A Deep Learning-Based Model for Forest Canopy Height Mapping Using Multisource Remote Sensing Data

Forest canopy height is a critical structural parameter for accurately assessing forest carbon storage. This study integrates Global Ecosystem Dynamics Investigation (GEDI) LiDAR data with multisource remote sensing features to construct a multidimensional feature space comprising 13 parameters. By...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Jiapeng Huang, Yue Zhang, Xiaozhu Yang, Fan Mo
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2026-01-01
Schriftenreihe:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Schlagworte:	Deep learning model forest canopy height Global Ecosystem Dynamics Investigation (GEDI) multisource remote sensing neural network-guided interpolation (NNGI)
Online-Zugang:	https://ieeexplore.ieee.org/document/11348094/
Tags:	Tag hinzufügen Keine Tags, Fügen Sie das erste Tag hinzu!