Post wildfire reforestation with veritree

Approximately 10% of the funding for tree planting goes to protecting the forest for the initial 3-5 years - the critical years where trees are most vulnerable. veritree also establishes buffer pools to cover any potential abnormal losses.

Beyond simply allocating money, veritree works closely with the local communities to showcase the immense value newly planted forests bring to their families. Through education and by involving them in the monitoring and reporting process, veritree finds local communities quickly develop a deep sense of ownership and pride over their trees, leading to effective management over the long-term, keeping forests not just alive, but keeping them thriving.

Veritree views additionality and permanence as critical to helping to feel confident that their projects are having an impact (not just doing something that would have happened anyway), and that the impact is long lasting.

To help ensure additionality, veritree collaborates with both corporate partners and local planting partners from tree order to tree planting and beyond to help ensure a seamless process that creates new carbon sinks that otherwise would not have been planted.

To help ensure permanence, veritree gathers on-the-ground monitoring to ensure trees remain in the ground over the first five years of its life. Part of the tree planting funding goes towards protection (from animals and human intervention) and by year 5 and onward, remote sensing (e.g. satellite imagery) is incorporated to monitor the long-term

• Level 1 - The first level of verification is complete once planting planters have submitted their planting form, activities, and applicable evidence through veritree's Collect App.  
• Level 2 - During the second level of verification, planting site management (leads and supervisors) check the data and information submitted via a planting site review and data check.  
• Level 3 - The third and final level of verification is conducted by veritree. At this stage all submitted data and evidence is analyzed by their internal machine learning algorithms to check for accuracy.