Abstract
Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., ≥ 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).
Highlights
Fostering information extraction in forested areas, in particular at the stand level, is driven by two main goals : statistical inventory and mapping
The results presented were produced using the RFo classification as it provides the best tree species classification
The regularisation was performed with λp = 1 and λi = 1 for the 4 multispectral bands, Normalized Difference Vegetation Index (NDVI), height and λi = 0 otherwise
Summary
Fostering information extraction in forested areas, in particular at the stand level, is driven by two main goals : statistical inventory and mapping. In statistical forest inventory, segmentation is helpful for extracting statistically meaningful sample points of field surveys and reliable features (basal area, dominant tree height, etc.) (Means et al, 2000, Kangas and Maltamo, 2006). A coarse forest stand delineation is performed on the 3-band feature image using the Mean-Shift algorithm, with high value of the parameters in order to obtain under-segmented raw forest stands. A forest mask is applied to the segmented image in order to retrieve forest and non-forest raw stands It may create some small isolated areas that will be merged to their most similar neighbour until their size is larger than a user-defined threshold. Instead of using the original pixels from the 3-band feature image, superpixels are generated with
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