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Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., ≥ 2 ha) of homogeneous tree\udspecies composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very\udhigh resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In\udthis paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for\udautomatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree\udspecies whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both\udat pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the\udobject level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at\udtree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through\udthe tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is\udthen minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a\udcontrolled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results\udboth in terms of stand labelling and delineation (matching ranges between 94% and 99%)

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

FOREST STAND SEGMENTATION USING AIRBORNE LIDAR DATA AND VERY HIGH RESOLUTION MULTISPECTRAL IMAGERY