Preview

PDF 148012 - A comp...pdf | Download (2MB) | Preview

Abstract

Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of ALS data. At the tree level, individual tree detection (ITD) algorithms have been developed to detect and/or delineate individual trees either from ALS point cloud data or from ALS- or DAP-based canopy height models. An examination of the application of ITDs to DAP-based point clouds has not yet been reported. In this research, we evaluate the suitability of DAP-based point clouds for individual tree detection in the Pinus radiata plantation. Two ITD algorithms designed to work with point cloud data are applied to dense point clouds generated from small- and medium-format photography and to an ALS point cloud. Performance of the two ITD algorithms, the influence of stand structure on tree detection rates, and the relationship between tree detection rates and canopy structural metrics are investigated. Overall, we show that there is a good agreement between ALS- and DAP-based ITD results (proportion of false negatives for ALS, SFP, and MFP was always lower than 29.6%, 25.3%, and 28.6%, respectively, whereas, the proportion of false positives for ALS, SFP, and MFP was always lower than 39.4%, 30.7%, and 33.7%, respectively). Differences between small- and medium-format DAP results were minor (for SFP and MFP, differences between recall, precision, and F-score were always less than 0.08, 0.03, and 0.05, respectively), suggesting that DAP point cloud data is robust for ITD. Our results show that among all the canopy structural metrics, the number of trees per hectare has the greatest influence on the tree detection rates

Item Type:	Article
Authors/Creators:	Iqbal, IA and Osborn, J and Stone, C and Lucieer, A
Keywords:	forest inventory, Pinus radiata plantation, individual tree detection, airborne laser scanning, photogrammetry, digital aerial photography, small-format photography, medium-format photography, image point cloud
Journal or Publication Title:	Remote Sensing
Publisher:	MDPI AG
ISSN:	2072-4292
DOI / ID Number:	https://doi.org/10.3390/rs13173536
Copyright Information:	Copyright 2021 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
Related URLs:	UTAS Profile: Osborn, J Google Scholar: Lucieer, A UTAS Profile: Lucieer, A
Item Statistics:	View statistics for this item

Actions (login required)

Item Control Page