Publications Published in Remote Sensing Environment
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.
Remote Sensing Environment:
Greenhouse gas inventories and emissions reduction programs require robust methods to quantify carbon sequestration in forests.We compare forest carbon estimates fromLight 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 heightmetrics 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, Pb0.05). Comparison of stand-level Lidar height metrics with field-derived Lorey's mean height showed significant correlation (Garcia–Mailliard r=0.94, Pb0.0001; North Yuba R=0.89, Pb0.0001). 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±35Mg 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 theMonte 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. idar achieved uncertainties of b1%, 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 fieldmeasurements, Lidar, andMonte Carlo, can produce robust wall-to-wall spatial data on forest carbon.