| 2. |
- Duncanson, Laura, et al.
(författare)
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Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- 2022
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 270
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Tidskriftsartikel (refereegranskat)abstract
- NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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| 3. |
- Wolever, Thomas M S, et al.
(författare)
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Measuring the glycemic index of foods: interlaboratory study.
- 2008
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Ingår i: The American journal of clinical nutrition. - 0002-9165 .- 1938-3207. ; 87:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Many laboratories offer glycemic index (GI) services. OBJECTIVE: We assessed the performance of the method used to measure GI. DESIGN: The GI of cheese-puffs and fruit-leather (centrally provided) was measured in 28 laboratories (n=311 subjects) by using the FAO/WHO method. The laboratories reported the results of their calculations and sent the raw data for recalculation centrally. RESULTS: Values for the incremental area under the curve (AUC) reported by 54% of the laboratories differed from central calculations. Because of this and other differences in data analysis, 19% of reported food GI values differed by >5 units from those calculated centrally. GI values in individual subjects were unrelated to age, sex, ethnicity, body mass index, or AUC but were negatively related to within-individual variation (P=0.033) expressed as the CV of the AUC for repeated reference food tests (refCV). The between-laboratory GI values (mean+/-SD) for cheese-puffs and fruit-leather were 74.3+/-10.5 and 33.2+/-7.2, respectively. The mean laboratory GI was related to refCV (P=0.003) and the type of restrictions on alcohol consumption before the test (P=0.006, r2=0.509 for model). The within-laboratory SD of GI was related to refCV (P<0.001), the glucose analysis method (P=0.010), whether glucose measures were duplicated (P=0.008), and restrictions on dinner the night before (P=0.013, r2=0.810 for model). CONCLUSIONS: The between-laboratory SD of the GI values is approximately 9. Standardized data analysis and low within-subject variation (refCV<30%) are required for accuracy. The results suggest that common misconceptions exist about which factors do and do not need to be controlled to improve precision. Controlled studies and cost-benefit analyses are needed to optimize GI methodology. The trial was registered at clinicaltrials.gov as NCT00260858.
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