Height-Diameter Modeling of Cinnamomum tamala Grown in Natural Forest in Mid-Hill of Nepal by Mukti Ram Subedi, Bishwa Nath Oli, Surendra Shrestha, Sophan Chhin

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Understanding forest insect outbreak dynamics: a comparative analysis of machine learning techniques by Roberto Molowny-Horas, Saeed Harati-Asl, Liliana Perez

“…Accurate modeling and simulation of forest land cover change resulting from epidemic insect outbreaks play a crucial role in equipping scientists and forest managers with essential insights. …”
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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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Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests by Sara Winsemius, Chad Babcock, Van R. Kane, Kat J. Bormann, Hugh D. Safford, Yufang Jin

“…When evaluated against two commonly referenced regional estimates based on Landsat optical imagery, root mean square error, relative standard error, and bias of our estimations were substantially lower, demonstrating the benefits of local modeling for subalpine forests. …”
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Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise by Shantaram B. Nadkarni, G. S. Vijay, Raghavendra C. Kamath

“…Their performance is assessed based on mean-squared error, coefficient of determination, training time, and standard deviation. …”
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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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Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate forest by Shashika Himandi Gardeye Lamahewage, Chandi Witharana, Rachel Riemann, Robert Fahey, Thomas Worthley

“…Abstract Plants sequester carbon in their aboveground components, making aboveground tree biomass a key metric for assessing forest carbon storage. Traditional methods of aboveground biomass (AGB) estimation via Forest Inventory and Analysis (FIA) plots lack sufficient sampling intensity to directly produce accurate estimates at fine granularities. …”
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A low resistance circular diverter tee based on an improved random forest model by Ao Tian, Angui Li, Ran Gao, Ruoyin Jing, Yi Wang, Yan Tian, Yibu Gao, Junkai Ren, Yingying Wang

“…This paper takes a tee as an example and proposes a novel resistance reduction method for building transmission and distribution systems that utilizes an improved random forest model. Unlike existing studies on local component resistance reduction that rely on trial-and-error empirical methods, this study introduces a posterior optimization approach that can obtain a global optimal solution within a given range. …”
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An Efficient Random Forest Classifier for Detecting Malicious Docker Images in Docker Hub Repository by Maram Aldiabat, Qussai M. Yaseen, Qusai Abu Ein

“…The results show that the Random Forest classifier demonstrates exceptional accuracy, achieving a 99% F1-score and an AUC of 100%. …”
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Aerial biomass allometric models for Prosopis affinis Spreng. in native Espinal forests of Argentina by Silvana M. J. Sione, Hernán J. Andrade-Castañeda, Silvia G. Ledesma, Leandro J. Rosenberger, José D. Oszust, Marcelo G. Wilson

“…ABSTRACT Estimation of carbon stored in forest ecosystems requires accurate biomass prediction tools. …”
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Modelling forest canopy reflectance over sloping terrain with an extended GSV and PROSAIL Model by Siqi Li, Guyue Hu, Shaoda Li, Chenghao Liu, Xiao Wang, Ronghao Yang, Junxiang Tan

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Mapping Tropical Forested Wetlands Biomass with LiDAR: A Machine Learning Comparison by Jonathan V. Solórzano, Candelario Peralta-Carreta, J. Alberto Gallardo-Cruz

“…The results indicate that the random forest model outperformed the others, achieving the lowest root mean squared error (<i>RMSE</i> = 20.25 Mg/ha, <i>rRMSE</i> = 12.25%, <i>R</i>² = 0.88). …”
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Development of a stereo vision-based UGV guidance system for bareroot forest nurseries by Sharif Shabani, Ashish R. Mulaka, Thomas A. Stokes, Tanzeel U. Rehman, Yin Bao

“…The US forest nursery industry still relies heavily on manual labor for inventories during the growing season. …”
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Soft detection model of corrosion leakage risk based on KNN and random forest algorithms by Yang YANG, Chengzhi LI, Xuan DU, Xiao YU, Shaohua DONG

“…These identified indicators were then employed to develop an intelligent soft detection model that integrates pipeline and environmental data, based on the K-Nearest Neighbor (KNN) and Random Forest algorithms. Results The model conducted predictions on missing detection data and achieved indirect measurements of key indicators, with a relative error between predicted and measured values staying below 25%, meeting acceptable standards. …”
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Estimation of the aboveground carbon stocks based on tree species identification in Saihanba plantation forest by Ao Zhang, Xiaohong Wang, Xin Gu, Xiangyao Xu, Xintong Gao, Linlin Jiao

“…Furthermore, the discrepancy between this estimation and the direct measurement outcomes of the forest management inventory (FMI) was minimal, exhibiting a relative error of only −5.2%, estimation precision of 94.8%. …”
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Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Pro... by Yuwen Peng, Huiyi Su, Min Sun, Mingshi Li

“…Historical forest fire risk databases are vital for evaluating the effectiveness of past forest management approaches, enhancing forest fire warnings and emergency response capabilities, and accurately budgeting potential carbon emissions resulting from fires. …”
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Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults by Konstantinos Kofidis, Cătălina Lucia Cocianu

“…The models are evaluated using metrics such as Mean Squared Error (MSE), F1 score, and Accuracy, and their proficiency in addressing imbalanced datasets and elucidating intricate data relationships is highlighted. …”
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