06 Nature Credit for Restoring Rainforest and Peatland in Borneo

AI habitat classification model for Central Borneo. We developed a novel algorithm to remove persistent cloud cover over rainforest from images so as to obtain multi-spectral data for continuous habitat classification. The AI Habitat classification algorithm we developed provides a basis for continuous monitoring of habitat condition and change. It can also be used to monitor the development of competing palm oil plantations in the rainforest area. The data is used to outline how a Nature Credit mechanism could be developed. We advocate the Nature Credit would represent uplift across 7 domains across biodiversity habitat, water quality, flood prevention, fauna, carbon, sustainable production. Rather than derive proprietary new metrics for each one, we instead recommend the adoption of the Wallacea Foundation methodolgy for biodiversity uplift. We then using our NCA package turn the natural capital stocks and flows in to monetary value using Natural Capital Accounting to quantify net gain which quantifies how many Nature Credit can be issued.

This will be of interest to international organisations and governments wishing to develop a robust, transparant and verifiable Nature Credit that support nature restoration, protection, carbon, improve water quality and sustainable development that supports local communities.