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A comparison of area-based forest attributes derived from airborne laser scanner, small-format and medium-format digital aerial photography

Citation

Iqbal, IA and Musk, RA and Osborn, J and Stone, C and Lucieer, A, A comparison of area-based forest attributes derived from airborne laser scanner, small-format and medium-format digital aerial photography, International Journal of Applied Earth Observation and Geoinformation, 76 pp. 231-241. ISSN 0303-2434 (2019) [Refereed Article]

Copyright Statement

2018 Elsevier B.V. All rights reserved.

DOI: doi:10.1016/j.jag.2018.12.002

Abstract

Forest inventory operations have greatly benefitted from remotely sensed data particularly airborne laser scanning (ALS) which has become a popular technology choice for large-area forest inventories. For remote regions, for fragmented estates or for single stand-level inventories ALS may be unsuitable because of the high cost of data acquisition. Point cloud data generated from digital aerial photography (DAP) is emerging as a cost-effective alternative to ALS. In this study we compared area-based forest inventory attributes derived from point cloud datasets sourced from ALS, small-format and medium-format digital aerial photography (SFP and MFP). Non-parametric modelling approach, namely RandomForest, was employed to model forest structural attributes at both plot- and stand-levels. The results were evaluated using field data collected at 105 inventory plots. At plot-level, the maximum difference among relative RMSEs of basal area (BA), top height (Htop), stocking (N) and total stem volume (TSV) of the three datasets was 2.46%, 0.55%, 13.29% and 2.53%, respectively. At stand-level, the maximum difference among relative RMSEs of BA, Htop, N and TSV of the three datasets was 3.86%, 1.25%, 7.85% and 6.04%, respectively. This study demonstrates the robustness of DAP across different sensors, and thus informs forest managers planning data acquisition solutions to best suit their operational needs.

Item Details

Item Type:Refereed Article
Keywords:forest inventory, Pinus radiata, airborne laser scanning, digital aerial photography, photogrammetry, small-format photography, medium-format photography, image point cloud, Random Forest, remote sensing, forestry
Research Division:Engineering
Research Group:Geomatic Engineering
Research Field:Photogrammetry and Remote Sensing
Objective Division:Plant Production and Plant Primary Products
Objective Group:Forestry
Objective Field:Softwood Plantations
UTAS Author:Iqbal, IA (Mr Irfan Iqbal)
UTAS Author:Osborn, J (Dr Jon Osborn)
UTAS Author:Lucieer, A (Professor Arko Lucieer)
ID Code:129721
Year Published:2019
Web of Science® Times Cited:4
Deposited By:Geography and Spatial Science
Deposited On:2018-12-17
Last Modified:2019-03-25
Downloads:0

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