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1. |
Record Nr. |
UNIBAS000031594 |
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Autore |
Boccaccio, Giovanni <1313-1375> |
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Titolo |
Amorosa visione / a cura di Vittore Branca Ninfale fiesolano / a cura di Armando Balduino Trattatello in laude di Dante / a cura di Pier Giorgio Ricci |
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Pubbl/distr/stampa |
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Descrizione fisica |
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Collana |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910557112103321 |
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Autore |
Matese Alessandro |
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Titolo |
Forestry Applications of Unmanned Aerial Vehicles (UAVs) 2019 |
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Pubbl/distr/stampa |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
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Descrizione fisica |
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1 online resource (184 p.) |
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Soggetti |
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Biology, life sciences |
Forestry & related industries |
Research & information: general |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, |
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autonomy, and capability of timely provision of high-resolution data. The main adopted image-based technologies are RGB, multispectral, and thermal infrared. LiDAR sensors are becoming commonly used to improve the estimation of relevant plant traits. In comparison with other permanent ecosystems, forests are particularly affected by climatic changes due to the longevity of the trees, and the primary objective is the conservation and protection of forests. Nevertheless, forestry and agriculture involve the cultivation of renewable raw materials, with the difference that forestry is less tied to economic aspects and this is reflected by the delay in using new monitoring technologies. The main forestry applications are aimed toward inventory of resources, map diseases, species classification, fire monitoring, and spatial gap estimation. This Special Issue focuses on new technologies (UAV and sensors) and innovative data elaboration methodologies (object recognition and machine vision) for applications in forestry. |
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