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Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook

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/persons/resource/sebastian.bathiany

Bathiany,  Sebastian
Potsdam Institute for Climate Impact Research;

Bastiaansen,  Robbin
External Organizations;

Bastos,  Ana
External Organizations;

/persons/resource/lana.blaschke

Blaschke,  Lana
Potsdam Institute for Climate Impact Research;

Lever,  Jelle
External Organizations;

/persons/resource/sina.loriani

Loriani,  Sina
Potsdam Institute for Climate Impact Research;

De Keersmaecker,  Wanda
External Organizations;

Dorigo,  Wouter
External Organizations;

Milenković,  Milutin
External Organizations;

Senf,  Cornelius
External Organizations;

Smith,  Taylor
External Organizations;

Verbesselt,  Jan
External Organizations;

/persons/resource/Niklas.Boers

Boers,  Niklas
Potsdam Institute for Climate Impact Research;

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Zitation

Bathiany, S., Bastiaansen, R., Bastos, A., Blaschke, L., Lever, J., Loriani, S., De Keersmaecker, W., Dorigo, W., Milenković, M., Senf, C., Smith, T., Verbesselt, J., Boers, N. (2024 online): Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook. - Surveys in Geophysics.
https://doi.org/10.1007/s10712-024-09833-z


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_29851
Zusammenfassung
As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research.