Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California

REMOTE SENSING OF ENVIRONMENT

卷:114

Article

Gonzalez, Patrick¹  Asner, Gregory P.²  Battles, John J.^1,3  Lefsky, Michael A.⁴  Waring, Kristen M.¹  Palace, Michael^5,6 

[1] Univ Calif Berkeley, Ctr Forestry, Berkeley, CA 94720 USA

[2] Carnegie Inst, Dept Global Ecol, Stanford, CA 94305 USA

[3] Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA

[4] Colorado State Univ, Dept Forest Rangeland & Watershed Stewardship, Ft Collins, CO 80523 USA

[5] Univ New Hampshire, Complex Syst Res Ctr, Durham, NH 03824 USA

[6] Univ Oxford, Environm Change Inst, Oxford OX1 3QY, England

关键词: Coast redwood;    Forest carbon;    Greenhouse gas inventories;    Lidar;    Monte Carlo analysis;    QuickBird;    Sierra Nevada;   

DOI  :  10.1016/j.rse.2010.02.011

来源: Elsevier

【 摘 要 】

Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon sequestration in forests. We compare forest carbon estimates from Light Detection and Ranging (Lidar) data and QuickBird high-resolution satellite images, calibrated and validated by field measurements of individual trees. We conducted the tests at two sites in California: (1) 59 km(2) of secondary and old-growth coast redwood (Sequoia sempervirens) forest (Garcia-Mailliard area) and (2) 58 km(2) of old-growth Sierra Nevada forest (North Yuba area). Regression of aboveground live tree carbon density, calculated from field measurements, against Lidar height metrics and against QuickBird-derived tree crown diameter generated equations of carbon density as a function of the remote sensing parameters. Employing Monte Carlo methods, we quantified uncertainties of forest carbon estimates from uncertainties in field measurements, remote sensing accuracy, biomass regression equations, and spatial autocorrelation. Validation of QuickBird crown diameters against field measurements of the same trees showed significant correlation (r=0.82, P<0.05). Comparison of stand-level Lidar height metrics with field-derived Lorey's mean height showed significant correlation (Garcia-Mailliard r=0.94, P<0.0001: North Yuba R=0.89, P<0.0001). Field measurements of five aboveground carbon pools (live trees, dead trees, shrubs, coarse woody debris, and litter) yielded aboveground carbon densities (mean +/- standard error without Monte Carlo) as high as 320 +/- 35 Mg ha(-1) (old-growth coast redwood) and 510 +/- 120 Mg ha(-1) (red fir [Abies magnifica] forest), as great or greater than tropical rainforest. Lidar and QuickBird detected aboveground carbon in live trees, 70-97% of the total. Large sample sizes in the Monte Carlo analyses of remote sensing data generated low estimates of uncertainty. Lidar showed lower uncertainty and higher accuracy than QuickBird, due to high correlation of biomass to height and undercounting of trees by the crown detection algorithm. Lidar achieved uncertainties of <1%, providing estimates of aboveground live tree carbon density (mean +/- 95% confidence interval with Monte Carlo) of 82 +/- 0.7 Mg ha(-1) in Garda-Mailliard and 140 +/- 0.9 Mg ha(-1) in North Yuba. The method that we tested, combining field measurements, Lidar, and Monte Carlo, can produce robust wall-to-wall spatial data on forest carbon. (C) 2010 Elsevier Inc. All rights reserved.

【 授权许可】

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