Measuring phenology uncertainty with large scale image processing
| Autor: | Guilherme Rezende Alles, Lucas Mello Schnorr, Jean-Marc Vincent, Shin Nagai, João Luiz Dihl Comba |
|---|---|
| Přispěvatelé: | Instituto de Informática da UFRGS (UFRGS), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Performance analysis and optimization of LARge Infrastructures and Systems (POLARIS), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Computer science Big data Image processing computer.software_genre 010603 evolutionary biology 01 natural sciences Digital image Histogram Ecology Evolution Behavior and Systematics Ecology business.industry 010604 marine biology & hydrobiology Applied Mathematics Ecological Modeling [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Computer Science Applications Visualization Workflow Computational Theory and Mathematics Modeling and Simulation Noise (video) Data mining [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] Scale (map) business computer |
| Zdroj: | Ecological Informatics Ecological Informatics, 2020, 59, pp.101109. ⟨10.1016/j.ecoinf.2020.101109⟩ Ecological Informatics, Elsevier, 2020, 59, pp.101109. ⟨10.1016/j.ecoinf.2020.101109⟩ |
| ISSN: | 1574-9541 |
| DOI: | 10.1016/j.ecoinf.2020.101109⟩ |
| Popis: | International audience; One standard method to capture data for phenological studies is with digital cameras, taking periodic pictures of vegetation. The large volume of digital images introduces the opportunity to enrich these studies by incorporating big data techniques. The new challenges, then, are to efficiently process large datasets and produce insightful information by controlling noise and variability. On these grounds, the contributions of this paper are the following. (a) A histogram-based visualization for large scale phenological data. (b) Phenological metrics based on the HSV color space, that enhance such histogram-based visualization. (c) A mathematical model to tackle the natural variability and uncertainty of phenological images. (d) The implementation of a parallel workflow to process a large amount of collected data efficiently. We validate these contributions with datasets taken from the Phenological Eyes Network (PEN), demonstrating the effectiveness of our approach. The experiments presented here are reproducible with the provided companion material |
| Databáze: | OpenAIRE |
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