A 50-Year Perspective on Changes in a Pacific Northwest Breeding Forest Bird Community Reveals General Stability of Abundances by Nolan M. Clements, Fang-Yu Shen, W. Douglas Robinson

“…Abundances of breeding forest birds have apparently declined in North America during the last five decades, possibly influenced by anthropogenic effects. …”
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Optimizing hybrid models for forest leaf and canopy trait mapping from EnMAP hyperspectral data with limited field samples by Nizom Farmonov, Susanne Walden, Eric Martinée, Christian Lampei, Mona Schreiber, Lars Opgenoorth, Anjaharinony Andry Ny Aina Rakotomalala, Tobias Müller, Nina Farwig, Stefan Pinkert, Lucy Saueressig, Annabell Rosemarie Wagner, Robert R. Junker, Jochem Verrelst, Jörg Bendix

“…The application of optimized hybrid GPR models to the study area enabled area-wide landscape forest trait mapping with minimal computational effort.…”
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Synergistic approaches in forest fire risk mapping using fuzzy AHP and machine learning models in the Chure Tarai Madhesh Landscape (CTML) of Nepal by Milan Dhakal, Balram Bhatta, Prakash Lamichhane, Ashok Parajuli

“…Forest fires are recurrent natural hazards threatening ecosystems, biodiversity, and nearby communities. …”
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Neighborhood competition improves biomass estimation for Scots pine (Pinus sylvestris L.) but not Pyrenean oak (Quercus pyrenaica Willd.) in young mixed forest stands by Eric Cudjoe, Ricardo Ruiz-Peinado, Hans Pretzsch, Shamim Ahmed, Felipe Bravo

“…Neighborhood competition is a critical driver of individual tree growth, and aboveground biomass (AGB) accumulation, which together play key roles in forest dynamics and carbon storage. Therefore, accurate biomass estimation is essential for understanding ecosystem functioning and informing forest management strategies to mitigate climate change. …”
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Estimating characteristics of planted forests’ relative yield index using low pulse density LiDAR and satellite remote sensing by Asahi Hashimoto, Shodai Inokoshi, Chen-Wei Chiu, Yuichi Onda, Takashi Gomi, Yoshimi Uchiyama

“…The Ry estimation index (Ry_estimated) calculated using ΩST and ΩLAI was correlated with the Ry estimated from LiDAR data (correlation coefficient; r = 0.61–0.65), confirming its high accuracy (root mean square error; RMSE = 0.07–0.11). By applying this method to a 3,650 km2 area of planted Japanese cedar and cypress forests in the Kanto region of Japan, large-scale and detailed information on various forest characteristics was obtained. …”
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Identifikasi Dini Curah Hujan Berpotensi Banjir Menggunakan Algoritma Long Short-Term Memory (Lstm) Dan Isolation Forest by Ahmad Wijayanto, Aris Sugiharto, Rukun Santoso

“…Prediksi LSTM dievaluasi menggunakan Mean Square Error (terbaik 19,11) dan Root Mean Square Error (terbaik 4,37) sebelum dilakukan forecasting jangka panjang. …”
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A framework for upscaling aboveground biomass from an individual tree to landscape level and qualifying the multiscale spatial uncertainties for secondary forests by Ye Ma, Jungho Im, Zhen Zhen, Yinghui Zhao

“…Secondary forests, a typical forest type in the sub-frigid zone of Northeast China, have significant potential for carbon sequestration. …”
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Random Forest Regression May Become the Optimal Regression Model for Osteoarthritis of the Knee in Elderly, in the Context of Embodied Cognition and Psychosomatic Medicine by Ma G, Chen J, Li J, Shi H, Chen Y

“…Five regression techniques—non-negative linear regression, stochastic gradient descent (SGD), AdaBoost, Random Forest, and Gradient Boosting Decision Trees (GBDT)—were evaluated using R², mean squared error (MSE), and mean absolute error (MAE). …”
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Back-Analysis of Parameters of Jointed Surrounding Rock of Metro Station Based on Random Forest Algorithm Optimized by Cuckoo Search Algorithm by Xinping Guo, Annan Jiang, Xiang Liu

“…This study combines the ubiquitous-joint model, random forest algorithm (RF), and cuckoo search algorithm (CS) to construct the parameters identification method of a jointed rock mass. …”
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MF‐RF: A detection approach based on multi‐features and random forest algorithm for improved collusive interest flooding attack by Meng Yue, Silin Peng, Wenzhi Feng

“…Test results show that the proposed detection approach outperforms other existing approaches with a detection rate of 98.1%, error rate of 1.9%, and false positive rate of 1.5%.…”
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Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S by Joseph St. Peter, Jason Drake, Paul Medley, Eben Broadbent, Gang Chen, Victor Ibeanusi

“…Quantifying basal area in terms of diameter classes is important for informing forest management decisions. It is commonly derived from stand diameter distributions using field measurements, LiDAR, and a distribution function. …”
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Machine learning using random forest to model heavy metals removal efficiency using a zeolite-embedded sheet in water by N.D. Takarina, N. Matsue, E. Johan, A. Adiwibowo, M.F.N.K. Rahmawati, S.A. Pramudyawardhani, T. Wukirsari

“…The machine learning analysis to model the heavy metal removal efficiency using zeolite-embedded sheet was performed using the random forest method. The random forest models were then validated using the root mean square error, mean square of residuals, percentage variable explained and graphs depicting out-of-bag error of a random forest.FINDINGS: The results show the heavy metal removal efficiency was 5.51-95.6 percent, 42.71-98.92 percent and 13.39-95.97 percent for copper, lead and zinc, respectively. …”
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Evaluation of Parametric and Non-Parametric Models in Estimating Canopy Cover Density of Zagros Forests Using Remote Sensing and Machine Learning by Sajad Alimahmoodi Sarab, MohamadHadi Moayery, MOhammad Mirzavand, Shaban Shataee Jouibary, Alireza Rashki

“…Research Topic: Evaluation of Parametric and Non-Parametric Models in Estimating Canopy Cover Density of Zagros Forests Using Remote Sensing and Machine LearningObjective: This study aims to compare parametric and non-parametric methods for estimating the percentage of forest canopy cover in a section of the Zagros ecosystem.Method: In order to achieve the research objective, field sampling was conducted to determine the percentage of canopy cover, and high-resolution satellite imagery was utilized. …”
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High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau by Zhenghao Li, Qiangqiang Yuan, Xin Su

“…Overall, on the basis of 10-fold cross-validation, the modified Deep Forest model performed the best, with estimate accuracy of 0.834 and 0.038 m3·m−3 in terms of coefficient of determination ([Formula: see text]) and unbiased Root Mean Square Error (ubRMSE), respectively. …”
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Improving Tropical Forest Canopy Height Mapping by Fusion of Sentinel-1/2 and Bias-Corrected ICESat-2–GEDI Data by Aobo Liu, Yating Chen, Xiao Cheng

“…Accurately estimating the forest canopy height is essential for quantifying forest biomass and carbon storage. …”
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Study of Changing Land Use Land Cover from Forests to Cropland on Rainfall: Case Study of Alabama’s Black Belt Region by Salem Ibrahim, Gamal El Afandi, Amira Moustafa, Muhammad Irfan

“…The control run demonstrated a Root Mean Square Error (RMSE) of 1.64, indicating accurate rainfall predictions. …”
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Estimation of microbial biomass based on water-extractable organic matter from air-dried soils from Japanese forests and pasture by Hirohiko Nagano, Yuki Kanda, Yuri Suzuki, Syuntaro Hiradate, Jun Koarashi, Mariko Atarashi-Andoh, Zhibin Guo

“…Moreover, the relationships with soil physiochemical properties were similar between WEOC and microbial biomass C (R 2 = 1.00, root mean square error (RMSE) = 0.04), whereas those were less similar between WETN and microbial biomass N (R 2 = 0.73, RMSE = 0.28). …”
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Integrating groundwater pumping data with regression-enhanced random forest models to improve groundwater monitoring and management in a coastal region by Jamie Kim, Yueling Ma, Yueling Ma, Reed M. Maxwell, Reed M. Maxwell, Reed M. Maxwell

“…This work studies the implementation of a regression-enhanced random forest (RERF) model to predict WTD anomalies with pumping as a major input for New Jersey, a coastal state in the United States. …”
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A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems by Tsikai S. Chinembiri, Onisimo Mutanga, Timothy Dube

“…Using a Bayesian hierarchical inferential framework, we employed a multi-source data approach (i.e. remote sensing derived anthropogenic, climatic and topographic set of variables) to model Carbon (C) stock in a managed plantation forest ecosystem in Zimbabwe’s Eastern Highlands. …”
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Forest classification and carbon stock estimation with integration of airborne LiDAR and satellite Gaofen-6 data in a subtropical region by Ruoqi Wang, Dengsheng Lu, Guiying Li, Yisa Li, Wenjing Liu

“…Another important factor influencing FCS estimation accuracy is the quality of forest classification. To address these limitations, this study developed a multi-scale decision-level fusion framework combined with a ResNet deep learning algorithm for fine forest classification, and proposed an HBA-based FCS estimation model by incorporating different stratification schemes –single-stratum (based on either forest type or canopy height distribution (CHD)) and double-strata (integrating both forest type and CHD) based on airborne LiDAR and satellite GaoFen-6 data in a subtropical region, the Baisha State-owned Forest Farm of Fujian Province, China. …”
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