Fusion of multispectral and LIDAR remote sensing data for the estimation of forest attributes in an Alpine region

This paper presents an analysis on the integration of airborne LIDAR and satellite multispectral data (IRS 1C LISS) for the prediction of forest stem volume at plot level. A set of variables has been extracted from both LIDAR and multispectral data and some models have been defined considering data source (LIDAR, multispectral and a combination of both) and the species composition of the plot areas. The analyzed data set comprises 799 ground-truth plots within the forested areas of the Trento Province, Italy (about 3 000 km²), in the Italian Alps. This area is characterized by a large heterogeneity in terms of ecological environments, species composition, morphology, and altitude. Experimental results show that the combination of LIDAR and IRS 1C LISS data for the estimation of forest attributes is effective. The best model developed comprises variables extracted from both these dataset, even if variables derived from LIDAR data provide the most important contribution.