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Shi, Yifang Individual silver fir (Abies alba) trees accurately mapped using hyperspectral and LiDAR data in a Central European mixed forest Poster
Yifang Shi1, Tiejun Wang1, Andrew K. Skidmore1,4, Stefanie Holzwarth2, Uta Heiden2 and Marco Heurich3
(1) Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Hengelosestraat 99, P.O. Box 217, 7500 AE Enschede, The Netherlands(2) German Aerospace Center (DLR), German Remote Sensing Data Center (DFD) Oberpfaffenhofen, 82234 Wessling, Germany(3) Department of Nature Protection and Research, Bavarian Forest National Park, Freyunger Str. 2, 94481 Grafenau, Germany(4) Department of Environmental Science, Macquarie University, NSW 2109, Australia

Silver fir (Abies alba) is considered an important ecological and functional balancer of European forests. However, this tree species has experienced a widespread decline across Europe during the last centuries. This study aims to accurately map individual silver fir trees in a mixed temperate forest in Germany using integrated airborne hyperspectral and LiDAR data. Tree species and remotely sensed data were collected in the study area between 2015 and 2017. We extracted a set of spectral and structural features from the hyperspectral and LiDAR data, respectively. We compared the performances of three one-class classifiers (i.e. one-class support vector machine, biased support vector machine, and maximum entropy) for mapping individual silver fir trees. Our results showed that the biased support vector machine classifier yielded the highest mapping accuracy, with the area under the curve for positive and unlabeled samples (puAUC) achieved being 0.95 and kappa value at 0.90. We found that the intensity value of 95th percentile of normalized tree height and the percentage of first returns were the most influential structural features, which effectively captured the main morphological difference between silver fir and Norway spruce at the top tree crown. We also found that the wavebands at 700.1 nm, 714.5 nm, and 1201.6 nm were the most important spectral bands, which are strongly affected by chlorophyll and foliar water content. Our study demonstrates that discovering links between spectral and structural features captured by remotely sensed data and species-specific traits can help to improve the mapping accuracy of individual tree species in a natural temperate forest.