Small Field Plots Can Cause Substantial Uncertainty in Gridded Aboveground Biomass Products from Airborne Lidar Data

被引:5
|
作者
Cushman, K. C. [1 ]
Saatchi, Sassan [1 ]
McRoberts, Ronald E. [2 ,3 ]
Anderson-Teixeira, Kristina J. [4 ,5 ]
Bourg, Norman A. [4 ]
Chapman, Bruce [1 ]
McMahon, Sean M. [5 ,6 ]
Mulverhill, Christopher [1 ,7 ]
机构
[1] CALTECH, Jet Prop Lab, Pasadena, CA 91011 USA
[2] Raspberry Ridge Analyt, Hugo, MN 55038 USA
[3] Univ Minnesota, Dept Forest Resources, St Paul, MN 55108 USA
[4] Smithsonians Natl Zoo & Conservat Biol Inst, Conservat Ecol Ctr, Front Royal, VA 22630 USA
[5] Smithsonian Trop Res Inst, Forest Global Earth Observ, Panama City 084303092, Panama
[6] Smithsonian Environm Res Ctr, Edgewater, MD 21037 USA
[7] Univ British Columbia, Dept Forest Resources Management, Vancouver, BC V6T 1Z4, Canada
基金
美国国家科学基金会; 美国国家航空航天局;
关键词
aboveground biomass; forest structure; prediction uncertainty; lidar; NISAR; ASSISTED ESTIMATION; FOREST; BOOTSTRAP; MODELS; CARBON; VOLUME;
D O I
10.3390/rs15143509
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8-33.3 Mg ha(-1) for site-specific models (one standard deviation), 11.1-28.2 Mg ha(-1) for ecoregion-specific models, and 21.1-22.1 Mg ha(-1) for the general model for pixels in the AGB range of 80-100 Mg ha(-1). Only 3 of 11 site-specific models had a total uncertainty of <15 Mg ha(-1) in this biomass range, suitable for the calibration or validation of AGB map products. Using two additional sites with larger field plots, we show that lidar-based models calibrated with larger field plots can substantially reduce 1 ha pixel AGB uncertainty for the same range from 18.2 Mg ha(-1) using 0.04 ha plots to 10.9 Mg ha(-1) using 0.25 ha plots and 10.1 Mg ha(-1) using 1 ha plots. We conclude that the estimated AGB uncertainty from models estimated from small field plots may be unacceptably large, and we recommend coordinated efforts to measure larger field plots as reference data for the calibration or validation of satellite-based map products at landscape scales (& GE;0.25 ha).
引用
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页数:17
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