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1 INTRODUCTION Forests worldwide are increasingly affected by climate change, particularly through the rise of extreme weather events, such as prolonged droughts, heatwaves and intense storms. These stressors may occur independently or in close sequence, potentially forming compound events that can severely impact forest ecosystems.
4 hours ago A 78-year-old climate protester has had her prison sentence extended by 20 days for being "unlawfully at large", after the authorities failed to find a tracking bracelet that fitted her. Gaie Delap, a retired teacher from Bristol, was sentenced to 20 months imprisonment for her part in a Just Stop …
Abstract With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models.
1 INTRODUCTION Phenology analyses the plant response to climatically driven seasonal variability (Menzel et al., 2020; Piao et al., 2019). Multiple plant physiological metrics for characterising plant phenological conditions are routinely applied. At the plant level, observations such as bud-burst, leaf-out or flowering are recorded to understand phenological shifts across years or over geographical space.
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