Airborne Laser Scanning (ALS) data hold a great deal of promise in monitoring the reduction of single trees and forests with high accuracy. In the literature, the canopy height model (CHM) is the main input used frequently for forest change detection. ALS also has the key capability of delivering 3D point clouds, not only from the top canopy surface, but also from the entire canopy profile and also from the terrain. We investigated the use of two additional parameters, which exploit these capabilities for assessing the reduction of wooded area: Slope-adapted echo ratio (sER) and Sigma0. In this study, two ALS point cloud data sets (2005 and 2011) were used to calculate Digital Surface Model (DSM), sER, and Sigma0 in 1.5 km2 forest area in Vorarlberg, Austria. Image differencing was applied to indicate the change in the three difference models individually and in their combinations. Decision trees were used to classify the area of removed trees with the minimum mapping unit of 13 m2. The final results were evaluated by a knowledge-based manual digitization using completeness and correctness measures. The best result is achieved using the combination of sER and DSM, namely a correctness of 92% and a completeness of 85%.