eCite Digital Repository

Using tree detection algorithms to predict stand sapwood area, basal area and stocking density in Eucalyptus regnans forest

Citation

Jaskierniak, D and Kuczera, G and Benyon, R and Wallace, L, Using tree detection algorithms to predict stand sapwood area, basal area and stocking density in Eucalyptus regnans forest, Remote Sensing, 7, (6) pp. 7298-7323. ISSN 2072-4292 (2015) [Refereed Article]


Preview
PDF
7Mb
  

Copyright Statement

2015 the authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) http://creativecommons.org/licenses/by/4.0/

DOI: doi:10.3390/rs70607298

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) (R2 = 0.48).

Item Details

Item Type:Refereed Article
Research Division:Engineering
Research Group:Geomatic Engineering
Research Field:Photogrammetry and Remote Sensing
Objective Division:Environment
Objective Group:Ecosystem Assessment and Management
Objective Field:Ecosystem Assessment and Management of Forest and Woodlands Environments
Author:Wallace, L (Dr Luke Wallace)
ID Code:105726
Year Published:2015
Web of Science® Times Cited:6
Deposited By:Geography and Environmental Studies
Deposited On:2016-01-13
Last Modified:2016-05-26
Downloads:61 View Download Statistics

Repository Staff Only: item control page