Modeling plant phenology database: Blending near-surface remote phenology with on-the-ground observations

Abstract 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.

[1]  Leopoldo Magno Coutinho,et al.  O conceito do cerrado , 1978 .

[2]  W. Köppen,et al.  Grundriss der Klimakunde , 1931 .

[3]  H. G. Baker,et al.  Tropical Plant Phenology: Applications for Studies in Community Ecology , 1974 .

[4]  L. Morellato,et al.  Effects of environmental conditions associated to the cardinal orientation on the reproductive phenology of the cerrado savanna tree Xylopia aromatica (Annonaceae). , 2011, Anais da Academia Brasileira de Ciencias.

[5]  Jonas Dierenbach,et al.  The plant phenological online database (PPODB): an online database for long-term phenological data , 2013, International Journal of Biometeorology.

[6]  Maria Gabriela G. Camargo,et al.  Fruit color and contrast in seasonal habitats – a case study from a cerrado savanna , 2013 .

[7]  Jurandy Almeida,et al.  Visual rhythm-based time series analysis for phenology studies , 2013, 2013 IEEE International Conference on Image Processing.

[8]  Marco Antonio Assis,et al.  Estrutura e composição florística de um Cerrado sensu stricto e sua importância para propostas de restauração ecológica , 2013 .

[9]  L. A. Fournier,et al.  Un método cuantitativo para la medición de características fenológicas em árboles , 1974 .

[10]  L. Patrícia,et al.  Phenology of Atlantic Rain Forest Trees: A Comparative Study1 , 2000 .

[11]  Maria Gabriela G. Camargo,et al.  A Review of Plant Phenology in South and Central America , 2013 .

[12]  Jurandy Almeida,et al.  Deriving vegetation indices for phenology analysis using genetic programming , 2015, Ecol. Informatics.

[13]  L. Morellato,et al.  Reproductive phenology of coastal plain Atlantic forest vegetation: comparisons from seashore to foothills , 2011, International journal of biometeorology.

[14]  D. Eamus,et al.  Reproductive Phenology of Woody Species in a North Australian Tropical Savanna 1 , 1999 .

[15]  R. Borchert Phenology and control of flowering in tropical trees , 1983 .

[16]  Irene L. Hudson,et al.  The Influence of Sampling Method, Sample Size, and Frequency of Observations on Plant Phenological Patterns and Interpretation in Tropical Forest Trees , 2010 .

[17]  Andrew D Richardson,et al.  Near-surface remote sensing of spatial and temporal variation in canopy phenology. , 2009, Ecological applications : a publication of the Ecological Society of America.

[18]  Jurandy Almeida,et al.  Shape-based time series analysis for remote phenology studies , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[19]  D. Hollinger,et al.  Use of digital webcam images to track spring green-up in a deciduous broadleaf forest , 2007, Oecologia.

[20]  D. H. Knight,et al.  Aims and Methods of Vegetation Ecology , 1974 .

[21]  C. J. Date An Introduction to Database Systems , 1975 .

[22]  Jurandy Almeida,et al.  Plant Species Identification with Phenological Visual Rhythms , 2013, 2013 IEEE 9th International Conference on e-Science.

[23]  M. D. Schwartz Phenology: An Integrative Environmental Science , 2003, Tasks for Vegetation Science.

[24]  Jurandy Almeida,et al.  Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation , 2014, Ecol. Informatics.

[25]  H. G. Baker,et al.  A new classification for plant phenology based on flowering patterns in lowland tropical rain forest trees at La Selva, Costa Rica , 1994 .

[26]  C. Augspurger Phenology, flowering synchrony, and fruit set of six neotropical shrubs , 1983 .

[27]  L. P. Morellato,et al.  Comparação de dois métodos de avaliação da fenologia de plantas, sua interpretação e representação , 2002 .

[28]  Ramez Elmasri,et al.  Fundamentals of Database Systems , 1989 .

[29]  Leonor Patricia C. Morellato,et al.  Mtodos de amostragem e avaliao utilizados em estudos fenolgicos de florestas tropicais , 2004 .

[30]  Jurandy Almeida,et al.  Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees , 2014, Ecol. Informatics.