Remote Sensing in Ecology & Conservation
@RSECJournal
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RSEC is open access peer-reviewed journal for novel, multidisciplinary research at the interface between remote sensing science & ecology and conservation
Joined June 2014
Find us over on Bluesky 🦋 https://t.co/kT3haGmPja
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What if sensor equipped drones could autonomously hop around landscapes to survey biodiversity? Would data be reliable? Could we adapt survey design in real-time? This and more in our latest @RSECJournal publication led by @PeggyBevan2
https://t.co/KSQ83GdtCN
zslpublications.onlinelibrary.wiley.com
Deploying and maintaining sensors is often a major bottleneck in collecting rapid biodiversity data. We tested whether autonomous hopping drones equipped with acoustic recorders could collect...
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🚨New paper published in @RSECJournal 👉 https://t.co/kNnexBqLJr 📷First study assessing #dugong body condition across the Indo-Pacific using #drone footage Dugongs from larger populations were 'fatter' Led by @marbec @ird_fr in partnership with @SelamatIJL
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Automated extraction of right whale morphometric data from drone aerial photographs. Bagchi et al. Read the full paper here: https://t.co/OSbaafauSE
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New paper! Remotely sensing coral bleaching in the Red Sea. Elamurugu Alias Gokul et al. Read the paper here: https://t.co/sjf86XojGJ
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New paper! Remotely sensing coral bleaching in the Red Sea. Elamurugu Alias Gokul et al. Read the paper here: https://t.co/sjf86XojGJ
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🐆 A new analysis, published in Remote Sensing in Ecology and Conservation, distinguished individual leopards at 93% accuracy, by analysing the unique temporal patterns in their roars. 🔗 Read more: https://t.co/fzgQlgCAhb
#NatureAfrica
@RSECJournal @J_Growcott99
nature.com
Nature Africa - Technique to analyse animals’ unique vocalizations pinpoints individual leopards with 93% accuracy, which could help wildlife monitoring.
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Identifying hedgerow gaps using deep learning reveals potential opportunities for new planting schemes. Read the new paper by @josh_NFM et al here: https://t.co/6tbwh6oPeg
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Mapping oil palm plantations and their implications on forest and great ape habitat loss in Central Africa. Ozigis et al. Read the full paper here: https://t.co/pavjQt7kde
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New paper: Amazonian manatee critical habitat revealed by artificial intelligence-based passive acoustic techniques Erbs et al. https://t.co/2t7JX6GI28
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🦉🦉🦉 Automated identification of avian vocalizations with deep convolutional neural networks. Ruff et al. 🦉🦉🦉 https://t.co/woLAiPcSs9
zslpublications.onlinelibrary.wiley.com
Passive acoustic monitoring is a powerful tool for wildlife conservation and research but depends on researchers’ ability to isolate signals of interest in the resulting data. We developed a data-p...
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🐀🐀🐀 Remote sensing-supported mapping of the activity of a subterranean landscape engineer across an afro-alpine ecosystem. Wraase et al. 🐀🐀🐀 https://t.co/1k4UbREtrV
zslpublications.onlinelibrary.wiley.com
We compared three machine learning-based modeling strategies, which included field- and remote-sensing data to a different extent, for predicting the distribution of a subterranean species. We used...
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🦇🦇🦇 Camera traps with white flash are a minimally invasive method for long-term bat monitoring. Krivek et al. 🦇🦇🦇 https://t.co/Gd8EPgEhEj
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🚀👽🌌 The role of space agencies in remotely sensed essential biodiversity variables. Paganini et al. 🌌👽🚀 https://t.co/6jCUEZqbdI
zslpublications.onlinelibrary.wiley.com
Outline of the overall process by which remotely sensed essential biodiversity variables should be developed and matured. The Group on Earth Observations Biodiversity Observation Network (GEO BON)...
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🌜🌕🌙 Quantifying nocturnal thrush migration using sensor data fusion between acoustics and vertical-looking radar. Giuntini et al. 🌙🌕🌜 https://t.co/sPSZdepp0N
zslpublications.onlinelibrary.wiley.com
This study addresses the challenge of studying nocturnal bird migration, typically hindered by limited taxonomic information from radar data. To enhance resolution, we combined acoustic recordings...
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🕷️🐞🐛🕸️ Real-time insect tracking and monitoring with computer vision and deep learning. Bjerge et al. 🕸️🐛🐞🕷️ https://t.co/QxNXY4opmM
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🦇🦇🦇 Drone-based thermal remote sensing provides an effective new tool for monitoring the abundance of roosting fruit bats. McCarthy et al. 🦇🦇🦇 https://t.co/UrqhXD19MF
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In honour of Halloween, why not revisit some 'spooky' RSEC papers... 🦇🦇🦇 BatNet: a deep learning-based tool for automated bat species identification from camera trap images. Krivek et al. 🦇🦇🦇 https://t.co/ZdbGGpkcFO
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Combining satellite and field data reveals Congo's forest types structure, functioning and composition. Picard et al. https://t.co/aFUUhy5aZU
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Early spectral dynamics are indicative of distinct growth patterns in post-wildfire forests. Smith-Tripp et al. https://t.co/ARKXO4wChp
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