M
Matteo Mura
Researcher at University of Florence
Publications - 19
Citations - 751
Matteo Mura is an academic researcher from University of Florence. The author has contributed to research in topics: Forest inventory & Forest management. The author has an hindex of 13, co-authored 19 publications receiving 585 citations. Previous affiliations of Matteo Mura include University of Molise.
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Journal ArticleDOI
A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data
Gherardo Chirici,Matteo Mura,Daniel McInerney,Nicolas Py,Erkki Tomppo,Lars T. Waser,Davide Travaglini,Ronald E. McRoberts +7 more
TL;DR: In the framework of Working Group 2 of COST Action FP1001, the scientific literature for forestry applications of k-NN was reviewed and qualitative and quantitative information from 260 experimental tests described in 148 scientific papers were extracted.
Journal ArticleDOI
Comparison of carbon estimation methods for European forests
Mathias Neumann,Adam Moreno,Volker Mues,Sanna Härkönen,Matteo Mura,Olivier Bouriaud,Mait Lang,Wouter Achten,Wouter Achten,Alain Thivolle-Cazat,Karol Bronisz,Ján Merganič,Mathieu Decuyper,Iciar Alberdi,Rasmus Astrup,Frits Mohren,Hubert Hasenauer +16 more
TL;DR: In this article, the results of the analysis show discrepancies in the resulting estimates for total tree carbon and for single tree compartments across the countries analyzed of up to 140 t carbon/ha.
Journal ArticleDOI
The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas
Emanuele Santi,Simonetta Paloscia,Simone Pettinato,Giacomo Fontanelli,Matteo Mura,Catherine Zolli,Fabio Maselli,Marta Chiesi,Lorenzo Bottai,Gherardo Chirici +9 more
TL;DR: In this article, the authors investigated the use of multi-frequency SAR data from different sensors (ALOS/PALSAR and ENVISAT/ASAR) for estimating forest biomass in two test areas in Central Italy (San Rossore and Molise), where detailed in-situ measurements and Airborne Laser Scanning (ALS) data were available.
Journal ArticleDOI
Exploiting the capabilities of the Sentinel-2 multi spectral instrument for predicting growing stock volume in forest ecosystems
Matteo Mura,Francesca Bottalico,Francesca Giannetti,Remo Bertani,Raffaello Giannini,Marco Mancini,Simone Orlandini,Davide Travaglini,Gherardo Chirici +8 more
TL;DR: Evaluating the performance of the S2-MSI imagery for estimating the growing stock volume of forest ecosystems found that S2 worked better than Landsat in 37.5% of the cases and in 62.5%.
Journal ArticleDOI
Estimating and mapping forest structural diversity using airborne laser scanning data
TL;DR: In this paper, the authors developed regression models for predicting indices of forest structural diversity for a study area in Molise, Italy, and constructed maps depicting the spatial pattern of the structural diversity indices.