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Evaluating photogrammetric point clouds for forest inventory

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Iqbal, IA ORCID: 0000-0002-0793-2412 2020 , 'Evaluating photogrammetric point clouds for forest inventory', PhD thesis, University of Tasmania.

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Abstract

Accurate and up-to-date information about forest resources is crucial to effective forest management. Remote sensing technologies are increasingly being applied to forest resource assessment, with airborne laser scanning (ALS) now employed for operational forest management in many countries. Statistical relationship between ALS point cloud data and forest structural data collected in the field are exploited to arrive at spatially explicit estimates of forest inventory attributes.
Digital aerial photography (DAP) has recently emerged as a potential alternative to ALS that may provide advantages such as lower deployment and data collection costs and easier access to a greater variety of sensors and platforms. However, the robustness and reliability of DAP methods need to be well understood and proven prior to operationalisation of a photogrammetric approach to point cloud measurement of forest structure.
Pinus radiata is the dominant plantation tree species in Australia and New Zealand. Using a P. radiata study site located in north east Tasmania, Australia, this thesis characterises DAP-based point clouds and evaluates the potential to apply DAP point cloud data to forest inventory.
The thesis investigates the capability of DAP-based point cloud to characterise forest structure, including the influences of photogrammetric processing strategies, terrain slope, canopy occlusion, canopy cover, photo-overlap and camera location. Canopy metrics commonly used in statistical models to estimate forest inventory attributes are examined for three different point cloud generation strategies and are shown to be robust to the choice of strategy.
Point cloud data from small- and medium-format DAP is utilised to estimate area-based forest inventory variables (top height, basal area, stocking and total stem volume). Accuracy as well as precision of the statistically modelled inventory variables are compared with those derived from ALS and with field data at both plot- and stand-level. It is concluded that forest inventory attributes can be estimated using DAP-based point cloud data sourced from cameras with different technical specifications (i.e. small- and medium-format cameras) and that accuracy levels are similar to those derived from ALS.
The suitability of DAP-based point cloud for individual tree detection (ITD) is evaluated. Two point cloud based ITD algorithms are applied to point cloud data sourced from small- and medium-format photography and from ALS. Using each of the ITD algorithms, the accuracy of tree-detection is reported for each of the data types. The influence of canopy structure and the relationship of canopy structural metrics with tree detection are investigated.
This thesis contributes to the understanding of the robustness of DAP-based point cloud data, the capability of DAP-based point cloud data to characterise forest canopy and to be used to derive estimates of forest inventory attributes. The thesis demonstrates the robustness of DAP-based point clouds for area-based forest inventory. The findings will assist forest managers to optimally choose from a variety of sensors and platforms that best suit their operational needs and to optimise flight planning and photo-acquisition for forest inventory applications.

Item Type: Thesis - PhD
Authors/Creators:Iqbal, IA
Keywords: Forest inventory, Pinus radiata, Airborne laser scanning, Digital aerial photography, Photogrammetry, Small format photography, Medium format photography, Image point cloud, Random Forest
DOI / ID Number: 10.25959/100.00035033
Copyright Information:

Copyright 2019 the author

Additional Information:

Chapter 2 appears to be the equivalent of an Accepted Manuscript of an article published by Taylor & Francis in Australian forestry on 9 July 2018, available online: http://www.tandfonline.com/10.1080/00049158.2018.1482799

Chapter 3 appears to be the equivalent of a post-print version of an article published as: Iqbal, I. A., Musk, R. A., Osborn, J., Stone, C., Lucieer, A., 2019. 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, 231–241

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