• Title of article

    Mapping LAI in a Norway spruce forest using airborne laser scanning

  • Author/Authors

    Solberg، نويسنده , , Svein and Brunner، نويسنده , , Andreas and Hanssen، نويسنده , , Kjersti Holt and Lange، نويسنده , , Holger and Nوsset، نويسنده , , Erik and Rautiainen، نويسنده , , Miina and Stenberg، نويسنده , , Pauline، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2009
  • Pages
    11
  • From page
    2317
  • To page
    2327
  • Abstract
    In this study we demonstrate how airborne laser scanning (ALS) can be applied to map effective leaf area index (LAIe) in a spruce forest, after being calibrated with ground based measurements. In 2003 and 2005, ALS data and field estimates of LAIe were acquired in a Norway spruce forest in SE Norway. We used LI-CORʹs LAI-2000® Plant canopy analyzer (“LAI-2000”) and hemispherical images (“HI”) for field based estimates of LAIe. ALS penetration rate calculated from first echoes and from first and last echoes was strongly related to field estimates of LAIe. We fitted regression models of LAIe against the log-transformed inverse of the ALS penetration rate, and in accordance with the Beer–Lambert law this produced a linear, no-intercept relationship. This was particularly the case for the LAI-2000, having R2 values > 0.9. The strongest relationship was obtained by selecting ALS data from within a circle around each plot with a radius of 0.75 times the tree height. We found a slight difference in the relationship for the two years, which can be attributed to the differences in the ALS acquisition settings. The relationship was valid across four age classes of trees representing different stages of stand development, except in one case with newly regenerated stands which most likely was an artifact. Using LAIe based on HI data produced weaker relationships with the ALS data. This was the case even when we simulated LAI-2000 measurements based on the HI data.
  • Keywords
    LAI , Norway spruce , LIDAR
  • Journal title
    Remote Sensing of Environment
  • Serial Year
    2009
  • Journal title
    Remote Sensing of Environment
  • Record number

    1629395