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

作　　者：刘超 李平 武运涛 潘胜难 贾舟 刘玲莉 LIU Chao;LI Ping;WU Yun-Tao;PAN Sheng-Nan;JIA Zhou;LIU Ling-Li(State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences,Beijing 100093,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[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

出　　处：《植物生态学报》2022年第10期1280-1288,共9页Chinese Journal of Plant Ecology

基　　金：中国科学院战略性先导科技专项(A类)(XDA23080301)。

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(R^(2))=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。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(R^(2))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.

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 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置] S812[自动化与计算机技术—控制科学与工程]