Healey, Sean: USDA Forest Service (Project Lead)
Cohen, Warren: USDA Forest Service (Co-Investigator)
Patterson, Paul: USDA Forest Service (Co-Investigator)
Yang, Zhiqiang: USDA Forest Service (Co-Investigator)
Andersen, Hans: U.S. Forest Service Pacific Northwest Research Station (Collaborator)
Wilson, Sylvia: USGS / SilvaCarbon (Stakeholder)
Project Funding:
2017 - 2020
NRA: 2016 NASA: Carbon Monitoring System
Funded by NASA
Abstract:
NASA's GEDI (Global Ecosystem Dynamics Investigation) mission will mount an innovative lidar instrument on the International Space Station; the mission will provide unprecedented detail about the structure of Earth’s forests. The number, quality, and international consistency of GEDI’s tree height measurements represent a matchless global tool for describing how much carbon our forests store and how that storage is affected by ecological change. However, the only biomass product GEDI is required (and currently funded) to produce is a 1km grid of estimated mean biomass (with standard errors). While there are important science applications for this grid, many scientists, landowners, and government agencies would benefit from easy access to GEDI-based biomass estimates over more flexible spatial domains. The GEDI Science Team (led by the PI of this proposal) has developed an approach to making 1km biomass estimates using sample theory applied to modeled observations of biomass made at each GEDI footprint (GEDI is not a wall-to-wall instrument). This approach accounts for both sampling uncertainty and biomass model error. There is no theoretical obstacle preventing this approach from being applied across areas defined by customized political, ownership, or ecological boundaries. This proposal, first, will pilot a web app that will support monitoring, reporting, and verification of local carbon storage (with uncertainty) over any spatial domain of interest, using exactly the same lidar data and sampling theory as the GEDI gridded product. This pilot application will be constructed in collaboration with the Forest Service FIA (Forest Inventory and Analysis) unit, which already maintains a national-to-local carbon monitoring system and has a legal mandate to improve the spatial detail at which forest characteristics can be reported. In addition to providing a potential long-term home for GEDI’s contribution to practical carbon monitoring, FIA will provide data the project will use to build validation case studies as well as to hone the community’s ability to use a single point-in-time lidar sample to study how changing forests affect carbon storage. Like GEDI itself, this proposal benefits from earlier CMS investments in strategic collection of lidar and field data (PI: Cohen, 2013- 2016) and development of statistical methods that apply sampling theory to estimating biomass from lidar (PI: Healey, 2012-2014). The proposed activities are needed to fully realize GED’s potential in how we plan and compensate forest management that results in augmented carbon storage.
Publications:
Healey, S. P., Yang, Z., Gorelick, N., Ilyushchenko, S. 2020. Highly Local Model Calibration with a New GEDI LiDAR Asset on Google Earth Engine Reduces Landsat Forest Height Signal Saturation. Remote Sensing. 12(17), 2840. DOI: 10.3390/rs12172840
Healey, S., Menlove, J. 2019. The Stability of Mean Wood Specific Gravity across Stand Age in US Forests Despite Species Turnover. Forests. 10(2), 114. DOI: 10.3390/f10020114
Hurtt, G. C., Andrews, A., Bowman, K., Brown, M. E., Chatterjee, A., Escobar, V., Fatoyinbo, L., Griffith, P., Guy, M., Healey, S. P., Jacob, D. J., Kennedy, R., Lohrenz, S., McGroddy, M. E., Morales, V., Nehrkorn, T., Ott, L., Saatchi, S., Sepulveda Carlo, E., Serbin, S. P., Tian, H. 2022. The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps. Environmental Research Letters. 17(6), 063010. DOI: 10.1088/1748-9326/ac7407
Menlove, J., Healey, S. P. 2020. A Comprehensive Forest Biomass Dataset for the USA Allows Customized Validation of Remotely Sensed Biomass Estimates. Remote Sensing. 12(24), 4141. DOI: 10.3390/rs12244141
Patterson, P. L., Healey, S. P., Stahl, G., Saarela, S., Holm, S., Andersen, H., Dubayah, R. O., Duncanson, L., Hancock, S., Armston, J., Kellner, J. R., Cohen, W. B., Yang, Z. 2019. Statistical properties of hybrid estimators proposed for GEDI--NASA's global ecosystem dynamics investigation. Environmental Research Letters. 14(6), 065007. DOI: 10.1088/1748-9326/ab18df
Saarela, S., Holm, S., Healey, S., Andersen, H., Petersson, H., Prentius, W., Patterson, P., Naesset, E., Gregoire, T., Stahl, G. 2018. Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data. Remote Sensing. 10(11), 1832. DOI: 10.3390/rs10111832
More details may be found in the following project profile(s):
