Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models by Leah T. Dreesmann, Timothy R. Johnson, Jason W. Karl

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Beyond imperfect maps: Evidence for EUDR‐compliant agroforestry by Meine vanNoordwijk, Sonya Dewi, Peter A. Minang, Rhett D. Harrison, Beria Leimona, Andre Ekadinata, Paul Burgers, Maja Slingerland, Marieke Sassen, Cathy Watson, Jeffrey Sayer

“…In targeting ‘deforestation‐free’ trade, it forces a complex social–ecological reality into an oversimplified forest–non‐forest representation. The forest definition used refers to tree cover but excludes farmer‐managed agroforestry (AF). …”
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Using multisource satellite products to estimate forest aboveground biomass in Oita prefecture: a novel approach with improved accuracy and computational efficiency by Hantao Li, Tomomichi Kato, Masato Hayashi, Jianhong Liu

“…Accurate estimation of forest aboveground biomass (AGB) using satellite information is crucial for quantitatively evaluating forest carbon stock for climate change mitigation. …”
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Selecting of global phenological field observations for validating coarse AVHRR-derived forest phenology products based on spatial heterogeneity and temporal consistency by Qi Shao, Chao Huang, Yuanjun Xiao, Li Liu, Weiwei Liu, Ran Huang, Chang Zhou, Wei Weng, Jingfeng Huang

“…Based on MSPT method, the capability of global forest phenological field observations to support coarse-scale remote sensing validation was evaluated. …”
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Diameter at Breast Height (DBH) Estimation and Stem Cross-Section Shape Analysis of Eucalyptus Trees Using LiDAR Data after Noisy Removal by Matheus Ferreira da Silva, Renato Cesar dos Santos, Antonio Maria Garcia Tommaselli, Mauricio Galo

“… LiDAR data offer new possibilities for obtaining geometric parameters of forest areas, such as diameter at breast height (DBH), basal area, height, volume, biomass, and carbon stock. …”
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Modelling effective soil depth at field scale from soil sensors and geomorphometric indices by Mauricio Castro Franco, Marisa Domenech, José Luis Costa, Virginia Carolina Aparicio

“…To do this, a Random Forest (RF) analysis was applied. RF was able to select those variables according to their predictive potential for ESD. …”
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A Cross-sectional Study on Stature Estimation from Arm Lengths among North Indian Population using Machine Learning by Arunima Dutta, Gyamar Anya

“…It also reveals a strong positive correlation between TAL and stature for both males (r-value=0.951) and females (r-value=0.975). The decision forest model achieved an accuracy of 0.951 and a Root Mean Square Error (RMSE) of 1.75. …”
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Intelligent Modeling; Single (Multi-layer perceptron) and Hybrid (Neuro-Fuzzy Network) Method in Forest Degradation (Case Study: Sari County) by somayeh mehrabadi

“…Then, the degraded and non-degraded forest areas were sampled in 200 locations. Seven factors identified as the most effective factors in forest degradation, including the distance from the features like city, river, village, sea, and road, elevation and slope were measured for the 200 locations. …”
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Space-Time Distribution Laws of Tunnel Excavation Damaged Zones (EDZs) in Deep Mines and EDZ Prediction Modeling by Random Forest Regression by Qiang Xie, Kang Peng

“…The root-mean-square error (RMSE) and mean absolute error (MAE) are used as reliable indicators to validate the model. …”
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Predicting Crown-width of Dominant Trees on Teak Plantation from Clonal Seed Orchards in Ngawi Forest Management Unit, East Java by Ronggo Sadono

“…The research was carried out in Ngawi Forest Management Unit on the good quality teak compartment having stands age from 6 to 15 years old. …”
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Mapping Subalpine Forest Aboveground Biomass in Qilian Mountain National Park Using UAV-LiDAR, GEDI, and Multisource Satellite Data by Yanyun Nian, Siwen Chen, Jie Chen, Minglu Che, Wenhui Zhang, Junejo Sikandar Ali, Hao Zhang, Xingbang Wang, Bingzhi Liao, Xufeng Wang

“…Third, by extrapolating biomass from discrete GEDI footprints and incorporating variables from Sentinel-1 and Landsat 8 OLI, a continuous, high-accuracy forest biomass map for the entire Qilian Mountain National Park was generated (<italic>R</italic>² &#x003D; 0.66, root-mean-square error &#x003D; 19.08 Mg&#x002F;ha, and relative root-mean-square error &#x003D; 11.04&#x0025;). …”
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Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images by Xintao Ling, Gui Zhang, Ying Zheng, Huashun Xiao, Yongke Yang, Fang Zhou, Xin Wu

“…Accurately simulating and predicting this dynamic process can provide a scientific basis for forest fire control and suppression decisions. In this study, five typical forest fires located in different regions of China were used as the study object. …”
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Improving Winter Wheat Yield Estimation Under Saline Stress by Integrating Sentinel-2 and Soil Salt Content Using Random Forest by Chuang Lu, Maowei Yang, Shiwei Dong, Yu Liu, Yinkun Li, Yuchun Pan

“…This study proposed a method integrating Sentinel-2 data and field-measured soil salt content (SC) using a random forest (RF) method to improve yield estimation of winter wheat in Kenli County, a typical saline area in China’s Yellow River Delta. …”
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Estimating the Compressive Strength of Cement-Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models by Hongxia Ma, Jiandong Liu, Jia Zhang, Jiandong Huang

“…The support vector machine (SVM), decision tree (DT), and random forest (RF) models were developed and compared. The beetle antennae search (BAS) algorithm was employed to tune the hyperparameters of the developed machine learning models. …”
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Water quality evaluation in Liaoning Province large reservoirs: a new method integrating random forest-TOPSIS and Monte Carlo simulation by Chong Zhang, Mo Chen, Yi Wang

“…The study further confirmed the model’s robustness by outlining its optimal assessment accuracy within a 5% error margin under normal distribution.…”
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Monitoring Sea Surface Temperature and Sea Surface Salinity Around the Maltese Islands Using Sentinel-2 Imagery and the Random Forest Algorithm by Gareth Craig Darmanin, Adam Gauci, Monica Giona Bucci, Alan Deidun

“…Subsequently, the numerical data generated by the random forest algorithm were validated with different error metrics and converted into visual representations to illustrate the sea surface salinity and sea surface temperature variations across the Maltese Islands. …”
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Evaluating organic carbon in living and dead trees using GLCM features and explainable machine learning: insights from Italian national forest by Mehdi Fasihi, Alex Falcon, Giorgio Alberti, Luca Cadez, Francesca Giannetti, Antonio Tomao, Giuseppe Serra

“…Finally, we assess model uncertainty using jackknife resampling and error bar analysis. The results indicate that CatBoost and Random Forest models deliver the highest performance for OC estimation. …”
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Towards monitoring biodiversity in Amazonian forests: how regular samples capture meso-scale altitudinal variation in 25 km2 plots. by Darren Norris, Marie-Josée Fortin, William E Magnusson

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Estimation of elbow flexion torque using equilibrium optimizer on feature selection of NMES MMG signals and hyperparameter tuning of random forest regression by Raphael Uwamahoro, Raphael Uwamahoro, Kenneth Sundaraj, Farah Shahnaz Feroz

“…These models often suffer from reduced estimation accuracies due to the presence of redundant and irrelevant information within the rapidly expanding complex biomedical datasets as well as suboptimal hyperparameters configurations.MethodsThis study utilized a random forest regression (RFR) model to estimate elbow flexion torque using mechanomyography (MMG) signals recorded during electrical stimulation of the biceps brachii (BB) muscle in 36 right-handed healthy subjects. …”
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Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm-Random Forest Regression by Muhammad Shahzad Nazir, Sami ud Din, Wahab Ali Shah, Majid Ali, Ali Yousaf Kharal, Ahmad N. Abdalla, Padmanaban Sanjeevikumar

“…The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. …”
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