# Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}were also analyzed. Forest stand heights were estimated from ULS and USP data metrics by linear regression and the prediction accuracy was assessed by 10-fold cross validation. The results showed that the prediction accuracy of the stand heights using metrics from USP was basically as good as that of ULS. Lorey’s height had the highest prediction accuracy, followed by dominated height, mean height, and median height. The correlation between height percentiles metrics from ULS and USP increased with the increased height. Different stand heights had their corresponding best height percentiles as variables based on stand height characteristics. Furthermore, canopy height model (CHM)-based metrics performed slightly better than normalized point cloud (NPC)-based metrics. The USP was not able to extract exact terrain information in a continuous coniferous plantation for forest canopy cover (CC) over 0.49. The combination of USP and terrain from ULS can be used to estimate forest stand heights with high accuracy. In addition, the estimation accuracy of each forest stand height was slightly affected by point density, which can also be ignored.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Site

^{2}(Figure 1). The elevation ranges from 800 m to 1890 m above sea level and it belongs to a semi-arid warm temperate continental climate. The annual temperature changes from −31° to 36 °C with an annual average temperature of 4.2 °C. The annual precipitation is about 400~600 mm, which mainly occurs in July and August. The dominate tree species of the plantation are L. principis-rupprechtii (Larix principis-rupprechtii) and P. tabuliformis (Pinus tabuliformis) while other areas are covered by Betula platyphylla and Betula dahurica Pallas. The plantation stage ranges from young to near-mature forest.

#### 2.2. Field Data

^{2}. The descriptive statistics of plots are summarized in Table 1.

_{L}), mean height (H

_{A}), dominated height (H

_{Dom}), and median height (H

_{Med}) were calculated as forest stand height. H

_{L}, calculated using DBH and height, the two critical parameters of each tree, could better represent the stand height. H

_{A}is the average height of all individual trees with DBH ≥5 cm in each ground plot. H

_{Dom}is the average height of 20% top trees [34]. H

_{Med}is the median height of each plot.

_{i}is the height of i

^{th}individual tree; n is the number of sample plots; G

_{i}is basal area at 1.3 m height of i

^{th}individual tree; and D

_{i}is diameter of i

^{th}individual tree.

#### 2.3. ULS and USP Datasets

^{2}. The average point density varied from 107 to 152 pts/m

^{2}in the 19 blocks. The vertical accuracy of LiDAR ranged from −0.36 m to 0.19 m.

#### 2.4. Data Pre-Processing

#### 2.4.1. Dense Point Clouds Generation from Images

^{2}at 1.0 m resolution.

#### 2.4.2. Point Cloud Normalization and CHM Generation

^{2}using the ThinData model in Fusion software, respectively [40]. The algorithm first figures out the cover area of each input point cloud data, then divides the data into grids with a certain cell size, and identifies the thinned ratio based on the target point cloud density and the pulse density of each grid cell. Finally, the thinning is performed by randomly removing until achieving the desired pulse density. In this study, the certain grid cell size was set to the default value 5 m × 5 m. To evaluate the uncertainty in the thinning process and reduce the random effects from the thinned point clouds, we executed 30 random repetitions for original point cloud datasets to the target density. An example of thinned point clouds can be found in Figure 4 and Figure 5. It can be found from Figure 4 that the thinning algorithm evenly thinned the point clouds. Correspondingly, the vertical distribution profiles did not have significant differences except for lower point densities (0.8 pts/m

^{2}, 1 pts/m

^{2}), as seen in Figure 5.

#### 2.4.3. Feature Metrics Generation

_{10}, h

_{20}, h

_{30}, …, h

_{90}, and h

_{95}), 6 height statistical metrics (h

_{max}, h

_{cv}, h

_{sd}, h

_{mean}, h

_{med}, and h

_{IQ}), and 4 density statistical metrics [41]. The h

_{cv}and h

_{sd}were the coefficients of variation and the standard deviation of heights representing the variation and heterogeneity of forest canopy height. In summary, four datasets including normalized point clouds from ULS and USP (L-NPC, P-NPC), CHM from ULS, and USP (L-CHM and P-CHM) were used in this study.

#### 2.5. Data Analysis

_{10}from L-NPC and h

_{10}from P-NPC using the Pearson’s coefficients (r) and mean difference (MD).

_{i}is the estimated stand height of i

^{th}from ULS data and p

_{i}is the estimated stand height of i

^{th}from USP data.

^{2}from one dataset were extracted as the explanatory variable for modeling simple linear regression. Then, 10-fold cross validation was applied for estimating the accuracy of each linear regression model using R

^{2}, RMSE, and rRMSE based on the observed values and the corresponding estimated values. In this study, three forest cases were considered including ALL plots, LYS plots alone, and YS plots alone, respectively. In total, 48 models for original point cloud density and 40 models for five thinned point cloud densities were analyzed in this study.

_{i}is the measured stand height of i

^{th}plot; $\widehat{{y}_{i}}$ is the predicted stand height of i

^{th}plot; and $\overline{y}$ is the mean of observed stand height.

^{2}, RMSE, and 10-fold cross validation.

## 3. Results

#### 3.1. Visual Comparison of ULS Point Clouds and USP Point Clouds

#### 3.2. Forest Stand Heights Modeling Using ULS and USP Metrics

_{10}~h

_{95}), h

_{med}and h

_{mean}, and less correlation with height statistical metrics h

_{cv}, h

_{sd}, and h

_{IQ}with R

^{2}ranging from 0.006 to 0.460 and RMSE varying from 10.015 to 13.06 m.

^{2}and RMSE of height percentiles (h

_{10}~h

_{95}), h

_{med}, h

_{mean}, and forest stand heights ranged from 0.734 to 0.944, 0.861 m to 7.681 m for L-NPC, respectively, and from 0.735 to 0.952, 0.822 m to 7.518 m for L-CHM, respectively. Like with the USP data, the R

^{2}and RMSE of the height metrics ranged from 0.800 to 0.942, 0.890 m to 5.517 m for the NPC metrics, respectively, and from 0.795 to 0.942, 0.868 m to 5.463 m for the CHM metrics, respectively.

_{A}and H

_{Med}had a higher correlation with the lower height percentiles (h

_{30}, h

_{40}), h

_{med}, and h

_{mean}, while H

_{L}and H

_{Dom}had a higher correlation with the upper height percentiles such as h

_{80}and h

_{90}.

_{A}, H

_{L}, H

_{Dom}, and H

_{Med}using height metrics generated from the ULS and USP datasets was applied to fit each forest stand height. The best explanatory variables for different models, and corresponding modeling accuracy of simple linear regression models using NPC and CHM metrics alone extracted from ULS and USP, respectively, are summarized in Figure 9 and Table 5.

_{80}, h

_{90}, and h

_{80}of L-NPC were the best explanatory variables for H

_{L}in the corresponding forest types (ALL plots, LYS plots, and YS plots, respectively), while h

_{70}, h

_{80}, and h

_{60}obtained from P-NPC were the corresponding best explanatory variables for H

_{L}. This tendency was also obvious for CHM metrics.

_{Dom}of LYS plots, while the best explanatory variables from L-NPC metrics were higher or the same as that from the L-CHM metrics.

_{L}and H

_{Dom}, their best explanatory variables were mostly the upper height percentiles such as h

_{80}, h

_{90}, and h

_{95}, while the lower and median height percentiles were the best independent variables for H

_{A}and H

_{Med}such as h

_{30}, h

_{40}, h

_{50}, and h

_{60}. For different forest types, the best explanatory variables for the same stand height varied within a certain range. The best independent variables for forest stand heights from the LYS plots were higher than those of the YS plots. For example, for H

_{A}, H

_{L}, H

_{Dom}, and H

_{Med}modeled from the L-NPC metrics, their corresponding best variables h

_{60}, h

_{90}, h

_{95}, and h

_{70}from the LYS plots were higher than h

_{40}, h

_{80}, h

_{90}, and h

_{30}from the YS plots, respectively.

#### 3.3. Forest Stand Height Estimation Accuracy

^{2}= 0.88~0.96, RMSE = 0.77~1.23 m, and rRMSE = 5.69~9.72%. The performances of the L-CHM metrics models were slightly higher than the results of L-NPC (ΔR

^{2}= 0~0.01, ΔRMSE = −0.08~0 m, ΔrRMSE = −0.61~0.05%).

_{L}had the highest accuracy with R

^{2}= 0.93~0.95, RMSE = 0.82~0.85 m, and rRMSE = 6.06~6.37%. H

_{Dom}had a higher accuracy with R

^{2}= 0.91~0.94, RMSE = 0.96~1.05 m, and rRMSE = 6.18~6.93%, followed by H

_{A}with R

^{2}= 0.91~0.93, RMSE = 0.97~1.05 m, and rRMSE = 7.63~8.47%, and H

_{Med}had the lowest accuracy (R

^{2}= 0.88~0.93, RMSE = 1.04~1.23 m, and rRMSE = 7.81~9.72%). Compared with the accuracies from the L-NPC models, the results from the L-CHM models showed a similar tendency, where H

_{L}had the highest accuracy with R

^{2}= 0.94~0.96, RMSE = 0.77~0.78 m, and rRMSE = 5.69~5.90%, followed by H

_{Dom}with R

^{2}= 0.91~0.95, RMSE = 0.88~1.01 m, and rRMSE = 5.72~6.71%, and H

_{A}with R

^{2}= 0.92~0.94, RMSE = 0.88~0.93 m, and rRMSE = 7.02~7.51%. H

_{Med}had the lowest accuracy (R

^{2}= 0.90~0.94, RMSE = 0.97~1.13 m, and rRMSE = 7.32~8.99%).

^{2}= 0.93~0.96, RMSE = 0.78~1.04 m and rRMSE = 5.72~7.81%) performed better than those of the YS plots alone (ΔR

^{2}= 0.02~0.05, ΔRMSE = −0.13~0.03 m, ΔrRMSE = −1.74~0.21%) and those of the ALL plots (ΔR

^{2}= 0.01~0.04, ΔRMSE = −0.19~0 m, ΔrRMSE = −1.91~−0.13%).

^{2}= 0.87~0.96, RMSE = 0.78~1.25 m, and rRMSE = 5.69~10.3%. The performances of the P-CHM metrics models were mostly consistent with the results of P-NPC (ΔR

^{2}= 0~0.01, ΔRMSE = −0.03~0.04 m, and ΔrRMSE = −0.28~0.07%).

_{L}still had the highest accuracy with R

^{2}= 0.93~0.96, RMSE = 0.78~0.86 m, and rRMSE = 5.69~6.56%, followed by H

_{Dom}(R

^{2}= 0.89~0.95, RMSE = 0.92~1.11 m, and rRMSE = 5.92~7.37%) and H

_{A}(R

^{2}= 0.89~0.94, RMSE = 0.93~1.07 m, and rRMSE = 7.13~8.91%), and H

_{Med}still had the lowest accuracy with R

^{2}= 0.87~0.94, RMSE = 0.87~1.25 m, and rRMSE = 6.98~10.3%.

^{2}= 0.94~0.96, RMSE = 0.78~0.94 m, and rRMSE = 5.69~7.17%) than the ALL plots (R

^{2}= 0.89~0.94, RMSE = 0.86~1.23 m, and rRMSE = 6.4~9.76%) and YS plots (R

^{2}= 0.87~0.93, RMSE = 0.86~1.25 m, and rRMSE = 6.53~10.3%).

^{2}= 0.88~0.96, RMSE = 0.77~1.23 m, and rRMSE = 5.69~9.72%), USP showed a similar performance (R

^{2}= 0.87~0.96, RMSE = 0.78~1.25 m, and rRMSE = 5.69~10.3%) (Figure 11). Among the results of the 10-fold cross validation from four datasets, L-CHM had the highest accuracy with R

^{2}= 0.9~0.96, RMSE = 0.78~1.13 m, and rRMSE = 5.69~8.99% than L-NPC with ΔR

^{2}= 0~0.02, ΔRMSE = −0.12~−0.04 m, and ΔrRMSE = −1.91~−0.22%, P-NPC with ΔR

^{2}= 0~0.03, ΔRMSE = −0.15~0 m, and ΔrRMSE = −1.55~0%, and P-CHM with ΔR

^{2}= 0~0.03, ΔRMSE = −0.18~−0.01 m, and ΔrRMSE = −1.53~−0.07%.

#### 3.4. Estimation Results of Forest Stand Heights with Different Point Density

^{2}, and became more stable, which could also be found from the corresponding RMSE results. As such, mean R

^{2}varied from 0.933 (±0.002) to 0.935 (±0) with increasing point density for H

_{A}using the L-NPC data, and from 0.923 (±0.002) to 0.931 (±0) with increasing point density for H

_{A}using the P-NPC data. Mean RMSE varied from 1.040 (±0.015) to 1.036 (±0.003) with increasing point density for H

_{A}using the L-NPC data, and from 1.056 (±0.014) to 1.037 (±0.001) with increasing point density for H

_{A}using the P-NPC data.

_{Med}(Figure 12a4,b4). The H

_{Med}estimated using USP was higher than ULS. H

_{Dom}exhibited the least variation with increasing point density (Figure 12a3,b3).

## 4. Discussion

#### 4.1. The Capability of USP and ULS to Estimate Forest Stand Heights

#### 4.2. The Best Explanatory Variables for Forest Stand Heights

_{90}and h

_{95}, which was consistent with the results in [4,18]. Mean height was strongly correlated with median height percentiles (h

_{40}, h

_{50}, and h

_{60}), which were affected by the whole distribution of all heights of each plot. Jensen and Mathews [28] estimated the H

_{A}and H

_{M}

_{ed}using h

_{mean}and h

_{med}metrics by simple linear regression with R

^{2}= 0.90 and R

^{2}= 0.89, respectively. The high height percentiles were the best variables for dominated height because the dominated height was calculated using the 20% tallest heights of each plot, representing the characteristics of upper trees. Puliti et al. [12] estimated the H

_{L}and H

_{Dom}using multiple linear regression by taking the height percentiles and density metrics as explanatory variables with R

^{2}= 0.71, RMSE = 1.4 m and R

^{2}= 0.97, RMSE = 0.7 m, respectively.

#### 4.3. The Influence of Point Density on the Estimation Accuracy

^{2}for ULS point cloud and USP point cloud data were analyzed in this study. The predication accuracy of forest stand heights were slightly affected by thinned point clouds, which was mostly attributed to the height percentile metrics from different point densities showing little variability [4]. The results were also in keeping with other studies [44]. Consequently, it may be possible to take a higher flight height or lower overlap to acquire lower point cloud density, which could collect the data over the same area using a shorter time, thus potentially reducing the cost.

#### 4.4. The Performances of Point Clouds and CHM

#### 4.5. The Limitation of This Study

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Nomenclature

Acronyms | Full Name |

UAV | Unmanned Aerial Vehicle |

ULS | UAV Laser Scanning |

USP | UAV Stereo Photogrammetry |

DEM | Digital Elevation Model |

DSM | Digital Surface Model |

NPC | Normalized Point Clouds |

L-NPC | NPC of LiDAR |

P-NPC | NPC of USP |

CHM | Canopy Height Model |

L-CHM | CHM of ULS |

P-CHM | CHM of USP |

DBH | Diameter at Breast Height |

H_{A} | Mean height |

H_{L} | Lorey’s height |

H_{Dom} | Dominated height |

H_{Med} | Median height |

## Appendix A

All Plots NPC Metrics | LYS Plots NPC Metrics | YS Plots NPC Metrics | All Plots CHM Metrics | LYS Plots CHM Metrics | YS Plots CHM Metrics | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

MD | r | MD | r | MD | r | MD | r | MD | r | MD | r | |

h_{10} | −2.180 | 0.962 | −1.947 | 0.965 | −2.351 | 0.962 | −2.076 | 0.948 | −1.807 | 0.955 | −2.273 | 0.939 |

h_{20} | −1.591 | 0.976 | −1.388 | 0.981 | −1.740 | 0.970 | −1.405 | 0.972 | −1.181 | 0.979 | −1.569 | 0.963 |

h_{30} | −1.270 | 0.984 | −1.121 | 0.986 | −1.379 | 0.980 | −1.050 | 0.982 | −0.887 | 0.988 | −1.170 | 0.975 |

h_{40} | −1.022 | 0.989 | −0.922 | 0.990 | −1.096 | 0.986 | −0.795 | 0.988 | −0.694 | 0.991 | −0.869 | 0.984 |

h_{50} | −0.828 | 0.991 | −0.757 | 0.993 | −0.880 | 0.988 | −0.596 | 0.990 | −0.529 | 0.994 | −0.645 | 0.987 |

h_{60} | −0.660 | 0.992 | −0.616 | 0.994 | −0.692 | 0.989 | −0.425 | 0.993 | −0.394 | 0.995 | −0.448 | 0.990 |

h_{70} | −0.500 | 0.993 | −0.492 | 0.995 | −0.506 | 0.991 | −0.278 | 0.994 | −0.277 | 0.996 | −0.278 | 0.992 |

h_{80} | −0.322 | 0.995 | −0.354 | 0.996 | −0.298 | 0.993 | −0.116 | 0.995 | −0.159 | 0.997 | −0.084 | 0.994 |

h_{90} | −0.130 | 0.995 | −0.184 | 0.996 | −0.090 | 0.993 | 0.051 | 0.995 | −0.007 | 0.997 | 0.093 | 0.994 |

h_{95} | −0.009 | 0.995 | −0.074 | 0.996 | 0.039 | 0.993 | 0.150 | 0.996 | 0.090 | 0.997 | 0.194 | 0.994 |

h_{max} | −0.012 | 0.977 | −0.014 | 0.970 | −0.011 | 0.986 | −0.004 | 0.977 | −0.008 | 0.971 | −0.002 | 0.985 |

h_{mean} | −1.030 | 0.990 | −0.959 | 0.991 | −1.081 | 0.989 | −0.842 | 0.988 | −0.766 | 0.990 | −0.898 | 0.987 |

h_{med} | −0.828 | 0.991 | −0.757 | 0.993 | −0.880 | 0.988 | −0.596 | 0.990 | −0.529 | 0.994 | −0.645 | 0.987 |

h_{cv} | 0.096 | 0.848 | 0.087 | 0.832 | 0.103 | 0.863 | 0.093 | 0.797 | 0.081 | 0.793 | 0.102 | 0.797 |

h_{sd} | 0.869 | 0.834 | 0.818 | 0.640 | 0.907 | 0.885 | 0.901 | 0.807 | 0.831 | 0.632 | 0.951 | 0.864 |

h_{IQ} | 1.002 | 0.859 | 0.816 | 0.629 | 1.139 | 0.889 | 1.013 | 0.861 | 0.794 | 0.709 | 1.174 | 0.887 |

**Table A2.**The explanatory variables and parameters of different forest stand heights from the ULS and USP metrics.

Height | L-NPC Metrics | L-CHM Metrics | P-NPC Metrics | P-CHM Metrics | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

ALL | LYS | YS | ALL | LYS | YS | ALL | LYS | YS | ALL | LYS | YS | |

H_{A} | h_{50} | h_{60} | h_{40} | h_{40} | h_{50} | h_{30} | h_{40} | h_{50} | h_{30} | h_{40} | h_{50} | h_{40} |

a = 0.859 | a = 1.667 | a = −0.564 | a = 0.971 | a = 1.418 | a = 0.256 | a = 0.108 | a = 1.003 | a = −1.377 | a = 0.005 | a = 0.949 | a = −1.696 | |

b = 1.037 | b = 0.936 | b = 1.237 | b = 1.054 | b = 0.971 | b = 1.210 | b = 1.061 | b = 0.967 | b = 1.237 | b = 1.065 | b = 0.968 | b = 1.211 | |

H_{L} | h_{80} | h_{90} | h_{80} | h_{80} | h_{90} | h_{70} | h_{70} | h_{80} | h_{60} | h_{70} | h_{80} | h_{60} |

a = 0.339 | a = 0.506 | a = −0.655 | a = 0.073 | a = −0.025 | a = −0.076 | a = 0.053 | a = 0.489 | a = −0.834 | a = −0.022 | a = 0.449 | a = −0.981 | |

b = 1.019 | b = 0.961 | b = 1.093 | b = 1.018 | b = 0.981 | b = 1.079 | b = 1.050 | b = 0.987 | b = 1.160 | b = 1.051 | b = 0.985 | b = 1.168 | |

H_{Dom} | h_{90} | h_{95} | h_{90} | h_{90} | h_{90} | h_{80} | h_{80} | h_{90} | h_{80} | h_{80} | h_{80} | h_{80} |

a = 0.898 | a = 1.057 | a = 0.051 | a = 0.611 | a = 0.975 | a = 0.764 | a = 0.975 | a = 1.070 | a = 0.071 | a = 0.889 | a = 1.490 | a = −0.081 | |

b = 1.056 | b = 1.005 | b = 1.121 | b = 1.057 | b = 1.030 | b = 1.113 | b = 1.087 | b = 1.029 | b = 1.165 | b = 1.087 | b = 1.033 | b = 1.171 | |

H_{Med} | h_{50} | h_{70} | h_{30} | h_{40} | h_{60} | h_{30} | h_{30} | h_{50} | h_{10} | h_{30} | h_{50} | h_{10} |

a = 0.618 | a = 0.980 | a = −0.565 | a = 0.740 | a = 0.751 | a = −0.062 | a = 0.139 | a = 0.673 | a = −1.005 | a = 0.025 | a = 0.619 | a = −1.005 | |

b = 1.082 | b = 0.980 | b = 1.358 | b = 1.100 | b = 1.013 | b = 1.269 | b = 1.120 | b = 1.017 | b = 1.388 | b = 1.126 | b = 1.018 | b = 1.376 |

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**Figure 1.**Overview of study area. (

**a**) Location of the study site in the Wangyedian forest farm in Inner Mongolia Autonomous Province, China; and (

**b**) Spatial distribution of plots (red dots indicate plot centers overlaid on ASTER GDEM of 30 m resolution, the grey line is the boundary of Wangyedian forest farm).

**Figure 2.**Histogram of tree heights of different forest cases (ALL plots (

**a**), LYS plots (

**b**), YS plots (

**c**)).

**Figure 3.**The boxplots of the four forest stand heights within different forest cases (ALL plots (

**a**), LYS plots (

**b**), YS plots (

**c**)).

**Figure 4.**An example of thinned datasets with 0.8, 1, 5, 10, and 30 pts/m

^{2}and original data from ULS (

**a1**–

**a3**,

**b1**–

**b3**) and USP (

**c1**–

**c3**,

**d1**–

**d3**) at plot YD11-1Y.

**Figure 5.**An example of the vertical distribution curves of thinned datasets with 0.8, 1, 5, 10, and 30 pts/m

^{2}and original point cloud density data from ULS (

**a**) and USP (

**b**) at plot YD11-1Y.

**Figure 6.**Comparison of the ULS point clouds and USP point clouds within different CC. 3D distribution of ULS point clouds (

**a1**–

**a3**), 3D distribution of USP point clouds (

**b1**–

**b3**), the original point cloud profile within a transect of 1 m width (

**c1**–

**c3**), and the normalized point cloud profile within a transect of 1 m width (

**d1**–

**d3**).

**Figure 7.**Correlation between the stand heights and height metrics calculated from the L-NPC metrics (

**a1**,

**b1**) and L-CHM metrics (

**a2**,

**b2**) for ALL plots.

**Figure 8.**Correlation between the stand heights and height metrics calculated from the P-NPC metrics (

**a1**,

**b1**) and P-CHM metrics (

**a2**,

**b2**) for ALL plots.

**Figure 9.**The boxplots of the best explanatories for each stand height modeled by L-NPC, L-CHM, P-NPC, and P-CHM alone. (

**a**) Mean height, (

**b**) Lorey’s height, (

**c**) dominated height, and (

**d**) median height.

**Figure 10.**Scatterplots of estimated forest stand height and field measured height using ULS metrics. (

**a1**–

**a4**,

**b1**–

**b4**) for L-NPC. (

**c1**–

**c4**,

**d1**–

**d4**) for L-CHM.

**Figure 11.**The scatterplot of estimated forest stand height and field measured height using USP metrics. (

**a1**–

**a4**,

**b1**–

**b4**) for P-NPC. (

**c1**–

**c4**,

**d1**–

**d4**) for P-CHM.

**Figure 12.**Boxplots of forest stand height estimates using thinned point clouds. (

**a1**,

**b1**) for mean height, (

**a2**,

**b2**) for Lorey’s height, (

**a3**,

**b3**) for dominated height, (

**a4**,

**b4**) for median height.

Forest Parameters | Symbol | Range | Median | Mean | SD |
---|---|---|---|---|---|

ALL plots (n = 71) | |||||

Stand density/(stems ha^{−1}) | N | 435–4097 | 1714 | 1728 | 695 |

Mean DBH/cm | DBH | 7.70–31.20 | 14.00 | 15.18 | 4.73 |

Canopy cover | CC | 0.42–0.87 | 0.63 | 0.63 | 0.09 |

Lorey’s height/m | H_{L} | 6.80–20.80 | 12.80 | 13.39 | 3.49 |

Mean height/m | H_{A} | 6.60–20.50 | 12.20 | 12.39 | 3.49 |

Dominated height/m | H_{Dom} | 8.00–22.70 | 15.30 | 15.24 | 3.69 |

Median height/m | H_{Med} | 6.6–20.70 | 12.40 | 12.66 | 3.69 |

Volume/(m^{3} ha^{−2}) | V | 62.40–374.40 | 206.40 | 209.67 | 70.70 |

LYS plots (n = 30) | |||||

Stand density/(stems ha^{−1}) | N | 528–4097 | 1794 | 1902 | 768 |

Mean DBH/cm | DBH | 7.70–22.50 | 14.15 | 14.02 | 3.89 |

Canopy cover | CC | 0.43–0.87 | 0.66 | 0.65 | 0.10 |

Lorey’s height/m | H_{L} | 6.80–20.80 | 14.80 | 13.76 | 3.85 |

Mean height/m | H_{A} | 6.60–20.50 | 13.85 | 13.00 | 3.77 |

Dominated height/m | H_{Dom} | 8.00–22.70 | 16.55 | 15.45 | 4.06 |

Median height/m | H_{Med} | 6.60–22.70 | 14.30 | 13.29 | 3.96 |

Volume/(m^{3} ha^{−2}) | V | 62.40–374.40 | 220.00 | 208.27 | 83.45 |

YS plots (n = 41) | |||||

Stand density/(stems ha^{−1}) | N | 435–2536 | 1674 | 1600 | 615 |

Mean DBH/cm | DBH | 10.80–31.20 | 13.80 | 16.03 | 5.14 |

Canopy cover | CC | 0.42–0.78 | 0.62 | 0.62 | 0.08 |

Lorey’s height/m | H_{L} | 8.70–20.30 | 12.30 | 13.12 | 3.22 |

Mean height/m | H_{A} | 7.80–19.60 | 11.10 | 11.94 | 3.24 |

Dominated height/m | H_{Dom} | 10.10–22.00 | 14.40 | 15.09 | 3.44 |

Median height/m | H_{Med} | 7.30–20.30 | 11.20 | 12.19 | 3.45 |

Volume/(m^{3} ha^{−2}) | V | 108.80–372.80 | 206.4 | 210.69 | 60.80 |

LiDAR | |||
---|---|---|---|

UAV model | RC6-2000 | Rotor | 8 |

LiDAR model | Riegl VUX-1 | PRF | 10 Hz~200 Hz |

Laser wavelength | 905 nm | Laser divergence | 3 mrad |

Scan pattern | Rotate Mirror | Scan FOV | 30° × 360° |

Echoes | 2 | Max Scan frequency | 20 Hz |

Range | 3 m~−920 m | Vertical Accuracy | <5 cm |

Photogrammetry | |||

UAV model | DJI Phantom 4 RTK | Rotor | 3 |

Camera model | 1 mm CMOS | Pixels | 50,320,896 |

CMOS size | 36.0 mm × 24.0 mm | Image size | 4864 × 3648 pixels |

FOV | Horizonal 70° Vertical ±10° | Focal length | 9 mm |

Pixel unit | 4.1 µm × 4.1 µm | Bands | R/G/B |

Category | Parameters | Describing |
---|---|---|

Height percentiles | h_{10}, h_{20}, _{…}, h_{80}, h_{90}, h_{95} | Height percentile value for point clouds or CHM cells over 2 m |

Height statistical metrics | h_{max} | Maximum value for point clouds or CHM cells over 2 m |

h_{mean} | Mean value for point clouds or CHM cells over 2 m | |

h_{med} | Median value for point clouds or CHM cells over 2 m | |

h_{cv} | Coefficient of variation for point clouds or CHM cells over 2 m | |

h_{sd} | Standard deviation for point clouds or CHM cells over 2 m | |

h_{IQ} | h_{75} minus h_{25} | |

Density statistical metrics | CRR | (h_{mean} − h_{min})/(h_{max} − h_{min}) |

CC_{2m} | Percentage of point cloud above 2 m | |

CC_{mean} | Percentage of point cloud above mean | |

CC_{mode} | Percentage of point cloud above mode |

CC Level | CC | L-NPC | P-NPC | ||||
---|---|---|---|---|---|---|---|

pts/m^{2} | Minimum | Maximum | pts/m^{2} | Minimum | Maximum | ||

High CC | 0.92 | 71 | 0 | 17.36 | 47 | 4.65 | 17.02 |

Median CC | 0.75 | 97 | 0 | 13.21 | 46 | 0 | 13.00 |

Low CC | 0.49 | 47 | 0 | 8.65 | 44 | 0 | 8.59 |

**Table 5.**The accuracy assessments of the four stand height predictive models from the ULS and USP metrics.

Data Type | Stand Height | ALL Plots | LYS Plots | YS Plots | ||||||
---|---|---|---|---|---|---|---|---|---|---|

R^{2} | RMSE /m | rRMSE % | R^{2} | RMSE /m | rRMSE % | R^{2} | RMSE /m | rRMSE % | ||

L-NPC | H_{A} | 0.91 | 1.01 | 8.16 | 0.94 | 0.93 | 7.14 | 0.92 | 0.92 | 7.70 |

H_{L} | 0.94 | 0.82 | 6.14 | 0.96 | 0.78 | 5.66 | 0.94 | 0.79 | 6.03 | |

H_{Dom} | 0.93 | 0.98 | 6.45 | 0.95 | 0.88 | 5.71 | 0.91 | 1.01 | 6.75 | |

H_{Med} | 0.89 | 1.21 | 9.53 | 0.91 | 0.98 | 7.39 | 0.89 | 1.12 | 9.20 | |

L-CHM | H_{A} | 0.93 | 0.90 | 7.27 | 0.94 | 0.91 | 6.98 | 0.92 | 0.88 | 7.40 |

H_{L} | 0.95 | 0.76 | 5.66 | 0.96 | 0.75 | 5.47 | 0.94 | 0.81 | 6.18 | |

H_{Dom} | 0.94 | 0.93 | 6.08 | 0.96 | 0.82 | 5.33 | 0.91 | 0.99 | 6.58 | |

H_{Med} | 0.91 | 1.11 | 8.78 | 0.93 | 1.04 | 7.79 | 0.84 | 1.38 | 11.3 | |

P-NPC | H_{A} | 0.91 | 1.04 | 8.37 | 0.94 | 0.87 | 6.71 | 0.9 | 1.02 | 8.56 |

H_{L} | 0.94 | 0.83 | 6.23 | 0.96 | 0.74 | 5.36 | 0.93 | 0.84 | 6.39 | |

H_{Dom} | 0.92 | 1.01 | 6.63 | 0.95 | 0.85 | 5.53 | 0.90 | 1.07 | 7.08 | |

H_{Med} | 0.89 | 1.21 | 9.55 | 0.95 | 0.88 | 6.62 | 0.88 | 1.2 | 9.86 | |

P-CHM | H_{A} | 0.91 | 1.02 | 8.23 | 0.94 | 0.88 | 6.75 | 0.89 | 1.07 | 8.98 |

H_{L} | 0.94 | 0.83 | 6.22 | 0.96 | 0.76 | 5.56 | 0.93 | 0.86 | 6.54 | |

H_{Dom} | 0.92 | 1.01 | 6.60 | 0.95 | 0.85 | 5.51 | 0.90 | 1.05 | 6.98 | |

H_{Med} | 0.90 | 1.18 | 9.36 | 0.93 | 1.03 | 7.78 | 0.83 | 1.42 | 11.64 |

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**MDPI and ACS Style**

Li, M.; Li, Z.; Liu, Q.; Chen, E.
Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry. *Remote Sens.* **2021**, *13*, 2885.
https://doi.org/10.3390/rs13152885

**AMA Style**

Li M, Li Z, Liu Q, Chen E.
Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry. *Remote Sensing*. 2021; 13(15):2885.
https://doi.org/10.3390/rs13152885

**Chicago/Turabian Style**

Li, Mei, Zengyuan Li, Qingwang Liu, and Erxue Chen.
2021. "Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry" *Remote Sensing* 13, no. 15: 2885.
https://doi.org/10.3390/rs13152885