P
Pedro Rodriguez-Veiga
Researcher at University of Leicester
Publications - 24
Citations - 570
Pedro Rodriguez-Veiga is an academic researcher from University of Leicester. The author has contributed to research in topics: Forest inventory & Biomass (ecology). The author has an hindex of 10, co-authored 19 publications receiving 296 citations. Previous affiliations of Pedro Rodriguez-Veiga include Kyoto University & University of Vigo.
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Journal ArticleDOI
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
Maurizio Santoro,Oliver Cartus,Nuno Carvalhais,Nuno Carvalhais,Danaë M. A. Rozendaal,Valerio Avitabile,Arnan Araza,Sytze de Bruin,Martin Herold,Shaun Quegan,Pedro Rodriguez-Veiga,Heiko Balzter,João M. B. Carreiras,Dmitry Schepaschenko,Dmitry Schepaschenko,Dmitry Schepaschenko,Mikhail Korets,Masanobu Shimada,Takuya Itoh,Álvaro Moreno Martínez,Jura Čavlović,Roberto Cazzolla Gatti,Polyanna da Conceição Bispo,Polyanna da Conceição Bispo,Nasheta Dewnath,Nicolas Labrière,Jingjing Liang,Jeremy A. Lindsell,Edward T. A. Mitchard,Alexandra C. Morel,Ana Maria Pacheco Pascagaza,Ana Maria Pacheco Pascagaza,Casey M. Ryan,Ferry Slik,Gaia Vaglio Laurin,Hans Verbeeck,Arief Wijaya,Simon Willcock +37 more
TL;DR: Santoro et al. as discussed by the authors used satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010 to generate a global, spatially explicit dataset of above ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1'ha.
Journal ArticleDOI
Quantifying forest biomass carbon stocks from space
TL;DR: In this paper, a review of cutting-edge methods and current and forthcoming satellite remote sensing technologies to map aboveground biomass (AGB) is presented, which is of key importance to understand the global carbon cycle and for the functioning of international economic mechanisms aiming to protect and enhance forest carbon stocks.
Journal ArticleDOI
Magnitude, spatial distribution and uncertainty of forest biomass stocks in Mexico
TL;DR: In this paper, the authors used a maximum entropy (MaxEnt) algorithm to generate forest biomass (AGB ), its associated uncertainty, and forest probability maps, which showed a root mean square error of 17.31 when validated at the 250m pixel scale with inventory plots.
Journal ArticleDOI
Forest biomass retrieval approaches from earth observation in different biomes
Pedro Rodriguez-Veiga,Shaun Quegan,João M. B. Carreiras,Henrik J. Persson,Johan E. S. Fransson,Agata Hoscilo,Dariusz Ziółkowski,Krzysztof Stereńczak,Sandra Lohberger,Matthias Stängel,Anna Berninger,Florian Siegert,Valerio Avitabile,Martin Herold,Stéphane Mermoz,Alexandre Bouvet,Thuy Le Toan,Nuno Carvalhais,Nuno Carvalhais,Maurizio Santoro,Oliver Cartus,Yrjö Rauste,Renaud Mathieu,Renaud Mathieu,Gregory P. Asner,Christian Thiel,Carsten Pathe,C. Schmullius,Frank Martin Seifert,Kevin Tansey,Heiko Balzter +30 more
TL;DR: The widest inter-comparison of regional-to-national AGB maps to date in terms of area, forest types, input datasets, and retrieval methods is compared, showing better agreement with field data than previously developed and widely used pan-tropical or northern hemisphere datasets.
Journal ArticleDOI
Predicting scenic beauty of forest stands in Catalonia (North-east Spain).
Elena Blasco,José Ramón González-Olabarria,Pedro Rodriguez-Veiga,Timo Pukkala,Osmo Kolehmainen,Marc Palahí +5 more
TL;DR: In this article, the relative priority of a forest stand was analyzed using regression methods for pairwise comparison between virtual reality images and photos of forest stands, and two models were developed based on two different groups of stands.