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

研究论文

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

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-Jin,2,*, BAI Yong-Fei,2,*

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

通讯作者: *(Zhao YJ,zhaoyj@ibcas.ac.cn;Bai YF,yfbai@ibcas.ac.cn)

编委: 苏艳军

责任编辑: 赵航

收稿日期: 2022-03-23   接受日期: 2022-07-6  

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)

Corresponding authors: *(zhaoyj@ibcas.ac.cn;Bai YF,yfbai@ibcas.ac.cn)

Received: 2022-03-23   Accepted: 2022-07-6  

Fund supported: 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

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.

Keywords： grassland; plant functional diversity; Sentinel-2; functional diversity index; stepwise regression; partial least squares regression; random forest regression

PDF (10331KB) 元数据 多维度评价 相关文章 导出 EndNote| Ris| Bibtex  收藏本文

引用本文

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

草地是陆地生态系统的重要组成部分, 是世界上分布最广的植被类型之一, 为人类提供了一系列重要的物质产品(肉奶、皮毛、各种草药等)和生态服务(防风固沙、大气调节、涵养水源等)(Bai et al., 2004)。然而, 由于气候变化和人类活动干扰, 草地的生物多样性和生态系统功能发生了巨大变化(Hoover et al., 2014)。因此, 加强对草地生物多样性及生态系统功能的监测与评估, 对草地生物多样性保护政策的制定和生态系统适应性管理至关重要。

在以往的研究中, 生态学家主要利用物种为基本单元的多样性指数来探讨群落间的物种多样性, 如: Shannon-Weiner指数, Simpson指数等(韩涛涛等, 2021)。然而, 物种间的差异往往体现在长期进化过程中, 以物种为基本单元的研究很难体现不同生态系统间功能的差异(Naeem et al., 2012; Mouillot et al., 2013)。植物功能性状能够适应环境变化, 并将环境与植物个体和生态系统结构、过程与功能联系起来(Díaz et al., 2004)。基于植物功能性状和功能多样性的研究可以从植物的生长、防御、抵抗等不同的功能策略方面解释不同生态系统间的物种组成、功能特征, 从而有助于进一步理解它们的生态过程和功能(Tilman, 1997; Leps et al., 2006; 韩涛涛等, 2021)。

近年来, 以植物功能性状为基础的研究方法被广泛应用到各个尺度中, 根据不同的尺度, 可选用不同的性状指标评估特定尺度下生态系统的功能(Tilman et al., 1997; Walker et al., 1999; Gillison, 2013; 刘晓娟和马克平, 2015; 韩涛涛等, 2021)。功能多样性是生物多样性的重要组成部分, 是解释和预测生态系统过程和功能的重要驱动力(Petchey & Gaston, 2002; Petchey et al., 2004), 反映了群落、景观、甚至区域尺度内功能性状的变异性, 决定了生态系统的功能和稳定性(Tilman, 1997; Ruiz-Benito et al., 2014)。由于对于功能多样性的不同理解会产生不同的研究方法(Mason et al., 2003, 2005; Botta-Dukát, 2005; Laliberté & Legendre, 2010), 而这些方法对于植物功能性状选取、功能多样性计算、生态系统功能评估的影响较大, 因此有必要对功能多样性做一个统一的定义。Tilman (2001)认为功能多样性是影响生态系统功能的群落中所有物种及有机物的功能特征值及其变动范围, 强调的是特征值的差异性。由于此概念具有较强的概括性和可操作性, 因而被普遍接受并广泛用于计算生态系统结构与功能的各个方面(Petchey & Gaston, 2002; Mason et al., 2003, 2005; Petchey et al., 2004)。

早期对于功能多样性的研究大多采用物种多样性及功能型组, 但随着计算机技术的高速发展, 很多复杂的数学公式被引入到功能多样性的研究中, 例如用影响生态系统结构与功能的功能性状替代物种间的遗传距离, 从而可以得出一些计算功能多样性的方法(Botta-Dukát, 2005; Violle et al., 2007)。随后, 研究者们认为仅采用一种指数难以代表生态系统功能多样性(Mouillot et al., 2005), 从而将功能多样性进一步细分为: (1)功能丰富度(FRic); (2)功能均匀度(FEve); (3)功能离散度(FDiv)(Villéger et al., 2008)。功能丰富度代表群落中物种占据生态位空间的大小, 体现的是生态位空间利用程度的指数, 常使用物种或群落生态位中性状空间的凸包体积计算获得, 而功能均匀度和功能离散度则代表生态位空间中功能性状分布的均匀程度或离散程度。

传统的功能多样性的计算通常依赖大量的野外调查, 基于野外获得的生物多样性和功能性状进一步得到功能多样性, 测定手段费时费力, 同时也受时间和空间尺度的制约, 不适用于长期大规模的区域监测。遥感数据具有探测范围广、数据获取周期短、可重复等特点, 使得其在大尺度生物多样性监测和评估及制图方面具有极大的优势(Roughgarden et al., 1991), 已经成为评估生态系统功能多样性的重要工具。目前, 基于遥感的功能多样性的监测方法已经在森林生态系统中成功应用。Asner等(2017)和Schneider等(2017)分别基于聚类分析和基于像元的功能多样性遥感反演方法, 利用机载高光谱数据成功绘制了高空间分辨率的植物功能多样性空间分布图。Durán等(2019)利用成像光谱学和叶片性状相结合的方法, 基于偏最小二乘回归(PLSR)估算了亚马孙至安第斯山脉热带森林不同海拔高度的功能多样性, 同时发现通过遥感获得的单一性状或多性状计算得到的功能多样性是净初级生产力和总生产力变化的重要预测因素。但由于草地类型多样、覆盖度低、植株矮小且多数混合生长等特点, 相较于森林生态系统, 在大尺度上进行草地生态系统监测难度较高(辛晓平等, 2018)。

群落生产力表征区域环境条件下植被的生产能力, 反映了生态系统的生长特征和健康状况, 早期的很多研究认可了物种多样性对生产力的贡献, 但近年来, 一些研究认为相对于物种多样性, 功能多样性更能影响群落生产力(Díaz & Cabido, 2001; Römermann et al., 2001)。因此开展对天然草地的功能多样性遥感监测及其与生产力关系的研究可以更好地探究功能多样性与生态系统功能之间的关系。然而, 功能多样性与生产力的关系较为复杂, 主要有“质量比假说” (Grime, 1998)和“多样性假说” (Tilman et al., 1997)。前者认为优势物种的性状大小决定了群落的生产力, 后者认为多样性高导致功能性状差异加大, 从而提高了资源的利用效率。两种假说的相对重要性至今仍有争论, 是研究的热点。地上生物量作为群落生产力的有力度量指标, 因其观测相对便利常用来代替群落生产力(Kassen et al., 2000; Bai et al., 2007)。基于单种栽培草地平台, Zhao等(2021)发现无人机高光谱反演的与光合作用相关的叶绿素、碳、氮含量等叶片生理性状, 是草地单种栽培群落地上生物量的重要预测因子, 进一步分析了功能多样性与生产力的关系。然而, 当前基于天然草地的功能多样性遥感监测及其与生产力的关系还鲜有研究。

当前的功能多样性监测主要以机载和星载的高光谱遥感数据为主, 相较于机载高光谱遥感, 星载高光谱遥感观测尺度更大、监测时间序列更长、数据获取成本更低(Saatchi et al., 2008; Paganini et al., 2016)。Sentinel-2号卫星数据作为目前可免费获取的最高空间分辨率的多光谱遥感数据, 是唯一一个在红边范围内含有3个波段的卫星遥感数据, 对监测植被健康十分重要。本研究拟利用Sentinel-2影像, 在内蒙古自治区锡林郭勒盟乌拉盖管理区草甸草原, 探究利用逐步回归、随机森林(RFR)、PLSR 3种方法的草地植物功能多样性反演精度, 进一步分析遥感反演的草地功能多样性与生产力的关系, 为能在大尺度上进行草地功能多样性的评估提供可行的方法和依据, 为草地利用与管理提供参考。

1 材料和方法

1.1 实验区概况

本研究在内蒙古自治区锡林郭勒盟乌拉盖管理区草甸草原(图1)进行。该地区位于锡林郭勒盟东北部, 地处锡盟、兴安盟、通辽市交界地带, 南临霍林郭勒市, 东北与阿尔山市接壤, 介于118.73°-119.83° E, 45.48°-46.63° N之间, 全区总面积5 013.67 km2, 为半湿润半干旱大陆性气候, 海拔在855.1-1 334.9 m之间, 四季交替明显, 昼夜温差大。年平均气温-0.9 ℃, 10 ℃及以上年有效积温1 900-2 100 ℃, 年日照时间2 700 h, 日照百分率61%, 年降水量342 mm, 多集中于6-8月。主要草种有北柴胡(Bupleurum chinense)、麻花头(Klasea centauroides)、羊草(Leymus chinensis)、细叶沙参(Adenophora capillaris subsp. paniculata)、蒙古韭(Allium mongolicum)、黄芩(Scutellaria baicalensis)等。

图1

新窗口打开| 下载原图ZIP| 生成PPT

图1   内蒙古锡林郭勒盟乌拉盖管理区草甸草原研究区采样点位置(A)及样方布设示意图(B)。B中蓝色框为1 m × 1 m小样方; 红色框为小样方中心1/4的面积; 绿色框为10 m × 10 m样方组成方式。NDVI, 归一化植被指数。

Fig. 1   Location of the study area (A) and sample settings (B) across the meadow steppe in the Ulgai Management Area of Xilin Gol League in Nei Mongol. In B, blue boxes represent 1 m × 1 m small sample squares, red boxes represent the area of 1/4 of the center of small sample squares, and the green box represents the composition of 10 m × 10 m sample square. NDVI, normalized difference vegetation index.


1.2 数据采集

1.2.1 野外数据采集

于2020年7-8月在乌拉盖管理区内开展群落和物种调查, 在研究地选取不同植被覆盖度的样地共6个(图1A)以提升模型的准确性。在每个样地布设2-5个30 m × 30 m的大样方, 在大样方对角线1/4及中点处布设1 m × 1 m的小样方共5个(图1B), 同时在每个小样方中心1/4的面积进行物种调查, 记录出现的物种名称、个数、高度并将其剪下, 共获得95组样方数据。然后将每个样方中的小样方按图中所示的方式组合成4个10 m × 10 m的样方, 以便与Sentinel-2像元数据匹配, 共得到76个样方的数据。用Trimble Pro 6H GPS系统(Trimble, Sunnyvale, USA)记录每个30 m × 30 m、10 m × 10 m及1 m × 1 m样方四角的GPS位置(差分处理后水平误差为10 cm)。

1.2.2 植物叶片性状测定
1.2.2.1 叶片生理性状及生物量的测定

对样方中每个物种随机选取3个植株, 从上到下分别选取3片叶子, 将植物叶片鲜样保存到冰盒中, 然后至实验室内随机选取完全展开的叶片进行叶面积扫描, 测定新鲜叶片的面积, 并称量记录新鲜叶片质量。其他部分的叶子则在105 ℃杀青2 h后, 再在65 ℃下烘干48 h达到恒质量后获得地上生物量(g·m-2)数据, 再称质量并做化学处理。功能性状的具体测量如下:

(1)叶片面积(cm2)

使用LI-3000C便携式叶面积扫描仪(Li-COR, Lincoln, USA)扫描叶片的面积, 获得叶片面积数据。

(2)比叶面积(SLA, cm2·g-1)

比叶面积=叶片面积/叶片干质量

(3)叶片含水量(W, %)

叶片含水量= (叶片鲜质量-叶片干质量)/叶片鲜质量× 100%

(4)叶片色素含量

叶绿素a、b (Chl a、Chl b)首先用95%乙醇溶液萃取, 然后将萃取的溶液利用紫外可见光分光光度计(DU 800, Beckman Coulter, San Francisco, USA)在波长665和649 nm下测定其吸光度, 按照以下公式计算色素浓度:

Ca=13.95A665−6.8A649
Cb=24.96A649−7.32A665

式中, A649、A665分别代表649 nm和665 nm波段下的吸光度, Ca、Cb分别为Chl a、Chl b的浓度。然后再通过以下公式计算相关含量:

Chl a/b含量(mg·g-1) = C×V×NW×1000
Chl含量(mg·g-1) = Chl a含量+ Chl b含量

式中, C为色素含量(mg·L-1), V为提取液体积(mL), N为稀释倍数, W为样品鲜质量或干质量(g)。

(5)叶片氮(N)含量

利用元素分析仪(vario MACRO cude, Elementar, Langenselbold, German)测量叶片总氮含量(%)。

1.2.2.2 叶片尺度上推到冠层尺度

基于单位质量的叶片物质含量, 利用各物种的地上生物量将上述单位质量的叶片性状上推到冠层性状(Homolová et al., 2013), 公式如下:

 Traitcanopy=∑1i(Traitmassi×Biomassi)

式中, Traitcanopy是冠层性状, Traitmassi是物种i基于质量的叶片性状, Biomassi是物种i的生物量占样地总生物量的比值。

1.2.3 遥感数据采集及预处理

Sentinel-2是由两颗同时运作的相同卫星A (Sentinel-2A)与B (Sentinel-2B)组成的高分辨率多光谱成像卫星群, 每颗卫星都搭载相同的多光谱影像仪(MSI), 一颗卫星的重访周期为10 d, 两颗互补, 重访周期缩短至5 d, 轨道高度为786 km, 幅宽为290 km。影像覆盖了13个光谱波段, 分别为空间分辨率为10 m的4个可见光波段(蓝色、绿色、红色和近红外波段), 空间分辨率为20 m的短波红外波段(SWIR1和SWIR2)、卷云波段(SWIR-Cirrus)和红边波段(红边波段1-4)和分辨率为60 m的海岸/气溶胶波段、水蒸气波段。同步于地面调查,选取2020年7月23日的4景Sentinel-2卫星影像(S2B_MSIL2A_20200723T025549_N0214_R032_T50TPR_20200723T064657,S2B_MSIL2A_20200723T025549_N0214_
R032_T50TQR_20200723T064657,S2B_MSIL2A_20200723T025549_N0214_R032_T50TPS_20200723T064657,
S2B_MSIL2A_20200723T025549_N0214_R032_T50TNS_20200723T064657)开展草地植物功能多样性遥感监测。

利用Sen2Cor (http://step.esa.int/main/snap-supported-plugins/sen2cor/)插件对下载的影像进行辐射定标和大气校正, 大气校正后的影像去除了B10卷云波段。对于经过大气校正以后的影像在SNAP (http://step.esa.int/main/download/snap-download/)中进行重采样, 分辨率设置为10 m, 然后将处理后的影像在ENVI软件中进行波段的调整、合并、镶嵌和裁剪等处理。对裁剪之后的影像进行辐射滤波, 计算影像的归一化植被指数(NDVI), 通过选取草地、土壤、阴影、建筑物和水体样本并统计其NDVI, 确定NDVI的阈值大于0.35来去掉土壤、建筑等非植被区域的影响, 最后提取地面样地(10 m × 10 m)对应的草地像元的光谱和植被指数。

1.3 研究方法

本研究通过实地数据计算得到功能多样性和地上生物量, 并选取基于像元计算的光谱多样性指数凸包面积(CHA)等与植物多样性相关的34个植被指数和12个波段信息, 共46个特征变量, 代入逐步回归、RFR、PLSR这3种当前比较流行的回归模型进行建模, 选取最优回归模型反演草地功能多样性和生物量。其中, 逐步回归和RFR由于变量共线性等影响, 需剔除相关变量后再进行回归。同时, 探究了遥感预测的功能多样性和生产力的关系, 具体的流程如图2所示。

图2

新窗口打开| 下载原图ZIP| 生成PPT

图2   研究方法流程图。FDiv, 功能离散度; FEve, 功能均匀度; FRic, 功能丰富度; NDVI, 归一化植被指数。

Fig. 2   Flow chart of the research method. FDiv, functional diversity; FEve, functional evenness; FRic, functional richness; NDVI, normalized difference vegetation index.


1.3.1 实测功能多样性计算

基于各样地每个物种的比叶面积、叶片含水量、叶绿素含量和总氮含量等生理性状与各物种的生物量占总生物量的比值, 计算FRic、FEve和FDiv指数。FRic通过计算物种或群落生态位中性状空间的CHA获得。FEve是利用所有成对物种的多度权重距离计算多维性状空间的最小生成树, 然后测量最小生成树分支长度的均匀性。

 EWI=dist(i,j)wi+wjPEWI=EWI∑i=1SEWIFEve=∑i=1SminPEWI,1S−1−1S−11−1S−1

式中, S为物种数, EW为均匀度权重, dist(i, j)为物种i和j的欧式距离, wi为物种i的相对多度, I为分支长, PEWI为分支长权重。

FDiv表征物种多度在物种所占据的功能性状空间的分布情况, 通过计算性状到性状凸包重心距离多度权重的离散度获取:

dGi=∑k=1T(xik−gk)2gk=1S∑i=1SxikΔ|d|=∑i=1Swi×|dGi−dG¯|Δd=∑i=1Swi×(dGi−dG¯)dG¯=1S∑i=1SdGi FDiv =Δd+dG¯Δ|d|+ dG¯

式中, xik为物种i的k性状值, gk为性状k的重心, S为物种数, T为性状数, dGi为xik距离重心的距离(这里用欧式距离表示), dG为物种i距离重心的平均距离, d为多度权重离散度, wi为物种i的相对多度。

所有操作均使用R软件“FD”包(Laliberté & Legendre, 2010), 基于各样地每个物种的比叶面积、叶片含水量、叶绿素含量和总氮含量等生理性状与各物种在样地中的生物量占总生物量的比值, 计算FRic、FEve和FDiv指数。

1.3.2 特征变量选取

本研究采用了Sentinel-2影像的12个波段信息和33个基于Sentinel-2影像衍生计算得出的植被指数和CHA作为特征变量。相关植被指数的计算公式如表1所示。

表1   植被指数计算公式

Table 1  Calculating formula of vegetation index

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
TCARI3[(R699.19 - R668.98) - 0.2(R699.19 - R550.67)(R699.19/R668.98)]B3, B4, B5Kim et al., 1994
OSAVI(1 + 0.16)(R750 - R705)/(R750 + R705 + 0.16)B5, B6Wu et al., 2008
OSAVI2(1 + 0.16) (R800 - R670)/(R800 + R670 + 0.16)B4, B7Rondeaux et al., 1996
DR730/R706B5, B6Zarco-Tejada et al., 2003
Datt(R850 - R710)/(R850 - R680)B4, B5, B8Datt, 1999
Datt2R850/R710B5, B8Datt, 1999
Gitelson1/R700B5Gitelson et al., 1999
SRR750/R700B5, B6Gitelson & Merzlyak, 1997
SR2R700/R670B4, B5McMurtrey III et al., 1994
MSIR1600/R819B8, B11Hunt Jr & Rock, 1989
NDVI705(R750 - R705)/( R750 + R705)B5, B6Sims & Gamon, 2002
CRI11/R510 - 1/R550B2, B3Gitelson et al., 2003
CRI21/R510 - 1/R700B2, B5Gitelson et al., 2002
ARI11/R550 - 1/R700B3, B5Sims & Gamon, 2002
ARI2R800(1/R550 - 1/R700)B3, B5, B7Gitelson et al., 2002
NDVI(R842 - R665)/(R842 + R665)B4, B8Huete et al., 1997
GNDVI(R783 - R560)/(R783 + R560)B3, B7Rozenstein et al., 2019
TNDVI[(R842 - R665)/(R842 + R665) + 0.5]0.5B4, B8Rozenstein et al., 2019
WDVIR842 - 0.5R665B4, B8Rozenstein et al., 2019
NDI45(R705 - R665)/(R705 + R665)B4, B5Delegido et al., 2011
SAVI(1 + 0.5) × (R799.09 - R680.045)/(R799.09 + R680.045 + 0.5)B4, B7Huete, 1988
SAVI2R799.09/(R680.045 + b/a) (a = 0.97, b = 0.08)B4, B7Major et al., 1990
ARVIARVI = (R799.09 -R680.045 + R444.5 + R680.045)/(R799.09 + R680.045 - R444.5 - R680.045)B1, B4, B7Kaufman & Tanre, 1992
SARVIRB = R680.045 - (R444.5 - R680.045)
SARVI = (1 + 0.5)(R799.09 -R680.045 + R444.5 + R680.045)/(R799.09 + R680.045 -
R444.5 - R680.045 + 0.5)		B1, B4, B7Kaufman & Tanre, 1992
EVI2.5(R799.09 - R680.045)/(R799.09 + 6R680.045 - 7.5R444.5 + 1)B1, B4, B7Huete et al., 1997
IRECI(R783 - R665)/(R705/R740)B4, B5, B6, B7Frampton et al., 2013
IPVIR842/(R842 + R665)B4, B8Rozenstein et al., 2019
PSSRAR783/R665B4, B7Rozenstein et al., 2019
RVIR842/R665B4, B8Rozenstein et al., 2019
mNDVI705(R750 - R705)/( R750 + R705 - 2R445)B1, B5, B6Datt, 1999
mSR705(R750 - R445)/( R705 + R445)B1, B6Datt, 1999
SIPI(R800 - R445)/( R800 - R680)B1, B7Penuelas et al., 1995
NDWI(R865 - R1614)/( R865 - R1614)B8A, B11McFeeters, 1996

R表示range, 其右下角数值表示光谱值范围。ARI, 花青素反射指数; ARVI, 耐大气植被指数; CRI, 类胡萝卜素反射指数; D, 双峰光学指数; DATT, DATT植被指数; EVI, 增强型植被指数; Gitelson, Gitelson植被指数; GNDVI, 绿色归一化差异植被指数; IPVI, 红外植被百分比指数; IRECI, 倒红边叶绿素指数; mNDVI705, 改进红边归一化植被指数; MSI, 水分胁迫指数; mSR705, 改进红边比值植被指数; NDI45, 归一化差异指数; NDII, 归一化红外指数; NDVI, 归一化植被指数; NDWI, 归一化差值水体指数; OSAVI, 优化型土壤调节植被指数; PSRI, 植物衰老反射指数; PSSRA, 特征色素简单比值指数; RVI, 比值植被指数; SARVI, 土壤大气阻抗植被指数; SAVI, 土壤调节植被指数; SIPI, 结构不敏感色素植被指数; SR, 比值植被指数; TCARI, 转换型叶绿素吸收植被指数; TNDVI, 转化后的归一化植被指数; WDVI, 加权差分植被指数。

R is range, and its lower-right value represents the spectral value range. ARI, anthocyanin reflectance index; ARVI, atmospherically resistant vegetation index; CRI, carotenoid reflectance index; D, double-peak optical index; DATT, DATT vegetation index; EVI, enhanced vegetation index; Gitelson, Gitelson vegetation index; GNDVI, green normalized difference vegetation index; IRECI, inverted red-edge chlorophyll index; IPVI, infrared percentage vegetation index; mNDVI705, modified red edge normalized difference vegetation index; MSI, moisture stress index; mSR705, modified red edge simple ratio index; NDI45, normalized difference index 45; NDII, normalized difference infrared index; NDVI, normalized difference vegetation index; NDWI, normalized difference water index; OSAVI, optimization soil-adjusted vegetation index; PSRI, plant senescence reflectance index; PSSRA, pigment specific simple ratio; RVI, ratio vegetation index; SARVI, soil atmospherically resistant vegetation index; SAVI, soil adjusted vegetation index; SIPI, structure insensitive pigment index; SR, simple ratio index; TCARI, transformed chlorophyll-absorbing vegetation index; TNDVI, transformed normalized difference vegetation index; WDVI, weighted difference vegetation index.

新窗口打开| 下载CSV


CHA是一种光谱多样性指数(Deng et al., 2016; Gholizadeh et al., 2018), 是样方内各草本植物像元的所有波段凸包面积的平均值, 即:

CHA¯L=∑K=1xCHA(RK,L,R¯L)x

式中, x为该样方中所有像元的数量, R¯L是样方L所有草本植物像元的平均值, RK,L是样方内第K个草本像元的光谱值。

本研究先将波段值进行归一化处理, 再使用MATLAB的convhull函数计算CHA。

1.3.3 回归方法
1.3.3.1 逐步回归

逐步回归是一种利用线性回归模型选择自变量的方法, 其基本思想是将变量逐个引入, 引入的条件是其偏回归平方和经过假设检验是显著的。同时, 每引入一个新变量后, 对已入选回归模型的老变量逐个进行检验, 将经检验后不显著的变量删除, 以保证所得自变量子集中每一个变量都是显著的。此过程经过若干步直到不能再引入新变量为止。这时回归模型中所有变量对因变量的作用都是显著的, 且没有严重多重共线性。本研究使用SPSS软件中的逐步回归分析将46个特征变量与实测草地功能多样性和生物量建立回归模型, 剔除掉多重共线性后, 基于所选变量构建最终的逐步回归模型。

1.3.3.2 偏最小二乘回归(PLSR)

PLSR是一种线性非参数回归模型(Verrelst et al., 2015), 是为了解决多变量共线性和数据规模大的问题而开发的一种多元回归算法(Schweiger et al., 2017)。PLSR分析在建模过程中集中了主成分分析、典型相关分析和线性回归分析方法的特点, 因此在分析结果中, 除了可以提供一个更为合理的回归模型外, 还可以同时完成一些类似于主成分分析和典型相关分析的研究内容, 提供一些更丰富、深入的信息。同时, 它考虑到多指数结合法的预测准确性较差和物理模型的输入参数较为复杂, PLSR将目标变量与全波段光谱信息联系在一起, 易于实现并可以较优的精度反演多种性状, 已成功地应用于森林和草地生态系统中的性状研究中(Asner & Martin, 2008; Dahlin et al., 2013; Schweiger et al., 2017), 是目前较优的反演方法。本研究使用Unscrambler X 10.4软件, 基于46个特征变量, 利用PLSR方法, 与实测草地功能多样性和生物量建立回归模型, 预测FRic、FEve、FDiv和生物量并反演成图。

1.3.3.3 随机森林回归(RFR)

随机森林最早是由Leo Breiman在2001年提出, 并经Adele Cutler等(Cutler & Zhao, 2001; Cutler & Stevens, 2006)修改完善的一种由决策树构成的集成算法, 可以解释若干个自变量对因变量的作用。本研究通过随机森林回归算法构建了基于图像的草地功能多样性反演模型, 分为如下几步: (1)通过自助法(bootstrap)重采样技术, 从原始样本S中随机选取m (m < S)个样本点, 得到S1……Sn个训练集, 再利用每个训练集生成对应的决策树, 测试样本为每次重采样中未被抽到的样本所构成的袋外数据(OOB); (2)随机选取一定数量的候选特征, 从中选择最适合的特征作为分裂节点; (3)重复步骤(2)直到不能被分裂; (4)生成的决策树就构成了随机森林回归模型, 其中每一个决策树最终的预测结果为该样本点到叶节点的均值, 随机森林最终的预测结果为所有决策树预测结果的均值。回归的精度评价采用OOB预测的残差均方表示如下:

MSEOOB=S−1∑1s(xi−x^iOOB)2
RRF2=1−MSEOOBδ^x2

式中, xi表示袋外数据中因变量的实际值, x^i表示对袋外数据的预测值, δ^x2表示袋外数据预测值的方差。由于某些变量可能对功能多样性的估算作用很小或者变量之间存在共线性, 从而影响回归结果, 因此, 我们首先通过逐步回归去除方差膨胀因子(VIF)大于10的变量, 再利用剩下的变量构建最终的随机森林回归模型, 随机森林回归模型在R语言的“randomforest”包中实现。

以上3种回归模型, 为减少冗余信息的干扰, 均随机选取样本, 利用十折重复交叉验证, 基于决定系数(R2)和均方根误差(RMSE)开展精度评估(Genuer et al., 2010; Edwards et al., 2018), 从而选择最优的回归模型反演草地功能多样性。

2 结果

2.1 实测功能多样性及地上生物量

图3是基于各样地每个物种的比叶面积、叶片含水量、叶绿素含量和总氮含量等生理性状与各物种在样地中的生物量占总生物量的比值, 获取的实测功能多样性与地上生物量。FRic、FEve、FDiv的范围分别在2-28、0.64-0.85、0.65-0.95之间, 其平均值分别为12.34、0.75、0.77; 地上生物量的范围则在250-900 g·m-2之间, 平均值为563 g·m-2。

图3

新窗口打开| 下载原图ZIP| 生成PPT

图3   实测功能多样性及地上生物量。

Fig. 3   Measured functional diversity and aboveground biomass.


2.2 变量选择

由于变量间可能存在共线性, 对于随机森林回归和逐步回归首先要去除VIF > 10的变量, 再筛选出用于构建随机森林和逐步回归最终模型的特征变量。其中, 波段B11、优化型土壤调节植被指数(OSAVI)、水波段指数(WBI)的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的解释度最高; 波段B5、B9、B10、B11、加权差分植被指数(WDVI)、CHA等对FDiv的解释度最高。对输入变量的重要性程度进行排序, 各指数选择的特征变量排序如图4所示。而PLSR够在自变量存在严重共线性的情况下进行回归建模, 在最终模型中将包含原有的所有自变量。

图4

新窗口打开| 下载原图ZIP| 生成PPT

图4   各功能多样性特征变量重要性排序。B5, B6, B9, B10, B11, B12表示波段5、6、9、10、11、12; CHA, 凸包面积; CRI1,类胡萝卜素反射指数; D, 双峰光学指数; NDI45, 归一化差值指数; OSAVI, 优化型土壤调节植被指数; WBI, 水波段指数; WDVI, 加权差分植被指数。

Fig. 4   Importance ranking of functional diversity feature variables. B5, B6, B9, B10, B11, B12 means bands 5, 6, 9, 10, 11, 12; CHA, convex hull area; CRI1, carotenoid reflectance index 1; D, double-peak optical index; NDI45, normalized difference index 45; OSAVI, optimized soil-adjusted vegetation index; WBI, water band index; WDVI, weighted differential vegetation index.


2.3 基于3种方法估算草地植物功能多样性及反演成图

基于3种回归方法, 利用十折重复交叉验证的验证精度如表2所示, 发现利用逐步回归估算的FRic (R2 = 0.52, RMSE = 4.51)和FEve (R2 = 0.42, RMSE = 0.03)解释度远高于其他两种回归方法, 使用PLSR方法估算的FDiv指数最高(R2 = 0.61, RMSE = 0.04)。因此, 本研究分别利用逐步回归方法反演FRic和FEve, 用PLSR方法反演FDiv并对研究区域的草地植物功能多样性FRic、FEve和FDiv进行区域成图(图5)。FRic、FEve和FDiv的值基本集中在52-78、0.44-0.75及0.74以上; 在空间上, FRic呈现南高北低的分布特征, 在西部部分区域略有降低, 而FEve和FDiv则呈现南低北高的分布特征。这种分布特征表明研究地区西南部生态空间利用程度高, 各物种在生态位上竞争较弱、对资源利用率较低。

表2   三种回归方法的验证精度

Table 2  Validation accuracy of the three regression methods

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
R2RMSER2RMSER2RMSE
FRic0.524.510.405.180.364.88
FEve0.430.030.320.040.350.03
FDiv0.540.040.560.040.610.04

R2, 决定系数; RMSE, 均方根误差。FRic, 功能丰富度; FEve, 功能均匀度; FDiv, 功能离散度。

R2, determination coefficient; RMSE, root mean square error; FRic, functional richness; FEve, functional evenness; FDiv, functional divergence.

新窗口打开| 下载CSV


图5

新窗口打开| 下载原图ZIP| 生成PPT

图5   乌拉盖管理区植物功能多样性分布图。A, 功能丰富度。B, 功能均匀度。C, 功能离散度。

Fig. 5   Maps of plant functional diversity metrics in the Ulgai Management Area. A, Functional richness. B, Functional evenness. C, Functional divergence.


2.4 功能多样性与地上生物量的关系

在本研究中, 利用3种回归方法分别对研究区域群落地上生物量进行建模, 逐步回归和RFR精度分别为R2 = 0.53 (RMSE = 101.16, p < 0.001)和R2 = 0.56 (RMSE = 106.34, p < 0.001), 而PLSR精度为R2 = 0.61 (RMSE = 94.15, p < 0.001)(图6)。因此选用PLSR模型对研究区域的群落地上生物量进行区域成图(图7), 整个区域的生物量大多在450-700 g·m-2之间。同时, 本研究发现表征功能丰富度的FRic对群落地上生物量的解释度最高(R2 = 0.40), 两者呈正相关关系, 而表征性状空间均匀度的FEve (R2 = 0.27)和离散度的FDiv (R2 = 0.28)与群落地上生物量关系相对较弱(图8)。

图6

新窗口打开| 下载原图ZIP| 生成PPT

图6   地上生物量反演精度验证。实线和虚线分别表示线性回归模型拟合线和1:1线。所有回归分析均具有统计学意义(p < 0.001)。RMSE为均方根误差。

Fig. 6   Validation of the remotely-sensed aboveground biomass based on field-measured data. Solid and dashed lines depict the linear regression model and the 1:1 line, respectively. All regression analyses were statistically significant (p < 0.001). RMSE is root mean square error.


图7

新窗口打开| 下载原图ZIP| 生成PPT

图7   乌拉盖管理区地上生物量(g·m-2)分布图。

Fig. 7   Distribution of the remotely-sensed aboveground biomass (g·m-2) in the Ulgai Management Area.


图8

新窗口打开| 下载原图ZIP| 生成PPT

图8   遥感预测的功能多样性与地上生物量的关系。

Fig. 8   Relationships between remotely-sensed functional diversity and aboveground biomass.


3 讨论

3.1 特征变量选择

本研究基于逐步回归法筛选出与各功能多样性指数相关的特征变量, 这些变量都与用于计算实际功能多样性的性状相关, 如波段B5、B6、B10、B11、B12分别为红边范围波段和短波红外波段, 它们与植物多种理化性质有关, 同时也是描述植物色素状态和健康状况的重要指示波段, 如B5 (703.9 nm)、B6 (740.2 nm)所在波长范围能够反映植被叶绿素含量变化, 而B10 (945.0 nm)、B11 (1 613.7 nm)、B12 (2 202.4 nm)的波长范围则能反映叶片氮和水分含量等生理特征, 同时有研究发现植被覆盖度越高、叶面积指数越大、红边斜率也就越大, 相应地植被生长状态越好; 归一化差值植被指数(NDI45)对监测植被健康信息十分重要; CRI1对叶片中的类胡萝卜素非常敏感, 间接反映叶片中的叶绿素含量; WBI对冠层水分状态的变化非常敏感; OSAVI有较好的抗土壤干扰能力, 能够稳定地表达植被覆盖信息, 适合植被监测。

遥感反演功能多样性的原理是基于系统发育差异和资源限制导致的植物性状不同(Schweiger et al., 2018), 进而影响光学遥感测量的光谱反射率(Wang et al., 2018)。而功能多样性计算中涉及的功能性状需要既考虑其生态意义(如生存策略和生态系统过程或功能), 也要兼顾其遥感反演精度。然而, 对于不同的物种组成类型, 在不同的环境梯度下, 哪些性状和多少性状最能反映生态策略或功能的主要变化, 目前尚不清楚, 因此在具体研究中应考虑二者的权衡来反演功能多样性。

3.2 草地功能多样性遥感反演

本研究基于3种回归方法反演草地功能多样性, 结果发现利用逐步回归对FRic (R2 = 0.52, RMSE = 4.51)和FEve (R2 = 0.42, RMSE = 0.03)指数有较好的反演结果, 而利用PLSR法对FDiv指数有较好的反演结果(R2 = 0.61, RMSE = 0.04)。这可能与3种回归方法选择特征变量的方式有关。逐步回归剔除了具有多重共线性且不显著的特征变量得到最终的回归模型, 而RFR仅剔除了具有多重共线性的特征变量后建立了回归模型, 变量在模型中显著与否没有得到证实。同时, RFR无法控制模型内部的运行, 只能在不同参数和随机种子之间进行尝试, 对于小样本数据或者低维数据(变量较少的数据), 可能不能产生很好的分类。本研究选取了46个特征变量, 但去掉具有多重共线性的变量后仅剩3-6个变量进行回归建模, 且实验样本数量较少, 可能会对模型估算精度有影响。PLSR能够在自变量存在严重多重共线性的条件下进行回归建模, 在最终模型中将包含原有的所有自变量且更易于辨识系统信息与噪声(Farifteh et al., 2007), 这一方法已经在森林功能多样性反演中得到广泛应用(Asner et al., 2017)。因此, 在反演过程中, 要根据实际情况选择合适的模型进行估算。

另一方面, 尽管基于遥感的功能多样性监测在森林中得到了很好的应用, 如利用机载激光雷达、成像光谱学和高光谱数据获取森林冠层或叶片性状, 成功估算热带森林的功能多样性及其变化(Asner et al., 2017; Durán et al., 2019)。但相比于森林生态系统, 草地物种个体小, Sentinel-2数据计算的不是各物种间的变异而是像元所代表的群落间的变异, 估算精度也会存在一定差异(Mallinis et al., 2020)。因此, 当影像空间分辨率提升到草地物种个体大小(cm级), 功能多样性的估算精度可能会有所提升。进一步, 高光谱数据的应用也会提升功能性状的反演精度, 进而提升功能多样性的反演精度。无人机具有成本低、操作灵活的优点, 结合地面调查, 辅以高光谱传感器, 可实现对高空间、高光谱和高时间分辨率的草地功能多样性监测, 进一步集成地面观测, 构建多维度、多尺度、高频率的天空地一体化草地多样性监测技术体系, 促使遥感技术方法与生态学理论的有机融合, 为实现小尺度研究到大尺度监测的无缝链接提供了强有力的技术手段。

3.3 功能多样性与地上生物量的关系

本研究利用PLSR对地上群落生物量进行了反演, 反演精度R2 = 0.61 (RMSE = 94.15, p < 0.001), 此结果相比基于Sentinel-2数据估算草地生物量的研究精度更低。如, Kong等(2019)以高寒草地的植被为研究对象, 利用冠层光谱数据和生物量与MOD13 NDVI数据相结合, 基于逐步回归方法反演高山草地生物量, 拟合R2为0.869; Dou等(2020)以滨海湿地的植被为研究对象反演生物量的精度, R2大于0.85, 本研究结果与之相比精度略低, 这可能是受研究区域空间尺度的影响(Kong et al., 2019)。不同草地类型的生物量遥感反演结果表明, 草地类型也是影响生物量估算精度的重要因子, 且多种草地类型的数据混合, 有利于提升模型的准确性(Shen et al., 2008), 本研究仅选择草甸草原, 可能会对结果产生一定影响。

本研究中遥感预测的功能多样性与地上生物量的关系与实测相一致, 证明了基于遥感反演分析两者关系的潜力。通过遥感预测功能多样性与地上群落生物量的关系发现, FRic对群落地上生物量的解释度最高(R2 = 0.40), 而FEve (R2 = 0.27)和FDiv (R2 = 0.28)与群落地上生物量关系较弱, 这与Zhao等(2021)的研究结果一致。植物的光合作用是物质生产的主要来源(Blackburn, 2007), 与光合作用有关的叶片性状叶绿素、类胡萝卜素和叶片氮含量等与群落生产力直接相关(Marron et al., 2005; da Silveira Pontes et al., 2010; Durán et al., 2019)。本研究中用于计算功能多样性的叶绿素含量、比叶面积、叶片氮含量和叶片含水量4种功能性状均被大量研究证实与生产力直接或间接相关, 随着这4种功能性状在植物体内含量的增加, 物种光合作用加强, FRic增加, 生产力上升(Cadotte et al., 2011; Fyllas et al., 2017; Durán et al., 2019)。Bernhardt等(2011)的研究结果也表明群落生产力有随着FRic指数增加而增加的趋势。

FEve与群落地上生物量的关系最弱, 且呈负相关关系, 即群落生产力随FEve的增加而降低, 说明生产力可能主要源于对某一资源集中利用的物种的生物量。FEve被用作指示资源的利用程度, 低的均匀度表示有些生态位空间虽被占据但未充分利用, 不对生物量的增加起促进作用。本研究结果支持“质量比”假说, 即群落中起支配作用的物种的属性和相对多度对生态系统过程有决定性作用(Grime, 1998), 优势种利用了大部分资源, 而非优势种则对生物量的贡献较小, 因此地上生物量随FEve的降低而升高(马文静, 2014)。FDiv与地上生物量间的相关性并不显著, 功能离散度表示群落功能性状的多度在性状空间内的离散程度, 离散度高说明植物在性状空间边缘较多分布。研究结果显示, 地上生物量随FDiv的增加表现出降低趋势, 可能是由于可利用资源较少, 使得植物功能性状的分布发生改变, 为了获取更多资源, 植物性状不在边缘聚集, 也与之前的研究结果(李晓刚等, 2011; 王海东等, 2013)一致。有研究发现FDiv对生产力的影响在施肥或者干扰的条件下显著, 说明在资源条件较好的情况下, 植物种间竞争增强, 促进了植物不同功能性状的分化, 有利于植物生存和对资源的有效利用, 群落生产力增加(Bernhardt et al., 2011)。

越来越多的研究证明, 功能多样性即群落中物种的功能差异性(Tilman, 2001; Petchey & Gaston, 2002)是生态系统过程的决定因素(Díaz & Cabido, 2001; Loreau et al., 2001; 臧岳铭等, 2009)。植物功能性状是研究生态系统结构与功能变化的中间桥梁(孙国钧等, 2003)。Wan等(2011)指出可以通过改变植物的种类组成和性状来影响区域尺度上的功能多样性, 本研究所选的生态系统功能仅为地上生物量, 覆盖面较小。因此, 研究各性状有何种功能, 各种性状组合计算得到的功能多样性具体又指代生态系统的何种功能, 可作为下一步研究的重点。

4 结论

本研究依托内蒙古自治区锡林郭勒盟乌拉盖管理区草甸草原, 基于遥感数据和野外调查数据, 比较了基于逐步回归、RFR、PLSR这3种当前比较流行的方法对草地植物功能多样性的反演精度, 并选取最优的特征变量反演草地功能多样性, 同时探究草地功能多样性与地上生物量的关系。结果发现:

(1)波段B11、OSAVI、WBI对FRic解释度最高, 波段B6、B10、B12、CRI1、D、NDI45等6个特征变量对FEve解释度最高, 波段B5、B9、B10、B11、WDVI、CHA等对FDiv解释度最高, 这些特征变量的选取与实测功能多样性指数计算时的性状选取有关;

(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) FRic与群落地上生物量的相关性最高(R2 = 0.40), 其次为FDiv (R2 = 0.28)和FEve (R2 = 0.27)。

本研究目前只是在草甸草原中展开, 由于地理差异, 如果要在不同草地类型乃至全国尺度推广时, 还需要在更大的数据集上进行进一步的验证和评估。同时, 在后续研究中还需要考虑植被结构参数、土壤反射率及一些环境因素, 如地形、海拔等的影响, 在方法上还有一定的局限性。本研究为能在大尺度上进行草地功能多样性和生产力估算提供可行的方法和依据, 这对草地管理利用和草地资源保护有重要意义。

参考文献

Asner GP, Martin RE. (2008).

Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels

Remote Sensing of Environment, 112, 3958-3970.

DOI:10.1016/j.rse.2008.07.003      URL     [本文引用: 1]

Asner GP, Martin RE, Knapp DE, Tupayachi R, Anderson CB, Sinca F, Vaughn NR, Llactayo W. (2017).

Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation

Science, 355, 385-388.

DOI:10.1126/science.aaj1987      PMID:28126815      [本文引用: 3]

Functional biogeography may bridge a gap between field-based biodiversity information and satellite-based Earth system studies, thereby supporting conservation plans to protect more species and their contributions to ecosystem functioning. We used airborne laser-guided imaging spectroscopy with environmental modeling to derive large-scale, multivariate forest canopy functional trait maps of the Peruvian Andes-to-Amazon biodiversity hotspot. Seven mapped canopy traits revealed functional variation in a geospatial pattern explained by geology, topography, hydrology, and climate. Clustering of canopy traits yielded a map of forest beta functional diversity for land-use analysis. Up to 53% of each mapped, functionally distinct forest presents an opportunity for new conservation action. Mapping functional diversity advances our understanding of the biosphere to conserve more biodiversity in the face of land use and climate change.Copyright © 2017, American Association for the Advancement of Science.

Bai YF, Han XG, Wu JG, Chen ZZ, Li LH. (2004).

Ecosystem stability and compensatory effects in the Inner Mongolia grassland

Nature, 431, 181-184.

DOI:10.1038/nature02850      URL     [本文引用: 1]

Bai YF, Wu JG, Pan QM, Huang JH, Wang QB, Li FS, Buyantuyev A, Han XG. (2007).

Positive linear relationship between productivity and diversity: evidence from the Eurasian Steppe

Journal of Applied Ecology, 44, 1023-1034.

DOI:10.1111/j.1365-2664.2007.01351.x      URL     [本文引用: 1]

Bernhardt RM, Roemermann C, Sperlich S, Schmidt W. (2011).

Explaining grassland biomass—The contribution of climate, species and functional diversity depends on fertilization and mowing frequency

Journal of Applied Ecology, 48, 1088-1097.

DOI:10.1111/j.1365-2664.2011.01968.x      URL     [本文引用: 2]

Blackburn GA. (2007).

Hyperspectral remote sensing of plant pigments

Journal of Experimental Botany, 58, 855-867.

DOI:10.1093/jxb/erl123      PMID:16990372      [本文引用: 1]

The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.

Botta-Dukát Z. (2005).

Raoʼs quadratic entropy as a measure of functional diversity based on multiple traits

Journal of Vegetation Science, 16, 533-540.

DOI:10.1111/j.1654-1103.2005.tb02393.x      URL     [本文引用: 2]

Breiman L. (2001).

Random forests

Machine Learning, 45, 5-32.

DOI:10.1023/A:1010933404324      URL     [本文引用: 1]

Cadotte MW, Carscadden K, Mirotchnick N. (2011).

Beyond species: functional diversity and the maintenance of ecological processes and services

Journal of Applied Ecology, 48, 1079-1087.

DOI:10.1111/j.1365-2664.2011.02048.x      URL     [本文引用: 1]

Cutler A, Stevens JR. (2006).

Random forests for microarrays

Methods in Enzymology, 411, 422-432.

PMID:16939804      [本文引用: 1]

Random Forests is a powerful multipurpose tool for predicting and understanding data. If gene expression data come from known groups or classes (e.g., tumor patients and controls), Random Forests can rank the genes in terms of their usefulness in separating the groups. When the groups are unknown, Random Forests uses an intrinsic measure of the similarity of the genes to extract useful multivariate structure, including clusters. This chapter summarizes the Random Forests methodology and illustrates its use on freely available data sets.

Cutler A, Zhao G. (2001).

PERT—Perfect random tree ensembles

Computer Science and Statistics, 1-8.

[本文引用: 1]

da Silveira Pontes L, Louault F, Carrère P, Maire V, Andueza D, Soussana JF. (2010).

The role of plant traits and their plasticity in the response of pasture grasses to nutrients and cutting frequency

Annals of Botany, 105, 957-965.

DOI:10.1093/aob/mcq066      PMID:20354073      [本文引用: 1]

Although plant functional traits (PFTs) appear to be important indicators of species' responses to land use changes, there is no clear understanding of how the variations in traits and their plasticity determine variations in species performance. This study investigated the role of functional shoot traits and their plasticity for variation in above-ground net primary productivity (ANPP) due to changes in N supply and in cutting frequency for 13 native perennial C(3) grass species.Monocultures of the grass species were grown in a fully factorial block design combining plant species, cutting frequency and N supply as factors.Four major trait associations were obtained by reducing the dimensions of 14 PFTs with a principal component analysis (PCA).Variations in species' productivity in response to an increase in cutting frequency was mainly explained by traits linked to the first PCA axis, opposing high plant stature from lower shoot cellulose and lignin contents and high leaf N content. Variation in species productivity in response to change in N supply was mainly explained by a set of predictor variables combining traits (average flowering date) and a trait's plasticity (tiller density per unit land area and leaf dry matter content, i.e. mg dry matter g fresh mass(-1)). These traits involved are linked to the second PCA axis ('nutrient acquisition-conservation'), which opposes distinct strategies based on response to nutrient supply.Variations in ANPP of species in response to an increase in cutting frequency and a decrease in N supply are controlled by a group of traits, rather than by one individual trait. Incorporating plasticity of the individual traits into these trait combinations was the key to explaining species' productivity responses, accounting for up to 89 % of the total variability in response to the changes in N supply.

Dahlin KM, Asner GP, Field CB. (2013).

Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem

Proceedings of the National Academy of Sciences of the United States of America, 110, 6895-6900.

DOI:10.1073/pnas.1215513110      PMID:23569241      [本文引用: 1]

Understanding how and why plant communities vary across space has long been a goal of ecology, yet parsing the relative importance of different influences has remained a challenge. Species-specific models are not generalizable, whereas broad plant functional type models lack important detail. Here we consider plant trait patterns at the local scale and ask whether plant chemical traits are more closely linked to environmental gradients or to changes in species composition. We used the visible-to-shortwave infrared (VSWIR) spectrometer of the Carnegie Airborne Observatory to develop maps of four plant chemical traits--leaf nitrogen per mass, leaf carbon per mass, leaf water concentration, and canopy water content--across a diverse Mediterranean-type ecosystem (Jasper Ridge Biological Preserve, CA). For all four traits, plant community alone was the strongest predictor of trait variation (explaining 46-61% of the heterogeneity), whereas environmental gradients accounted for just one fourth of the variation in the traits. This result emphasizes the critical role that species composition plays in mediating nutrient and carbon cycling within and among different communities. Environmental filtering and limits to similarity can act strongly, simultaneously, in a spatially heterogeneous environment, but the local-scale environmental gradients alone cannot account for the variation across this landscape.

Datt B. (1999).

A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus leaves

Journal of Plant Physiology, 154, 30-36.

DOI:10.1016/S0176-1617(99)80314-9      URL     [本文引用: 4]

Delegido J, Verrelst J, Alonso L, Moreno J. (2011).

Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content

Sensors, 11, 7063-7081.

DOI:10.3390/s110707063      PMID:22164004      [本文引用: 1]

ESA's upcoming satellite Sentinel-2 will provide Earth images of high spatial, spectral and temporal resolution and aims to ensure continuity for Landsat and SPOT observations. In comparison to the latter sensors, Sentinel-2 incorporates three new spectral bands in the red-edge region, which are centered at 705, 740 and 783 nm. This study addresses the importance of these new bands for the retrieval and monitoring of two important biophysical parameters: green leaf area index (LAI) and chlorophyll content (Ch). With data from several ESA field campaigns over agricultural sites (SPARC, AgriSAR, CEFLES2) we have evaluated the efficacy of two empirical methods that specifically make use of the new Sentinel-2 bands. First, it was shown that LAI can be derived from a generic normalized difference index (NDI) using hyperspectral data, with 674 nm with 712 nm as best performing bands. These bands are positioned closely to the Sentinel-2 B4 (665 nm) and the new red-edge B5 (705 nm) band. The method has been applied to simulated Sentinel-2 data. The resulting green LAI map was validated against field data of various crop types, thereby spanning a LAI between 0 and 6, and yielded a RMSE of 0.6. Second, the recently developed "Normalized Area Over reflectance Curve" (NAOC), an index that derives Ch from hyperspectral data, was studied on its compatibility with simulated Sentinel-2 data. This index integrates the reflectance curve between 643 and 795 nm, thereby including the new Sentinel-2 bands in the red-edge region. We found that these new bands significantly improve the accuracy of Ch estimation. Both methods emphasize the importance of red-edge bands for operational estimation of biophysical parameters from Sentinel-2.

Deng S, Katoh M, Yu X, Hyyppa J, Gao T. (2016).

Comparison of tree species classifications at the individual tree level by combining ALS data and RGB images using different algorithms

Remote Sensing, 8, 1034. DOI: 10.3390/rs8121034.

DOI:10.3390/rs8121034      [本文引用: 1]

Díaz S, Cabido M. (2001).

Vive la différence: plant functional diversity matters to ecosystem processes

Trends in Ecology & Evolution, 16, 646-655.

DOI:10.1016/S0169-5347(01)02283-2      URL     [本文引用: 2]

Díaz S, Hodgson JG, Thompson K, Cabido M, Cornelissen JHC, Jalili A, Montserrat-Martí G, Grime JP, Zarrinkamar F, Asri Y, Band SR, Basconcelo S, Castro-Díez P, Funes G, Hamzehee B, et al. (2004).

The plant traits that drive ecosystems: evidence from three continents

Journal of Vegetation Science, 15, 295-304.

DOI:10.1111/j.1654-1103.2004.tb02266.x      URL     [本文引用: 1]

Dou ZG, Li YZ, Cui LJ, Pan X, Ma QF, Huang YL, Lei YR, Li J, Zhao XS, Li W. (2020).

Hyperspectral inversion of Suaeda salsa biomass under different types of human activity in Liaohe Estuary wetland in north-eastern China

Marine and Freshwater Research, 71, 482-492.

DOI:10.1071/MF19030      URL     [本文引用: 1]

Durán SM, Martin RE, Díaz S, Maitner BS, Malhi Y, Salinas N, Shenkin A, Silman MR, Wieczynski DJ, Asner GP, Bentley LP, Savage VM, Enquist BJ. (2019).

Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient

Science Advances, 5, eaaw8114. DOI: 10.1126/sciadv.aaw8114.

DOI:10.1126/sciadv.aaw8114      [本文引用: 4]

Edwards JA, Santos-Medellin CM, Liechty ZS, Nguyen B, Lurie E, Eason S, Phillips G, Sundaresan V. (2018).

Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice

PLOS Biology, 16, e2003862. DOI: 10.1371/journal.pbio.2003862.

DOI:10.1371/journal.pbio.2003862      [本文引用: 1]

Farifteh J, van der Meer F, Atzberger C, Carranza EJM. (2007).

Quantitative analysis of salt-affected soil reflectance spectra: a comparison of two adaptive methods (PLSR and ANN)

Remote Sensing of Environment, 110, 59-78.

DOI:10.1016/j.rse.2007.02.005      URL     [本文引用: 1]

Frampton WJ, Dash J, Watmough G, Milton EJ. (2013).

Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation

ISPRS Journal of Photogrammetry and Remote Sensing, 82, 83-92.

DOI:10.1016/j.isprsjprs.2013.04.007      URL     [本文引用: 1]

Fyllas NM, Bentley LP, Shenkin A, Asner GP, Atkin OK, Díaz S, Enquist BJ, Farfan-Rios W, Gloor E, Guerrieri R, Huasco WH, Ishida Y, Martin RE, Meir P, Phillips O, et al. (2017).

Solar radiation and functional traits explain the decline of forest primary productivity along a tropical elevation gradient

Ecology Letters, 20, 730-740.

DOI:10.1111/ele.12771      PMID:28464375      [本文引用: 1]

One of the major challenges in ecology is to understand how ecosystems respond to changes in environmental conditions, and how taxonomic and functional diversity mediate these changes. In this study, we use a trait-spectra and individual-based model, to analyse variation in forest primary productivity along a 3.3 km elevation gradient in the Amazon-Andes. The model accurately predicted the magnitude and trends in forest productivity with elevation, with solar radiation and plant functional traits (leaf dry mass per area, leaf nitrogen and phosphorus concentration, and wood density) collectively accounting for productivity variation. Remarkably, explicit representation of temperature variation with elevation was not required to achieve accurate predictions of forest productivity, as trait variation driven by species turnover appears to capture the effect of temperature. Our semi-mechanistic model suggests that spatial variation in traits can potentially be used to estimate spatial variation in productivity at the landscape scale.© 2017 John Wiley & Sons Ltd/CNRS.

Genuer R, Poggi JM, Tuleau-Malot C. (2010).

Variable selection using random forests

Pattern Recognition Letters, 31, 2225-2236.

DOI:10.1016/j.patrec.2010.03.014      URL     [本文引用: 1]

Gholizadeh H, Gamon JA, Zygielbaum AI, Wang R, Schweiger AK, Cavender-Bares J. (2018).

Remote sensing of biodiversity: soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems

Remote Sensing of Environment, 206, 240-253.

DOI:10.1016/j.rse.2017.12.014      URL     [本文引用: 1]

Gillison AN. (2013).

Plant functional types and traits at the community ecosystem and world level

//van der Maarel E, Franklin J. Vegetation Ecology. John Wiley & Sons, Oxford. 347-386.

[本文引用: 1]

Gitelson AA, Buschmann C, Lichtenthaler HK. (1999).

The chlorophyll fluorescence ratio F735/F700 as an accurate measure of the chlorophyll content in plants

Remote Sensing of Environment, 69, 296-302.

DOI:10.1016/S0034-4257(99)00023-1      URL     [本文引用: 1]

Gitelson AA, Gritz Y, Merzlyak MN. (2003).

Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves

Journal of Plant Physiology, 160, 271-282.

DOI:10.1078/0176-1617-00887      PMID:12749084      [本文引用: 1]

Leaf chlorophyll content provides valuable information about physiological status of plants. Reflectance measurement makes it possible to quickly and non-destructively assess, in situ, the chlorophyll content in leaves. Our objective was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation in leaves with a wide range of pigment content and composition using reflectance in a few broad spectral bands. Spectral reflectance of maple, chestnut, wild vine and beech leaves in a wide range of pigment content and composition was investigated. It was shown that reciprocal reflectance (R lambda)-1 in the spectral range lambda from 520 to 550 nm and 695 to 705 nm related closely to the total chlorophyll content in leaves of all species. Subtraction of near infra-red reciprocal reflectance, (RNIR)-1, from (R lambda)-1 made index [(R lambda)(-1)-(RNIR)-1] linearly proportional to the total chlorophyll content in spectral ranges lambda from 525 to 555 nm and from 695 to 725 nm with coefficient of determination r2 > 0.94. To adjust for differences in leaf structure, the product of the latter index and NIR reflectance [(R lambda)(-1)-(RNIR)-1]*(RNIR) was used; this further increased the accuracy of the chlorophyll estimation in the range lambda from 520 to 585 nm and from 695 to 740 nm. Two independent data sets were used to validate the developed algorithms. The root mean square error of the chlorophyll prediction did not exceed 50 mumol/m2 in leaves with total chlorophyll ranged from 1 to 830 mumol/m2.

Gitelson AA, Merzlyak MN. (1997).

Remote estimation of chlorophyll content in higher plant leaves

International Journal of Remote Sensing, 18, 2691-2697.

DOI:10.1080/014311697217558      URL     [本文引用: 1]

Gitelson AA, Zur Y, Chivkunova OB, Merzlyak MN. (2002).

Assessing carotenoid content in plant leaves with reflectance spectroscopy

Photochemistry and Photobiology, 75, 272-281.

DOI:10.1562/0031-8655(2002)075<0272:accipl>2.0.co;2      PMID:11950093      [本文引用: 2]

Spectral reflectance of maple, chestnut and beech leaves in a wide range of pigment content and composition was investigated to devise a nondestructive technique for total carotenoid (Car) content estimation in higher plant leaves. Reciprocal reflectance in the range 510 to 550 nm was found to be closely related to the total pigment content in leaves. The sensitivity of reciprocal reflectance to Car content was maximal in a spectral range around 510 nm; however, chlorophylls (Chl) also affect reflectance in this spectral range. To remove the Chl effect on the reciprocal reflectance at 510 nm, a reciprocal reflectance at either 550 or 700 nm was used, which was linearly proportional to the Chl content. Indices for nondestructive estimation of Car content in leaves were devised and validated. Reflectances in three spectral bands, 510+/-5 nm, either 550+/-15 nm or 700+/-7.5 nm and the near infrared range above 750 nm are sufficient to estimate total Car content in plant leaves nondestructively with a root mean square error of less than 1.75 nmol/cm2.

Grime JP. (1998).

Benefits of plant diversity to ecosystems: immediate, filter and founder effects

Journal of Ecology, 86, 902-910.

DOI:10.1046/j.1365-2745.1998.00306.x      URL     [本文引用: 2]

Han TT, Tang X, Ren H, Wang J, Liu N, Guo QF. (2021).

Community/ecosystem functional diversity: measurements and development

Acta Ecologica Sinica, 41, 3286-3295.

[本文引用: 3]

[韩涛涛, 唐玄, 任海, 王俊, 刘楠, 郭勤峰 (2021).

群落/生态系统功能多样性研究方法及展望

生态学报, 41, 3286-3295.]

[本文引用: 3]

Homolová L, Malenovský Z, Clevers JGPW, García-Santos G, Schaepman ME. (2013).

Review of optical-based remote sensing for plant trait mapping

Ecological Complexity, 15, 1-16.

DOI:10.1016/j.ecocom.2013.06.003      URL     [本文引用: 1]

Hoover DL, Knapp AK, Smith MD. (2014).

Resistance and resilience of a grassland ecosystem to climate extremes

Ecology, 95, 2646-2656.

DOI:10.1890/13-2186.1      URL     [本文引用: 1]

Huete AR. (1988).

A soil-adjusted vegetation index (SAVI)

Remote Sensing of Environment, 25, 295-309.

DOI:10.1016/0034-4257(88)90106-X      URL     [本文引用: 1]

Huete AR, Liu HQ, Batchily K, van Leeuwen W. (1997).

A comparison of vegetation indices over a global set of TM images for EOS-MODIS

Remote Sensing of Environment, 59, 440-451.

DOI:10.1016/S0034-4257(96)00112-5      URL     [本文引用: 2]

Hunt Jr ER, Rock BN. (1989).

Detection of changes in leaf water content using Near- and Middle-Infrared reflectances

Remote Sensing of Environment, 30, 43-54.

DOI:10.1016/0034-4257(89)90046-1      URL     [本文引用: 1]

Kassen R, Buckling A, Bell G, Rainey PB. (2000).

Diversity peaks at intermediate productivity in a laboratory microcosm

Nature, 406, 508-512.

DOI:10.1038/35020060      URL     [本文引用: 1]

Kaufman YJ, Tanre D. (1992).

Atmospherically resistant vegetation index (ARVI) for EOS-MODIS

IEEE Transactions on Geoscience and Remote Sensing, 30, 261-270.

DOI:10.1109/36.134076      URL     [本文引用: 2]

Kim MS, Daughtry C, Chappelle EW, McMurtrey III JE, Walthall CL. (1994).

The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation

// ISPRS Sixth International Colloquium on Physical Measurements and Signatures in Remote Sensing. 17-21.

[本文引用: 1]

Kong B, Yu H, Du RX, Wang Q. (2019).

Quantitative estimation of biomass of alpine grasslands using hyperspectral remote sensing

Rangeland Ecology & Management, 72, 336-346.

DOI:10.1016/j.rama.2018.10.005      URL     [本文引用: 2]

Laliberté E, Legendre P. (2010).

A distance-based framework for measuring functional diversity from multiple traits

Ecology, 91, 299-305.

PMID:20380219      [本文引用: 2]

A new framework for measuring functional diversity (FD) from multiple traits has recently been proposed. This framework was mostly limited to quantitative traits without missing values and to situations in which there are more species than traits, although the authors had suggested a way to extend their framework to other trait types. The main purpose of this note is to further develop this suggestion. We describe a highly flexible distance-based framework to measure different facets of FD in multidimensional trait space from any distance or dissimilarity measure, any number of traits, and from different trait types (i.e., quantitative, semi-quantitative, and qualitative). This new approach allows for missing trait values and the weighting of individual traits. We also present a new multidimensional FD index, called functional dispersion (FDis), which is closely related to Rao's quadratic entropy. FDis is the multivariate analogue of the weighted mean absolute deviation (MAD), in which the weights are species relative abundances. For unweighted presence-absence data, FDis can be used for a formal statistical test of differences in FD. We provide the "FD" R language package to easily implement our distance-based FD framework.

Lepš J, de Bello F, Lavorel S, Berman S. (2006).

Quantifying and interpreting functional diversity of natural communities: practical considerations matter

Preslia, 78, 481-501.

[本文引用: 1]

Li XG, Zhu ZH, Zhou XS, Yuan FR, Fan RJ, Xu ML. (2011).

Effects of clipping, fertilizing and watering on the relationship between species diversity, functional diversity and primary productivity in alpine meadow of China

Chinese Journal of Plant Ecology, 35, 1136-1147.

DOI:10.3724/SP.J.1258.2011.01136      URL     [本文引用: 1]

[李晓刚, 朱志红, 周晓松, 袁芙蓉, 樊瑞俭, 许曼丽 (2011).

刈割、施肥和浇水对高寒草甸物种多样性、功能多样性与初级生产力关系的影响

植物生态学报, 35, 1136-1147.]

DOI:10.3724/SP.J.1258.2011.01136      [本文引用: 1]

通过在高寒矮嵩草(Kobresia humilis)草甸为期4年的刈割(留茬1 cm、3 cm及不刈割)、施肥(尿素7.5 g&middot;m<sup>&ndash;2</sup>&middot;a<sup>&ndash;1</sup> +磷酸二铵1.8 g&middot;m<sup>&ndash;2</sup>&middot;a<sup>&ndash;1</sup>、不施肥)和浇水(20.1 kg&middot;m<sup>&ndash;2</sup>&middot;a<sup>&ndash;1</sup>、不浇水)控制实验, 对该生态系统中功能多样性与物种多样性的变化及其与初级生产力关系进行了研究。结果表明: 刈割和施肥显著影响植物性状和生产力, 而浇水作用微弱。刈割对物种多样性无影响, 但对不同功能多样性指数的影响不同; 施肥降低了物种多样性和以所有性状(除植株高度外)量化的功能多样性; 浇水仅对物种丰富度有微弱影响。各功能多样性的平均值能更好地反映群落物种间的功能差异。功能多样性和物种多样性呈正相关或不相关, 且二者与初级生产力的关系可表现为正相关、负相关和不相关, 说明三者间的关系是性状依赖的, 并受生境资源状况和刈割扰动的影响。植物功能性状对群落初级生产力有更加直接的影响。在考虑生物多样性对生态系统功能的影响时, 应更加注重对植物功能属性变化的研究。

Liu XJ, Ma KP. (2015).

Plant functional traits—Concepts, applications and future directions

Scientia Sinica Vitae, 45, 325-339.

DOI:10.1360/N052014-00244      URL     [本文引用: 1]

[刘晓娟, 马克平 (2015).

植物功能性状研究进展

中国科学: 生命科学, 45, 325-339.]

[本文引用: 1]

Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, Hooper DU, Huston MA, Raffaelli D, Schmid B, Tilman D, Wardle DA. (2001).

Biodiversity and ecosystem functioning: current knowledge and future challenges

Science, 294, 804-808.

DOI:10.1126/science.1064088      PMID:11679658      [本文引用: 1]

The ecological consequences of biodiversity loss have aroused considerable interest and controversy during the past decade. Major advances have been made in describing the relationship between species diversity and ecosystem processes, in identifying functionally important species, and in revealing underlying mechanisms. There is, however, uncertainty as to how results obtained in recent experiments scale up to landscape and regional levels and generalize across ecosystem types and processes. Larger numbers of species are probably needed to reduce temporal variability in ecosystem processes in changing environments. A major future challenge is to determine how biodiversity dynamics, ecosystem processes, and abiotic factors interact.

Ma WJ. (2014). The Relationship Between Plant Functional Diversity and Primary Productivity in Desert Steppe of Inner Mongolia. Master degree desertation, Inner Mongolia University, Hohhot.

[本文引用: 1]

[马文静 (2014). 内蒙古荒漠草原植物功能多样性与初级生产力的关系. 硕士学位论文, 内蒙古大学, 呼和浩特.]

[本文引用: 1]

Major DJ, Baret F, Guyot G. (1990).

A ratio vegetation index adjusted for soil brightness

International Journal of Remote Sensing, 11, 727-740.

DOI:10.1080/01431169008955053      URL     [本文引用: 1]

Mallinis G, Chrysafis I, Korakis G, Pana E, Kyriazopoulos AP. (2020).

A random forest modelling procedure for a multi-sensor assessment of tree species diversity

Remote Sensing, 12, 1210. DOI: 10.3390/rs12071210.

DOI:10.3390/rs12071210      [本文引用: 1]

Marron N, Villar M, Dreyer E, Delay D, Boudouresque E, Petit JM, Delmotte FM, Guehl JM, Brignolas F. (2005).

Diversity of leaf traits related to productivity in 31 Populus deltoides × Populus nigra clones

Tree Physiology, 25, 425-435.

DOI:10.1093/treephys/25.4.425      URL     [本文引用: 1]

Mason NWH, MacGillivray K, Steel JB, Wilson JB. (2003).

An index of functional diversity

Journal of Vegetation Science, 14, 571-578.

DOI:10.1111/j.1654-1103.2003.tb02184.x      URL     [本文引用: 2]

Mason NWH, Mouillot D, Lee WG, Wilson JB. (2005).

Functional richness, functional evenness and functional divergence: the primary components of functional diversity

Oikos, 111, 112-118.

DOI:10.1111/j.0030-1299.2005.13886.x      URL     [本文引用: 2]

McFeeters SK. (1996).

The use of the normalized difference water index (NDWI) in the delineation of open water features

International Journal of Remote Sensing, 17, 1425-1432.

DOI:10.1080/01431169608948714      URL     [本文引用: 1]

McMurtrey III JE, Chappelle EW, Kim MS, Meisinger JJ, Corp LA. (1994).

Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements

Remote Sensing of Environment, 47, 36-44.

DOI:10.1016/0034-4257(94)90125-2      URL     [本文引用: 1]

Mouillot D, Graham NAJ, Villéger S, Mason NWH, Bellwood DR. (2013).

A functional approach reveals community responses to disturbances

Trends in Ecology & Evolution, 28, 167-177.

DOI:10.1016/j.tree.2012.10.004      URL     [本文引用: 1]

Mouillot D, Mason WHN, Dumay O, Wilson JB. (2005).

Functional regularity: a neglected aspect of functional diversity

Oecologia, 142, 353-359.

PMID:15655690      [本文引用: 1]

Functional diversity has been identified as a key to understanding ecosystem and community functioning. However, due to the lack of a sound definition its nature and measurement are still poorly understood. In the same way that species diversity can be split into species richness and species evenness, so functional diversity can be split into functional richness (i.e. the amount of functional trait/character/attribute space filled) and functional evenness (i.e. the evenness of abundance distribution in functional trait space). We propose a functional regularity index (FRO) as a measure of functional evenness for situations where species are represented only by a single functional trait value (e.g. mean, median or mode), and species abundances are known. This new index is based on the Bulla O index of species evenness. When dealing with functional types or categorical functional traits, the Bulla O or any other accepted species evenness index may be used directly to measure functional evenness. The advantage of FRO is that it supplies a measure of functional evenness for continuous trait data. The FRO index presented in this paper fulfils all the a priori criteria required. We demonstrate with two example datasets that a range of FRO values may be obtained for both plant and animal communities. Moreover, FRO was strongly related to ecosystem function as seen in photosynthetic biomass in plant communities, and was able to differentiate sampling stations in a lagoon based on the functional traits of fish. Thus, the FRO index is potentially a highly useful tool for measuring functional diversity in a variety of ecological situations.

Naeem S, Duffy JE, Zavaleta E. (2012).

The functions of biological diversity in an age of extinction

Science, 336, 1401-1406.

DOI:10.1126/science.1215855      PMID:22700920      [本文引用: 1]

Ecosystems worldwide are rapidly losing taxonomic, phylogenetic, genetic, and functional diversity as a result of human appropriation of natural resources, modification of habitats and climate, and the spread of pathogenic, exotic, and domestic plants and animals. Twenty years of intense theoretical and empirical research have shown that such biotic impoverishment can markedly alter the biogeochemical and dynamic properties of ecosystems, but frontiers remain in linking this research to the complexity of wild nature, and in applying it to pressing environmental issues such as food, water, energy, and biosecurity. The question before us is whether these advances can take us beyond merely invoking the precautionary principle of conserving biodiversity to a predictive science that informs practical and specific solutions to mitigate and adapt to its loss.

Paganini M, Leidner AK, Geller G, Turner W, Wegmann M. (2016).

The role of space agencies in remotely sensed essential biodiversity variables

Remote Sensing in Ecology and Conservation, 2, 132-140.

DOI:10.1002/rse2.29      URL     [本文引用: 1]

Penuelas J, Baret F, Filella I. (1995).

Semi-empirical indices to assess carotenoids/chlorophyll-a ratio from leaf spectral reflectance

Photosynthetica, 31, 221-230.

[本文引用: 1]

Petchey OL, Gaston KJ. (2002).

Functional diversity (FD), species richness and community composition

Ecology Letters, 5, 402-411.

DOI:10.1046/j.1461-0248.2002.00339.x      URL     [本文引用: 3]

Petchey OL, Hector A, Gaston KJ. (2004).

How do different measures of functional diversity perform?

Ecology, 85, 847-857.

DOI:10.1890/03-0226      URL     [本文引用: 2]

Römermann M, Römermann C, Sperlich S, Schmidt W. (2001).

Explaining grassland biomass—The contribution of climate, species and functional diversity depends on fertilization and mowing frequency

Journal of Applied Ecology, 48, 1088-1091.

DOI:10.1111/j.1365-2664.2011.01968.x      URL     [本文引用: 1]

Rondeaux G, Steven M, Baret F. (1996).

Optimization of soil-adjusted vegetation indices

Remote Sensing of Environment, 55, 95-107.

DOI:10.1016/0034-4257(95)00186-7      URL     [本文引用: 1]

Roughgarden J, Running SW, Matson PA. (1991).

What does remote sensing do for ecology?

Ecology, 72, 1918-1922.

DOI:10.2307/1941546      URL     [本文引用: 1]

Rozenstein O, Haymann N, Kaplan G, Tanny J. (2019).

Validation of the cotton crop coefficient estimation model based on Sentinel-2 imagery and eddy covariance measurements

Agricultural Water Management, 223, 105715. DOI: 10.1016/j.agwat.2019.105715.

DOI:10.1016/j.agwat.2019.105715      [本文引用: 6]

Ruiz-Benito P, Gómez-Aparicio L, Paquette A, Messier C, Kattge J, Zavala MA. (2014).

Diversity increases carbon storage and tree productivity in Spanish forests

Global Ecology and Biogeography, 23, 311-322.

DOI:10.1111/geb.12126      URL     [本文引用: 1]

Saatchi S, Buermann W, Ter Steege H, Mori S, Smith TB. (2008).

Modeling distribution of Amazonian tree species and diversity using remote sensing measurements

Remote Sensing of Environment, 112, 2000-2017.

DOI:10.1016/j.rse.2008.01.008      URL     [本文引用: 1]

Schneider FD, Morsdorf F, Schmid B, Petchey OL, Hueni A, Schimel DS, Schaepman ME. (2017).

Mapping functional diversity from remotely sensed morphological and physiological forest traits

Nature Communications, 8, 1441. DOI: 10.1038/s41467-017-01530-3.

DOI:10.1038/s41467-017-01530-3      [本文引用: 1]

Schweiger AK, Cavender-Bares J, Townsend PA, Hobbie SE, Madritch MD, Wang R, Tilman D, Gamon JA. (2018).

Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function

Nature Ecology & Evolution, 2, 976-982.

[本文引用: 1]

Schweiger AK, Schütz M, Risch AC, Kneubühler M, Haller R, Schaepman ME. (2017).

How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response

Methods in Ecology and Evolution, 8, 86-95.

DOI:10.1111/2041-210X.12642      URL     [本文引用: 2]

Shen M, Tang Y, Klein J, Zhang P, Gu S, Shimono A, Chen J. (2008).

Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau

Journal of Plant Ecology, 1, 247-257.

DOI:10.1093/jpe/rtn025      URL     [本文引用: 1]

Sims DA, Gamon JA. (2002).

Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages

Remote Sensing of Environment, 81, 337-354.

DOI:10.1016/S0034-4257(02)00010-X      URL     [本文引用: 2]

Sun GJ, Zhang R, Zhou L. (2003).

Trend and advances in researches on plant functional diversity and functionals groups

Acta Ecologica Sinica, 7, 1430-1435.

[本文引用: 1]

[孙国钧, 张荣, 周立 (2003).

植物功能多样性与功能群研究进展

生态学报, 7, 1430-1435.]

[本文引用: 1]

Tilman D. (1997).

Distinguishing between the effects of species diversity and species composition

Oikos, 80, 185. DOI: 10.2307/3546532.

DOI:10.2307/3546532      [本文引用: 2]

Tilman D. (2001).

Functional diversity

//Levin SA. Encyclopedia of Biodiversity. Elsevier, Amsterdam, the Netherlands. 109-120.

[本文引用: 2]

Tilman D, Knops J, Wedin D, Reich P, Ritchie M, Siemann E. (1997).

The influence of functional diversity and composition on ecosystem processes

Science, 277, 1300-1302.

DOI:10.1126/science.277.5330.1300      URL     [本文引用: 2]

Verrelst J, Camps-Valls G, Muñoz-Marí J, Rivera JP, Veroustraete F, Clevers JGPW, Moreno J. (2015).

Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties—A review

ISPRS Journal of Photogrammetry and Remote Sensing, 108, 273-290.

DOI:10.1016/j.isprsjprs.2015.05.005      URL     [本文引用: 1]

Villéger S, Mason NWH, Mouillot D. (2008).

New multidimensional functional diversity indices for a multifaceted framework in functional ecology

Ecology, 89, 2290-2301. DOI: 10.1890/07-1206.1.

DOI:10.1890/07-1206.1      PMID:18724739      [本文引用: 1]

Functional diversity is increasingly identified as an important driver of ecosystem functioning. Various indices have been proposed to measure the functional diversity of a community, but there is still no consensus on which are most suitable. Indeed, none of the existing indices meets all the criteria required for general use. The main criteria are that they must be designed to deal with several traits, take into account abundances, and measure all the facets of functional diversity. Here we propose three indices to quantify each facet of functional diversity for a community with species distributed in a multidimensional functional space: functional richness (volume of the functional space occupied by the community), functional evenness (regularity of the distribution of abundance in this volume), and functional divergence (divergence in the distribution of abundance in this volume). Functional richness is estimated using the existing convex hull volume index. The new functional evenness index is based on the minimum spanning tree which links all the species in the multidimensional functional space. Then this new index quantifies the regularity with which species abundances are distributed along the spanning tree. Functional divergence is measured using a novel index which quantifies how species diverge in their distances (weighted by their abundance) from the center of gravity in the functional space. We show that none of the indices meets all the criteria required for a functional diversity index, but instead we show that the set of three complementary indices meets these criteria. Through simulations of artificial data sets, we demonstrate that functional divergence and functional evenness are independent of species richness and that the three functional diversity indices are independent of each other. Overall, our study suggests that decomposition of functional diversity into its three primary components provides a meaningful framework for its quantification and for the classification of existing functional diversity indices. This decomposition has the potential to shed light on the role of biodiversity on ecosystem functioning and on the influence of biotic and abiotic filters on the structure of species communities. Finally, we propose a general framework for applying these three functional diversity indices.

Violle C, Navas ML, Vile D, Kazakou E, Fortunel C, Hummel I, Garnier E. (2007).

Let the concept of trait be functional!

Oikos, 116, 882-892.

DOI:10.1111/j.0030-1299.2007.15559.x      URL     [本文引用: 1]

Walker B, Kinzig A, Langridge J. (1999).

Original articles: plant attribute diversity, resilience, and ecosystem function: the nature and significance of dominant and minor species

Ecosystems, 2, 95-113.

DOI:10.1007/s100219900062      URL     [本文引用: 1]

Wan HW, Bai YF, Schöenbach P, Gierus M, Taube F. (2011).

Effects of grazing management system on plant community structure and functioning in a semiarid steppe: scaling from species to community

Plant and Soil, 340, 215-226.

DOI:10.1007/s11104-010-0661-2      URL     [本文引用: 1]

Wang HD, Zhang LL, Zhu ZH. (2013).

Effects of clipping and fertilizing on the relationships between species diversity and ecosystem functioning and mechanisms of community stability in alpine meadow

Chinese Journal of Plant Ecology, 37, 279-295.

DOI:10.3724/SP.J.1258.2013.00028      URL     [本文引用: 1]

[王海东, 张璐璐, 朱志红 (2013).

刈割、施肥对高寒草甸物种多样性与生态系统功能关系的影响及群落稳定性机制

植物生态学报, 37, 279-295.]

DOI:10.3724/SP.J.1258.2013.00028      [本文引用: 1]

植物群落中不同&ldquo;功能身份&rdquo;物种的多样性与特定生态系统功能之间具有何种关系及其作用机制尚不明确。通过在高寒矮嵩草(Kobresia humilis)草甸为期5年的刈割(不刈割、留茬3 cm、留茬1 cm)、施肥(施肥、不施肥)和浇水(浇水、不浇水)控制实验, 研究了刈割与土壤资源获得性梯度上不同&ldquo;功能身份&rdquo;物种(群落中所有物种、响应物种、作用物种和共有物种)的多样性变化与群落地上净初级生产力和稳定性的关系以及稳定性机制。研究结果显示: 群落中响应物种、作用物种和共有物种数分别占全部物种数的36.6%、18.3%和64.8%, 物种多样性对生态系统功能具有不同的效应, 净初级生产力主要受响应物种和作用物种的多样性变化影响, 而稳定性则主要由共有物种的多样性变化决定; 群落稳定性的维持主要依赖于共有物种的多样性增加, 其作用机制是投资组合效应, 而超产效应和异步性效应对稳定性并无作用; 刈割和施肥对物种多样性、稳定性和净初级生产力具有相反的影响, 前者能增加物种多样性和稳定性, 并降低净初级生产力, 而后者的作用正相反。这与群落中全部物种的多样性变化受刈割影响较大, 而作用物种的多样性变化受资源获得性影响较大有关。上述结果表明高寒草甸生态系统地上净初级生产力主要由少数影响生产力的作用物种的多样性决定, 而稳定性则由大量共有物种的多样性所掌控。投资组合效应是物种多样性导致稳定性的机制。由于群落中不同物种的多样性效应具有分异性, 对于特定的生态系统功能而言, 物种的&ldquo;功能身份&rdquo;可能比物种多样性本身更重要, 不加区别地笼统定义物种多样性与生态系统功能的关系可能欠妥。

Wang R, Gamon JA, Schweiger AK, Cavender-Bares J, Townsend PA, Zygielbaum AI, Kothari S. (2018).

Influence of species richness, evenness, and composition on optical diversity: a simulation study

Remote Sensing of Environment, 211, 218-228.

DOI:10.1016/j.rse.2018.04.010      URL     [本文引用: 1]

Xin XP, Xu DW, He XL, Li ZW, Ding L, Shen BB, Mao PP. (2018).

Research progress of grassland carbon cycle using remote sensing technology

China Agricultural Informatics, 30, 1-16.

[本文引用: 1]

[辛晓平, 徐大伟, 何小雷, 李振旺, 丁蕾, 沈贝贝, 毛平平 (2018).

草地碳循环遥感研究进展

中国农业信息, 30, 1-16.]

[本文引用: 1]

Zang YM, Zhu ZH, Li YN, Wang WJ, Xi B. (2009).

Effects of species diversity and functional diversity on primary productivity of alpine meadow Chinese

Journal of Ecology, 28, 999-1005.

[本文引用: 1]

[臧岳铭, 朱志红, 李英年, 王文娟, 席博 (2009).

高寒矮嵩草草甸物种多样性与功能多样性对初级生产力的影响

生态学杂志, 28, 999-1005.]

[本文引用: 1]

Zarco-Tejada PJ, Pushnik JC, Dobrowski S, Ustin SL. (2003).

Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects

Remote Sensing of Environment, 84, 283-294.

DOI:10.1016/S0034-4257(02)00113-X      URL     [本文引用: 1]

Zhao YJ, Sun YH, Lu XM, Zhao XZ, Yang L, Sun ZY, Bai YF. (2021).

Hyperspectral retrieval of leaf physiological traits and their links to ecosystem productivity in grassland monocultures

Ecological Indicators, 122, 107267. DOI: 10.1016/j.ecolind.2020.107267.

DOI:10.1016/j.ecolind.2020.107267      [本文引用: 2]

/

〈 〉

Copyright © 2022 版权所有 《植物生态学报》编辑部
地址: 北京香山南辛村20号, 邮编: 100093
Tel.: 010-62836134, 62836138;　Fax: 010-82599431;　E-mail: apes@ibcas.ac.cn, cjpe@ibcas.ac.cn
备案号: 京ICP备16067583号-19