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CollectiveCrunch launches near real-time forest monitoring tool

CollectiveCrunch, creator of the AI-powered platform that enables sustainable forestry at scale, is launching Linda Planet, a new trustworthy tool that enables forestry professionals to assess the health of forests by monitoring and tracking changes in both forest biodiversity and carbon storages in near real-time.

“Our goal is to change the world’s understanding of forests by providing the most accurate, scalable and timely forest analytics possible,” said CollectiveCrunch co-founder Rolf Schmitz. “Through solutions like Linda Planet, we can enable sustainable forestry, assist in the mitigation of carbon losses, and bring transparency to forest carbon markets to spearhead the fight against climate change.”

The urgent need for transparency in the carbon market

The AI-based Linda Planet solution eliminates the uncertainty and subjectivity associated with forest carbon assessments by providing a scientific and data-driven approach to forest carbon management. It does this by helping mitigate forest carbon value loss by tracking changes and potential damages in near real-time, and assists in forecasting the growth and loss of forest carbon stocks.

Linda Planet ensures the accountability of carbon assets behind an offset and provides the transparency necessary for credible carbon development through the AI backbone of the Linda platform. Nominal check-ups with five-year manual auditing cycles of forest carbon assets have proven to be insufficient. In order to have foolproof understanding of the progress and permanence of the natural capital, continuous and accurate visibility into the assets is essential – which is what Linda offers.

Linda Planet is uniquely positioned to bring credible baselines to the market at large scale. Credible baselines are necessary to build a strong foundation for the future of carbon offsets. A data-driven forest carbon inventory helps to define stored carbon and rates of sequestration throughout the forested lands, and assists forest management efforts with precision and high quality.

Tracking biodiversity unveils the reality behind biodiversity loss

A transition to sustainable forestry at a global scale requires analytics ranging from deforestation and carbon stock changes to habitat destruction and biodiversity loss. Ensuring that the world’s forests are on a path towards increased resilience and enhanced biodiversity requires access to the best available intelligence. Resilient forests better withstand natural disturbances and require less time for recovery, reducing the need for clearing trees. Biodiverse forests provide habitats for a wide range of species and ensure that ecosystems remain intact. The ability to track changes in resilience and biodiversity at scale is necessary to address the environmental challenges on the road towards global sustainable forestry.

“The dramatic loss of biodiversity in the forestry ecosystem is a serious global challenge. Linda Planet provides continuous visibility into the forest status and timely assessment of disturbances,” said Schmitz. “We’re thrilled to launch this product to the market to fundamentally change how the forestry industry monitors forest health.”

CollectiveCrunch is a solutions provider that is approved by Verra, a nonprofit organization that operates standards in environmental and social markets, including the world’s leading carbon crediting program, the Verified Carbon Standard (VCS) Program. CollectiveCrunch’s involvement in the Verra carbon accreditation program will further solidify the organization’s methodologies and add a baseline for near-real time carbon-storing data to support the program.

CollectiveCrunch has been developing state-of-the-art AI-based technologies for the forest sector since 2016. Linda Forest today covers 23 million hectares of forest. Continuous monitoring is an essential part of understanding the changes in the sensitive ecosystem of forests and providing visibility into biodiversity and the changes in the forest carbon storages near real-time.