CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Effects of plot size, stand density, and scan density on the relationship between airborne laser scanning metrics and the gini coefficient of tree size inequality
Authors
S Adnan
DA Coomes
M Maltamo
R Valbuena
Publication date
28 September 2017
Publisher
Canadian Journal of Forest Research
Doi
Cite
Abstract
© 2017, Canadian Science Publishing. All rights reserved. Estimation of the Gini coefficient (GC) of tree sizes using airborne laser scanning (ALS) can provide maps of forest structure across the landscape, which can support sustainable forest management. A challenge arise s in determining the optimal spatial resolution that maximizes the stability and precision of GC estimates, which in turn depends on stand density or ALS scan density. By subsampling different plot sizes within large field plots, we evaluated the optimal spatial resolution by observing changes in GC estimation and in its correlation with ALS metrics. We found that plot size had greater effects than either stand density or ALS scan density on the relationship between GC and ALS metrics. Uncertainty in GC estimates fell as plot size increased. Correlation with ALS metrics showed convex curves with maxima at 250–450m 2 , which thus was considered the optimal plot size and, consequently, the optimal spatial resolution. By thinning the density of the ALS point cloud, we deduced that at least 3 points·m −2 were needed for reliable GC estimates. Many nationwide ALS scan densities are sparser than this, so may be unreliable for GC estimation. Ours is a simple approach for evaluating the optimal spatial resolution in remote sensing estimation of any forest attribute
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
University of Toronto Research Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:tspace.library.utoronto.ca...
Last time updated on 17/10/2023
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
info:doi/10.1139%2Fcjfr-2017-0...
Last time updated on 23/04/2021
TSpace Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:localhost:1807/79708
Last time updated on 05/04/2020
Sustaining member
Apollo (Cambridge)
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:www.repository.cam.ac.uk:1...
Last time updated on 12/03/2018