Estimating stand-level structural and biophysical variables of lowland dipterocarp forest using airborne LiDAR data

Muhamad Afizzul M., . and Siti Yasmin Y., . and Hamdan O., . and Tan S. A., . Estimating stand-level structural and biophysical variables of lowland dipterocarp forest using airborne LiDAR data. pp. 312-323. ISSN 0128-1283

Light Detection and Ranging (LiDAR) has been used in a wide range of applications including forestry. This study aims to investigate the potential use of airborne lidar scanning (ALS) data in estimating stand-level structural and biophysical variables of lowland dipterocarp forest. Five forest variables namely mean height (Hm) basal area (BA) square mean diameter (Dg) stand density (S) and above ground biomass (AGB) were tested based on 40 field plots. A total of 34 ALS metrics were generated and tested for model development. A multiple linear regression approach was performed to generate the best model for estimating the variables. Models for BA and AGB gave strong precisions with an adjusted-R of 0.77 and 0.82 and RMSE of 5.45 m ha- and 71.12 Mg ha-. The Hm and Dg gave moderate precisions with R of 0.61 and 0.44 and RMSE of 2.35 m and 6.07 cm respectively while S gave the lowest precision with an adjusted-R of 0.27 and RMSE of 149.48 stem ha-. This study demonstrated that ALS data performs better in estimating stand-level structural and biophysical parameters of tropical forest which is important for forest managers towards better monitoring planning and managing their forests by using this technology.

Full text available from: Official URL: https://info.frim.gov.my/infocenter_applications/j...

Abstract

Light Detection and Ranging (LiDAR) has been used in a wide range of applications including forestry. This study aims to investigate the potential use of airborne lidar scanning (ALS) data in estimating stand-level structural and biophysical variables of lowland dipterocarp forest. Five forest variables namely mean height (Hm) basal area (BA) square mean diameter (Dg) stand density (S) and above ground biomass (AGB) were tested based on 40 field plots. A total of 34 ALS metrics were generated and tested for model development. A multiple linear regression approach was performed to generate the best model for estimating the variables. Models for BA and AGB gave strong precisions with an adjusted-R of 0.77 and 0.82 and RMSE of 5.45 m ha- and 71.12 Mg ha-. The Hm and Dg gave moderate precisions with R of 0.61 and 0.44 and RMSE of 2.35 m and 6.07 cm respectively while S gave the lowest precision with an adjusted-R of 0.27 and RMSE of 149.48 stem ha-. This study demonstrated that ALS data performs better in estimating stand-level structural and biophysical parameters of tropical forest which is important for forest managers towards better monitoring planning and managing their forests by using this technology.

[error in script]

Item Type:	Article
AGROVOC Term:	Dipterocarps
AGROVOC Term:	Tropical forests
AGROVOC Term:	Lowland
AGROVOC Term:	Stand structure
AGROVOC Term:	Stand characteristics
AGROVOC Term:	Biomass
AGROVOC Term:	Diameter
AGROVOC Term:	Plot design
AGROVOC Term:	Multivariate analysis
AGROVOC Term:	Monitoring
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:54
URI:	http://webagris.upm.edu.my/id/eprint/8204

Actions (login required)

View Item