Assessing the local and landscape drivers of post-fire understory early Pinus radiata invasions in the Coastal Maulino forest using remote sensing data and deep learning techniques

The project “Assessing the local and landscape drivers of post-fire understory early Pinus radiata invasions in the Coastal Maulino forest using remote sensing data and deep learning techniques” aimed to examine the local and landscape effects on post-fire understory P. radiata invasions in the Coastal Maulino forest using remote sensing data and deep learning techniques.

Specific objectives

1. Estimate the overstory canopy cover of P. radiata in all the Coastal Maulino forest before and after five years of the Las Maquinas fire of 2017.
2. Assess understory P. radiata early invasion success in the Coastal Maulino forest after five years of the Las Maquinas fire.
3. Determine the essential factors influencing post-fire understory early P. radiata invasions success in the Coastal Maulino forest at the landscape- and local-levels.