Image series are increasingly being used to output ecological indicators because they provide the ability to reanalyze data that has already been collected and they improve temporal as well as spatial resolution. We address both the increased utilization and the need to diversify the way they are produced by introducing an open source Python (www.python.org) library called EcoIS that creates image series from unaligned pictures of specially equipped plots. We use EcoIS to sample flowering phenology plots in a high arctic environment and create image series that later generate phenophase counts and automatically estimate phenological dates of interest. Our results exhibit one day difference between EcoIS estimations of local indicators and the ones calculated with the established field-based process. We show that EcoIS' error is similar to the one of image series generated with fixed camera setups. We see that EcoIS processes an image in 3.8 s and show how it is equipped to handle data intensive scenarios. We additionally identify in-camera automatic image formatting and image acquiring oversight as contributing factors for increasing the overall error. Our main conclusion is that EcoIS creates usable image series that maintain the spatiotemporal qualities of the original images and can successfully be utilized to generate ecological indicators. EcoIS is relevant as a replacement for traditional image series infrastructure where the cost of deploying EcoIS is smaller or less intrusive to the ecosystem.