Aboveground biomass density maps for post-hurricane Ian forest monitoring in Florida by Inacio T. Bueno, Carlos A. Silva, Caio Hamamura, Victoria M. Donovan, Ajay Sharma, Jiangxiao Qiu, Jinyi Xia, Kody M. Brock, Monique B. Schlickmann, Jeff W. Atkins, Denis R. Valle, Jason Vogel, Andres Susaeta, Mauro A. Karasinski, Carine Klauberg

“…Abstract Hurricane Ian caused aboveground biomass density (AGBD) losses across Florida’s forests in the United States, highlighting the need for accurate, large-scale monitoring tools. …”
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Estimating latent heat flux of subtropical forests using machine learning algorithms by Harekrushna Sahu, Pramit Kumar Deb Burman, Palingamoorthy Gnanamoorthy, Qinghai Song, Yiping Zhang, Huimin Wang, Yaoliang Chen, Shusen Wang

“…Nonetheless, validation against the in situ eddy covariance measurement reveals significant errors in MODIS LE estimation. Our study integrates ground‐measured, reanalysis and satellite data to predict LE by leveraging the advantage of the data‐driven method. …”
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A Comparative Analysis of Burned Area Datasets in Canadian Boreal Forest in 2000 by Laia Núñez-Casillas, José Rafael García Lázaro, José Andrés Moreno-Ruiz, Manuel Arbelo

“…Results showed that burned area data from MODIS provided accurate dates of burn but great omission error, partially caused by calibration problems. …”
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Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing by Thomas Leditznig, Hermann Klug

“…Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. …”
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How many plots are needed to estimate sapling density and stocking in temperate forests? by François Guillemette, Marie-Claude Lambert, Hugues Power, Patricia Raymond

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Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic by Efraín Duarte, Erick Zagal, Juan A. Barrera, Francis Dube, Fabio Casco, Alexander J. Hernández

“…In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0–15 cm) in forest lands of the Dominican Republic. …”
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Optimizing public health management with predictive analytics: leveraging the power of random forest by Hongman Wang, Yifan Song, Yifan Song, Hua Bi

“…This study employs a Random Forest Algorithm (RFA) to address this limitation and enhance the predictive modeling of community health outcomes. …”
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Applying the greenhouse gas inventory calculation approach to predict the forest carbon sink by Fredric Mosley, Jari Niemi, Sampo Soimakallio

“…Second, we use it to predict GHG balances in year leading up to 2035 at various roundwood and forest residue harvest rates. The tool can replicate forest GHG balances for forest land with an average annual error of 1.0 Mt CO2, representing 4% of the average annual forest carbon sink. …”
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Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites by Feng Bin, Shahab Hosseini, Jie Chen, Pijush Samui, Hadi Fattahi, Danial Jahed Armaghani

“…We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). …”
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Global Elevation Inversion for Multiband Spaceborne Lidar: Predevelopment of Forest Canopy Height by Haowei Zhang, Wei Gong, Hu He, Yue Ma, Weibiao Chen, Jiqiao Liu, Ge Han, Zhiyu Gao, Wanqi Zhong, Xin Ma

“…Compared with ICESat-2, GEDI and airborne scanning data in Finland, the geographic elevation results of MBFA showed average biases of –0.09, 0.1, and –0.95 m, with root mean square errors (RMSE) of 3.68, 4.51, and 7.70 m, respectively. …”
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Allometric Models for Estimating Tree Volume and Aboveground Biomass in Lowland Forests of Tanzania by Wilson Ancelm Mugasha, Ezekiel Edward Mwakalukwa, Emannuel Luoga, Rogers Ernest Malimbwi, Eliakimu Zahabu, Dos Santos Silayo, Gael Sola, Philippe Crete, Matieu Henry, Almas Kashindye

“…The findings show that site specific ht-dbh model appears to be suitable in estimating tree height since the tree allometry was found to differ significantly between studied forests. The developed general volume models yielded unbiased mean prediction error and hence can adequately be applied to estimate tree volume in dry and wet lowland forests in Tanzania. …”
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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

“…However, the three-baseline method still yields acceptable results, with a root-mean-square error ranging from 4.92 to 6.07 m and a correlation coefficient (<italic>R</italic>²) from 0.32 to 0.85, within hectare-scale forest height statistics. …”
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Large-scale inventory in natural forests with mobile LiDAR point clouds by Jinyuan Shao, Yi-Chun Lin, Cameron Wingren, Sang-Yeop Shin, William Fei, Joshua Carpenter, Ayman Habib, Songlin Fei

“…The proposed framework was able to localize and measure all 5838 stems at individual tree level in a 20 ha natural forest in less than 20 min using UAV LiDAR. DBH measurement results on trees’ DBH larger than 38.1 cm (15 in) showed that backpack LiDAR was able to achieve 1.82 cm of Root Mean Square Error (RMSE) and UAV LiDAR was able to achieve 3.13 cm of RMSE. …”
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Allometric equation for estimating aboveground biomass of Acacia-Commiphora forest, southern Ethiopia by Wondimagegn Amanuel, Chala Tadesse, Moges Molla, Desalegn Getinet, Zenebe Mekonnen

“…Data was transformed to logarithmic functions and multiple linear regression techniques were used to develop model to estimate aboveground biomass (AGB). The root mean square error (RMSE) was used for measuring model bias, precision, and accuracy. …”
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Sample intensity in ombrophilous open forest at Verde Para Sempre Extractive Reserve, Porto de Moz, PA by Fábio Miranda Leão, Luiz Fernandes Silva Dionisio, Loirena do Carmo Moura Sousa, Marlon Costa de Menezes, Marcelo Henrique Silva de Oliveira, Raphael Lobato Prado Neves

“…Was simulated a random sampling with sampling units of 1 ha and several sample intensities: 5%, 10%, 15% and 20% in an area of 200 ha that it was submitted to a forest census. It was evaluated the phytosociological parameters such as structure and diversity, and the estimated errors in the sampling intensities for the volume. …”
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GROWTH AND PRODUCTION OF Eucalyptus CLONES IN SILVOPASTORAL SYSTEM by Ricardo Fernandes Pena, Marcelo Dias Müller, Silvio Nolasco de Oliveira Neto, Domingos Sávio Campos Paciullo, Gabriel Soares Lopes Gomes, Adênio Louzeiro de Aguiar Júnior

“…The Spurr (1952) model showed a high quality of fit and adjusted coefficient of determination and low residual standard error. The I144 clone showed a larger diameter and higher productivity compared to GG100 clone. …”
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Regression analysis and artificial neural networks for predicting pine species volume in community forests by Wenceslao Santiago-García

“…Volume prediction models are fundamental in forestry, as they support forest inventories, sustainable forest management strategies, and comprehensive environmental planning. …”
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Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach by Hai-Bang Ly, Thuy-Anh Nguyen, Binh Thai Pham

“…The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. …”
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A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment by Jiwen Jia, Junhua Kang, Lin Chen, Xiang Gao, Borui Zhang, Guijun Yang

“…On the Mid-Air dataset, the Transformer-based DepthAnything demonstrates a 54.2% improvement in RMSE for the global error metric compared to the CNN-based Adabins. On the LOBDM dataset, the CNN-based MiDas has the depth edge completeness error of 93.361, while the Transformer-based Metric3D demonstrates the significantly lower error of only 5.494. …”
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General Approach for Forest Woody Debris Detection in Multi-Platform LiDAR Data by Renato César dos Santos, Sang-Yeop Shin, Raja Manish, Tian Zhou, Songlin Fei, Ayman Habib

“…Woody debris (WD) is an important element in forest ecosystems. It provides critical habitats for plants, animals, and insects. …”
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