A 30 m aboveground biomass dataset for multiple vegetation types in China (2020)

Area of Science:

• Ecology
• Remote Sensing
• Environmental Science

Background:

• Accurate estimation of aboveground biomass (AGB) is critical for global carbon cycle research and climate policy development.
• China's extensive and varied ecosystems hold a significant portion of global carbon stock, necessitating detailed biomass mapping.
• Existing AGB products often lack the spatial resolution and consistency required for comprehensive analysis of China's diverse landscapes.

Purpose of the Study:

• To develop a high-resolution (30 m), nationwide dataset of aboveground biomass density (AGBD) for China in 2020.
• To integrate multiple vegetation types, including forests, grasslands, shrublands, croplands, and wetlands, into a single, consistent AGBD product.
• To provide an updated and accessible baseline for biomass assessment, carbon accounting, and biodiversity monitoring in China.

Main Methods:

• Integration of multi-source, openly accessible remote sensing data, including LiDAR, optical, and radar imagery.
• Development of a consistent mapping methodology applicable across diverse vegetation types.
• Rigorous validation using field observations, national statistical data, and comparison with existing AGB products.

Main Results:

• A 30 m-resolution nationwide AGBD dataset for China in 2020 was successfully created, covering multiple vegetation types.
• The dataset demonstrated high accuracy, with an average R² of 0.85, RMSE of 31.26 Mg/ha, and rRMSE of 50.04%.
• The estimated total carbon stock across China's vegetated areas is 20.20 Petagrams (Pg).

Conclusions:

• The new AGBD dataset offers an unprecedented level of detail and consistency for China's diverse ecosystems.
• This resource is crucial for enhancing the accuracy of national and global carbon accounting.
• The dataset will significantly support ongoing efforts in climate change mitigation, carbon monitoring, and biodiversity conservation in China.