Urban land use and land cover mapping: proposal of a classification system with remote sensing

被引:2
作者
Azevedo, Thiago [1 ]
Matias, Lindon Fonseca [1 ]
机构
[1] Univ Estadual Campinas, Unicamp, Campinas, SP, Brazil
来源
AGUA Y TERRITORIO | 2024年 / 23期
关键词
CBERS; 04A; Land use and land cover classification; Urban space; Remote sensing;
D O I
10.17561/at.23.7251
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
The Brazilian urbanization process produced a complex urban space, with a variety of urban land use and cover as a result. The study of these forms through a classification system is essential, but most current systems don't capture this complexity, condensing it. Urban forms are difficult to distinguish and classify, resulting in the need for a system with a high degree of detail, for a more accurate urban planning. The objective of this work is to propose a classification system for urban land use and cover, which can demonstrate the multiplicities through remote sensing, using data from CBERS 04A satellite. The methodology surveys the visual elements of remote sensing images, through visual interpretation, relating them to each proposed use and cover class. With this, a classification system was developed that covers the urban space in 17 classes, being an effective way to raise information about the different urban forms.
引用
收藏
页码:73 / 82
页数:10
相关论文
共 28 条
[1]  
Anderson J., 1979, Sistema de classificacao do uso da terra e do revestimento do solo para utilizacao com dados de sensores remoto
[2]  
Araujo A. S., 2015, dissetacao de mestrado
[3]  
Barreto J. R., 2016, Anais do XI Coloquio Internacional Sobre Comercio e Consumo Urbano, V6, P298
[4]  
CORREA R L., 1989, O Espaco Urbano
[5]  
CROSTA AP., 1992, PROCESSAMENTO DIGITA
[6]  
Herold M, 2010, REMOTE SENS DIGIT IM, V10, P47, DOI 10.1007/978-1-4020-4385-7_4
[7]  
Heymann Y., 1994, Corine Land Cover -Technical Guide
[8]   Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery [J].
Huang, Bo ;
Zhao, Bei ;
Song, Yimeng .
REMOTE SENSING OF ENVIRONMENT, 2018, 214 :73-86
[9]  
IBGE, 2013, MAN TECN US TERR
[10]  
Jensen J., 2008, Sensoriamento Remoto do Ambiente: Uma Perspectiva em Recursos Terrestres