Fecha/hora: 11/06/2024 21:45:25

Biblioteca(s): | INIA Las Brujas.
Fecha corriente: | 23/10/2020
Fecha de la última actualización: | 09/04/2021
Autoria: | HASTINGS, F.; FUENTES, I.; PÉREZ-BIDEGAIN, M.; NAVAS, R.; GORGOGLIONE, A.
Afiliación: | FLORENCIA HASTINGS, School of Agronomy Universidad de la República, Montevideo, Uruguay; Directorate of Natural Resources, Ministry of Agriculture, Livestock and Fisheries, Montevideo, Uruguay; IGNACIO FUENTES, School of Life and Environmental Sciences, University of Sydney, Sydney, Australia; MARIO PÉREZ-BIDEGAIN, School of Agronomy, Universidad de la República, Montevideo, Uruguay; RAFAEL NAVAS NÚÑEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ÁNGELA GORGOGLIONE, School of Engineering, Universidad de la República, Montevideo, Uruguay.
Título: | Land-cover mapping of agricultural areas using machine learning in Google Earth engine. (Conference paper)
Año de publicación: | 2020
Fuente/Imprenta: | In: Gervasi O. et al. (eds) Computational Science and Its Applications - ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12252. International Conference on Computational Science and Its Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_52
ISBN: | e-ISBN: 978-3-030-58811-3
DOI: | 10.1007/978-3-030-58811-3_52
Idioma: | Inglés

Notas:

Article history:  First Online 29 September 2020. Volume Editors: Gervasi O.,Murgante B.,Misra S. .,Garau C.,Blecic I.,Taniar D.,Apduhan B.O.,Rocha A.M.A.C.,Tarantino E.,Torre C.M.,Karaca Y. Publisher: Springer Science and Business Media Deutschland GmbH.
20th International Conference on Computational Science and Its Applications, ICCSA 2020; Cagliari; Italy; 1 July 2020 through 4 July 2020; Code 249529.
Corresponding author: Hastings, F.; School of Agronomy, Universidad de la República, Av. Gral. Eugenio Garzón 780, Montevideo, Uruguay; email:fhastings@mgap.gub.uy

Contenido: | Land-cover mapping is critically needed in land-use planning and policy making. Compared to other techniques, Google Earth Engine (GEE) offers a free cloud of satellite information and high computation capabilities. In this context, this article examines machine learning with GEE for land-cover mapping. For this purpose, a five-phase procedure is applied: (1) imagery selection and pre-processing, (2) selection of the classes and training samples, (3) classification process, (4) post-classification, and (5) validation. The study region is located in the San Salvador basin (Uruguay), which is under agricultural intensification. As a result, the 1990 land-cover map of the San Salvador basin is produced. The new map shows good agreements with past agriculture census and reveals the transformation of grassland to cropland in the period 1990?2018. © 2020, Springer Nature Switzerland AG.
Palabras-Clave: | Agricultural region; Google earth engine; Land-cover map; Supervised classification.
Categoría del tema: | A50 Investigación agraria
Registro original: | INIA Las Brujas (LB)
Ejemplares: | LB - (102424-1) - PXI - DD - DD - ICCSA 2020 -  -  - DIS