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

Remote Sensing

Dominik Jaskierniak¹  George Kuczera²  Richard Benyon¹  Luke Wallace³  Lars T. Waser⁴ 

[1] Department of Forest and Ecosystem Science, University of Melbourne, Parkville, VIC 3010, Australia; E-Mail:;School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia; E-Mail:;School of Land and Food, University of Tasmania, Sandy Bay, TAS 7005, Australia; E-Mail:;Department of Forest and Ecosystem Science, University of Melbourne, Parkville, VIC 3010, Australia; E-Mail

关键词: LiDAR;    normalised cut;    local maximum filtering;    tree detection;    forest hydrology;    stand sapwood area;    basal area;    stocking density;    forest inventory;   

DOI  :  10.3390/rs70607298

来源: mdpi

【 摘 要 】

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 m⁻², we applied a local maximum filtering (LMF) method and normalised cut (NCut) algorithm to predict stocking density (S_Den) 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 (BA_Ha) per hectare and sapwood area (SA_Ha) per hectare, a well-established proxy for transpiration. Sapwood area was also indirectly estimated with allometric relationships dependent on LiDAR derived S_Den and BA_Ha 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 S_Den across large forested areas. Using NCut derived ITD segments, observed versus predicted stand BA_Ha 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 BA_Ha. For extrapolating one ha resolution SA_Ha estimates across large forested catchments, we found that directly relating SA_Ha to NCut derived LiDAR indices (R² = 0.56) was slightly more accurate but computationally more demanding than indirect estimates of SA_Ha using allometric relationships consisting of BA_Ha (R² = 0.50) or a sapwood perimeter index, defined as (BA_HaS_Den)^½ (R² = 0.48).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.