Optimized autoencoder deep learning for carbon stock estimation using multi source remote sensing data in tropical rainforests

Abstract Accurate estimation of aboveground carbon stocks in tropical rainforests is fundamental for global climate change mitigation, sustainable forest management, and carbon accounting. Traditional field-based carbon assessment methods are often time-consuming, labour-intensive, and spatially lim...

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Detalhes bibliográficos

Principais autores:	John Khoo, Rayner Alfred, Kim On Chin, Ervin Gubin Moung, Oliver Valentine Eboy, Normah Awang Besar Raffie, Luaran Nosius
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Springer 2026-02-01
coleção:	Discover Artificial Intelligence
Assuntos:	Carbon stock estimation Auto-encoders Deep learning Remote sensing Hyper-parameter optimization Environmental monitoring
Acesso em linha:	https://doi.org/10.1007/s44163-026-01010-y
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