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Remote Sens. 2015, 7(6), 7298-7323; doi:10.3390/rs70607298

Using Tree Detection Algorithms to Predict Stand Sapwood Area, Basal Area and Stocking Density in Eucalyptus regnans Forest

1
Department of Forest and Ecosystem Science, University of Melbourne, Parkville, VIC 3010, Australia
2
School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia
3
School of Land and Food, University of Tasmania, Sandy Bay, TAS 7005, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Lars T. Waser and Prasad S. Thenkabail
Received: 2 December 2014 / Revised: 22 May 2015 / Accepted: 29 May 2015 / Published: 3 June 2015
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Abstract

Managers of forested water supply catchments require efficient and accurate methods to quantify changes in forest water use due to changes in forest structure and density after disturbance. Using Light Detection and Ranging (LiDAR) data with as few as 0.9 pulses m2, we applied a local maximum filtering (LMF) method and normalised cut (NCut) algorithm to predict stocking density (SDen) of a 69-year-old Eucalyptus regnans forest comprising 251 plots with resolution of the order of 0.04 ha. Using the NCut method we predicted basal area (BAHa) per hectare and sapwood area (SAHa) per hectare, a well-established proxy for transpiration. Sapwood area was also indirectly estimated with allometric relationships dependent on LiDAR derived SDen and BAHa using a computationally efficient procedure. The individual tree detection (ITD) rates for the LMF and NCut methods respectively had 72% and 68% of stems correctly identified, 25% and 20% of stems missed, and 2% and 12% of stems over-segmented. The significantly higher computational requirement of the NCut algorithm makes the LMF method more suitable for predicting SDen across large forested areas. Using NCut derived ITD segments, observed versus predicted stand BAHa had R2 ranging from 0.70 to 0.98 across six catchments, whereas a generalised parsimonious model applied to all sites used the portion of hits greater than 37 m in height (PH37) to explain 68% of BAHa. For extrapolating one ha resolution SAHa estimates across large forested catchments, we found that directly relating SAHa to NCut derived LiDAR indices (R2 = 0.56) was slightly more accurate but computationally more demanding than indirect estimates of SAHa using allometric relationships consisting of BAHa (R2 = 0.50) or a sapwood perimeter index, defined as (BAHaSDen)½ (R2 = 0.48). View Full-Text
Keywords: LiDAR; normalised cut; local maximum filtering; tree detection; forest hydrology; stand sapwood area; basal area; stocking density; forest inventory LiDAR; normalised cut; local maximum filtering; tree detection; forest hydrology; stand sapwood area; basal area; stocking density; forest inventory
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jaskierniak, D.; Kuczera, G.; Benyon, R.; Wallace, L. Using Tree Detection Algorithms to Predict Stand Sapwood Area, Basal Area and Stocking Density in Eucalyptus regnans Forest. Remote Sens. 2015, 7, 7298-7323.

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