The Use of Remote Sensing Indices for Land Cover Change Detection

Authors

  • Gustavo Facincani Dourado Universidade Federal de Mato Grosso do Sul, Faculdade de Engenharias, Arquitetura e Urbanismo e Geografia, Cidade Universitária, s/n, 79070-900 Campo Grande, MS, Brasil
  • Jaíza Santos Motta Universidade Federal de Mato Grosso do Sul, Faculdade de Engenharias, Arquitetura e Urbanismo e Geografia, Cidade Universitária, s/n, 79070-900 Campo Grande, MS, Brasil
  • Antonio Conceição Paranhos Filho Universidade Federal de Mato Grosso do Sul, Faculdade de Engenharias, Arquitetura e Urbanismo e Geografia, Cidade Universitária, s/n, 79070-900 Campo Grande, MS, Brasil
  • David Findlay Scott The University of British Columbia Okanagan, Department of Earth and Environmental Sciences, I. K. Barber School of Arts and Sciences, 3247, University Way, Kelowna, British Columbia, V1V 1V7, Canada
  • Sandra Garcia Gabas Universidade Federal de Mato Grosso do Sul, Faculdade de Engenharias, Arquitetura e Urbanismo e Geografia, Cidade Universitária, s/n, 79070-900 Campo Grande, MS, Brasil

DOI:

https://doi.org/10.11137/2019_2_72_85

Keywords:

Landsat, Multi-temporal, NDVI, NDWI, QGIS, Time-series

Abstract

Remote sensing technology has been applied to monitor anthropogenic changes in the landscape that produce impacts on natural resources, such as environmental degradation, changes in the hydrological cycle and in ecosystems structure and functioning. As digital change detection may be a difficult task to perform, this study proposes a simple and logical technique to display land cover changes using Landsat imagery. Open source geoprocessing tools were used to acquire information for mapping changes on the land surface. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite images of four dates between 1984 and 2016 were used in RGB composites. The method was used to map gains and losses of vegetation cover and liquid water content in a spatiotemporal scale. The results indicate that this change detection method can effectively reflect the variations occurred over the years. Although both indices have similar responses, NDWI may provide opposite information to NDVI in certain areas, such as in wetlands and riparian zones, presenting wetness losses even in places that exhibit gains in vegetation. This method has applicability to other regions for deriving historical changes.

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Published

2019-12-01

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Article