Monitoring forest disturbance recovery using metrics derived from multi-spectral satellite time-series: introducing the spectral recovery open-source package with European and Canadian use cases

Forests globally are experiencing unprecedented levels of disturbances, negatively impacting ecosystem functioning and services. Ecosystem restoration (ER) is a global priority to counteract and reverse the effects of disturbances, highlighted by initiatives such as the UN Decade for ER and the Convention on Biological Diversity 30x30 target.   

With increased investments in ER, more effective monitoring is required. Conventionally, ER monitoring relies on field surveys which are costly and infeasible for large or remote restoration sites. Recent advances in remote sensing technologies are seeing this technology increasingly being used to evaluate impacts of natural disturbances on forest ecosystems. Previous research has demonstrated strong correlations between remotely sensed spectral data and the recovery of forest ecosystems post-disturbance. These remote sensing recovery monitoring methods have relied on pre-disturbance status to assess recovery progress. However, increasingly multidisciplinary initiatives and ER management in practice require more flexibility in defining recovery targets. Additionally, ER practitioners face barriers to use remote sensing technology due to computational demands and complexity of time series analysis. 

To address these issues, the Pioneer Earth Observation apPlications for the Environment (PEOPLE) ER project, funded by the European Space Agency, developed spectral-recovery, an open-source, flexible, remote sensing tool to support monitoring of vegetation recovery in forested ecosystems. Written in the open-source Python programming language, the spectral-recovery package provides simple computational methods for analyzing Sentinel-2 or Landsat satellite data time series, with straightforward interfaces that allow users to select from a variety of spectral indices and recovery metrics to monitor recovery trends and trajectories over time. To facilitate the integration of the tool with existing ER practices, users have the flexibility to determine recovery targets using either a historic method, based on the restoration site's historical conditions, or a reference method, which uses reference sites for target conditions. The tool produces raster layers for each index and recovery metric, along with recovery trajectory graphs for each restoration site. This allows for flexible post-tool analysis and mapping visualizations. In this presentation, the potential of this tool is demonstrated via case studies in Canada and Europe of detecting and quantifying forest recovery from wildfire verified by using airborne laser scanning (ALS) data. Results in the Canada case study found that 84% of the tool's estimated recovered area also had met structural recovery targets of height and/or cover, supporting the use of the spectral-recovery tool to monitor, quantify, and map post-disturbance forest recovery at multiple scales. The tool’s ability to provide wall-to-wall recovery estimates over entire restoration sites or landscapes enables the comparison of various restoration activities over time and space through continuous monitoring and consistent metrics, addressing the most prevalent limitations of current ER monitoring efforts.  

The spectral-recovery tool is openly available via Github with demonstration notebooks and documentation, and is presented as an important tool for monitoring forest recovery, and assisting European and other countries in monitoring commitments under international agreements, EU policies, and at national level.