Terrestrial and mobile laser scanning for national forest inventories: From theory to implementation - Holvoet, Justin; Eichhorn, Markus P.; Giannetti, Francesca; Kükenbrink, Daniel; Liang, Xinlian; Mokroš, Martin; Novotný, Jan; Pitkänen, Timo P.; Puliti, Stefano; Skudnik, Mitja; Stereńczak, Krzysztof; Terryn, Louise; et al. (Elsevier, 2025)
Light Detection and Ranging (LiDAR) has emerged as an important data source for monitoring forest resources. Terrestrial laser scanning (TLS) and Mobile laser scanning (MLS) have already shown high potential in further advancing forest inventory development. By enabling the retrieval of new forest attributes in addition to traditional ones, these technologies could drive forest inventories into a new paradigm by introducing innovative approaches to measuring and monitoring forests. The debate on the possible implementation of TLS and MLS in forest inventories, particularly in national forest inventories (NFIs), continues in both the scientific community and the public institutions. To date, few studies have evaluated the application of TLS and MLS technologies in large-scale forest inventories or assessed their practical operational limits. In this practice-oriented paper, we first detail TLS and MLS data acquisition and processing for tree attribute estimation, assessing their maturity and main limitations. We then explore three European case studies—from the French, Finnish, and Swiss National Forest Inventories (NFIs)—where these technologies have been tested. Based on these experiences, we identify the main constraints and challenges for operational implementation. Lastly, we discuss the prospects for TLS and MLS within the NFI context and the requirements for their successful adoption. We conclude that TLS and MLS should be viewed not as a replacement for, but as a complement to and enhancement of, traditional NFI practices. Emphasis should be placed on the new opportunities these technologies offer, rather than on direct comparisons with conventional methods.
Keywords: Enhanced NFI; Close-range remote sensing; Ground-based LiDAR; Point cloud; Tree attribute accuracy; Explorative implementation