High-Resolution Mapping of Forest Parameters in Tropical Rainforests Through AutoML Integration of GEDI With Sentinel-1/2, Landsat 8, and ALOS-2 Data

High-Resolution Mapping of Forest Parameters in Tropical Rainforests Through AutoML Integration of GEDI With Sentinel-1/2, Landsat 8, and ALOS-2 Data

Forests are vital carbon sinks, with tree height and biomass critical for carbon research. NASA's GEDI spaceborne LiDAR enhances vegetation monitoring through 3D structure analysis. This study established relationships between GEDI products and Sentinel-1/2, Landsat 8, ALOS, and GLO-30 features...

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Bibliographic Details
Main Authors: Bo Zhang, Li Zhang, Min Yan, Jian Zuo, Yuqi Dong, Bowei Chen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
ALOS-2 PALSAR-2
forest parameters
Global Ecosystem Dynamics Investigation (GEDI)
Landsat 8
Sentinel-1/2
Online Access:https://ieeexplore.ieee.org/document/10924716/
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Summary:Forests are vital carbon sinks, with tree height and biomass critical for carbon research. NASA's GEDI spaceborne LiDAR enhances vegetation monitoring through 3D structure analysis. This study established relationships between GEDI products and Sentinel-1/2, Landsat 8, ALOS, and GLO-30 features using the AutoML method. We constructed a total of 432 features, primarily from 14 types of earth observation features. In a 95.56% forest-covered area, AutoML improved canopy height (FCH) and biomass (AGBD) accuracy by up to 5.25 m and 32.18 Mg/ha over other methods. Polarization interference features, especially phase, explain 20% of forest parameters, showing high stability. Wavelet and Fourier-based texture features also demonstrate strong potential. Two mapping methods are proposed: 10 m resolution (FCH <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> = 0.53, RMSE = 11.49 m; AGBD <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> = 0.53, RMSE = 133.56 Mg/ha) and 500 m resolution (FCH <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> = 0.64, RMSE = 10.06 m; AGBD <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> = 0.66, RMSE = 114.25 Mg/ha). Compared to existing maps (AGBD: <inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$&lt;$</tex-math></inline-formula> 0.1, RMSE <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 180 Mg/ha; FCH: <inline-formula><tex-math notation="LaTeX">$R &lt;$</tex-math></inline-formula> 0.2, RMSE <inline-formula><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 15 m), our method (AGBD <inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula> = 0.74, RMSE = 131.39 Mg/ha; FCH <inline-formula><tex-math notation="LaTeX">$R$</tex-math></inline-formula> = 0.73, RMSE = 11.30 m) significantly improves accuracy. The approach shows minimal saturation effects and broad applicability for forest parameter estimation.
ISSN:1939-1404
2151-1535

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