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
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