# Comparing Forest Structural Attributes Derived from UAV-Based Point Clouds with Conventional Forest Inventories in the Dry Chaco

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## Abstract

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^{2}in a subtropical dry forest. Our data indicate that the UAV-SfM indicators provide a valuable alternative for ground-based forest inventory’ indicators of the upper canopy structure. Based on the correlation between ground-based measures and UAV-SfM derived indicators, we can state that the UAV-SfM technique provides reliable estimates of the mean and maximum height of the upper canopy. The performance of UAV-SfM techniques to characterize the undergrowth forest structure is low, as UAV-SfM indicators derived from the point cloud in the lower forest strata are not suited to provide correct estimates of the vegetation density in the lower strata. Besides structural information, UAV-SfM derived indicators, such as canopy cover, can provide relevant ecological information as the indicators are related to structural, functional, and/or compositional aspects, such as biomass or compositional dominance. Although UAV-SfM techniques cannot replace the wealth of data collected during ground-based forest inventories, its strength lies in the three-dimensional (3D) monitoring of the tree canopy at cm-scale resolution, and the versatility of the technique to provide multi-temporal datasets of the horizontal and vertical forest structure.

## 1. Introduction

^{2}, with a spatial resolution of 100 to 100,000 points per m

^{2}[9]. Clapuyt et al. [10] showed that the precision of measurements is in the order of centimeters for identical replications.

^{2}. The novelty of this research lies in (i) the validation of UAV-SfM forest structural attributes based on forest inventory indicators of 64 circular plots of 1000 m

^{2}, and (ii) the publication of—to our knowledge—one of the first applications of UAV-SfM techniques to the Dry Chaco forest ecosystem. Furthermore, this study analyses multiple ecological attributes by accounting for forest structure, composition, and function.

## 2. Methods

#### 2.1. Study Area

^{2}. The intact landscape of the region was mostly characterized by patches of hard wood forests and natural pastures [23]. The Dry Chaco (Figure 1A), the eastern part of the Gran Chaco covers an area of 0.8 million km

^{2}and it extends over Argentina, Paraguay, and Bolivia. Its vegetation is adapted to arid conditions with broadleaf and deciduous or semi-deciduous species [24]. Cabrera [25] discerned three vertical forest strata. The first consists the canopy stratum dominated by Schinopsis lorentzii (quebracho colorado santiagueño), Bulnesia sarmientoi (Palo Santo), and Aspidosperma quebracho-blanco (quebracho blanco). The second arboreal or sub-canopy stratum comprises Ziziphus mistol, Geoffroea decorticans, Caesalpinia paraguariensis, Tabebuia nodosa, Prosopis alba, P. nigra, P. kuntzei, and others. The third shrub stratum can be very dense and is typically composed of Acacia spp. Schinus spp., Salta triflora, Capparis spp., Celtis spp., and others.

#### 2.2. Forest Inventory Data

^{2}(Figure 2), resulting in 16 × 4, i.e., 64 forest inventory plots. With a Garmin 12XL and eTrex Legend H GPS, we determined the absolute location of the center of each plot with a precision of 2 to 3 m. The position of individual trees within the plot was then measured in a relative coordinate system, measuring the azimuths and distances to the plot center. For the field inventory, the circular plots are divided in two concentric circles, with an inner and outer circle of respectively 500 and 1000 m

^{2}(Figure 2). In the inner circle, all trees with a diameter at breast height (DBH) greater than 0.1 m are recorded, while only trees with a DBH greater than 0.2 m are documented in the outer circle. During the forest inventory, the team documented the tree species, and measured for each individual tree (n = 2022) the DBH with diameter tape (m). The tree height (m) was determined in 24 of the 64 inventory plots using trigonometric methods using a clinometer and laser distance meter. In 16 plots, tree height was estimated using visual estimations techniques. The basal area (m

^{2}) was derived from the DBH. The aboveground biomass (Mg·ha

^{−1}) of tree species was estimated from the DBH measurements and published data on wood specific gravity [33] using the allometric equations presented in Table 1 [34,35].

#### 2.3. UAV Data Acquisition and SfM Image Processing

^{2}, the UAV survey covered an area of 300 m by 300 m centered on the forest plot. The surveys were realized with the team that participated in the forest inventories, to ensure that the forest plots were correctly represented in the UAV surveys. The ground surface was surveyed twice, using both a nadir-oriented and a 30°-tilted camera. The inclusion of oblique images in the SfM algorithm decreases systematic errors in topographic reconstruction and captures better complex 3-D structures as shown by Clapuyt et al. [10]. The frontal and side overlap were set to 80%, following Dandois et al. [40] who suggested 80% as an optimal overlap for UAV-SfM reconstructions of forest structure. The UAV platform flew at a constant height of 80 to 120 m above the take-off point, leading to a ground sample distance between 21.8 and 45.3 mm. Table 2 resumes the camera, flight, and imaging parameters of the flights.

^{2}. The 3D point clouds were georeferenced directly using position information from the onboard GPS/GLONASS receiver. This approach ensured a mean absolute horizontal accuracy of 2.6 m. Given horizontal errors of c. 2 to 3 m on the location of 3D point clouds and forest inventory plots, the two datasets were manually co-registered by identifying pairs of corresponding survey trees and aerial detected trees using the cm-scale resolution aerial imagery.

#### 2.4. Image Processing and Variable Extraction

^{2}. At the plot level, the percentage of the plot with vegetation openings (%) was extracted as an indicator of the horizontal forest structure. Table 4 resumes the four indicators that were derived at the plot level: the mean height of trees taller than 6 m (m), the maximum height of the tallest canopy patch (m), the canopy cover (%), and vegetation openings (%).

#### 2.5. Data Analysis

^{2}) statistic from the Kruskal-Wallis test and the pair-wise Wilcoxon test were used to detect significant differences between groups. Finally, scatterplots and Spearman correlation analyses were used to visualize and analyze the correlation between biomass and the UAV-SfM structural indicators.

## 3. Results

#### 3.1. Forest Structure, Composition, and Function Based on Ground-Based Measures

^{2}for which tree height data are available, the mean height of the upper canopy ranges between 7.5 and 16.0 m, with a mean and standard deviation of 10.3 ± 1.8 m. The distribution is slightly left-skewed (Figure 4A), and similar to the distribution of values of the height of the tallest tree. The tallest tree is on average 15.3 ± 2.7 m, with a minimum of 10.2 and a maximum a 21.7 m. The number of trees above 6 m is slightly left-skewed, ranges from 70 to 300 trees per hectare, with a mean of 153.0 ± 60.7 trees·ha

^{−1}(Figure 4C). The percentage of trees in the shrubby and the sub-canopy strata varies strongly between plots, with values ranging between 0.0 and 64.1% for the shrubby and between 6.2 to 55.0% for the sub-canopy. The average and standard deviation for the 0.5 to 4 m and 4 to 6 m height strata are respectively 10.4 ± 16.5% and 21.2 ± 14.9%.

^{−1}. With regard to forest structure, we observe that more than half of the trees (i.e., 107 ± 86.8 trees·ha

^{−1}) have a DBH < 0.20 m, followed by slightly less than 30% having a DBH between 0.2 and 0.3 m, and less than 20% having a DBH > 0.3 m. However, the number of trees < 0.2 m had a high range and variance (i.e., respectively 480 and 86.8 tree·ha

^{−1}). Additionally, there are strong differences between plots in the above ground biomass, with values ranging between 12.8 and 62.9 Mg·ha

^{−1}with an average of 30 ± 11.3 Mg·ha

^{−1}. About 50% of the 64 plots have the iconic tree species of the dry Chaco forest as dominant species, i.e., Aspidosperma quebracho-blanco, Schinopsis lorentzii, or Bulnesia sarmientoi. Forty percent of the plots have a dominance of sub-canopy species and the remaining 10% have a dominance of pioneer species.

#### 3.2. Horizontal and Vertical Forest Complexity Based on UAV-SfM Data

#### 3.3. Performance of UAV-SfM Based Indicators on Forest Structure

#### 3.4. Relevance of UAV-SfM Based Indicators for Forest Composition and Function

## 4. Discussion

#### 4.1. Forest Structural Indicators as Ecological Indicators

#### 4.2. Horizontal Forest Complexity

#### 4.2.1. Indicators of the Upper Canopy Structure

#### 4.2.2. Canopy Cover

#### 4.2.3. Vegetation Openings

#### 4.3. Vertical Forest Complexity

#### 4.3.1. Sub-Canopy and Shrub Stratum

#### 4.3.2. Vertical Distribution and Vertical Complexity

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Location of the study sites: (

**A**) the study area (red rectangle) situated in the central part of the Dry Chaco within South America, (

**B**) the 16 clusters of 4 forest inventory plots. The map shows the average tree cover (%) derived from Landsat composites from the year 2015 [31].

**Figure 2.**One cluster of four forest inventory plots of 1000 m

^{2}each. For field data collection, the inventory plots are divided in two concentric circles, where all trees with breast height (DBH) > 0.1 m are documented for the inner circle of 500 m

^{2}and only trees with DBH > 0.2 m for the outer circle. The background image is the orthophoto of the cluster that was reconstructed from the aerial photographs taken by the digital camera on the unmanned aerial vehicle (UAV).

**Figure 3.**Extraction of information on canopy cover and vegetation openings based on the canopy height model for a given forest plot: (

**A**) The polygons of canopy patches (green) and vegetation openings (red) are overlying the orthophoto taken by the UAV, (

**B**) they are plotted on the canopy height model (CHM) of the same plot.

**Figure 6.**Comparison of the forest height indicators that were derived from the UAV-SfM data and the field-based measures of canopy height. The field data are plotted on the X-axis, while the UAV-SfM derived indicators are plotted on the Y-axis with the mean height of vegetation patches for (

**A**) and the height of the tallest vegetation patch for (

**B**). The Spearman correlation coefficients are calculated at the plot level (n = 40). If the p-value of the correlation analysis is less than 0.05, the correlation coefficient is flagged with one star (*).

**Figure 7.**Comparison of forest structural indicators on vegetation density per stratum. The field data are plotted on the X-axis, while the UAV-SfM based indicators are plotted on the Y-axis.

**Panels A and B**show the overall relative point density (ORD) in the 0.5 to 4 m stratum against respectively the percentage of trees in the same stratum and the percentage of trees with DBH < 0.2 m.

**Panels C and D**show the same information for the 4 to 6 m stratum. The Spearman correlation coefficients are calculated at the plot level (n = 64). If the p-value of the correlation analysis is less than 0.05, the correlation coefficient is flagged with one star (*).

**Figure 8.**The UAV-SfM data (n = 64) on vegetation openings, compared with ground measurements on the total tree density (ha

^{−1}) for

**panel A**, and the density of trees (ha

^{−1}) with DBH < 0.2 m for

**panel B**, between 0.2 and 0.3 m for

**panel C**and > 0.3 m for

**panel D**. The field data are plotted on the X-axis, while the UAV-SfM based indicator on vegetation openings is plotted on the Y-axis. The Spearman correlation coefficients are calculated at the plot level (n = 64). If the p-value of the correlation analysis is less than 0.05, the correlation coefficient is flagged with one star (*).

**Figure 9.**The UAV-SfM data on canopy cover (%), compared with (

**A**) ground measurements on the number of trees taller than 6 m (ha

^{−1}), and (

**B**) the number of trees with DBH above 0.3 m. If the p-value of the correlation analysis is less than 0.05, the correlation coefficient is flagged with one star (*).

**Figure 10.**Boxplots showing the distribution of four UAV-SfM structural indicators for the different classes of forest composition as they were defined in Table 1. The probability density at different values is smoothed by a kernel density estimator. The y-axis displays the structural indicators from the UAV-SfM products. The chi-squared (X

^{2}) from the Kruskal-Wallis test was implemented and if the p-value of the correlation analysis is less than 0.05, the correlation coefficient is flagged with one star (*). Groups with the different letters (a and b) are groups that are significantly different according to the pair-wise Wilcoxon test.

**Figure 11.**Scatterplots relating all the forest indicators that were derived from the UAV-SfM data to the field-based measures of biomass (Mg/ha). The field data are plotted on the X-axis, while the UAV-SfM derived indicators are plotted on the Y-axis. The Spearman correlation coefficients are calculated at the plot level. If the p-value of the correlation analysis is less than 0.05, the correlation coefficient is flagged with one star (*).

**Table 1.**Plot-level indicators on forest structure, composition, and function as they were derived from the field data on individual trees during the forest inventory. The above ground biomass is calculated with allometric equations using DBH values (expressed in cm) and the oven dry over green volume (p, in g/cm

^{3}).

Stand Element | Variable | Description | Unit | |
---|---|---|---|---|

STRUCTURE | Tree height | Mean height of upper canopy | Mean height of trees taller than 6 m | m |

Height tallest tree | Height of the tallest tree | m | ||

Number of trees > 6 m | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{taller}\mathrm{than}6\mathrm{m}}{\mathrm{Surface}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{plot}}$ | ha^{−1} | ||

Percentage of trees in 0.5–4 m height stratum | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{with}\mathrm{height}\mathrm{between}0.5\mathrm{and}4\mathrm{m}}{\mathrm{Total}\mathrm{number}\mathrm{of}\mathrm{trees}\mathrm{in}\mathrm{the}\mathrm{plot}}\times 100$ | % | ||

Percentage of trees in 4–6 m height stratum | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{with}\mathrm{height}\mathrm{between}4\mathrm{and}6\mathrm{m}}{\mathrm{Total}\mathrm{number}\mathrm{of}\mathrm{trees}\mathrm{in}\mathrm{the}\mathrm{plot}}\times 100$ | % | ||

Tree spacing | Tree density | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{in}\mathrm{the}\mathrm{plot}}{\mathrm{Surface}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{plot}}$ | ha^{−1} | |

Tree diameter at breast height (DBH) | Number of trees with DBH < 0.2 m | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{with}\mathrm{DBH}0.2\mathrm{m}}{\mathrm{Surface}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{plot}}$ | ha^{−1} | |

Number of trees with 0.2 < DBH < 0.3 m | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{with}0.2\mathrm{m}\mathrm{DBH}0.3\mathrm{m}}{\mathrm{Surface}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{plot}}$ | ha^{−1} | ||

Number of trees with DBH > 0.3 m | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{with}\mathrm{DBH}0.3\mathrm{m}}{\mathrm{Surface}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{plot}}$ | ha^{−1} | ||

Percentage of trees with DBH < 0.2 m | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{with}\mathrm{DBH}0.2\mathrm{m}}{\mathrm{Total}\mathrm{number}\mathrm{of}\mathrm{trees}\mathrm{in}\mathrm{the}\mathrm{plot}}\times 100$ | % | ||

Percentage of trees with 0.2 < DBH < 0.3m | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{with}0.2\mathrm{DBH}0.3\mathrm{m}}{\mathrm{Total}\mathrm{number}\mathrm{of}\mathrm{trees}\mathrm{in}\mathrm{the}\mathrm{plot}}\times 100$ | % | ||

Percentage of trees with DBH > 0.3 m | $\frac{\mathrm{Number}\mathrm{of}\mathrm{trees}\mathrm{with}\mathrm{DBH}0.3\mathrm{m}}{\mathrm{Total}\mathrm{number}\mathrm{of}\mathrm{trees}\mathrm{in}\mathrm{the}\mathrm{plot}}\times 100$ | % | ||

COMPOSITIO | Species | Classification based on dominant tree species | Cat 1: Aspidosperma quebracho-blanco, Schinopsis lorentzii, Bulnesia sarmientoi Cat 2: Zizyphus mistol Cat 3: Caesalpinia paraguariensis and Tabebuia nodosa Cat 4: colonizer or pioneer species, like Prosopis nigra | |

FUNCTION | Tree diameter and species | Above ground biomass (AGB) | For trees with DBH < 0.6 m: $AGB=p\times {e}^{-0.667+1.784\mathrm{ln}\left(DBH\right)+0.207{\left(\mathrm{ln}\left(DBH\right)\right)}^{2}-0.0281\left(\mathrm{ln}\left(DBH\right)\right)3}$ For trees with DBH > 0.6 m: $AGB=p\times {e}^{1.589+2.284\mathrm{ln}\left(DBH\right)+0.129{\left(\mathrm{ln}\left(DBH\right)\right)}^{2}-0.0197{\left(\mathrm{ln}\left(DBH\right)\right)}^{3}}$ | Mg ha^{−1} |

Camera model | Phantom 4 Pro camera |

Lens model | FOV 84° (8.8 mm/24 mm) f/2.8–f/11 |

Image resolution | 5472 × 3648 |

Crop factor | 1 |

Approximate sensor size | 24 mm |

Pixel size | 2.41 × 2.41 μm |

Shutter speed | 8–1/8000 s |

ISO Range | 100–3200 |

Mean f number | 2.8–11 |

Flight velocity | 2 m s^{−1} |

Flight height | 80–120 m |

Ground sample distance | 21.8–45.3 mm |

Number of pictures | 160–217 |

Point cloud density | 212–437 pt m^{−2} |

CHM resolution | 3.4–9.1 cm pix^{−1} |

Horizontal absolute accuracy | 2.6 m |

**Table 3.**Derivation of forest structure indicators from the UAV-SfM (Unmanned Aerial Vehicle—Structure from Motion) images. The UAV-SfM data are converted to a canopy height model (CHM) and three-dimensional (3D) point cloud for the extraction of the forest structural indicators.

Data Source | Processing | Indicators | Units |
---|---|---|---|

Canopy Height Model | Canopy patches | Mean height of vegetation patches Height of the tallest vegetation patch Canopy cover | m m % |

Vegetation openings | Vegetation openings | % | |

Vegetation point cloud | Height distribution of the point cloud | Stratum independent: 99th percentile Vertical distribution Vertical complexity | m |

Stratum dependent: Overall relative point density of 0.5 to 4 m stratum Overall relative point density of 4 to 6 m stratum | % % |

**Table 4.**Plot-level indicators on horizontal and vertical forest structure, as they were derived from the canopy height model and 3D point cloud.

Forest Structure | Data Source | Indicator | Description | Unit |
---|---|---|---|---|

Horizontal complexity | CHM (Vegetation data points) | Mean height of tree crown patches | Mean of the maximum heights of the tree crown patches | m |

Height of the tallest vegetation patch | Maximum height of the tallest canopy patch | m | ||

Canopy cover | $\frac{\mathrm{Surface}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{tree}\mathrm{crown}\mathrm{patches}}{\mathrm{Total}\mathrm{surface}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{plot}}\times 100$ | % | ||

CHM (ground data points) | Vegetation openings | $\frac{\mathrm{Surface}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{vegetation}\mathrm{openings}}{\mathrm{Total}\mathrm{surface}\mathrm{area}\mathrm{of}\mathrm{the}\mathrm{plot}}\times 100$ | % | |

Vertical complexity | 3D point cloud (Percentile based) | 99th percentile | Height of 99th percentile of vegetation point cloud | m |

Vertical distribution | $\frac{\mathrm{Height}\mathrm{of}99\mathrm{th}\mathrm{percentile}-\mathrm{Height}\mathrm{of}50\mathrm{th}\mathrm{percentile}}{\mathrm{Height}\mathrm{of}99\mathrm{th}\mathrm{percentile}}$ | |||

Vertical complexity | $(-{{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{HB}}{\mathrm{p}}_{\mathrm{i}}\mathrm{ln}{\mathrm{p}}_{\mathrm{i}})/\mathrm{ln}\mathrm{HB}$ where p_{i} is the proportional abundance of points within the height bin i; and HB is the total number of height bins of 1 m | |||

3D point cloud (ORD based) | Overall relative point density of 0.5 to 4 m stratum | $\frac{\mathrm{Number}\mathrm{of}\mathrm{points}\mathrm{between}0.5\mathrm{and}4\mathrm{m}}{\mathrm{Total}\mathrm{number}\mathrm{of}\mathrm{points}}\times 100$ | % | |

Overall relative point density of 4 to 6 m stratum | $\frac{\mathrm{Number}\mathrm{of}\mathrm{points}\mathrm{between}4\mathrm{and}6\mathrm{m}}{\mathrm{Total}\mathrm{number}\mathrm{of}\mathrm{points}}\times 100$ | % |

**Table 5.**Summary of values for ground-based forest structure indicators, with indication of average, standard deviation, minimum and maximum values, and the number of plots where observations were made.

Average | S.D. | Min. | Max. | # of Plots | |
---|---|---|---|---|---|

Mean height of upper canopy | 10.3 | 1.8 | 7.5 | 16.1 | 40 |

Height tallest tree | 15.2 | 2.7 | 10.2 | 21.7 | 40 |

Number of trees > 6 m | 153.0 | 60.7 | 70.0 | 300.0 | 40 |

Percentage of trees in 0.5–4 m height stratum | 10.4 | 16.5 | 0.0 | 64.1 | 40 |

Percentage of trees in 4–6 height stratum | 21.2 | 14.9 | 6.2 | 55.0 | 40 |

Tree density | 231.1 | 94.4 | 100 | 560 | 64 |

Number of trees with DBH < 0.2 m | 107 | 86.8 | 0 | 480 | 64 |

Number of trees with 0.2 < DBH < 0.3m | 60 | 29.9 | 20 | 140 | 64 |

Number of trees with DBH > 0.3 m | 42 | 18.6 | 0 | 90 | 64 |

Dominant species by classification | - | - | - | - | 64 |

Above ground biomass (AGB) | 30.0 | 11.3 | 12.8 | 62.9 | 64 |

**Table 6.**Summary of values for UAV-SfM indicators, with indication of average, standard deviation, minimum and maximum values, and the number of plots where observations were made.

Average | S.D. | Min. | Max. | # of Plots | |
---|---|---|---|---|---|

Mean height of vegetation patches | 8.1 | 1.2 | 6.1 | 11.2 | 64 |

Height of the tallest vegetation patch | 12.8 | 2.4 | 6.9 | 17.6 | 64 |

Canopy cover | 25.6 | 10.8 | 2.8 | 56.6 | 64 |

Vegetation openings | 8.3 | 8.1 | 0.0 | 34.3 | 64 |

99th percentile | 11.3 | 2.2 | 5.9 | 16 | 64 |

Vertical distribution | 0.7 | 0.1 | 0.4 | 0.9 | 64 |

Vertical complexity | 0.7 | 0.1 | 0.5 | 0.8 | 64 |

ORD of 0.5 to 4 m stratum | 50.2 | 15.2 | 14.7 | 82.3 | 64 |

ORD of 4 to 6 m stratum | 15.9 | 9.5 | 2.6 | 47.5 | 64 |

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## Share and Cite

**MDPI and ACS Style**

Gobbi, B.; Van Rompaey, A.; Loto, D.; Gasparri, I.; Vanacker, V. Comparing Forest Structural Attributes Derived from UAV-Based Point Clouds with Conventional Forest Inventories in the Dry Chaco. *Remote Sens.* **2020**, *12*, 4005.
https://doi.org/10.3390/rs12234005

**AMA Style**

Gobbi B, Van Rompaey A, Loto D, Gasparri I, Vanacker V. Comparing Forest Structural Attributes Derived from UAV-Based Point Clouds with Conventional Forest Inventories in the Dry Chaco. *Remote Sensing*. 2020; 12(23):4005.
https://doi.org/10.3390/rs12234005

**Chicago/Turabian Style**

Gobbi, Beatriz, Anton Van Rompaey, Dante Loto, Ignacio Gasparri, and Veerle Vanacker. 2020. "Comparing Forest Structural Attributes Derived from UAV-Based Point Clouds with Conventional Forest Inventories in the Dry Chaco" *Remote Sensing* 12, no. 23: 4005.
https://doi.org/10.3390/rs12234005