Results 201 to 210 of about 245,568 (297)

DuckNet: an open‐source deep learning tool for waterfowl species identification in UAV imagery

Remote Sensing in Ecology and Conservation, EarlyView.
Using drones with thermal‐RGB sensors and a deep learning model (RetinaNet with ResNet‐50), we surveyed non‐breeding waterfowl across restored wetlands in the Mississippi Alluvial Valley. Our model, DuckNet, achieved high accuracy and offers an open‐source, customizable tool for automated waterfowl detection to support conservation monitoring ...
Zack Loken, Kevin M. Ringelman, Anne Mini, J. Dale James, Mike Mitchell   +4 more
wiley   +1 more source

High-resolution dataset (2017-2023) of physical-geographical predictors for machine learning modelling of fluvial flooding: the Gidra river case, Slovakia. [PDF]

Data Brief
Vojtek M, Benko Ľ, Kapusta J, Držík D, Munk M, Munková D, Drlík M, Vojteková J.   +7 more
europepmc   +1 more source

Investigating boreal forest successional stages in Alaska and Northwest Canada using UAV‐LiDAR and RGB and a community detection network

Remote Sensing in Ecology and Conservation, EarlyView.
This study develops a novel application of UAV‐LiDAR and Red Green Blue (RGB) data and network analysis to enhance our understanding of boreal forest succession. The results indicate that tree height and spectral variables are the most influential predictors of plant functional type in random forest algorithms, and high overall accuracies were attained.
Léa Enguehard, Birgit Heim, Ulrike Herzschuh, Viktor Dinkel, Glenn Juday, Santosh Panda, Nicola Falco, Jacob Schladebach, Jakob Broers, Stefan Kruse   +9 more
wiley   +1 more source

Localization of Radio Signal Sources for Situational Awareness Enhancement. [PDF]

Sensors (Basel)
Malon K, Skokowski P, Pavlin G.
europepmc   +1 more source

UAVs unveil the role of small scale vegetation structure on wader nest survival

Remote Sensing in Ecology and Conservation, EarlyView.
In this study, we combine high‐resolution vegetation structural metrics derived from unmanned aerial vehicle (UAV) imagery with on‐field wader nest survival monitoring. We show that the immediate vegetation height and heterogeneity within a 2‐meter buffer surrounding the clutch of the recorded ground‐nesting wader species positively influenced its ...
Miguel Silva‐Monteiro, Miguel Villoslada, Thaisa Bergamo, Triin Kaasiku, Camilo Carneiro, David Kleijn   +5 more
wiley   +1 more source

Research on LiDAR-Assisted Optimization Algorithm for Terrain-Aided Navigation of eVTOL. [PDF]

Sensors (Basel)
Zhang G, Zhou J, Duan Z, Zhao W.
europepmc   +1 more source

Planetary Surface Image Generation for Testing Future Space Missions with PANGU [PDF]

, 2019
Dunstan, Martin, Martin, Iain, Sanchez Gestido, Manuel   +2 more
core   +1 more source

Ground‐truthing of satellite imagery to assess seabird colony size: A test using Adélie penguins

Remote Sensing in Ecology and Conservation, EarlyView.
Adélie penguin colony size can be estimated from space using very high‐resolution (VHR; 0.3–0.6 m resolution) satellite imagery due to the contrast between their guano stain and the surrounding terrain. Our study assessed the utility of VHR imagery for making indirect assessments of changes in colony size.
Alexandra J. Strang, Dean P. Anderson, Esme Robinson, Grant Ballard, Annie E. Schmidt, David G. Ainley, Kerry Barton, Fiona Shanhun, Elissa Z. Cameron, Michelle A. LaRue   +9 more
wiley   +1 more source

Protocol to discover terrain-precipitation relationships with interpretable artificial intelligence. [PDF]

STAR Protoc
Xu H, Chen Y, Zeng Z, Li N, Li J, Zhang D.   +5 more
europepmc   +1 more source

Wall‐to‐wall Amazon forest height mapping with Planet NICFI, Aerial LiDAR, and a U‐Net regression model

Remote Sensing in Ecology and Conservation, EarlyView.
Tree canopy height is a key indicator of forest biomass and structure, yet accurate mapping across the Amazon remains challenging. Here, we generated a canopy height map of the Amazon forest at ~4.8 m resolution using Planet NICFI imagery and a deep learning U‐Net model trained with airborne LiDAR data.
Fabien H. Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi C. M. Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, Jean P. H. B. Ometto, Lorena Alves, Cynthia Creze, Stephanie P. George‐Chacon, Shuang Li, Zhihua Liu, Adugna Mullissa, Yan Yang, Erone G. Santos, Sarah R. Worden, Martin Brandt, Philippe Ciais, Stephen C. Hagen, Sassan Saatchi   +21 more
wiley   +1 more source