Populus species, important economic species combining rapid growth with broad ecological adaptability, play a critical role in sustainable forestry and bioenergy production. In this study, we performed whole-genome resequencing of 707 individuals from a full-sib family to develop comprehensive single nucleotide polymorphism (SNP) markers and constructed a high-density genetic linkage map of 19 linkage groups. The total genetic length of the map reached 3623.65 cM with an average marker interval of 0.34 cM. By integrating multidimensional phenotypic data, 89 quantitative trait loci (QTL) associated with growth, wood physical and chemical properties, disease resistance, and leaf morphology traits were identified, with logarithm of odds (LOD) scores ranging from 3.13 to 21.72 and phenotypic variance explained between 1.7 and 11.6%. Notably, pleiotropic analysis revealed significant colocalization hotspots on chromosomes LG1, LG5, LG6, LG8, and LG14, with epistatic interaction network analysis confirming genetic basis of coordinated regulation across multiple traits. Functional annotation of 207 candidate genes showed that R2R3-MYB and bHLH transcription factors and pyruvate kinase-encoding genes were significantly enriched, suggesting crucial roles in lignin biosynthesis and carbon metabolic pathways. Allelic effect analysis indicated that the frequency of favorable alleles associated with target traits ranged from 0.20 to 0.55. Incorporation of QTL-derived favorable alleles as random effects into Bayesian-based genomic selection models led to an increase in prediction accuracy ranging from 1 to 21%, with Bayesian ridge regression as the best predictive model. This study provides valuable genomic resources and genetic insights for deciphering complex trait architecture and advancing molecular breeding in poplar.

Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L. trees affected by ash dieback, a major threat to common ash populations across Europe. In this study, both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery. The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR–NDVI image, whereas the coarse segmentation relied on the segment anything model (SAM). Both methods successfully delineated tree crown outlines, however, only the fine segmentation accurately captured internal canopy gaps. Despite these structural differences, mean NDVI values calculated per tree crown revealed no significant differences between the two approaches, indicating that coarse segmentation is sufficient for mean vegetation index assessments. Nevertheless, the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees, underscoring its value for detailed structural and health analyses. Furthermore, the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys. For applications focused on structural integrity and spatial variation in canopy health, the fine segmentation approach is recommended.

Urban and community forestry is a specialized discipline focused on the meticulous management of trees and forests within urban, suburban, and town environments. This field often entails extensive civic involvement and collaborative partnerships with institutions. Its overarching objectives span a spectrum from preserving water quality, habitat, and biodiversity to mitigating the Urban Heat Island (UHI) effect. The UHI phenomenon, characterized by notably higher temperatures in urban areas compared to rural counterparts due to heat absorption by urban infrastructure and limited urban forest coverage, serves as a focal point in this study. The study focuses on developing a methodological framework that integrates Geographically Weighted Regression (GWR), Random Forest (RF), and Suitability Analysis to assess the Urban Heat Island (UHI) effect across different urban zones, aiming to identify areas with varying levels of UHI impact. The framework is designed to assist urban planners and designers in understanding the spatial distribution of UHI and identifying areas where urban forestry initiatives can be strategically implemented to mitigate its effect. Conducted in various London areas, the research provides a comprehensive analysis of the intricate relationship between urban and community forestry and UHI. By mapping the spatial variability of UHI, the framework offers a novel approach to enhancing urban environmental design and advancing urban forestry studies. The study’s findings are expected to provide valuable insights for urban planners and policymakers, aiding in creating healthier and more livable urban environments through informed decision-making in urban forestry management.

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Investing in projects that support environmental benefits, such as tree harvesting, has the potential to reduce air pollution levels in the atmosphere in the future. However, this kind of investment may increase the current level of emissions. Therefore, it is necessary to estimate how much the policy affects the current level of CO2 emissions. This makes sure the policy doesn’t increase the level of CO2 emissions. This study aims to analyze the effect of the One Billion Trees program on CO2 emissions in New Zealand by employing the 2020 input–output table analysis. This investigation examines the direct and indirect effects of policy on both the demand and supply sides across six regions of New Zealand. The results of this study for the first year of plantation suggest that the policy increases the level of CO2 emissions in all regions, especially in the Waikato region. The direct and indirect impact of the policy leads to 64 kt of CO2 emissions on the demand side and 270 kt of CO2 emissions on the supply side. These lead to 0.19 and 0.74% of total CO2 emissions being attributed to investment shocks. Continuing the policy is recommended, as it has a low effect on CO2 emissions. However, it is crucial to prioritize the use of low-carbon machinery that uses fossil fuels during the plantation process.