Wall-to-wall forest biomass estimation using Google Earth Engine
ForestSAT2026 Workshop Series
Description
Forest biomass is a key indicator for estimating forest carbon stocks. Biomass maps help identify
carbon pools, explore carbon credit opportunities, and connect these insights to biodiversity
conservation and protection.
Wall-to-wall mapping uses reference biomass data to produce a continuous, full-coverage biomass
map across forested landscapes. This hands-on workshop teaches a well-documented wall-to-wall
mapping workflow in Google Earth Engine (GEE). Through guided exercises, participants will learn how
to prepare biomass reference data (GEDI L4A/L4B), generate a wall-to-wall biomass map,
evaluate accuracy, interpret spatial patterns, and download final outputs.
By the end, participants will have practical skills that can be applied to ecological monitoring
and resource management.
Learning objectives
Writing code in Google Earth Engine (GEE)
Accessing GEDI and satellite imagery datasets through GEE
Designing samples, building models, and producing wall-to-wall maps
This course is supported by step-by-step tutorials and example materials to guide participants
through in-depth analysis.
Target audience
Students, researchers, and professionals in forestry, ecology, remote sensing, or environmental
sciences interested in scalable tools for forest monitoring. Basic GEE coding knowledge is recommended.
Format & activities
Short lectures and live demonstrations
Step-by-step guided exercises in GEE
Tutorials and example applications for extended practice
Expected outcomes
Build a wall-to-wall forest biomass map using GEE
Confidently use GEE for geospatial analysis and forest monitoring workflows
Language & requirements
Language: English
Requirements: Computer + Google Earth Engine access (web-based)
School of Forest, Fisheries, and Geomatics Sciences, University of Florida
Gainesville, FL 32611, USA
OpenForest4D is a web-based platform providing tools and educational resources that support transformative research in forest science, ecosystem protection, and hazard mitigation.
This work is supported by the U.S. National Science Foundation (NSF) under Awards #2409885, #2409886, and #2409887.