Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations

Liu, Xiao; Neigh, Christopher S.R.; Pardini, Matteo; Forkel, Matthias

doi:10.1080/01431161.2024.2343134

International Journal of Remote Sensing

2024

DOI: 10.1080/01431161.2024.2343134

|View full text |Cite|

Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations

Xiao Liu,

Christopher S.R. Neigh,

Matteo Pardini

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Quantifying forest stocking changes in Sundarbans mangrove using remote sensing data

Ali,

Rahman

2025

Science of Remote Sensing

View full text Add to dashboard Cite

Quantifying forest stocking changes in Sundarbans mangrove using remote sensing data

Ali,

Rahman

2025

Science of Remote Sensing

View full text Add to dashboard Cite

Aboveground biomass models for Acacia mangium Willd. growing at the eastern plains of Colombia

Barrios-Trilleras,

López-Aguirre

2024

Floresta Ambient.

View full text Add to dashboard Cite

Canopy height mapping in French Guiana using multi-source satellite data and environmental information in a U-Net architecture

Lahssini,

Baghdadi,

le Maire

et al. 2024

Front. Remote Sens.

View full text Add to dashboard Cite

Canopy height is a key indicator of tropical forest structure. In this study, we present a deep learning application to map canopy height in French Guiana using freely available multi-source satellite data (optical and radar) and complementary environmental information. The potential of a U-Net architecture trained on sparse and unevenly distributed GEDI data to generate a continuous canopy height map at a regional scale was assessed. The developed model, named CHNET, successfully produced a canopy height map of French Guiana at a 10-m spatial resolution, achieving relatively good accuracy compared to a validation airborne LiDAR scanning (ALS) dataset. The study demonstrates that relevant environmental descriptors, namely, height above nearest drainage (HAND) and forest landscape types (FLT), significantly contribute to the model’s accuracy, highlighting that these descriptors bring important information on canopy structural properties and that the CHNET framework can efficiently use this information to improve canopy height prediction. Another critical aspect highlighted is the necessity of addressing GEDI data inaccuracies and geolocation uncertainties, which is essential for any GEDI-based application. However, challenges remain, particularly in characterizing tall canopies, as our CHNET model exhibits a tendency to underestimate canopy heights greater than 35 m. A large part of this error arises from the use of GEDI measurements as reference, given the fact these data exhibit certain saturation in tropical biomes. Future improvements in the analysis of GEDI signal as well as the implementation of robust models are essential for better characterization of dense and tall tropical forest ecosystems.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations

Cited by 4 publications

References 73 publications

Quantifying forest stocking changes in Sundarbans mangrove using remote sensing data

Quantifying forest stocking changes in Sundarbans mangrove using remote sensing data

Aboveground biomass models for Acacia mangium Willd. growing at the eastern plains of Colombia

Canopy height mapping in French Guiana using multi-source satellite data and environmental information in a U-Net architecture

Contact Info

Product

Resources

About