TY - JOUR

T1 - Estimation of forest resources from a country wide laser scanning survey and national forest inventory data

AU - Nord-Larsen, Thomas

AU - Schumacher, Johannes

PY - 2012

Y1 - 2012

N2 - Airborne laser scanning may provide a means for assessing local forest biomass resources. In this study, national forest inventory (NFI) data was used as reference data for modeling forest basal area, volume, aboveground biomass, and total biomass from laser scanning data obtained in a countrywide scanning survey. Data covered a wide range of forest ecotypes, stand treatments, tree species, and tree species mixtures. The four forest characteristics were modeled using nonlinear regression and generalized method-of-moments estimation to avoid biased and inefficient estimates. The coefficient of determination was 68% for the basal area model and 77–78% for the volume and biomass models. Despite the wide range of forest types model accuracy was comparable to similar studies. Model predictions were unbiased across the range of predicted values and crown cover percentages but positively biased for deciduous forest and negatively biased for coniferous forest. Species type specific (coniferous, deciduous, or mixed forest) models reduced root mean squared error by 3–12% and removed the bias. In application, model predictions will be improved by stratification into deciduous and coniferous forest using e.g. infrared orthophotos or satellite images.

AB - Airborne laser scanning may provide a means for assessing local forest biomass resources. In this study, national forest inventory (NFI) data was used as reference data for modeling forest basal area, volume, aboveground biomass, and total biomass from laser scanning data obtained in a countrywide scanning survey. Data covered a wide range of forest ecotypes, stand treatments, tree species, and tree species mixtures. The four forest characteristics were modeled using nonlinear regression and generalized method-of-moments estimation to avoid biased and inefficient estimates. The coefficient of determination was 68% for the basal area model and 77–78% for the volume and biomass models. Despite the wide range of forest types model accuracy was comparable to similar studies. Model predictions were unbiased across the range of predicted values and crown cover percentages but positively biased for deciduous forest and negatively biased for coniferous forest. Species type specific (coniferous, deciduous, or mixed forest) models reduced root mean squared error by 3–12% and removed the bias. In application, model predictions will be improved by stratification into deciduous and coniferous forest using e.g. infrared orthophotos or satellite images.

U2 - 10.1016/j.rse.2011.12.022

DO - 10.1016/j.rse.2011.12.022

M3 - Journal article

VL - 119

SP - 148

EP - 157

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -