Remote Sensing of Forest Gap Dynamics in the Białowieża Forest: Comparison of Multitemporal Airborne Laser Scanning and High-Resolution Aerial Imagery Point Clouds

Abstract

Remote sensing technologies like airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have emerged as efficient tools for detecting and analysing canopy gaps (CGs). Comparing these technologies is essential to determine their functionality and applicability in various environments. Thus, this study aimed to assess CG dynamics in the temperate European Białowieża Forest between 2015 and 2022 by comparing ALS data and image-derived point clouds (IPC) from DAP, to evaluate their respective capabilities in describing and analysing forest CG dynamics. Our results demonstrated that ALS-based point clouds provided more detailed and precise spatial information about both the vertical and horizontal structure of forest CGs compared to IPC. ALS detected 27,754 (54%) new CGs between 2015 and 2022, while IPC identified 23,502 (75%) new CGs. Both the average gap area and the total gap area significantly increased over time in both methods. ALS data not only identified a greater number of CGs, particularly smaller ones (below 500 m2), but also produced a more precise representation of CG shape and structure. In conclusion, precise, multi-temporal remote sensing data on the distribution and size of canopy gaps enable effective monitoring of structural changes and disturbances in forest stands, which in turn supports more efficient forest management, e.g., planning of forest regeneration.

Code and data availability

The supplied blocks contain no public paper-specific asset. The article's canopy gap data (ALS/IPC point clouds, gap maps) are not deposited publicly: 'The data underlying this article will be shared on reasonable request to the corresponding author.' No author analysis code, scripts, workflows, or trained models are公开

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