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Comparing yield estimates derived from LiDAR and aerial photogrammetric point-cloud data with cut-to-length harvester data in a Pinus radiata plantation in Tasmania

Citation

Caccamo, G and Iqbal, AI and Osborn, J and Bi, H and Arkley, K and Melville, G and Aurik, D and Stone, C, Comparing yield estimates derived from LiDAR and aerial photogrammetric point-cloud data with cut-to-length harvester data in a Pinus radiata plantation in Tasmania, Australian Forestry pp. 1-11. ISSN 0004-9158 (2018) [Refereed Article]

Copyright Statement

Copyright 2018 Crown Copyright in the Commonwealth of Australia. State of New South Wales through the Department of Industry, Skills and Regional Development

DOI: doi:10.1080/00049158.2018.1458582

Abstract

Accurate mapping of timber resources in commercial forestry is essential to support planning and management operations of forest growers. Over the last two decades, Light Detection and Ranging (LiDAR) systems have been successfully deployed for the collection of point-cloud data for accurate modelling of forest attributes that are traditionally obtained from plot-based inventory. In recent years, studies conducted in North America and Scandinavia have shown that three-dimensional point clouds derived from digital aerial photogrammetric (AP) data can be used to model forest attributes with a level of accuracy similar to traditional LiDAR-based approaches. A comparative analysis of the performance of the two point-cloud technologies has never been attempted in Australian plantations. In this study, we compared the performance of LiDAR-based and AP-based point clouds for estimating total recoverable volume in a Pinus radiata plantation at Springfield in north-eastern Tasmania, using volume data collected by harvesting machines as a reference. Our results showed that AP point clouds can be used for mapping total recoverable volume in P. radiata plantations with levels of accuracy that are comparable to LiDAR-based estimates. Plot-level relative root mean squared error (RMSE%) values were 23.85% for LiDAR and ranged from 22.07 to 27.10% for the three AP dense point-cloud settings evaluated. At the stand level, RMSE% decreased to 9.86 and 8.91% for LiDAR and AP, respectively. Both LiDAR-based and AP-based modelled volumes showed a close agreement with volumes measured using harvester head data, demonstrating the potential of AP technology for the management and planning of forestry operations in softwood plantations.

Item Details

Item Type:Refereed Article
Keywords:remote sensing, photogrammetry, forestry, forest inventory
Research Division:Engineering
Research Group:Geomatic Engineering
Research Field:Photogrammetry and Remote Sensing
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Technology
Author:Iqbal, AI (Mr Akhtar Iqbal)
Author:Osborn, J (Dr Jon Osborn)
ID Code:127709
Year Published:2018
Deposited By:Geography and Spatial Science
Deposited On:2018-08-10
Last Modified:2018-09-13
Downloads:0

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