This map shows the Terrestrial Biomes, the eight major terrestrial biomes on Earth are each distinguished by characteristic temperatures and amount of precipitation. Comparing the annual totals of precipitation and fluctuations in precipitation from one biome to another provides clues as to the importance of abiotic factors in the distribution of biomes. Temperature variation on a daily and seasonal basis is also important for predicting the geographic distribution of the biome and the vegetation type in the biome. The distribution of these biomes shows that the same biome can occur in geographically distinct areas with similar climates.

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Côte d'Ivoire and Ghana are the main largest producers of cocoa in the world, however, the cultivation of this crop has led to the loss of vast tracts of forest areas in both countries. The efficient and accurate methods for remotely identifying cocoa farms are essential for the implementation of sustainable cocoa practices and the periodic and effective monitoring of forests. In this study, a multi-feature Random Forest (RF) algorithm was developed to map cocoa farms from other classes. Normalized difference vegetation index (NDVI) and second-order texture features were input variables for the RF model to discriminate cocoa farms in both countries. The estimated area for cocoa in Cote d'Ivoire was 4.8Mha and 2.3Mha for Ghana. The Produce Accuracy (PA) and User Accuracy (UA) of the RF model were 95.08% and 83.69% respectively. The results demonstrate that a combination of the RF model and multi-feature classification can accurately discriminate cocoa plantations, effectively reduce feature dimensions and improve classification efficiency.

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This map shows species richness in country assessed by the International Union for the Conservation of Nature (IUCN) and documented in the IUCN Red List of Threatened Species TM (RLTS). Country summary statistics are expert based as reported by the IUCN in their summary tables: https://www.iucnredlist.org/resources/summarystatistics

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Key Landscape for Conservation (KLC) were proposed in the European Commission publication “Larger than Elephants” (EC, 2015). The polygons shown in the report did not always emcompass the target protected areas and their limits were difficult to use in quantitative spatial analyses.

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The Natural Land Pattern Index (NLPI) assesses the spatial pattern of the natural/semi-natural land by reporting the area (in km2) covered by six spatial pattern classes (core, edge, perforation, islet, margin, core-opening) in which natural/semi-natural land has been classified as of 2015. The Natural Land Pattern Dynamics (NLPD) reports the trends in the area occupied by these pattern classes in the last 20 years (1995-2015). The six pattern classes are determined based on the spatial context and size of the patches of natural/semi-natural land cover, accounting for its proximity to non-natural (agricultural and urban) areas. See below (Use and Interpretation section) for a detailed description of these six classes.

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Industrial mature oil palm plantation in Country (v1) Smallholder mature oil palm plantation in Country (v2)

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The deforestation year is the year when the TMF has been deforested for the first time (followed or not by a regrowth).

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The transition map captures the sequential dynamics of changes by providing transition stages from the initial observation period to the end of the year 2019. It depicts five main land cover types with a few sub-types: (i) remaining undisturbed moist forests (including the mangroves), (ii) degraded forests with two sub-types corresponding mostly to either logged or burned forests, (iii) young forest regrowth, (iv) deforested land that includes three subcategories of converted land cover: (a) water bodies (new dams and river flow changes); (b) tree plantations; and (c) other land cover that includes infrastructure, agriculture and mining, and (v) non-TMF cover (including afforestation).

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