Results 201 to 210 of about 207,214 (277)

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

open access: yesRemote 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   +9 more
wiley   +1 more source

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

open access: yesRemote 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   +5 more
wiley   +1 more source

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

open access: yesRemote 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   +9 more
wiley   +1 more source

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

open access: yesRemote 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   +21 more
wiley   +1 more source

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