Forest aboveground carbon storage estimation and uncertainty analysis by coupled multi-source remote sensing data in Liaoning Province by Hancong Fu, Hengqian Zhao, Ge Liu, Yujiao Zhang, Xiadan Huangfu, Jinbao Jiang

“…Accurate mapping of large-scale forest aboveground carbon (AGC) stock is essential for understanding the role of forests in the global carbon cycle. …”
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Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest by Tássia Fraga Belloli, Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha, Carina Cristiane Korb

“…This study examined how different sample data treatments and plot sizes impact a random forest model’s performance based on RS for AGB and Corg prediction. …”
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Feasibility of automating the determination of changes in forest areas using satellite images (Case Study: Central Alborz protected area) by Amir Satari Rad, Behzad Rayegani, Ali Jahani, Hamid Goshtasb Meigooni

“…The 16-day NDVI images were converted to monthly images with the maximum value algorithm and the PCA algorithm with 25 components was applied to them to eliminate errors and noise. Using the Google Earth system, 5 random polygons were selected on virgin forest areas, in order to estimate the value of the pixels and finally determine the thresholds. …”
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Calibrated Adaptive Teacher for Domain-Adaptive Intelligent Fault Diagnosis by Florent Forest, Olga Fink

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Estimation of elemental composition of agricultural soils from West Azerbaijan, Iran, using mid-infrared spectral models by Maryam Ghebleh Goydaragh, Ali Asghar Jafarzadeh, Farzin Shahbazi, Shahin Oustan, Ruhollah Taghizadeh-Mehrjardi, Marcos Lado

“…Random Forest provided the important spectral bands related to the content of each element. …”
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Static evaluation of a virtual fence collar: precision and accuracy relative to a conventional GNSS collar across habitats by Edward W. Bork, Sydney G. Lopes, Alexandra J. Harland, Matthew J. Francis, Carolyn J. Fitzsimmons, Cameron N. Carlyle, Francisco J. Novais

“…The precision and accuracy of both collars also varied over time, including between days of observation and even within days, and were reduced in forested habitats relative to shrublands and grasslands where obstructive canopy cover was greater. …”
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The Least Limiting Water Range to Estimate Soil Water Content Using Random Forest Integrated with GIS and Geostatistical Approaches by Orhan Dengiz, Pelin Alaboz

“…As a result of the study, it was determined that LLWR can be obtained with a low error rate with the RF algorithm, and distribution maps can be created with lower error in surface soils.…”
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Research and validation of forest carbon sequestration measurement model based on biomass method-A case study of Guizhou Province. by Zhang Min, Wu Yang, Qiu Yan, Yan Jun, Li Chunling, Ran Wen Rui

“…Compared with the existing forestry carbon sequestration project evaluation, the results showed that the average relative error of the model was 6. 09%, and the absolute error range was 0. 348-4. 262/hm2, and the model effect was good. …”
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Random Forest-Based Retrieval of XCO₂ Concentration from Satellite-Borne Shortwave Infrared Hyperspectral by Wenhao Zhang, Zhengyong Wang, Tong Li, Bo Li, Yao Li, Zhihua Han

“…This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. …”
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Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine by Ibrahim Shomope MS, Kelly M. Percival BS, Nabil M. Abdel Jabbar PhD, Ghaleb A. Husseini PhD

“…RF and SVM models were trained and evaluated using mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R²), and the a20 index as performance metrics. …”
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A random-forest-derived 35-year snow phenology record reveals climate trends in the Yukon River Basin by C. G. Pan, K. Lasko, S. P. Griffin, J. S. Kimball, J. Du, T. G. Meehan, P. B. Kirchner

“…Model evaluation against station observations yielded a mean absolute error (MAE) of 10.5 d and a root mean square error (RMSE) of 13.7 d for snowmelt onset. …”
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An efficient method for predicting temperature field of PC beams with CSWs using thermocouple numerical analysis and random forest algorithm by Haiping Zhang, Hao Long, Fanghuai Chen, Yuan Luo, Xinhui Xiao, Yang Deng, Naiwei Lu, Yang Liu

“…Comparison of the coefficient of determination (R2) and mean squared error (MSE) for the three models indicated that the random forest model outperformed the other two in both computational efficiency and prediction accuracy. …”
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SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data by Yuhan Liu, Yuanyuan Zha, Gulin Ran, Yonggen Zhang, Liangsheng Shi

“…In this study, we present a novel machine learning (ML) based framework for generating a continuously updated, multilayer global SM dataset: SMRFR (Soil Moisture via Random Forest Regression). Leveraging publicly available reanalysis and remote sensing data, SMRFR provides daily SM estimates at five soil layers (0–5, 5–10, 10–30, 30–50 and 50–100 cm) with a spatial resolution of 9 km, covering the period from 2000 to 2023. …”
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Improving airborne laser scanning-based species-specific forest volume estimation using sentinel-2 time series by Katri Mäkinen, Lauri Korhonen, Matti Maltamo

“…Species-specific timber volume estimates are required to support forest planning and conservation. We evaluated whether additional predictors from a Sentinel-2 time series can improve airborne laser scanning (ALS)-based estimation of species-specific timber volumes. …”
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Classification of forest development stages from national low-density lidar datasets: a comparison of machine learning methods by R. Valbuena, M. Maltamo, P. Packalen

“…The area-based method has become a widespread approach in airborne laser scanning (ALS), being mainly employed for the estimation of continuous variables describing forest attributes: biomass, volume, density, etc. …”
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Collaborative Optimization on Both Weight and Fatigue Life of Fifth Wheel Based on Hybrid Random Forest with Improved BP Algorithm by Huan Xue, Chang Guo, Xiaojian Peng, Saiqing Xu, Kaixian Li, Jianwen Li

“…We proposed a prediction model combining a random forest algorithm with an optimized back propagation (BP) neural network. …”
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Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forest by Loventa Anyango Otieno, Terry Amolo Otieno, Brian Rotich, Katharina Löhr, Harison Kiplagat Kipkulei

“…The Random Forest (RF) regression model was employed to predict PVR and identify factors that significantly contribute to PVR in the Mount Kenya Forest ecosystem. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…The results show that: first, spatial structural parameters C and U have a certain contribution to the forest growth, and C and U can explain 21.5&#x0025;, 15.2&#x0025;, and 9.3&#x0025; of the variance in DBH, H, and CW growth models, respectively; second, CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance; third, based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018 to 2022 and found that DBH and H&#x0027;s fitting performance in measured and predicted data was highly consistent with <italic>R</italic>² and root-mean-square error (RMSE) of 86.8&#x0025;, 2.06 cm in DBH and 79.2&#x0025;, 1.11 m in H, but CW&#x0027;s <italic>R</italic>² and RMSE of 72.2&#x0025;, 0.65 m caused crowding (C) inconsistency.…”
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Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…Effective detection and classification of forest fire imagery are critical for timely and efficient wildfire management. …”
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A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer by Hisham Allahem, Sameh Abd El-Ghany, A. A. Abd El-Aziz, Bader Aldughayfiq, Menwa Alshammeri, Malak Alamri

“…However, manual examination of brain MRI scans is prone to errors and heavily depends on radiologists’ experience and fatigue levels. …”
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