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

Patrick Gonzalez, Gregory P. Asner, John J. Battles, Michael A. Lefsky, Kristen M Waring, Michael Palace

Research output: Contribution to journalArticle

137 Citations (Scopus)

Abstract

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 km2 of secondary and old-growth coast redwood (Sequoia sempervirens) forest (Garcia-Mailliard area) and (2) 58 km2 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 Garcia-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.

Original languageEnglish (US)
Pages (from-to)1561-1575
Number of pages15
JournalRemote Sensing of Environment
Volume114
Issue number7
DOIs
StatePublished - Jul 15 2010
Externally publishedYes

Fingerprint

lidar
QuickBird
uncertainty
Carbon
carbon
Sequoia sempervirens
Abies magnifica
tree crown
remote sensing
Light measurement
Remote sensing
Coastal zones
Biomass
coasts
carbon footprint
Monte Carlo method
coarse woody debris
detection
Uncertainty
biomass

Keywords

  • Coast redwood
  • Forest carbon
  • Greenhouse gas inventories
  • Lidar
  • Monte Carlo analysis
  • QuickBird
  • Sierra Nevada

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Soil Science
  • Geology

Cite this

Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. / Gonzalez, Patrick; Asner, Gregory P.; Battles, John J.; Lefsky, Michael A.; Waring, Kristen M; Palace, Michael.

In: Remote Sensing of Environment, Vol. 114, No. 7, 15.07.2010, p. 1561-1575.

Research output: Contribution to journalArticle

Gonzalez, Patrick ; Asner, Gregory P. ; Battles, John J. ; Lefsky, Michael A. ; Waring, Kristen M ; Palace, Michael. / Forest carbon densities and uncertainties from Lidar, QuickBird, and field measurements in California. In: Remote Sensing of Environment. 2010 ; Vol. 114, No. 7. pp. 1561-1575.
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