In 2021, Tajogaite erupted along the Cumbre Vieja ridge on the island of La Palma, Canary Islands. Immense sulfur emissions impacted pine forests in up to a 7-km radius around the new crater as can be observed in satellite imagery. Although such extreme events have taken place on the island before, volcanic activity is not the only major disturbance regime governing the pine forest. Fire burns naturally and frequently on the island as evidenced by the highly fire adapted Pinus canariensis. The Canary Island pine is unique among pine species due to its ability to resprout from its aboveground organs almost immediately following disturbances like fire or intense sulfur exposure. At the time of the eruption, the neighboring forests were still recovering from the last fire in 2016 and also experienced fires in 2009 and 2012. Thus, the eruption presents the opportunity to examine the fire return interval on La Palma and to test contemporary ideals regarding compound disturbances. Here, we investigate the regeneration of the pine forest surrounding Tajogaite crater by conducting a time series image analysis of remotely sensed NDVI. We then deploy a machine learning algorithm to identify the most important variables influencing the recovery of Pinus canariensis following the Tajogaite eruption.