Monte Carlo simulation was used to determine how violation of the independence assumption affects the empirical probability distribution and Type I error rates of Revusky's Rn statistical test. Simulation results show that the probability distribution of Rn was distorted when the data were autocorrelated. A corrected Rn statistic was proposed to reach a reasonable fit between theoretical (exact...

Error propagation modeling for terrain analysis can provide insights into the robustness of terrain derivatives. For unconstrained terrain derivatives, such as stream and watershed delineation, the most widely used technique for error propagation modeling employs a Monte Carlo simulation of a spatially autocorrelated error model. The current study seeks to make this methodology more accessible ...

[1] The accurate specification of observing and/or modeling error statistics presents a remaining challenge to the successful implementation of many land data assimilation systems. Recent work has developed adaptive filtering approaches that address this issue. However, such approaches possess a number of known weaknesses, including a required assumption of serially uncorrelated error in assimi...

Abundance trends are the basis for many classifications of threat and recovery status, but they can be a challenge to interpret because of observation error, stochastic variation in abundance (process noise) and temporal autocorrelation in that process noise. To measure the frequency of incorrectly detecting a decline (false-positive or false alarm) and failing to detect a true decline (falsene...