Forest disturbances in central Europe caused by fungal pests may result in widespread tree mortality. To assess the state of health and to detect disturbances of entire forest ecosystems, up-to-date knowledge of the tree species diversity is essential. The German state Mecklenburg-Vorpommern is severely affected by ash (Fraxinus excelsior) dieback caused by the fungal pathogen Hymenoscyphus pseudoalbidus. In this study, species diversity and the magnitude of ash mortality was assessed by classifying seven different tree species and multiple levels of damaged ash. The study is based on a multispectral WorldView-2 (WV-2) scene and uses object-based supervised classification methods based on multinomial logistic regressions. Besides the original multispectral image, a set of remote sensing indices (RSI) was derived, which significantly improved the accuracies of classifying different levels of damaged ash but only slightly improved tree species classification. The large number of features was reduced by three approaches, of which the linear discriminant analysis (LDA) clearly outperformed the more commonly used principal component analysis (PCA) and a stepwise selection method. Promising overall accuracies (83%) for classifying seven tree species and (73%) for classifying four different levels of damaged ash were obtained. Detailed tree damage and tree species maps were visually inspected using aerial images. The results are of high relevance for forest OPEN ACCESSRemote Sens. 2014, 6 4516 managers to plan appropriate cutting and reforestation measures to decrease ash dieback over entire regions.

<p>The north-East of Europe is affected by the ash (Fraxinus excelsior) dieback caused by the fungal pathogen Hymenoscyphus pseudoalbidus. A great variety of studies utilize remote sensing data and subsequently derived spectral indices to estimate the magnitude and spatial distribution of the damage for different tree types. </p><p>Often, structural indices, such as the NDVI are applied to detect already affected tree (sometimes even for early detection). However, there are differences in the suitability of an index. While a structural index, might have advantages when the canopy is not closed, pigment-based indices can show more variation within a full crown coverage forest. Therefore, the season of data acquisition might define the preferred index-selection. The same accounts not just for seasonal but for inter-annual changes, too. Here, the pigment indices show a higher sensitivity towards changes due to damages than structural indices.</p><p>To show these differences, the presented study is evaluating a variety of indices derived by hyperspectral imagery for affected ash trees in north-east Germany. This includes images from different phenological stages within one year (2015) and a comparison between 2011, 2015, and 2019 because the decline increased severely within this timespan for the observed trees. The indices were compared with tree damage estimations from the regional forest administration. </p><p>Preliminary results show a better relation for structural indices in autumn, but higher relation for pigment-based indices in spring and summer, once the crown is closed. A higher sensitivity to changes between 2011 and 2019 can be shown for pigment-based indices.</p>