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中国沙漠 ›› 2024, Vol. 44 ›› Issue (5): 50-59.DOI: 10.7522/j.issn.1000-694X.2024.00032

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基于无人机多源数据的梭梭( Holoxylon ammodendron )地上生物量估算

鲍莉莉1,2(), 李锦荣2(), 韩兆恩1,2, 刘悦1,2, 单浩东1,2   

  1. 1.内蒙古农业大学 沙漠治理学院,呼和浩特 010018
    2.中国水利水电科学研究院 内蒙古阴山北麓草原生态水文国家野外科学观测研究站,北京 100038
  • 收稿日期:2023-12-20 修回日期:2024-02-16 出版日期:2024-09-20 发布日期:2024-10-15
  • 通讯作者: 李锦荣
  • 作者简介:李锦荣(E-mail: lijinrong918@126.com
    鲍莉莉(1997—),女,青海西宁人,硕士研究生,研究方向荒漠化防治。E-mail: 937309774@qq.com
  • 基金资助:
    中国水利水电科学研究院十四五“五大人才”计划“三型人才”项目(MK0199A122021);国家自然科学基金项目(42071021)

Estimation of aboveground biomass of Haloxylon ammodendron based on UAV multi-source data

Lili Bao1,2(), Jinrong Li2(), Zhaoen Han1,2, Yue Liu1,2, Haodong Shan1,2   

  1. 1.College of Desert Control Science and Engineering,Inner Mongolia Agricultural University,Hohhot 010018,China
    2.Yinshanbeilu National Field Research Station of Steppe Eco-hydrological System,China Institute of Water Resources and Hydropower Research,Beijing 100038,China
  • Received:2023-12-20 Revised:2024-02-16 Online:2024-09-20 Published:2024-10-15
  • Contact: Jinrong Li

摘要:

梭梭(Holoxylon ammodendron)是荒漠生态系统的重要组成,其地上生物量(aboveground biomass,AGB)在一定程度上反映了群落及生态系统的健康程度,对荒漠生态系统碳储量的估算具有重要意义。以乌兰布和沙漠梭梭为研究对象,基于无人机激光雷达提取的株高(H)、冠幅(C)参数和可见光数据构建的植被指数(VI)以及二者结合的3类特征指标,采用多元逐步回归、主成分回归和偏最小二乘回归,对梭梭地上生物量进行估算研究,利用决定系数R2和均方根误差RMSE进行评价。结果表明:(1)无人机LiDAR点云数据提取的株高(R2=0.85,RMSE=0.32 m)和冠幅(R2=0.80,RMSE=0.73 m)精度较高。(2)多元逐步回归、主成分回归和偏最小二乘回归模型对梭梭地上生物量拟合效果均较好,R2均大于0.8,而偏最小二乘回归(PLSR)模型的均方根误差最小(2.86 kg·株-1),可以作为梭梭AGB估算的最优模型。利用无人机激光雷达数据可以有效地提取梭梭单株参数因子(H、C),并开展荒漠低矮植被地上生物量的估算研究。

关键词: 地上生物量, 梭梭, 无人机激光雷达, 株高, 冠幅, 植被指数

Abstract:

As one of the important components of desert ecosystem, the aboveground biomass (AGB) of Haloxylon ammodendron reflects the health of community and ecosystem to a certain extent, which is of great significance to the carbon cycle of desert ecosystem. In this paper, H. ammodendron in Ulan Buh Desert was taken as the research object. Based on the plant height (H), crown width (C), visible light vegetation index (VI) extracted by UAV laser radar and the three characteristic indexes of the combination of the two, multiple stepwise regression (MSR), principal component regression (PCR) and partial least squares regression (PLSR) were used to estimate the above-ground biomass of H. ammodendron. The determination coefficient R2 and root mean square error RMSE were used for evaluation. The results showed that: (1) The accuracy of plant height (R2=0.85, RMSE=0.32 m) and crown width (R2=0.80, RMSE=0.73 m) extracted from UAV LiDAR point cloud data was high. (2) MSR, PCR and PLSR models had good fitting effects, and R2 was greater than 0.8, while the root mean square error of PLSR model was the smallest (2.86 kg·plant-1), which could be used as the optimal model for AGB estimation of H. ammodendron. The results show that the use of UAV LiDAR data can effectively extract the single tree parameter factors (H, C) of H. ammodendron, and carry out the estimation of aboveground biomass of low vegetation in desert.

Key words: aboveground biomass, Haloxylon ammodendron, UAV laser radar, plant height, crown width, vegetation index

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