A Forest Fire Prediction Model Based on Cellular Automata and Machine Learning by Xuan Sun, Ning Li, Duoqi Chen, Guang Chen, Changjun Sun, Mulin Shi, Xuehong Gao, Kuo Wang, Ibrahim M. Hezam

“…Forest fires constitute a widespread and impactful natural disaster, annually ravaging millions of hectares of forests and posing a severe threat to human life and property. …”
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Contrasting Trends in Onset of Spring Green‐Up Between Grasslands and Forests in China by Ying Hu, Bojie Fu, Katerina Michaelides, Martin G. De Kauwe, Jian Wang, Miaogen Shen, Wenmin Zhang, Yue Wang, Xiangming Xiao, Yuanwei Qin, Xiaoming Feng, Chaoyang Wu, Yongzhe Chen, Zhuangzhuang Wang, Lingfan Wan

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Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters by Xingyin Wei, Jing Chen, Su Zhang, Feilong He, Yunbo Zhao, Xuran He, Yongjie Fang, Xinhao Chen, Hao Yang

“…We present a novel cluster finder approach called forest fire clustering (FFC). FFC combines iterative label propagation with parallel Monte Carlo simulation to achieve internal validation of clustering results. …”
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Protection of vineyards from Zonda wind: Evaluation of forest windbreaks. Mendoza, Argentina by Rodolfo Abel Dematte, Ernesto Gandolfo Raso, Josefina Huespe

“…The simulations considered the Zonda wind speed type 2 (25 m/s), together with varying levels of optical porosity and geometries of the forest curtains. …”
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Spatial networks reveal how forest cover decreases the spread of agricultural pests by Débora C. Rother, Leandro G. Cosmo, Julia Tavella, Fredric M. Windsor, Mariano Devoto, Darren M. Evans, Paulo R. Guimarães Jr.

“…Therefore, using only information about the direct and indirect pathways of the spatial network and the initial focus of infestation, we were able to predict with nearly 80% accuracy the most susceptible sites to pest spread in the simulated landscape. By adjusting parameters such as pest mobility, and interaction with landscape features, our model can simulate different agricultural systems and pest behaviors, showing that forest cover can be used to control pest occurrence and that direct and indirect pathways in spatial networks can be used as a predictive tool to manage the pest spread in agricultural landscapes.…”
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The role of non-timber forest products in reducing rural poverty in Burkina Faso by Issoufou Ouedraogo, Eugenie Maiga, Lars Esbjerg

“…Using data collected in 2023 on five hundred and thirty (530) randomly selected rural households in two regions of Burkina Faso, this research investigates the contribution of Non-Timber Forest Products (NTFP) exploitation to rural poverty reduction using two different approaches. …”
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Climate constrains the enhancement of CO2 fertilization on forest gross primary productivity by Xinyuan Wei, Daniel J Hayes, Christopher R Schwalm, Joshua B Fisher, Deborah N Huntzinger, Lei Ma, Rodrigo Vargas, Nathaniel A Brunsell

“…In this study, eddy covariance flux measurements from 50 forest ecosystems were integrated with simulations from 14 terrestrial biosphere models to investigate how climate conditions and atmospheric CO _2 concentrations regulate forest GPP. …”
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Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial With Causal Forests by Erik Sverdrup, Maria Petukhova, Stefan Wager

“…Aims This paper gives an accessible tutorial demonstrating the use of the causal forest algorithm, available in the R package grf. …”
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Women’s Preferences and Perspectives on the Use of Parks and Urban Forests: A Case Study by Marta Anna Skiba, Inna Abramiuk

“…This research investigates the behavioural experiences of women in Zielona Góra, Poland, focusing on municipal parks and forests. A mixed-methods approach was applied, including on-site observations, in-depth interviews, online surveys and scenario modelling using Fuzzy Cognitive Maps (FCMs), involving 204 women aged 15–85. …”
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Random forest–based feature selection and detection method for drunk driving recognition by ZhenLong Li, HaoXin Wang, YaoWei Zhang, XiaoHua Zhao

“…A method for drunk driving detection using Feature Selection based on the Random Forest was proposed. First, driving behavior data were collected using a driving simulator at Beijing University of Technology. …”
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Firefront Forecasting in Boreal Forests: Machine Learning Approach to Predict Wildfire Propagation by A.-M. Raita-Hakola, I. Pölönen

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Forest Carbon Modeling Improved Through Hierarchical Assimilation of Pool‐Based Measurements by Yu Zhou, Christopher A. Williams

“…Abstract Accurate assessment of forest carbon dynamics is a critical element of appraising forest‐based Natural Climate Solutions. …”
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Random Forest Development and Modeling of Gross Primary Productivity in the Hudson Bay Lowlands by Jason Beaver, Elyn R. Humphreys, Douglas King

“…Using MODIS data, individual sites’ daily GPP could be simulated with minimal bias, R2 up to 0.89 and mean absolute error as low as 0.37 g C m−2 day−1. …”
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Predicting Short-Range Weather in Tropical Regions Using Random Forest Classifier by Sellappan Palaniappan, Rajasvaran Logeswaran, Anitha Velayutham, Bui Ngoc Dung

“…To address these challenges, we trained a Random Forest classifier on a synthetic (simulated) dataset comprising 1,500 samples, each representing a specific weather scenario. …”
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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

“…First, the BIOMASS data were simulated using the airborne P-band SAR acquisitions collected over two different tropical forests in Paracou, French Guiana, and Mondah, Gabon. …”
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Integrating fire predisposition assessment into decision support systems for mountain forest management by S. Mutterer, J. Schweier, L.G. Bont, G.B. Pezzatti, M. Conedera, C. Temperli, V.C. Griess, C. Blattert

“…Multi-criteria decision support systems (DSSs) address this challenge by integrating climate-sensitive forest modelling into frameworks for the evaluation of BES provision under simulated climate and management trajectories. …”
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Turbulent Transport Efficiency of Momentum and Sscalar over Forest Canopy in Southern Sichuan by Yi DING, Yu ZHANG, Demin FAN, Youqi SU, Qian ZHANG, Yanqi WANG, Yanqi WANG

“…The turbulent transport characteristics of momentum and scalar over the canopy were studied by using the three levels （20 m， 38 m and 56 m） turbulence data measured at the 60m forest micro meteorological tower in southern Sichuan from May 1 to June 30， 2021.Coherent structure is the main form of turbulent motion， which is composed of updraft （ejection） and downdraft （sweep）.In this paper， the quadrant analysis method is used to analyze the boundary of the roughness sublayer， roughness sublayer and constant flux layer above the forest canopy and the ejection-sweep motion characteristics of the constant flux layer， including the difference of ejection and sweep contribution to flux， the difference between momentum and scalar transport， and the difference between different scalar （T， q， CO2） transport.The results show that under unstable and stable conditions， the ejection dominates the scalar transport at all three levels， while the sweep is the main eddy current motion for scalar transport above the roughness sublayer under neutral conditions.For the momentum flux， under unstable conditions， the ejection dominates at all three levels.Under stable conditions， the ejection dominates at the roughness sublayer and the constant flux layer， while at the boundary of the roughness sublayer and the constant flux layer， the effect of the sweep is greater than that of the ejection.Under neutral conditions， the flux contribution of the sweep is greater than that of the ejection except for the roughness sublayer.The third-order cumulant expansion method （CEM） can more accurately express the flux contribution caused by ejection and sweep， while the incomplete cumulant expansion method （ICEM） is poor in simulating the temperature at the boundary of roughness sublayer and constant flux layer.Through the calculation of transmission efficiency， the difference between momentum and scalar transmission is further quantified.The turbulent transfer efficiency of momentum decreases with increasing instability， while the heat transfer efficiency is the opposite.Atmospheric stability is an important factor controlling momentum and scalar transfer， and the transfer efficiency of water vapor is less affected by atmospheric stability.Under the condition of strong instability， the heat transfer efficiency is more effective than other scalar transfer.…”
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Implications of land use and land cover change in Mampong municipality, Ghana by James Kofi Blay, Isaac Abunyuwah

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COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA by Ni Gusti Ayu Putu Puteri Suantari, Anwar Fitrianto, Bagus Sartono

“…This study aimed to compare the performance of the Survival Support Vector Machine and Random Survival Forest methods using simulation studies. Simulation results on right-censored survival data using binary predictor variables scenario indicate that the Survival Support Vector Machine (SSVM) method with Radial Basic Function Kernel (RBF Kernel) has the best model performance on data with small volumes, whereas when the data volume becomes larger, the method that has the best performance is Survival Support Vector Machine using Additive Kernel. …”
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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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