AGBD: A Global-scale Biomass Dataset

TL;DR

This paper introduces a comprehensive, high-resolution, global biomass dataset combining satellite data and reference measurements, enabling improved machine learning models for estimating above ground biomass worldwide.

Contribution

It provides the first publicly available, high-resolution, globally distributed AGB dataset integrating multiple satellite sources and reference data, along with benchmark models.

Findings

Significant variability in biomass estimates across vegetation types

The dataset enables more accurate global AGB estimation

Benchmark models demonstrate the dataset's utility

Abstract

Accurate estimates of Above Ground Biomass (AGB) are essential in addressing two of humanity's biggest challenges: climate change and biodiversity loss. Existing datasets for AGB estimation from satellite imagery are limited. Either they focus on specific, local regions at high resolution, or they offer global coverage at low resolution. There is a need for a machine learning-ready, globally representative, high-resolution benchmark dataset. Our findings indicate significant variability in biomass estimates across different vegetation types, emphasizing the necessity for a dataset that accurately captures global diversity. To address these gaps, we introduce a comprehensive new dataset that is globally distributed, covers a range of vegetation types, and spans several years. This dataset combines AGB reference data from the GEDI mission with data from Sentinel-2 and PALSAR-2 imagery. Additionally, it includes pre-processed high-level features such as a dense canopy height map, an elevation map, and a land-cover classification map. We also produce a dense, high-resolution (10m) map of AGB predictions for the entire area covered by the dataset. Rigorously tested, our dataset is accompanied by several benchmark models and is publicly available. It can be easily accessed using a single line of code, offering a solid basis for efforts towards global AGB estimation. The GitHub repository github.com/ghjuliasialelli/AGBD serves as a one-stop shop for all code and data.