一种基于数码相机图像和群落冠层结构调查的草地地上生物量估算方法

doi:10.17521/cjpe.2022.0235

植物生态学报 ›› 2022, Vol. 46 ›› Issue (10): 1280-1288.DOI: 10.17521/cjpe.2022.0235

一种基于数码相机图像和群落冠层结构调查的草地地上生物量估算方法

刘超¹^,², 李平¹, 武运涛¹^,², 潘胜难¹^,², 贾舟¹^,², 刘玲莉¹^,^*()

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, 北京 100049

收稿日期:2022-06-06 接受日期:2022-08-16 出版日期:2022-10-20 发布日期:2022-08-28
通讯作者: 刘玲莉
作者简介:^*ORCID:刘玲莉: 0000-0002-5696-3151(lingli.liu@ibcas.ac.cn)
基金资助:
中国科学院战略性先导科技专项(A类)(XDA23080301)

Estimation of grassland aboveground biomass using digital photograph and canopy structure measurements

LIU Chao¹^,², LI Ping¹, WU Yun-Tao¹^,², PAN Sheng-Nan¹^,², JIA Zhou¹^,², LIU Ling-Li¹^,^*()

¹State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
²University of Chinese Academy of Sciences, Beijing 100049, China

Received:2022-06-06 Accepted:2022-08-16 Online:2022-10-20 Published:2022-08-28
Contact: LIU Ling-Li
Supported by:
Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080301)

1. 附录草地地上生物量估算所有模型的泛化能力表现评估结果.pdf(2517KB)

摘要/Abstract

摘要：

草地地上生物量是影响其生态系统功能最重要的因素之一, 也是草地生态学研究中不可或缺的监测指标。草地地上生物量监测多采用收割法进行, 但这种破坏性取样方法会对研究区域带来巨大干扰, 尤其是面积较小的长期定位监测或者控制实验研究样地, 从而使得地上生物量监测的频次受到很大限制。因此, 通过获取某些原位易测变量, 建立地上生物量的估算方法具有重要意义。该研究依托内蒙古典型草地刈割控制实验平台, 通过数码照片获取不同土地利用方式下的植被覆盖度, 并对样方内的叶面积指数、植被高度、物种多样性等参数进行了测定, 最后利用一元回归模型、逐步回归模型和随机森林模型对地上生物量进行估算。结果表明, 植被覆盖度、叶面积指数、植被平均高度、植被最大高度和物种丰富度是影响地上生物量的主要驱动因素。通过构建适宜于本地的逐步回归模型, 可以实现草地地上生物量的准确预测。在该研究区域中, 预测模型的决定系数(R²) = 0.91, 均方根误差(RMSE) = 35.60 g·m^-2。该研究提供了一种快速、准确且非破坏性测定草地地上生物量的方法, 可作为传统收割法的有效补充。

关键词: 植被覆盖度, 叶面积指数, 植被高度, 逐步回归模型, 随机森林模型, 最大似然分类

Abstract:

Aims Aboveground biomass (AGB) is one of the most important factors affecting grassland ecosystem function and is commonly measured in grassland research. AGB is often measured using the harvest method, which can cause great disturbance to plant communities, especially for those long-term monitoring plots. A non-destructive method for AGB estimation is thus needed.

Methods Here, we conducted field measurements at a land-use manipulation experiment in a typical steppe in Nei Mongol, China. We obtained the fractional vegetation cover (FVC) using digital photographs. We also measured leaf area index (LAI), vegetation height, and plant species richness. Three different models were used to estimate AGB: univariate regression model, stepwise regression model, and random forest model.

Important findings We found that FVC, LAI, mean vegetation height, maximum vegetation height and richness were highly correlated with AGB variation. AGB can be accurately predicted by a stepwise regression model developed based on the local plant community. The determination coefficient (R²) and root-mean-square error (RMSE) of the stepwise regression model can reach 0.91 and 35.60 g·m^-2, respectively. Overall, our study provides a rapid and non-destructive method for AGB measurement that can be used as an alternative to the traditional harvest method.

Key words: fractional vegetation cover, leaf area index, vegetation height, stepwise regression model, random forest model, maximum likelihood classification

刘超, 李平, 武运涛, 潘胜难, 贾舟, 刘玲莉. 一种基于数码相机图像和群落冠层结构调查的草地地上生物量估算方法. 植物生态学报, 2022, 46(10): 1280-1288. DOI: 10.17521/cjpe.2022.0235

LIU Chao, LI Ping, WU Yun-Tao, PAN Sheng-Nan, JIA Zhou, LIU Ling-Li. Estimation of grassland aboveground biomass using digital photograph and canopy structure measurements. Chinese Journal of Plant Ecology, 2022, 46(10): 1280-1288. DOI: 10.17521/cjpe.2022.0235

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2022.0235

https://www.plant-ecology.com/CN/Y2022/V46/I10/1280

图/表 4

图1 数码相机获取的原始植被图片(A)和ENVI处理后的植被图片(B)。B中绿色区域为植被区域, 浅色区域为非植被区域。

Fig. 1 Original digital photo obtained by digital camera (A) and the photo processed by ENVI (B). The green area in figure B indicates vegetation area, and light-colored area indicates non-vegetation area.

图2 草地地上生物量估算中各变量的相关矩阵。每个方框内的椭圆颜色和尺寸大小代表变量间的相关方向和程度, 蓝色代表正相关, 红色代表负相关, 颜色越深代表相关性越强。AGB, 地上生物量; CV, 高度变异系数; FVC, 植被覆盖度; H', 香农-维纳多样性指数; hmax, 植被最大高度; hmean, 植被平均高度; hmin, 植被最小高度; LAI, 叶面积指数; MC, 含水量; richness, 物种丰富度。*, p < 0.05; ***, p < 0.001。

Fig. 2 Correlation matrix among variables in grassland aboveground biomass estimation. The color and size of each ellipse represent the correlation direction and degree between variables; the blue and red colors represent positive and negative correlations, respectively, and the correlation degree is stronger when the red or blue color of each ellipse gets deeper. AGB, aboveground biomass; CV, coefficient of variation for vegetation height; FVC, fractional vegetation cover; H', Shannon-Wiener index; hmax, maximum vegetation height; hmean, mean vegetation height; hmin, minimum vegetation height; LAI, leaf area index; MC, moisture content; richness, species richness. *, p < 0.05; ***, p < 0.001.

图3 草地地上生物量一元回归模型的预测结果。决定系数(R2)和均方根误差(RMSE)以对应的颜色显示。

Fig. 3 Prediction results of univariate regression model of grassland aboveground biomass. The determination coefficient (R2) and root-mean-square error (RMSE) from linear regression (blue) and exponential regression (red) are given.

图4 草地地上生物量预测模型及变量相对重要性。蓝色实线代表逐步回归模型(A)的拟合线, 绿色实线代表随机森林模型(B)的拟合线, 灰色虚线为1:1线, 对应模型的决定系数(R2)和均方根误差(RMSE)在左上角进行标示。模型变量重要性: 逐步回归模型(C), 随机森林模型(D)。条柱旁边的数字代表变量对应的相对重要性, 冒号连接的变量代表变量间的交互作用; 逐步回归模型的变量重要性来源于不同变量对R2的贡献, 随机森林模型的变量重要性来源于均值递减精度的方法。CV, 高度变异系数; FVC, 植被覆盖度; hmean, 植被平均高度; hmax, 植被最大高度; hmin, 植被最小高度; H', 香农-维纳多样性指数; LAI, 叶面积指数。

Fig. 4 Grassland aboveground biomass prediction models and relative importance of variables. The blue and green solid lines represent the fitted lines of the stepwise regression model (A) and random forest model (B), respectively; the grey dashed line denotes the 1:1 line. The determination coefficient (R2) and root mean square error (RMSE) of the two models are also marked by the corresponding color. The relative importance of variables in the stepwise regression model (C) and random forest model (D) are shown; numbers next to the bars indicate the relative importance of each variable; variables connected by colons represent interactions between variables; the variable importance of stepwise regression model is derived from the contributions of different variables to R2, and the variable importance of random forest model is derived from the mean decrease accuracy. CV, coefficient of variation for vegetation height; FVC, fractional vegetation cover; hmean, mean vegetation height; hmax, maximum vegetation height; hmin, minimum vegetation height; H', Shannon-Wiener index; LAI, leaf area index.

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一种基于数码相机图像和群落冠层结构调查的草地地上生物量估算方法

Estimation of grassland aboveground biomass using digital photograph and canopy structure measurements

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