Chin J Plan Ecolo ›› 2012, Vol. 36 ›› Issue (10): 1095-1105.DOI: 10.3724/SP.J.1258.2012.01095

• Research Articles • Previous Articles     Next Articles

Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China

PANG Yong* and LI Zeng-Yuan   

  1. Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China
  • Received:2012-02-13 Revised:2012-09-12 Online:2012-10-01 Published:2012-09-26
  • Contact: PANG Yong
  • Supported by:

    NSFC;State Forest Administration of China

Abstract:

Aims Our purpose was to demonstrate the potential of using airborne laser to estimate biomass components of temperate forest. The airborne Lidar data and field data of concomitant plots were used in a forest of the Northeastern China.
Methods A set of biomass components, i.e., leaf biomass, branch biomass, trunk biomass, aboveground biomass and belowground biomass, were calculated from field data using species-specific allometric equations. Canopy height indices and density indices were calculated from Lidar point cloud data. The height indices evaluated included maximum height of all points, mean height of all points, quadratic mean height (square root of the mean squared height of each Lidar point) as well as height percentiles. Canopy density indices were computed as the proportions of laser points above each percentile height to total number of points. Then statistical models between these biomass components from field data and Lidar indices were built. Stepwise regression was used for variable selection and the maximum coefficient of determination (R2) improvement variable selection techniques were applied to select the ALS-derived variables to be included in the models. The least squares method was used generally and repeated until all the independent variables of the regression equation were accord with the requirements of entering models.
Important findings There were good correlations between biomass components and Lidar indices. The R2 was >0.6 for all the biomass components when we put all three types of forest (i.e., needle-leaved, broad-leaved and mixed) together. Needle-leaved forest had best estimation followed by broad-leaved and mixed forests when we built separate models for the three types of forest. This estimation capability is better when the regression models are built for different forest types.