Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation-Reference-Cited by-同舟云学术

Correction of global digital elevation models in forested areas using an artificial neural network-based method with the consideration of spatial autocorrelation

Published:2023-04-28 Issue:1 Volume:16 Page:1568-1588
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Li Yanyan¹²,Li Linye¹²,Chen Chuanfa¹²,Liu Yan¹²

Affiliation:

1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, People’s Republic of China

2. Key Laboratory of Geomatics and Digital Technology of Shandong Province, Shandong University of Science and Technology, Qingdao, People’s Republic of China

Funder

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation

Shandong Province Higher Educational Youth Innovation Science and Technology

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2203953

Reference76 articles.

1. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling