Estimation of PAIe using airborne LiDAR data in South Korea

Doo Ahn Kwak, Woo Kyun Lee, Menas Kafatos, Yowhan Son, Hyun Kook Cho

Research output: Contribution to conferencePaperpeer-review

Abstract

In this study, the Effective Plant Area Indices (PAIe) for areas of Korean Pine (Pinus koraiensis) and Oaks (Quercus spp.) were estimated by calculating the ratio of intercepted and penetrated LIDAR laser pulses for the canopies of the three forest types. Initially, the canopy gap fraction (G LiDAR) was generated by extracting the LiDAR data reflected from the canopy surface, or inner canopy area. The LiDAR-derived PAIe was then estimated by using GLIDAR with the Beer-Lambert law. A comparison of the LiDAR-derived and field-derived PAIe revealed the coefficients of determination for Korean Pine and Oak to be 0.82 and 0.59, respectively. These differences between field-based and LIDAR-based PAIe for the different forest types were attributed to the amount of leaves and branches in the forest stands. The probability that the LiDAR pulses are reflected from bare branches is low as compared to the reflection from branches with a high leaf density.

Original languageEnglish
Pages1645-1648
Number of pages4
DOIs
Publication statusPublished - 2012
Event2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany
Duration: 2012 Jul 222012 Jul 27

Other

Other2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
CountryGermany
CityMunich
Period12/7/2212/7/27

Keywords

  • beer-lambert law
  • gap fraction
  • leaf area index
  • plant area index LiDAR

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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