Correcting the nondetection bias of angle count sampling
2013 | journal article; research paper. A publication with affiliation to the University of Göttingen.
Jump to: Cite & Linked | Documents & Media | Details | Version history
Documents & Media
Details
- Authors
- Ritter, Tim; Nothdurft, Arne; Saborowski, Joachim
- Abstract
- The well-known angle count sampling (ACS) has proved to be an efficient sampling technique and has been applied in forest inventories for many decades. However, ACS assumes total visibility of objects; any violation of this assumption leads to a nondetection bias. We present a novel approach, in which the theory of distance sampling is adapted to traditional ACS to correct for the nondetection bias. Two new estimators were developed based on expanding design-based inclusion probabilities by model-based estimates of the detection probabilities. The new estimators were evaluated in a simulation study as well as in a real forest inventory. It is shown that the nondetection bias of the traditional estimator is up to -52.5%, whereas the new estimators are approximately unbiased.
- Issue Date
- 2013
- Journal
- Canadian Journal of Forest Research
- Organization
- Fakultät für Forstwissenschaften und Waldökologie ; Büsgen-Institut ; Abteilung Ökosystemmodellierung ; Abteilung Ökoinformatik, Biometrie und Waldwachstum
- ISSN
- 0045-5067
- Language
- English