Mostrar el registro sencillo del ítem
dc.contributor.author | García-Díaz, Diego | es_ES |
dc.contributor.author | Díaz-Delgado, Ricardo | es_ES |
dc.date.accessioned | 2023-02-07T09:17:23Z | |
dc.date.available | 2023-02-07T09:17:23Z | |
dc.date.issued | 2023-01-30 | |
dc.identifier.issn | 1133-0953 | |
dc.identifier.uri | http://hdl.handle.net/10251/191688 | |
dc.description.abstract | [EN] PhenoApp application have been developed within the framework of the eLTER Plus and SUMHAL projects, as a tool aimed at scientists and managers of the sites integrated in the eLTER network, for which long-term phenology monitoring can be assessed. The application provides a dynamic map that allows the selection of any site in the network and queries the phenological metrics of each pixel or group of pixels generated with the Sentinel-2 time series of images using the Ndvi2Gif and PhenoPY python libraries. The application also integrates phenology products from MODIS (MCD12Q2.006) and Copernicus Sentinel 2 High Resolution Vegetation Phenology Product (HR VPP). In addition, the application incorporates a web form that allows the user to provide the phenology data obtained in situ (through direct observation or phenocams), which will be used to perform a validation of the different products obtained via satellite. As an example, we carried out a preliminary validation in one of the sites of the eLTER network located in the Doñana Natural Area (END). We used in situ data provided by the network of phenocams in the Doñana Biological Reserve since 2016 installed by the Singular Scientific and Technical Infrastructure of Doñana (ICTS-Doñana). A preliminary validation analysis highlights the need to consider the discrepancies between the different products and methods according to the phenological variability inherent in each ecosystem. | es_ES |
dc.description.abstract | [ES] La aplicación PhenoApp ha sido desarrollada en el marco de los proyectos eLTER Plus y SUMHAL, como una herramienta dirigida a científicos y gestores de los sitios integrados en la red eLTER, con la cual puede realizarse un seguimiento de la fenología a largo plazo de diferentes cubiertas vegetales. La aplicación proporciona un mapa dinámico, que permite la selección de cualquier sitio de la red y consultar las métricas fenológicas de cada píxel o grupo de píxeles generadas con la serie de imágenes Sentinel 2 usando las librerías de Python Ndvi2Gif y PhenoPY. La aplicación integra también los productos de fenología de MODIS (MCD12Q2.006) y de Copernicus Sentinel 2 High Resolution Vegetation Phenology Product (HR-VPP). Además, la aplicación incorpora un formulario que permite al usuario proporcionar los datos de fenología obtenidos in situ (mediante observación directa o fenocámaras), que se usarán para realizar una validación de los distintos productos obtenidos vía satélite. A modo de ejemplo, se muestra la validación efectuada en uno de los sitios de la red eLTER ubicado en el Espacio Natural de Doñana (END), usando como datos in situ los proporcionados por la red de fenocámaras instaladas en la Reserva Biológica de Doñana a partir de 2016, dentro del marco de la Infraestructura Científica y Técnica Singular de Doñana (ICTS-Doñana). Un análisis de validación preliminar pone de manifiesto la necesidad de considerar las discrepancias entre los distintos productos y métodos de acuerdo con la variabilidad fenológica inherente a cada ecosistema. | es_ES |
dc.description.sponsorship | Este trabajo se financia gracias al proyecto eLTER Plus (INFRAIA, Horizonte 2020, Agreement No 871128) y a través de las actuaciones FEDER [SUMHAL, LIFEWATCH-2019-09-CSIC-13, POPE 2014-2020] por el Ministerio de Ciencia, Innovación y Universidades, Subtarea LWE2103022: Integration into VRE. | es_ES |
dc.language | Español | es_ES |
dc.publisher | Universitat Politècnica de València | es_ES |
dc.relation.ispartof | Revista de Teledetección | es_ES |
dc.rights | Reconocimiento - No comercial - Compartir igual (by-nc-sa) | es_ES |
dc.subject | Phenology | es_ES |
dc.subject | Phenocams | es_ES |
dc.subject | Google Earth Engine | es_ES |
dc.subject | Geemap | es_ES |
dc.subject | Python | es_ES |
dc.subject | Fenología | es_ES |
dc.subject | Fenocámaras | es_ES |
dc.title | PhenoApp. Una aplicación basada en Google Earth Engine para el monitoreo de la fenología | es_ES |
dc.title.alternative | PhenoApp. A Google Earth Engine based tool for monitoring phenology | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.4995/raet.2023.18767 | |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/871128 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FEDER/POPE 2014-2020/LIFEWATCH-2019-09-CSIC-13 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | García-Díaz, D.; Díaz-Delgado, R. (2023). PhenoApp. Una aplicación basada en Google Earth Engine para el monitoreo de la fenología. Revista de Teledetección. (61):73-81. https://doi.org/10.4995/raet.2023.18767 | es_ES |
dc.description.accrualMethod | OJS | es_ES |
dc.relation.publisherversion | https://doi.org/10.4995/raet.2023.18767 | es_ES |
dc.description.upvformatpinicio | 73 | es_ES |
dc.description.upvformatpfin | 81 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.issue | 61 | es_ES |
dc.identifier.eissn | 1988-8740 | |
dc.relation.pasarela | OJS\18767 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.description.references | Amani, M., Ghorbanian, A., Ali Ahmadi, S., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q. and Brisco, B. 2020. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326-5350. https://doi.org/10.1109/JSTARS.2020.3021052 | es_ES |
dc.description.references | Friedl, M., Gray, J., Sulla-Menashe, D., 2019. MCD12Q2 MODIS/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. 2022-02-09. | es_ES |
dc.description.references | García, D. 2020. Ndvi2Gif, Python Package Index - PyPI. Recuperado en mayo de 2020 de https://pypi.org/project/ndvi2gif/. | es_ES |
dc.description.references | Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031 | es_ES |
dc.description.references | Haase, P., Frenzel, M., Klotz, S., Musche, M., Stoll, S. 2016. The long-term ecological research (LTER) network: Relevance, current status, future perspective and examples from marine, freshwater and terrestrial long-term observation. Ecological Indicators, 65 1-3. https://doi.org/10.1016/j.ecolind.2016.01.040 | es_ES |
dc.description.references | Haase, P., Tonkin, J.D., Stoll, S., Burkhard, B., Frenzel, M., Geijzendorffer, I.R., Häuser, C., Klotz, S., Kühn, I., McDowell, W.H., Mirtl, M., Müller, F., Musche, M., Penner, J., Zacharias, S., Schmeller, D.S. 2018. The next generation of site-based long-term ecological monitoring: Linking essential biodiversity variables and ecosystem integrity. Science of The Total Environment 613-614, 1376-1384. https://doi.org/10.1016/j.scitotenv.2017.08.111 | es_ES |
dc.description.references | Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., Willing, C., Jupyter development team. 2016. Jupyter Notebooks - a publishing format for reproducible computational workflows. Loizides, Fernando and Scmidt, Birgit (eds.) In Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press. pp. 87-90. https://doi.org/10.3233/978-1-61499-649-1-87 | es_ES |
dc.description.references | Lopatín, J., Paredes, J. 2021. PhenoPY. Recuperado en diciembre de 2019 de https://github.com/JavierLopatin/PhenoPY | es_ES |
dc.description.references | Moore, R.T., Hansen, M.C. 2011. Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis 2011:IN43C-02. American Geophysical Union, Fall Meeting 2011. | es_ES |
dc.description.references | Morgen W.V. Burke, Bradley C. Rundquist., 2021. Scaling Phenocam GCC, NDVI, and EVI2 with Harmonized Landsat-Sentinel using Gaussian Processes. Agricultural and Forest Meteorology, 300, 108316, https://doi.org/10.1016/j.agrformet.2020.108316 | es_ES |
dc.description.references | Richardson, A., Hufkens, K., Milliman, T. et al., 2018. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Scientific Data 5, 180028. https://doi.org/10.1038/sdata.2018.28 | es_ES |
dc.description.references | Tian, F., Cai, Z., Jin, H., Hufkens, K., Scheifinger, H., Tagesson, T., Smets, B., Van Hoolst, R., Bonte, K., Ivits, E., Tong, X., Ardö, J., Eklundh, L. 2021. Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe. Remote Sensing of Environment, 260, https://doi.org/10.1016/j.rse.2021.112456 | es_ES |
dc.description.references | Wohner, C., Peterseil, J., Klug, H. 2022. Designing and implementing a data model for describing environmental monitoring and research sites. In Ecological Informatics, 70, p. 101708). Elsevier BV. https://doi.org/10.1016/j.ecoinf.2022.101708 | es_ES |
dc.description.references | Wu, Q. 2020. Geemap: A Python package for interactive mapping with Google Earth Engine. The Journal of Open Source Software, 5(51), 2305. https://doi.org/10.21105/joss.02305 | es_ES |
dc.description.references | Wu, Q. 2021. Interactive mapping and geospatial analysis with Leafmap and Jupyter. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on APIs and Libraries for Geospatial Data Science (SpatialAPI '21). Association for Computing Machinery, New York, NY, USA, Article 1, 1-2. https://doi.org/10.1145/3486189.3490015 | es_ES |