Landscape heterogeneity and integrated landscape management practices in Benin: a remote sensing, GIS, and stakeholder-based analysis

Introduction Balancing social, economic, and environmental priorities remains a central challenge in integrated landscape management (ILM). Despite increasing recognition of ILM as a framework for addressing these interconnected objectives, empirical evidence on how landscape heterogeneity influences the implementation of ILM practices remains limited, particularly in tropical socio-ecological systems. This study aimed to develop a systematic characterization of landscape unit types in the Agoua Forest Massifs, Monts Kouffé, and Wari-Maro region in central Benin, with a specific focus on integrated landscape management practices. Methods The study combined remote sensing and Geographic Information Systems (GIS) with a participatory approach involving local stakeholders. First, high-resolution satellite imagery was used to generate a detailed map of landscape unit types, highlighting dominant land-use and land-cover patterns. In a second step, household surveys conducted with 459 randomly selected households were used to identify the range of integrated landscape management practices implemented within each landscape unit. Adoption patterns were first summarized using descriptive statistics and Pearson’s chi-square tests, while composite adoption scores for the four ILM pillars (production systems, biodiversity conservation, livelihoods, and conflict management) were compared among landscape units using the Kruskal-Wallis test. Results Four landscape unit types were identified: Mixed Forest-Cropland Landscape (LU1), Agricultural Production Landscape (LU2), Forest-Dominated Landscape (LU3), and Agroforestry Landscape (LU4). LU1 is characterized by a predominance of natural woody vegetation (46% of land cover), whereas LU2 shows a higher proportion of annual cropland (38%). LU3 is strongly dominated by natural woody vegetation (70%), while LU4 exhibits the highest share of tree-based cropping systems (46%). The implementation of ILM practices differed significantly among landscape units across all four pillars (Kruskal-Wallis, p < 0.001). Practices related to production systems and biodiversity, such as crop diversification, agroforestry, and soil fertility management, were more frequently implemented in LU3 and LU4 than in LU1 and LU2. Livelihood diversification and conflict management practices also showed greater implementation in these more heterogeneous landscape units. Discussion Overall, the findings demonstrate that landscape heterogeneity is associated with distinct combinations of ILM practices and that the different landscape units play complementary roles in supporting agricultural production, biodiversity conservation, rural livelihoods, and local resource governance. This study provides a spatially explicit empirical basis for understanding the implementation of ILM in tropical landscapes and contributes to informing future landscape assessments in Sub-Saharan Africa.