Traditional field-based forest inventories tend to be expensive, time-consuming, and cover only a limited area of a forested region. Remote sensing (RS), especially airborne laser scanning (ALS) has opened new possibilities for operational forest inventories, particularly at the single-tree level, and in the prediction of single-tree characteristics. Throughout the world, forests have varying characteristics that necessitate the development of modern, effective, and versatile tools for ALS data processing. To address this need, we aimed to develop a tool for individual tree detection (ITD) utilising a self-calibrating algorithm procedure and to verify its accuracy using the complicated forest structure of near natural forests in the temperate zone.

This study was carried out in the Polish part of the Białowieża Forest (BF). The airborne laser scanner (ALS) and color-infrared (CIR) datasets were acquired for more than 60 000 ha. Field-based measurements were performed to provide reference data at the single tree level. We introduced a novel ITD method that is self-calibrated and uses a hierarchical analyses of the canopy height model.

There were more than 20 000 000 of trees in first layer in BF above 7 m height. Trees visible from above were divided into coniferous, deciduous and mixed trees that were then matched with an accuracy of 85 %, 85 % and 75 %, respectively. Compared to existing methods, the proposed method is more flexible and achieves better results, especially for deciduous species. Before application of the presented method to other regions, the calibration based on the developed optimisation procedure is needed.

传统的基于实地的森林清单往往昂贵，费时，并且仅覆盖森林区域的有限区域。遥感（RS），尤其是机载激光扫描（ALS）为操作性森林清单开辟了新的可能性，尤其是在单树级别以及单树特征的预测中。在全世界，森林具有变化的特征，因此有必要开发用于ALS数据处理的现代，有效且通用的工具。为了满足这一需求，我们旨在开发一种利用自校准算法程序进行个体树木检测（ITD）的工具，并使用温带地区近乎天然森林的复杂森林结构来验证其准确性。

这项研究是在Białowieża森林（BF）的波兰部分进行的。机载激光扫描仪（ALS）和彩色红外（CIR）数据集的采集时间超过6万公顷。进行基于现场的测量以提供单棵树级别的参考数据。我们介绍了一种新颖的ITD方法，该方法可以自我校准，并使用对冠层高度模型的分层分析。

高海拔7 m以上的高炉第一层有超过2亿棵树木。从上方可见的树木分为针叶树，落叶树和混合树，然后分别以85％，85％和75％的准确度进行匹配。与现有方法相比，该方法更加灵活，并且取得了更好的效果，尤其是对于落叶树种。在将提出的方法应用于其他区域之前，需要基于开发的优化程序进行校准。