Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

• Published in: Ecological informatics Vol. 23; pp. 49 - 61
• Main Authors: Almeida, Jurandy, dos Santos, Jefersson A., Alberton, Bruna, Torres, Ricardo da S., Morellato, Leonor Patricia C.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2014; Elsevier
• Subjects: Animal, plant and microbial ecology; Biological and medical sciences; Digital cameras; Forestry; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; General forest ecology; Generalities. Production, biomass. Quality of wood and forest products. General forest ecology; Image analysis; Machine learning; Methods and techniques (sampling, tagging, trapping, modelling...); Remote phenology; Tropical forests; Image analysis; Digital cameras; Tropical forests; Remote phenology; Machine learning; Learning; Biome; Phenology; Savanna woodland; Tropical forest; Cerrado
• DOI: 10.1016/j.ecoinf.2013.06.011
• ISSN: 1574-9541

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. •We monitored leaf-changing patterns of a Cerrado vegetation by taking daily images.•We extract color and texture features and correlate them with phenological changes.•We present a machine learning approach to detect phenological patterns in the images.•Our method is able to distinguish species and functional groups of plants by images.•This new tool can help phenology experts in the species identification on-the-ground.