Forest attributes estimation using discrete return airborne lidar: An individual tree based approach

Wan Shafrina Wan Mohd Jaafar, Iain Hector Woodhouse, Carlos Alberto Silva, Hamdan Omar, Khairul Nizam Abdul Maulud

Research output: Contribution to conferencePaperpeer-review

Abstract

Light Detection and Ranging (LiDAR) has become a common tool for predicting forest attributes. Forestattributes estimation such as aboveground biomass (AGB), crown width and tree height for forest inventories using areabased approach have reached the operational status, while methods using the individual tree-based approach still remaina great challenge. In this study, we present a step by step approach in developing predictive LiDAR-AGB model derivedat tree level and how this model can be used to validate the results on automatic delineation of individual trees at thelandscape level. The methodology in this study arranged according to the following order: (1) Pre-processing: LiDARnormalization, LiDAR metrics extraction and LiDAR-field individual tree extraction, (2) Individual tree crowndelineation and crown assessment (3) Predictive model assessment (4) Forest attributes assessment. The automatedmethod correctly delineated about 84% and 88% of the tree crowns in two forest site of tropical rainforest in PeninsularMalaysia. The correct extraction of individual tree helps to derive accurate forest parameters that are important for naturalresource managers to improve management decisions.

Original languageEnglish
Pages1631-1640
Number of pages10
Publication statusPublished - 1 Jan 2018
Event39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018 - Kuala Lumpur, Malaysia
Duration: 15 Oct 201819 Oct 2018

Conference

Conference39th Asian Conference on Remote Sensing: Remote Sensing Enabling Prosperity, ACRS 2018
Country/TerritoryMalaysia
CityKuala Lumpur
Period15/10/1819/10/18

Keywords

  • Forest attributes
  • ITC
  • LiDAR

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