0.104824781417851194500RMRSRMRSPublished to Web1Publication1Formally Refereed1Scientific Journal (JRNL)<![CDATA[Statistical properties of hybrid estimators proposed for GEDI - NASA’s global ecosystem dynamics investigation]]> 20192019NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission will collect waveform lidar data at a dense sample of ~25 m footprints along ground tracks paralleling the orbit of the International Space Station (ISS). GEDI’s primary science deliverable will be a 1 km grid of estimated mean aboveground biomass density (Mg ha-1), covering the latitudes overflown by ISS (51.6 ° S to 51.6 ° N). One option for using the sample of waveforms contained within an individual grid cell to produce an estimate for that cell is hybrid inference, which explicitly incorporates both sampling design and model parameter covariance into estimates of variance around the population mean. We explored statistical properties of hybrid estimators applied in the context of GEDI, using simulations calibrated with lidar and field data from six diverse sites across the United States. We found hybrid estimators of mean biomass to be unbiased and the corresponding estimators of variance appeared to be asymptotically unbiased, with under-estimation of variance by approximately 20% when data from only two clusters (footprint tracks) were available. In our study areas, sampling error contributed more to overall estimates of variance than variability due to the model, and it was the design-based component of the variance that was the source of the variance estimator bias at small sample sizes. These results highlight the importance of maximizing GEDI’s sample size in making precise biomass estimates. Given a set of assumptions discussed here, hybrid inference provides a viable framework for estimating biomass at the scale of a 1 km grid cell while formally accounting for both variability due to the model and sampling error.10.1088/1748-9326/ab18dfhttps://www.fs.fed.us/rm/pubs_journals/2019/rmrs_2019_patterson_p001.pdf1.0 MBhttps://www.fs.usda.gov/treesearch/pubs/58341583410Environmental Research Letters. 14: 065007.014065007T0Patterson, Paul L.; Healey, Sean P.; Stahl, Goran ; Saarela, Svetlana ; Holm, Soren ; Andersen, Hans-Erik ; Dubayah, Ralph O.; Duncanson, Laura ; Hancock, Steven ; Armston, John ; Kellner, James R.; Cohen, Warren B.; Yang, Zhiqiang ; 06-AUG-2019 14:05:5323-SEP-2020 20:07:22AY06-AUG-2019 14:06:5110Carbon44Inventory, Monitoring, & Analysis 77Bioenergy and biomassPatterson, Paul L.RMRS4801plpatterson1858011Healey, Sean P.RMRS4801seanhealey1857112Stahl, Goran 003Saarela, Svetlana 004Holm, Soren 005Andersen, Hans-Erik 006Dubayah, Ralph O.007Duncanson, Laura 008Hancock, Steven 009Armston, John 0010Kellner, James R.0011Cohen, Warren B.PNW266951wcohen10161112Yang, Zhiqiang 0013Patterson, Paul L.RMRS4801plpatterson1858011Healey, Sean P.RMRS4801seanhealey1857112Stahl, Goran 003Saarela, Svetlana 004Holm, Soren 005Andersen, Hans-Erik 006Dubayah, Ralph O.007Duncanson, Laura 008Hancock, Steven 009Armston, John 0010Kellner, James R.0011Cohen, Warren B.PNW266951wcohen10161112Yang, Zhiqiang 00137IAInventory and Monitoringhttp://www.fs.fed.us/research/inventory-monitoring-analysis/PNW-2669-2Increase the efficiency and add value to inventory and monitoring efforts through the development of new tools, techniques, and methodology.This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

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