Correcting for measurement errors in a long‐term aerial survey with auxiliary photographic data by Jamie L. Brusa, Matthew T. Farr, Joseph Evenson, Emily Silverman, Bryan Murphie, Thomas A. Cyra, Heather J. Tschaekofske, Kyle A. Spragens, Sarah J. Converse

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Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction by Lihua Shen, Baorui Du, He Fan, Hailong Yang

“…Compared to traditional random forest and neural network models, the MSE and MAE show average reductions of 32.3% and 25.3%. …”
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Estimation of Solar Diffuse Radiation in Chongqing Based on Random Forest by Peihan Wan, Yongjian He, Chaoyu Zheng, Jiaxiong Wen, Zhuting Gu

“…A high-precision DIFRA estimation model was developed using the random forest algorithm, and a distributed simulation of DIFRA in Chongqing was achieved. …”
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New Possibilities of Field Data Survey in Forest Road Design by Mihael Lovrinčević, Ivica Papa, David Janeš, Luka Hodak, Tibor Pentek, Andreja Đuka

“…Field data, as the basis for planning and designing forest roads, must have high spatial accuracy. Classical (using a theodolite and a level) and modern (based on total stations and GNSSs) surveying methods are used in current field data survey for forest road design. …”
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Anomaly detection research using Isolation Forest in Machine Learning by A. S. Kechedzhiev, O. L. Tsvetkova

“…The study includes data preprocessing, training the model on the training set, and evaluating the model's performance on the test set using accuracy metrics, error matrix, and classification report. To implement this research, the Python programming language and the scikit-learn library were chosen to implement the Isolation Forest, as well as Pandas for working with data.Result. …”
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LightGBM hybrid model based DEM correction for forested areas. by Qinghua Li, Dong Wang, Fengying Liu, Jiachen Yu, Zheng Jia

“…Despite extensive research on DEMs in recent years, significant errors still exist in forested areas due to factors such as canopy occlusion, terrain complexity, and limited penetration, posing challenges for subsequent analyses based on DEMs. …”
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YOLOGX: an improved forest fire detection algorithm based on YOLOv8 by Caixiong Li, Yue Du, Xing Zhang, Peng Wu

“…To tackle issues, including environmental sensitivity, inadequate fire source recognition, and inefficient feature extraction in existing forest fire detection algorithms, we developed a high-precision algorithm, YOLOGX. …”
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Forest Fire Risk Assessment: An Illustrative Example from Ontario, Canada by W. John Braun, Bruce L. Jones, Jonathan S. W. Lee, Douglas G. Woolford, B. Mike Wotton

“…This paper presents an analysis of ignition and burn risk due to wildfire in a region of Ontario, Canada using a methodology which is applicable to the entire boreal forest region. A generalized additive model was employed to obtain ignition risk probabilities and a burn probability map using only historic ignition and fire area data. …”
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Predicting forest understory habitat for Canada lynx using LIDAR data by Patrick A. Fekety, Rema B. Sadak, Joel D. Sauder, Andrew T. Hudak, Michael J. Falkowski

“…A common method to assess Canada lynx habitat used by the U.S. Forest Service is to measure horizontal cover using a cover board. …”
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Conversion from Forest to Agriculture in the Brazilian Amazon from 1985 to 2021 by Hugo Tameirão Seixas, Hilton Luís Ferraz da Silveira, Alan Pereira da Silva Falcão Mendes, Fabiana Da Silva Soares, Ramon Felipe Bicudo da Silva

“…The method consists of basic algebraic operation and recursion to identify every conversion from forest to agriculture between 1985 and 2021. The results show a correlation between environmental policies and the time required for the conversion to be completed, such as the adoption of the soy moratorium and the New Forest Code, that were followed by a search for old cleared areas for the establishment of new agricultural sites. …”
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Fuel detection in forest environments training deep learners with smartphone imagery by F. Pirotti, F. Pirotti, A. Carmelo, E. Kutchartt, E. Kutchartt, E. Kutchartt

“…Unmixing mixtures in images is one of the challenges for extracting information from data. Forest environments are particularly complex due to the relatively irregular structure of trees, shrubs and low vegetation. …”
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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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Exploring the Effects of Multi-Factors on User Emotions in Scenarios of Interaction Errors in Human–Robot Interaction by Wa Gao, Yuan Tian, Shiyi Shen, Yang Ji, Ning Sun, Wei Song, Wanli Zhai

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“Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region” by Ankit Singh, Nitesh Dhiman, K. C. Niraj, Dericks Praise Shukla

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Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery by Andrii Zaiats, T. Trevor Caughlin, Jennyffer Cruz, David S. Pilliod, Megan E. Cattau, Rongsong Liu, Richard Rachman, Maisha Maliha, Donna Delparte, John D. J. Clare

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Diagnosing aortic dissection: A review of this elusive, lethal diagnosis by Blake Briggs, David Cline

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Random Forest–Based Coal Mine Roof Displacement Prediction and Application by Hongxia Li, Rong Wu, Jianan Gao

“…R2, mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE) are selected to evaluate the performance of the models. …”
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Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…Evaluation metrics encompassing Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination (R2) are employed to validate its performance. …”
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Optimizing maize germination forecasts with random forest and data fusion techniques by Lili Wu, Yuqing Xing, Kaiwen Yang, Wenqiang Li, Guangyue Ren, Debang Zhang, Huiping Fan

“…The RF model stood out, with a training time of 5.18 s and the highest accuracy of 92.88%, along with a mean absolute error (MAE) of 0.913 and a root mean square error (RMSE) of 1.163. …”
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Harmonizing remote sensing and ground data for forest aboveground biomass estimation by Ying Su, Zhifeng Wu, Xiaoman Zheng, Yue Qiu, Zhuo Ma, Yin Ren, Yanfeng Bai

“…Accurate aboveground biomass (AGB) estimation is crucial for evaluating management and conservation policy of forests. However, the complexity of forest ecosystems and the diversity of geography bring great challenges to traditional biomass estimation methods. …”
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