Forest Carbon Estimation Using Two Vegetation Structural Indices Derived from Terrestrial Laser Scanner: Vegetation Area Index and Leaf Area Index

Abo ve-ground carbon at the stand level is often estimated from the combination of destructive and non-destructive methods using allometric equations. Today, terrestrial laser scanners provide three-dimensional (3D) data for forest structural assessments. We utilized the point clouds generated from light detection and ranging (LiDAR) to compute vegetation area index (VAI) and leaf area index (LAI), providing predictor variables besides the traditional above-ground carbon assessment. The stand above-ground carbon values of five forest plots were better using the VAI (R2 = 0.66) than LAI (R2 = 0.51) with the linear model fitting. However, the P-values are more than 0.05, which stands for no statistical significance. This study suggests scaling up to a large sample size and area, or downing the scale into individual trees that will promote better estimation of above-ground carbon in different forest types and conditions.

Authors

Publication Details

Journal
Chiang Mai Journal of Science
Published
2024-11-29
DOI
https://doi.org/10.12982/cmjs.2024.101
Citations
1
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forest Carbon Estimation Using Two Vegetation Structural Indices Derived from Terrestrial Laser Scanner: Vegetation Area Index and Leaf Area Index

Nattawut Sareein
1 citations
Chiang Mai Journal of Science
Remote Sensing and LiDAR Applications
0.21
article

Forest Carbon Estimation Using Two Vegetation Structural Indices Derived from Terrestrial Laser Scanner: Vegetation Area Index and Leaf Area Index

Nattawut Sareein
article en
1 citations

Abstract

Abo ve-ground carbon at the stand level is often estimated from the combination of destructive and non-destructive methods using allometric equations. Today, terrestrial laser scanners provide three-dimensional (3D) data for forest structural assessments. We utilized the point clouds generated from light detection and ranging (LiDAR) to compute vegetation area index (VAI) and leaf area index (LAI), providing predictor variables besides the traditional above-ground carbon assessment. The stand above-ground carbon values of five forest plots were better using the VAI (R2 = 0.66) than LAI (R2 = 0.51) with the linear model fitting. However, the P-values are more than 0.05, which stands for no statistical significance. This study suggests scaling up to a large sample size and area, or downing the scale into individual trees that will promote better estimation of above-ground carbon in different forest types and conditions.

Chiang Mai Journal of ScienceVol. 51(6)
Life in Land
Openalex Percentile: Top 48%
Remote Sensing and LiDAR Applications
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Forest Carbon Estimation Using Two Vegetation Structural Indices Derived from Terrestrial Laser Scanner: Vegetation Area Index and Leaf Area Index — Nattawut Sareein · Chiang Mai Journal of Science (2024) | TGRS Research Map | TGRS