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
收藏
页数:17
相关论文
共 44 条
  • [41] Importance of structure and its measurement in quantifying function of forest ecosystems
    Shugart, H. H.
    Saatchi, S.
    Hall, F. G.
    [J]. JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 2010, 115
  • [42] WU CFJ, 1986, ANN STAT, V14, P1261, DOI 10.1214/aos/1176350142
  • [43] Changes in global terrestrial live biomass over the 21st century
    Xu, Liang
    Saatchi, Sassan S.
    Yang, Yan
    Yu, Yifan
    Pongratz, Julia
    Bloom, A. Anthony
    Bowman, Kevin
    Worden, John
    Liu, Junjie
    Yin, Yi
    Domke, Grant
    McRoberts, Ronald E.
    Woodall, Christopher
    Nabuurs, Gert-Jan
    De-Miguel, Sergio
    Keller, Michael
    Harris, Nancy
    Maxwell, Sean
    Schimel, David L.
    [J]. SCIENCE ADVANCES, 2021, 7 (27)
  • [44] Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo
    Xu, Liang
    Saatchi, Sassan S.
    Shapiro, Aurelie
    Meyer, Victoria
    Ferraz, Antonio
    Yang, Yan
    Bastin, Jean-Francois
    Banks, Norman
    Boeckx, Pascal
    Verbeeck, Hans
    Lewis, Simon L.
    Muanza, Elvis Tshibasu
    Bongwele, Eddy
    Kayembe, Francois
    Mbenza, Daudet
    Kalau, Laurent
    Mukendi, Franck
    Ilunga, Francis
    Ebuta, Daniel
    [J]. SCIENTIFIC REPORTS, 2017, 7