Estimation of carbon dioxide stocks in forest using airborne LiDAR data

Sang Jin Lee; Yun Soo Choi; Ha Su Yoon

doi:10.7848/ksgpc.2012.30.3.259

Estimation of carbon dioxide stocks in forest using airborne LiDAR data

Sang Jin Lee, Yun Soo Choi, Ha Su Yoon

Department of Geoinformatics

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

This paper aims to estimate the carbon dioxide stocks in forests using airborne LiDAR data with a density of approximate 4.4 points per meter square. To achieve this goal, a processing chain consisting of bare earth Digital Terrain Model(DTM) extraction and individual tree top detection has been developed. As results of this experiment, the reliable DTM with type-II errors of 3.32% and tree positions with overall accuracy of 66.26% were extracted in the study area. The total estimated carbon dioxide stocks in the study area using extracted 3-D forests structures well suited with the traditional method by field measurements upto 7.2% error level. This results showed that LiDAR technology is highly valuable for replacing the existing forest resources inventory.

Original language	English
Pages (from-to)	259-268
Number of pages	10
Journal	Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
Volume	30
Issue number	3
DOIs	https://doi.org/10.7848/ksgpc.2012.30.3.259
State	Published - 2012

Keywords

Biomass
Carbon dioxide stocks
Digital terrain modeling
Individual tree detection
LiDAR Filtering

Access to Document

10.7848/ksgpc.2012.30.3.259

Cite this

@article{148a35d0de544151ae609b06720aeca0,

title = "Estimation of carbon dioxide stocks in forest using airborne LiDAR data",

abstract = "This paper aims to estimate the carbon dioxide stocks in forests using airborne LiDAR data with a density of approximate 4.4 points per meter square. To achieve this goal, a processing chain consisting of bare earth Digital Terrain Model(DTM) extraction and individual tree top detection has been developed. As results of this experiment, the reliable DTM with type-II errors of 3.32% and tree positions with overall accuracy of 66.26% were extracted in the study area. The total estimated carbon dioxide stocks in the study area using extracted 3-D forests structures well suited with the traditional method by field measurements upto 7.2% error level. This results showed that LiDAR technology is highly valuable for replacing the existing forest resources inventory.",

keywords = "Biomass, Carbon dioxide stocks, Digital terrain modeling, Individual tree detection, LiDAR Filtering",

author = "Lee, {Sang Jin} and Choi, {Yun Soo} and Yoon, {Ha Su}",

year = "2012",

doi = "10.7848/ksgpc.2012.30.3.259",

language = "English",

volume = "30",

pages = "259--268",

journal = "Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography",

issn = "1598-4850",

publisher = "Korean Society of Surveying",

number = "3",

}

TY - JOUR

T1 - Estimation of carbon dioxide stocks in forest using airborne LiDAR data

AU - Lee, Sang Jin

AU - Choi, Yun Soo

AU - Yoon, Ha Su

PY - 2012

Y1 - 2012

N2 - This paper aims to estimate the carbon dioxide stocks in forests using airborne LiDAR data with a density of approximate 4.4 points per meter square. To achieve this goal, a processing chain consisting of bare earth Digital Terrain Model(DTM) extraction and individual tree top detection has been developed. As results of this experiment, the reliable DTM with type-II errors of 3.32% and tree positions with overall accuracy of 66.26% were extracted in the study area. The total estimated carbon dioxide stocks in the study area using extracted 3-D forests structures well suited with the traditional method by field measurements upto 7.2% error level. This results showed that LiDAR technology is highly valuable for replacing the existing forest resources inventory.

AB - This paper aims to estimate the carbon dioxide stocks in forests using airborne LiDAR data with a density of approximate 4.4 points per meter square. To achieve this goal, a processing chain consisting of bare earth Digital Terrain Model(DTM) extraction and individual tree top detection has been developed. As results of this experiment, the reliable DTM with type-II errors of 3.32% and tree positions with overall accuracy of 66.26% were extracted in the study area. The total estimated carbon dioxide stocks in the study area using extracted 3-D forests structures well suited with the traditional method by field measurements upto 7.2% error level. This results showed that LiDAR technology is highly valuable for replacing the existing forest resources inventory.

KW - Biomass

KW - Carbon dioxide stocks

KW - Digital terrain modeling

KW - Individual tree detection

KW - LiDAR Filtering

UR - http://www.scopus.com/inward/record.url?scp=84875727274&partnerID=8YFLogxK

U2 - 10.7848/ksgpc.2012.30.3.259

DO - 10.7848/ksgpc.2012.30.3.259

M3 - Article

AN - SCOPUS:84875727274

SN - 1598-4850

VL - 30

SP - 259

EP - 268

JO - Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography

JF - Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography

IS - 3

ER -

Estimation of carbon dioxide stocks in forest using airborne LiDAR data

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this