Human Modification Map

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https://geoserver.iiasa.ac.at/geoserver/transparent-monitoring/wms?TILED=true

Mazur, E., M. Sims, E. Goldman, M. Schneider, M.D. Pirri, C.R. Beatty, F. Stolle, Stevenson, M. 2025. “SBTN Natural Lands Map v1.1: Technical Documentation”. Science Based Targets for Land Version 1-- Supplementary Material. Science Based Targets Network. https://sciencebasedtargetsnetwork.org/wp-content/uploads/2025/02/Technical-Guidance-2025-Step3-Land-v1_1-Natural-Lands-Map.pdf

The SBTN Natural Lands Map v1.1 is a 2020 baseline map of natural and non-natural land covers intended for use by companies setting science-based targets for nature, specifically the SBTN Land target #1: no conversion of natural ecosystems.

Natural Land

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Human Modification Threshold 30

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Human Modification Threshold 15

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Human Modification Threshold 10

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Human Modification Threshold 5

Natural lands map

This map is widely considered to be the most accurate land cover map of CDI, and hence the most accurate cocoa map.

The Cote d'Ivoire BNETD 2020 Land Cover Map was produced by the Ivorian Government through a national institution, the Center for Geographic Information and Digital from the National Study Office Techniques and Development (BNETD-CIGN), with technical and financial support from the European Union. The methodology used to produce the map was transparent, participatory and in line with international standards.

Cocoa 2020 (BNEDT)

Cocoa farming maps for Côte d'Ivoire and Ghana at a resolution of 10m. The maps are based on Sentinel-2 images taken between January 2019 and December 2021, which are interpreted with an ensemble of convolutional neural networks (CNN), each trained on a large corpus of geo-referenced ground truth cocoa farms and non-cocoa sites, including more than 100,000 farms. Each model predicts a binary cocoa map and the final map is computed by taking the average, indicating the degree of agreement among the models. Additionally, we compute the best threshold at 0.65 according to a held-out validation set, i.e. all values above 65 % in the probability map are classified as cocoa and all others as non-cocoa areas.

https://nk.users.earthengine.app/view/cocoa-map

Cocoa 2019-2021 (ETH)

This map appears to differ from all 3 other maps in terms of spatial distribution, with an almost uniform distribution of cocoa present across the growing region.

https://doi.org/10.1016/j.ecolind.2021.107863

Côte d'Ivoire and Ghana are the largest producers of cocoa in the world. In recent decades the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. Efficient and accurate methods for remotely identifying cocoa plantations are essential to the implementation of sustainable cocoa practices and for the periodic and effective monitoring of forests. In this study, a method for cocoa plantation identification was developed based on a multi-temporal stack of Sentinel-1 and Sentinel-2 images and a multi-feature Random Forest (RF) algorithm. The Normalized Difference Vegetation Index (NDVI) and second-order texture features were assessed for their importance in an RF classification, and their optimal combination was used as input variables for the RF model to identify cocoa plantations in both countries. The RF model-based cocoa map achieved 82.89% producer's and 62.22% user's accuracy, detecting 3.69 million hectares (Mha) and 2.15 Mha of cocoa plantations for Côte d'Ivoire and Ghana, respectively. The results demonstrate that a combination of an RF model and multi-feature classification can distinguish cocoa plantations from other land cover/use, effectively reducing feature dimensions and improving classification efficiency. The results also highlight that cocoa farms largely encroach into protected areas (PAs), as 20% of the detected cocoa plantation area is located in PAs, and almost 70% of the PAs in the study area house cocoa plantations.

https://doi.pangaea.de/10.1594/PANGAEA.917473

Cocoa 2019 (JMU, JRC, SU)

McKinsey acquired Vivid Economics in March 2021. The map is no longer available.

Object-based classification using the following data sources: Sentinel 1/2. Eight land use categories were retained. Cocoa was then extracted from the map for visualisation here. No field-based validation has taken place.

This map was acquired through direct contact with Vivid Economics. 