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

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

To remove vegetation bias (VB) from the global DEMs (GDEMs), an artificial neural network (ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper. Three study sites with different forest types (evergreen, mixed evergreen-deciduous, and deciduous) are...

Full description

Bibliographic Details
Main Authors: Yanyan Li, Linye Li, Chuanfa Chen, Yan Liu
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Series:International Journal of Digital Earth
Subjects:
vegetation bias
terrain parameter
elevation correction
machine learning
Online Access:http://dx.doi.org/10.1080/17538947.2023.2203953

Internet

http://dx.doi.org/10.1080/17538947.2023.2203953

Similar Items