Paper Info
Title
Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna
Abstract
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.
Year
DOI
Venue
2012
10.1109/eScience.2012.6404438
eScience
Keywords
DocType
Citations 
leafing phenological change,daily digital image,digital image,remote phenology,phenological observation,cerrado savanna,phenological change,RGB channel,phenological pattern,digital camera,color change information,functional group
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Ricardo da S. Torres180545.77
Bruna Alberton2627.50
Jurandy Almeida343135.15
Jefersson A. dos Santos4223.48
Leonor Patricia C. Morellato5648.54