Tree detection is a core task in forest inventory and mapping, yet reliable stem identification remains difficult in dense and structurally complex forests. This study systematically reviews the literature on terrestrial laser scanning (TLS)-based tree detection to summarize methodological development, identify persistent challenges, and highlight research gaps. Records were retrieved from Scopus and Web of Science (WoS). Following PRISMA 2020, 39 articles were included and analyzed using Bibliometrix v 5.2.1 package in R Studio 2026.01.1 and qualitative content coding. The reviewed studies were published between 2011 and 2025 in 20 peer-reviewed journals and involved 169 authors from 73 institutions across 24 countries. The literature was organized into three developmental phases: foundational development (2011–2015), rapid growth (2016–2020), and refinement and integration (2021–2025). Across these phases, methods evolved from geometric fitting and clustering to voxel-based and increasingly integrated workflows. Reported performance varied markedly with scan configuration, forest structure, and algorithm design, ranging from very low detection rates to near-complete detection under favorable conditions. Overall, TLS shows strong potential for forest inventory; however, dense stands, multilayered forests, and regeneration-rich environments remain major challenges.

This article enables preventive engineering and the risk of negative occurrences in the transport and temporary parking of vehicles with dangerous goods can be reduced, thus decreasing interactions with other traffic participants. An important element in transporting dangerous goods is defining suitable locations where these vehicles can be safely parked. The research, which consists of several phases, has been carried out along a highway network of approximately one thousand kilometres in length. Out of 95 potentially acceptable locations, 28 have been selected (for both directions) as the most suitable. In the first part of the research, a fuzzy Z multi-criteria decision-making (MCDM) model has been created to select the most suitable locations, followed by the formation of a total of 32 scenarios (16 for each direction), integrating the fuzzy PIvot Pair-wise RElative Criteria Importance Assessment (PIPRECIA) method with Z-numbers with a Geographic Information System (GIS) model. Such integration represents an original and unique model. Fuzzy MCDM model based on Z numbers enables more accuracy in defining the assessment of criteria due to considering probabilities of a given assessment. When evaluating the formed scenarios, a minimum distance between two adjacent locations has been taken into account, and it is 50 km. The obtained results show most suitable scenario S3 = S4 = 35.278 for the first direction, and S2 = S10 = 35.521 for the second direction. Sensitivity analysis through 24 sets shown that a few scenarios can be candidate for implementation depending from significance of criteria. The contribution of this article lies in its pioneering research of this kind and provides recommendations to relevant state authorities for implementing the best scenario, but final implementation depends from representative entities.