Terrestrial laser scanning (TLS) has become a vital tool in forestry for accurately measuring tree parameters, such as diameter at breast height (DBH). However, its application in Mexican forests remains underexplored. This study evaluates the performance of five two-dimensional DBH estimation algorithms (Nelder–Mead, least squares, Hough transform, RANSAC, and convex hull) within a temperate Mexican forest and explores their broader applicability across diverse ecosystems, using published point cloud data from various scanning devices. Results indicate that algorithm accuracy is influenced by local factors like point cloud density, occlusion, vegetation, and tree structure. In the Mexican study area, the Nelder–Mead algorithm achieved the highest accuracy (R² = 0.98, RMSE = 1.59 cm, MAPE = 6.12%), closely followed by least squares (R² = 0.98, RMSE = 1.67 cm, MAPE = 6.42%), with different outcomes in other sites. These findings advance DBH estimation methods by highlighting the importance of tailored algorithm selection and environmental considerations, thereby contributing to more accurate and efficient forest management across various landscapes.

Effective forest management and conservation are increasingly critical in addressing the challenges posed by climate change. Advances in remote sensing technologies, such as terrestrial laser scanning, offer promising tools for more accurate assessments in forestry research. This study explores the application of TLS in biomass estimation by conducting a bibliometric analysis of scientific articles indexed in Scopus and the Web of Science. By examining the literature from 2010 to 2024, the study identifies key trends, knowledge gaps, and emerging research opportunities, as well as practical applications in forest management and conservation. The analysis reveals a significant rise in scientific output on TLS, with an average annual growth rate of 8.16%. The most cited works address biomass estimation at the individual tree level using laser scanning data. China and the United States lead in the publication volume with 11 articles. The collaboration network highlights research disparities among regions such as Latin America. Overall, TLS has proven effective for the non-destructive measurement of forest variables and biomass.

On this topic focus on determining the volumetric yield of these products, showing a sawmill yield that ranges between 45% and 60% (Luna et al. 2012), so that approximately 40% becomes residues considered of little to no economic value (Fregoso-Madueño et al. 2017).

Los ecosistemas forestales desempeñan un papel clave en el almacenamiento de carbono, lo que subraya la importancia de estimar la biomasa de los árboles de manera precisa. El objetivo de la presente investigación fue estimar la biomasa forestal mediante un escáner láser (LiDAR, por sus siglas en inglés Light Detection and Ranging), específicamente un dispositivo terrestre (TLS, Terrestrial Laser Scanner), a nivel de árbol individual. Se seleccionaron 31 árboles de una masa regular de Pinus cooperi de los cuales se midieron las variables de diámetro a la altura del pecho (DAP) y la altura (h), de manera tradicional. Los datos de TLS se recolectaron con un escáner laser FARO® Focus M70, se procesaron para modelar tridimensionalmente los troncos y calcular su biomasa. Estos datos se contrastaron con estimaciones obtenidas por ecuaciones alométricas y mediciones tradicionales. Los resultados indican que el TLS es preciso para medir diámetros (R2=0.72 y RMSE=1.28 cm), respecto a los métodos tradicionales. Sin embargo, subestima la altura de los árboles (R2=0.79 y RMSE=1.68 m), lo que afecta la precisión en el cálculo de la biomasa. Aunque el TLS proporcionó estimaciones aceptables, estas fueron inferiores a las obtenidas mediante ecuaciones alométricas. Se concluye que el TLS es una herramienta prometedora para estudios no destructivos de biomasa. Futuros trabajos deben considerar con mayor detalle la influencia de las características del área estudiada, la metodología del escaneo y los algoritmos aplicados en la estimación de la biomasa.

Sustainable management of natural resources requires tools that enable a comprehensive analysis of patterns of use, change and conservation of ecosystems. In this context, Geographic Information Systems (GIS) and Remote Sensing (RS) have established themselves as key technologies for analyzing and promoting the sustainable management of forest ecosystems and other natural resources. This study aimed to analyze the evolution, structure, and trends in scientific output regarding the use of GIS and remote sensing in natural resource management, with an emphasis on forest management, during the period from 1985 to 2025. A systematic bibliometric analysis was conducted using Scopus, Web of Science, and ScienceDirect, with the literature identification and selection process reported in accordance with the PRISMA 2020 guidelines, yielding a final dataset of 679 documents. The results show sustained growth (5% annually), with an increase in citations. The output is concentrated in specialized journals and countries such as the United States and China. From a thematic perspective, the studies focus on forest ecosystems (81%), with a predominance of raster data use (91%). It is concluded that the research field is expanding, with significant advances since the year 2000, consolidating itself as a key area for the monitoring and sustainable management of forest resources. Likewise, the predominance of raster data use reflects an orientation toward large-scale spatial analyses, supported by open-access remote sensing platforms with continuous spatial coverage.

There is a perverse cycle: the poverty-forest cycle that feeds autonomously and is related to the deterioration of natural resources. Rural areas vary greatly in size and characteristics, depending on the country and region. It is observed how these areas play an essential role in agriculture and the preservation of the natural environment. (Word Bank, 2023). Approximately 250 million people reside in forest and savannah areas, these communities depend on forest products, goods and services for their survival and resilience during times of adversity. Globally, forests cover approximately 4.06 billion hectares, representing the largest area of forests on the planet. Corresponding to approximately 31% of the total surface of the Earth. (FAO, 2020). W. F. (2004) argues that three billion individuals, about half of the world’s population, reside in developing rural areas of the world, and 1,500 million of these survive on less than 2 dollars a day. This area encompasses both native forests and tree plantations. In 2023, approximately 3.42 billion people lived in rural areas, accounting for about 42.66% of the total population. However, the distribution of forests is not homogeneous: more than half of the world’s forests are located in just five nations: Russia, Brazil, Canada, the United States andChina. It is fascinating to observe how these areas play an essential role in agriculture and the preservation of the natural environment. (FAO, 2020). In Durango, deforestation causes environmental damage, but it also increases the supply of agricultural land and generates income and rural employment, sometimes sustainable and sometimes not. There is a trade-off between poverty reduction and environmental protection. The objective is to analyze income and the consequences of poverty and natural resources. No methodology is presented due to the descriptive nature of the study