Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics.

Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics. Here we analyzed 2 cm resolution RGB imagery collected by a Remotely Piloted Aircraft System (RPAS), also known as drone, together with two decades of bi-annual tree censuses for 2 ha of old growth forest in the Central Amazon. We delineated all crowns visible in the imagery and linked each crown to a tagged stem through field work. Canopy trees constituted 40% of the 1244 inventoried trees with diameter at breast height (DBH) > 10 cm, and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree diameter, passing through 50% at 23.5 cm DBH. Diameter growth was on average twice as large in canopy trees as in understory trees. Growth rates were unrelated to diameter in canopy trees and positively related to diameter in understory trees, consistent with the idea that light availability increases with diameter in the understory but not the canopy. The whole stand size distribution was best fit by a Weibull distribution, whereas the separate size distributions of understory trees or canopy trees > 25 cm DBH were equally well fit by exponential and Weibull distributions, consistent with mechanistic forest models. The identification and field mapping of crowns seen in a high resolution orthomosaic revealed new patterns in the structure and dynamics of trees of canopy vs. understory at this site, demonstrating the value of traditional tree censuses with drone remote sensing.

Introduction

Scientists have long sought to understand tropical forest structure and dynamics—the abundances of trees of different sizes and canopy positions, and their growth and mortality rates [1]. Whether trees are in the canopy (i.e., directly exposed to overhead light) or not has long been recognized as a critical determinant of tree performance [2-6]. However, the tall canopy height and dense understory of many tropical forests often make it difficult to establish whether an individual tree is in the canopy or in the understory, because it is hard to see the tops of crowns and assess their light exposure from the ground. Cameras mounted on drones, technically called remotely piloted aircraft systems (RPAS), produce high spatial resolution images with pixel size < 5cm that enable the visualization of individual tree crowns [7-12]. Thus, the integration of RPAS remote sensing and ground-based data provides the opportunity for the exact determination of canopy status to be linked with information on tree diameter, growth, etc., thereby enabling new insights into the structure and dynamics of tropical forests.

Remote sensing provides increasing amounts of information about tropical forests including phenology, photosynthesis, and functional composition, but the signal in many types of data is largely determined by canopy trees [13-16]. In contrast, ground-based plot data include both canopy and understory trees. Thus, a key issue in linking and integrating ground-based and remote sensing datasets is understanding which trees are in the canopy, and their role in the forest. In particular, what proportion of trees of different sizes are in the canopy, and what are their contribution to forest carbon stocks and woody productivity? It is well-known that larger trees are more likely to be in the canopy, that larger trees contribute disproportionately to forest carbon stocks, and that woody productivity increases with tree size and light exposure [17-25]. But few studies have quantified how canopy position varies with tree size and growth rates or how canopy trees contribute to growth rates and carbon stocks and fluxes. A rare exception is work combining airborne imagery and ground-based data for the old-growth moist tropical forest of Barro Colorado Island, Panama, to evaluate the canopy status of individual trees, quantify the proportion of trees in the canopy, and compare diameter growth of canopy and understory trees [26, 27].

Forest carbon stocks and productivity are closely related with tree size distributions, a fundamental attribute of forest structure [28-30]. Whole-forest tree size distributions can be understood as the sum of size distributions of understory and canopy trees, which are shaped by different processes [31]. However, to date, tests of theories explaining tree size distributions have been conducted exclusively at the level of the whole stand, without distinguishing between canopy and understory trees [32-36]. Metabolic ecology derives a power function tree size distribution from arguments regarding the scaling of metabolic rates with diameter, and specifically predicts that the diameter distribution follows a power function with exponent -2, i.e., N ~ D-2, for N trees of D diameter [32, 33]. Demographic equilibrium derives tree size distributions from the von Foerster equation and empirical relationships for growth and mortality with size, and predicts that diameter distributions will be better fit by Weibull and Quasi-Weibull functions [34-36]. In contrast, the more mechanistic approach of Farrior et al. [31] predicts that canopy trees will follow an exponential distribution whereas understory trees will follow a power function. The approach of Farrior et al. [31] parallels the structure of vegetation demographic models, taking into account multiple size-classes and light environments [37].

In this study, we use the combination of images collected with digital camera mounted on RPAS and detailed field mapping of tree crowns to determine the canopy status of individual trees and link this information to forest inventory data in an old-growth forest near Manaus, Brazil in the Central Amazon. We thus determine the proportions, growth rates and size distributions of canopy and understory trees, as well as the contributions of canopy and understory trees to total biomass and wood productivity. We specifically addressed the following questions: (i) What is the proportion of trees in the canopy and how does this vary with diameter? (ii) How do growth rates differ between canopy and understory trees, and how does that difference vary with diameter? (iii) What are the relative contributions of canopy and understory trees to aboveground biomass carbon stocks and aboveground wood productivity? (iv) What are the forms of size distributions of canopy trees and understory trees, how do they differ from each other and from whole-forest size distributions, and how do they fit with competing theories?

Materials and methods

Study site

The study was carried out in the northernmost 1020 m of a North-South Transect Plot that is located in the Estação Experimental de Silvicultura Tropical (EEST ZF-2) of the Instituto Nacional de Pesquisas da Amazônia (INPA) (Fig 1), a research reserve with 21,000 ha. The area is covered by old-growth terra-firme forest, characterized by a closed canopy with high tree species diversity and a dense understory [38, 39]. The North-South and East-West Transect Plots are permanent inventory plots that were installed in 1996 by the Jacaranda Project (a collaboration between INPA and Japan International Cooperation Agency, JICA) with dimensions of 20 x 2500 m each, totaling 10 ha. These transect plots were designed to representatively sample the dominant undulating topography of the region, which encompasses plateau, slope and valley positions and associated forest structural and functional differences (Fig 1b). The transects were subdivided into 20 m x 20 m subplots (125 for each transect) and the forest dynamics (growth, recruitment, mortality) was monitored with repeat censuses of trees with DBH > 10 cm (S1 File). Censuses were performed in 1996, 2000, 2002, 2004, 2006, 2008, 2010, 2011, 2013 and 2015, for a total of 10 inventories between 1996 and 2015, inclusive (S2 and S3 Files). The availability of ~two decades of bi-annual tree censuses and the proximity from the road (Fig 1) enabled the accomplishment of the present study in this plot.

Fig 1

Map of the study area.

(a) The EEST-ZF-2 study site is located 50 km north of Manaus, Brazil. (b) The North-South and East-West Transect Plots are permanent plots of 20 x 2500 m each. (c) The RPAS overflight covered the first 1020 m of the NS plot, which includes representation of plateau, slope and valley areas. Landsat-8 (a, b) and SRTM (c) images courtesy of the U.S. Geological Survey.

Image acquisition and processing

Digital RGB camera imaging with an RPAS was performed on the northernmost 1020 m of the North-South transect, covering 51 20 m x 20 m subplots, in April 2016 (Fig 1c). We employed a DJI Phantom 2 with an RGB camera mounted on a three-axis gimbal. We replaced the standard GoPro lens with a lens having a 5.5 mm focal length and 60° Field of View (FOV). The resulting photos have a resolution of 12 Mp with dimensions of 4000 x 3000 pixels. The flight was made at 80 m above ground, speed of 4 m.s-1 and spacing between flight lines of 10 m. Photos were taken every 1 second, covering an area of ~60 m wide at the height of the canopy. The minimum longitudinal and side overlap were 88% and 78%, respectively, in areas of peak canopy height on ridges; overlap was larger in areas with lower canopy height and in slope and valley areas.

We processed photos using the photogrammetry software Agisoft Photoscan (https://www.agisoft.com, v.1.3.0), which aligned the photos using the Scale Invariant Feature Transformation (SIFT) algorithm [40] and produced a point cloud model based on overlap among photos (because the camera was not integrated with the RPAS, GPS coordinates of the flight were not automatically assigned to photos). We georeferenced this model using as reference an Airborne Laser Scanning (ALS) dataset from the North-South Transect plot. We selected 15 control points evenly distributed across the flight area and extracted the XYZ coordinates (UTM—Universal Transverse Mercator projection Zone 20S, WGS84 horizontal datum). The control points were the center of crowns and palm trees, and the solar panels of two towers of the AmazonFACE project located in the plot, all well visible in both ALS data and photos. Georeferencing accuracy was assessed in terms of the Root Mean Square Error (RMSE) reported by the software. We then generated a 3D point cloud, a digital elevation model (DEM) and a 2 cm spatial resolution orthomosaic (e.g., Fig 2; S4 File) using Agisoft Photoscan.

Fig 2

Orthomosaic image showing canopy tree crowns mapped in the field.

Numbers in red are the assigned tree tags; the black lines correspond to the limits of each 20 m x 20 m subplots.

Crown delineation and linkage to tagged trees

Crowns visible in the orthomosaic were associated with tagged trees through field work, enabling us to link almost two decades of bi-annual forest dynamics data with detailed crown characteristics for the first time at this site. Maps with the orthomosaic were printed for each 20 m x 20 m subplot and crowns visible in the image were identified in the field, with reference to the forest inventory data on individual tree DBH, species identity, and tag number. We started delineating the crowns of trees with the biggest trunk diameters, looking from the base to the top and identifying the whole boundary of each crown. We drew the tree crowns boundaries on the map with their respective tag numbers. We then delineated the crowns of the smaller trees, identifying the neighboring trees which had crowns surrounding the biggest crowns. This in situ delineation of crowns allowed us to perceive that in some cases branches of the same tree, but located in different heights and positions, had different colors in the orthomosaic, such that examination of the imagery alone would suggest they belong to different individuals. After the field work, all the crowns were vectorized in GIS, with the creation of polygons to represent each crown (Fig 2; S5 File). We used the orthomosaic as the reference and vectorized the polygons using the same projected coordinate system. Finally, we returned to the field to address some questions that arose during the vectorization of crowns, and thereby minimize errors in the linkages of delineated crowns with forest inventory data.

Data analysis

Defining canopy status

For the purposes of this study, canopy trees were defined as those directly exposed to overhead light; other trees were classified as understory trees (as in Bohlman [27]). Specifically, trees were classified as being in the canopy if their crowns were totally or partially visible in the orthomosaic, and had a visible crown diameter greater than ~ 1.5 m. We note that some trees classified as canopy trees under this definition may have most of their crowns shaded and only a small part of the crown in direct light, and that small trees can be classed as canopy trees if they do not have larger trees above them. We also note that some trees classified as understory trees under this definition may received direct lateral light for part of the day.

Canopy status by size

We calculated the proportions of trees that were in the canopy (exposed) or understory (non-exposed) for each 10 cm wide diameter class, and for all trees combined. Confidence intervals on these proportions were obtained from 1000 bootstraps over the 20 m x 20 m subplots. To quantify size-dependence of canopy status, we conducted a logistic regression of canopy status against stem diameter (Eq 1): where α and b are parameters, x is the independent variable DBH in cm, and e is natural exponential basis.

Growth

We calculated the mean annual increment (MAI) of each tree from DBH measurements taken from 1996 to 2015 (1996, 2000, 2002, 2004, 2006, 2008, 2010, 2011, 2013, 2015). Specifically, we calculated the mean growth rate of each tree as the slope (b) of the regression of DBH against time. We included only trees that had at least four DBH measurements (i.e., trees that recruited in 2010 or earlier). We compared MAI distributions between canopy and understory trees using the Levene variance test and the Student’s t-test. We conducted log-log regressions of MAI against DBH, fitting separate functions for canopy and understory trees. Because negative and zero growth values cannot be included in log-log regressions, we replaced all negative MAI (16 trees) with half of the smallest positive MAI value for this analysis.

Predicting canopy status

We fitted logistic regressions for canopy status as a function of DBH alone, MAI alone, and DBH and MAI combined. We evaluated the classification accuracy of canopy/understory status based on the fitted functions (i.e., classifying trees as canopy or understory based on whether the predicted canopy probability was greater or smaller than 0.5, respectively) by calculating the Kappa concordance index and the global accuracy derived from the confusion matrix [41].

Contribution of canopy trees to carbon and above ground wood productivity

We estimated the above ground carbon stock of each tree using the biomass equation and water and carbon contents of Silva [42]: where AGB is the fresh above ground biomass in kg, DBH is the diameter at breast height in cm; AGC is the above ground carbon in kg, WC is the water content (here 0.408) and CC is the carbon content (here 0.485) [43]. We then calculated the proportional contribution of canopy trees to total estimated aboveground carbon.

We calculated the wood productivity (kg C.yr-1) of each tree and of the stand as a whole, and the proportional contribution of canopy trees, using two approaches. In the first approach, we calculated growth for 2011–2015 using observed DBHs for those years. In the second approach, we estimated DBH in 2011 and 2015 using the equation fit to the entire DBH time series from 1996 to 2015, thus effectively using average growth over that entire time period. In both cases, the observed or estimated DBHs in 2011 and 2015 were combined with Eqs 2 and 3 to estimate AGC on both dates, and their difference was used to calculate woody productivity in kg C.yr-1. Note that the first approach has the advantage that growth is closer in time to the canopy status measurements, but the disadvantage that individual measurement errors have more influence (in particular, some trees exhibited negative productivity).

Tree size distributions

We quantified the size distributions for all trees, canopy trees, and understory trees. In each case, we fit three alternative probability distributions—exponential, power, and Weibull (Eqs 4–6)–using maximum likelihood [44, 45]: where λ and k are fitted parameters, x is DBH in cm, and e is the natural exponential basis. Trees were first binned in 1 cm classes from Dmin = 11 cm to Dmax = 117 cm (the 10–11 cm size class was omitted from analysis because of inconsistencies in measurements associated with the lower size cutoff at 10 cm). For maximum likelihood estimation, the PDFs were normalized to sum to one over the focal diameter range, 11–117 cm. The maximum likelihood estimates of the parameters were those that maximized the likelihood function (Eq 7): where the summation is over the size class intervals i, N is the number of trees in size class i, F(x) is the cumulative probability distribution of f(x), x and x are the minimum and maximum DBH in size class i, and thus the quantity in square brackets is the total probability an individual is in size class i under the candidate parameters and probability density function. We used Akaike’s Information Criterion (AIC) to compare the goodness of fits of the different functions [46]. We obtained 95% confidence intervals on parameters from 1000 bootstraps over the 20 x 20 m subplots.

Results

Of the 1244 trees with DBH > 10 cm in the first 51 subplots of the NS Transect Plot, 40% (498 trees) were in the canopy, with at least parts of their crowns exposed and visible in the image. This represents a density of 249 canopy trees per ha. The proportion of trees in the canopy increased with diameter from 21% for trees 10–20 cm to 57% for 20–30 cm, up to 100% for trees above 70 cm (Fig 3, S1 Table). Logistic regression provided a reasonably good fit to the proportion of trees in the canopy (Fig 3). The fitted equation predicts that a tree 23.5 cm DBH has 50% probability of being in the canopy (Fig 3).

Fig 3

Proportions of trees in the canopy.

Observed proportions of trees in the canopy in each 10 cm DBH class (green points), together with the fitted logistic regression (solid green line). Dashed vertical bars give 95% confidence intervals from bootstrapping over subplots.

Growth of canopy trees averaged 2.34 mm.year-1 (CI 0.18 mm.year-1), just twice that of understory trees, which averaged 1.18 mm.year-1 (CI 0.07 mm.year-1; n = 484 and 696 trees, respectively; S1 Fig). Canopy and understory trees differed significantly in mean growth rates (t-test, p<0.001), and in the variances of growth rates (Levene variance test, p<0.001). Growth rates were approximately a power function of diameter for understory trees, and were not significantly related to diameter in canopy trees (Fig 4a and 4b). At small diameters, canopy trees had much higher growth rates than understory trees; this difference decreased with increasing diameter (Fig 4c).

Fig 4

Relationship of diameter growth with DBH for understory and canopy trees.

Fitted lines are linear regressions of log-transformed data, with solid lines indicating that the slope is significantly different from zero, and dashed lines that it is not.

The probability of a tree being in the canopy was reasonably well-predicted from DBH, and somewhat better predicted using DBH and prior growth (MAI) in combination (Table 1). Growth alone was not as good a predictor as DBH. The fitted logistic regression based on MAI alone crossed 50% probability of canopy status at 2.2 mm.year-1 (S2 Fig).

Table 1

Model coefficients and fit statistics for logistic regressions and associated classifications of canopy status of individual trees based on DBH, growth (MAI) or both combined.

	DBH only	Growth only	DBH and Growth
Intercept (a)	-3.276	-1.413	-3.723
DBH (b1)	0.140	NA	0.124
Growth (b2)	NA	0.658	0.457
Overall accuracy	0.764	0.681	0.790
Kappa	0.492	0.299	0.553

Canopy trees accounted for a disproportionately large share of carbon stocks and fluxes. Though canopy trees were only 40% of all trees greater than 10 cm DBH, they accounted for 67% of the total above ground carbon stocks. In terms of wood productivity, we estimated that canopy trees contributed 75% when using growth data for the last 4 years alone, and 68% of wood productivity when using growth data for the entire previous 19-year period.

The size distribution of canopy trees differed from those of understory trees, and both distributions differed from those of all trees combined (Fig 5; S4 Fig). Among the tested models, the best fit for all individuals was the Weibull distribution (Fig 5a and 5c; Table 2). The best fit for the understory trees was the exponential distribution, with the Weibull producing an almost equally good fit (Fig 5a and 5c; Table 2; S4 Fig). The canopy tree size distribution was unimodal (Fig 5b and 5d) and poorly fit by the exponential distribution (S3 Fig). Because trees with DBH ≥ 25 cm had more than 50% probability of being in the canopy (Fig 3), we took a DBH of 25 cm as a logical threshold diameter for separately fitting canopy trees. The size distribution of canopy trees with DBH ≥ 25 cm was best fit by the exponential function, with the Weibull producing a similarly good fit (Fig 5b and 5d; Table 2; S4 Fig).

Fig 5

Observed size distributions of understory trees, canopy trees, and both combined.

Size distributions of all 1244 trees with DBH > 10 cm (black points), understory trees (gray points), and canopy trees (green circles), shown together with best-fit probability density functions (lines). Distributions are shown on both linear (top) and log (bottom) scales. The vertical dashed black line indicates the minimum diameter (25 cm) for inclusion in the canopy tree fits. The Weibull distribution (Eq 6) had the best fit for all individuals combined; the exponential distribution (Eq 4) had the best fit for understory trees as well as for canopy trees with DBH ≥ 25 cm. Whereas data are graphed here for 10-cm size classes for visualization purposes, fits were carried out using 1-cm size classes (parameter values in Table 2).

Table 2

Parameter values and delta AIC values for maximum likelihood fits of exponential, power and Weibull probability density functions to size distributions for all trees, understory trees, canopy trees, and canopy trees ≥ 25 cm DBH.

Group	Distribution	λ (95% CI)	k (95% CI)	Delta AIC
All	Exponential	0.091 (0.085–0.097)		5.50
All	Power	2.539 (2.438–2.639)		77.26
All	Weibull	7.299 (4.914–9.762)	0.805 (0.691–0.925)	0.00
Understory	Exponential	0.165 (0.154–0.180)		0.00
Understory	Power	3.558 (3.410–3.741)		37.82
Understory	Weibull	4.206 (2.500–7.349)	0.851 (0.704–1.128)	0.54
Canopy	Exponential	0.056 (0.051–0.061)		39.49
Canopy	Power	1.734 (1.623–1.856)		167.64
Canopy	Weibull	27.082 (24.241–29.531)	1.641 (1.416–1.880)	0.00
Canopy≥25	Exponential	0.078 (0.070–0.086)		0.00
Canopy≥25	Power	3.451 (3.197–3.745)		18.91
Canopy≥25	Weibull	17.531 (5.528–27.771)	1.19 (0.716–1.796)	1.28

Delta AIC is the difference in AIC from the best model. The best-fit models for each dataset, and those within 2 delta AIC of the best model, are highlighted in bold.

Discussion

Our integration of high resolution drone imagery and forest inventory data enabled us to define canopy positions of individual trees, and quantify structural and dynamic contributions of canopy and understory trees in this old-growth tropical forest in Central Amazonia. We found that canopy trees constituted 40% of trees with DBH > 10 cm and accounted for ~70% of carbon stocks and wood productivity. Diameter growth of canopy trees was on average twice as large as that of understory trees, and was size-independent in canopy trees. The size distribution of canopy trees differed markedly from that of understory trees and from the whole-forest size distribution; distributions of understory trees and of canopy trees with DBH ≥ 25 cm were well fit by the exponential functions, whereas the whole-forest distribution was much better fit by a Weibull. These findings contributed to improve the understanding of the structure and dynamics of trees with similar light environments in tropical forests.

The probability of being in the canopy increased logistically with tree diameter, passing through 50% at 23.5 cm DBH. Overall there were 249 trees.ha-1 in the canopy, 40% of trees > 10 cm DBH at our study site. We know of only one comparable study, of old-growth moist tropical forest on Barro Colorado Island (BCI), Panama, where there were 215 trees.ha-1 in the canopy in 2015, constituting 50% of trees > 10 cm DBH, and where the probability of being in the canopy reached 50% at less than 20 cm DBH [26, 27]. Our site also has a substantially higher density of trees overall, with 622 trees > 10 cm per hectare, compared with 430 for BCI [47]. Thus, our site has a somewhat higher density of canopy trees, and a much higher density of understory trees (373 vs. 215 trees.ha-1), consistent with the presence of trees of larger sizes in BCI [48]. The observed proportions of trees in the canopy for different tree size classes at BCI matched those predicted by the perfect plasticity approximation (PPA) when run at the scale of 31.25 m width subplots [26]. The PPA algorithm “fills” the canopy layer crown by crown starting from the tallest trees (the canopy is full when the summed crown area of canopy trees equals or exceeds the area of the relevant subplot). Investigating the proportion of trees in the canopy per species or genus should bring more information on forest structure heterogeneity. At BCI, the proportion of gap-dependent species was higher in the canopy than the understory and increased with tree size, while the proportion of shade-tolerant species was smaller in the canopy and decreased with tree size [27]. Future studies should evaluate if the PPA can explain differences in the numbers and sizes of canopy trees among forests such as those between our site and BCI and explore differences in species and functional group compositions between canopy and understory. Expanding this approach for larger areas and other forest types would increase the understanding of the general proportion of trees occupying the canopy in relation to tree size, contributing to predictions of forest structure from satellite imagery.

Light availability is one factor that directly influences tree growth in tropical forests. In this study we found that growth of canopy trees was on average twice as large as that of understory trees. Similarly, a previous study of BCI found that diameter increment was 2.6 times larger in canopy trees than in understory trees [26]. Other studies showed a positive relationship of diameter growth with crown exposure as assessed by ground-based observers [17, 19–21]. We found that canopy trees did not show an increase in growth with diameter, consistent with all of them having high light exposure regardless of size. The positive relationship of growth with diameter in understory trees suggests that light availability increases with size within the understory. This implies that the general increase in growth with diameter across all trees combined is due to increasing average light with diameter [35].

Higher light availability and thus higher growth rates for canopy trees translate into a high proportion of stand-level woody productivity, here an estimated 68–75%. The higher estimate of wood productivity based on growth in the most recent 4 years is likely to be a more accurate representation of the true proportion of woody productivity in the canopy than the lower estimate based on average growth over the entire 19-year period. Because canopy status changes over time, measurements of growth closer in time to the canopy status assessments are more likely to be of trees in the same canopy status. In contrast, further back in time canopy status is increasingly likely to be different, which will decrease growth differences of trees classified as canopy versus understory. Thus, basing calculations only on the most recent 4 years leads to higher growth estimates for canopy versus understory trees, and a higher proportion of wood productivity in the canopy. In this study, we revealed the contributions of canopy and understory trees to carbon stocks and wood productivity, and at our knowledge we are the first study discussing this topic.

The differences in the size structure of canopy and understory trees observed in this study enable quantitative tests of previously presented theoretical models. Farrior et al. [31] developed a mechanistic model for the emergence of understory and canopy size distributions based on space-filling competition. Their model predicts that within individual patches, understory trees follow a power function and canopy trees follow an exponential distribution. Combining size distributions for patches of different ages under the assumption of a constant rate of patch disturbance results in predictions for the whole-forest size distributions. The overall predicted size distribution of smaller (mostly understory) trees is close to a power function (straight line on a log-log scale), whereas that of the larger (mostly canopy) trees is close to exponential (curving below a straight line in a J shape on a log-log scale). This is consistent with the size distribution observed here: if we take 25 cm as our empirically observed threshold diameter at which 50% of trees are in the canopy as a cutoff, the size distribution for smaller trees is close to a straight line on log-log scales, whereas for larger trees it is J-shaped (Fig 5c). Also consistent with Farrior et al. [31], the best-fit size distribution for canopy trees > 25 cm is the exponential distribution. The good fit of the Weibull to the whole-forest size distribution is also consistent with demographic equilibrium theory [34, 36, 49]. In contrast, the observed stand-level size distribution was inconsistent with metabolic theory, as the power function was a poor fit, and the best-fit power exponent was significantly different from -2.

In conducting the field work linking crowns to tagged stems, we observed a number of distinct strategies by which trees increased their access to light, strategies that would not have been apparent from either the drone-acquired imagery or the ground field work alone. We observed that some individuals extend long branches beneath the crowns of other trees to reach a gap in which they produce a second set of leaves. From the orthomosaic image alone, these would appear to be two separate crowns belonging to different individuals, highlighting the importance of the field work. Other trees lean sharply such that their crowns are strongly displaced from the rooting points of their trunks. Many small trees emerge from underneath the crowns of larger trees, and a substantial number of trees with DBH less than 10 cm are exposed to direct sunlight. These observations corroborate other studies of the plasticity of crowns [50, 51] and light as a highly influential factor in forest dynamics [17, 36]. It is important to note that many trees that have crowns within the plot have their trunks outside of the plot, and vice versa. This causes misinterpretation among the relationships of forest inventory data and remote sensing. Essentially this is an edge effect problem, with more severe errors for smaller plots [52].

Conclusion

The identification and field mapping of crowns seen in a high resolution orthomosaic revealed new patterns in the structure and dynamics of trees occupying different light environments in this Amazonian forest. In this study, we were able to determine canopy status of individual trees, and thereby quantify the proportion of trees in canopy and understory in relation to tree size, the contributions of canopy and understory trees to carbon stocks and wood productivity, and differences in stem growth and size distributions between canopy and understory trees. Less than half of the trees with DBH > 10 cm were in the canopy, but they were disproportionately larger trees and accounted for ~70% of carbon stocks and wood productivity. Diameter growth rates of canopy trees were unrelated to diameter, suggesting that the general increase in growth with diameter is due to greater light exposure for larger trees. The size distributions of understory and canopy trees were consistent with mechanistic models based on steady state emerging from local competition. Thus, this study demonstrates how the combination of high-resolution aerial imagery and ground-based field work has great potential to improve our understanding of the structure and dynamics of old-growth tropical forests having dense understories.

Number and proportion of trees in the canopy by 10 cm diameter class.

(DOCX)

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Mean diameter growth rates of individual trees in relation to their DBH and canopy status.

The dashed and dash-dot lines show the mean growth rates of 2.34 and 1.18 mm.year-1 for the canopy and understory groups, respectively.

(TIF)

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Probability to occupy the canopy status.

Canopy status in relation to DBH (a) and MAI (b) for individual trees (points), together with fitted logistic regressions (lines). The fitted lines cross 50% for at DBH of 23.5 cm and MAI of 2.2 mm.year-1.

(TIF)

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Exponential fit for canopy trees.

Observed size distribution of canopy trees (green circles) together with the exponential fit probability density functions (green line). The x-axis is the stem diameter class in cm, on a linear scale; the y-axis is the frequency of individuals per hectare per 1-cm size class.

(TIF)

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Exponential, power and Weibull fits for all, understory and canopy trees.

All 1244 trees with DBH > 10 cm (black points), understory trees (gray points), and canopy trees (green circles), shown together with fit probability density functions (lines). Distributions are shown on linear (top) and log (bottom) scales. The vertical dashed black line indicates the minimum diameter (25 cm) for inclusion in the canopy tree fits. The Weibull distribution (Eq 6) had the best fit for all individuals combined; the exponential distribution (Eq 4) had the best fit for understory trees as well as for canopy trees with DBH ≥ 25 cm (solid lines). The other fits are shown by dashed and dotted lines. Whereas data are graphed here for 10-cm size classes for visualization purposes, fits were carried out using 1-cm size classes (parameter values in Table 2).

(TIF)

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Shapefile of transect NS subplots.

(ZIP)

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Table containing forest inventory and crown delineation data.

(CSV)

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Metadata explaining the S2 File contents.

(TXT)

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Orthomosaic image resized to 50 cm spatial resolution.

(TIF)

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Shapefile of crown delineation polygons.

(ZIP)

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30 Jun 2020

PONE-D-20-08690

Forest dynamics and canopy structure from a high resolution remotely piloted aircraft imagery in the Central Amazon

PLOS ONE

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Additional Editor Comments (if provided):

The paper is interesting and research well structured. Therefore only minor revision is recommended. Please make sure you address all the issues raised by the reviewers, especially to define aims and research gaps you want to address in your study, add details on data collection and processing, and formulate clear conclusions.

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: No

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The study is exploring the usage of high resolution orthomosaics from UAV to identify the crowns within an Amazon forests but also authors are combining the data with data from ground measurements started in 1996. Altogether 1244 trees from 51 subplots (20x20) are investigated. The number of trees and also the temporal ground data are great basis for all analysis. The main idea is not to assess the performance of UAV for crown delineation but to use it as one of the data sources to come with conclusions. In general, I like the whole concept of the study, where authors are taking the UAV in account and they are aware of the shortcomings of it (not seeing understory trees). The main result is that 40% of trees are canopy trees but they are accounted for 70% of aboveground carbon stocks and wood productivity.

I have comments mainly for methods. I have not found flaws or main issues in introduction, results or discussion.

TITLE

I would rather use “Unmanned aerial vehicle” or “Unmanned aircraft system”. I understand that the RPAS is a most formal term but nowadays it is not used frequently within manuscripts. But if you decided to use this formal name correct the title: Forest dynamics and canopy structure from a high resolution remotely piloted aircraft system imagery in the Central Amazon.

DATA AVAILABILITY

You have stated that your data are fully available. Could you add it also to the article? Especially the way how to access them. This will give additional value to your paper in my opinion.

Suppl. Shp: I have downloaded “Shapefile_Crowns_WGS84_UTM20S” but I it was not possible to open it. It seems empty because the extent is 0,0,0,0.

METHODS

In general, you should add more details in the part dedicated to data collection and processing. It is not clear how did you georeferenced the data from UAV but also those on the ground. Or everything was in local coordinates? If you have used to georeferenced and scale your data based on Flytrex then you should add more info about this device and the reliability of it.

Secondly, the crown fitting with ground data is not very clear.

L146: Please add information for the overlap. Whether it is related to ground or crown.

L146-147: What is the accuracy of the Flytrex core 2.0. You should add it to the article.

L182: would be good to add also the number of trees that were considered for that analysis.

Reviewer #2: The presented study compares forest inventory data with tree crowns visible in high resolution UAV imagery. This study reveals that there is no trivial connection between forest structure from the ground and from the air, and that a combination of both perspectives can provide important insights. The manuscript thereby provide interest to a wide readership. The manuscript is generally well written and the methods seem sound.

General comments:

I recommend to streamline the introduction and define a proper research gap. After reading the introduction for the fist time I had problems to get a clear picture of the overall research aim. The research questions are very well defined but came a bit as a surprise to me. I guess you should drop some details and focus on a clear red thread (too much detail may distract from the overall red thread).

The results regarding the visibility in the imagery as a function of the diameter distributions are indeed interesting! The same applies for the growth rates. The manuscript as a whole will certainly benefit from clearer guidance on these aspects in the introduction.

Regarding the DBH and canopy trees relationship: Did you compare different species or at least genera? It is very likely that the different species show a very different relationship since they are likely to have different strategies. Some species may show a conservative growth and accumulate a lot of resources (e.g. high wood density). Other species may be more competitive and grow fast in height but not in DBH (they may aim to overtop neighbors). I was missing at least a discussion on this. A separation into species or genera (at least the dominant ones) may also allow to fit better models. I guess you would also see a very strong pattern in the species distribution as a function of canopy trees since shadow-tolerant species usually feature smaller heights.

I missed a conclusion section where you distil your main findings. This would also enable to create a nice frame for the manuscript and relate to your initial research questions.

I hope this review helps to further improve the manuscript. Best regards, Teja Kattenborn

Detailed comments:

Title: I think the title does not really resemble the content of your paper. I guess it would be better to include the aspect of comparing UAV with inventory data – this is what I understood the research gap and should, hence, be reflected in the title.

l.27 Crucial for what? To me the research gap is somewhere between the lines, but ideally the research gap should be explicitly formulated. Are you primarily interested in the forest structure and dynamics or in the methodology of deriving the latter?

l.31 The term ‚canopy trees‘ sounds odd to me, since every tree contributes to the canopy. This becomes even more clear when considering that a canopy can have multiple layers. At least you should clearly define what you mean with canopy trees before using such concept.

l.46 Limitted in terms of what? And does the number of studies matter or is it rather the missing knowledge that matters?

l.51 LiDAR does penetrate up to the ground. This should be reflected here.

l.63-72 I do not really understand how this paragraphs contributes to formulating the research gap. To me this is rather off-topic or distracting, respectively.

l.97 I cannot really follow the red line of your introduction. You start with tropical forest, then with remote sensing, then you jump again to forest structures and again to remote sensing. I recommend to streamline your introduction and address one topic after another. For instance along these lines: 1) Knowledge on the forest structuere in tropical forest remains scarce. There are different models and empirical findings. Inventory data alone is unlikely to reveal the structural diversity and causal processes. Remote sensing offers insights from another perspective. Combining Inventory data and remote sensing is key to address the above mentioned research gap.

l.104 There is no reference here. I guess this one suitable: Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2019.03.025

l.117 This section most importantly misses a motivation on why you chose this study area.

l.151-156 This description is a bit short in my opinion. To my experience from various similar works, linking UAV and in-situ data can be very challenging. Did you use a RTK-based GNSS? Based on what criteria did you assign a species to a stem coordinate? When looking at the RGB imagery with the red polygons it seems to me that there are some uncertainties with the delineation of the tree crowns. For instance polygon #1022, #1018 and #1035 seem to cover different tree species / individuals. How can you be sure that these interpretations are plausible? Consider to elaborate on your methods and discuss potential problems.

l.163 There might be gap constellations that enable a tree to receive direct sunlight for the major part of the day although these trees might not be fully visible from the bird perspective. From a process-based perspective such trees could be considered as ‘canopy trees’. You should consider to discuss such problems, since your method may not perfectly resemble the inherent process of the system.

Table 2 Maybe it would be worth to visualize the distributions vs your observations. I guess this would greatly help the reader to get an understanding of the data structure and the distributions.

l.294 This sentence may imply that you applied an automated crown delineation. Remote Sensing itself cannot detect trees, but an algorithm or interpreter can do. Consider to revise this sentence.

l.303 Here it appears that tree growth only depends on light. One would have to know the growth rate of small trees with sufficient access to light to make such a statement – at least I would state this more carefully. Especially considering that tropical forests are often rather limited by nutrients rather than by light. It could also be that the larger root system of bigger trees is indeed being the causal factor that drives higher growth rates. I do not want to state that your statement is entirely wrong – I just recommend to be more careful.

l.339 I do not really agree here. You indeed showed that there are some statistical patterns, but you did not present causal relationships (see comments above on species, nutrients, etc…).

l.343ff This connects to the issue I addressed above – given these aspects, how can you be sure that you delineated the trees appropriately? This should be discussed in my opinion.

l.361 I totally agree – maybe this aspect could be nicely paraphrased by saying that different perspectives, i.e. the ‘ground’ and the ‘bird perspective’, are needed to fully understand the complex structure of forests. In the light of your results it may also be worth to discuss that passive optical remote sensing is certainly limited in revealing the forest structure or its biomass. In this regard I was also missing reference to other studies that used more sophisticated sensors (e.g. LiDAR) to describe forest structures.

The data is not made available yet. There is no inventory data, no orthoimagery and the supplied shapefile is empty.

**********

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Reviewer #1: No

Reviewer #2: Yes: Teja Kattenborn

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14 Sep 2020

September 13, 2020

To:

Jana Müllerová, Academic Editor

PLOS ONE

With regards to our submission (PONE-D-20-08690), please find below an accounting of the changes we have made to address the helpful comments of the reviewers and editor. We have reformulated the introduction to better motivate the research questions, added details on data collection and processing, and revised the discussion including formulating a final conclusion paragraph. Note that we have changed the title, as suggested by both reviewers. We hope that the revised manuscript is now acceptable for publication in PLOS ONE. We thank the reviewers and editors for their contributions.

Sincerely,

Raquel Fernandes de Araujo

DETAILED RESPONSES IN ITALICS

Journal Requirements:

We note that Figure 1 in your submission contain [map/satellite] images which may be copyrighted.

R: Thank you for pointing this out. The images of Figure 1 are from US Geological Survey and they may be used and reproduced without copyright restriction. Following the USGS recommendation, we included the citation in the Figure 1 legend: “Landsat-8 (a, b) and SRTM (c) images courtesy of the U.S. Geological Survey”.

Reviewer #1

Title

I would rather use “Unmanned aerial vehicle” or “Unmanned aircraft system”. I understand that the RPAS is a most formal term but nowadays it is not used frequently within manuscripts. But if you decided to use this formal name correct the title: Forest dynamics and canopy structure from a high resolution remotely piloted aircraft system imagery in the Central Amazon.

R: We changed RPAS to drone in the title, as this is the more common popular usage these days. Within the manuscript itself, we use RPAS after defining it. We modified the title for: “Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics”

Data availability

You have stated that your data are fully available. Could you add it also to the article? Especially the way how to access them. This will give additional value to your paper in my opinion.

Suppl. Shp: I have downloaded “Shapefile_Crowns_WGS84_UTM20S” but I it was not possible to open it. It seems empty because the extent is 0,0,0,0.

R: We uploaded a correct crown delineation shapefile. We also included the forest inventory data, the orthomosaic image resized to 50 cm (resized due to file size limit to upload in the PLOS ONE system), and the shapefile of subplots as supporting information files. We double-checked the files and all of them are opening in GIS software.

Methods

In general, you should add more details in the part dedicated to data collection and processing. It is not clear how did you georeferenced the data from UAV but also those on the ground. Or everything was in local coordinates? If you have used to georeferenced and scale your data based on Flytrex then you should add more info about this device and the reliability of it.

R: Thank you for pointing this out. As requested, we added more details on the data collection and processing, including an explanation of the georeferencing. Indeed, we installed the GPS track logger model Flytrex Core 2.0, that recorded the flight geographic coordinates. But we realized that we used Flytrex data only in another analysis not included in this paper.

We used an Airborne Laser Scanning (ALS) data to georeference the orthomosaic. There the text now reads:

“We processed photos using the photogrammetry software Agisoft Photoscan (https://www.agisoft.com, v.1.3.0), which aligned the photos using the Scale Invariant Feature Transformation (SIFT) algorithm and produced a point cloud model based on overlap among photos. (Because the camera was not integrated with the RPAS, GPS coordinates of the flight were not automatically assigned to photos). We georeferenced this model using as reference an Airborne Laser Scanning (ALS) dataset from the North-South Transect plot. We selected 15 control points evenly distributed across the flight area and extracted the XYZ coordinates (UTM – Universal Transverse Mercator projection Zone 20S, WGS84 horizontal datum). The control points were the center of crowns and palm trees, and the solar panels of two towers of the AmazonFACE project located in the plot, all well visible in both ALS data and photos. Georeferencing accuracy was assessed in terms of the Root Mean Square Error (RMSE) reported by the software.”

Secondly, the crown fitting with ground data is not very clear.

R: We revised the text to more fully explain these methods. The text now reads:

“Maps with the orthomosaic were printed for each 20 m x 20 m subplot and crowns visible in the image were identified in the field, with reference to the forest inventory data on individual tree DBH, species identity, and tag number. We started delineating the crowns of trees with the biggest trunk diameters, looking from the base to the top and identifying the whole boundary of each crown. We drew the tree crowns boundaries on the map with their respective tag numbers. We then delineated the crowns of the smaller trees, identifying the neighboring trees which had crowns surrounding the biggest crowns. This in situ delineation of crowns allowed us to perceive that in some cases branches of the same tree, but located in different heights and positions, had different colors in the orthomosaic, such that examination of the imagery alone would suggest they belong to different individuals. After the field work, all the crowns were vectorized in GIS, with the creation of polygons to represent each crown (Fig 2, S5 File).”

L146: Please add information for the overlap. Whether it is related to ground or crown.

R: As requested, we added information on the overlap:

“The minimum longitudinal and side overlap were 88% and 78%, respectively, in areas of peak canopy height on ridges; overlap was larger in areas with lower canopy height and in slope and valley areas.”

L146-147: What is the accuracy of the Flytrex core 2.0. You should add it to the article.

R: As explained above, we installed the GPS track logger model Flytrex Core 2.0, that recorded the flight geographic coordinates. But we realized that we used Flytrex data only in another analysis not included in this paper, so we removed all mention of Flytrex from the methods.

L182: would be good to add also the number of trees that were considered for that analysis.

R: Following the suggestion, we added “n = 484 and 696 trees” for canopy and understory trees, respectively.

Reviewer #2

General comments:

I recommend to streamline the introduction and define a proper research gap. After reading the introduction for the fist time I had problems to get a clear picture of the overall research aim. The research questions are very well defined but came a bit as a surprise to me. I guess you should drop some details and focus on a clear red thread (too much detail may distract from the overall red thread).

The results regarding the visibility in the imagery as a function of the diameter distributions are indeed interesting! The same applies for the growth rates. The manuscript as a whole will certainly benefit from clearer guidance on these aspects in the introduction.

R: Thank you for the constructive criticism. We have now completely rewritten the introduction to better motivate the research questions and clarify the contribution of this work.

Regarding the DBH and canopy trees relationship: Did you compare different species or at least genera? It is very likely that the different species show a very different relationship since they are likely to have different strategies. Some species may show a conservative growth and accumulate a lot of resources (e.g. high wood density). Other species may be more competitive and grow fast in height but not in DBH (they may aim to overtop neighbors). I was missing at least a discussion on this. A separation into species or genera (at least the dominant ones) may also allow to fit better models. I guess you would also see a very strong pattern in the species distribution as a function of canopy trees since shadow-tolerant species usually feature smaller heights.

R: We agree that including species or genus in the analysis would give more information about different strategies to reach light. However, because of the high tree diversity in this forest, we have limited samples sizes for individual species, and thus we chose not to pursue this analysis with this dataset. We hope to explore it with a larger dataset in a future study.

I missed a conclusion section where you distil your main findings. This would also enable to create a nice frame for the manuscript and relate to your initial research questions.

R: As requested, we now included a conclusion section.

Detailed comments:

Title: I think the title does not really resemble the content of your paper. I guess it would be better to include the aspect of comparing UAV with inventory data – this is what I understood the research gap and should, hence, be reflected in the title.

R: Following the suggestion, we modified the title to: “Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory and quantify their contributions to forest structure and dynamics”

l.27 Crucial for what? To me the research gap is somewhere between the lines, but ideally the research gap should be explicitly formulated. Are you primarily interested in the forest structure and dynamics or in the methodology of deriving the latter?

R: We reformulate this statement. There the text now reads:

“The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics.”

l.31 The term ‚canopy trees‘ sounds odd to me, since every tree contributes to the canopy. This becomes even more clear when considering that a canopy can have multiple layers. At least you should clearly define what you mean with canopy trees before using such concept.

R: We agree that “canopy tree” has different meanings in different papers, which can be confusing. We considered a number of different options for wording (overstory vs. understory, exposed vs. nonexposed), but in the end it seemed to us that canopy and understory were the best terms to use. Our usage of these terms is consistent with a considerable prior literature, e.g., Bohlman 2015. We have now added parenthetical definition of what is meant by a canopy tree in the abstract at its first mention, and also stated the definition at greater length in the methods.

l.46 Limitted in terms of what? And does the number of studies matter or is it rather the missing knowledge that matters?

R: We completely rewrote this paragraph, and this sentence no longer appears.

l.51 LiDAR does penetrate up to the ground. This should be reflected here.

R: We completely rewrote this paragraph, and this sentence no longer appears.

l.63-72 I do not really understand how this paragraphs contributes to formulating the research gap. To me this is rather off-topic or distracting, respectively.

R: Following your suggestion, we reformulated this paragraph.

l.97 I cannot really follow the red line of your introduction. You start with tropical forest, then with remote sensing, then you jump again to forest structures and again to remote sensing. I recommend to streamline your introduction and address one topic after another. For instance along these lines: 1) Knowledge on the forest structuere in tropical forest remains scarce. There are different models and empirical findings. Inventory data alone is unlikely to reveal the structural diversity and causal processes. Remote sensing offers insights from another perspective. Combining Inventory data and remote sensing is key to address the above mentioned research gap.

R: Thank you for the constructive criticism. As noted above, we have now completely rewritten the introduction to better motivate the research questions and clarify the contribution of this work, very much in line with these suggestions.

l.104 There is no reference here. I guess this one suitable: Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2019.03.025

R: We included the reference, as suggested.

l.117 This section most importantly misses a motivation on why you chose this study area.

R: As requested, we included the motivation

“The availability of ~two decades of bi-annual tree censuses and the proximity from the road (Fig 1) enabled the accomplishment of the present study in this plot.”

l.151-156 This description is a bit short in my opinion. To my experience from various similar works, linking UAV and in-situ data can be very challenging. Did you use a RTK-based GNSS? Based on what criteria did you assign a species to a stem coordinate? When looking at the RGB imagery with the red polygons it seems to me that there are some uncertainties with the delineation of the tree crowns. For instance polygon #1022, #1018 and #1035 seem to cover different tree species / individuals. How can you be sure that these interpretations are plausible? Consider to elaborate on your methods and discuss potential problems.

R: We added more detail on data collection and processing, explaining better how we georeferenced the orthomosaic and linked crowns to ground data.

“We processed photos using the photogrammetry software Agisoft Photoscan (https://www.agisoft.com, v.1.3.0), which aligned the photos using the Scale Invariant Feature Transformation (SIFT) algorithm and produced a point cloud model based on overlap among photos. (Because the camera was not integrated with the RPAS, GPS coordinates of the flight were not automatically assigned to photos). We georeferenced this model using as reference an Airborne Laser Scanning (ALS) dataset from the North-South Transect plot. We selected 15 control points evenly distributed across the flight area and extracted the XYZ coordinates (UTM – Universal Transverse Mercator projection Zone 20S, WGS84 horizontal datum). The control points were the center of crowns and palm trees, and the solar panels of two towers of the AmazonFACE project located in the plot, all well visible in both ALS data and photos. Georeferencing accuracy was assessed in terms of the Root Mean Square Error (RMSE) reported by the software.”

“Maps with the orthomosaic were printed for each 20 m x 20 m subplot and crowns visible in the image were identified in the field, with reference to the forest inventory data on individual tree DBH, species identity, and tag number. We started delineating the crowns of trees with the biggest trunk diameters, looking from the base to the top and identifying the whole boundary of each crown. We drew the tree crowns boundaries on the map with their respective tag numbers. We then delineated the crowns of the smaller trees, identifying the neighboring trees which had crowns surrounding the biggest crowns. This in situ delineation of crowns allowed us to perceive that in some cases branches of the same tree, but located in different heights and positions, had different colors in the orthomosaic, such that examination of the imagery alone would suggest they belong to different individuals. After the field work, all the crowns were vectorized in GIS, with the creation of polygons to represent each crown (Fig 2, S5 File).”

l.163 There might be gap constellations that enable a tree to receive direct sunlight for the major part of the day although these trees might not be fully visible from the bird perspective. From a process-based perspective such trees could be considered as ‘canopy trees’. You should consider to discuss such problems, since your method may not perfectly resemble the inherent process of the system.

R: We agree that some of the trees we classified as “understory” trees may nonetheless receive direct lateral light for part of the day. We now explicitly acknowledge these cases and other limitations of our definitions when we state it in the methods. In full, this section now reads “For the purposes of this study, canopy trees were defined as those directly exposed to overhead light; other trees were classified as understory trees (as in Bohlman, 2015). Specifically, trees were classified as being in the canopy if their crowns were totally or partially visible in the orthomosaic, and had a visible crown diameter greater than ~ 1.5 m. We note that some trees classified as canopy trees under this definition may have most of their crowns shaded and only a small part of the crown in direct light, and that small trees can be classed as canopy trees if they do not have larger trees above them. We also note that some trees classified as understory trees under this definition may received direct lateral light for part of the day.”

Table 2 Maybe it would be worth to visualize the distributions vs your observations. I guess this would greatly help the reader to get an understanding of the data structure and the distributions.

R: This is an excellent suggestion. We included a new figure in the supplementary material (S4 Figure) showing fits for all models.

l.294 This sentence may imply that you applied an automated crown delineation. Remote Sensing itself cannot detect trees, but an algorithm or interpreter can do. Consider to revise this sentence.

R: Thank you for pointing this out. We reformulated this sentence:

“Canopy trees represented less than half of trees with DBH > 10 cm…”

l.303 Here it appears that tree growth only depends on light. One would have to know the growth rate of small trees with sufficient access to light to make such a statement – at least I would state this more carefully. Especially considering that tropical forests are often rather limited by nutrients rather than by light. It could also be that the larger root system of bigger trees is indeed being the causal factor that drives higher growth rates. I do not want to state that your statement is entirely wrong – I just recommend to be more careful.

R: As suggested, we modified to “Light availability is one factor that directly influences tree growth in tropical forests.”

l.339 I do not really agree here. You indeed showed that there are some statistical patterns, but you did not present causal relationships (see comments above on species, nutrients, etc…).

R: We recognize that our wording was potentially misleading. We modified this sentence to:

“In conducting the field work linking crowns to tagged stems, we observed a number of distinct strategies by which trees increased their access to light, strategies that would not have been apparent from either the drone-acquired imagery or the ground field work alone.”

l.343ff This connects to the issue I addressed above – given these aspects, how can you be sure that you delineated the trees appropriately? This should be discussed in my opinion.

R: We delineated crowns at field, we after vectorized in GIS, and returned to the field to check remaining doubts in the crown delineation.

l.361 I totally agree – maybe this aspect could be nicely paraphrased by saying that different perspectives, i.e. the ‘ground’ and the ‘bird perspective’, are needed to fully understand the complex structure of forests. In the light of your results it may also be worth to discuss that passive optical remote sensing is certainly limited in revealing the forest structure or its biomass. In this regard I was also missing reference to other studies that used more sophisticated sensors (e.g. LiDAR) to describe forest structures.

R: Indeed, the penetrability of airborne laser scanning is important to measure tree height and describe forest structure and dynamics. In this study we referenced studies using LiDAR in the introduction (reference numbers 1,30). We chose not discussing them because the LiDAR it’s not the focus of our study.

The data is not made available yet. There is no inventory data, no orthoimagery and the supplied shapefile is empty.

R: We included the forest inventory data, the orthomosaic image resized to 50 cm (resized due to file size limit to upload in the PLOS ONE system), the shapefiles of subplots and crown delineation as supporting information files. We double-checked the files and all of them are opening in GIS software.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

7 Oct 2020

PONE-D-20-08690R1

Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics

PLOS ONE

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Reviewer #2: The revised manuscript clearly improved in clarity and reading flow. All of my previous comments were addressed and misunderstandings were clarified. The manuscript reads nicely and the results are now more elegantly highlighted in the added conclusion section.

Besides some details (see below), the only further suggestion I have relates to a comment of the previous review: I suggested to study the species as a factor of canopy proportions, DBH and growth rates. I undersand, that your data may not enable to do such analysis. Nevertheless, I would (briefly! - maybe 1-2 sentences) discuss that looking deeper into species/genus-specific relationships may shed additional light on the forest structural heterogeneity. Especially, considering that some species are by design shadow tolerant (there is no need to reach the canopy to thrive), whereas other species (competitive species) may only succeed on the long run, if the eventually reach the canopy. There may, for example, be only a few species with exceptional high canopy heights containing the majority of timber. I would consider to add such thoughts to 1) explain the scatter in your results and 2) provide an outlook for future work.

Good job!

Teja Kattenborn

Minor Details:

l.141 Remove full stop before braket.

l.149 Consider to state that you generated the products using Agisoft.

l.310 Full stop after and not before the bracket.

Fig.5 In the text, you primarily refer to DBH, whereas in the figures you use ‘Diamter’ as axis label. Consider to also use DBH in the figures. Diameter is rather unspecific (and could, for example, also refer to crown diameter).

**********

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Reviewer #2: Yes: Teja Kattenborn

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20 Oct 2020

October 20, 2020

To:

Jana Müllerová, Academic Editor

PLOS ONE

With regards to our submission (PONE-D-20-08690R1), please find below the changes we have made to address the helpful comments of the reviewer. We have added the discussion of investigating the proportion of trees in the canopy per species/genus and addressed the minor suggestions. We thank the reviewers and editors for their contributions.

Sincerely,

Raquel Fernandes de Araujo

DETAILED RESPONSES IN ITALICS

Reviewer #2

Besides some details (see below), the only further suggestion I have relates to a comment of the previous review: I suggested to study the species as a factor of canopy proportions, DBH and growth rates. I undersand, that your data may not enable to do such analysis. Nevertheless, I would (briefly! - maybe 1-2 sentences) discuss that looking deeper into species/genus-specific relationships may shed additional light on the forest structural heterogeneity. Especially, considering that some species are by design shadow tolerant (there is no need to reach the canopy to thrive), whereas other species (competitive species) may only succeed on the long run, if the eventually reach the canopy. There may, for example, be only a few species with exceptional high canopy heights containing the majority of timber. I would consider to add such thoughts to 1) explain the scatter in your results and 2) provide an outlook for future work.

R: As suggested, we added these thoughts in the second paragraph of discussion section. There the text now reads:

“Investigating the proportion of trees in the canopy per species or genus should bring more information on forest structure heterogeneity. At BCI, the proportion of gap-dependent species was higher in the canopy than the understory and increased with tree size, while the proportion of shade-tolerant species was smaller in the canopy and decreased with tree size [27]. Future studies should evaluate if the PPA can explain differences in the numbers and sizes of canopy trees among forests such as those between our site and BCI and explore differences in species and functional group compositions between canopy and understory.”

Minor Details:

l.141 Remove full stop before bracket.

R: Thank you for pointing this out. We removed the full stop before the bracket.

l.149 Consider to state that you generated the products using Agisoft.

R: We reformulate this statement. There the text now reads:

“We then generated a 3D point cloud, a digital elevation model (DEM) and a 2 cm spatial resolution orthomosaic (e.g., Fig 2, S4 File) using Agisoft Photoscan.”

l.310 Full stop after and not before the bracket.

R: We replaced the full stop after the bracket (this is in the l.242).

Fig.5 In the text, you primarily refer to DBH, whereas in the figures you use ‘Diameter’ as axis label. Consider to also use DBH in the figures. Diameter is rather unspecific (and could, for example, also refer to crown diameter).

R: Following the suggestion, we replaced the x axis labels of all figures with “DBH (cm)”.

Submitted filename: Response_to_reviewers.docx

Click here for additional data file.

16 Nov 2020

Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics

PONE-D-20-08690R2

Dear Dr. Araujo,

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Kind regards,

Jana Müllerová, Ph.D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Congrats to your paper, it is now ready for publication. Regards

Reviewers' comments:

1 Dec 2020

PONE-D-20-08690R2

Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics

Dear Dr. Araujo:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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PLOS ONE