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.

Sudan, recognized as a forest degradation and deforestation hotspot, has experienced significant forest loss in recent decades. The Umfakarin Natural Forest Reserve (UNFR) is undergoing rapid forest degradation driven by increasing human land-use pressure within a conflict-affected dryland environment. This study assessed long-term land use/land cover dynamics (LULC) and mapped forest degradation in the UNFR. Landsat satellite imagery from 1988, 2002, and 2018 were analyzed using maximum-likelihood supervised classification, transition matrix analysis, and supported by GNSS field-based verification. The classification results revealed marked LULC changes over the study periods, where tree cover reached its peak in 2002 at 32.13%, compared to 19.89% in 1988 and the marked decline by 18.43% in 2018, indicating fluctuating forest conditions. Mixed vegetation increased sharply by 2018 (22.54%) relative to 1988 (16.08%) and 2002 (5.43%), while agricultural land expanded significantly in 2018 (31.96%), exceeding le

Accurate placement and geometry of simulation plots are essential for spatially explicit modeling of forest ecosystems. This study introduces a Monte Carlo-based approach for optimizing the spatial alignment of simulation plots with their source polygons, improving their ability to represent stand-level heterogeneity. The method is implemented in GenSimPlot, an open-source Python plugin for QGIS (version 3.30) that automates the generation, placement, and refinement of simulation plots using simple geometric shapes. Monte Carlo optimization iteratively adjusts translation, rotation, and scaling parameters to maximize spatial congruence, thereby enhancing the fidelity of forest growth simulations. A built-in hyperparameter tuning module based on random search enables users to explore optimal parameter settings systematically. In addition, GenSimPlot supports the extraction of qualitative and quantitative environmental variables and terrain from raster datasets, facilitating integration with forest growth models and broader ecological simulations. The proposed approach improves plot representativeness and enables robust scenario analysis across heterogeneous landscapes.

The compelling role of tree identification and measurement spans ecological and socio-economic domains and emphasizes its importance for environmental studies and forest management. The accuracy of tree detection and parameter estimation is crucial, which has led to the use of advanced technological methods in recent research. In this study, geometric algorithms for tree detection in point clouds from terrestrial laser scanning (TLS) are evaluated to contribute to forest inventory and geographic information systems. Conventional tree measurement methods are based on manual inspection, which, despite its widespread use, has disadvantages such as high cost, labor and human error, which reduces accuracy. Our study explores geometric algorithms for automated and precise solutions. The circle fitting method automates the detection of tree trunks in horizontal cross-sections at certain heights and proves its efficiency in processing point cloud data. However, in certain cases, the method is affected by the irregular shapes of tree trunks that deviate from a circular shape, resulting in inaccurate estimates of both tree position and diameter at breast height DBH. The circular Hough transform, which is known to refine and eliminate unwanted shapes, is beneficial for circle detection and noise reduction in point clouds. It improves tree detection compared to manual methods, especially in terms of processing speed and error reduction, but is limited in complete denoising. The Random Sample Consensus (RANSAC) algorithm closes this gap and excels in removing outliers and accurately detecting cylindrical shapes of tree trunks. The basic methodology of the RANSAC algorithm involves applying ellipses to incomplete datasets and fitting lines to collections of 3D points to solve problems in locating cylinders in range data. We also investigate the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, which is effective in detecting stems and eliminating irrelevant data. DBSCAN divides the data into clusters based on point density and removes regions of low density and noise but requires a minimum number of cluster points. It is effective in identifying and segmenting stems in complex high-density forest stands and complements another algorithm, such as Hough circle fitting, to better remove noise and avoid the impact on stem detection accuracy by the DBSCAN method in some cases. Our analysis highlights the utility of geometric algorithms in detecting trees and improving measurements in point clouds. These algorithms refine the shapes and filter the noise, which contributes to a more accurate estimation of tree parameters. However, each method has its advantages and limitations, with the choice of algorithm depending on specific requirements such as the type of point cloud data, the desired accuracy and the application purpose. In summary, our study provides a comprehensive evaluation of geometric algorithms for tree detection in point clouds and demonstrates the potential for the development of more sophisticated algorithms and methods. These results make an important contribution to forest inventory, especially in the application of terrestrial laser scanning in forestry.