DIMPEO

Motivation

‍In recent years, the forest damage caused by dry and hot summers has become a pressing concern for multiple stakeholders in Switzerland and neighboring regions. Understanding where and when climate extremes affect forest health and vitality is a crucial step for developing forecasting and early warning systems. Satellite-based remote sensing provides a powerful tool for monitoring forests, enabling the detection of anomalous “browning”, i.e. an unexpected decrease in plant growth or sudden reduction in plant productivity, of the vegetated land surface. However, working with large and noisy Earth Observation (EO) data presents challenges, so how can we accurately identify these anomalies? This project aims to implement a robust pipeline for detecting land surface browning anomalies using high-resolution EO time series data. By doing so, we can map and quantify the impacts of summer droughts on Swiss forests and identify drivers of browning events.

Proposed Approach / Solution

‍In the first step, suitable algorithms for greenness anomaly detection are identified and implemented. Surface greenness depends on the seasons, elevation, exposition, location, and the terrain, so these factors will have to be accounted for. The resulting anomaly detection model should scale to the high-resolution EO data covering the entirety of the Swiss forests. In the second step, the detected anomalies will be attributed to ecological drivers by relating them with respect to additional covariates, such as summer droughts, late frost, deforestation, and storm damage.

Impact

‍Developing capacity for drought impact monitoring and near-term forecasting (“early warning”, weeks-months lead time) has been declared a high priority by the Swiss Federal Council and a national program for its implementation has recently been launched as part of the National Centre for Climate Services (NCCS). Methodological developments achieved by this project will enable an open-access, robust, transparent, and updatable drought impact detection and quantification workflow and will serve to improve methods in drought impact and forest fire forecasting, achieved in future work.