# Forest aboveground biomass estimation using GEDI and earth observation data through attention-based deep learning

arXiv: https://arxiv.org/abs/2311.03067 · HTML: https://mlsanity.com/paper/2311.03067/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 66 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery](https://mlsanity.com/paper/2209.00727/) (2022)

## Built on by (most cited first, 2 of 2)

- [Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data](https://mlsanity.com/paper/2311.11777/) (2023)
- [Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin](https://mlsanity.com/paper/2606.05368/) (2026)

## Abstract (arXiv)

Accurate quantification of forest aboveground biomass (AGB) is critical for understanding carbon accounting in the context of climate change. In this study, we presented a novel attention-based deep learning approach for forest AGB estimation, primarily utilizing openly accessible EO data, including: GEDI LiDAR data, C-band Sentinel-1 SAR data, ALOS-2 PALSAR-2 data, and Sentinel-2 multispectral data. The attention UNet (AU) model achieved markedly higher accuracy for biomass estimation compared to the conventional RF algorithm. Specifically, the AU model attained an R2 of 0.66, RMSE of 43.66 Mg ha-1, and bias of 0.14 Mg ha-1, while RF resulted in lower scores of R2 0.62, RMSE 45.87 Mg ha-1, and bias 1.09 Mg ha-1. However, the superiority of the deep learning approach was not uniformly observed across all tested models. ResNet101 only achieved an R2 of 0.50, an RMSE of 52.93 Mg ha-1, and a bias of 0.99 Mg ha-1, while the UNet reported an R2 of 0.65, an RMSE of 44.28 Mg ha-1, and a substantial bias of 1.84 Mg ha-1. Moreover, to explore the performance of AU in the absence of spatial information, fully connected (FC) layers were employed to eliminate spatial information from the remote sensing data. AU-FC achieved intermediate R2 of 0.64, RMSE of 44.92 Mgha-1, and bias of -0.56 Mg ha-1, outperforming RF but underperforming AU model using spatial information. We also generated 10m forest AGB maps across Guangdong for the year 2019 using AU and compared it with that produced by RF. The AGB distributions from both models showed strong agreement with similar mean values; the mean forest AGB estimated by AU was 102.18 Mg ha-1 while that of RF was 104.84 Mg ha-1. Additionally, it was observed that the AGB map generated by AU provided superior spatial information. Overall, this research substantiates the feasibility of employing deep learning for biomass estimation based on satellite data.