Using canopy height model derived from UAV imagery as an auxiliary for spectral data to estimate the canopy cover of mixed broadleaf forests

Canopy cover is an important structural trait that is frequently used in forest inventories to assess sustainability as well as many other important aspects of forest stands. Remote sensing data is more effective and suitable for canopy cover estimating than traditional field measurements such as sample plots, especially at broad scales. Measurement and mapping this attribute in fine-scale is a difficult task. Aerial imagery using unmanned aerial vehicle (UAV) has been recognized as an excellent tool to estimate canopy attributes. In this study, we compared the potential of using digital hemispherical photography (DHP), digital cover photography (DCP), UAV RGB data, and canopy height model (CHM) for estimation of canopy cover of mix broad-leaved forest on seven different stands. The canopy cover was measured from two digital canopy photographic methods, including DHP (as the reference method) and DCP. The stand orthophotos were segmented using a multi-resolution image segmentation method. Afterward, the classification in two classes of the canopy cover and the non-canopy cover was conducted using minimum distance classification to estimate canopy cover. The CHM layer was generated based on the SfM algorithm and utilized in the canopy cover estimation in each stand as auxiliary data. The results showed a slight improvement when we used the CHM as auxiliary data. Overall, the results showed that the efficiency of the ground digital canopy photographic methods (zenith view) in multi-storied and dense forests is the lowest. In return, our method for digital aerial canopy photography (object-based canopy segmentation and classification) is simple, quick, efficient, and cost-effective.