Inventory of Close-to-Nature Forests Based on the Combination of Airborne LiDAR Data and Aerial Multispectral Images Using a Single-Tree Approach

Abstract

This study is concerned with the assessment of application possibilities for remote sensing data within a forest inventory in close-to-nature forests. A combination of discrete airborne laser scanning data and multispectral aerial images separately evaluated main tree and forest stand characteristics (i.e., the number of trees, mean height and diameter, tree species, tree height, tree diameter, and tree volume). We used eCognition software (Trimble GeoSpatial, Munich, Germany) for tree species classification and reFLex software (National Forest Centre, Zvolen, Slovakia) for individual tree detection as well as for forest inventory attribute estimations. The accuracy assessment was conducted at the ProSilva demo site Smolnícka Osada (Eastern Slovakia, Central Europe), which has been under selective management for more than 60 years. The remote sensing data were taken using a scanner (Leica ALS70-CM) and camera (Leica RCD30) from an average height of 1034 m, and the ground reference data contained the measured positions and dimensions of 1151 trees in 45 plots distributed across the region. This approach identified 73% of overstory and 28% of understory trees. Tree species classification within overstory trees resulted in an overall accuracy slightly greater than 65%. We also found that the mean difference between the remote-based results and ground data was −0.3% for tree height, 1.1% for tree diameter, and 1.9% for stem volume. At the stand level, the mean difference reached values of 0.4%, 17.9%, and −21.4% for mean height, mean diameter, and growing stock, respectively.

Keywords

• forest inventory
• airborne laser scanning
• aerial imaging
• individual tree detection approach
• object-oriented classification