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
  • [1] Comparison of Machine Learning Classifiers for Land Cover Changes using Google Earth Engine
    Mangkhaseum, Sackdavong
    Hanazawa, Akitoshi
    PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON AEROSPACE ELECTRONICS AND REMOTE SENSING TECHNOLOGY (ICARES 2021), 2021,
  • [2] Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine
    Xie, Shuai
    Liu, Liangyun
    Zhang, Xiao
    Yang, Jiangning
    Chen, Xidong
    Gao, Yuan
    REMOTE SENSING, 2019, 11 (24)
  • [3] Machine Learning-Driven Snow Cover Mapping Techniques using Google Earth Engine
    Panda, Saptarshi
    Anilkumar, Ritu
    Balabantaray, Bunil Kumar
    Chutia, Dibyajyoti
    Bharti, Rishikesh
    2022 IEEE 19TH INDIA COUNCIL INTERNATIONAL CONFERENCE, INDICON, 2022,
  • [4] Land use and land cover classification using machine learning algorithms in google earth engine
    Arpitha M
    S A Ahmed
    Harishnaika N
    Earth Science Informatics, 2023, 16 : 3057 - 3073
  • [5] Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh
    Biswas, Jayanta
    Abu Jobaer, Md
    Haque, Salman F.
    Shozib, Md Samiul Islam
    Limon, Zamil Ahamed
    HELIYON, 2023, 9 (11)
  • [6] Mapping Maize Cropland and Land Cover in Semi-Arid Region in Northern Nigeria Using Machine Learning and Google Earth Engine
    Abubakar, Ghali Abdullahi
    Wang, Ke
    Koko, Auwalu Faisal
    Husseini, Muhammad Ibrahim
    Shuka, Kamal Abdelrahim Mohamed
    Deng, Jinsong
    Gan, Muye
    REMOTE SENSING, 2023, 15 (11)
  • [7] Land use/land cover and change detection mapping in Rahuri watershed area (MS), India using the google earth engine and machine learning approach
    Pande, Chaitanya B.
    GEOCARTO INTERNATIONAL, 2022, 37 (26) : 13860 - 13880
  • [8] Multi-Temporal Land Cover Change Mapping Using Google Earth Engine and Ensemble Learning Methods
    Wagle, Nimisha
    Acharya, Tri Dev
    Kolluru, Venkatesh
    Huang, He
    Lee, Dong Ha
    APPLIED SCIENCES-BASEL, 2020, 10 (22): : 1 - 20
  • [9] Automatic land cover classification with SAR imagery and Machine learning using Google Earth Engine
    Desai, Geeta T.
    Gaikwad, Abhay N.
    INTERNATIONAL JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING SYSTEMS, 2022, 13 (10) : 909 - 916
  • [10] Assessing Machine Learning Algorithms for Land Use and Land Cover Classification in Morocco Using Google Earth Engine
    Ouchra, Hafsa
    Belangour, Abdessamad
    Erraissi, Allae
    Banane, Mouad
    IMAGE ANALYSIS AND PROCESSING - ICIAP 2023 WORKSHOPS, PT I, 2024, 14365 : 395 - 405
12345