Automatedly identify dryland threatened species at large scale by using deep learning-Reference-Cited by-同舟云学术

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Automatedly identify dryland threatened species at large scale by using deep learning

Published:2024-03 Issue: Volume:917 Page:170375
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Wang Haolin,Liu Qi,Gui Dongwei,Liu Yunfei,Feng Xinlong,Qu Jia,Zhao Jianping,Wei Guanghui

Funder

Xinjiang Uygur Autonomous Region Natural Science Foundation

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Elsevier BV

Reference104 articles.

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2. Detection and plant monitoring programs: lessons from an intensive survey of Asclepias meadii with five observers;Alexander;PLoS One,2012

3. UAV & satellite synergies for optical remote sensing applications: a literature review;Alvarez-Vanhard;Sci. Remote Sens.,2021

4. Assessing the effect of EWDP on vegetation restoration by remote sensing in the lower reaches of Tarim River;Bao;Ecol. Indic.,2017

5. Thermal imaging reveals significantly smaller Brazilian free-tailed bat colonies than previously estimated;Betke;J. Mammal.,2008

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