Spatio-temporal mangrove canopy variation (2001–2016) assessed using the MODIS enhanced vegetation index (EVI)

  • Marta Rocío Nepita-Villanueva
  • César Alejandro Berlanga-RoblesEmail author
  • Arturo Ruiz-Luna
  • J. Héctor Morales Barcenas


Variation patterns in mangrove canopies were evaluated for the Marismas Nacionales coastal ecosystem (northwest Mexico), based on a monthly time series (2001 to 2016) of the MODIS enhanced vegetation index (EVI). By using a non-centralized normalized principal component analysis (PCA), the imagery series was decomposed to the S and T modes, allowing the identification of recurrent temporal patterns as well as spatial patterns over time. It was found that the maximum vegetation vigor in mangroves occurs in autumn, 4–5 months after the driest season, while approximately 15% of the mangrove canopy displayed decreasing trends because of disturbance events, including anomalies in temperature and precipitation. Most of the mangrove canopy was stable (78%), while the remaining 7% was found to be in a recovery phase. The most vulnerable mangrove canopies were detected in areas defined in previous studies as dominated by Avicenia germinans, while resistant and resilient forests where located in areas dominated by Laguncularia racemosa.


Mangroves MODIS Enhanced vegetation index (EVI) Time series Principal component analysis (PCA) 



The authors thank the National Council for Science and Technology (CONACYT) for financing the SEP-CONACYT Basic Science Project 157533 “Modeling the relationships between the spatial patterns of the mangrove forest and the distribution and abundance of penaeid shrimp in the Teacapán-Agua Brava lagoon system, Mexico”, and for the grant 748017 to Marta R. Nepita. The MODQ131 products were retrieved online, courtesy of NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, DAAC. The climate data used in this study were courtesy of the National Center for Environmental Prediction (NCEP), Global Weather Data for SWAT.


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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Doctorado en Ciencias Marinas, Departamento de Recursos del MarCentro de Investigación y de Estudios Avanzados del IPNMéridaMexico
  2. 2.Centro de Investigación en Alimentación y Desarrollo A. C, Coordinación Regional MazatlánMazatlánMexico
  3. 3.Departamento de MatemáticasUniversidad Autónoma Metropolitana-IztapalapaCiudad de MéxicoMexico

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