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...

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Autori principali:	Jiapeng Huang, Yue Zhang, Xiaozhu Yang, Fan Mo
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IEEE 2026-01-01
Serie:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Soggetti:	Deep learning model forest canopy height Global Ecosystem Dynamics Investigation (GEDI) multisource remote sensing neural network-guided interpolation (NNGI)
Accesso online:	https://ieeexplore.ieee.org/document/11348094/
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