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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 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. Bayesian Adaptive Sampling (BAS) is a methodology that allows a system to examine currently available data in order to determine new locations at which to take new readings. This procedure leads to the identification of locations where new observations are likely to yield the most information about a process, thus minimizing the required data that must be collected. As an example of the application of this methodology, we examine the question of standing woods in the United States. In order to estimate the amount of carbon sequestered by trees in the United States, the amount of standing woods must be estimated with quantifiable uncertainty (Wheeler, 2006). Such estimates come from either satellite images or near ground measurements. The amounts of error in the estimates from these two approaches are currently unknown. To this end, an autonomous helicopter with differential GPS (Global Positioning System), LIDAR (Light Detection and Ranging), stereo imagers, and spectrometers has been developed as a testing platform for conducting further studies (Wheeler, 2006). These instruments are capable of measuring the reflectance data and the location of the Sun and helicopter in terms of the zenith and the azimuth angles (Figure 1). The objective is to develop a controlling software system for this robotic helicopter, which optimizes the required ground O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

Efficient Data Collection with an Automated Robotic Helicopter Using Bayesian Adaptive Sampling Algorithms for Control