Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating

Area of Science:

• Forestry
• Remote Sensing
• Ecosystem Modeling

Background:

• Accurate monitoring of forest aboveground biomass (AGB) is crucial for assessing ecosystem carbon sequestration.
• Spatio-temporal variations in forest AGB require effective mapping and updating methodologies.

Purpose of the Study:

• To develop and validate a physical-based approach for mapping and updating forest AGB in the Greater Khingan Mountains (GKM) of China.
• To dynamically update annual forest AGB from 2009 to 2012 using simulated net primary productivity (NPP).

Main Methods:

• Established a baseline forest AGB using Landsat ETM+ images from 2008.
• Incorporated annual AGB increment (ABI) simulated by the Boreal Ecosystem Productivity Simulator (BEPS) model.
• Validated the 2012 AGB estimates against field and aerial laser scanning (ALS) data.

Main Results:

• The BEPS-model-based AGB explained 31% of field-based AGB variation and 85% of ALS-based AGB variation.
• The model showed a tendency to underestimate/overestimate AGB in dense/sparse forests due to optical signal saturation and understory vegetation.
• Remotely sensed AGB estimates can initialize process-based models for NPP simulation.

Conclusions:

• The combination of baseline forest AGB and the BEPS model effectively updates the spatiotemporal distribution of forest AGB.
• This approach provides a robust method for monitoring forest carbon stocks and dynamics.