Spatial-temporal dynamics of Caatinga vegetation cover by remote sensing in the Brazilian semiarid region

Spatial-temporal dynamics of Caatinga vegetation cover by remote sensing in the Brazilian semiarid region

Abstract The Brazilian semiarid region is marked by water scarcity, which causes the loss of leaves from native vegetation to reduce transpiration. With the reduction of the Caatinga leaf area, the soil becomes more exposed, which makes it a great ally for environmental degradation. This study aimed to monitor and analyze the spatial-temporal dynamics of the Caatinga vegetation by orbital remote sensing in the semiarid region of Pernambuco, Brazil, in the rainy and dry season. The study was developed from Landsat-8 satellite images between the years 2013-2018. From the SEBAL algorithm, thematic maps of the biophysical parameters were determined: albedo and surface temperature, normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and leaf area index (LAI). The results show that in the dry season, there is a greater aptitude for environmental degradation to occur.

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Bibliographic Details
Main Authors: Rodrigues,Joez André de Moraes, Lopes,Pabricio Marcos Oliveira, Silva,Jhon Lennon Bezerra da, Araújo,Hélio Lopes, Silva,Marcos Vinícius da, Santos,Anderson dos, Batista,Pedro Henrique Dias, Moura,Geber Barbosa de Albuquerque
Format: Digital revista
Language:English
Published: Universidad Nacional de Colombia 2020
Online Access:http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532020000400109
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Summary:Abstract The Brazilian semiarid region is marked by water scarcity, which causes the loss of leaves from native vegetation to reduce transpiration. With the reduction of the Caatinga leaf area, the soil becomes more exposed, which makes it a great ally for environmental degradation. This study aimed to monitor and analyze the spatial-temporal dynamics of the Caatinga vegetation by orbital remote sensing in the semiarid region of Pernambuco, Brazil, in the rainy and dry season. The study was developed from Landsat-8 satellite images between the years 2013-2018. From the SEBAL algorithm, thematic maps of the biophysical parameters were determined: albedo and surface temperature, normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and leaf area index (LAI). The results show that in the dry season, there is a greater aptitude for environmental degradation to occur.

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