SIRENE

Summary

The SIRENE (Satellite Information for Resilience Monitoring and Early warning of Ecosystem Tipping Points) project aims to enhance understanding of terrestrial ecosystems and in particular the detection and prediction of their climate-driven tipping points

The project is developing improved methods using advanced Earth Observation (EO) techniques in combination with ground data to monitor vegetation changes - such as photosynthesis, mortality, and structural shifts. This will help assess resilience, focusing on vulnerable regions like rainforests, boreal forests, and drylands and identify early warning signals of potential ecosystem collapse.

The objective of the project is to refine resilience metrics, address uncertainties within the entire process, and explore the potential of machine learning for more accurate predictions. The ultimate goal is to create a robust framework for tracking tipping points, supporting climate risk assessments, and informing global reports like those from the IPCC and IPBES.

Background

From a policy perspective, the lack of reliable early warning systems prevents timely intervention. Due to uncertainties in climate risk assessments, reports from organizations like the IPCC and IPBES may underestimate ecosystem vulnerability. Furthermore, there still exists a disconnect between science and policy, as complex resilience indicators are difficult to translate into actionable measures.

In particular, a critical challenge is the uncertainty in defining intervention thresholds. Since tipping elements develop gradually, policymakers struggle to determine when action is necessary to prevent irreversible ecosystem collapse. Additionally, regional variations in climate impacts mean that policies must be localized rather than generalised.

One major scientific difficulty of detecting and predicting the complexity and nonlinearity of tipping points, making them unpredictable and sometimes irreversible. Current Earth system models (ESMs) struggle to incorporate abrupt ecological changes such as large-scale tree mortality, fire regimes, and ecosystem shifts. Additionally, data uncertainty remains a challenge, as satellite observations are affected by sensor biases, unrelated environmental conditions such as cloud cover, and variations in data processing methods (e.g., cloud correction, vegetation indices). This introduces high uncertainty to estimates of resilience and resilience changes.

Another issue is the lack of long-term observations. This is especially the case for slow-developing tipping elements like ice sheets and ocean circulation, yet it also challenges the robust investigation of vegetation tipping elements. In addition, few best-practices exist, making the standardization of resilience metrics difficult and thus, hindering the comparison of resilience results.

Statistical limitations further complicate predictions. Metrics like Critical Slowing Down (CSD), used to identify early warning signals, may be biased by measurement noise or external influences. Additionally, ecosystems respond differently to stressors such as drought, fire, and land-use change, making it difficult to generalize resilience indicators. Lastly, while AI and machine learning could enhance monitoring, their potential remains largely unexploited in ecosystem resilience studies.

Aims and objectives

The SIRENE project aims to enhance the detection and prediction of climate-driven tipping points in terrestrial ecosystems by developing more reliable resilience metrics and improving early warning systems.

One major focus is improving ecosystem resilience assessments. Current resilience metrics, such as those based on Critical Slowing Down (CSD), have limitations due to potential biases introduced by the noise, short time-series data, and external influences. This project seeks to refine these resilience estimation of terrestrial ecosystems by incorporating multi-sensor satellite data, enhanced and validated with ground-based data, long-term climate records, and robust resilience estimators. We will employ machine learning for detecting patterns and improving prediction accuracy.

To make the results more relevant for policymakers, we will quantify and propagate uncertainties from data and resilience estimation into the final results. This will allow clearer risk assessments. With this, the SIRENE project enables more effective climate adaptation strategies, ecosystem conservation efforts, and land-use planning decisions. We also seek to develop region-specific resilience assessments, recognizing that ecosystems respond differently to climate stressors such as drought, fire, and deforestation. Understanding how different types of ecosystems respond to climate extremes will help guide restoration efforts and improve ecosystem management.

From a broader perspective, the research aims to contribute to global climate risk assessments conducted by organizations like the IPCC and IPBES. By incorporating enhanced resilience indicators into these reports, the project hopes to supply valuable information to international climate policies and conservation strategies.

The SIRENE project envisions a future where climate-driven tipping points can be predicted with greater certainty, allowing for timely policy interventions and conservation actions. The integration of advanced satellite as well as ground-based data, AI-driven analysis, and Earth system modeling will provide a more holistic understanding of ecosystem resilience.

Furthermore, by standardizing resilience metrics and ensuring they are accessible to policymakers, the project aims to foster stronger science-policy collaboration. Ultimately, this research will contribute to global efforts to mitigate climate change impacts and protect vulnerable and valuable ecosystems.

Project plans

Understanding resilience is crucial for predicting vegetation ecosystems’ responses to environmental changes. The SIRENE project aims to improve resilience quantification by integrating Earth Observation (EO) and ground-based data, refining metrics, and addressing uncertainties.

• Review of state-of-the-art science

A comprehensive literature review will assess existing studies on vegetation resilience, focusing on the employed data’s spatial and temporal scales, methodologies, and key findings. This will help identify knowledge gaps and guide best practices for EO data usage. In addition, we will compile an inventory of remote sensing and ground-based datasets, selecting the most promising datasets with respect to data quality and applicability of resilience analysis.

• Advancing Methods for Resilience Monitoring

To enhance resilience monitoring, we will evaluate dataset quality, focusing on uncertainty assessment and fitness-for-purpose. Our analysis will determine whether dataset uncertainties are adequately represented. A ranking of available products will be conducted based on data reliability. For the refined list of products we will perform a gap analysis of uncertainty considerations and assess the overall maturity of the final products.

Furthermore, we will advance resilience metrics by improving Critical Slowing Down (CSD)-based resilience metrics. Traditional methods often assume stationary environmental conditions, which limits their applicability in dynamic ecosystems. Hence, we will ensure the advanced metrics account for state-dependent and time-dependent changes in vegetation.

• Benchmarking and Model-Based Assessment

Resilience metrics will be benchmarked against empirical recovery rates from disturbances like droughts and fires, in satellite but also ground-based data. Simulations of process-based models in controlled environments help us understand the expected metric consistency. Furthermore, we will employ Machine Learning to determine the optimal resilience quantifier by integrating different datasets.

• Bistability and Tipping Elements Analysis

Our project will investigate bistability in ecosystems, particularly in the Amazon basin, where an alternative stable state of low-treecover has been suggested. We will assess the validity of space-for-time assumptions and determine key environmental drivers influencing resilience shifts. Based on the gained knowledge, we will preform global resilience assessment, focusing on tropical rainforests, boreal forests, and drylands.

• Uncertainty Quantification and Trend Analysis

A key component of our research is integrating uncertainty propagation into resilience assessments: we will develop an uncertainty-aware framework to improve future resilience studies. Employing our own framework, we will evaluate the robustness of resilience trends, and relate them to environmental factors such as temperature, precipitation, CO₂ levels, and land-use changes. By addressing methodological gaps, refining resilience metrics, and incorporating uncertainty assessments, the SIRENE project will enhance the reliability of global vegetation resilience estimates and inform climate adaptation strategies.