Time series analysis to demonstrate restoration outcomes and system change from satellite data

Abstract

Introduction: Restoring functions to degraded ecosystems is needed to maintain a sustainable planet. Although restoration efforts are widespread, the majority of restoration projects are not monitored, limiting the ability to assess outcomes, adaptively manage, and improve future restoration projects. Remote sensing, with its multi-decadal data and global extent, offers new opportunities for restoration monitoring. However, remote sensing data require analytical approaches that may be unfamiliar to ecologists and practitioners. Objectives: We present a guide to applying time series analysis to assess restoration outcomes via change point detection, using publicly available remote sensing data. Methods: We demonstrate a range of time series analysis techniques for quantifying change at a river corridor restoration site. Results: The tools we present can detect if and when change occurs, what type of changes might be expected if restoration were performed at a similar site, and if restoration treatments cause measurable change. We introduce a flow diagram to help restoration professionals determine which change point detection method is most useful for their needs and software with an example to get started. Conclusions: We provide recommendations for choosing between different types of models for ecologists and practitioners interested in monitoring, assessing, and communicating restoration outcomes.