Field assessments on the impact of CO₂ concentration fluctuations along with complex-terrain flows on the estimation of the net ecosystem exchange of temperate forests by D. Teng, D. Teng, J. Zhu, J. Zhu, J. Zhu, T. Gao, T. Gao, T. Gao, F. Yu, F. Yu, Y. Zhu, Y. Zhu, X. Zhou, X. Zhou, B. Yang

“…The CO<span class="inline-formula">₂</span> storage dominates the NEE equation under a stable atmospheric stratification when the equation is used for forest ecosystems over complex terrains. However, estimating <span class="inline-formula"><i>F</i>_s</span> remains challenging due to the frequent gusts and random fluctuations in boundary-layer flows that lead to tremendous difficulties in capturing the true trend of CO<span class="inline-formula">₂</span> changes for use in storage estimation from eddy covariance along with atmospheric profile techniques. …”
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Daily-scale dataset of highly dynamic water of Poyang Lake by Yuqing Wang, Shuai Wang, Tingsong Gong, Haifeng Tian, Yaochen Qin, Yijie Ma, Xueyue Liang, Jie Pei, Li Wang

“…Third, we introduced water level, cumulative outflow from the Three Gorges Dam, and precipitation data to build a predictive model for random forests with reliable accuracy. Finally, we realized the spatial inversion of the predicted area and constructed a daily-scale water dataset of Poyang Lake (2016–2021). …”
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Leveraging Phenology to Assess Seasonal Variations of Plant Communities for Mapping Dynamic Ecosystems by Thilina D. Surasinghe, Kunwar K. Singh, Lindsey S. Smart

“…Using a temperate wetland complex as a case study, we leveraged NDVI time series from Sentinel imagery to refine a wetland classification scheme by identifying periods of maximum plant community distinction. …”
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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

“…Past studies have relied mainly on traditional machine learning models, but these models do not perform well for complex spatial patterns. To address this gap, this study uses two meta-heuristic algorithms (Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)) to provide an optimized Random Forest (RF) model with better predictive ability. …”
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Revealing causes of a surprising correlation: snow water equivalent and spatial statistics from Calibrated Enhanced-Resolution Brightness Temperatures (CETB) using interpretable ma... by Mahboubeh Boueshagh, Joan M. Ramage, Mary J. Brodzik, David G. Long, Molly Hardman, Hans-Peter Marshall

“…In snow-dominated regions with less surface heterogeneity, such as Monument Creek, SSDs can improve SWE estimation by capturing snow spatial variability. In complex environments like Jones Pass, SSDs aid SWE retrievals by accounting for factors such as soil moisture that impact snowpack dynamics. …”
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Respiratory Strength Training Versus Respiratory Relaxation Training in the Rehabilitation of Physical Impairment, Function, and Return to Participation After Stroke: Protocol for... by Dorian K Rose, Gina Brunetti, Kathryn Cavka, J Brooke Hoisington, Hannah Snyder, Wei Xue, Barbara K Smith

“…Longer-term societal participation is a complex domain that may be influenced by other factors beyond physical function. …”
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Performance Assessment of Maximum Likelihood, Random Forest and Support Vector Machines Classifier for Urban Land Use Classification: A Case Study of Dhaka Metropolitan City, Bangl... by Ha-mim Ebne Alam, Md. Nizam Uddin, Kazi Tawkir Ahmed, Md. Jahidul Hasan, Md. Yeasir Arafat, Md. Enamul Hoque

“…The classification was conducted by using three methods where the Support vector machines classification (SVMC) produced the best accuracy results of 83.2% overall accuracy and overall kappa coefficient value of 0.74 than both random forest classification (RFC) and maximum likelihood classification (MLC) methods with 86.34% and 83% spatial similarity rate respectively. …”
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Evaluating aufeis detection methods using Landsat imagery: Comparative assessment and recommendations by Julian Dann, Simon Zwieback, Paul Leonard, W. Robert Bolton

“…Currently, changes to the spatial and temporal distribution of large aufeis fields are predominantly monitored using optical satellite imagery. …”
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STGATN: a wind speed forecasting method based on geospatial dependency by Xingtong Ge, Ling Peng, Yi Yang, Cang Qin, Jiahui Chen, Hongze Liu, Zhaobo Li

“…Accurate wind speed forecasting is crucial for power systems, but wind speed as a spatially continuous field presents high randomness, fluctuation, and spatial heterogeneity under complex geographical environments, leading to challenges for predictive modeling. …”
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Deciphering Car Crash Dynamics in Greater Melbourne: a Multi-Model Machine Learning and Geospatial Analysis by Christopher JOHNSON, ZHOU Heng, Richard TAY, SUN Qian(Chayn)

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Trends and Drivers of Water Temperature Extremes in Mountain Rivers by A. vanHamel, M. I. Brunner

“…Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.…”
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Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data by Arifou Kombate, Guy Armel Fotso Kamga, Kalifa Goïta

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A genetic algorithm based method of optimizing dispersion matrix for RDSM system by Peng ZHANG, Xiaoping JIN, Dongxiao CHEN

“…Rectangular differential spatial modulation (RDSM) is a multi-antenna incoherent modulation technology with high spectral efficiency, low power consumption, and zero-overhead for channel estimation.RDSM is especially suitable for 6G communication systems, such as fast-moving Internet of vehicles, Internet of things, cellular networks, etc.However, the construction of the sparse rectangular unitary space-time dispersion matrix (DM) at transmitter is a problem.The proposed Genetic algorithm (GA) will result in less computational complexity than the currently used random research.The fitness of GA was calculated by the rank and determinant criterion (RDC) method to avoid discussions in differential system.Due to characteristics of constellation symbols of RDSM, the proposed method reduced the computational complexity during each single iteration in GA.The simulation results show that the optimized DMS can significantly improve the bit error rate (BER) performance of the RDSM system.Compared with random search, the low-complexity GA effectively improves the DMS optimization efficiency of RDSM.The computational complexity required for optimizing DMS is about 0.1% of random search optimization method.…”
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A Delayed Response in the Area‐Concentrated Search Can Improve Foraging Success by Thotsapol Chaianunporn, Thomas Hovestadt

“…The dACS rule does not rely on complex spatial memory but only on memorizing how long ago resources were found or not. …”
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Ecological and climatic transferability of airborne lidar-driven aboveground biomass models in Piñon-Juniper woodlands by Jessie F. Eastburn, Michael J. Campbell, Philip E. Dennison, William Rl Anderegg, Kevin J. Barrett, Patrick A. Fekety, Samuel W. Flake, David W. Huffman, Steven A. Kannenberg, Kelly L. Kerr, Andrew J. Sánchez Meador, Jody C. Vogeler

“…Piñon-juniper (PJ) woodlands are an expansive and dynamic dryland ecosystem in the US that encompass a wide range of spatial, temporal, and ecological diversity. The dynamism of PJ woodland extent and abundance over space and time is attributable to variability in species compositions, stand structures, climatic conditions, and disturbance patterns. …”
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Spatio-temporal distribution of soil microbial communities and nutrient availability around a municipal solid waste landfill by Mingye Zhan, Yanqiu Sun, Huanjie Lan, Tao Zhou, Youcai Zhao, Li Yang

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Improving the accuracy of remotely sensed TSS and turbidity using quality enhanced water reflectance by a statistical resampling technique by Kunwar Abhishek Singh, Dongryeol Ryu, Meenakshi Arora, Manoj Kumar Tiwari, Bhabagrahi Sahoo

“…Out of 80 cloud-contaminated images, we were able to use 70 images with 40%–50% clouds/cloud shadows for TSS and turbidity retrievals after applying the GMM-based masking and spatial aggregation. The resampled spectral data and in-situ TSS and turbidity measurements were used to train four ML models: Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). …”
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Evaluation of landslide susceptibility of mountain highway based on RF and SVM models by Qingfeng He, Shoulong Wu, Xia Zhao, Zhankun Hui, Zhengang Wang, Paraskevas Tsangaratos, Ioanna Ilia, Wei Chen, Yixin Chen, Yiheng Hao

“…Abstract Geological complexities along mountain highways frequently trigger landslides, posing significant threats to transportation safety and infrastructure. …”
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Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms i... by Yue Chen, Weili Kou, Wenna Miao, Xiong Yin, Jiayue Gao, Weiyu Zhuang

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The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale by Adrian Baggström, Robert Goodsell, Laura van Dijk, Ela Iwaszkiewicz-Eggebrecht, Andreia Miraldo, Ayco J.M. Tack, Tobias Andermann

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