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Causal inference for climate change events from satellite image time series using computer vision and deep learning

arXiv · Oct 25

The paper proposes a method for causal inference using satellite image time series to determine the impact of interventions on climate change, focusing on quantifying deforestation due to human causes. The method uses computer vision and deep learning to detect forest tree coverage levels over time and Bayesian structural causal models to estimate counterfactuals. The framework is applied to analyze deforestation levels before and after the hyperinflation event in Brazil in the Amazon rainforest region.

Where forests grow matters for their climate impact, global study finds

KAUST · Mar 16

A KAUST-led study analyzed the impact of natural forests on local temperatures across the globe, finding that forests provide the greatest climate benefits when conserved or restored in their native locations. The analysis showed that forests generally have net global cooling effects in areas where dense tree cover would naturally exist. The research highlights that forests buffer against extreme temperatures, providing warming effects in freezing areas and cooling effects in warm regions. Why it matters: The findings emphasize the importance of regional scientific evidence in guiding land-use and restoration decisions, ensuring effective and sustainable nature-based solutions in the face of climate change.