Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1016/j.jag.2016.07.018
Title (Primary) The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area
Author Richter, R.; Reu, B.; Wirth, C.; Doktor, D.; Vohland, M.
Source Titel International Journal of Applied Earth Observation and Geoinformation
Year 2016
Department CLE
Volume 52
Page From 464
Page To 474
Language englisch
Keywords Tree species classification; Hyperspectral data; PLS-DA; SVM; RF; Sample selection; Spectral variable selection; CARS
UFZ wide themes RU1;
Abstract The success of remote sensing approaches to assess tree species diversity in a heterogeneously mixed forest stand depends on the availability of both appropriate data and suitable classification algorithms. To separate the high number of in total ten broadleaf tree species in a small structured floodplain forest, the Leipzig Riverside Forest, we introduce a majority based classification approach for Discriminant Analysis based on Partial Least Squares (PLS-DA), which was tested against Random Forest (RF) and Support Vector Machines (SVM). The classifier performance was tested on different sets of airborne hyperspectral image data (AISA DUAL) that were acquired on single dates in August and September and also stacked to a composite product. Shadowed gaps and shadowed crown parts were eliminated via spectral mixture analysis (SMA) prior to the pixel-based classification. Training and validation sets were defined spectrally with the conditioned Latin hypercube method as a stratified random sampling procedure. In the validation, PLS-DA consistently outperformed the RF and SVM approaches on all datasets. The additional use of spectral variable selection (CARS, “competitive adaptive reweighted sampling”) combined with PLS-DA further improved classification accuracies. Up to 78.4% overall accuracy was achieved for the stacked dataset. The image recorded in August provided slightly higher accuracies than the September image, regardless of the applied classifier.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=17725
Richter, R., Reu, B., Wirth, C., Doktor, D., Vohland, M. (2016):
The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area
Int. J. Appl. Earth Obs. Geoinf. 52 , 464 - 474 10.1016/j.jag.2016.07.018