Deriving vegetation indices for phenology analysis using genetic programming

• We extract plant color information from images and correlate with leaf phenological changes.
• We use vegetation indices associated with plants for pattern analysis and knowledge extraction.
• We present a novel approach for deriving appropriate vegetation indices from images.
• Our method learns phenological patterns from plants through genetic programming.
• We obtain composite vegetation indices that better characterize plant phenology.

Plant phenology studies recurrent plant life cycle events and is a key component for understanding the impact of climate change. To increase accuracy of observations, new technologies have been applied for phenological observation, and one of the most successful strategies relies on the use of digital cameras, which are used as multi-channel imaging sensors to estimate color changes that are related to phenological events. We monitor leaf-changing patterns of a cerrado-savanna vegetation by taking daily digital images. We extract individual plant color information and correlate with leaf phenological changes. For that, several vegetation indices associated with plant species are exploited for both pattern analysis and knowledge extraction. In this paper, we present a novel approach for deriving appropriate vegetation indices from vegetation digital images. The proposed method is based on learning phenological patterns from plant species through a genetic programming framework. A comparative analysis of different vegetation indices is conducted and discussed. Experimental results show that our approach presents higher accuracy on characterizing plant species phenology.