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.

Implications for Practice

Remote sensing time series cannot replace in situ data collection, but can provide low-cost ways for monitoring high-level outcomes of restoration projects between visits. Available software packages for time series analysis provide complementary, but different, information about restoration outcomes, and choosing the correct method for understanding change is non-trivial. Continuous monitoring of sites is essential for quantifying change, and evaluating restoration impacts with remote sensing time series can help to understand and communicate these changes. While we present these tools for analyzing remote sensing time series, they are equally applicable to time series data collected in situ or any other pertinent source.

Recommended citation: Kolarik, N.E., Koehn, C., Nati-Johnson, E., Rojas Lucero, J.C., Martin, C., Caughlin, T.T., Iskin, E., Jochems, L., Neville, H., & Brandt, J. (2025). Time series analyses provide a low-cost and scalable way to assess restoration outcomes from satellite data. Restoration Ecology, e70184.
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