Deep learning-based rapid recognition of oasis-desert ecotone plant communities using UAV low-altitude remote-sensing data-Reference-Cited by-同舟云学术

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Deep learning-based rapid recognition of oasis-desert ecotone plant communities using UAV low-altitude remote-sensing data

Published:2020-05 Issue:10 Volume:79 Page:
ISSN:1866-6280
Container-title:Environmental Earth Sciences
language:en
Short-container-title:Environ Earth Sci

Author:

Ainiwaer Mireguli^ORCID,Ding Jianli,Kasim Nijat

Funder

National Natural Science Foundation of China

the Sino-German Interdisciplinary Joint Program for Innovative Talent Training funded by the China Scholarship Council

Publisher

Springer Science and Business Media LLC

Subject

Earth-Surface Processes,Geology,Pollution,Soil Science,Water Science and Technology,Environmental Chemistry,Global and Planetary Change

Link

https://link.springer.com/content/pdf/10.1007/s12665-020-08965-w.pdf

Reference33 articles.

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2. Bing L, Yu X, Zhang P, Xiong T, Yu A, Xue Z (2017) A semi-supervised convolutional neural network for hyperspectral image classification. Remote Sens Lett 8(9):839–848. https://doi.org/10.1080/2150704X.2017.1331053

3. Chigani HK, Khajeddin SJ, Karimzadeh HR (2012) Soil-vegetation relationships of three arid land plant species and their use in rehabilitating degraded sites. Land Degrad Dev 23(1):92–101. https://doi.org/10.1002/ldr.1057

4. Fensham RJ, Fairfax RJ, Archer SR (2010) Rainfall land use and woody vegetation cover change in semi-arid Australian savanna. J Ecol 93(3):596–606. https://doi.org/10.1111/j.1365-2745.2005.00998.x

5. Franklin J, Serra-Diaz JM, Syphard AD, Regan HM (2016) Global change and terrestrial plant community dynamics. Proc Natl Acad Sci 113(14):3725–3734. https://doi.org/10.1073/pnas.1519911113

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