Mapping salt marsh along coastal South Carolina using U-Net-Reference-Cited by-同舟云学术

Mapping salt marsh along coastal South Carolina using U-Net

Published:2021-09 Issue: Volume:179 Page:121-132
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Li Huixuan,Wang Cuizhen,Cui Yuxin,Hodgson Michael

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference97 articles.

1. Adam, 1990. Saltmarsh Ecology. Cambridge University Press. New York.

2. Modeling and prediction of future urban growth in the Charleston region of South Carolina: a GIS-based integrated approach;Allen;Conservation Ecology,2003

3. Allen, D.M., W. B. Allen, R. F. Feller, and J. S. Plunket (Eds.). 2014. Site profile of the North Inlet-Winyah Bay National Estuarine Research Reserve. North Inlet-Winyah Bay National Estuarine Research Reserve. Georgetown, South Carolina. 432 pp.

4. Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing;Belluco;Remote Sens. Environ.,2006

5. Relationship between habitat area and the distribution of tidal marsh birds;Benoit;The Wilson Journal of Ornithology,2002

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