Reliable information concerning stand volume is fundamental to making strategic decisions in sustainable forest management. A variety of remotely sensed data and different inventory methods have been used for the estimation of forest biometric parameters. Particularly, airborne laser scanning (ALS) point clouds are widely used for the estimation of stand volume and forest biomass using an area-based approach (ABA) framework. This method relies on the reference measurements of field plots with the necessary prerequisite of a precise co-registration between ground reference plots and the corresponding ALS samples. In this research, the allometric area-based approach (AABA) is proposed in the context of stand volume estimation of Scots pine (Pinus sylvestris L.) stands. The proposed method does not require detailed information about the coordinates of the field plots. We applied Polish National Forest Inventory data from 9400 circular field plots (400 m²) to develop a plot level stand volume allometric model using two independent variables: top height (TH) and relative spacing index (RSI). The model was developed using the multiple linear regression method with a log–log transformation of variables. The hypothesis was that, the field measurements of TH and RSI could be replaced with corresponding ALS-derived metrics. It was assumed that TH could be represented by the maximum height of the ALS point cloud, while RSI can be calculated based on the number of tree crowns delineated within the ALS-derived canopy height model. Performance of the developed AABA model was compared with the semi-empirical ABA_SE (with two predictors: TH and RSI) and empirical ABA_E (several point cloud metrics as predictors). The models were validated at the plot level using 315 forest management inventory plots (400 m²) and at the stand level using the complete field measurements from 42 Scots pine dominated forest stands in the Milicz forest district (Poland). The AABA model showed a comparable accuracy to the traditional ABA models with relatively high accuracy at the plot (relative root mean square error (RMSE) = 22.8 per cent; R² = 0.63) and stand levels (RMSE = 17.8 per cent, R² = 0.65). The proposed novel approach reduces time- and cost-consuming field work required for the classic ABA method, without a significant reduction in the accuracy of stand volume estimations. The AABA is potentially applicable in the context of forest management inventory without the necessity for field measurements at local scale. The transportability of the approach to other species and more complex stands needs to be explored in future studies.

中文翻译：

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基于面积的测绘方法-一种基于ALS和NFI数据的经济有效的林分体积估计方法

有关林分数量的可靠信息对于制定可持续森林管理的战略决策至关重要。各种各样的遥感数据和不同的清单方法已用于森林生物特征参数的估计。特别是，使用基于区域的方法（ABA）框架，机载激光扫描（ALS）点云被广泛用于估算林分数量和森林生物量。此方法依赖于场图的参考测量，并具有在地面参考图和相应的ALS样本之间进行精确共配准的必要前提。在这项研究中，基于樟子松（Pinus sylvestrisL.）站立。所提出的方法不需要关于场标坐标的详细信息。我们应用了来自9400个圆形田地（400 m ²），以使用两个独立变量：最高高度（TH）和相对间距指数（RSI）来开发地块级林木体积立体模型。该模型是使用具有变量对数对数转换的多元线性回归方法开发的。假设是，TH和RSI的现场测量值可以替换为相应的ALS衍生指标。假定TH可以由ALS点云的最大高度表示，而RSI可以根据ALS派生的树冠高度模型中描绘的树冠数量来计算。将开发的AABA模型的性能与半经验ABA _SE（具有两个预测因子：TH和RSI）和经验ABA _{E进行比较}（几个点云指标作为预测指标）。使用315个森林管理调查样地（400 m ²）在样地一级验证模型，并使用来自Milicz林区（波兰）的42个以苏格兰松树为主的林分进行完整的实地测量，对模型进行了验证。AABA模型显示出与传统ABA模型相当的精度，在图上具有相对较高的精度（相对均方根误差（RMSE）= 22.8％；R ²  = 0.63）和林分水平（RMSE = 17.8％，R ² = 0.65）。所提出的新颖方法减少了经典ABA方法所需的时间和成本消耗的现场工作，而没有显着降低林分体积估计的准确性。AABA可能适用于森林管理清单，而无需在当地进行实地测量。该方法在其他物种和更复杂林分中的可运输性需要在未来的研究中加以探讨。