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QGIS and applications in agriculture and forest |
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Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesQGIS and applications in agriculture and forest, ch. 6. Vegetation cartography from Sentinel-1 radar images / Pierre-Louis Frison (2018)
contenu dans QGIS in Remote Sensing, Volume 2. QGIS and applications in agriculture and forest / Nicolas Baghdadi (2018)
Titre de série : QGIS and applications in agriculture and forest, ch. 6 Titre : Vegetation cartography from Sentinel-1 radar images Type de document : Chapitre/Contribution Auteurs : Pierre-Louis Frison Editeur : Londres : ISTE Editions Année de publication : 2018 Autre Editeur : New York, Londres, Hoboken (New Jersey), ... : John Wiley & Sons Note générale : bibliographie Langues : Anglais (eng) Résumé : (auteur) Remote sensing is particularly well suited to monitoring terrestrial surfaces, especially vegetation. The cartography from spaceborne images allows the estimation of land use over large areas. The Sentinel‐1 mission consists of the orbiting of two satellites (Sentinel‐1A [S1A] and 1B [S1B]) carrying a synthetic aperture radar (SAR) sensor. This chapter provides a brief review of the main classification methods used in remote sensing, and details the pre‐processing of Sentinel‐1 radar data required for use in classification algorithms. These classifications aim to map main land use entities. The processing presented is developed in Python language, based on the Orfeo Toolbox (OTB) algorithms, and integrated into quantum geographic information system (QGIS) software, in order to make them accessible to non‐specialists in radar data. A classification algorithm aims to generate, from one or more remote sensing images, an image containing different thematic classes. The chapter presents the application of random forest (RF) classification, which requires training polygons. Numéro de notice : H2018-007 Affiliation des auteurs : UPEM-LASTIG (2016-2019) Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1002/9781119457107.ch6 Date de publication en ligne : 19/01/2018 En ligne : https://doi.org/10.1002/9781119457107.ch6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91928
