Anuário do Instituto de Geociências (Mar 2019)

Comparison of the Normalized Difference Vegetation Index (NDVI) Between the Sensors OLI-Landsat Satellite-8 and MSI-Sentinel-2 Satellite in Semi-Arid Region

  • Ulisses Alencar Bezerra,
  • Leidjane Maria Maciel de Oliveira,
  • Ana Lúcia Bezerra Candeias,
  • Bernardo Barbosa da Silva,
  • Antônio Celso Leite de Sousa Leite,
  • Luisa Thaynara Muricy de Souza Silva

DOI
https://doi.org/10.11137/2018_3_167_177
Journal volume & issue
Vol. 41, no. 3
pp. 167 – 177

Abstract

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The use of remote sensing techniques in support of environmental studies has become common in recent years, and the availability of satellite images for free has also boosted this growth. The use of orbital images for the accomplishment of these studies allows a reduction of the costs involved, greater agility and constancy in the access to the data and, it consents, a holistic evaluation, analyzing with more precision and detail several environmental components present in the region of interest. This paper aimed to analyze the spectral responses in the visible and infrared bands and to compare values of the Normalized Difference Vegetation Index (NDVI) obtained by the OLI - Landsat 8 and MSI - Sentinel 2 sensors in the semi - arid region that comprises part of the territory of the basin of the Moxotó river. When analyzing the spectral response of each of the sensors, it is evident that there are clear differences between the bands that provide the computation of the NDVI, which are the red and near infrared bands that could generate different values of the same index in the same area. Despite the differences between the Landsat-8 / OLI and Sentinel-2 / MSI optical sensors, they presented similar statistical moments between the bands compared. The NDVI in the studied period presented mean values for Landsat-8 and Sentinel-2 equal to 0.383 and 0.387, respectively, and a strong correlation equal to 0.871 among the sensors. The MSI-Sentinel-2 sensor allowed a greater delineation of the targets due to its greater spatial resolution, allowing greater confidence for monitoring and management of the environment.

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