Correcting the nondetection bias of angle count sampling. Ritter, T., Nothdurft, A., & Saborowski, J. Canadian Journal of Forest Research, 43(January):344–354, 2013.
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Pdf doi abstract bibtex 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
@Article{Ritter2013,
author = {Ritter, Tim and Nothdurft, Arne and Saborowski, Joachim},
title = {{Correcting the nondetection bias of angle count sampling}},
journal = {Canadian Journal of Forest Research},
year = {2013},
volume = {43},
number = {January},
pages = {344--354},
issn = {0045-5067},
url_pdf = {http://uni-goettingen.de/de/document/download/3c0188500934b105bca9661b33555343.pdf/2013_CanadJFR_Ritter_etal.pdf},
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},
comment = {public},
doi = {10.1139/cjfr-2012-0408},
}Downloads: 0
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