Results 221 to 230 of about 1,631,931 (302)

Scale dependence in remotely sensed biodiversity: Leveraging continental‐scale imaging spectroscopy from the National Ecological Observatory Network

Remote Sensing in Ecology and Conservation, EarlyView.
Imaging spectroscopy enables large‐scale biodiversity assessment, yet spectral diversity metrics are scale dependent. Across 15 NEON ecosystems, we find that spectral richness increases sub‐linearly from 3600 m2 to 4 km2, whereas spectral divergence shows weak or inconsistent scaling with area, underscoring the importance of scale‐aware interpretation ...
Meghan T. Hayden, Matthew W. Rossi, Laura E. Dee, Kyle Kovach, Cibele H. Amaral, Jacob Nesslage, Madeline Slimp, Rachel S. Meyer, E. Natasha Stavros   +8 more
wiley   +1 more source

Uncovering the spatio-temporal patterns and drivers of 30 years of greening in South UK landscapes. [PDF]

Landsc Ecol
Castro G, Koricheva J, Moat J, Wilkes P, Wilkinson T, Garcia C.   +5 more
europepmc   +1 more source

Time‐series digital camera photos combined with machine learning algorithms can realize accurate observation of flowering phenology

Remote Sensing in Ecology and Conservation, EarlyView.
Intelligent approaches are required to extract valuable phenological information from time‐series digital camera photos. In this research, we employed YOLO‐based object detection and semantic segmentation models to identify flowers and flower pixels, acquire flower count and flower cover data, and extract phenophases such as first, peak, and end ...
Chuangye Song, Yuan Jia, Lin Zhang, Dongxiu Wu   +3 more
wiley   +1 more source

PaSTO-GNN: prompt-aware spatio-temporal graph neural networks for automatic essay scoring. [PDF]

Front Artif Intell
Alhothali A.
europepmc   +1 more source

Scalable low‐cost seabed landers: the missing link for sustained, integrated, long‐term observations in dynamic shallow seas

Remote Sensing in Ecology and Conservation, EarlyView.
Seabed landers, when equipped with a range of biotic and abiotic sensors, offer a non‐invasive and cost‐effective solution for ecosystem‐scale monitoring of Essential Biodiversity Variables (EBVs) and Essential Ocean Variables (EOVs) in dynamic shallow seas.
Arienne Calonge, Roeland Develter, Carlota Muñiz, Clea Parcerisas, Jan Reubens, Wieter Boone, Klaas Deneudt, Elisabeth Debusschere   +7 more
wiley   +1 more source

Correction: ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation. [PDF]

Front Neurorobot
Liu X, Xie F, Xu K, Liu Z, Wei S, Li G, Sun X.   +6 more
europepmc   +1 more source

Ground‐based robotic remote sensing for standardized biodiversity monitoring in coastal habitats

Remote Sensing in Ecology and Conservation, EarlyView.
Illustrated workflow of the proposed citizen‐to‐robot monitoring pipeline: (i) expert‐validated citizen observations are translated into AI models, (ii) deployed on a ground‐based robotic platform for proximal sensing of coastal dune habitats, (iii) enabling standardized detection of ecological targets (e.g., Pancratium maritimum & Brithys crini), and (
Giovanni Di Lorenzo, Simonetta Bagella, Micaela del Valle Rasino, Maria Laura Carranza, Manolo Garabini, Franco Angelini   +5 more
wiley   +1 more source

Spatio-temporal modelling of COVID-19 infection and associated risk factors in Dakar, Senegal. [PDF]

PLOS Glob Public Health
Gadiaga AN, Tine MW, Diene AN, Linard C, Speybroeck N, Yankey O, Chaudhuri S, Nnanatu CC, Cleary E, Lai S, Lazar AN, Tatem AJ.   +11 more
europepmc   +1 more source

frontiermetrics: An R Package for Characterizing Deforestation Frontiers

Remote Sensing in Ecology and Conservation, EarlyView.
Deforestation frontiers are major drivers of biodiversity loss, carbon emissions, and ecosystem degradation, yet our understanding of how deforestation progresses in these frontiers is limited. We present frontiermetrics, an R package to quantify a range of metrics describing the speed, severity, and spatio‐temporal patterns of forest loss. The package
Pablo Yair Huais, Ana Buchadas, Eduardo Lacerda, Javier Nori, Matthias Baumann, Tobias Kuemmerle   +5 more
wiley   +1 more source

Federated spatio-temporal graph neural network for privacy-preserving vehicle trajectory prediction in autonomous driving. [PDF]

Front Artif Intell
Joshi A, P A, Prajapati V, Anbalagan S, G S.   +4 more
europepmc   +1 more source