Predicting the spectral information of future land cover using machine learning-Reference-Cited by-同舟云学术

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Predicting the spectral information of future land cover using machine learning

Published:2017-06-22 Issue:20 Volume:38 Page:5592-5607
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Patil Sopan D.¹^ORCID,Gu Yuting²,Dias Felipe S. A³,Stieglitz Marc³,Turk Greg²

Affiliation:

1. School of Environment, Natural Resources and Geography, Bangor University, Bangor, UK

2. School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA

3. School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Funder

National Science Foundation

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2017.1343512

Reference72 articles.

1. Effects of harvest on carbon and nitrogen dynamics in a Pacific Northwest forest catchment

2. Catchment hydrological responses to forest harvest amount and spatial pattern

3. Interactions Across Spatial Scales among Forest Dieback, Fire, and Erosion in Northern New Mexico Landscapes

4. A validation of heat and carbon fluxes from high-resolution land surface and regional models

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