Land-Cover Mapping of Agricultural Areas Using Machine Learning in Google Earth Engine

被引：3
|
作者：
Hastings, Florencia [1 ,2 ]
Fuentes, Ignacio [3 ]
Perez-Bidegain, Mario [1 ]
Navas, Rafael [4 ]
Gorgoglione, Angela [5 ]
机构：
[1] Univ Republica, Sch Agron, Av Gral Eugenio Garzon 780, Montevideo, Uruguay
[2] Minist Agr Livestock & Fisheries, Directorate Nat Resources, Av Gral Eugenio Garzon 456, Montevideo, Uruguay
[3] Univ Sydney, Sch Life & Environm Sci, Sydney, NSW 2006, Australia
[4] Inst Nacl Invest Agr, Programa Nacl Invest Prod & Sustentabilidad Ambie, Montevideo, Uruguay
[5] Univ Republica, Sch Engn, Julio Herrera y Reissig 565, Montevideo, Uruguay
来源：
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2020, PART IV | 2020年 / 12252卷
关键词：
Land-cover map; Supervised classification; Google earth engine; Agricultural region; CLASSIFICATION; CROPLAND;
D O I：
10.1007/978-3-030-58811-3_52
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：721 / 736
页数：16
相关论文
共 50 条
  • [31] Tracking land use land cover changes in the twin cities of Odisha, India using a machine learning based Google Earth Engine approach
    Nayak, Abhayaa
    Kar, Anil Kumar
    URBAN WATER JOURNAL, 2025, 22 (03) : 291 - 312
  • [32] A preliminary study on machine learning and google earth engine for mangrove mapping
    Kamal, Muhammad
    Farda, Nur Mohammad
    Jamaluddin, Ilham
    Parela, Artha
    Wikantika, Ketut
    Prasetyo, Lilik Budi
    Irawan, Bambang
    FIFTH INTERNATIONAL CONFERENCES OF INDONESIAN SOCIETY FOR REMOTE SENSING: THE REVOLUTION OF EARTH OBSERVATION FOR A BETTER HUMAN LIFE, 2020, 500
  • [33] Improved machine-learning mapping of local climate zones in metropolitan areas using composite Earth observation data in Google Earth Engine
    Chung, Lamuel Chi Hay
    Xie, Jing
    Ren, Chao
    BUILDING AND ENVIRONMENT, 2021, 199
  • [34] RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine
    Naboureh, Amin
    Ebrahimy, Hamid
    Azadbakht, Mohsen
    Bian, Jinhu
    Amani, Meisam
    REMOTE SENSING, 2020, 12 (21) : 1 - 16
  • [35] Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing
    Midekisa, Alemayehu
    Holl, Felix
    Savory, David J.
    Andrade-Pacheco, Ricardo
    Gething, Peter W.
    Bennett, Adam
    Sturrock, Hugh J. W.
    PLOS ONE, 2017, 12 (09):
  • [36] Google Earth Engine and machine learning for Earth monitoring
    Uzhinskiy, Alexander
    Proceedings of Science, 2022, 429
  • [37] Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation
    Pande, Chaitanya Baliram
    Srivastava, Aman
    Moharir, Kanak N.
    Radwan, Neyara
    Sidek, Lariyah Mohd
    Alshehri, Fahad
    Pal, Subodh Chandra
    Tolche, Abebe Debele
    Zhran, Mohamed
    ENVIRONMENTAL SCIENCES EUROPE, 2024, 36 (01)
  • [38] Mapping and quantifying agricultural irrigation in heterogeneous landscapes using Google Earth Engine
    Zurqani, H. A.
    Allen, J. S.
    Post, C. J.
    Pellett, C. A.
    Walker, T. C.
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2021, 23
  • [39] Characterization of Land-Cover Changes and Forest-Cover Dynamics in Togo between 1985 and 2020 from Landsat Images Using Google Earth Engine
    Kombate, Arifou
    Folega, Fousseni
    Atakpama, Wouyo
    Dourma, Marra
    Wala, Kperkouma
    Goita, Kalifa
    LAND, 2022, 11 (11)
  • [40] BULC-U: Sharpening Resolution and Improving Accuracy of Land-Use/Land-Cover Classifications in Google Earth Engine
    Lee, Jacky
    Cardille, Jeffrey A.
    Coe, Michael T.
    REMOTE SENSING, 2018, 10 (09)
12345