基于Sentinel-2数据的草地植物功能多样性遥感反演及其与生产力的关系

doi:10.17521/cjpe.2022.0104

植物生态学报 ›› 2022, Vol. 46 ›› Issue (10): 1234-1250.DOI: 10.17521/cjpe.2022.0104

所属专题：生态遥感及应用

基于Sentinel-2数据的草地植物功能多样性遥感反演及其与生产力的关系

赵晏平¹, 王忠武¹, 温都日根³, 赵玉金²^,^*(), 白永飞²^,^*()

¹内蒙古农业大学草原与资源环境学院, 呼和浩特 010000
²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
³正蓝旗草原工作站, 内蒙古锡林浩特 027200

收稿日期:2022-03-23 接受日期:2022-07-06 出版日期:2022-10-20 发布日期:2022-09-28
通讯作者: 赵玉金,白永飞
作者简介:Bai YF, yfbai@ibcas.ac.cn)
^* (Zhao YJ, zhaoyj@ibcas.ac.cn;
基金资助:
内蒙古自治区科技重大专项(2021ZD0011-04);国家自然科学基金(41801230);中国科学院战略性先导科技专项(A类)(XDA23080303)

Remotely sensed monitoring method of grassland plant functional diversity and its relationship with productivity based on Sentinel-2 satellite data

ZHAO Yan-Ping¹, WANG Zhong-Wu¹, WENDU Rigen³, ZHAO Yu-Jin²^,^*(), BAI Yong-Fei²^,^*()

¹College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot 010000, China
²State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinses Academy of Science, Beijing 100093, China
³Grassland Workstation of Zhenglan Banner, Xilinhot, Nei Mongol 027200, China

Received:2022-03-23 Accepted:2022-07-06 Online:2022-10-20 Published:2022-09-28
Contact: ZHAO Yu-Jin,BAI Yong-Fei
Supported by:
Key Science & Technology Special Program of Inner Mongolia(2021ZD0011-04);National Natural Science Foundation of China(41801230);Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080303)

摘要/Abstract

摘要：

生物多样性与生态系统功能的关系是当前生态学研究的焦点和难点。植物功能多样性是影响生态系统功能的重要指标, 开展植物功能多样性的研究对了解生物多样性与生态系统功能之间的关系有着重要意义。传统的草地植物功能多样性研究多以实地调查为主, 不仅费时费力, 而且由于受到时空的限制, 很难拓展到大尺度的研究中。遥感技术的发展为评估草地功能多样性提供了一种经济、有效的手段。该研究选取内蒙古自治区锡林郭勒盟乌拉盖管理区草甸草原为研究区, 利用Sentinel-2卫星影像和野外实测数据, 选取了波段及植被指数等46个特征变量, 探讨了逐步回归、偏最小二乘法(PLSR)和随机森林(RFR)等3种不同方法对草地植物功能丰富度(FRic)、功能均匀度(FEve)和功能离散度(FDiv)的反演精度, 并基于PLSR反演草地地上生物量, 进一步分析了研究区功能多样性与生产力的关系。研究结果表明: (1)波段B11、优化型土壤调节植被指数(OSAVI)、水波段指数(WBI)对FRic解释度最高; 波段B6、B10、B12、类胡萝卜素反射指数1 (CRI1)、双峰光学指数(D)、归一化差值指数45 (NDI45)等6个特征变量对FEve解释度最高; 波段B5、B9、B10、B11、加权差分植被指数(WDVI)、凸包面积等对FDiv解释度最高; (2)基于十折重复交叉验证, 利用逐步回归估算的FRic和FEve反演精度远高于其他两种回归方法, R²分别为0.52和0.44; 而利用PLSR方法估算的FDiv反演精度最高(R² = 0.61); (3)群落地上生物量反演精度为R² = 0.61; FRic与地上生产力的关系最好(R² = 0.40), 其次为FDiv (R² = 0.28)和FEve (R² = 0.27)。研究发现, 基于Sentinel-2卫星影像能较好地反演草地功能多样性和生产力, 为下一步能在大尺度上进行草地功能多样性估算及其与生产力关系研究提供了参考和依据。

关键词: 草地, 植物功能多样性, Sentinel-2, 功能多样性指数, 逐步回归, 偏最小二乘法(PLSR), 随机森林回归

Abstract:

Aims The relationship between biodiversity and ecosystem function is an important ecological issue that is increasingly receiving global attention. Plant functional diversity, as one of the most important components of biodiversity, is directly linked to ecosystem functions. Traditional in-situ monitoring of grassland plant functional diversity is not only time-consuming and laborious, but also difficult to expand to large-scale research due to the limitations of time and space. The development of remote sensing technology provides an economical and effective means for assessing the grassland functional diversity over large areas. We estimated functional diversity and aboveground biomass based on Sentinel-2 satellite images and field data across the meadow steppe in the Ulgai Management Area of Xilin Gol League in Nei Mongol.

Methods We selected 46 spectral feature variables from the Sentinel-2 satellite imagery in the study area. Next, three methods, including stepwise regression, partial least squares regression (PLSR), and random forest regression (RFR) were applied to retrieve the grassland functional richness (FRic), functional evenness (FEve) and functional divergence (FDiv). Finally, the grassland aboveground biomass was also estimated using PLSR method, and the relationships between remotely sensed grassland functional diversity and grassland aboveground biomass were analyzed.

Important findings Our results showed that: (1) Band 11, optimized soil adjusted vegetation index (OSAVI), water band index (WBI) were the most important predictor of FRic; Band 6, Band 10, Band 12, carotenoid reflectance index 1 (CRI1), double-peak optical index (D), normalized difference index 45 (NDI45) were significantly related to FEve; and Band 5, Band 9, Band10, Band11, weighted difference vegetation index (WDVI), convex hull area played a critical role in predicting FDiv. (2) Based on 10-fold cross-validation, the retrieval accuracies of FRic and FEve estimated by stepwise regression were much higher than that of the other two regression methods, with R² of 0.52 and 0.44, respectively. However, the FDiv was best estimated by PLSR (R² = 0.61). (3) Grassland aboveground biomass was estimated with an accuracy of R² = 0.61, and FRic was the best indicator of aboveground biomass (R² = 0.40), followed by FDiv (R² = 0.28) and FEve (R² = 0.27). Our findings indicated the ability of Sentinel-2 satellite images to estimate grassland plant functional diversity, providing reference and basis for grassland plant functional diversity estimation at a large regional scale.

Key words: grassland, plant functional diversity, Sentinel-2, functional diversity index, stepwise regression, partial least squares regression, random forest regression

赵晏平, 王忠武, 温都日根, 赵玉金, 白永飞. 基于Sentinel-2数据的草地植物功能多样性遥感反演及其与生产力的关系. 植物生态学报, 2022, 46(10): 1234-1250. DOI: 10.17521/cjpe.2022.0104

ZHAO Yan-Ping, WANG Zhong-Wu, WENDU Rigen, ZHAO Yu-Jin, BAI Yong-Fei. Remotely sensed monitoring method of grassland plant functional diversity and its relationship with productivity based on Sentinel-2 satellite data. Chinese Journal of Plant Ecology, 2022, 46(10): 1234-1250. DOI: 10.17521/cjpe.2022.0104

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植被指数 Vegetation index	计算公式 Calculate formula	Sentinel-2波段 Sentinel-2 band used	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)]	B3, B4, B5	Kim et al., 1994
OSAVI	(1 + 0.16)(R₇₅₀ - R₇₀₅)/(R₇₅₀ + R₇₀₅ + 0.16)	B5, B6	Wu et al., 2008
OSAVI2	(1 + 0.16) (R₈₀₀- R₆₇₀)/(R₈₀₀+ R₆₇₀+ 0.16)	B4, B7	Rondeaux et al., 1996
D	R₇₃₀/R₇₀₆	B5, B6	Zarco-Tejada et al., 2003
Datt	(R₈₅₀- R₇₁₀)/(R₈₅₀- R₆₈₀)	B4, B5, B8	Datt, 1999
Datt2	R₈₅₀/R₇₁₀	B5, B8	Datt, 1999
Gitelson	1/R₇₀₀	B5	Gitelson et al., 1999
SR	R₇₅₀/R₇₀₀	B5, B6	Gitelson & Merzlyak, 1997
SR2	R₇₀₀/R₆₇₀	B4, B5	McMurtrey III et al., 1994
MSI	R₁₆₀₀/R₈₁₉	B8, B11	Hunt Jr & Rock, 1989
NDVI₇₀₅	(R₇₅₀ - R₇₀₅)/( R₇₅₀+ R₇₀₅)	B5, B6	Sims & Gamon, 2002
CRI1	1/R₅₁₀- 1/R₅₅₀	B2, B3	Gitelson et al., 2003
CRI2	1/R₅₁₀- 1/R₇₀₀	B2, B5	Gitelson et al., 2002
ARI1	1/R₅₅₀- 1/R₇₀₀	B3, B5	Sims & Gamon, 2002
ARI2	R₈₀₀(1/R₅₅₀- 1/R₇₀₀)	B3, B5, B7	Gitelson et al., 2002
NDVI	(R₈₄₂- R₆₆₅)/(R₈₄₂+ R₆₆₅)	B4, B8	Huete et al., 1997
GNDVI	(R₇₈₃- R₅₆₀)/(R₇₈₃ + R₅₆₀)	B3, B7	Rozenstein et al., 2019
TNDVI	[(R₈₄₂- R₆₆₅)/(R₈₄₂ + R₆₆₅) + 0.5]^0.5	B4, B8	Rozenstein et al., 2019
WDVI	R₈₄₂- 0.5R₆₆₅	B4, B8	Rozenstein et al., 2019
NDI45	(R₇₀₅- R₆₆₅)/(R₇₀₅+ R₆₆₅)	B4, B5	Delegido et al., 2011
SAVI	(1 + 0.5) × (R_799.09- R_680.045)/(R_799.09+ R_680.045+ 0.5)	B4, B7	Huete, 1988
SAVI2	R_799.09/(R_680.045+ b/a) (a = 0.97, b = 0.08)	B4, B7	Major et al., 1990
ARVI	ARVI = (R_799.09-R_680.045+ R_444.5+ R_680.045)/(R_799.09+ R_680.045- R_444.5- R_680.045)	B1, B4, B7	Kaufman & Tanre, 1992
SARVI	RB = R_680.045- (R_444.5- R_680.045) SARVI = (1 + 0.5)(R_799.09-R_680.045+ R_444.5+ R_680.045)/(R_799.09+ R_680.045- R_444.5- R_680.045 + 0.5)	B1, B4, B7	Kaufman & Tanre, 1992
EVI	2.5(R_799.09- R_680.045)/(R_799.09+ 6R_680.045- 7.5R_444.5+ 1)	B1, B4, B7	Huete et al., 1997
IRECI	(R₇₈₃- R₆₆₅)/(R₇₀₅/R₇₄₀)	B4, B5, B6, B7	Frampton et al., 2013
IPVI	R₈₄₂/(R₈₄₂+ R₆₆₅)	B4, B8	Rozenstein et al., 2019
PSSRA	R₇₈₃/R₆₆₅	B4, B7	Rozenstein et al., 2019
RVI	R₈₄₂/R₆₆₅	B4, B8	Rozenstein et al., 2019
mNDVI₇₀₅	(R₇₅₀- R₇₀₅)/( R₇₅₀ + R₇₀₅- 2R₄₄₅)	B1, B5, B6	Datt, 1999
mSR₇₀₅	(R₇₅₀- R₄₄₅)/( R₇₀₅ + R₄₄₅)	B1, B6	Datt, 1999
SIPI	(R₈₀₀- R₄₄₅)/( R₈₀₀- R₆₈₀)	B1, B7	Penuelas et al., 1995
NDWI	(R₈₆₅- R₁₆₁₄)/( R₈₆₅- R₁₆₁₄)	B8A, B11	McFeeters, 1996

植被指数 Vegetation index	计算公式 Calculate formula	Sentinel-2波段 Sentinel-2 band used	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)]	B3, B4, B5	Kim et al., 1994
OSAVI	(1 + 0.16)(R₇₅₀ - R₇₀₅)/(R₇₅₀ + R₇₀₅ + 0.16)	B5, B6	Wu et al., 2008
OSAVI2	(1 + 0.16) (R₈₀₀- R₆₇₀)/(R₈₀₀+ R₆₇₀+ 0.16)	B4, B7	Rondeaux et al., 1996
D	R₇₃₀/R₇₀₆	B5, B6	Zarco-Tejada et al., 2003
Datt	(R₈₅₀- R₇₁₀)/(R₈₅₀- R₆₈₀)	B4, B5, B8	Datt, 1999
Datt2	R₈₅₀/R₇₁₀	B5, B8	Datt, 1999
Gitelson	1/R₇₀₀	B5	Gitelson et al., 1999
SR	R₇₅₀/R₇₀₀	B5, B6	Gitelson & Merzlyak, 1997
SR2	R₇₀₀/R₆₇₀	B4, B5	McMurtrey III et al., 1994
MSI	R₁₆₀₀/R₈₁₉	B8, B11	Hunt Jr & Rock, 1989
NDVI₇₀₅	(R₇₅₀ - R₇₀₅)/( R₇₅₀+ R₇₀₅)	B5, B6	Sims & Gamon, 2002
CRI1	1/R₅₁₀- 1/R₅₅₀	B2, B3	Gitelson et al., 2003
CRI2	1/R₅₁₀- 1/R₇₀₀	B2, B5	Gitelson et al., 2002
ARI1	1/R₅₅₀- 1/R₇₀₀	B3, B5	Sims & Gamon, 2002
ARI2	R₈₀₀(1/R₅₅₀- 1/R₇₀₀)	B3, B5, B7	Gitelson et al., 2002
NDVI	(R₈₄₂- R₆₆₅)/(R₈₄₂+ R₆₆₅)	B4, B8	Huete et al., 1997
GNDVI	(R₇₈₃- R₅₆₀)/(R₇₈₃ + R₅₆₀)	B3, B7	Rozenstein et al., 2019
TNDVI	[(R₈₄₂- R₆₆₅)/(R₈₄₂ + R₆₆₅) + 0.5]^0.5	B4, B8	Rozenstein et al., 2019
WDVI	R₈₄₂- 0.5R₆₆₅	B4, B8	Rozenstein et al., 2019
NDI45	(R₇₀₅- R₆₆₅)/(R₇₀₅+ R₆₆₅)	B4, B5	Delegido et al., 2011
SAVI	(1 + 0.5) × (R_799.09- R_680.045)/(R_799.09+ R_680.045+ 0.5)	B4, B7	Huete, 1988
SAVI2	R_799.09/(R_680.045+ b/a) (a = 0.97, b = 0.08)	B4, B7	Major et al., 1990
ARVI	ARVI = (R_799.09-R_680.045+ R_444.5+ R_680.045)/(R_799.09+ R_680.045- R_444.5- R_680.045)	B1, B4, B7	Kaufman & Tanre, 1992
SARVI	RB = R_680.045- (R_444.5- R_680.045) SARVI = (1 + 0.5)(R_799.09-R_680.045+ R_444.5+ R_680.045)/(R_799.09+ R_680.045- R_444.5- R_680.045 + 0.5)	B1, B4, B7	Kaufman & Tanre, 1992
EVI	2.5(R_799.09- R_680.045)/(R_799.09+ 6R_680.045- 7.5R_444.5+ 1)	B1, B4, B7	Huete et al., 1997
IRECI	(R₇₈₃- R₆₆₅)/(R₇₀₅/R₇₄₀)	B4, B5, B6, B7	Frampton et al., 2013
IPVI	R₈₄₂/(R₈₄₂+ R₆₆₅)	B4, B8	Rozenstein et al., 2019
PSSRA	R₇₈₃/R₆₆₅	B4, B7	Rozenstein et al., 2019
RVI	R₈₄₂/R₆₆₅	B4, B8	Rozenstein et al., 2019
mNDVI₇₀₅	(R₇₅₀- R₇₀₅)/( R₇₅₀ + R₇₀₅- 2R₄₄₅)	B1, B5, B6	Datt, 1999
mSR₇₀₅	(R₇₅₀- R₄₄₅)/( R₇₀₅ + R₄₄₅)	B1, B6	Datt, 1999
SIPI	(R₈₀₀- R₄₄₅)/( R₈₀₀- R₆₈₀)	B1, B7	Penuelas et al., 1995
NDWI	(R₈₆₅- R₁₆₁₄)/( R₈₆₅- R₁₆₁₄)	B8A, B11	McFeeters, 1996

功能多样性指数 Functional diversity index	逐步回归 Stepwise regression		随机森林回归 Random forest regression		偏最小二乘回归 Partial least squares regression
功能多样性指数 Functional diversity index	R²	RMSE	R²	RMSE	R²	RMSE
FRic	0.52	4.51	0.40	5.18	0.36	4.88
FEve	0.43	0.03	0.32	0.04	0.35	0.03
FDiv	0.54	0.04	0.56	0.04	0.61	0.04

功能多样性指数 Functional diversity index	逐步回归 Stepwise regression		随机森林回归 Random forest regression		偏最小二乘回归 Partial least squares regression
功能多样性指数 Functional diversity index	R²	RMSE	R²	RMSE	R²	RMSE
FRic	0.52	4.51	0.40	5.18	0.36	4.88
FEve	0.43	0.03	0.32	0.04	0.35	0.03
FDiv	0.54	0.04	0.56	0.04	0.61	0.04

基于Sentinel-2数据的草地植物功能多样性遥感反演及其与生产力的关系

Remotely sensed monitoring method of grassland plant functional diversity and its relationship with productivity based on Sentinel-2 satellite data

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