Allometric Models for Estimating Tree Volume and Aboveground Biomass in Lowland Forests of Tanzania by Wilson Ancelm Mugasha, Ezekiel Edward Mwakalukwa, Emannuel Luoga, Rogers Ernest Malimbwi, Eliakimu Zahabu, Dos Santos Silayo, Gael Sola, Philippe Crete, Matieu Henry, Almas Kashindye

“…The findings show that site specific ht-dbh model appears to be suitable in estimating tree height since the tree allometry was found to differ significantly between studied forests. The developed general volume models yielded unbiased mean prediction error and hence can adequately be applied to estimate tree volume in dry and wet lowland forests in Tanzania. …”
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Optimizing public health management with predictive analytics: leveraging the power of random forest by Hongman Wang, Yifan Song, Yifan Song, Hua Bi

“…This study employs a Random Forest Algorithm (RFA) to address this limitation and enhance the predictive modeling of community health outcomes. …”
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Applying the greenhouse gas inventory calculation approach to predict the forest carbon sink by Fredric Mosley, Jari Niemi, Sampo Soimakallio

“…Second, we use it to predict GHG balances in year leading up to 2035 at various roundwood and forest residue harvest rates. The tool can replicate forest GHG balances for forest land with an average annual error of 1.0 Mt CO2, representing 4% of the average annual forest carbon sink. …”
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Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites by Feng Bin, Shahab Hosseini, Jie Chen, Pijush Samui, Hadi Fattahi, Danial Jahed Armaghani

“…We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). …”
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Capabilities of BIOMASS Three-Baseline PolInSAR Mode for the Characterization of Tropical Forests by Yanzhou Xie, Laurent Ferro-Famil, Yue Huang, Thuy Le Toan, Jianjun Zhu, Haiqiang Fu, Peng Shen

“…However, the three-baseline method still yields acceptable results, with a root-mean-square error ranging from 4.92 to 6.07 m and a correlation coefficient (<italic>R</italic>²) from 0.32 to 0.85, within hectare-scale forest height statistics. …”
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Large-scale inventory in natural forests with mobile LiDAR point clouds by Jinyuan Shao, Yi-Chun Lin, Cameron Wingren, Sang-Yeop Shin, William Fei, Joshua Carpenter, Ayman Habib, Songlin Fei

“…The proposed framework was able to localize and measure all 5838 stems at individual tree level in a 20 ha natural forest in less than 20 min using UAV LiDAR. DBH measurement results on trees’ DBH larger than 38.1 cm (15 in) showed that backpack LiDAR was able to achieve 1.82 cm of Root Mean Square Error (RMSE) and UAV LiDAR was able to achieve 3.13 cm of RMSE. …”
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Allometric equation for estimating aboveground biomass of Acacia-Commiphora forest, southern Ethiopia by Wondimagegn Amanuel, Chala Tadesse, Moges Molla, Desalegn Getinet, Zenebe Mekonnen

“…The aim of the study is to develop site-specific biomass estimation models and validate and evaluate the existing generic models developed for pan-tropical forest and newly developed allometric models. Total of 140 trees was harvested from each diameter class biomass model development. …”
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Exploring the Link: A Systematic Review and Meta‐Analysis on the Prevalence and Association Between Refractive Errors and Intermittent Exotropia by Najah K. Mohammad, Ibrahim Ali Rajab, Mohammed Tareq Mutar, Mustafa Ismail

“…ABSTRACT Background and Aims Refractive errors and intermittent exotropia are prevalent conditions in pediatric populations, impacting visual development and quality of life. …”
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Sample intensity in ombrophilous open forest at Verde Para Sempre Extractive Reserve, Porto de Moz, PA by Fábio Miranda Leão, Luiz Fernandes Silva Dionisio, Loirena do Carmo Moura Sousa, Marlon Costa de Menezes, Marcelo Henrique Silva de Oliveira, Raphael Lobato Prado Neves

“…Was simulated a random sampling with sampling units of 1 ha and several sample intensities: 5%, 10%, 15% and 20% in an area of 200 ha that it was submitted to a forest census. It was evaluated the phytosociological parameters such as structure and diversity, and the estimated errors in the sampling intensities for the volume. …”
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Predicting onset of myopic refractive error in children using machine learning on routine pediatric eye examinations only by Yonina Ron, Tchelet Ron, Naomi Fridman, Anat Goldstein

“…This study introduces an AI-based method to identify children at higher risk for the onset of myopic refractive error, enabling personalized follow-up and treatment plans. …”
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General Approach for Forest Woody Debris Detection in Multi-Platform LiDAR Data by Renato César dos Santos, Sang-Yeop Shin, Raja Manish, Tian Zhou, Songlin Fei, Ayman Habib

“…Woody debris (WD) is an important element in forest ecosystems. It provides critical habitats for plants, animals, and insects. …”
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Comment on “Opinion: Can uncertainty in climate sensitivity be narrowed further?” by Sherwood and Forest (2024) by N. Lewis

“…<p>This comment addresses assertions made by Sherwood and Forest (2024) (SF24) regarding the narrowing of the range of equilibrium climate sensitivity (ECS). …”
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Biodiversity characteristics of large forest plots in Qinghai area of Qilian Mountain National Park by WANG Dinghui, SUONAN Cairang, YU Hongyan, DU Yangong

“…[Objective] Long-term monitoring of plant community dynamics in large forest plots helps reveal the spatial patterns and underlying mechanisms that sustain species diversity. …”
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Bias in transect counts of forest birds: An agent-based simulation model and an empirical assessment by Asko Lõhmus, Ants Kaasik

“…Compared with these field errors, record interpretation had smaller effect on the density estimates. …”
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Mobile Mapping System for Point Cloud Acquisition in a Forest Environment with an Action Camera by A. Pinhal, C. Lázaro, C. Lázaro, J. A. Gonçalves, J. A. Gonçalves

“…The georeferencing of the point cloud relies on the camera's GNSS-derived projection centres, which can be interpolated for each extracted frame. However, in forested environments, the reduced positional accuracy of the GNSS can introduce significant errors in the scale and orientation, limiting the accuracy of extracted dimensional parameters. …”
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Performance analysis of ultra-wideband positioning for measuring tree positions in boreal forest plots by Zuoya Liu, Harri Kaartinen, Teemu Hakala, Heikki Hyyti, Juha Hyyppä, Antero Kukko, Ruizhi Chen

“…The experimental results show that UWB data-driven method is able to map individual tree locations accurately with total root-mean-squared-errors (RMSEs) of 0.17 m, 0.2 m, and 0.26 m for “Easy”, “Medium” and “Difficult” forest plots, respectively, providing a strong reference for forest surveys.…”
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GROWTH AND PRODUCTION OF Eucalyptus CLONES IN SILVOPASTORAL SYSTEM by Ricardo Fernandes Pena, Marcelo Dias Müller, Silvio Nolasco de Oliveira Neto, Domingos Sávio Campos Paciullo, Gabriel Soares Lopes Gomes, Adênio Louzeiro de Aguiar Júnior

“…The Spurr (1952) model showed a high quality of fit and adjusted coefficient of determination and low residual standard error. The I144 clone showed a larger diameter and higher productivity compared to GG100 clone. …”
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Regression analysis and artificial neural networks for predicting pine species volume in community forests by Wenceslao Santiago-García

“…Volume prediction models are fundamental in forestry, as they support forest inventories, sustainable forest management strategies, and comprehensive environmental planning. …”
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Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach by Hai-Bang Ly, Thuy-Anh Nguyen, Binh Thai Pham

“…The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. …”
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A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment by Jiwen Jia, Junhua Kang, Lin Chen, Xiang Gao, Borui Zhang, Guijun Yang

“…These findings highlight the potential of Transformer-based approaches for monocular depth estimation in low-altitude forest environments, with implications for high-throughput plant phenotyping, environmental monitoring, and other forest-specific applications.…”
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