Fusion of airborne LiDAR and multispectral sensors reveals synergic capabilities in forest structure characterization. Issue 6 (1st November 2016)

• Record Type: Journal Article
• Authors: Manzanera, Jose A.; García-Abril, Antonio; Pascual, Cristina; Tejera, Rosario; Martín-Fernández, Susana; Tokola, Timo; Valbuena, Ruben
• Is Part Of: GIScience & remote sensing. Volume 53:Issue 6(2016)
• Page Start: 723
• Page End: 738
• Publication Date: 2016-11-01
• Subjects: airborne laser scanning -- data fusion -- forest Structural Types -- multispectral imagery -- Stand structure; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2016.1231605
• Languages: English
• ISSNs: 1548-1603

Abstract

Abstract : Forest stand structure is an important concept for ecology and planning in sustainable forest management. In this article, we consider that the incorporation of complementary multispectral information from optical sensors to Light Detection and Ranging (LiDAR) may be advantageous, especially through data fusion by back-projecting the LiDAR points onto the multispectral image. A multivariate data set of both LiDAR and multispectral metrics was related with a multivariate data set of stand structural variables measured in a Scots pine forest through canonical correlation analysis (CCA). Four statistically significant pairs of canonical variables were found, which explained 83.0% accumulated variance. The first pair of canonical variables related indicators of stand development, i.e. height and volume, with LiDAR height metrics. CCA also found attributes describing stand density to be related to LiDAR and spectral variables determining canopy coverage. Other canonical variables pertained to Lorenz curve-derived attributes, which are measures of within-stand tree size variability and heterogeneity, able to discriminate even-sized from uneven-sized stands. The most relevant result was to find that metrics derived from the multispectral sensor showed significant explanatory potential for the prediction of these structural attributes. Therefore, we concluded that metrics derived from the optical sensor have potential for complementing the information from the LiDAR sensor in describing structural properties of forest stands. We recommend the use of back-projecting for jointly exploiting the synergies of both sensors using similar types of metrics as they are customary in forestry applications of LiDAR.