Imaging of charcoal particles extracted from lake sediments provides an important way to understand past fire regimes. Imaging of large numbers of particles can be time consuming. In this note we explore the effects of subsampling and extrapolation of area on estimates of sum charcoal area, using resampling of real and simulated data sets and propose a protocol in which all particles are counted with only the first 100 encountered being imaged. Extrapolated estimates of sum total area of charcoal for 40 real samples were nearly identical to actual values, and error introduced due to subsampling was low (Coefficient of variation <0.2) for all but samples originally containing fewer than 50 particles. Similarly, error was low for simulated data (CV <0.02). Extrapolation provided better estimates of charcoal area than did a regression-based approach. Our results suggest that imaging a fixed number of pieces of charcoal (n = 100) and counting any additional pieces represents a time efficient way to estimate charcoal area while at the same time retaining useful information on particle size and shape

从湖泊沉积物中提取的木炭颗粒的成像提供了一种了解过去火灾状况的重要方法。大量颗粒的成像可能很耗时。在本说明中，我们使用真实和模拟数据集的重采样来探索面积的二次采样和外推对总炭面积估计的影响，并提出一种协议，其中仅对遇到的前100个粒子进行成像，对所有粒子进行计数。对于40个真实样品的木炭总面积的外推估算值几乎与实际值相同，并且除最初包含少于50个颗粒的样品外，所有样品因二次采样而引入的误差均很低（变异系数<0.2）。同样，模拟数据的误差也很低（CV <0.02）。与基于回归的方法相比，外推法可以更好地估计木炭面积。我们的结果表明，对固定数量的木炭进行成像（n  = 100）并计数任何其他碎片代表了一种估算木炭面积的省时方法，同时保留了有关粒径和形状的有用信息