NDLI: A new composite k-tree estimator of stem density

Content Provider	Springer Nature Link
Author	Magnussen, Steen
Copyright Year	2012
Abstract	This study presents a generally applicable and robust k-tree composite estimator of density. We propose to estimate stem density by a weighted average $$ \left( {\hat{\lambda }_{\text{aic}} } \right) $$ of 16 individual density estimators. The weights given to individual estimators are inversely proportional to the relative fit (Akaike’s corrected information criterion) of each estimator to the assumed distribution of observed k-tree distances. The performance of the proposed estimator is evaluated in simulated simple random sampling with k = 3 and 6 in 58 forest stands (54 actual and 4 simulated) and 600 replications. Sample sizes were 15 and 30 locations per stand. Eleven estimators were novel, including three designed for regular spatial patterns. Absolute stand-level bias with k = 6 varied from 0.1 to 8.1% (mean 1.8%), and a bias larger than 6% was limited to 3 stands with either pronounced density gradients or a strong clustering of stem locations. Root mean squared errors were approximately 16% (k = 6 and n = 15) versus 12% for sampling with comparable fixed-area plots. Coverage of computed 95% confidence intervals ranged from 0.72 to 0.99 (median = 0.98 with n = 15 and 0.95 with n = 30), with 98% of all intervals achieving a coverage of 0.85 or better. In seven stands used in an assessment of a novel spatial point pattern reconstruction k-tree density estimator (RDE) by Nothdurft et al. (Can J For Res 40:953–967, 2010), the average absolute bias of $$ \hat{\lambda }_{\text{aic}} $$ with k = 6 was 1.5 versus 0.7% for $$ \hat{\lambda }_{\text{RDE}} $$ .
Starting Page	1513
Ending Page	1527
Page Count	15
File Format	PDF
ISSN	16124669
Journal	Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch
Volume Number	131
Issue Number	5
e-ISSN	16124677
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-03-09
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Plotless sampling Fixed-count sampling Point-to-tree distance Distance sampling Akaike’s information criterion Plant Ecology Forestry Plant Sciences
Content Type	Text
Resource Type	Article
Subject	Plant Science Forestry

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

An adaptive composite density estimator for k-tree sampling

Erratum to: An adaptive composite density estimator for k-tree sampling

Two new density estimators for distance sampling

Erratum to: An adaptive composite density estimator for k-tree sampling

From points to numbers: a database-driven approach to convert terrestrial LiDAR point clouds to tree volumes

Improved fixed-count stand-level estimators of stem density, mean diameter, and basal area

Both stem and crown mass affect tree resistance to uprooting

Information networking for new partnerships in forestry

Analysis of wood density profiles of tree stems: incorporating vertical variations to optimize wood sampling strategies for density and biomass estimations

A new composite k-tree estimator of stem density

Similar Documents

An adaptive composite density estimator for k-tree sampling

Erratum to: An adaptive composite density estimator for k-tree sampling

Two new density estimators for distance sampling

Erratum to: An adaptive composite density estimator for k-tree sampling

From points to numbers: a database-driven approach to convert terrestrial LiDAR point clouds to tree volumes

Improved fixed-count stand-level estimators of stem density, mean diameter, and basal area

Both stem and crown mass affect tree resistance to uprooting

Information networking for new partnerships in forestry

Analysis of wood density profiles of tree stems: incorporating vertical variations to optimize wood sampling strategies for density and biomass estimations

A new composite k-tree estimator of stem density