Shallow-water benthic habitat classification of coral reefs based on satellite remote sensing is an important part of coral reef monitoring. Leveraging its potent capacity for feature learning, and generalization, deep learning emerges as a robust method
Hui Chen +7 more
doaj +1 more source
Bottom-grab samplers have long been the standard to describe nearshore marine habitats both qualitatively and quantitively. However, sediment samplers are designed to collect specific grain sizes and therefore have biases toward those sediments. Here, we
Sean Terrill +3 more
doaj +1 more source
Decision-making in land-use and conservation planning requires relevant and good quality information over wide areas, collected in a cost-efficient manner. This study focuses on the use of medium-resolution satellite imagery in landscape level studies of
Tuuli Toivonen, Miska Luoto
doaj
Subtidal kelp habitat classification at the Isles of Shoals: An integrative Random Forest approach using bathymetry and Landsat imagery. [PDF]
Tyler CN +3 more
europepmc +1 more source
Automated classification of natural habitats using ground-level imagery. [PDF]
Tourian M +9 more
europepmc +1 more source
Integrating InVEST and machine learning to model mangrove habitat degradation trend in in Northern Persian Gulf, Iran. [PDF]
Kazemi M +3 more
europepmc +1 more source
Machine Learning and Geospatial Modeling of Climate Change Impacts on Ethiopian Honeybees for Conservation and Resilient Agriculture. [PDF]
Tulu D +4 more
europepmc +1 more source
Water Level Regulation Regime Shifts Drive Divergent Foraging Habitat Use by Wintering Hooded Cranes (<i>Grus monacha</i>) in Shallow Gate-controlled Lakes of the Yangtze Floodplain. [PDF]
Fang Y, Zhong Y, Zhou L, Liang M.
europepmc +1 more source
Predicting habitat suitability of Korean Lindera as Tertiary relict plants under climate change scenarios. [PDF]
Seol J, Kwon HJ, Jung S, Cho YC.
europepmc +1 more source

