A LiDAR-based approach for a multi-purpose characterization of Alpine forests: an Italian case study

Abstract

Several studies have verified the suitability of LiDAR for the estimation of forest metrics over large areas. In the present study we used LiDAR as support for the characterization of structure, volume, biomass and naturalistic value in mixed-coniferous forests of the Alpine region. Stem density, height and structure in the test plots were derived using a mathematical morphology function applied directly on the LiDAR point cloud. From these data, digital maps describing the horizontal and vertical forest structure were derived. Volume and biomass were then computed using regression models. A strong agreement (accuracy of the map = 97%, Kappa Cohen = 94%) between LiDAR land cover map (i.e., bare soil, forest, shrubs) and ground data was found, while a moderate agreement between coniferous/broadleaf map derived from LiDAR data and ground surveys was detected (accuracy = 73%, Kappa Cohen = 60%). An analysis of the forest structure map derived from LiDAR data revealed a prevalence of even-age stands (66%) in comparison to the multilayered and uneven-aged forests (20%). In particular, the even-age stands, whether adult or mature, were overwhelming (33%). A moderate agreement was then detected between this map and ground data (accuracy = 68%, Kappa Cohen = 58%). Moreover, strong correlations between LiDAR-estimated and ground-measured volume and aboveground carbon stocks were detected. Related observations also showed that stem density can be rightly estimated for adult and mature forests, but not for younger categories, because of the low LiDAR posting density (2.8 points m-2). Regarding environmental issues, this study allowed us to discriminate the different contribution of LiDAR-derived forest structure to biodiversity and ecological stability. In fact, a significant difference in floristic diversity indexes (species richness - R, Shannon index - H’) was found among structural classes, particularly between pole wood (R=15 and H’=2.8; P <0.01) and multilayer forest (R=31 and H’=3.4) or thicket (R=28 and H’=3.4) where both indexes reached their maximum values

Using low density LiDAR data to map Mediterranean forest characteristics by means of an area-based approach and height threshold analysis

“…and for forest in Canada (Treitz et al, 2010) (Lindberg and Hollaus, 2012), and mixed forest in Italy (R 2 = 0.58) (Alberti et al, 2013). The results of the present study (in terms of adj.…”

“…In the case of mixed forest (adj R 2 = 0.61, rRMSE = 20.42%), the results were similar to those obtained for mixed forest in Italy (adj. R 2 = 0.64) (Alberti et al, 2013), with a laser pulse density of 2.8 pulses m -2 , and for subtropical rainforest (adj. R 2 = 0.61) in complex terrain in Australia (Ediriweera et al, 2014), with a point density of 1-1.3 pulses m -2 .…”

“…This approach is also very expensive as it requires spatially dense LiDAR data. The area-based approach (ABA) is a less expensive alternative and has commonly been used to generate maps of forest attributes over a wide range of forest types: Temperate (Hall et al, 2005), Boreal (Naesset and Gobakken 2008), Atlantic (Gonçalves-Seco et al, 2011;González-Ferreiro et al, 2012) and Mediterranean forests (González-Olabarria et al, 2012;Alberti et al, 2013). This approach establishes statistical relationships between plot-level LiDAR metrics and stand attributes such as stand height, basal area and volume, which are derived from field measurements.…”

Processing lidar waveform data for 3D visual assessment of forest environments

“…Tree canopies have characteristic texture with gaps which allow the laser beam to reach the ground surface (Lefsky et al, 2002). Information on echoes from the ground and from the canopy allow to model crowns and estimate number of stems and tree heights with sufficient accuracy to be used for inventorial purposes, depending on canopy closure and tree structure (Alberti et al, 2013;Pirotti, 2011). In several forest types and conditions, the estimation of biomass in a stand has been proven to give important results, due to the capability for prediction of lidar-derived stand height or mean height of dominant and codominant trees (Hall et al, 2005;Lefsky et al, 1999;Means, 1999).…”

Terrestrial laser scanning in monitoring of anthropogenic objects

“…Milan, 2009), forestry (e.g. Alberti et al, 2013), archaeology (Valzano et al, 2005), (Cruthley, 2009), hydraulic engineering and geodetic engineering (Zaczek-Peplinska et al, 2014), glaciology (Pętlicki and Kinnard, 2016) and many other fi elds. Laser scanning technology can be distinguished into Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS).…”