Module 3 Summary

OBIWAN is based upon the measurements, model, and statistical methods assembled by the GEDI mission, but it is also designed with specific user needs in mind:

• Clear, repeatable measures of uncertainty are needed for use of any OBIWAN outputs in a market- or treaty-based framework.
• “Additionality” is important in forest carbon projects - the carbon stored in one property needs to be compared against storage under a business-as-usual scenario from another time or place.
• User needs in an operational forest carbon verification system vary. Developers working for individual users can choose to build applications around OBIWAN with appropriate choices related to: visualization; authentication; documentation; time period of interest; and location of interest.
• Users don’t want to wait for on-the-fly bootstrap error simulations. OBIWAN pre-computes and stores those simulations, with help from Google, so that users get their estimates fast.

OBIWAN is still under construction in many parts of the country and world. A goal is to store 100 bootstrapped map time series everywhere, with more bootstraps in areas where users have expressed interest.

References

Statistical estimation of biomass using GEDI: Dubayah, R., Armston, J., Healey, S.P., Bruening, J.M., Patterson, P.L., Kellner, J.R., Duncanson, L., Saarela, S., Ståhl, G., Yang, Z. and Tang, H., 2022. GEDI launches a new era of biomass inference from space. Environmental Research Letters, 17(9), p.095001.

Patterson, P.L., Healey, S.P., Ståhl, G., Saarela, S., Holm, S., Andersen, H.E., Dubayah, R.O., Duncanson, L., Hancock, S., Armston, J. and Kellner, J.R., 2019. Statistical properties of hybrid estimators proposed for GEDI—NASA’s global ecosystem dynamics investigation. Environmental Research Letters, 14(6), p.065007.

Bootstrap uncertainty of GEDI biomass estimates

Saarela, S., Healey, S.P., Yang, Z., Roald, B.E., Patterson, P.L., Gobakken, T., Næsset, E., Hou, Z., McRoberts, R.E. and Ståhl, G., 2025. A Separable Bootstrap Variance Estimation Algorithm for Hierarchical Model‐Based Inference of Forest Aboveground Biomass Using Data From NASA’s GEDI and Landsat Missions. Environmetrics, 36(1), p.e2883.