Statistical properties of hybrid estimators proposed for GEDI—NASA's global ecosystem dynamics investigation. (13th June 2019)

• Record Type: Journal Article
• Authors: Patterson, Paul L; Healey, Sean P; Ståhl, Göran; Saarela, Svetlana; Holm, Sören; Andersen, Hans-Erik; Dubayah, Ralph O; Duncanson, Laura; Hancock, Steven; Armston, John; Kellner, James R; Cohen, Warren B; Yang, Zhiqiang
• Is Part Of: Environmental research letters. Volume 14:Number 6(2019:Jun.)
• Publication Date: 2019-06-13
• Subjects: carbon monitoring -- lidar -- forest biomass; Environmental sciences -- Periodicals; Human ecology -- Research -- Periodicals; Environmental health -- Periodicals; 333.7
• DOI: 10.1088/1748-9326/ab18df
• Languages: English
• ISSNs: 1748-9326

Abstract

Abstract: NASA'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.