Rolf Schmitz, Co-Founder & Co-CEO of CollectiveCrunch

Rolf Schmitz is the Co-Founder & Co-CEO of CollectiveCrunch, a platform changing the world’s understanding of forests by providing the most accurate, scalable, timely analytics globally and enabling sustainable forestry and bring transparency to carbon trading markets.

Rolf is an Engineer by education and holds an MBA from Manchester Business School. He has deep experience in global Business Development and Sales, having built teams in Asia, USA and Europe.

Could you share the genesis story behind CollectiveCrunch?

We are steeped in handling large amounts of data and deriving insights from them. Our initial idea when starting CollectiveCrunch was to combine climate data with business processes as we felt that was an overlooked aspect of climate change.

Initially, we pursued logistics and energy. We built a product that predicts energy generation from wind farms, which is essential in maintaining stability of energy grids. The product is active at Fingrid, the national grid in Finland. However, we found logistics and energy crowded markets that would be hard for a small company to build a leadership role in.

Via a friend of Jarkko, one of our Co-Founders, we became aware of the challenges in creating and maintaining forest inventories. We thought that there was a shockingly low level of technical sophistication. As a result, inventories were expensive, inaccurate, and only done every 5-10 years. The importance of forests in climate change mitigation, ecosystem services and Nature-based Solutions was clear at the time. That’s how CollectiveCrunch became a “forestry AI company.” On a personal level, we all grew up in the countryside, so we had a natural affinity to forests. That’s how we came to build AI models for forests.

What types of tools and cameras are used to monitor a forest?

Our approach is to not specialize on any one sensory method, but to combine all relevant data sources we can get our hands on. Any one sensory method has strengths and weaknesses; combining data sources enables us to counter the weaknesses. For example, optical images are very useful, but they are not available from satellites when there is cloud coverage. In our business satellite-originating data is important, but also LIDAR scans as far as they are available. From a business model perspective, we do not engage in data acquisition, like flying drones or renting planes to scan areas.

Other than the gamut of satellite-based sensory data, LIDAR is a very important tool or method. High-res optical images taken with areal campaigns are less prominent than LIDAR, but also used. A tool that is surprisingly widely in use still is the good old 19^th century method of samples taken manually. With many statistics involved, I’d still call it a tool.

Is the system able to be trained for different localized ecosystems to identify pathogenic infections, abnormalities, and disturbances, or other types of tree diseases?

There is adaptation for different regional ecosystems, including change detection. Tree species, growth patterns and forest management practices vary greatly across regions. The same holds for data acquisition methods and practices. So, it’s not just the trees but also the training data that are different.

What type of actionable insights can be gained from this information?

• Grouped under the term “change detection,” you have detection of storm damage, identification of pest outbreaks and other negative impacts that require intervention to enable intervention on the ground and limit the impact of the damage in question.
• Carbon inventories bring transparency to carbon projects and facilitate the decisions around valuation and purchase of such projects and credits.
• In afforestation projects, the viability of newly planted trees depends on the right amount of moisture in the soil. Detecting excessive dryness or wetness can trigger intervention to prevent such young trees from failing.
• Forest inventories in commercial forestry inform decisions such as thinning of areas (which boosts growth) and optimization of harvests. Species detection makes supply chain more efficient and boost margins. Together, this enables the industry to use the forest resources more efficiently. This is crucial as much of commercial forest is key to sustaining rural communities and in driving the adoption of circular products and packaging.
• Tracking of biodiversity can trigger intervention in case an area is suffering from degradation. Biodiversity is crucial for our forests to become more resilient as we go through this phase of accelerating climate change.

How do analytics benefit sustainable forest ownership?

Several benefits came into play. Firstly, commercial forestry is constantly adopting new measures to become more sustainable. Many of these require better and deeper analytics. By way of example: Clear-cuts, where a forest area is cut 100%, has a strong impact on the local ecosystem. It’s done for efficiency reasons – many sustainable products such as fiber-based packaging could not compete with less sustainable alternatives if the forest industry became less efficient. The industry is exploring alternatives where only the largest trees in each area are cut. It’s much more sustainable, but from a logistics and cost perspective it is a very serious challenge. And it can only be done with state-of-the-art analytics.

Biodiversity is essential for the resilience of forests. Tracking biodiversity and enabling interventions where needed is crucial to the viability of forest in the short and long term.

For carbon capture projects how does the system verify that a project is reducing greenhouse gas emissions as advertised?

The system achieves a certain accuracy for the forest inventory in question, which is verifiable. Most of the greenwashing does not happen on the analytics level but in the way projects are structured. Forest carbon projects that aim at avoiding deforestation mostly suffer from two problems:

• Baselines: This is the set of assumptions projecting what would happen without intervention. The intervention is then calculated as the “additionality” above the baseline. Baselines today do not come out of a data-driven analysis but are often crude averages. Moreover, the baseline is calculated by the project managers themselves, who are in a conflict of interest: the lower the baseline, the more credits are being created.
• Spillage: The phenomenon that the positive things that are happening within the defined project areas (such as reduced logging) are counterbalanced by what’s happening outside of the defined project area. Very often such areas are not tracked, so the project gets credits while the upside is lost to surrounding forests.

The fundamental problem here is that there is a lack of data-driven analytics to independently track what’s going on. It’s possible today, we can do this at scale, but there is a very slow adaptation of state-of-the-art technology in this field. In short, the problem is not the analytics, it’s what the calculation of credits are based on.

Do you have any case studies that you can share of clients using this system?

• ENCE, the largest forest owner in Spain uses our system.
• Our first and largest customer is Metsähallitus (Finnish State Forest).
• Our partner Forliance, one of the largest and most respected carbon project managers globally, works with us in one of the largest carbon projects in Columbia.
• 7 of the Top 10 forestry countries in the European Nordics are our customers. The latest addition is Metsä Group, one of the “big 3” in Finland.

What is your vision for the future of forestry conservation?

Our vision is data-driven with facts-based analytics in Nature-based solutions. It is very clear that we need to move fast to mitigate climate change. Currently, the vast number of forests on the globe gets inventoried every 5-10 years. We should reduce this to monthly tracking to understand what’s going on. On top of that, we need to track biodiversity. Without biodiversity we lose the resilience of our forests in the middle of a climate crisis.

Is there anything else that you would like to share about CollectiveCrunch?

Yes: we can do this at scale. We currently cover 20 million hectares, around 50 million acres of forest. We do this at an accuracy better than the conventional methods we replace. This is real, and it enables transparency in carbon trading markets.

Thank you for the great interview, readers who wish to learn more should visit CollectiveCrunch.