Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data  
[PERSON]  
1 ESRI Deutschland, Department Science and Education, Ringstr. 7, 85402 Kranzberg, Germany; [EMAIL_ADDRESS] (M.W. ); [EMAIL_ADDRESS] (M.B.) Department of Geoinformatics--Z_GIS, University of Salzburg, Schillerrstr. 30, 5020 Salzburg, Austria 1  
[PERSON]  
1 ESRI Deutschland, Department Science and Education, Ringstr. 7, 85402 Kranzberg, Germany; [EMAIL_ADDRESS] (M.W. ); [EMAIL_ADDRESS] (M.B.) Department of Geoinformatics--Z_GIS, University of Salzburg, Schillerrstr. 30, 5020 Salzburg, Austria 1  
[PERSON]  
available. In recent years, deca-metric-resolution imagery (e.g., Landsat) was easier accessible and often cost-free for a broad majority of users, leading to many research projects. With the launch of the Sentinel-2 series in 2015 (Sentinel-2A) [10; 11; 12], a new mission of free and open satellite data with the main objective in land monitoring, new possibilities for research came into existence. The 180\({}^{\circ}\) phased twin-satellite constellation was completed with the launch of Sentinel-2B in March 2017. In the context of forest analysis, the Sentinel-2 mission is very important due to 10 m spatial resolution bands in the visible and the near infrared region (VNIR) as well as four bands (5, 6, 7, 8a) of 20 m resolution in the red-edge region of the electromagnetic spectrum and two bands (11,12) of 20 m resolution in the shortwave infrared (SWIR) [13]. The red-edge region is especially interesting, as it is well known for vegetation analysis and Sentinel-2 offers more bands in this spectral range than comparable satellite missions like the Landsat series. [PERSON] et al. [14] highlights that the respective visible to shortwave infrared Sentinel-2 wavelength regions mainly cover with absorption features of plant pigments and water, making it an ideal sensor for the analysis of vegetation characteristic. Thus, Sentinel-2 can set new standards for vegetation analysis with deca-metric-resolution imagery for areas that do not have a high complexity at a small scale. As the twin constellation of Sentinel-2 is phased at 180\({}^{\circ}\) in the same orbit, a high temporal revisit frequency of 5 days facilitates change detection analysis. It also has the advantage that many different scenes are available for one area of interest, which allows access to cloud-free data. There exist only few studies using Sentinel-2 data for forest analysis as of now: [PERSON] et al. [15] used Sentinel-2 data for their research at two forest sites as well as cropland in Bavaria (Germany). They tested object-based image analysis (OBIA) and pixel-based (PB) methods, with the conclusion, that for their study area OBIA achieved slightly better overall accuracies (66.2% accuracy vs 63.5% accuracy) for the forest classification of seven tree groups. They mentioned the red edge part of the spectrum and the shortwave infrared region as highly important for their study. [PERSON] et al. [16] evaluated vegetation indices for the classification of vegetation using Sentinel-2 and Pleiades data. They mapped Prosopis (negative impacts on biodiversity) and Vachella (positive biodiversity effects as a natural resource) trees as the Prosopis tree invaded Kenya strongly in recent years. In their classification, they tested the Random Forest classification algorithm (RF) with the conclusion, that the blue, green and near-infrared bands of Sentinel-2 were important for classification purposes. Compared to the commercial Pleiades data with a 2 m spatial resolution of the multispectral bands, Sentinel-2 data showed similar results in their study. First positive results with Sentinel-2 data were also reported by [PERSON] et al. [17] in a study about forest defoliation. They tested Random Forest (RF) and Support Vector Machine (SVM) algorithms to investigate the defoliation of Scots pines in Poland with the conclusion that Sentinel-2 data were well-suited for the purpose. [PERSON] et al.