Chin J Plant Ecol ›› 2020, Vol. 44 ›› Issue (6): 598-615.DOI: 10.17521/cjpe.2019.0347
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YI Hai-Yan1,2, ZENG Yuan1,2,*(), ZHAO Yu-Jin3, ZHENG Zhao-Ju1, XIONG Jie1,2, ZHAO Dan1
Received:
2019-12-12
Accepted:
2020-02-07
Online:
2020-06-20
Published:
2020-03-26
Contact:
ZENG Yuan
Supported by:
YI Hai-Yan, ZENG Yuan, ZHAO Yu-Jin, ZHENG Zhao-Ju, XIONG Jie, ZHAO Dan. Forest species diversity mapping based on clustering algorithm[J]. Chin J Plant Ecol, 2020, 44(6): 598-615.
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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2019.0347
结构参数 Structural parameter | 描述 Description | 引用 Reference |
---|---|---|
95%分位数高度 95% quantile height | 近似于森林冠层峰值高度, 由首次回波统计获得 Approximates the peak height in meters of the forest canopy, obtained from the point cloud of the first echo | |
平均植被高度 Mean vegetation height | 植被的平均高度, 由各植被分层首次回波和末次回波统计获得 The mean height of vegetation, obtained from the point cloud of the first and last echo of each vegetation layer | |
植被穿透率 Vegetation permeability | 植被首次回波在二次回波中的比例, 由植被的首次回波和所有二次回波计算得到 Proportion of first vegetation returns (see above) for which there is a second return, obtained from the point cloud of the first echo and all secondary echoes of vegetation | |
叶高度多样性 Foliage height diversity | 描述植被剖面的叶密度和高度分布, 公式: $h=-\mathop{\sum }^{}{{p}_{i}}\ln {{p}_{i}}$, 其中, pi表示不同高度间隔内的点云返回值与所有返回值的比例, h表示树高 Metric intended to characterize the density and height distribution of foliage in a vegetation profile. Formula: $h=-\mathop{\sum }^{}{{p}_{i}}\ln {{p}_{i}}$, where pi is the ratio of return value of point cloud in different height intervals to all return values, h is the tree height | |
标准偏差(首次回波) Standard deviation (first return) | 反映单木树高的离散程度 Metric the dispersion of each individual tree | |
平均绝对偏差 Mean absolute deviation | 所有单木树高值与其算术平均值的偏差的绝对值的平均 Mean of absolute value of the deviation of tree height from the mean | |
偏度(首次回波) Skewness (first return) | 与峰态(首次回波)高度相关 Highly correlated with kurtosis |
Table 2 Canopy structural parameters derived from LiDAR
结构参数 Structural parameter | 描述 Description | 引用 Reference |
---|---|---|
95%分位数高度 95% quantile height | 近似于森林冠层峰值高度, 由首次回波统计获得 Approximates the peak height in meters of the forest canopy, obtained from the point cloud of the first echo | |
平均植被高度 Mean vegetation height | 植被的平均高度, 由各植被分层首次回波和末次回波统计获得 The mean height of vegetation, obtained from the point cloud of the first and last echo of each vegetation layer | |
植被穿透率 Vegetation permeability | 植被首次回波在二次回波中的比例, 由植被的首次回波和所有二次回波计算得到 Proportion of first vegetation returns (see above) for which there is a second return, obtained from the point cloud of the first echo and all secondary echoes of vegetation | |
叶高度多样性 Foliage height diversity | 描述植被剖面的叶密度和高度分布, 公式: $h=-\mathop{\sum }^{}{{p}_{i}}\ln {{p}_{i}}$, 其中, pi表示不同高度间隔内的点云返回值与所有返回值的比例, h表示树高 Metric intended to characterize the density and height distribution of foliage in a vegetation profile. Formula: $h=-\mathop{\sum }^{}{{p}_{i}}\ln {{p}_{i}}$, where pi is the ratio of return value of point cloud in different height intervals to all return values, h is the tree height | |
标准偏差(首次回波) Standard deviation (first return) | 反映单木树高的离散程度 Metric the dispersion of each individual tree | |
平均绝对偏差 Mean absolute deviation | 所有单木树高值与其算术平均值的偏差的绝对值的平均 Mean of absolute value of the deviation of tree height from the mean | |
偏度(首次回波) Skewness (first return) | 与峰态(首次回波)高度相关 Highly correlated with kurtosis |
Fig. 4 Validation result of individual tree isolation (A) and individual tree isolation result of plot No. 25 (B) in Gutianshan study area. The red polygon represents plot border, the white polygons represent canopy locations, and the red dots represent field-measured positions of the base of tree trunk.
Fig. 5 16 standardized biochemical components of 17 dominant tree species in Gutianshan study area. Car, carotenoids; Cel, cellulose; Chl a, chlorophyll a; Chl b, chlorophyll b; Lig, lignin; SLA, specific leaf area; EWT, equivalent water thickness.
生化组分 Biochemical component | 叶绿素a Chl a | 叶绿素b Chl b | 类胡萝卜素 Car | 比叶面积 SLA | 水分含量 EWT | 纤维素 Cel | 木质素 Lig | 碳 Carbon | 氮 N | 磷 P | 钙 Ca | 钾 K | 镁 Mg | 锰 Mn | 锌 Zn | 硼 B |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | 0.70 | 0.60 | 0.69 | 0.79 | 0.67 | 0.66 | 0.76 | 0.72 | 0.63 | 0.63 | 0.51 | 0.23 | 0.48 | 0.10 | 0.09 | 0.21 |
Table 3 Validation result of leaf biochemical components
生化组分 Biochemical component | 叶绿素a Chl a | 叶绿素b Chl b | 类胡萝卜素 Car | 比叶面积 SLA | 水分含量 EWT | 纤维素 Cel | 木质素 Lig | 碳 Carbon | 氮 N | 磷 P | 钙 Ca | 钾 K | 镁 Mg | 锰 Mn | 锌 Zn | 硼 B |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
R2 | 0.70 | 0.60 | 0.69 | 0.79 | 0.67 | 0.66 | 0.76 | 0.72 | 0.63 | 0.63 | 0.51 | 0.23 | 0.48 | 0.10 | 0.09 | 0.21 |
生化组分 Biochemical component | 植被指数 Vegetation index | 计算公式 Formula | 引用 Reference |
---|---|---|---|
叶绿素a/b Chl a/b | TCARI/OSAVI | TCARI/OSAVI [705,750] = 3[(R750.66 - R704.6) - 0.2(R750.66 - R550.67)(R750.66/R704.6)]/[(1 + 0.16)(R750.66 - R704.6)/(R750.66 + R704.6 + 0.16)] | |
类胡萝卜素 Car | CRI | CRI = 1/R510 - 1/R550 | |
水分 EWT | WBI | WBI = R895/R972 | |
氮/磷 N/P | CCCI | CCCI = (0.7415R790 - 0.6965R720)/(0.0319R790 - 0.281R720) | |
比叶面积 SLA | RVI | RVI = R750/R705 | |
纤维素/木质素 Cel/Lig | PRI | PRI = (R531 - R570)/(R531 + R570) |
Table 4 Vegetation indices corresponding to the optimal biochemical components
生化组分 Biochemical component | 植被指数 Vegetation index | 计算公式 Formula | 引用 Reference |
---|---|---|---|
叶绿素a/b Chl a/b | TCARI/OSAVI | TCARI/OSAVI [705,750] = 3[(R750.66 - R704.6) - 0.2(R750.66 - R550.67)(R750.66/R704.6)]/[(1 + 0.16)(R750.66 - R704.6)/(R750.66 + R704.6 + 0.16)] | |
类胡萝卜素 Car | CRI | CRI = 1/R510 - 1/R550 | |
水分 EWT | WBI | WBI = R895/R972 | |
氮/磷 N/P | CCCI | CCCI = (0.7415R790 - 0.6965R720)/(0.0319R790 - 0.281R720) | |
比叶面积 SLA | RVI | RVI = R750/R705 | |
纤维素/木质素 Cel/Lig | PRI | PRI = (R531 - R570)/(R531 + R570) |
结构多样性参数 Structural parameter | Simpson 指数 Simpson index | Shannon- Wiener 指数 Shannon- Wiener index | 物种丰富度 Species richness | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
95%分位 数高度 95% Quantile height | 平均植 被高度 Mean vegetation height | 平均绝对偏差 Mean absolute deviation | 标准偏差 Standard deviation | 偏度 Skewness | 叶高度 多样性 Foliage height diversity | 植被 穿透率 Vegetation permeability | |||||
垂直结构 Vertical structure | 95%分位数高度 | 1.000 | 0.884** | 0.757** | 0.811** | -0.320 | -0.233 | 0.233 | 0.455** | 0.320 | 0.088 |
平均植被高度 | 0.884** | 1.000 | 0.428* | 0.511** | -0.578** | 0.171 | 0.318 | -0.352* | -0.187 | 0.012 | |
平均绝对偏差 | 0.757** | 0.428* | 1.000 | 0.988** | -0.055 | -0.763** | 0.134 | -0.314 | -0.236 | 0.002 | |
标准偏差 | 0.811** | 0.511** | 0.988** | 1.000 | -0.167 | -0.700** | 0.214 | -0.328 | -0.244 | 0.010 | |
内部结构 Inner structure | 偏度 | -0.320 | -0.578** | -0.055 | -0.167 | 1.000 | -0.281 | -0.546** | -0.171 | -0.225 | 0.152 |
叶高度多样性 | -0.233 | 0.171 | -0.763** | -0.700** | -0.281 | 1.000 | 0.227 | 0.020 | 0.020 | 0.123 | |
植被穿透率 | 0.233 | 0.318 | 0.134 | 0.214 | -0.546** | 0.227 | 1.000 | -0.095 | -0.075 | 0.149 |
Table 5 Correlation analysis of LiDAR-derived structural parameters
结构多样性参数 Structural parameter | Simpson 指数 Simpson index | Shannon- Wiener 指数 Shannon- Wiener index | 物种丰富度 Species richness | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
95%分位 数高度 95% Quantile height | 平均植 被高度 Mean vegetation height | 平均绝对偏差 Mean absolute deviation | 标准偏差 Standard deviation | 偏度 Skewness | 叶高度 多样性 Foliage height diversity | 植被 穿透率 Vegetation permeability | |||||
垂直结构 Vertical structure | 95%分位数高度 | 1.000 | 0.884** | 0.757** | 0.811** | -0.320 | -0.233 | 0.233 | 0.455** | 0.320 | 0.088 |
平均植被高度 | 0.884** | 1.000 | 0.428* | 0.511** | -0.578** | 0.171 | 0.318 | -0.352* | -0.187 | 0.012 | |
平均绝对偏差 | 0.757** | 0.428* | 1.000 | 0.988** | -0.055 | -0.763** | 0.134 | -0.314 | -0.236 | 0.002 | |
标准偏差 | 0.811** | 0.511** | 0.988** | 1.000 | -0.167 | -0.700** | 0.214 | -0.328 | -0.244 | 0.010 | |
内部结构 Inner structure | 偏度 | -0.320 | -0.578** | -0.055 | -0.167 | 1.000 | -0.281 | -0.546** | -0.171 | -0.225 | 0.152 |
叶高度多样性 | -0.233 | 0.171 | -0.763** | -0.700** | -0.281 | 1.000 | 0.227 | 0.020 | 0.020 | 0.123 | |
植被穿透率 | 0.233 | 0.318 | 0.134 | 0.214 | -0.546** | 0.227 | 1.000 | -0.095 | -0.075 | 0.149 |
单木样方 Individual tree plot | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
R2 | 0.784 | 0.836 | 0.741 | 0.774 | 0.715 | 0.785 |
RMSE | 1.196 | 0.894 | 0.684 | 1.083 | 1.323 | 1.132 |
Table 6 Validation result of LiDAR-derived individual tree height
单木样方 Individual tree plot | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
R2 | 0.784 | 0.836 | 0.741 | 0.774 | 0.715 | 0.785 |
RMSE | 1.196 | 0.894 | 0.684 | 1.083 | 1.323 | 1.132 |
样方号 Plot No. | 实测物种丰富度 Measured species richness | 实测物种 丰富度* Measured species richness* | 实测物种 丰富度** Measured species richness** | 聚类数 (预测值) Prediction | 样方号 Plot No. | 实测物种丰富度 Measured species richness | 实测物种 丰富度* Measured species richness* | 实测物种 丰富度** Measured species richness** | 聚类数 (预测值) Prediction |
---|---|---|---|---|---|---|---|---|---|
1 | 14 | 9 | 8 | 10 | 18 | 13 | 7 | 6 | 8 |
2 | 10 | 6 | 5 | 8 | 19 | 19 | 10 | 6 | 9 |
3 | 17 | 9 | 9 | 8 | 20 | 17 | 11 | 8 | 8 |
4 | 9 | 6 | 5 | 7 | 21 | 16 | 10 | 6 | 8 |
5 | 11 | 9 | 7 | 8 | 22 | 16 | 10 | 9 | 7 |
6 | 20 | 14 | 11 | 13 | 23 | 8 | 5 | 5 | 5 |
7 | 19 | 14 | 10 | 11 | 24 | 9 | 8 | 5 | 5 |
8 | 11 | 6 | 5 | 8 | 25 | 13 | 6 | 3 | 6 |
9 | 6 | 4 | 3 | 6 | 26 | 13 | 9 | 7 | 7 |
10 | 14 | 8 | 5 | 7 | 27 | 14 | 6 | 4 | 5 |
11 | 18 | 9 | 6 | 6 | 28 | 7 | 5 | 5 | 7 |
12 | 10 | 7 | 6 | 9 | 29 | 16 | 8 | 5 | 7 |
13 | 16 | 7 | 5 | 5 | 30 | 20 | 12 | 8 | 8 |
14 | 16 | 10 | 8 | 8 | 31 | 15 | 7 | 6 | 8 |
15 | 20 | 12 | 8 | 9 | 32 | 16 | 8 | 7 | 9 |
16 | 18 | 14 | 8 | 8 | 33 | 15 | 8 | 3 | 5 |
17 | 16 | 8 | 5 | 6 | 34 | 17 | 13 | 9 | 8 |
R2 | 0.29 | 0.37 | 0.56 | RMSE | 7.75 | 2.41 | 1.82 |
Table 7 Prediction and validation for species richness in Gutianshan study area
样方号 Plot No. | 实测物种丰富度 Measured species richness | 实测物种 丰富度* Measured species richness* | 实测物种 丰富度** Measured species richness** | 聚类数 (预测值) Prediction | 样方号 Plot No. | 实测物种丰富度 Measured species richness | 实测物种 丰富度* Measured species richness* | 实测物种 丰富度** Measured species richness** | 聚类数 (预测值) Prediction |
---|---|---|---|---|---|---|---|---|---|
1 | 14 | 9 | 8 | 10 | 18 | 13 | 7 | 6 | 8 |
2 | 10 | 6 | 5 | 8 | 19 | 19 | 10 | 6 | 9 |
3 | 17 | 9 | 9 | 8 | 20 | 17 | 11 | 8 | 8 |
4 | 9 | 6 | 5 | 7 | 21 | 16 | 10 | 6 | 8 |
5 | 11 | 9 | 7 | 8 | 22 | 16 | 10 | 9 | 7 |
6 | 20 | 14 | 11 | 13 | 23 | 8 | 5 | 5 | 5 |
7 | 19 | 14 | 10 | 11 | 24 | 9 | 8 | 5 | 5 |
8 | 11 | 6 | 5 | 8 | 25 | 13 | 6 | 3 | 6 |
9 | 6 | 4 | 3 | 6 | 26 | 13 | 9 | 7 | 7 |
10 | 14 | 8 | 5 | 7 | 27 | 14 | 6 | 4 | 5 |
11 | 18 | 9 | 6 | 6 | 28 | 7 | 5 | 5 | 7 |
12 | 10 | 7 | 6 | 9 | 29 | 16 | 8 | 5 | 7 |
13 | 16 | 7 | 5 | 5 | 30 | 20 | 12 | 8 | 8 |
14 | 16 | 10 | 8 | 8 | 31 | 15 | 7 | 6 | 8 |
15 | 20 | 12 | 8 | 9 | 32 | 16 | 8 | 7 | 9 |
16 | 18 | 14 | 8 | 8 | 33 | 15 | 8 | 3 | 5 |
17 | 16 | 8 | 5 | 6 | 34 | 17 | 13 | 9 | 8 |
R2 | 0.29 | 0.37 | 0.56 | RMSE | 7.75 | 2.41 | 1.82 |
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