3D Landscape structure and carbon storage mapping in Mediterranean Dehesas of southern Spain

High resolution mapping is a challenge for 3D landscape pattern and process identification. In addition to the usual geospatial data (vector and raster formats), LiDAR (Light Detection and Ranging) data has become an important information source for a comprehensive study of 3D landscape structure and detailed forest inventories (Dragut et al. 2010, Hyppa et al. 2012, Leitold et al. 2015). Furthermore, aboveground biomass and carbon density at local and regional scale are estimated using LiDAR data (e.g. Zhao et al. 2012; Dalponte and Coomes 2016, Meng et al. 2016). High density LiDAR point clouds are either not available or costly for large areas (Wulder et al. 2012). In Spain, low point density LiDAR data is available from the National Geographic Institute (IGN; PNOA-LiDAR Project). LiDAR data is generated by airborne laser scanning over all Spain territory since 2009. The landscape of Mediterranean dehesas in mountain ranges of southern Spain, i.e. savanna-like open forest, consists of grassland with tree cover of sclerophyllous species, and include forest patches of high tree density and shrublands. The spatial pattern of trees in dehesas is a challenge for low density LiDAR point cloud applications, though some case studies in pine forests are known (Montalvo et al. 2013, Cabrera et al. 2014).
The specific objectives using low density LiDAR point clouds are: (1) to develop an automated method for describing a comprehensive 3D height and spatial arrangement of tree vegetation; (2) to define the different types of landscape units based on their 3D vegetation structure, and (3) to describe the spatial distribution of carbon storage in aboveground biomass. The study area is a farm of 705 hectares, located in the Parque Natural Sierra de Hornachuelos (Sierra Morena, south of Spain). This farm presents a typical dehesa landscape with tree cover of cork oak (Quercus suber) and holm oak (Quercus ilex subsp. ballota), mixed with other tree species, at a specific location, such as carob tree (Ceratonia siliqua) and wild olive tree (Olea europaea var. sylvestris). We used the LiDAR data of IGN. This LiDAR data is recorded by a sensor up to four echoes per laser light pulse. It allows us to obtain a 3D point cloud with a variable height and density related to the vegetation spatial pattern. Figure 1 shows examples of LiDAR point cloud applied to describe the structure of the landscape. The vegetation height and other attributes (LiDAR-derived variables) have been estimated using Digital Terrain Models, Surface Terrain Models and GIS methods. Vegetation LiDAR data are used for segmentation of tree cover units within landscape (Figure 2). Carbon storage in aboveground tree biomass (stem wood) was estimated at landscape units using measured variables at sample plots and bibliographical data (Montero et al., 2005). LiDAR-variables were calibrated with field data. Results show a reasonable description and mapping of 3D vegetation patterns at landscape scale.