Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

• Autor(a) principal: Almeida, Jurandy
• Data de Publicação: 2012
• Outros Autores: Dos Santos, Jefersson A., Alberton, Bruna [UNESP], Torres, Ricardo Da S., Morellato, Leonor Patricia C. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/eScience.2012.6404438; http://hdl.handle.net/11449/73807
• topic: Cerrado; Color changes; Digital image; Global change; Leaf color; Machine learning approaches; Multichannel imaging; New technologies; Phenological changes; Phenological observations; Plant phenology; Plant species; Species identification; Biology; Colorimetry; Forestry; Learning systems; Phenols

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.