Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

Edith Olmos-Trujillo, Julián González-Trinidad, Hugo Júnez-Ferreira, Anuard Pacheco-Guerrero, Carlos Bautista-Capetillo, Claudia Avila-Sandoval and Eric Galván-Tejada
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Edith Olmos-Trujillo: Doctorado en Ciencias de la Ingeniería, Universidad Autónoma de Zacatecas, Campus siglo XXI, Zacatecas 98160, Mexico
Julián González-Trinidad: Doctorado en Ciencias de la Ingeniería, Universidad Autónoma de Zacatecas, Campus siglo XXI, Zacatecas 98160, Mexico
Hugo Júnez-Ferreira: Doctorado en Ciencias de la Ingeniería, Universidad Autónoma de Zacatecas, Campus siglo XXI, Zacatecas 98160, Mexico
Anuard Pacheco-Guerrero: Doctorado en Ciencias de la Ingeniería, Universidad Autónoma de Zacatecas, Campus siglo XXI, Zacatecas 98160, Mexico
Carlos Bautista-Capetillo: Doctorado en Ciencias de la Ingeniería, Universidad Autónoma de Zacatecas, Campus siglo XXI, Zacatecas 98160, Mexico
Claudia Avila-Sandoval: Doctorado en Ciencias de la Ingeniería, Universidad Autónoma de Zacatecas, Campus siglo XXI, Zacatecas 98160, Mexico
Eric Galván-Tejada: Doctorado en Ciencias de la Ingeniería, Universidad Autónoma de Zacatecas, Campus siglo XXI, Zacatecas 98160, Mexico

Sustainability, 2020, vol. 12, issue 5, 1-18

Abstract: In this research, vegetation indices (VIs) were analyzed as indicators of the spatio-temporal variation of vegetation in a semi-arid region. For a better understanding of this dynamic, interactions between vegetation and climate should be studied more widely. To this end, the following methodology was proposed: (1) acquire the NDVI, EVI, SAVI, MSAVI, and NDMI by classification of vegetation and land cover categories in a monthly period from 2014 to 2018; (2) perform a geostatistical analysis of rainfall and temperature; and (3) assess the application of ordinary and uncertainty least squares linear regression models to experimental data from the response of vegetation indices to climatic variables through the BiDASys (bivariate data analysis system) program. The proposed methodology was tested in a semi-arid region of Zacatecas, Mexico. It was found that besides the high values in the indices that indicate good health, the climatic variables that have an impact on the study area should be considered given the close relationship with the vegetation. A better correlation of the NDMI and EVI with rainfall and temperature was found, and similarly, the relationship between VIs and climatic factors showed a general time lag effect. This methodology can be considered in management and conservation plans of natural ecosystems, in the context of climate change and sustainable development policies.

Keywords: data analysis; statistical methods; LANDSAT 8; remote sensing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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