Our database serves as the foundation that helps us store, organize and make sense of all our tree planting data.
While we have always stored data about individual trees and seedlings, species’ properties and optimal forestry practices, these databases – much like our company – have grown more sophisticated with every passing year. Today’s challenges lay in gathering, analyzing and applying all the data we have amassed over the years, along with scientific peer-reviewed agroforestry and synthesizing a structure of information that we can work with now and scale-up in the future. Land Life Company’s own software engineers are currently busy building the back-end infrastructure to enable this.
Nowadays, we collect information focused on more than just trees. For example, our monitoring systems app gathers information about local climate, soil, planting site statistics, (a)biotic stress, the performance of the Cocoon’s shelter component and so on. In other words, we are busy actively expanding our databases to collect data beyond merely the ‘tree’ as an entity independent from various other environmental factors. Our broader data collection remains relevant to optimizing the growth, health and resilience of our seedlings.
We use open-source relational databases, which enable us to store and retrieve data efficiently and securely. Our databases contain many advanced features such as GIS –geographic information system– support. Land Life Company trees have a specific coordinate, and each plot of land we plant in has a polygon area. When we use our drone, one single fly over video consists of over 20,000 still photos, each with corresponding spatial GIS tags. Subsequently, as we begin utilizing more sophisticated technology in the field, our databases are going to grow exponentially. We are also currently busy developing a drone algorithm to automate the gathering of this influx of data fully.
One aspect of our success is that we do not build our databases so that only software engineers can navigate them. Our in-house tech team made both a mobile app and a web application to help our operations and science team, both in and out of the field. This user-friendly interface allows staff to both input and access information from the databases. These same databases are also used to supply up-to-date site information from the field directly to our client’s Customer Dashboards.
In the future, we want to take these millions of data points about trees, forestry and specific locations and process it through a machine learning algorithm. In this way, when a user says, “I wish to plant in this part of Spain, which has certain soil conditions and weather patterns,” the algorithm suggests a set of species that will be most successful, based on our own site data and historical data. The algorithm can put forth an optimal planting combination and then ties this to data models that will project CO2 sequestrations. As such, the Land Life Company team is rapidly building the databases’ capacity to store and interpret data through machine learning.