Assessing surface area of coarse woody debris with line intersect and perpendicular distance sampling

Coarse woody debris (CWD) plays an important role in many terrestrial and aquatic ecosystem processes. In recent years, a number of new methods have been proposed to sample CWD. Of these methods, perpendicular distance sampling (PDS) is one of the most efficient methods for estimating CWD volume in terms of both estimator variance and field effort. This study extends the results for PDS to the estimation of the surface area of CWD. The PDS estimator is also compared to two line intersect sampling (LIS) estimators, where one of the LIS estimators requires the measurement of surface area on each log and the other estimates surface area using a single measurement of log circumference at the point of intersection between the log and the line. The first estimator approximates the true surface area by assuming either a conic or parabolic stem form and requires measurements of the end diameters of each log, which is more time consuming than a single measurement. The performance of the three estimators was compared using a computer simulation. The results of the simulation indicate that, given the same number of pieces of CWD sampled at each point, equal variances can be achieved with PDS using sample sizes that range from about 10% to in excess of 100% the size of a comparable LIS estimator. When the LIS estimators were compared, the estimator that required the measurement of surface area was only about 3%-6% more efficient than the alternative estimator, but the bias associated with assuming a conic or parabolic stem form ranged from roughly 5% to 15%. We conclude that PDS will generally outperform either of the LIS estimators. Another important conclusion is that the LIS estimator based on a measured surface area is likely to have a higher mean squared error than an LIS estimator that employs a single measurement of circumference. Thus, LIS sampling strategies that require the least amount of field work will often have the smallest mean square error.