基于双时相无人机激光雷达数据的长白落叶松人工林地上生物量监测

doi:10.13287/j.1001-9332.202603.005

摘要/Abstract

摘要： 为精准监测人工林生物量变化、支撑森林碳动态评估与森林资源管理,本研究以黑龙江省孟家岗林场3个龄组的长白落叶松人工林为研究对象,采集双时相无人机激光雷达点云数据构建冠层高度模型,分别提取2期冠层高度统计量、冠层覆盖度以及冠层占据垂直空间的体积等特征,并引入Weibull函数拟合冠层表面高度分布得到阈值、尺度和形状参数,在此基础上,对比分析了直接法(直接采用两期特征差值对生物量变化量建模)和间接法(分别预测单期生物量后计算其差值)2种生物量变化量估算方法的精度与适用性。结果表明:间接法对生物量的拟合精度较高(R²=0.94,相对均方根误差rRMSE=9.2%),其间接预测的生物量变化量的精度也较高(偏差Bias=-0.16 Mg·hm^-2,平均绝对误差MAE=4.78 Mg·hm^-2),其中冠层占据垂直空间的体积和Weibull阈值参数是关键预测变量。而直接法对生物量变化量的拟合精度较低(R²=0.60,rRMSE=26.2%),预测精度也相对较低(Bias=0.37 Mg·hm^-2,MAE=4.95 Mg·hm^-2)。间接法预测结果表明,孟家岗林场幼龄林生物量增长最快,林分生物量变化量为6.48 Mg·hm^-2·a^-1,其次是中龄林(5.29 Mg·hm^-2·a^-1),近熟林生物量增长最慢(3.75 Mg·hm^-2·a^-1)。本研究验证了双时相无人机激光雷达数据在人工林生物量监测中的有效性,为森林生物量监测与碳储量动态评估提供了技术参考与方法支撑。

关键词: 无人机激光雷达, 双时相, 地上生物量, 长白落叶松

Abstract: This study aimed to accurately monitor plantation biomass change, support the assessment of forest carbon dynamics, and improve forest resource management. We focused on Larix olgensis plantations from three age groups at the Mengjiagang Forest Farm, Heilongjiang Province. By using dual-temporal UAV LiDAR point cloud data, we constructed canopy height models to extract canopy height statistics, canopy cover, and vertical volume occupied by the canopy for both periods. The Weibull function was used to fit the height distribution of the canopy surface, obtaining threshold, scale, and shape parameters. A comparative analysis was conducted on the accuracy and applicability of two biomass change estimation methods: the direct method (modeling biomass change directly using the difference between two periods' features) and the indirect method (predicting biomass for each period separately and calculating the difference). The results showed that the indirect method achieved high fitting accuracy for biomass [R²=0.94, relative root mean square error (rRMSE)=9.2%], and the indirectly derived biomass change also exhibited high estimation accuracy [Bias=-0.16 Mg·hm^-2, mean absolute error (MAE)=4.78 Mg·hm^-2]. The canopy volume and the Weibull threshold parameter were identified as key predictors. In contrast, the direct method showed lower fitting accuracy for biomass change (R²=0.60, rRMSE=26.2%) and correspondingly lower prediction accuracy (Bias=0.37 Mg·hm^-2, MAE=4.95 Mg·hm^-2). Predictions based on the indirect method indicated that young forest of the Mengjiagang Forest Farm had the fastest biomass growth (mean stand biomass change rate was 6.48 Mg·hm^-2·a^-1), followed by middle-aged forests (5.29 Mg·hm^-2·a^-1), while near-mature forests showed the slowest growth (3.75 Mg·hm^-2·a^-1). This study validated the effectiveness of bi-temporal UAV LiDAR data in plantation biomass monitoring and offered a technical and methodological reference for forest biomass monitoring and carbon stock assessment.

Key words: unmanned aerial vehicle laser scanning, bi-temporal, above-ground biomass, Larix olgensis

肖翔, 刘鑫, 董利虎, 郝元朔. 基于双时相无人机激光雷达数据的长白落叶松人工林地上生物量监测[J]. 应用生态学报, 2026, 37(3): 758-768.

XIAO Xiang, LIU Xin, DONG Lihu, HAO Yuanshuo. Monitoring aboveground biomass in Larix olgensis plantations using bi-temporal unmanned aerial vehicle laser scanning data[J]. Chinese Journal of Applied Ecology, 2026, 37(3): 758-768.

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