Module 4 Summary

This module presented methods, techniques, and modeling for forest biomass estimation using field, airborne and satellite lidar (GEDI) data developed in Acre, in the southwestern Amazon of Brazil. Participants will understand the advantages, limitations, and considerations for applying such methodology on your own. In addition, participants can customize the methods by changing model parameters, testing different ML models (eg., RF, XGB, Cubist etc.), and using their own data to develop a local model to a particular area of interest.

References

Duncanson, L., Kellner, J.R., Armston, J., Dubayah, R., Minor, D.M., Hancock, S., Healey, S.P., Patterson, P.L., Saarela, S., Marselis, S. and Silva, C.E., 2022. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sensing of Environment, 270, p.112845.

Fareed, N., Numata, I., Cochrane, M.A., Novoa, S., Tenneson, K., Melo, A.W.F.D., da Silva, S.S., Oliveira, M.V.N.D., Nicolau, A. and Zutta, B., 2025. Aboveground biomass modeling using simulated Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and forest inventories in Amazonian rainforests. Forest Ecology and Management, 578, p.122491.

Gorelick, N., Yang, Z., Arévalo, P., Bullock, E.L., Insfrán, K.P. and Healey, S.P., 2023. A global time series dataset to facilitate forest greenhouse gas reporting. Environmental Research Letters, 18(8), p.084001.

Longo, M., Keller, M., dos‐Santos, M.N., Leitold, V., Pinagé, E.R., Baccini, A., Saatchi, S., Nogueira, E.M., Batistella, M. and Morton, D.C., 2016. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Global Biogeochemical Cycles, 30(11), pp.1639-1660.

Zhu, Z. and Woodcock, C.E., 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of Environment, 144, pp.152-171.