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Relationship between densification and NDVI loss. A study using the Google Earth Engine at local scale

Celemin Juan Pablo () and Arias Maria Eugenia
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Celemin Juan Pablo: Institute of Geography, History and Social Sciences, National Scientific and Technical Research Council, Pinto 399, Tandil, Buenos Aires Province, Argentina
Arias Maria Eugenia: Institute of Geography, History and Social Sciences, National Scientific and Technical Research Council, Pinto 399, Tandil, Buenos Aires Province, Argentina

Environmental & Socio-economic Studies, 2022, vol. 10, issue 3, 33-42

Abstract: Latin American cities are amongst those with the highest rates of urbanization in the world. This process has involved their territorial expansion as well as the densification of some of its neighborhoods, in mainly central areas. This is the case of the city of Santiago del Estero (Argentina) that increased its population by 33% between 1991 and 2010 with the consequent transformations of the local space. In this context, this study analyzes the evolution of vegetated areas and densification of the central area of the city using satellite data. We analyzed two indices: normalized difference vegetation index (NDVI) and Urban Index (UI) time-series data, for the 1992–2011 year period, using the Google Earth Engine for processing Landsat 5 TM images. We found that the NDVI showed a decreasing trend in the timelapse under consideration, while the UI performance registered the opposite trend. The mean NDVI decreased from 0.161 (1992) to 0.103 (2011) while the UI mean increased from 0.003 to 0.036 in the same timelapse. Further, the NDVI has a strong negative correlation with UI (R-squared = -0.862). The results are consistent with the census information that recorded an important demographic and housing growth for the entire city in this period.

Keywords: vegetation cover loss; densification; NDVI; Google Earth Engine; Santiago del Estero; Argentina (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:enviro:v:10:y:2022:i:3:p:33-42:n:7

DOI: 10.2478/environ-2022-0015

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