Manuel Fernández Urrutia

Manuel Fernández Urrutia
Irish Centre for High-End Computing | ICHEC · Earth Observation

Master of Research
GIS Computational Scientist at Irish Center for High End Computing (ICHEC), National University of Ireland Galway(NUIG)

About

6
Publications
1,459
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3
Citations
Additional affiliations
September 2015 - September 2015
University of the Azores
University of the Azores
  • Ce3C / Azorean Biodiversity Group - University of the Azores
  • Ponta Delgada, Portugal
Position
  • PhD Student

Publications

Publications (6)
Figure 1. Different growing stages of paddy rice and its approximate...
Figure 4. Contribution of journals in percentage to the total number of...
Breakdown of all information collected per each article.
Table showing the location of the studies by continent.
+1 Table showing the most represented satellites and sensors in the review.
Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review
Article
Full-text available
  • Aug 2023
Rice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review p...
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Figure 1. Overview of the ForestAz app Graphical User Interface. Left...
Figure 2. A) Vegetation indices monitoring methodological workflow. B)...
Figure 3. Examples of outputs provided by the ForestAz app: Vegetation...
Resource Communication: ForestAz - Using Google Earth Engine and Sentinel data for forest monitoring in the Azores Islands (Portugal)
Article
Full-text available
  • Jul 2022
Google Earth Engine App: https://manuferu.users.earthengine.app/view/forestaz |||| Open Source Code available at GitHub: https://github.com/Manuferu/ForestAZ |||| Original article based at https://revistas.inia.es/index.php/fs/article/view/18929 |||| Full citation: Fernández-Urrutia M., & Gil A. (2022). Resource Communication: ForestAz - Using Goog...
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AI-Ready Training Datasets for Earth Observation: Enabling FAIR data principles for EO training data.
Poster
  • Apr 2021
The ESA-funded AIREO project [1] sets out to produce AI-ready training dataset specifications and best practices to support the training and development of machine learning models on Earth Observation (EO) data. While the quality and quantity of EO data has increased drastically over the past decades, availability of training data for machine learn...
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Using Open Remote Sensing and Geographic Data for SMART Monitoring of Nature-based TOURISM in the Azores Islands Natural Parks: towards (more) Sustainability
Conference Paper
Full-text available
  • Jul 2020
Abstract. A remote sensing-based operational tool using Sentinel-2 and Landsat-8 free and open access satellite multispectral data, and based on the Google Earth Engine (GEE) platform, is under development for monitoring land-cover/land-use changes in the Azores Islands Natural Park. Citation: Gil A., Fernández-Urrutia M., Tiago F., & Borges-Tiag...
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Figure 2: Graphic representation of the regional dimension of the...
Sentinel-based Azores Regional Forest Inventory
Chapter
Full-text available
  • Nov 2018
The Azorean Regional Forest Inventory constitutes the core tool for forest planning and management and also the most accurate and reliable official Land Use / Land Cover map in the Azores Autonomous Region (Portugal), being widely used by local and regional authorities for supporting both spatial planning and forestry policies purposes. The curren...
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