Global forest resilience change from 2001 to 2022

ABSTRACT

Forest resilience, the ecosystem’s capacity to withstand perturbations and retain primary functions and characteristics, is an essential indicator in evaluating the fate of an ecosystem under rapid climate change. Detailed information about forest resilience change, such as number of changes, direction (reduced vs increased), timing (start/end year), or duration of the change, is critical but is often not well demonstrated at the global scale. Here, we applied lag-one autocorrelation (AC1) as a forest resilience indicator on time series MODIS vegetation index images from 2001 to 2022 and employed LandTrendr spectral-temporal segmentation algorithms to track forest resilience change. Our results showed that the resilience of global forests changed nearly 3 times on average, and over 50% of global forests had an overall downward trend from the early 2000s up to recently. However, the current condition is changing for the better globally with approximately 53% of global forests currently showing an increased resilience during the recent change, and average patches of reduced resilience becoming smaller and more scattered. Over half of the forests in arid and temperate domains still show decreased resilience recently, which highlights the need for improved management practices.

KEYWORDS:

• Forest resilience
• time series
• LandTrendr

Acknowledgements

The authors are grateful for the help provided by Pauline Lovell, Cornelius Senf, Martí Bosch Padrós, and Thomas Wohlgemuth. This work was supported by the National Key R&D Program of China (2021YFC3000300 and 2023YFB3907500).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data used in this article can be found in the related reference.

Author contribution

Jing Guo designed the study and performed the analysis. Jing Guo drafted the paper with contributions from all co-authors. Zhiliang Zhu revised the paper.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/01431161.2024.2380546

Additional information

Funding

The work was supported by the National Key R&D Program of China [2021YFC3000300,2023YFB3907500].