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Agustín Lobo
Researcher at Spanish National Research Council
Publications - 58
Citations - 1412
Agustín Lobo is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Land cover & Normalized Difference Vegetation Index. The author has an hindex of 18, co-authored 56 publications receiving 1193 citations. Previous affiliations of Agustín Lobo include University of Barcelona & Autonomous University of Barcelona.
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Pedodiversity and global soil patterns at coarse scales (with discussion)
TL;DR: In this article, the diversity and distribution of major soil groups by continents and climatic zones on the basis of data compiled by the FAO at the scale I : 5,000,OOO were studied.
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Image segmentation and discriminant analysis for the identification of land cover units in ecology
TL;DR: The segmentation algorithm, iterative mutually optimum region merging (IMORM), is presented and used to partition images into elements that are thereafter classified by linear canonical discriminant analysis and a maximum likelihood allocation rule.
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Classification of Mediterranean crops with multisensor data: per-pixel versus per-object statistics and image segmentation
TL;DR: In this paper, per-field statistics derived from multi-spectral imagery enhances separability between different crops and terrain categories, and image segmentation is a convenient way to apply this approach avoiding field digitizing by computing per-segment statistics of training fields.
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Woody plant richness and ndvi response to drought events in catalonian (northeastern spain) forests
Francisco Lloret,Agustín Lobo,Helena Estevan,Philippe Maisongrande,Jordi Vayreda,Jaume Terradas +5 more
TL;DR: The results show that a shift on the diversity-stability relationship appears across the regional, climatic gradient, and a positive relationship appears in drier localities, supporting a null model where the probability of finding a species able to cope with drier conditions increases with the number of species.
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Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data.
Hector A. Orengo,Francesc Cecilia Conesa,Arnau Garcia-Molsosa,Agustín Lobo,Adam S. Green,Marco Madella,Marco Madella,Marco Madella,Cameron A. Petrie +8 more
TL;DR: The results largely expand the known concentration of Indus settlements in the Cholistan Desert in Pakistan, with the detection of hundreds of new sites deeper in the desert than previously suspected including several large-sized (>30 ha) urban centers.