Urban Phytophysiognomy Characterization Using NDVI from Satellites Images and Free Software

Authors

  • Ariadne Barbosa Gonçalves Universidade Católica Dom Bosco, Departamento de Ciências Ambientais e Sustentabilidade Agropecuária, INOVISAO, Avenida Tamandaré, n° 6000, Jardim Seminário, CEP 79117-900, Campo Grande, Mato Grosso do Sul, Brasil
  • Raquel de Faria Godoi Universidade Federal de Mato Grosso do Sul, Faculdade de Engenharia, Arquitetura, Urbanismo e Geografia, LABGIS, Avenida Filinto Muller, n° 1555, Cidade Universitária, CEP 79074-460 Campo Grande, Mato Grosso do Sul, Brasil
  • Antonio Conceição Paranhos Filho Universidade Federal de Mato Grosso do Sul, Faculdade de Engenharia, Arquitetura, Urbanismo e Geografia, LABGIS, Avenida Filinto Muller, n° 1555, Cidade Universitária, CEP 79074-460 Campo Grande, Mato Grosso do Sul, Brasil
  • Marcelo Theophilo Folhes IbiGeo Ciências Aplicadas, Avenida Afonso Pena, n° 5723, Cidade Jardim, CEP 79031-010 Campo Grande, Mato Grosso do Sul, Brasil
  • Hemerson Pistori Universidade Católica Dom Bosco, Departamento de Ciências Ambientais e Sustentabilidade Agropecuária, INOVISAO, Avenida Tamandaré, n° 6000, Jardim Seminário, CEP 79117-900, Campo Grande, Mato Grosso do Sul, Brasil

DOI:

https://doi.org/10.11137/2018_3_24_36

Keywords:

Remote sensing, Urban modelling, Landsat 8, Rapideye

Abstract

These paper reports applications using satellite images to the identification of vegetation types in the Campo Grande city. This identification allows studies of urban vegetation, palynology and environmental changes. Images from Landsat 8 and Rapideye satellites from the Campo Grande urban area were used. A soil coverage map was done for each one of the seven sub-regions. The Normalized Difference Vegetation Index was applied. In addition, a field survey was carried out to confirm the vegetation types sites through satellite images. Satellite images and in situ data validation allowed the distinction of the following features: water, urban structure, herbaceous, open and dense vegetation. For the identification of urban vegetation, Rapideye images were the most suitable for this type of study. The Rapideye satellite sensor detected 6.55% more dense vegetation area than Landsat 8 images.

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Published

2019-10-16

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Article