Assessing structural changes at the forest edge using kernel density estimation.

• Automatic quantification and detection of forest edge changes for a large area. • Using kernel density estimation (KDE) to describe ALS data. • 3 indicators describe the KDE curves and reflected the complexity. • Promising tool for using ALS data to analyze the change of forest edges. Due to the strong impact of the composition and structure of the forest edge on forest ecosystems, the value of investigating changes at the forest edge has been recognized for a few decades. Automatic quantification and detection of forest edge changes has advantages over fieldwork with respect to the time and cost required. Based on the structural features extracted from Airborne Laser Scanning (ALS) data, in this study an automatic method using Kernel Density Estimation (KDE) was developed to describe ALS point data related to the forest edge in terms of a finite number of interpretable indicators. The method was applied to identify patterns in data sets at the forest edge between two given dates (2001 and 2014) in European temperate mixed forests. The study area has an extent of 1403 km² and the total length of forest boundary is approx. 5000 km. The calculated structural features explained the vegetation's height distribution and the structural variance at the forest edge. The kernel density curves represented the general structural diversity of the study area. Three extracted indicators described the shape of the kernel density curves and reflected the dynamics of the studied forest edge area. Overall, results from this study show that the proposed approach is a promising tool for using remote sensing data to analyze whether or not the vertical structure of the forest edge for the entire study area has changed and thus to assess automatically the progress of forest management at forest edge. [ABSTRACT FROM AUTHOR]