OEMC Project Use Case: Tools and Data for Improved Biomass Estimation

Description

Improved biomass estimation reduces uncertainties of the terrestrial carbon sink assessment. Current methods of estimating forest biomass, such as cutting down trees and weighing them, are not feasible on a large scale. ESA CCI Biomass maps, for example, minimize estimation errors globally but can have suboptimal performance when locally assessed, particularly in regions with highly heterogeneous forests. Biomass validation is important for developing reliable biomass models but is also challenging because of the lack of field data and because most national forest inventory data are not open. Therefore, this use case will focus on developing auxiliary tools and data for improved biomass estimation for the ESA CCI biomass map over forests with reported lower performance. This will involve the preparation of high-quality satellite LiDAR data and their integration with other satellite imagery and forest management data using advanced modeling techniques. We will also analyze the potential of a mobile citizen-science biomass app to provide open forest field measurements and support the validation of EO-based biomass maps. This app will allow users to estimate biomass in the field quickly using a mobile phone. The goal is to provide high-quality satellite LiDAR heights that can be readily used to produce more accurate carbon maps and make the tools and data open and accessible so that a wide range of communities can use them.

Stakeholder needs: 

• High-quality satellite LiDAR heights that are open and in a cloud-optimized data format
• Functionality for assessing plot-level aboveground biomass implemented in an existing open citizen-science app

Planned implementation:

The focus will be on preparing satellite LiDAR data, developing an open library (tool) for easily accessing this data, and selecting high-quality satellite LiDAR points. Furthermore, the focus will be on implementing the Bitterlich method for assessing aboveground biomass within the IIASA citizen science app.  

Implementation steps include: 

• Preparation and storing of satellite LiDAR data in a cloud-optimized format
• Developing an open library for accessing and filtering this data
• Implementing the Bitterlich method in the IIASA citizen science app: Geo-Qest
• Validation of implemented Bitterlich method together with Stakeholders
• Developing a biomass mapping example with Stakeholders that use high-quality satellite LiDAR data and the open library

Recorded talks:

• "Citizen Science Mobile App for Measuring Trees" by Milutin Milenkovic (IIASA, Austria), GEO-OPEN-HACK-2024
• "Citizen Science Mobile App and Data in Support of Forest Mapping: Laxenburg Park Campaign" by Milutin Milenkovic (IIASA, Austria) and Silvia Rodrigues (Stakeholder, University of Brasília, Brazil ), Open-Earth-Monitor — Global Workshop 2024

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