This study investigates the application of remote sensing technologies to identify the biophysical characteristics of marine ecosystems for spatial planning, focusing on optimal conservation scenarios within the Raja Ampat Marine Protected Area (MPA) on Arefi Island, Southwest Papua, Indonesia. Indigenous communities manage this area. WorldView-3 satellite imagery, combined with an object-based image analysis (OBIA) approach, was used to classify and map coastal ecosystems. A Marine Reserve Design using the Spatially Explicit Annealing (Marxan) model was applied to delineate conservation areas and propose zoning strategies. Based on ecological values (EV), three scenarios were tested to prioritize conservation features while ensuring sustainable ecosystem use. Image analysis revealed that Arefi Island's coastal ecosystems cover 64.78 hectares, consisting of seagrass beds (45.41%), coral reefs (36.35%), and mangroves (18.24%), with a kappa accuracy of 0.82. Results indicate that EV3 selects the highest number of planning units, ensuring broader conservation coverage, while EV1 selects the fewest. EV2 is the most budget-friendly option with the lowest cost, whereas EV3 is the most expensive. Ecological Scenario II provided a balanced approach, allocating larger areas for local community use while preserving conservation integrity. Moreover, sensitivity analysis confirmed that a conservation objective targeting 40% of the total area (EV II) is the most effective model for Arefi Island. The zoning breakdown under this scenario includes a core zone of 19.53 hectares, a utilization zone of 15.96 hectares, a sustainable fisheries zone of 15.67 hectares, and other zones covering 92.89 hectares. This study highlights the effectiveness of remote sensing and spatial planning tools, such as Marxan, in marine conservation within indigenously managed areas, emphasizing the importance of balancing conservation efforts with sustainable community use for future planning.
Keywords: Marine protected area; Marxan model; Spatial planning; indigenous management; remote sensing.
©2025 Darmawan et al.