D’une superficie de 225 000 ha, la Réserve de biosphère de Yangambi (République démocratique du Congo) est l’une des principales aires protégées menacées par l’anthropisation dans la région. Malgré son appartenance au réseau mondial des réserves de biosphère depuis les années 1970, les connaissances sur les dynamiques d’occupation du sol y sont encore lacunaires. Une démarche fondée sur l’analyse d’images (Landsat TM, ETM+ et OLI) et la réalité du terrain a été mise en œuvre pour étudier la dynamique d’occupation des sols de la réserve entre 1986 et 2016. Le taux annuel de déforestation est passé de 0,18 % au cours de la période 1986-2003 à 0,38 % en 2003-2016. Durant cette dernière période, la forêt primaire a connu un taux de déforestation plus élevé qu’en forêt secondaire (respectivement 4,5 % et 0,4 %). Quatre zones aux dynamiques socio-environnementales spécifiques ont été identifiées, nécessitant des approches de gestion différentes. Il s’agit notamment de la concession de l’INERA, des zones proches d’axes principaux de circulation, des zones situées sur des axes routiers plus ou moins abandonnés et des zones isolées du cœur de la réserve. Les principales causes de déforestation mentionnées par les habitants sont l’agriculture itinérante sur brûlis (54 % des ménages), l’exploitation artisanale des minerais (17 %) et la fabrication des pirogues (8 %). La pression démographique, sans amélioration du niveau de vie des communautés ou des techniques agricoles, et le développement du réseau routier, favorisant les échanges commerciaux, en sont les causes sous-jacentes. Cette dynamique de déforestation dans une forêt rattachée au réseau mondial des réserves de biosphère entame l’objectif fixé par l’État d’étendre le réseau d’aires protégées à 15 % du territoire national.

The evaluation of deforestation by optical remote sensing remains a challenge in the humid tropical region due to high cloud cover. This paper develops a simple and reproducible method for mapping deforestation of the old-growth forest using open access software. A map of old-growth forest depletion was created using composites from three different dates (2003, 2010, 2016). Four models were tested: the first model using spectral bands (nir, swir1, swir2 and red), the second model was based on the association of spectral bands and spectral indices (NDVI, B54R, NDWI and NBR), the third model was constructed using spectral bands and geomorphological indices (DEM, Slope and Roughness) and the last model combined spectral bands, spectral indices and geomorphological indices. The optimal random forest ntrees and Mtry parameters were determined for each model to optimize the mapping in each model. The out-of-bag error for these four models was 2.15 %, 2.05 %, 1.86 % and 1.85 %, respectively. The fourth model had the lowest error and was hence used to predict deforestation of the old-growth forest. The annual rates of deforestation amounted 0.26 % (69861 ha) and 0.66 % (145768 ha) between 2003 – 2010 and 2010 – 2016, respectively. The area of the old-growth forest in 2016 was 3601607 ha and 215629 ha of forest lost between 2003 and 2016. These results showed that the Random Forest Classification (RFC) model was able to effectively map the reduction of old-growth forests.

The Ituri-Epulu-Aru landscape (IEAL) is experiencing deforestation and forest degradation. This deforestation is at the root of many environmental disturbances in a region characterized by endemism in biodiversity. The importance of this article is to provide useful information for those who wish to discuss a model that can be replicated for other territories affected by deforestation and changes in natural and anthropogenic forest structure. This article focuses on the triangulation of spatialized prospective scenarios in order to identify future trajectories based on the knowledge of historical dynamics through the diachronic analysis of three satellite images (2003–2010–2014–2016). The scenarios were designed in a supervised model implemented in the DINAMICA EGO platform. The three scenarios: business as-usual (BAU), rapid economic growth (REG) and sustainable management of the environment (SME), extrapolating current trends, show that by 2061 this landscape will always be dominated forests (+84%). Old-growth forests occupy 74.2% of the landscape area in the BAU scenario, 81.4% in the SEM scenario and 61.2% in the REG scenario. The SEM scenario gives hope that restoration and preservation of biodiversity priority habitats is still possible if policy makers become aware of it.

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The Ituri-Epulu-Aru landscape (IEAL) is experiencing deforestation and forest degradation. This deforestation is at the root of many environmental disturbances in a region characterized by endemism in biodiversity. This article focuses on the triangulation of spatialized prospective scenarios in order to identify future trajectories based on the knowledge of historical dynamics through the diachronic analysis of three satellite images (2003-2010-2014-2016). The scenarios were de-signed in a supervised model implemented in the DINAMICA EGO platform. The three scenarios Business-As-Usual (BAU), Rapid Economic Growth (REG) and Sustainable Management of the Environment (SME), extrapolating current trends, show that by 2061 this landscape will always be dominated forests (+ 84%). Old-growth forests occupy 74.2% of the landscape area in the BAU scenario, 81.4% in the SEM scenario and 61.2% in the REG scenario. The SEM scenario gives hope that restoration and preservation of biodiversity priority habitats is still possible if policy makers become aware of it.