Phenology research handles multifaceted information that needs to be organized and made promptly accessed by scientific community. We propose the conceptual design and implementation of a database to store, manage, and manipulate phenological time series and associated ecological information and environmental data. The database was developed in the context of the e-phenology project and integrates ground-based conventional plant phenology direct observations with near-surface remote phenology using repeated images from digital cameras. It also includes site-base information, sensor derived data from the study site weather station and plant ecological traits (e.g., pollination and dispersal syndrome, flower and fruit color, and leaf exchange strategy) at individual and species level. We validated the database design through the implementation of a Web application that generates the time series based on queries, exemplified in two case studies investigating: the relationship between flowering phenology and local weather; and the consistency between leafing patterns derived from ground-based phenology on leaf flush and from vegetation image indices (%Green). The database will store all the information produced in the e-phenology project, monitoring of 12 sites from cerrado savanna to rainforest, and will aggregate the legacy information of other studies developed in the Phenology Laboratory (UNESP, Rio Claro, Brazil) over the last 20 years. We demonstrate that our database is a powerful tool that can be widely used to manage complex temporal datasets, integrating legacy and live phenological information from diverse sources (e.g., conventional, digital cameras, seed traps) and temporal scales, improving our capability of producing scientific and applied information on tropical phenology.
Phenology is a traditional science that investigates the periodic phenomena of plants and animals and their relations to environmental conditions. Typically plant phenological studies are based on observations made by phenology experts in the field over time and the correlation with climate data collected by weather sensors. Although within the visualization community several approaches have been proposed for visualizing data that vary over time, many of them have a specific purpose and cannot be applied to phenology studies. Besides that, phenology experts increasingly need tools for managing appropriately long-term time series with many variables of different data types, as well as to identify cyclical temporal patterns. In this work, we propose a novel approach to visualize phenological data by combining radial visual structures along with visual rhythms. Radial visual structures are used to provide contextual insights regarding cyclical phenomena, while the visual rhythm encoding is used to summarize long-term time series into compact representations. We developed, evaluate, and validate our proposal with phenology experts using plant phenology direct observational data both at individuals and species levels.
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