Updating lidar-derived crown cover density products with sentinel-2

Crown cover density (CCD) is one important forest attribute used in forest management. With remote sensing, crown cover density maps can be derived from the spectral information of optical satellite imagery or from a normalized digital surface model (nDSM). LiDAR data based applications provide the most accurate results, but LiDAR campaigns are expensive and available data are often outdated. We propose a new method to update LiDAR-derived CCD products and to map forest change. The method is based on Sentinel-2 imagery and an outdated LiDAR nDSM used to train a kNN classifier. CCD estimations are derived for two tests sites in Austria. Results are compared with the LiDAR CCD values in unchanged forest and with the latest tree cover density product of the European Copernicus High Resolution Layers Forest. Results demonstrate the operability of the workflow. User accuracies for forest change detection are very high with 87.3% and 94.8%.