| Texto completo | |
| Autor(es): |
Almeida, Jurandy
;
dos Santos, Jefersson A.
;
Alberton, Bruna
;
Torres, Ricardo da S.
;
Morellato, Leonor Patricia C.
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | ECOLOGICAL INFORMATICS; v. 23, p. 13-pg., 2014-09-01. |
| Resumo | |
Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. (C) 2013 Elsevier B.V. All rights reserved. (AU) | |
| Processo FAPESP: | 10/52113-5 - E-fenologia: aplicação de novas tecnologias para monitorar a fenologia e mudanças climáticas nos trópicos |
| Beneficiário: | Leonor Patricia Cerdeira Morellato |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - Regular |
| Processo FAPESP: | 07/52015-0 - Métodos de aproximação para computação visual |
| Beneficiário: | Jorge Stolfi |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 07/59779-6 - Estudo comparativo da diversidade e da fenologia reprodutiva e vegetativa entre borda e interior num fragmento de cerrado em itirapina, sao paulo. |
| Beneficiário: | Leonor Patricia Cerdeira Morellato |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 11/11171-5 - Gestão de Séries Temporais do e-Phenology |
| Beneficiário: | Jurandy Gomes de Almeida Junior |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 08/58528-2 - Classificacao semi-automatica de regioes em imagens de sensoriamento remoto utilizando realimentacao de relevancia |
| Beneficiário: | Jefersson A dos Santos |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |
| Processo FAPESP: | 09/18438-7 - Classificação e busca em grande escala para dados complexos |
| Beneficiário: | Ricardo da Silva Torres |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |