Time series of images to improve tree species classification

• Autor(a) principal: Miyoshi, G. T. [UNESP]
• Data de Publicação: 2017
• Outros Autores: Imai, N. N. [UNESP], De Moraes, M. V.A. [UNESP], Tommaselli, A. M.G. [UNESP], Näsi, R.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.5194/isprs-archives-XLII-3-W3-123-2017; http://hdl.handle.net/11449/175481
• topic: Hyperspectral image; Random Forest; SAM; SID; Time series; Tree species classification; UAV

Tree species classification provides valuable information to forest monitoring and management. The high floristic variation of the tree species appears as a challenging issue in the tree species classification because the vegetation characteristics changes according to the season. To help to monitor this complex environment, the imaging spectroscopy has been largely applied since the development of miniaturized sensors attached to Unmanned Aerial Vehicles (UAV). Considering the seasonal changes in forests and the higher spectral and spatial resolution acquired with sensors attached to UAV, we present the use of time series of images to classify four tree species. The study area is an Atlantic Forest area located in the western part of São Paulo State. Images were acquired in August 2015 and August 2016, generating three data sets of images: only with the image spectra of 2015; only with the image spectra of 2016; with the layer stacking of images from 2015 and 2016. Four tree species were classified using Spectral angle mapper (SAM), Spectral information divergence (SID) and Random Forest (RF). The results showed that SAM and SID caused an overfitting of the data whereas RF showed better results and the use of the layer stacking improved the classification achieving a kappa coefficient of 18.26%.