Remote Sensing in Ecology and Conservation

Papers
(The H4-Index of Remote Sensing in Ecology and Conservation is 19. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-07-01 to 2024-07-01.)
ArticleCitations
Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes74
Real‐time insect tracking and monitoring with computer vision and deep learning51
Extending deep learning approaches for forest disturbance segmentation on very high‐resolution satellite images41
Monitoring spring phenology of individual tree crowns using drone‐acquired NDVI data39
Automated detection of Hainan gibbon calls for passive acoustic monitoring38
Machine learning to detect marine animals in UAV imagery: effect of morphology, spacing, behaviour and habitat36
Ecoacoustics: acoustic sensing for biodiversity monitoring at scale34
Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species‐rich grasslands34
Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery33
Discovery of new colonies by Sentinel2 reveals good and bad news for emperor penguins30
21 000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning29
Regional matters: On the usefulness of regional land‐cover datasets in times of global change27
Dual visible‐thermal camera approach facilitates drone surveys of colonial marshbirds24
Integration of close‐range underwater photogrammetry with inspection and mesh processing software: a novel approach for quantifying ecological dynamics of temperate biogenic reefs23
HydroMoth: Testing a prototype low‐cost acoustic recorder for aquatic environments22
Automatic flower detection and phenology monitoring using time‐lapse cameras and deep learning21
Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation20
Multispecies detection and identification of African mammals in aerial imagery using convolutional neural networks19
Monitoring ash dieback (Hymenoscyphus fraxineus) in British forests using hyperspectral remote sensing19
Regional‐scale forest restoration effects on ecosystem resiliency to drought: a synthesis of vegetation and moisture trends on Google Earth Engine19
Using drones to reduce human disturbance while monitoring breeding status of an endangered raptor19
Random encounter model is a reliable method for estimating population density of multiple species using camera traps19
0.023305177688599