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Article

A LiDAR-Driven Effective Leaf Area Index Inversion Method of Urban Forests in Northeast China

by
Chang Zhai
1,†,
Mingming Ding
1,2,†,
Zhibin Ren
3,
Guangdao Bao
2,*,
Ting Liu
2,*,
Zhonghui Zhang
2,
Xuefei Jiang
4,
Hongbo Ma
5 and
Haisen Lin
1
1
College of Landscape Architecture, Changchun University, Changchun 130022, China
2
Institute of Forest Management, Jilin Provincial Academy of Forestry Sciences, Changchun 130033, China
3
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
4
College of Forestry, Beijing Forestry University, Beijing 100083, China
5
Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(10), 2084; https://doi.org/10.3390/f14102084
Submission received: 13 September 2023 / Revised: 12 October 2023 / Accepted: 17 October 2023 / Published: 18 October 2023
(This article belongs to the Special Issue Urban Green Infrastructure and Urban Landscape Ecology)
Browse Figures
Versions Notes

Abstract

:
Leaf area index (LAI) stands as a pivotal parameter for the quantitative assessment of vegetation growth dynamics, and the rapid acquisition of the effective leaf area index (LAIe) in different scales is crucial for forest ecological monitoring. In this study, forest structure parameters were derived from fusion point cloud data obtained through Airborne Laser Scanning and Terrestrial Laser Scanning in three coniferous forests. The influence of point diameter on the extraction of different forest structure parameters was examined, and an in-depth analysis of the correlations between these parameters and measured LAIe was undertaken. The LAIe inversion model was constructed, and its performance for different forest types was studied. The results show that the precision of the extracted forest structure parameters was highest when the point diameter was set to 0.1 cm. Among the 10 forest structure parameters, internal canopy structures such as canopy openness (CO), gap fraction (GF) and canopy closure (CC) were significantly correlated with measured LAIe (p < 0.01), and the correlations between different forest types were significantly different. In addition, the multiparameter LAIe inversion model was able to distinguish forest type and thus better stimulate measured LAIe; also, it appeared closer to the 1:1 relationship line than the voxel model. This study made up for the inefficiency of LAIe measurement with optical instruments and the inaccuracy of passive remote sensing measurement and proved the possibility of LAIe extraction at a large scale via LiDAR in the future.

1. Introduction

Leaf area index (LAI) is an important parameter characterizing the structure of vegetation canopy. It is defined as half of the total leaf area on the ground at the unit level [1]. It is closely related to vegetation photosynthesis [2] and the carbon cycle [3] and is a key parameter in quantitatively describing the growth and development characteristics of forest leaves [4]. LAI provides an effective method for quantitative growth analysis of plant communities and has been widely applied to crop yield estimation [5], the breeding of improved varieties [6] and forest productivity comparation at the landscape level [7]. The measurement of LAI has been divided into the direct method and indirect method [8]. The direct method usually includes litter collection and destructive sampling to obtain LAI through manual measurement. However, it is difficult to collect litter data, and the processing process is complicated. The destructive method often causes irreversible damage to vegetation [9]. Meanwhile, the indirect method, which involves tools such as optical instruments and remote sensing [10], is used to calculate LAI by analyzing the light absorption mode and reflection characteristics of the vegetation canopy [11]. Optical instruments can obtain stand-scale LAI with high precision and repeatability but can only obtain spatially discrete sample data, thus hindering large-scale continuous collection of vegetation information and posing high technical requirements [12]. For example, the LAI-2200C plant canopy analyzer is commonly used to measure the ground LAI, and it specifies that the measurement should take place on a cloudy day or in the early morning [13]. Before the measurement, the measurement points of the sample site should be calculated and laid out first. Then, all sky data should be measured in the open space outside the sample site and recorded as value A; the measurement points should be measured successively and recorded as value B. Finally, the ground LAI of the sample site should be calculated by matching values A and B [14]. Remote sensing technology allows for macroscopic and efficient ground object observation and can overcome the defects of traditional methods in parameter extraction [15]. However, optical passive remote sensing is susceptible to light saturation and LAI underestimation, so accurate ground data are needed for accuracy verification [16].
Light Detection and Ranging (LiDAR) is an active remote sensing technology that uses a laser as a light source [17]. The laser pulses emitted by LiDAR can penetrate vegetation canopy and obtain the three-dimensional structure information of forests accurately, making up for the light saturation defect of passive remote sensing. LiDAR has gradually become the main means by which to obtain the effective leaf area index (LAIe) [18,19,20]. LAIe is the macroscopic LAI value obtained through optical instrument measurement or remote sensing under the assumption of random leaf distribution [21]. Both LAIe and LAI reflect the life vitality, living environment and canopy structure of vegetation. At the macro scale, an LAIe obtained using optical instruments or remote sensing methods has more scientific basis and validity than an LAI acquired through complex calculation, and LAIe can replace LAI as an effective parameter for monitoring vegetation information dynamics. LAIe has the same function as LAI—characterizing the number of leaves—and the direct measurement result of LAI-2200C is LAIe. However, due to the limitations of the measurement angle of the sensor and the occlusion effect of LiDAR from different platforms [22,23], some point cloud data are missing, leading to uncertainty in LAI estimation. For example, You et al. [24] carried out a study on the inversion accuracy of LAI by employing Airborne Laser Scanning (ALS) in coniferous forests and found that the laser could not penetrate the dense canopy, resulting in the overestimation of LAI. Moreover, in the estimation of LAI in a north tropical forest, Xie et al. [25] mentioned that when Terrestrial Laser Scanning (TLS) was severely obscured, the estimation accuracy was lower than the LAI-2200C value. In order to overcome such a limitation, Guo et al. [26] estimated the LAI of a Pinus koraiensis forest by combining TLS and ALS data. It was confirmed that the combination of multi-source data could reduce the canopy shielding effect and improve LAI inversion accuracy. Many researchers have found that compared with data combination, data fusion can provide rich ground feature information and reflect the spatial structure characteristics of vegetation directly, while retaining the repeatability of information observation [27,28,29]. In 2022, Piao et al. [30] carried out LAIe inversion research by means of data fusion and found that the method had higher prediction accuracy and could avoid redundant errors caused by combining a large number of data. Therefore, the three-dimensional structure information of forests can be described more comprehensively using fusion data, but the factors that affect the accuracy of point cloud information still exist, such as point cloud density and point cloud thinning. In recent years, there have been studies showing the relationship between point cloud density, point cloud thinning and point cloud data [31,32], while the impact of point diameter on point cloud data has rarely been reported. Point clouds are composed of a massive collection of points with target surface characteristics. The point diameter can drive the internal structure of target point clouds to change to different degrees, affecting the accuracy of obtained forest structure parameters. Therefore, it is of great significance to explore the extraction of forest structure parameters from point cloud data based on the optimal point diameter to improve LAIe inversion accuracy.
Methods for estimating LAIe using LiDAR are divided into the physical model method and the empirical model method [33]. The physical model method is based on the spectrophotometric method and calculates the light attenuation through the vegetation canopy and the estimation of LAI [34]. It has the advantages of clear physical meaning, wide practicability and strong universality. Compared with the physical model, the empirical model can be used to establish a regression model by extracting the statistical parameters of point cloud data and measuring LAI [35]. With high flexibility and few influencing factors, this method has significant advantages over remote sensing methods in LAI estimation [36]. In recent years, in order to meet the accuracy requirements of LAI estimation via remote sensing technology, a large number of scholars built LAI inversion models based on vegetation index, laser penetration index and echo intensity [20,37,38]. For example, Luo et al. [39] estimated LAI in the forest area of Dayakou City based on the laser penetration index in 2013 and obtained a high-precision LAI inversion model. However, in previous research, the echo intensity needed to be corrected, and in mountainous areas with a high slope, the echo intensity was greatly affected by distance and incidence angle, resulting in a decrease in the estimation accuracy of LAI. In 2022, Stobbelaar et al. [40] inverted the LAI of mixed broadleaf–conifer forest based on seven vegetation indices and compared it with the LAI value estimated using surface reflectance, proving that the prediction accuracy of modeling with additional variables was higher. However, due to the heterogeneity of leaf characteristics and growth rhythm among different tree species [41], it was difficult to avoid the influence of the vegetation’s canopy size, diameter at breast height (DBH) and tree height parameters, resulting in large fluctuations in the estimated results of LAIe. Therefore, the inversion of LAIe based on multiple forest structure parameters of different forest types can reveal the relationship between LAIe and canopy structure more comprehensively, thus improving the LAIe inversion accuracy.
This study took the LAIe of three different conifer forests in Changchun City as the research object. Based on the fusion point cloud data derived from TLS and ALS, we explored the influence of different point diameters on forest structure parameter extraction and correlation. Ten forest structure parameters, including DBH and stand density, were extracted at the optimal diameter, and the relationship between different forest structure parameters and measured LAIe was analyzed. Using the polynomial regression method, LAIe inversion models driven by point cloud data were constructed for different forest types, and the variation rules of LAIe in different forest types were also investigated. Specifically, the three objectives of this study were as follows: (1) to explore the influence of point cloud diameter on the estimation of forest structure parameters and clarify the best parameters and formulas for LAIe estimation of different forest types; (2) to develop a high-precision LAIe estimation method driven by LiDAR point cloud data and explore the spatial distribution of and differences in LAIe in different forest types; and (3) to simplify LAIe estimation at the sample plot scale and provide a reference for fine LAIe estimation using remote sensing inversion on a large scale. This study attempted to make use of the accurate restoration characteristics of LiDAR point cloud data to evaluate forest structure and proposed an LAIe inversion method completely based on point cloud data, so as to break through the limitations of traditional methods regarding light environment changes and leaf spatial distribution and thus accelerate data acquisition efficiency, enhance estimation accuracy and improve calculation speed. Also, it provided a new way to estimate the LAIe of urban forests and validate large-scale LAI products.

2. Methods

2.1. Study Area

This study was conducted in Jingyuetan National Forest Park (125°44′–125°50′ N, 43°77′–43°79′ E), Changchun City, Northeast China. Changchun City is located in the warm temperate zone, which is a continental monsoon climate area [42] with an average annual temperature of 7.1 °C, average relative humidity of 69%, annual precipitation of 662 mm and annual sunshine duration of 2688 h. The main soil types are black soil, meadow soil and chernozem [43]. The main conifer species in the study area are Larix olgensis, Pinus tabulaeformis, Pinus koraiensis and Pinus sylvestris. The main broad-leaved tree species are Populus davidiana, Quercus mongolica and Tilia mandschurica.
In this study, we selected P. tabulaeformis forest, L. olgensis forest and P. koraiensis forest as the research objects. Based on the different terrain conditions, 2 plots of each forest type were selected. Each plot was divided into a 40 m × 40 m quadrat, and the 6 quadrats were numbered as A, B, C, D, E and F, as shown in Figure 1.

2.2. LAIe Measurement in Sample Plots

An LAI-2200C plant canopy analyzer (LI-COR, Lincoln, NE, USA) was used to measure the LAIe of sample plots. The data were obtained on 25 July 2022. Nine sample points with a distance of 10 m were measured sequentially in the same direction, and the covering cap was selected to be 90° [25], as shown in Figure 2. To avoid the influence of low vegetation in the forest, the analyzer was set at 1.5 m height, and each point was measured 3 times. The average LAIe was used as the measured value of the sample plot (Table 1). To reduce the measurement errors caused by light conditions, the measurements were taken before 9:00 am and after 16:00 pm. The tree height and DBH of each tree were measured with TruPulse 200L (Laser Technology, Inc., Centennial, CO, USA) and a diameter ruler.

2.3. LiDAR Data Acquisition

The LiDAR data were collected via TLS and ALS on 28 July 2022, and the parameters are shown in Table 2. We made use of a GeoSLAM laser scanner (Velodyne LiDAR, Silicon Valley, CA, USA) to collect TLS data, and the specific collection methods were as follows: the operator held the LiDAR scanner, and, starting from the corner of each sample plot, took the measurement point as the center, followed the “S” route for data acquisition, conducted a complete scan of the sample plot, and finally returned to the starting point. The scanning route is shown in Figure 3. In addition, Pegasus D2000 (Pegasus Robot Technology Co., Ltd., Shenzhen, Guangdong, China) was used to collect ALS data. The unmanned aerial vehicle automatically conducted flight scanning according to the scanning route designed by the ground control station in advance (Figure 3). Pegasus D2000 started to fly at the corner of each plot, conducted all-round scanning of the sample plot at a height of 100 m and a speed of 8 m/s, and finally returned to the starting point. Also, UnistrongRTK-G10A (Star Map Instrument Co., Ltd., Hangzhou, Zhejiang, China) was used to measure the absolute coordinate values of the sample sites, and the plane accuracy range was 2–4 cm. All data were collected using WGS84 UTM projection.

2.4. LiDAR Data Processing

The point cloud data were an important basis for quantitative calculation using LiDAR. The collected point cloud data were affected by many factors, such as terrain and the occlusion effect. Therefore, this study conducted pre-processing via denoising and resampling for TLS and ALS data, respectively, and then fused the point cloud data and carried out point cloud normalization and single-wood segmentation. Statistical outlier analysis [38] was used to remove noise points. Data fusion was used for rough registration with absolute coordinates, and then the ICP algorithm was used for fine registration [44]. The improved progressive triangulation filtering algorithm was used to extract ground points for elevation normalization [45]. Single-tree segmentation was conducted based on seed points generated by the canopy height model [46]. Figure 4 demonstrates the differences before and after point cloud data processing. All processes were performed in LiDAR360 (Green Vally International, Beijing, China).

2.5. Canopy Parameter Acquisition

In order to improve the inversion accuracy of LAIe and depict the relationship between forest canopy and LAIe comprehensively, 10 forest structure parameters, canopy closure (CC), gap fraction (GF), canopy openness (CO), stand density (SD), crown volume (CV), crown area (CA), crown thickness (CT), canopy height (CH, which represents the distance from the bottom of the canopy to the ground), tree height (TH) and DBH [47,48,49], were extracted in this study. Among them, SD, CV, CA, CT, CH, DBH and TH were extracted from the re-edited point cloud data via single-wood segmentation. CC, GF and CO were calculated using the package lidR and raster in R (CRAN project), the equations for which are as follows [38,49,50]:
C C = n v e g n t o t a l ,
where n v e g represents the vegetation points and n t o t a l represents the total cloud points at the sample plot.
G F = n t n ,
where n t is the number of ground points through which the laser passes through in the vegetation canopy and n is the total number of points.
C O = r R
The whole-sky view was regarded as a grid, and the grid not covered by canopy was the open area, which was determined using a single parametric algorithm, where r is the number of grids without canopy components and R is the number of all grids.

2.6. Model Building and Evaluation

Because it is difficult to invert LAIe accurately with a single variable and the relationship between different parameters and different forest types is not constant [51,52], we divided the measured data into two categories: distinguished forest type sample and full sample. The relationship between forest structure parameters such as DBH and TH, which were extracted from point cloud data, and the LAIe of different forest types was established. The optimal model was selected by using significant structure parameters, and the stepwise multiple regression method [53]. The accuracy of LAIe estimation with multiple parameters was analyzed, and whether the use of distinguished forest type can improve the inversion effect of LAIe was also discussed. In order to ensure the accuracy of the model, multiple parameters and the voxel method model were built in R based on the same verification data [54]. The stability of the model was tested via cross-validation of the retained samples [23]. Among the 54 groups of sample data, 2/3 of the data were randomly selected as the experimental data, and the remaining 1/3 were used for the verification. In addition, the modeling verification process was repeated 20 times, and the average values of modeling parameters and evaluation indicators were compared to improve the generalization ability of the model and reduce the influence of randomly divided samples on the generalization ability index. The determination coefficient (R2) and Akaike Information Criterion (AIC) were used for model evaluation. The larger the R2 and the smaller the AIC, the higher the accuracy of model prediction.

3. Result

3.1. Effect of Point Diameter on Forest Structure Parameters Extraction

Figure 5A–C show the changing trends of forest structure parameters of different forest types at different point diameters. At different sizes, the values of CC, GF and CO were significantly different. When the point diameter was 0.05 cm, CC was 0.41, GF was 0.73 and CO was 0.28 in the P. tabulaeformis forest. Meanwhile, when the point diameter was set to 0.25 cm, CC, GF and CO were 0.91, 0.17 and 0.07, respectively. The CC value gradually increased with the increase in point diameter. On the contrary, the trend of GF and CO decreased gradually, and the change trend of the three forest types was the same. When the point diameter was 0.15 cm, the value of GF in the P. tabulaeformis forest decreased by 0.2 compared with a 0.1 cm point diameter. Although the value of GF in the P. koraiensis forest increased by 0.3 from a 0.15 cm point diameter to a 0.1 cm point diameter, the overall trend decreased.
Figure 5D–F show the correlation between the forest structure parameters extracted from different point diameters and the measured LAIe. When the point diameter was 0.1 cm, the correlation was the strongest, with the highest correlation coefficient being 0.83. The significance decreased gradually with increasing point diameter. Moreover, the correlation between different forest types and the three parameters was obviously different. At the 0.2 cm point diameter, the P. tabulaeformis forest had the strongest correlation with GF, the L. olgensis forest had the strongest correlation with CO, and the P. koraiensis forest had the highest correlation with CC. The correlations between the three forest types and different parameters changed constantly with the point diameter. When the point diameter exceeded 0.2 cm, the data density of single-tree point clouds was too large, and the correlation between LAIe and parameters of different forest types decreased steadily. Therefore, the best cloud point diameter found in this study was 0.1 cm.

3.2. Correlation between LAIe and Forest Structure Parameters

Except for CH, DBH and TH, the other parameters showed significant correlations with the measured LAIe (p < 0.01), and the correlation coefficients were all higher than 0.56 (Table 3). In addition, the correlation between the LAIe of the full-sample group and CO was strongest, with a correlation coefficient of 0.69. In the distinguished forest type samples, the correlation coefficient between the CO and LAIe of the P. koraiensis forest reached 0.81, which was the highest in all groups. Also, the correlation coefficients of the P. tabulaeformis forest and L. olgensis forest were extremely significant, with values of 0.73 and 0.79, respectively. The correlation coefficient increased by at least 0.03 when the method of distinguishing forest type was applied. The correlation trends between the distinguished forest type samples and the 10 parameters were different. Among them, the correlation between CC and LAIe in the P. tabulaeformis forest was the strongest (p < 0.001), while the LAIe of the L. olgensis forest and P. koraiensis forest showed an extremely significant correlation with CO (p < 0.001). Moreover, the correlation coefficient between the LAIe and TH of the L. olgensis forest was the lowest (p < 0.05), and the correlation between the LAIe and CV of P. tabulaeformis and P. koraiensis forests was the weakest (p < 0.05).

3.3. Construction of LAIe Inversion Model

Table 4 lists the composition and accuracy of LAIe inversion models of different forest types. It demonstrates that the P. tabulaeformis forest had the best estimation accuracy, followed by the L. olgensis forest, P. koraiensis forest and full-sample group. The determination coefficient of the P. tabulaeformis forest was the highest (0.969), and the lowest was found for the full-sample model. The R2 of the three distinguished forest type groups were all above 0.82, which is 3% higher than that of the full-sample model. The AIC used in distinguishing forest type models were all negative. The AIC value of the P. tabulaeformis forest and L. olgensis forest models were −67.42 and −52.07, respectively, and the full-sample model had the highest AIC of −42.44. The AIC of the distinguished forest type model was at least 20% lower than that of the full-sample model. In addition, the number of parameters involved in modeling was different among different forest types. The number of optimal modeling parameters for the P. tabulaeformis forest and L. olgensis forest was six, while that for the P. koraiensis forest was four, and the optimal model composition of the three forest types was completely different.

3.4. Comparison of Multiparameter Model and Voxel Model

Compared to the full-sample model, the voxel model had an overestimation phenomenon in the low-value region (Figure 6), and the R2 was 0.73. However, the multiparameter model was closer to the 1:1 relationship line, and its R2 was 8% higher than that of the voxel model. In addition, the performance of the multiparameter model and voxel model showed different trends, and the distinguished forest type models had higher accuracy than the full-sample model. The R2 of the P. tabulaeformis forest, L. olgensis forest and P. koraiensis forest estimated using the voxel method was 0.82, 0.79 and 0.82, respectively. The R2 of the multiparameter model was at least 3% higher than that of the model fitted with full-sample data.

3.5. Spatial Distribution of LAIe in Different Forest Types

Figure 7 and Table 5 illustrate the LAIe spatial distribution based on the optimal models. It was concluded that the LAIe values of the three forest types in the study area ranged from 0.19 to 6.59, and the average value ranged from 2.17 to 4.35. Among different forest types, the LAIe of the P. koraiensis forest was the highest (6.59), with an average value of 4.35, and most areas had a value between 4 and 6. The LAIe value of the P. tabulaeformis forest was mostly concentrated between 4 and 5. The LAIe values of the L. olgensis forest were generally lower than those of the other two forest types, with an average value of 2.95, and LAIe values were mostly concentrated between 2 and 4. Therefore, the multiparameter model introduced by distinguished forest types can better predict the LAIe value in the study area and objectively reflect the difference in LAIe spatial distribution.

4. Discussion

4.1. Effect of Point Diameter on Forest Structure Parameters

To obtain an accurate LAIe estimation model, the changes in forest structure parameters at different point diameters were investigated based on the point cloud data of three forest types. Figure 8 shows a comparison of point cloud data at 0.1 cm and 0.25 cm point diameters. When the point diameter was 0.1 cm, the point cloud could clearly reflect the environmental appearance of vegetation (Figure 8A); however, when the point diameter was too large, the internal structure of the forest canopy changed accordingly, and the light transmittance decreased significantly (Figure 8B). But inherent structures such as CV, DBH and TH did not change with the point size. Nevertheless, CC is the ratio describing the forest area covered by the canopy, GF represents the ratio of pore volume in the canopy to total canopy volume, and CO can be used as an indirect light environment as well as a spatial competition index of individual trees. All three parameters can reflect the internal structure of the canopy and the light environment of the stand. Therefore, this paper only discussed the relationship between CC, CO and GF and point diameter. Point diameter can affect the extraction accuracy of forest structure parameters and had different effects on different forest types, but the change trend was almost the same (Figure 5). In 2012, Zheng et al. [55] studied the LAI of homogenous and heterogeneous forests and found that point cloud density varied greatly in different canopy structures. The canopy structures of homogenous forests were similar, and point cloud density had little impact on LAI, while the canopy structures of heterogeneous forests had significant differences, and the point cloud density had a great influence on the results. In this study, it was also concluded that the point diameter mainly changed the point cloud density inside a single tree, and there were great differences among different forest types. This may be due to the differences in the composition of leaves. When the point diameter changes, the density of the canopy structure increases, and the distance and angle between leaves in different vegetation canopies change to different degrees. In addition, it can be seen from Figure 5D–F that the larger the point diameter, the higher the precision of forest structure parameter extraction. In this paper, the point diameter of 0.1 cm was used to extract the forest structure parameters. The reason for this may be that when the point diameter reached 0.1 cm, the point cloud can restore the forest information more accurately, but when the point diameter exceeds or falls below this value, the description of the point cloud will exaggerate or shrink the forest vegetation information, resulting in information errors which affect the LAIe inversion value.
Table 6 lists the average DBH and TH extracted from the fusion data based on the optimal point diameter and the average DBH and TH measured in the plot. It demonstrates that the average DBH difference between the P. tabulaeformis forest and P. koraiensis forest extracted from the fusion data was between 1% and 3%, and the average DBH difference in the L. olgensis forest was 5%. The maximum difference between measured tree height and point cloud height was about 5%. Therefore, the method of extracting forest structure parameters was more accurate and can be used as an effective parameter for LAIe inversion.

4.2. Correlation between Forest Structure Parameters and Measured LAIe

By comparing the correlation between the LAIe of 3 forest types and 10 forest structure parameters, it was concluded that the correlation between different forest types and parameters was significantly different (Table 3). This may be due to the large differences in the canopy structure characteristics of different forest types, resulting in different echo information collected by the laser emitted by LiDAR in the process of penetrating the canopy of vegetation [56], and thus the correlation between different parameters and different forest types was significantly different. This result is consistent with Zhu et al. [54], who used multivariate inversion of a broad-leaved forest and a mixed broad-leaved and conifer forest to obtain LAI. As shown in Table 3, the correlations between the 10 parameters and LAIe in descending order were CO, GF, CC, CA, CV, CT, CH, DBH and TH. Therefore, internal canopy structures such as CO, GF and CC have a greater impact on the inversion accuracy of LAIe, followed by canopy morphology parameters such as CA and CV. This may be because LAIe is a key parameter in describing the intensity of vegetation photosynthesis, while CC, CO and GF can affect the amount of light penetrating the canopy of vegetation and describe the intensity of light absorption by vegetation [57]. Structure parameters such as CA and CV mainly describe the size of the vegetation canopy [52] and indirectly reflect the intensity of sunlight absorption. Therefore, CO, CC and GF have a greater impact on LAIe. On the other hand, TH and DBH can indirectly reflect plant growth status and forest age [58] and thus vegetation canopy information, so the correlation with LAIe is not significant. Nevertheless, most of the parameters have certain impacts on LAIe, so the use of multiple parameters to estimate LAIe can reflect the vegetation canopy information more comprehensively and increase the inversion accuracy of LAIe. In addition, it can be seen from Table 3 that the correlation between the 10 parameters and the full-sample group was lower than their correlation with the distinguished forest type groups. Therefore, by distinguishing forest types, correlation relationships can be established according to tree species canopy attributes and parameters, thereby increasing the inversion accuracy of LAIe.

4.3. Model Optimization Method

In recent years, the voxel method has become a popular method of estimating LAIe, but some factors, such as extinction coefficient and voxel size, cannot be used as a constant value to estimate LAIe [59,60]. As shown in Figure 6, compared with voxel method, the accuracy of LAIe estimation based on multiple parameters of distinguished forest type models was higher. Through the introduction of a variety of parameter combinations, remote sensing data can be mined more deeply, a more comprehensive canopy structure can be depicted, and LAIe can be accurately calculated according to different forests’ attributes. In 2008, Darvishzadeh et al. [61] estimated the LAI of heterogeneous grasslands using a multiparameter model and emphasized the importance of multivariate LAI estimation in their study. Furthermore, the stepwise multiple regression method can improve LAI inversion. In 2019, Yu et al. [62] estimated LAI based on different forest types and found that the multiparameter model was superior to the univariate model in estimating LAI, achieving the highest R2 of 0.79, and the accuracy R2 of distinguished forest types was at least 0.0079 higher than that achieved with the full-sample model. In addition, in order to carry out accurate statistical analysis of the sample data, this study used the method of stepwise multiple regression, which calculated the contribution value of variables and included them in the regression analysis in turn. Additionally, different variables were included, and the AIC value was calculated to determine whether the variables selected earlier in the equation changed due to the selection of other variables. This method not only avoids the possible interdependence between variables but also balances the complexity of the estimated model and the goodness of the model fitting data, which is also consistent with Solomon et al. [31].

4.4. Limitations of LAIe Inversion Model

This study further verified the possibility of integrating ALS and LAS data to retrieve LAIe and laid a foundation for large-scale remote sensing LAIe inversion in the future. However, the data collected in this study were collected in the same period, and the comparison information between different periods was lacking. In the future, more field data could be added, and leaves should be collected more frequently according to the period of tree defoliation for further verification and improvement. Thus, the results of this study still show the influence of the xylem and aggregation effect. Seven forest parameters, including CC, GF and CO, were used to invert LAIe, and during the inversion process, the xylem and aggregation effects also greatly affected the accuracy. In next step, the xylem could be removed or quantified according to the woody proportion aggregation index [25] before calculating the LAIe. Finally, only the coniferous forest type was considered in this study, and the validation of broad-leaved forest was lacking, so it is uncertain whether this method is suitable for broad-leaved forests.

5. Conclusions

In order to address the problems of the complicated process of LAIe measurement using optical instruments and inaccurate estimation when employing a passive remote sensing method, this study integrated TLS and ALS data to estimate LAIe, proposed the multiparameter inversion method and distinguished forest types to improve the accuracy. The main conclusions can be drawn as follows:
(1)
The point diameter of point clouds can affect the extraction accuracy of forest structure parameters, especially the parameters related to the internal structure of the canopy.
(2)
Among the 10 extracted forest structure parameters, 7 parameters, including CC, GF and CV, were significantly correlated with the measured LAIe, indicating that we cannot rely on a single factor to meet the needs of constructing an LAIe inversion model. However, the correlations between the same parameter were different with different forest types.
(3)
Combining multiple parameters can increase the estimation accuracy of LAIe, and introducing distinguished forest types can improve the inversion effect of LAIe. The LAIe spatial distribution of different forest types was significantly different, as the P. koraiensis forest had the biggest LAIe, and the L. olgensis forest had the smallest value.

Author Contributions

C.Z., M.D., Z.R. and G.B. collected and analyzed the data. Field surveys were conducted by M.D., X.J., H.M. and H.L. The manuscript was written by C.Z. and M.D. The article was revised by G.B., T.L. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Research and Development Project of Jilin Province (No. 20230202098NC), Natural Science Foundation of Jilin Province (No. 20220101315JC and No. YDZJ202201ZYTS446), Major Special Project of Science and Technology Department of Jilin Province (No. 20230303006SF) and the Research Start-up Funds for Doctoral Talents of Changchun University (No. ZKQD202301).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 14 02084 g001
Figure 1. Overview of the study area. Note: (A,B) are P. tabulaeformis forest plots, (C,D) are L. olgensis forest plots and (E,F) are P. koraiensis forest plots.
Figure 1. Overview of the study area. Note: (A,B) are P. tabulaeformis forest plots, (C,D) are L. olgensis forest plots and (E,F) are P. koraiensis forest plots.
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Figure 2. LAI-2200C measurement.
Figure 2. LAI-2200C measurement.
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Figure 3. Laser radar scanning route map. Note: the red line was the route of the ground-based laser scanner, blue line was the route of the airborne scanner, and green dots were the location of trees in the sample plot. This map shows the C L. olgensis forest plot.
Figure 3. Laser radar scanning route map. Note: the red line was the route of the ground-based laser scanner, blue line was the route of the airborne scanner, and green dots were the location of trees in the sample plot. This map shows the C L. olgensis forest plot.
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Figure 4. Comparison of point cloud data before and after processing. Note: (A) is the map of the point cloud before processing and (B) shows the map after processing. This map shows the C L. olgensis forest plot.
Figure 4. Comparison of point cloud data before and after processing. Note: (A) is the map of the point cloud before processing and (B) shows the map after processing. This map shows the C L. olgensis forest plot.
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Figure 5. Effect of point diameter on forest structure parameter value. Note: (A,D) are P. tabulaeformis plots, (B,E) are L. olgensis plots and (C,F) are P. koraiensis plots. *** Indicates significance at 0.001 level; ** indicates significance at 0.01 level; * indicates significance at 0.05 level.
Figure 5. Effect of point diameter on forest structure parameter value. Note: (A,D) are P. tabulaeformis plots, (B,E) are L. olgensis plots and (C,F) are P. koraiensis plots. *** Indicates significance at 0.001 level; ** indicates significance at 0.01 level; * indicates significance at 0.05 level.
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Figure 6. Comparison of multiparameter model and voxel model validation results. Note: (A) is the LAIe inversion model of L. olgensis forest, (B) represents the LAIe inversion model of the full sample, (C) is the LAIe inversion model of P. koraiensis forest, and (D) represents the LAIe inversion model of P. tabulaeformis forest. Red line is the voxel model, blue line represents the multiparameter model, and the red dashed line is the 1:1 relationship line. The blue dots represent the multiparameter model data and the red triangles represent the voxel model data.
Figure 6. Comparison of multiparameter model and voxel model validation results. Note: (A) is the LAIe inversion model of L. olgensis forest, (B) represents the LAIe inversion model of the full sample, (C) is the LAIe inversion model of P. koraiensis forest, and (D) represents the LAIe inversion model of P. tabulaeformis forest. Red line is the voxel model, blue line represents the multiparameter model, and the red dashed line is the 1:1 relationship line. The blue dots represent the multiparameter model data and the red triangles represent the voxel model data.
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Figure 7. Spatial distribution of LAIe in each sample plot. Note: (A,B) are P. tabulaeformis forest, (C,D) are L. olgensis forest and (E,F) are P. koraiensis forest.
Figure 7. Spatial distribution of LAIe in each sample plot. Note: (A,B) are P. tabulaeformis forest, (C,D) are L. olgensis forest and (E,F) are P. koraiensis forest.
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Figure 8. Comparison of point cloud data at different diameters. Note: (A) is the point cloud map with a point diameter of 0.1 cm, and (B) is the point cloud map with a point diameter of 0.25 cm. The picture is the point cloud data of P. tabulaeformis forest.
Figure 8. Comparison of point cloud data at different diameters. Note: (A) is the point cloud map with a point diameter of 0.1 cm, and (B) is the point cloud map with a point diameter of 0.25 cm. The picture is the point cloud data of P. tabulaeformis forest.
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Table 1. Measured values of LAI in sample plots.
Table 1. Measured values of LAI in sample plots.
IDForest TypeNumber/TreeLAIeminLAIemaxLAIemeanHeightmean/mDBHmean/cm
AP. tabulaeformis412.163.482.7612.2 32.1
BP. tabulaeformis462.523.472.9611.9 29.3
CL. olgensis652.724.483.3714.7 20.2
DL. olgensis592.473.482.8620.7 24.7
EP. koraiensis482.163.542.910.6 19.8
FP. koraiensis453.014.143.219.8 18.1
Table 2. Technical parameters of the LiDAR scanner.
Table 2. Technical parameters of the LiDAR scanner.
ParametersGeoSLAMD2000
Wavelength16 line/905 nm905 nm
Point cloud acquisition300,000 point/s113 point/m2
Measuring range100 m190 m
Ranging accuracy±3 cm±2 cm
Fieldvertical 30° (+15°–−15°); level 360°vertical 4.5°/77.2°; level 70.4°
Echo13
Frequency5–20 Hz20 Hz
Table 3. Correlation coefficients between forest parameters of different forest types and LAIe. Note: *** indicates significance at 0.001 level; ** indicates significance at 0.01 level; * indicates significance at 0.05 level.
Table 3. Correlation coefficients between forest parameters of different forest types and LAIe. Note: *** indicates significance at 0.001 level; ** indicates significance at 0.01 level; * indicates significance at 0.05 level.
ParametersP. tabulaeformis ForestL. olgensis ForestP. koraiensis ForestFull Sample
CC0.76 ***0.64 **0.74 ***0.61 **
GF−0.73 ***−0.65 **−0.65 **−0.62 **
CO0.73 ***0.79 ***0.81 ***0.67 **
SD0.66 **0.5 *0.69 **0.47 *
CA0.67 **0.62 **0.72 ***0.55 *
CV0.57 *0.56 *0.58 *0.51 *
CT0.62 **0.53 *0.67 **0.51 *
CH0.060.51 *0.48 *0.31
DBH−0.21−0.31−0.26−0.48 *
TH−0.12−0.45−0.2−0.35
Table 4. Parameters and evaluation of LAIe inversion model.
Table 4. Parameters and evaluation of LAIe inversion model.
Model CoefficientP. tabulaeformis ForestL. olgensis ForestP. koraiensis ForestFull Sample
Constant−2.56−1.23−10.37−2.8
CC 13.035.15
GF−3.761.21142.68
CO−103.8−19.6−9.53
SD7.052.58
CA0.010.18 −0.02
CV0.03−0.040.04
CT0.770.57 0.71
ModelingR20.9690.8360.8270.794
AIC−67.42−52.77−48.76−42.44
p-value0.00010.00010.00010.0001
Table 5. LAIe estimation results of different forest types based on multiparameter inversion models.
Table 5. LAIe estimation results of different forest types based on multiparameter inversion models.
IDTypeLAIemeanLAIemaxLAIemin
AP. tabulaeformis forest3.044.870.19
BP. tabulaeformis forest3.355.580.37
CL. olgensis forest2.954.470.25
DL. olgensis forest2.174.920.28
EP. koraiensis forest4.356.590.78
FP. koraiensis forest3.416.470.59
Table 6. Comparison of measured mean DBH and tree height with point cloud extraction value. Note: DBH is the measured DBH, and DBHt is the DBH extracted from the point cloud. TH is the measured tree height, and THt is the tree height extracted from the point cloud. All values are the average values of the sample plots.
Table 6. Comparison of measured mean DBH and tree height with point cloud extraction value. Note: DBH is the measured DBH, and DBHt is the DBH extracted from the point cloud. TH is the measured tree height, and THt is the tree height extracted from the point cloud. All values are the average values of the sample plots.
Forest TypesDBH (cm)DBHt (cm)TH (m)THt (m)
P. tabulaeformis32.1 32.6 12.2 12.8
P. tabulaeformis29.3 28.3 11.3 11.5
L. olgensis24.6 23.3 20.7 21.2
L. olgensis20.2 20.2 17.7 18.1
P. koraiensis18.0 18.5 9.8 10.3
P. koraiensis19.8 19.9 10.6 10.7
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Zhai, C.; Ding, M.; Ren, Z.; Bao, G.; Liu, T.; Zhang, Z.; Jiang, X.; Ma, H.; Lin, H. A LiDAR-Driven Effective Leaf Area Index Inversion Method of Urban Forests in Northeast China. Forests 2023, 14, 2084. https://doi.org/10.3390/f14102084

AMA Style

Zhai C, Ding M, Ren Z, Bao G, Liu T, Zhang Z, Jiang X, Ma H, Lin H. A LiDAR-Driven Effective Leaf Area Index Inversion Method of Urban Forests in Northeast China. Forests. 2023; 14(10):2084. https://doi.org/10.3390/f14102084

Chicago/Turabian Style

Zhai, Chang, Mingming Ding, Zhibin Ren, Guangdao Bao, Ting Liu, Zhonghui Zhang, Xuefei Jiang, Hongbo Ma, and Haisen Lin. 2023. "A LiDAR-Driven Effective Leaf Area Index Inversion Method of Urban Forests in Northeast China" Forests 14, no. 10: 2084. https://doi.org/10.3390/f14102084

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