基于Sentinel-2A数据的东北森林植物多样性监测方法研究

doi:10.17521/cjpe.2021.0373

植物生态学报 ›› 2022, Vol. 46 ›› Issue (10): 1251-1267.DOI: 10.17521/cjpe.2021.0373

所属专题：遥感生态学

基于Sentinel-2A数据的东北森林植物多样性监测方法研究

周楷玲¹^,², 赵玉金¹^,^*(), 白永飞¹^,³^,^*()

¹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
²中国科学院大学生命科学学院, 北京 100049
³中国科学院大学资源与环境学院, 北京 100049

收稿日期:2021-10-15 接受日期:2022-01-14 出版日期:2022-10-20 发布日期:2022-05-21
通讯作者: ^*(白永飞, yfbai@ibcas.ac.cn; 赵玉金, zhaoyj@ibcas.ac.cn)
基金资助:
中国科学院战略性先导科技专项(A类)(XDA23080303)

Study on forest plant diversity monitoring based on Sentinel-2A satellite data in northeast China

ZHOU Kai-Ling¹^,², ZHAO Yu-Jin¹^,^*(), BAI Yong-Fei¹^,³^,^*()

¹State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinses Academy of Science, Beijing 100093, China
²College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
³College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2021-10-15 Accepted:2022-01-14 Online:2022-10-20 Published:2022-05-21
Contact: ^*(BAI Yong-Fei, yfbai@ibcas.ac.cn; ZHAO Yu-Jin, zhaoyj@ibcas.ac.cn)
Supported by:
Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080303)

摘要/Abstract

摘要：

植物多样性监测是开展生物多样性评估, 制定生物多样性保护政策的基础。传统的森林植物多样性监测以实地调查为主, 难以快速获取森林植物多样性的空间分布及其动态变化信息。遥感技术的发展为评估区域尺度森林植物多样性提供了重要工具。该研究选取凉水、丰林和珲春3个国家级自然保护区, 利用Sentinel-2A卫星影像和野外实测数据, 探讨了基于像元和聚类的光谱多样性直接估算方法, 以及基于随机森林回归的森林植物多样性反演方法。研究结果表明: (1)在像元尺度, 基于凸包面积计算的光谱多样性指数对Shannon-Wiener多样性指数的估算精度(R²= 0.74)优于基于变异系数的方法(R²= 0.60); (2)基于像元的光谱多样性估算方法对Shannon-Wiener多样性指数的估算精度优于聚类分析方法(R²= 0.59); (3)基于6个特征变量, 利用随机森林回归算法对Shannon-Wiener多样性指数的估算精度最高(R²= 0.79); (4)上述方法均不能精确估算Simpson多样性指数和物种丰富度。研究发现基于Sentinel-2A卫星影像能较好地反演Shannon-Wiener多样性指数, 为下一步能在大尺度上进行森林植物多样性估算提供了参考和依据。

关键词: 森林植物多样性, Sentinel-2A, 光谱多样性, 聚类分析, 随机森林回归

Abstract:

Aims Plant diversity monitoring is the basis of biodiversity assessment and developing conservation policy. Traditional forest plant diversity monitoring is mainly based on field surveys, which is difficult to quickly obtain the spatial distribution and dynamic change of forest plant diversity. The development of remote sensing technology provides an important tool for assessing forest plant diversity at the regional scale. In this study, we explored two methods of forest plant diversity estimation based on Sentinel-2A satellite images and field data in three selected national nature reserves (Liangshui, Fenglin, and Hunchun).

Methods We used two methods to estimate forest plant diversity: (1) Direct estimation based on spectral diversity at the pixel and cluster scales, respectively; (2) Indirect estimation based on random forest regression. The spectral diversity was calculated based on the coefficient of variation and convex hull area at the pixel scale, respectively. K-means clustering method was used for cluster analysis to calculate the spectral diversity between clusters. For the indirect estimation, we used 10-fold cross validation to select characteristic variables for later diversity calculation.

Important findings Our results showed that: (1) At the pixel scale, the estimation accuracy of Shannon-Wiener diversity index based on convex hull area (R²= 0.74) was better than that of coefficient of variation (R²= 0.60); (2) The pixel-based estimation accuracy of Shannon-Wiener diversity index outperformed clustering basis (R²= 0.59); (3) Based on six feature variables, the Shannon-Wiener diversity index was best estimated using the random forest regression algorithm (R²= 0.79); (4) Both the Simpson diversity index and species richness could not be accurately estimated by the above methods. Our findings indicate the capability of Sentinel-2A satellite images to estimate the Shannon-Wiener diversity index, providing reference and basis for forest plant diversity estimation at a large scale.

Key words: forest plant diversity, Sentinel-2A, spectral diversity, cluster analysis, random forest regression

周楷玲, 赵玉金, 白永飞. 基于Sentinel-2A数据的东北森林植物多样性监测方法研究. 植物生态学报, 2022, 46(10): 1251-1267. DOI: 10.17521/cjpe.2021.0373

ZHOU Kai-Ling, ZHAO Yu-Jin, BAI Yong-Fei. Study on forest plant diversity monitoring based on Sentinel-2A satellite data in northeast China. Chinese Journal of Plant Ecology, 2022, 46(10): 1251-1267. DOI: 10.17521/cjpe.2021.0373

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图/表 9

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样地 Sample plot	地理位置 Geographical position	郁闭度 Crown density	物种丰富度 Number of species	Shannon-Wiener多样性指数 Shannon-Wiener diversity index	Simpson多样性指数 Simpson diversity index
1	129.19° E, 48.12° N	0.85	6	1.56	0.76
2	129.19° E, 48.12° N	0.75	10	1.89	0.82
3	129.19° E, 48.12° N	0.80	7	1.67	0.79
4	129.18° E, 48.13° N	0.85	10	1.92	0.82
5	129.18° E, 48.13° N	0.75	10	1.96	0.83
6	131.11° E, 43.44° N	0.70	11	0.87	0.83
7	131.11° E, 43.44° N	0.60	10	0.88	0.85
8	131.11° E, 43.44° N	0.65	8	0.77	0.78
9	131.07° E, 43.47° N	0.60	6	0.65	0.74
10	131.07° E, 43.47° N	0.70	4	0.44	0.56
11	131.07° E, 43.47° N	0.60	8	0.73	0.74
12	128.90° E, 47.18° N	0.60	5	1.34	0.80
13	128.89° E, 47.18° N	0.70	7	1.27	0.66
14	128.89° E, 47.18° N	0.70	6	1.56	0.82
15	128.89° E, 47.18° N	0.65	7	1.01	0.73
16	128.89° E, 47.19° N	0.60	6	1.17	0.72
17	128.89° E, 47.18° N	0.60	4	0.96	0.67
18	128.86° E, 47.20° N	0.70	10	2.06	0.86
19	128.86° E, 47.20° N	0.70	11	2.17	0.84
20	128.86° E, 47.20° N	0.70	9	1.82	0.79

样地 Sample plot	地理位置 Geographical position	郁闭度 Crown density	物种丰富度 Number of species	Shannon-Wiener多样性指数 Shannon-Wiener diversity index	Simpson多样性指数 Simpson diversity index
1	129.19° E, 48.12° N	0.85	6	1.56	0.76
2	129.19° E, 48.12° N	0.75	10	1.89	0.82
3	129.19° E, 48.12° N	0.80	7	1.67	0.79
4	129.18° E, 48.13° N	0.85	10	1.92	0.82
5	129.18° E, 48.13° N	0.75	10	1.96	0.83
6	131.11° E, 43.44° N	0.70	11	0.87	0.83
7	131.11° E, 43.44° N	0.60	10	0.88	0.85
8	131.11° E, 43.44° N	0.65	8	0.77	0.78
9	131.07° E, 43.47° N	0.60	6	0.65	0.74
10	131.07° E, 43.47° N	0.70	4	0.44	0.56
11	131.07° E, 43.47° N	0.60	8	0.73	0.74
12	128.90° E, 47.18° N	0.60	5	1.34	0.80
13	128.89° E, 47.18° N	0.70	7	1.27	0.66
14	128.89° E, 47.18° N	0.70	6	1.56	0.82
15	128.89° E, 47.18° N	0.65	7	1.01	0.73
16	128.89° E, 47.19° N	0.60	6	1.17	0.72
17	128.89° E, 47.18° N	0.60	4	0.96	0.67
18	128.86° E, 47.20° N	0.70	10	2.06	0.86
19	128.86° E, 47.20° N	0.70	11	2.17	0.84
20	128.86° E, 47.20° N	0.70	9	1.82	0.79

植被指数 Vegetation index	计算公式 Calculate formula	参考文献 Reference
TCARI	3[(R_699.19 - R_668.98) - 0.2(R_699.19 - R_550.67)(R_699.19/R_668.98)]	Kim et al., 1994
OSAVI	(1 + 0.16)(R₇₅₀ - R₇₀₅)/(R₇₅₀+ R₇₀₅+ 0.16)	Wu et al., 2008
OSAVI2	(1 + 0.16)(R₈₀₀ - R₆₇₀)/(R₈₀₀+ R₆₇₀+ 0.16)	Rondeaux et al., 1996
DATT	(R₈₅₀ - R₇₁₀)/(R₈₅₀ - R₆₈₀)	Datt, 1999
DATT2	R₈₅₀/R₇₁₀	Datt, 1999
Gitelson	1/R₇₀₀	Gitelson et al., 1999
SR1	R₇₅₀/R₇₀₀	Gitelson & Merzlyak, 1997
SR2	R₇₀₀/R₆₇₀	McMurtrey III et al., 1994
SR3	R₇₃₀/R₇₀₆	Zarco-Tejada et al., 2003
SR4	R₆₇₅/R₇₀₀	Gitelson et al., 2003
MSI	R₁₆₀₀/R₈₁₉	Hunt & Rock, 1989
NDII	(R₈₁₉ - R₁₆₄₉)/(R₈₁₉+ R₁₆₄₉)	Hardisky et al., 1983
CRI1	1/R₅₁₀ - 1/R₅₅₀	Gitelson et al., 2002
CRI2	1/R₅₁₀ - 1/R₇₀₀	Gitelson et al., 2002
ARI	1/R₅₅₀ - 1/R₇₀₀	Sims & Gamon, 2002
PSRI	(R₆₈₀ - R₅₀₀)/R₇₅₀	Merzlyak et al., 1999
NDVI	(R_750.66 - R_704.6)/(R_750.66+ R_704.6)	Gitelson & Merzlyak, 1994
GNDVI	(R₇₈₃ - R₅₆₀)/(R₇₈₃+ R₅₆₀)	Rozenstein et al., 2019
TNDVI	((R₈₄₂ - R₆₆₅)/(R₈₄₂+ R₆₆₅) + 0.5)^0.5	Rozenstein et al., 2019
WDVI	R₈₄₂ - R₆₆₅ × 0.5	Rozenstein et al., 2019
NDI45	(R₇₀₅ - R₆₆₅)/(R₇₀₅+ R₆₆₅)	Delegido et al., 2011
SAVI	(1 + L) × (R_799.09 - R_680.045)/(R_799.09 + R_680.045+ L) (L = 0.5)	Huete, 1988
SAVI2	R_799.09/(R_680.045+ b/a) (a = 0.969 1, b = 0.084 726)	Major et al., 1990
ARVI	RB = R_680.045 - r(R_444.5 - R_680.045) (r = 1) ARVI = (R_799.09 - RB)/(R_799.09+ RB)	Kaufman & Tanre, 1992
SARVI	RB = R_680.045 - r(R_444.5 - R_680.045) (r = 1, L = 0.5) SARVI = (1 + L)(R_799.09 - RB)/(R_799.09+ RB + L)	Kaufman & Tanre, 1992
EVI	G(R_799.09 - R_680.045)/(R_799.09 + C₁R_680.045 - C₂R_444.5+ L) (G = 2.5, C₁= 6, C₂= 7.5, L = 1)	Huete et al., 1997
IRECI	(R₇₈₃ - R₆₆₅)/(R₇₀₅/R₇₄₀)	Frampton et al., 2013
IPVI	R₈₄₂/(R₈₄₂+ R₆₆₅)	Rozenstein et al., 2019
PSSRA	R₇₈₃/R₆₆₅	Rozenstein et al., 2019
RVI	R₈₄₂/R₆₆₅	Rozenstein et al., 2019

植被指数 Vegetation index	计算公式 Calculate formula	参考文献 Reference
TCARI	3[(R_699.19 - R_668.98) - 0.2(R_699.19 - R_550.67)(R_699.19/R_668.98)]	Kim et al., 1994
OSAVI	(1 + 0.16)(R₇₅₀ - R₇₀₅)/(R₇₅₀+ R₇₀₅+ 0.16)	Wu et al., 2008
OSAVI2	(1 + 0.16)(R₈₀₀ - R₆₇₀)/(R₈₀₀+ R₆₇₀+ 0.16)	Rondeaux et al., 1996
DATT	(R₈₅₀ - R₇₁₀)/(R₈₅₀ - R₆₈₀)	Datt, 1999
DATT2	R₈₅₀/R₇₁₀	Datt, 1999
Gitelson	1/R₇₀₀	Gitelson et al., 1999
SR1	R₇₅₀/R₇₀₀	Gitelson & Merzlyak, 1997
SR2	R₇₀₀/R₆₇₀	McMurtrey III et al., 1994
SR3	R₇₃₀/R₇₀₆	Zarco-Tejada et al., 2003
SR4	R₆₇₅/R₇₀₀	Gitelson et al., 2003
MSI	R₁₆₀₀/R₈₁₉	Hunt & Rock, 1989
NDII	(R₈₁₉ - R₁₆₄₉)/(R₈₁₉+ R₁₆₄₉)	Hardisky et al., 1983
CRI1	1/R₅₁₀ - 1/R₅₅₀	Gitelson et al., 2002
CRI2	1/R₅₁₀ - 1/R₇₀₀	Gitelson et al., 2002
ARI	1/R₅₅₀ - 1/R₇₀₀	Sims & Gamon, 2002
PSRI	(R₆₈₀ - R₅₀₀)/R₇₅₀	Merzlyak et al., 1999
NDVI	(R_750.66 - R_704.6)/(R_750.66+ R_704.6)	Gitelson & Merzlyak, 1994
GNDVI	(R₇₈₃ - R₅₆₀)/(R₇₈₃+ R₅₆₀)	Rozenstein et al., 2019
TNDVI	((R₈₄₂ - R₆₆₅)/(R₈₄₂+ R₆₆₅) + 0.5)^0.5	Rozenstein et al., 2019
WDVI	R₈₄₂ - R₆₆₅ × 0.5	Rozenstein et al., 2019
NDI45	(R₇₀₅ - R₆₆₅)/(R₇₀₅+ R₆₆₅)	Delegido et al., 2011
SAVI	(1 + L) × (R_799.09 - R_680.045)/(R_799.09 + R_680.045+ L) (L = 0.5)	Huete, 1988
SAVI2	R_799.09/(R_680.045+ b/a) (a = 0.969 1, b = 0.084 726)	Major et al., 1990
ARVI	RB = R_680.045 - r(R_444.5 - R_680.045) (r = 1) ARVI = (R_799.09 - RB)/(R_799.09+ RB)	Kaufman & Tanre, 1992
SARVI	RB = R_680.045 - r(R_444.5 - R_680.045) (r = 1, L = 0.5) SARVI = (1 + L)(R_799.09 - RB)/(R_799.09+ RB + L)	Kaufman & Tanre, 1992
EVI	G(R_799.09 - R_680.045)/(R_799.09 + C₁R_680.045 - C₂R_444.5+ L) (G = 2.5, C₁= 6, C₂= 7.5, L = 1)	Huete et al., 1997
IRECI	(R₇₈₃ - R₆₆₅)/(R₇₀₅/R₇₄₀)	Frampton et al., 2013
IPVI	R₈₄₂/(R₈₄₂+ R₆₆₅)	Rozenstein et al., 2019
PSSRA	R₇₈₃/R₆₆₅	Rozenstein et al., 2019
RVI	R₈₄₂/R₆₆₅	Rozenstein et al., 2019

基于Sentinel-2A数据的东北森林植物多样性监测方法研究

Study on forest plant diversity monitoring based on Sentinel-2A satellite data in northeast China

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