Estimating over- and understorey canopy density of temperate mixed stands by airborne LiDAR data

Abstract

Estimating forest structural attributes is one of the essential forestry-related remote sensing applications. The methods applied so far typically concentrate on the structure of the overstorey. For various conservation and management applications, however, information on lower layers is also of great interest. Detecting understorey cover by remote sensing is challenging, as passive sensors do not penetrate to the forest ground layer. An alternative to these is 3D metrics from active light detection and ranging (LiDAR). Here, we evaluate this technique for describing the vegetation density of multiple stand layers within the temperate stands of a large protected area in south-eastern Germany. We combined LiDAR metrics and information on forest habitat types with regression models to investigate LiDAR metrics that are significantly correlated with vegetation density. The top canopy and the herbal layer showed strong correlations with the applied LiDAR metrics, whereas the predictive power was lower for the intermediate stand layers. Moreover, our results suggest that the relationship between LiDAR predictors and vegetation density depends on the forest type. A comparison of the regression models with random forest predictions showed no major improvement in predictive error. In conclusion, this study highlights the value of the LiDAR metrics for characterizing the structural properties of lower forest layers, which has implications for wildlife and forest management applications, especially in protected areas.