Classification of forest vertical structure in South Korea from aerial orthophoto and lidar data using an artificial neural network

Soo Kyung Kwon, Hyung Sup Jung, Won Kyung Baek, Daeseong Kim

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Every vegetation colony has its own vertical structure. Forest vertical structure is considered as an important indicator of a forest's diversity and vitality. The vertical structure of a forest has typically been investigated by field survey, which is the traditional method of forest inventory. However, this method is very time- and cost-consuming due to poor accessibility. Remote sensing data such as satellite imagery, aerial photography, and lidar data can be a viable alternative to the traditional field-based forestry survey. In this study, we classified forest vertical structures from red-green-blue (RGB) aerial orthophotos and lidar data using an artificial neural network (ANN), which is a powerful machine learning technique. The test site was Gongju province in South Korea, which contains single-, double-, and triple-layered forest structures. The performance of the proposed method was evaluated by comparing the results with field survey data. The overall accuracy achieved was about 70%. It means that the proposed approach can classify the forest vertical structures from the aerial orthophotos and lidar data.

Original languageEnglish
Article number1046
JournalApplied Sciences (Switzerland)
Volume7
Issue number10
DOIs
StatePublished - 12 Oct 2017

Keywords

  • ANN (Artificial Neural Network)
  • Aerial orthophoto
  • Forest inventory
  • Forestry vertical structure
  • Lidar (light detection and ranging)
  • Machine learning
  • Stratification

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