Mapping individual trees with airborne laser scanning data in an European lowland forest using a self-calibration algorithm

Abstract 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 Bialowieza 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.