Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Juan Pablo Rivera Caicedo"'
Autor:
Lizette Zareh Cortés-Macías, Juan Pablo Rivera-Caicedo, Jushiro Cepeda-Morales, Óscar Ubisha Hernández-Almeida, Ricardo García-Morales, Pablo Velarde-Alvarado
Publikováno v:
Revista de Teledetección, Iss 62, Pp 39-55 (2023)
El lago-cráter de Santa María del Oro en el estado de Nayarit, México, presenta Florecimientos Algales (FA) de manera cíclica anual, el florecimiento y posterior decaimiento de estas poblaciones de crea cambios de color en el agua, generalmente e
Externí odkaz:
https://doaj.org/article/cde2c39394304123bac3b0ba817882a7
Autor:
Miguel Morata, Bastian Siegmann, Adrian Perez-Suay, Jose Luis Garcia-Soria, Juan Pablo Rivera-Caicedo, Jochem Verrelst
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 762-772 (2023)
Hyperspectral satellite imagery provides highly resolved spectral information for large areas and can provide vital information. However, only a few imaging spectrometer missions are currently in operation. Aiming to generate synthetic satellite-base
Externí odkaz:
https://doaj.org/article/2849d0b95e664482a94a2a729873c785
Autor:
Mafalda Reis Pereira, Jochem Verrelst, Renan Tosin, Juan Pablo Rivera Caicedo, Fernando Tavares, Filipe Neves dos Santos, Mário Cunha
Publikováno v:
Agronomy, Vol 14, Iss 3, p 493 (2024)
Early and accurate disease diagnosis is pivotal for effective phytosanitary management strategies in agriculture. Hyperspectral sensing has emerged as a promising tool for early disease detection, yet challenges remain in effectively harnessing its p
Externí odkaz:
https://doaj.org/article/8445b86576474d678955472c44c72c33
An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF
Autor:
José Roberto Lomelí-Huerta, Juan Pablo Rivera-Caicedo, Miguel De-la-Torre, Brenda Acevedo-Juárez, Jushiro Cepeda-Morales, Himer Avila-George
Publikováno v:
PeerJ Computer Science, Vol 8, p e979 (2022)
This paper proposes an approach to fill in missing data from satellite images using data-intensive computing platforms. The proposed approach merges satellite imagery from diverse sources to reduce the impact of the holes in images that result from a
Externí odkaz:
https://doaj.org/article/17d772fecc83403187e7553af47e9a6a
Autor:
Masoumeh Aghababaei, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi, Adrián Pérez-Suay, Miguel Morata, Jose Luis Garcia, Juan Pablo Rivera Caicedo, Jochem Verrelst
Publikováno v:
Remote Sensing, Vol 14, Iss 18, p 4452 (2022)
Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and r
Externí odkaz:
https://doaj.org/article/3ccc01f7b08040b9b35a550ecf57909d
Autor:
Gabriel Caballero, Alejandro Pezzola, Cristina Winschel, Alejandra Casella, Paolo Sanchez Angonova, Juan Pablo Rivera-Caicedo, Katja Berger, Jochem Verrelst, Jesus Delegido
Publikováno v:
Remote Sensing, Vol 14, Iss 18, p 4531 (2022)
Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development a
Externí odkaz:
https://doaj.org/article/66c19f0598c44606b4b0d422fa96bb0b
Autor:
Ana B. Pascual-Venteo, Enrique Portalés, Katja Berger, Giulia Tagliabue, Jose L. Garcia, Adrián Pérez-Suay, Juan Pablo Rivera-Caicedo, Jochem Verrelst
Publikováno v:
Remote Sensing, Vol 14, Iss 10, p 2448 (2022)
In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models w
Externí odkaz:
https://doaj.org/article/4f9c184bc8e8433fab32cbbc4796d7fa
Autor:
Gabriele Candiani, Giulia Tagliabue, Cinzia Panigada, Jochem Verrelst, Valentina Picchi, Juan Pablo Rivera Caicedo, Mirco Boschetti
Publikováno v:
Remote Sensing, Vol 14, Iss 8, p 1792 (2022)
In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This mission will provide an unprecedented amount of hyperspectral data, enabling new research possibilitie
Externí odkaz:
https://doaj.org/article/64ca091a600a4b2880ff5e7c635e80da
Autor:
Pablo Reyes-Muñoz, Luca Pipia, Matías Salinero-Delgado, Santiago Belda, Katja Berger, José Estévez, Miguel Morata, Juan Pablo Rivera-Caicedo, Jochem Verrelst
Publikováno v:
Remote Sensing, Vol 14, Iss 6, p 1347 (2022)
Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3
Externí odkaz:
https://doaj.org/article/e82611827de54fedadefc0953b2af57b
Autor:
Katja Berger, Tobias Hank, Andrej Halabuk, Juan Pablo Rivera-Caicedo, Matthias Wocher, Matej Mojses, Katarina Gerhátová, Giulia Tagliabue, Miguel Morata Dolz, Ana Belen Pascual Venteo, Jochem Verrelst
Publikováno v:
Remote Sensing, Vol 13, Iss 22, p 4711 (2021)
Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of
Externí odkaz:
https://doaj.org/article/b67b72b2c2b04aa3b26cbc5ac254af74