Stand density estimators based on individual tree detection and stochastic geometry-Reference-Cited by-同舟云学术

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Stand density estimators based on individual tree detection and stochastic geometry

Published:2016-11 Issue:11 Volume:46 Page:1359-1366
ISSN:0045-5067
Container-title:Canadian Journal of Forest Research
language:en
Short-container-title:Can. J. For. Res.

Author:

Kansanen Kasper¹,Vauhkonen Jari²,Lähivaara Timo³,Mehtätalo Lauri¹

Affiliation:

1. School of Computing, University of Eastern Finland, Postal Box 111, 80101 Joensuu, Finland.

2. School of Forest Sciences, University of Eastern Finland, Postal Box 111, 80101 Joensuu, Finland.

3. Department of Applied Physics, University of Eastern Finland, Postal Box 1627, Kuopio, Finland.

Publisher

Canadian Science Publishing

Subject

Ecology,Forestry,Global and Planetary Change

Link

http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfr-2016-0181

Reference33 articles.

1. Baddeley, A., and Turner, R. 2005. spatsat: an R package for analyzing spatial point patterns. J. Stat. Softw. 12(6). 10.18637/jss.v012.i06.

2. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

3. Chiu, S.N., Stoyan, D., Kendall, W.S., and Mecke, J. (Editors). 2013. Stochastic geometry and its applications. 3rd edition. Wiley, New York. 10.1002/9781118658222.

4. Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations

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