BAYESIAN ADAPTIVE SAMPLING FOR BIOMASS ESTIMATION WITH QUANTIFIABLE UNCERTAINTY

Pinky Thakkar, Steven M. Crunk, Marian Hofer, Gabriel Cadden, Shikha Naik, Kim T. Ninh

Abstract

Traditional methods of data collection are often expensive and time consuming. We propose a novel data collection technique, called Bayesian Adaptive Sampling (BAS), which enables us to capture maximum information from minimal sample size. In this technique, the information available at any given point is used to direct future data collection from locations that are likely to provide the most useful observations in terms of gaining the most accuracy in the estimation of quantities of interest. We apply this approach to the problem of estimating the amount of carbon sequestered by trees. Data may be collected by an autonomous helicopter with onboard instrumentation and computing capability, which after taking measurements, would then analyze the currently available data and determine the next best informative location at which a measurement should be taken. We quantify the errors in estimation, and work towards achieving maximal information from minimal sample sizes. We conclude by presenting experimental results that suggest our approach towards biomass estimation is more accurate and efficient as compared to random sampling.

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Paper Citation


in Harvard Style

Thakkar P., M. Crunk S., Hofer M., Cadden G., Naik S. and T. Ninh K. (2007). BAYESIAN ADAPTIVE SAMPLING FOR BIOMASS ESTIMATION WITH QUANTIFIABLE UNCERTAINTY . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 4: ICINCO, ISBN 978-972-8865-83-2, pages 229-236. DOI: 10.5220/0001628702290236


in Bibtex Style

@conference{icinco07,
author={Pinky Thakkar and Steven M. Crunk and Marian Hofer and Gabriel Cadden and Shikha Naik and Kim T. Ninh},
title={BAYESIAN ADAPTIVE SAMPLING FOR BIOMASS ESTIMATION WITH QUANTIFIABLE UNCERTAINTY},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 4: ICINCO,},
year={2007},
pages={229-236},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001628702290236},
isbn={978-972-8865-83-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 4: ICINCO,
TI - BAYESIAN ADAPTIVE SAMPLING FOR BIOMASS ESTIMATION WITH QUANTIFIABLE UNCERTAINTY
SN - 978-972-8865-83-2
AU - Thakkar P.
AU - M. Crunk S.
AU - Hofer M.
AU - Cadden G.
AU - Naik S.
AU - T. Ninh K.
PY - 2007
SP - 229
EP - 236
DO - 10.5220/0001628702290236