R
Rubén Ramo
Researcher at University of Alcalá
Publications - 12
Citations - 808
Rubén Ramo is an academic researcher from University of Alcalá. The author has contributed to research in topics: Remote sensing (archaeology) & Support vector machine. The author has an hindex of 8, co-authored 11 publications receiving 492 citations.
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Developing a Random Forest Algorithm for MODIS Global Burned Area Classification
Rubén Ramo,Emilio Chuvieco +1 more
TL;DR: A global burned area (BA) algorithm for MODIS BRDF-corrected images based on the Random Forest (RF) classifier is developed, which should be applicable to other biomes and years, as they were trained with a global set of reference BA sites.
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A data mining approach for global burned area mapping
TL;DR: Four different classification algorithms, widely used in remote sensing, such as Random Forest, Support Vector Machine, SVM, Neural Networks and a well-known decision tree algorithm are explored for classifying burned areas at global scale through a data mining methodology using 2008 MODIS data.
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Global Detection of Long-Term (1982-2017) Burned Area with AVHRR-LTDR Data
TL;DR: This paper presents the first global burned area (BA) product derived from the land long term data record (LTDR), a long-term 0.05-degree resolution dataset generated from advanced very high resolution radiometer (AVHRR) images.
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About Validation-Comparison of Burned Area Products
TL;DR: The research focuses on the tropical regions of Northern Hemisphere South America and Northern Hemisphere Africa and studies the accuracy of the BA products, finding that biomass, total BA, and the number of fragments affect the BA product accuracy.
Journal ArticleDOI
Correction: Otón, G., et al. Global Detection of Long-Term (1982─2017) Burned Area with AVHRR-LTDR Data. Remote Sensing 2019, 11, 2079
TL;DR: The authors wish to make the following corrections to this paper:.