be
possible to accomplish true wall-to-wall mapping of
tree species diversity in the pan-tropics and beyond.
5. Conclusion
Canopy height alone, used as a proxy for niche
volume, can predict a limited percentage of the
variation in tree species diversity across a savanna-
tropical forest landscape in Gabon, Africa. Including
information on the vertical canopy structure, used as a
proxy for vertical niche occupation, derived from large
footprint full-waveform lidar data improved these
models significantly. This structure-diversity relation-
ship and themodels developed here show potential for
application to data from active remote sensing satel-
lites such as ICESat, TanDEM-X, BIOMASS and
GEDI. Further research is encouraged to study the
structure-diversity relationship across the tropics to
establish the potential of mapping pantropical tree
species diversity using measurements of canopy
structure.
Acknowledgments
This work is supported by NASA Headquarters under
the NASA Earth and Space Science Fellowship Pro-
gram—Grant 80NSSC17K0321, under theNASANew
Investigator Program—Grant 80NSSC18K0708,
under NASA’s Global Ecosystem Dynamics Invest-
igation (GEDI, Grant NNL15AAO3C) and NASA’s
AfriSAR campaign. The AfriSAR campaign was con-
ducted in Gabon in collaboration with l’Agence
gabonaise d’études et d’observation spatiale (AGEOS)
and the Agence Nationale des Parcs Nationaux
(ANPN). Research permission was granted by the
Centre National de la Recherche Scientifique et
Technologique (CENAREST—permit numbers
AR0011/17/MESRSFC/CENAREST/CG/CST/
CSAR and AR0024/10/MESRDT/CENAREST/CG/
CST/CSAR) and the Agence Nationale des Parcs
Nationaux (ANPN). Lidar data collection in Lopé,
Mondah, Mabounié and Rabi was carried out by the
NASALandVegetation and Ice Sensor (LVIS) team led
by Bryan Blair,MichelleHofton andDavid Rabine and
hosted by AGEOS (Ghislain Moussavou, Tanguy
Gahouma), we express our sincere gratitude to their
team for their effort to collect these data.
We are grateful to ANPN and AGEOS for the pro-
vision of logistical support that facilitated both field
work and lidar data collection, specifically to Flore
Koumba Pambo, Josue EdzangNdong andDavid Leh-
mann from ANPN for their support. The field data
collection in Lopé was funded by ESA through the
AfriSAR campaign, coordinated by Simon Lewis and
guided by Nicolas Labrière, and hosted by ANPN and
the University of Stirling at the SEGC field station. We
would like to specifically thank Carl Ditougou,
Pacôme Dimbonda, Arthur Dibambou, Edmond
Dimoto, and Napo Milamizokou. The field data col-
lection in Mondah was funded by NASA through the
AfriSAR campaign, coordinated and guided by John
Poulsen, and hosted by ANPN. The field data collec-
tion in Mabounié was led by Nicolas Barbier and per-
formed in collaboration with the Missouri Botanical
Garden (Tariq Stevart) and Golder Associates and we
would specifically like to thank P Ploton, V Droissart,
and Y Issembe for their help and expertise. The field
data collection in Rabi was funded through Shell
Gabon and the Smithsonian Conservation Biology
Institute. The data were collected under guidance of
Alfonso Alonso, HerveMemiaghe, David Kenfack and
Pulcherie Bissiengou. This is contribution No. 171 of
the Gabon Biodiversity Program. We express our
11
Environ. Res. Lett. 14 (2019) 094013
sincere gratitude to everyone involved in these field
campaigns and for making the data available for this
study.
ORCID iDs
HaoTang https://orcid.org/0000-0001-7935-5848
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