NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias.
Cocoa production has been identified as a major global driver of deforestation, but its precise contribution to deforestation dynamics in West Africa remains unclear. It is also unknown to what degree companies and international markets are able to trace their cocoa imports, and satisfy their sustainable sourcing commitments. Here, we use publicly-available remote-sensing and supply chain data for Côte d’Ivoire, the world’s largest cocoa producer, to quantify cocoa-driven deforestation and trace 2019 cocoa exports and the associated deforestation from their department of origin, via trading companies, to international markets. We find 2.4 Mha of cocoa deforestation and degradation over 2000–2019, i.e. 125 000 ha y−1, representing 45% of the total deforestation and forest degradation over that period. Only 43.6% (95% CI: 42.6%–44.7%) of exports can be traced back to a specific cooperative and department. The majority of cocoa (over 55%) thus remains untraced, either indirectly sourced from local intermediaries by major traders (23.9%, 95% CI: 22.9%–24.9%), or exported by untransparent traders—who disclose no information about their suppliers (32.4%). Traceability to farm lags further behind, and is insufficient to meet the EU due-diligence legislation’s proposed requirement for geolocation of product origins. We estimate that trading companies in the Cocoa and Forests Initiative have mapped 40% of the total farms supplying them, representing only 22% of all Ivorian cocoa exports in 2019. We identify 838 000 hectares of deforestation over 2000–2015 associated with 2019 EU imports, 56% of this arising through untraced sourcing. We discuss issues of company- and state-led traceability systems, often presented as solutions to deforestation, and stress the need for transparency and for the sector to work beyond individual supply chains, at landscape-level, calling for collaboration, stronger regulatory policies, and investments to preserve the remaining stretches of forests in West Africa.
Côte d’Ivoire and Ghana, the world’s largest producers of cocoa, account for two thirds of the global cocoa production. In both countries, cocoa is the primary perennial crop, providing income to almost two million farmers. Yet precise maps of the area planted with cocoa are missing, hindering accurate quantification of expansion in protected areas, production and yields and limiting information available for improved sustainability governance. Here we combine cocoa plantation data with publicly available satellite imagery in a deep learning framework and create high-resolution maps of cocoa plantations for both countries, validated in situ. Our results suggest that cocoa cultivation is an underlying driver of over 37% of forest loss in protected areas in Côte d’Ivoire and over 13% in Ghana, and that official reports substantially underestimate the planted area (up to 40% in Ghana). These maps serve as a crucial building block to advance our understanding of conservation and economic development in cocoa-producing regions.
Cocoa production has been identified as a major global driver of deforestation, but its precise contribution to deforestation dynamics in West Africa remains unclear. It is also unknown to what degree companies and international markets are able to trace their cocoa imports, and satisfy their sustainable sourcing commitments. Here, we use publicly-available remote-sensing and supply chain data for Côte d'Ivoire, the world's largest cocoa producer, to quantify cocoa-driven deforestation and trace 2019 cocoa exports from their department of origin, via trading companies, to international markets. We find 2.5 Mha of cocoa deforestation and degradation over 2000-2019, i.e., 166,257 ha/y, representing 46% of the total deforestation and forest degradation over that period. Only 43.6% (95% CI: 42.6 - 44.7%) of exports can be traced back to a specific cooperative and department. The majority of cocoa (over 55%) thus remains untraced, either indirectly sourced from local intermediaries by major traders (23.9%, 95% CI: 22.9 - 24.9%), or exported by traders who disclose no information about their suppliers (32.4%). Traceability to farm lags further behind, and is insufficient to meet the EU due-diligence legislation's proposed requirement for geolocation of product origins. We estimate that trading companies in the Cocoa and Forests Initiative have mapped 40% of the total farms supplying them, representing only 22% of all Ivorian cocoa exports in 2019. We identify 890,000 hectares of deforestation over 2000-2015 associated with 2019 EU imports, 56% of this arising through untraced sourcing. We discuss issues and prospects for traceability systems, often presented as solutions to deforestation, and stress the need for transparency and for the sector to work beyond individual supply chains, at landscape-level, calling for collaboration and investments to preserve the remaining stretches of forests in West Africa.
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