Dynamic occupancy models provide a flexible framework for estimating and mapping species occupancy patterns over space and time for large-scale monitoring programs (e.g., the North American Bat Monitoring Program (NABat), the Amphibian Research and Monitoring Initiative). Challenges for designing surveys using the dynamic occupancy modeling framework include defining appropriate derived trend parameters, and providing usable tools for researchers to conduct project-specific sample size investigations. We present a simulation-based power analysis framework for dynamic occupancy models that allows for the incorporation of the underlying environmental space (i.e., as covariates) within a specific study region to inform sample size estimation. We investigate two definitions of temporal trend: (1) a gradual, sustained (linear or nonlinear) change over a period of many years, and (2) an abrupt increase or decrease between two time periods. We draw upon pilot data collected following NABat protocols to inform assumed data generating values in a demonstration of our approach. Due to the complicated parameter structure of dynamic occupancy models, we emphasize the importance of visualizing simulated changes over time based on different parameter settings prior to conducting a power analysis. Our simulations revealed that the linearity of short-term trends (five years in our investigation) conferred higher power with lower sample size than longer trends where occupancy probabilities approached zero (ten years in our investigation). We provide an example of how to use our tools to conduct customized investigations using questions posed by NABat, and in doing so, we shed light on general guidelines that can be applied to programs monitoring species occupancy for other taxa. Importantly, we created an R package to execute our approach for informing program-, species-, and study-specific investigations aimed at identifying changes in species occupancy.