SNAPP: Landscape-Scale Forest Assessments to Support Zero-Deforestation Supply Chains

Most tropical deforestation is caused by the production of internationally traded commodities including oil palm, cattle, and pulp and paper. Many companies involved in these sectors have agreed to eliminate deforestation from their operations and supply chains. Achieving these commitments will require scientifically grounded and commonly agreed upon standards and tools. In the oil palm and timber sectors, the High Carbon Stock (HCS) approach is emerging as the primary methodology for the identification of forests that must be protected as part of a zero-deforestation commitment. Developed through a series of negotiations between NGOs and corporations, the HCS Approach provides transparent and science-based steps to identify biodiverse and carbon-rich forests. However, the HCS approach faces several challenges including: 1) High costs of field surveys that threaten to exclude smallholder producers from zero-deforestation supply chains; 2) Heterogeneous methods that may undermine trust and complicate monitoring; and 3) Limitations in spatial scales that complicate conservation planning. Here, we propose a project to synthesize field observations collected through previously completed HCS assessments. Using this data within Google Earth Engine, we will train a machine learning classifier to predict HCS forests across the islands of Borneo and Sumatra. Collaborating with project participants from academia, industry and non-profits, we will conduct scientific analysis to enable 1) greater participation of smallholder producers in zero-deforestation supply chains; 2) higher confidence in the benefits of corporate zero-deforestation commitments; and 3) improved conservation planning through the integration of landscape-scale connectivity assessment. Project outputs will include scientific publications, technical guidelines and a publicly available tool to support transparent, homogenous, and low-cost HCS assessments.

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