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

TL;DR

This paper introduces a novel method combining computer vision, deep learning, and Bayesian causal modeling to quantify the effects of interventions like deforestation on forest cover using satellite image time series.

Contribution

It presents a new framework integrating satellite imagery analysis with causal inference techniques to assess intervention impacts on climate-related events.

Findings

Successfully quantified deforestation effects in Brazil's Amazon region.

Demonstrated causal attribution of human activities to forest cover change.

Provided a scalable approach for analyzing climate interventions using satellite data.

Abstract

We propose a method for causal inference using satellite image time series, in order to determine the treatment effects of interventions which impact climate change, such as deforestation. Simply put, the aim is to quantify the 'before versus after' effect of climate related human driven interventions, such as urbanization; as well as natural disasters, such as hurricanes and forest fires. As a concrete example, we focus on quantifying forest tree cover change/ deforestation due to human led causes. The proposed method involves the following steps. First, we uae computer vision and machine learning/deep learning techniques to detect and quantify forest tree coverage levels over time, at every time epoch. We then look at this time series to identify changepoints. Next, we estimate the expected (forest tree cover) values using a Bayesian structural causal model and projecting/forecasting the counterfactual. This is compared to the values actually observed post intervention, and the difference in the two values gives us the effect of the intervention (as compared to the non intervention scenario, i.e. what would have possibly happened without the intervention). As a specific use case, we analyze deforestation levels before and after the hyperinflation event (intervention) in Brazil (which ended in 1993-94), for the Amazon rainforest region, around Rondonia, Brazil. For this deforestation use case, using our causal inference framework can help causally attribute change/reduction in forest tree cover and increasing deforestation rates due to human activities at various points in time.