Estimation of Forest Structural Parameters Using UAV-LiDAR Data and a Process-Based Model in Ginkgo Planted Forests
Estimation of Forest Structural Parameters Using UAV-LiDAR Data and a Process-Based Model in Ginkgo Planted Forests
复制标题
利用无人机激光雷达数据和基于过程的模型估算银杏人工林的森林结构参数
DOI：
10.1109/jstars.2019.2918572
复制
发表时间：
2019-11-01
影响因子：
5.5
通讯作者：
Liu, Hao
中科院分区：
文献类型：
--
作者：
Cao, Lin;Liu, Kun;Liu, Hao
开发一个准确的模型来估计人工林的森林结构参数，对于预测森林生产力是至关重要的，并可以更好地了解气候变化下的碳循环。无人驾驶飞行器-光探测和测距(UAV-LiDAR)系统是一种很有前途的主动遥感技术，有可能用于森林资源清查。此外，基于生理学原理和环境因子的基于过程的模型-生理原理预测生长(3-PG)已被应用于不同管理水平、立地条件和气候变化的影响下的同龄单种森林的生长估计。在这项研究中，评估了UAV-LiDAR指标的性能，并将其应用于使用多元线性回归(MLR)方法估计森林结构参数。将3-PG参数化，模拟了中国东部银杏人工林的胸径、树干密度、蓄积量和地上生物量。此外，还对3-PG模型的输入参数进行了敏感性分析。结果表明，基于UAV-LiDAR数据的MLR和3-PG的进展模型在估计森林结构参数(R-2>；0.70，相对均方误差>；20)方面都有很好的潜力。对3-PG参数的敏感性分析也证实了参数“树冠覆盖年龄”(FullCanAge)对3-PG模型至关重要，并与模拟结果呈正相关。本文提出的方法是对传统森林结构参数估计方法的改进，因为它更清楚地考虑了3-PG模型中包含的气候影响。
Developing an accurate model for estimating the forest structural parameters of planted forests is crucial for forest productivity predictions and can provide a better understanding of the carbon cycle under climate change. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represents a promising active remote sensing technology that has the potential to be used for forest inventories. In addition, the process-based model, physiological principles predicting growth (3-PG), which is based on physiological principles and environmental factors, has been applied to estimate the growth of even-aged, mono-specific forests under the effect of different management levels, site conditions, and climate change. In this study, the performance of UAV-LiDAR metrics was assessed and applied to estimate forest structural parameters using a multivariate linear regression (MLR) method. The 3-PG was parameterized and used to simulate the diameter at breast height, stem density, volume and above-ground biomass of a planted ginkgo forest in eastern China. In addition, a sensitivity analysis was conducted on the 3-PG models input parameters. The results demonstrated that both the MLR based on UAV-LiDAR data and a progress model of the 3-PG have a promising potential for estimating forest structural parameters (R-2 > 0.70, relative root squared error > 20). A sensitivity analysis of the 3-PG parameters also confirmed that the parameter "age at canopy cover" (fullCanAge) is vital for the 3-PG model, and positively correlation with the simulated results. The method presented here represents an improvement on traditional methods for estimating forest structural parameters because it more explicitly accounts for climatic effects included in the 3-PG model.