利用聚类算法监测森林乔木物种多样性

doi:10.17521/cjpe.2019.0347

植物生态学报 ›› 2020, Vol. 44 ›› Issue (6): 598-615.DOI: 10.17521/cjpe.2019.0347

所属专题：遥感生态学

利用聚类算法监测森林乔木物种多样性

衣海燕¹^,², 曾源¹^,²^,^*(), 赵玉金³, 郑朝菊¹, 熊杰¹^,², 赵旦¹

¹中国科学院空天信息创新研究院遥感科学国家重点实验室, 北京 100101
²中国科学院大学, 北京 100049
³中国科学院植物研究所植被与环境变化国家重点实验室, 北京 100093

收稿日期:2019-12-12 接受日期:2020-02-07 出版日期:2020-06-20 发布日期:2020-03-26
通讯作者: 曾源
基金资助:
国家重点研发计划项目(2016YFC0500201);国家自然科学基金(41671365)

Forest species diversity mapping based on clustering algorithm

YI Hai-Yan¹^,², ZENG Yuan¹^,²^,^*(), ZHAO Yu-Jin³, ZHENG Zhao-Ju¹, XIONG Jie¹^,², ZHAO Dan¹

¹State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
²University of Chinese Academy of Sciences, Beijing 100049, China
³State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

Received:2019-12-12 Accepted:2020-02-07 Online:2020-06-20 Published:2020-03-26
Contact: ZENG Yuan
Supported by:
National Key R&D Program of China(2016YFC0500201);National Natural Science Foundation of China(41671365)

摘要/Abstract

摘要：

该研究基于机载激光雷达(LiDAR)和高光谱数据, 从森林物种叶片的生理化学源头探寻生化特征与光谱特征的内在关联, 探讨生化多样性、光谱多样性与物种多样性之间的响应机制, 选择最优植被指数并结合最优结构参数, 通过聚类方法构建森林物种多样性遥感估算模型, 在古田山自然保护区开展森林乔木物种多样性监测。研究结果表明: (1)从16种叶片生化组分中, 筛选出叶绿素a、叶绿素b、类胡萝卜素、叶片含水量、比叶面积、纤维素、木质素、氮、磷和碳可通过偏最小二乘法用叶片光谱有效模拟(R² = 0.60-0.79, p < 0.01), 并选择有效的植被指数: 转换型吸收反射指数/优化型土壤调整指数(TCARI/OSAVI)、类胡萝卜素反射指数(CRI)、水波段指数(WBI)、比值植被指数(RVI)、生理反射指数(PRI)和冠层叶绿素浓度指数(CCCI)表征相应的最优生化组分; (2)基于机载LiDAR数据利用结合形态学冠层控制的分水岭算法获得高精度单木分离结果(R ² = 0.77, RMSE = 16.48), 同时采用逐步回归方法从常用的森林结构参数中选取树高和偏度作为最优结构参数(R ² = 0.32, p < 0.01); (3)基于6个最优植被指数和2个最优结构参数, 以20 m × 20 m为窗口通过自适应模糊C均值方法进行聚类, 实现了研究区森林乔木物种丰富度(Richness, R ²= 0.56, RMSE = 1.81)和多样性指数Shannon-Wiener (R ² = 0.83, RMSE = 0.22)与Simpson (R ² = 0.85, RMSE = 0.09)的成图。该研究在冠层尺度上获取了与物种多样性相关的生化、光谱和结构参数, 将单木个体作为最小单元, 利用聚类算法直接估算物种类别差异, 无需判定具体的树种属性, 是利用遥感数据进行区域尺度森林物种多样性监测与成图的实践, 可为亚热带地区常绿阔叶林的物种多样性监测提供借鉴。

关键词: 森林物种多样性, 自适应模糊C均值聚类, 高光谱, 激光雷达, 单木分离

Abstract:

Aims Monitoring forest species diversity continuously and efficiently is important to maintain ecosystem services and achieve sustainability and conservation goals. In this paper, we explored the relationship between leaf biochemical and spectral properties and their inner linkage with species diversity, then estimated the forest species diversity based on a clustering algorithm using airborne imaging spectroscopy and Light Detection and Ranging (LiDAR) data in the Gutianshan National Nature Reserve of China.
Methods Firstly, we isolated individual tree crowns (ITCs) with the watershed algorithm from the LiDAR data. Then we calculated the optimal vegetation indices (VIs) representing the key biochemical properties from the hyperspectral data and selected optimal structural parameters from commonly used LiDAR-derived structural parameters based on correlation and stepwise regression analysis with the field samples. Finally, a self-adaptive Fuzzy C-Means (FCM) clustering algorithm was applied to map the species diversity (i.e. Richness, Shannon-Wiener index and Simpson index) in the study area for each 20 m × 20 m moving window.
Important findings The results indicated that biochemical components (chlorophyll a & b, total carotenoids, equivalent water thickness, specific leaf area, cellulose, lignin, nitrogen, phosphorus and carbon) could be well quantified by leaf spectrum using partial least squares regression (R² = 0.60-0.79, p < 0.01), and represented by hyperspectral VIs, namely, Transformed Chlorophyll Ratio Index/Optimization of Soil-adjusted Vegetation Index (TCARI/OSAVI), Carotenoid Reflectance Index (CRI), Water Band Index (WBI), Ratio Vegetation Index (RVI), Photochemical Reflectance Index (PRI) and Canopy Chlorophyll Concentration Index (CCCI). The individual tree isolation showed high accuracy (R ² = 0.77, RMSE = 16.48). The correlation and stepwise regression analysis showed tree height and skewness were the optimal structural parameters among seven commonly used forest structural parameters (R ² = 0.32, p < 0.01). The species diversity indices calculated from the self-adaptive FCM clustering algorithm based on the six VIs and two optimal structural parameters correlated well with the field measurements (species richness, R ² = 0.56, RMSE = 1.81; Shannon-Wiener index, R ² = 0.83, RMSE = 0.22; Simpson index, R ² = 0.85, RMSE = 0.09). With the clustering method combined with crown-by-crown variations in hyperspectral biochemical VIs and LiDAR-derived structural parameters, we created continuous maps of forest species diversity in the examined subtropical forest without the need to identify specific tree species. Our case study in Gutianshan showed the potential of airborne hyperspectral and LiDAR data in mapping species diversity of the subtropical evergreen broad-leaved forest. It could also provide a pathway for monitoring the state and changes of forest biodiversity at regional scales.

Key words: forest species diversity, self-adaptive Fuzzy C-Means clustering algorithm, imaging spectroscopy, Light Detection and Ranging, individual tree isolation

衣海燕, 曾源, 赵玉金, 郑朝菊, 熊杰, 赵旦. 利用聚类算法监测森林乔木物种多样性. 植物生态学报, 2020, 44(6): 598-615. DOI: 10.17521/cjpe.2019.0347

YI Hai-Yan, ZENG Yuan, ZHAO Yu-Jin, ZHENG Zhao-Ju, XIONG Jie, ZHAO Dan. Forest species diversity mapping based on clustering algorithm. Chinese Journal of Plant Ecology, 2020, 44(6): 598-615. DOI: 10.17521/cjpe.2019.0347

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2019.0347

https://www.plant-ecology.com/CN/Y2020/V44/I6/598

图/表 14

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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	Ria?o et al., 2004
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	Lefsky et al., 2002
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	Moffiet et al., 2005
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, p_i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 p_i is the ratio of return value of point cloud in different height intervals to all return values, h is the tree height	Clawges et al., 2008
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	Nelson et al., 1988
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	Zhao et al., 2018
偏度(首次回波) Skewness (first return)	与峰态(首次回波)高度相关 Highly correlated with kurtosis	Antonarakis et al., 2008

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	Ria?o et al., 2004
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	Lefsky et al., 2002
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	Moffiet et al., 2005
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, p_i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 p_i is the ratio of return value of point cloud in different height intervals to all return values, h is the tree height	Clawges et al., 2008
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	Nelson et al., 1988
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	Zhao et al., 2018
偏度(首次回波) Skewness (first return)	与峰态(首次回波)高度相关 Highly correlated with kurtosis	Antonarakis et al., 2008

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[(R_750.66 - R_704.6) - 0.2(R_750.66 - R_550.67)(R_750.66/R_704.6)]/[(1 + 0.16)(R_750.66 - R_704.6)/(R_750.66 + R_704.6 + 0.16)]	Daughtry et al., 2000; Wu et al., 2008
类胡萝卜素 Car	CRI	CRI = 1/R₅₁₀ - 1/R₅₅₀	Gitelson et al., 2002
水分 EWT	WBI	WBI = R₈₉₅/R₉₇₂	Penuelas et al., 1993
氮/磷 N/P	CCCI	CCCI = (0.7415R₇₉₀ - 0.6965R₇₂₀)/(0.0319R₇₉₀ - 0.281R₇₂₀)	El-Shikha et al., 2007
比叶面积 SLA	RVI	RVI = R₇₅₀/R₇₀₅	Jordan, 1969
纤维素/木质素 Cel/Lig	PRI	PRI = (R₅₃₁ - R₅₇₀)/(R₅₃₁ + R₅₇₀)	Gamon et al., 1992

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[(R_750.66 - R_704.6) - 0.2(R_750.66 - R_550.67)(R_750.66/R_704.6)]/[(1 + 0.16)(R_750.66 - R_704.6)/(R_750.66 + R_704.6 + 0.16)]	Daughtry et al., 2000; Wu et al., 2008
类胡萝卜素 Car	CRI	CRI = 1/R₅₁₀ - 1/R₅₅₀	Gitelson et al., 2002
水分 EWT	WBI	WBI = R₈₉₅/R₉₇₂	Penuelas et al., 1993
氮/磷 N/P	CCCI	CCCI = (0.7415R₇₉₀ - 0.6965R₇₂₀)/(0.0319R₇₉₀ - 0.281R₇₂₀)	El-Shikha et al., 2007
比叶面积 SLA	RVI	RVI = R₇₅₀/R₇₀₅	Jordan, 1969
纤维素/木质素 Cel/Lig	PRI	PRI = (R₅₃₁ - R₅₇₀)/(R₅₃₁ + R₅₇₀)	Gamon et al., 1992

		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	Simpson 指数 Simpson index	Shannon- Wiener 指数 Shannon- Wiener index	物种丰富度 Species richness
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

利用聚类算法监测森林乔木物种多样性

Forest species diversity mapping based on clustering algorithm

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参考文献 73

相关文章 15

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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

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