An Integrated Conceptual Framework for Adapting Forest Management Practices to Alternative Futures by Tony Prato, Travis B. Paveglio

“…This paper proposes an integrated, conceptual framework that forest managers can use to simulate the multiple objectives/indicators of sustainability for different spatial patterns of forest management practices under alternative futures, rank feasible (affordable) treatment patterns for forested areas, and determine if and when it is advantageous to adapt or change the spatial pattern over time for each alternative future. …”
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Improving forest ecosystem functions by optimizing tree species spatial arrangement by Rémy Beugnon, Georg Albert, Georg Hähn, Wentao Yu, Sylvia Haider, Stephan Hättenschwiler, Andréa Davrinche, Benjamin Rosenbaum, Benoit Gauzens, Nico Eisenhauer

“…Our simulations suggest that increasing tree species spatial heterogeneity in forests composed of eight tree species leads to higher biomass production, more evenly distributed litterfall, increased litter decomposition, and associated nitrogen and carbon cycling. …”
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Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods by Natasha Harvey, Sean P. Burns, Keith N. Musselman, Holly Barnard, Peter D. Blanken

“…At the Niwot Ridge Subalpine Forest AmeriFlux site in the Colorado Front Range, USA, we compared methods that estimate or simulate the presence of snow interception. …”
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Bayesian weighted random forest for classification of high-dimensional genomics data by Oyebayo Ridwan Olaniran, Mohd Asrul A. Abdullah

“…The new model Bayesian Weighted Random Classification Forest (BWRCF) arises from the modification of the existing random classification forest in two ways. …”
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Research on forest fire information propagation model based on IoV by Xiaodan Dong, Siyang Xia

“…The wireless communication in the forest fire scenario was simulated, and the experimental results show that by using EDCA, fire related emergency safety information will be preferentially sent through the high-priority transmission queue, with the minimum transmission delay less than 0.2 ms and the maximum packet delivery ratio up to 0.995. …”
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Conceptual Framework of an Effective Decision Support System in Forest Fire Management by Kadir Alperen Coşkuner, Ertuğrul Bilgili

“…These subsystems are; i) Fire Information System; a smart database for fire managers to input, store, update and manage data easily, ii) Fire Occurrence Prediction System; forest fire risk and danger determiner using constant environmental variables iii) Fire Weather Index System; fire weather index calculator for the ignition potential of fuels, potential rate of spread and fire intensity, iv) Fire Behavior Prediction System; fire propagation simulator for predicting fire behavior under varying weather, fuel and topographic conditions. …”
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Forest land cover optimization for water management in the Ichawaynochaway creek basin by Chambers J. English, Seth E. Younger, Steven T. Brantley, Puneet Dwivedi, Daniel Markewitz, Jeffery B. Cannon

“…Converting all upland forests to restored longleaf pine forests increases water yield by a maximum of 167.09 L s−1, but moderate increases in low flows up to 85 L s−1 could also be achieved through the conversion of 10% of the convertible forested area of the watershed to loblolly pine savannas at a greater cost efficiency (<$1 million year-1) than longleaf pine restoration. …”
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A Prototype Forest Fire Decision Support System for Uttarakhand, India by Neelesh Yadav, Shrey Rakholia, Peter Moore, Laura Patricia Ponce-Calderón, Mithun Kumar S R, Reuven Yosef

“…The development of this DSS involves an open-source R Shiny framework, enabling any decision maker at the execution or planning level to access these key datasets and simulate the spatial solutions cost-effectively. Hence, this study aimed to internalize key decision support tools and datasets based on extensive statistical analysis for data-driven forest fire planning and management.…”
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Energy consumption forecasting and thermal insulator selection with random forest regression by Mohammed Fellah, Salma Ouhaibi, Naoual Belouaggadia, Khalifa Mansouri

“…The model used in this study is Random Forest (RF), which belongs to the family of ensemble learning models.The data used in this study come from numerical simulations carried out with Matlab and consist of 1400 samples, derived from the analysis of 35 thermal insulators distributed across 20 climate zones. …”
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Unmanned Aerial Surveillance and Tracking System in Forest Areas for Poachers and Wildlife by R. G. Sangeetha, Yash Srivastava, C. Hemanth, Harsh Sankar Naicker, Abhijith P. Kumar, Sreejith Vidhyadharan

“…Simulations are done to validate navigation, collision avoidance, and poacher detection in the forest landscape. …”
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Assessment and improvement of GEDI canopy height estimation in tropical and temperate forests by Myung-Sik Cho, David P. Roy, Herve B. Kashongwe, Lin Yan, Meicheng Shen

“…The approach is demonstrated at a tropical evergreen lowland forest site in the Democratic Republic of Congo (MNDP), a temperate pine and hardwood forest site in Alabama (TALL), and a temperate mix-species forest site in Maryland (SERC). …”
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Sustainable fertilizer management for reduction of greenhouse gases emission in bamboo forest by Jiang Jianhui, Zhang Jianying, Hu Hong

“…It is supposed to reduce current GHG emission from bamboo plantation without inhibiting bamboo production.Integrated with the surveys/statistics data, the DNDC (denitrification-decomposition) biogeochemical model was applied to simulate seven typical fertilization scenarios of a Moso bamboo forest in Anji County, Zhejiang Province during 2000—2010. …”
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Optimizing spatial allocation schemes with a focus on carbon fixation services by the integration of GIS and a robust algorithmic approach by Eryan Guo, Jing He

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Windthrow modifies soil solution chemistry and nutrient leaching in the Canadian black spruce boreal forest by Marie Renaudin, Daniel Houle, Jean-David Moore, Louis Duchesne

“…Soil solution chemistry is directly related to vegetation nutrition and growth in forest ecosystems. However, the impacts of natural disturbances on boreal forest soil solution composition and nutrient fluxes remain unclear. …”
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An Innovative Proposal for Developing a Dynamic Urban Growth Model Through Adaptive Vector Cellular Automata by Ahmet Emir Yakup, Ismail Ercument Ayazli

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MultiOptForest: An interactive multi-objective optimization tool for forest planning and scenario analysis [version 2; peer review: 1 approved, 2 approved with reservations, 1 not... by Kyle Eyvindson, Markus Hartikainen, Daniel Burgas, Julian Klein, Jussi Hakanen, Clara Antón-Fernández, Michael Emmerich, Tord Snäll, Mikko Mönkkönen, Marta Vergarechea, Astor Toraño Caicoya, Clemens Blattert

“…It aims to find the optimal portfolio of management regimes that balance the objectives regarding multiple forest ecosystem services and biodiversity. The software flexibly imports data, allowing for the use of a variety of forest simulator outputs. …”
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Measuring and modeling waterlogging tolerance to predict the future for threatened lowland ash forests by E. J. Gustafson, D. R. Bronson, M. A. Windmuller-Campione, R. A. Slesak, D. M. Donner

“…Broad-scale models can help identify management strategies to maintain lowland ash ecosystems. Simulating lowland forest dynamics in landscape models has been problematic because lowland hydrology is extremely complex, making most hydrology algorithms intractable at the landscape scale. …”
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Trees outside forests in Ghana: current state, spatial distribution, and future projection by Kwadwo Kyenkyehene Kusi

“…Trees outside forests (TOF) are increasingly recognized for their vital contributions to environmental sustainability and socioeconomic development. …”
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Assessing the Role of Forest Grazing in Reducing Fire Severity: A Mitigation Strategy by Raffaella Lovreglio, Julian Lovreglio, Gabriele Giuseppe Antonio Satta, Marco Mura, Antonio Pulina

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Intelligent resource allocation in internet of things using random forest and clustering techniques by Nahideh Derakhshanfard, Lida Hosseinzadeh, Fahimeh Rashid Jafari, Ali Ghaffari

“…Initially, IoT devices are grouped using the K-Means algorithm based on features such as energy consumption and bandwidth requirements. A Random Forest model is then trained to accurately predict the resource needs of each cluster, enabling optimal allocation. …”
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