Coastal areas play a key role in the adaptation of ocean-climate change due to their land-sea interface. The mapping and monitoring of their use and cover are crucial to understand where are the most exposed and vulnerable zones and how to manage them in a sustainable way. The finest spatial resolution possible is required to empower the diagnosis and prognosis of coastal objects subject to current and future erosion and/or submersion risks. To date, unmanned aerial vehicles (UAVs) consist of the best platforms to bear sensors capable to provide centimeter-scale 2D and 3D coastal information. The active lidar instrument scans coastal landscapes with a rate of hundreds of thousands points per second propagating at the speed of light. UAV-based lidar products enable to reach the best accuracy and precision in xyz data among the airborne/spaceborne tools. However lidar intensity remains poorly harnessed in Earth Observation from satellite to drone.

Along the bay of Mont-Saint-Michel (France), classifications of nine representative coastal habitats (sediment, soil, salt marsh, dry grass, grass, shrub, tree, car, road) at 1 cm spatial resolution were run based on 2300 pixels of calibration and 2300 pixels of validation for every class, using the DJI Zenmuse L1 data, mounted on a DJI M300-RTK quadcopter. The L1 sensor gathers an active lidar Livox Avia, a passive one-inch blue-green-red (BGR) 20 MP camera, and an inertial measurement unit. The 450m-range Avia instrument emits a 905nm laser at 240 kHz while receiving up to 2 returns.

Landscape-scale classification results were satisfactory based on BGR data (Overall Accuracy, OA: 84,57%), and were substantially improved by 4,14% when adding the mean lidar intensity (OA: 88,71%). At the class-level, road, grass and soil showed better producer’s accuracies (12,83%, 11,3% and 8,95%, respectively), while soil, tree, salt marsh and dry grass were better classified (9,48%, 9,28%, 4,56% and 2,35% of user’s accuracies, respectively) when mean lidar intensity was integrated.