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Mapping tree species in tropical seasonal

Accurately mapping the spatial distribution of tree species in tropical environments provides valuable insights for ecologists and forest managers. This process may play an important role in reducing fieldwork costs, monitoring

changes in canopy biodiversity, and locating parent trees to collect seeds for forest restoration efforts. However, mapping tree species in tropical forests with remote sensing data is a challenge because of high floristic and spectral

diversity. In this research,we discriminated andmapped tree species in tropical seasonal semi-deciduous forests (Brazilian Atlantic Forest Biome) by using airborne hyperspectral and simulatedmultispectral data in the 450

to 2400nmwavelength range. After quantifying the spectral variabilitywithin and among individual tree crowns of eight species, three supervised machine learning classifiers were applied to discriminate the species at the

pixel level. Linear Discriminant Analysis outperformed Support Vector Machines with Linear and Radial Basis Function (RBF-SVMs) kernels and Random Forests in almost all the tested cases.


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