Trend and variability of NDVI of the main vegetation types in the Cape Region of Baja California Sur



Palabras clave:

Landsat 7, Vegetation cover variability, Vegetation cover trend, Coefficient of variation


The Cape Region (CR) of Baja California Sur is located in the southernmost tip of the Baja California peninsula, Mexico. The vegetation of this region is dominated by scrubland (SRB) and tropical dry forest (TDF), but oak (OW) and oak-pine woodland (OWP) are also found in Sierra La Laguna Biosphere Reserve. These vegetation types, in this relatively small area, make the CR a contrasting and highly biodiverse region. This research used the Normalised Difference Vegetation Index (NDVI) to describe spatiotemporal variability of the vegetation cover in the CR. The analysed NDVI data derived from Landsat 7 during the growing season (September-November) from 2001-2015. Its spatial distribution showed the highest values in the TDF, OW and OPW. Higher variability was observed in SRB. Temporal variations of the annual NDVI mean of the growing season fluctuated in a range from 0.292 to 0.522, and with the highest values in 2014 while the lowest were observed in 2011. A general increasing trend was observed for the growing season over the period 2001-2015 for all analysed vegetation types. Remote sensing change detection methods combined with Geographic Information Systems, suggested that the vegetation of the area was well conserved during the period of study.

Biografía del autor/a

Raúl Octavio Martínez-Rincón, Centro de Investigaciones Biológicas del Noroeste, S.C.

Catedrático CONACYT comisionado al CIBNOR desde 2014. Doctor en ciencias por el Centro Interdisciplinario de Ciencias Marinas (IPN-CICIMAR).


Alvarez-Borrego, S. (2002). Physical oceanography. In Case T. J., Cody, M. L., & Ezcurra, E. (Eds.), A new island biogeography of the Sea of Cortés (pp. 41–59). New York, USA: Oxford University Press.

Arriaga, L., & Mercado, C. (2004). Seed bank dynamics and tree-fall gaps in a Northwestern Mexican Quercus-Pinus Forest. Journal of Vegetation Science, 15, 661–668.

Arriaga, L., & Ortega-Rubio, A. (1988). La Sierra de la Laguna de Baja California Sur. México. Baja California Sur, Mexico: Centro Investigaciones Biologicas de Baja California Sur, A.C.

Bivand, R. (2018). RGdal: Bindings for the geospatial data abstraction library. R package 2.6–7. [Accessed Nov 2018]:

Breceda, A., Sosa, J., Jiménez, C., & Ortega-Rubio, A. (2014). Conservación en la Reserva de la Biosfera Sierra la Laguna, Baja California Sur: logros y retos. Investigación y Ciencia de la Universidad Autónoma de Aguascalientes, 60, 78–84.

CONANP, Comisión Nacional de Áreas Naturales Protegidas. (2003). Programa de manejo Reserva de la Biosfera Sierra La Laguna. México, D. F: CONANP.

Congedo, L. (2016). Semi-automatic classification plugin documentation. [Accessed Nov 2018] DOI:

Corredor, X. (2018). CloudMasking Qgis plugin (version 18.2.7), SMByC-IDEAM and FAO. [Accessed Nov 2018]:

De Jong, R., de Bruin, S., de Wit, S., Schaepman, M. E., & Dent, D. L. (2011). Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115, 692–702.

Díaz, S. C, Touchan, R., & Swetnam, T. W. (2001). A tree-ring reconstruction of past precipitation for Baja California Sur, Mexico. International Journal of Climatology, 21, 1007–1019.

Díaz, S. C., Salinas-Zavala, C. A., & Hernández-Vázquez, S. (2008). Variability of rainfall from tropical cyclones in north western Mexico and its relation to SOI and PDO. Atmosfera, 21, 213–223.

Eckert, S., Hüsler, ., Linigier, H., & Hodel, E. (2015). Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments, 113, 16–28.

Franklin, A. B., Noon, .B. R., & George, T. L. (2002). What is habitat fragmentation? Studies in Avian Biology, 25, 20–29.

García, E. (1973). Modificaciones al sistema de clasificación climática de Köppen. 2. Ed. Instituto de Geografía, Universidad Nacional Autónoma de México, Mexico.

Gillespie, T. W, Willis, K., & Ostermann-Kelm, S. (2014). Spaceborne remote sensing of the world’s protected areas. Progress in Physical Geography, 39, 1–17.

Gillespie, T. W., Ostermann-Kelm, S., Dong, C., Willis, K. S., Okin, G. S., & Macdonald, G. M. (2018). Monitoring changes of NDVI in protected areas of southern California. Ecolological Indicators, 88, 485–494.

Gu, X., & Wylie, B. K. (2015). Downsclaing 250-m MODIS growing season NDVI based on multiple-date Landsat images and data mining approaches. Remote Sensing, 7, 3489–3506.

Hijmans, R. J. (2018). Raster: geographic data analysis and modeling. R package version 2.6–7. [Accesed Nov 2018]:

Huang, C., Goward, S. N., Schleeweis, K., Thomas, N., Masek, J. F., & Zhu, Z. (2009). Dynamics of national forests assessed using the Landsat record: case studies in eastern United States. Remote Sensensing of Environment, 113, 1430–1442.

INEGI (Instituto Nacional de Estadística y Geografía). (2010). Censos generales de población y vivienda. Tabulados básicos. Mexico: INEGI.

INEGI (Instituto Nacional de Estadística y Geografía). (2015). Encuesta intercensal. Tabulados básicos. Mexico: INEGI

INEGI (Instituto Nacional de Estadística y Geografía). (2016). Datos de uso del suelo y vegetación: escala 1:250,000: version 3. Mexico: INEGI

Jiang, W., Yuan, L., Wang, W., Cao, R., Zhang, Y., & Shen, W. (2015). Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 51, 117–126.

León-de la Luz, J. L., Pérez-Navarro, J. J., & Breceda, A. (2000). A transitional xerophytic tropical plant community of the Cape Region, Baja California. Journal of Vegetation Science, 11, 555–564.

León-de la Luz, J. L., & Breceda, A. (2006). Using endemic plant species to establish critical habitats in the Sierra de La Laguna Biosphere reserve, Baja California Sur, Mexico. Biodiversity & Conservation, 15, 1043–1055.

León-de la Luz, J. L., Domínguez-Cadena, R., & Medel-Narváez, A. (2012) Florística de la selva baja caducifolia de la península de Baja California, México. Botanical Sciences, 90, 143–162.

Mallegowda, P., Rengaiang, G., Krishnan, J., & Niphadkar, M. (2015). Assessing habitat quality of forest-corridors through NDVI analysis in dry tropical forests of south India: implications for conservation. Remote Sensing, 7, 1619–1639.

Milich, L., & Weiss, E. (2000). GAC NDVI inter annual coefficient of variation (CoV) images: ground truth sampling of the Sahel along north-south transects. International Journal of Remote Sensing, 21, 235–260.

Nagendra, H. (2008). Do parks work? Impact of protected areas on land cover clearing. AMBIO:A Journal of the Human Environment, 37, 330–337.

Nagendra, H., Lucas, R., Honrado, J. P., Jongman, R. H., Tarantino, C., Adamo, M., & Mairota, P. (2013). Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecological Indicators, 33, 45–59.

Nelson, A., & Chomitz, K. M. (2011). Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods. Plos ONE, 6, e22722.

Padilla G., Pedrin, S., & Díaz, E. (1988). Historia geológica y paleoecología, Arriaga, L., Ortega-Rubio, A. (Eds.), La Sierra de La Laguna en Baja California Sur. (pp. 27–36). Baja California Sur, Mexico: Centro de Investigaciones Biológicas de Baja California Sur A.C.

Pang, G., Wang, X., & Yang, M. (2017). Using the NDVI to identify variations in, and responses of, vegetation to climate change on the Tibetan Plateau from 1982 to 2012. Quaternary International, 444, 87–96.

Peng, J., Liu, Z., Liu, Y., Wu, J., & Han, Y. (2012). Trend analysis of vegetation dynamics in Qinghai-Tibet Plateau using Hurst Exponent. Ecological Indicators, 14, 28–39.

Prieto-Amparan, J., Villareal-Guerrero, F., Martínez-Salvador, M., Manjarrez-Domínguez, C., Santellano E., & Pinedo-Álvarez, A. (2018). Atmospheric and radiometric correction algorithms for the multitemporal assessment of grasslands productivity. Remote Sensing, 10, 1–23.

QGIS. Development Team. (2018). QGIS Geographic Information System. Open Source Geospatial Foundation. [Accessed Jan 2019]:

R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Accessed at Feb 2019]:

Rascón-Ayala, J. M., Alanís-Rodríguez, E., Mora-Olivio, A., Buenída-Rodríguez, E., Sánchez-Castillo, L., & Silva-García, J. E. (2018). Differences in vegetation structure and diversity of a forest in an altitudinal gradient of the Sierra La Laguna Biosphere Reserve, México. Botanical Sciences, 96, 598–608.

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the great plains with ERTS. NASA Special Publication, 351, 309–318.

Salinas-Zavala, C. A., Martínez-Rincón R. O., & Morales-Zarate, M. V. (2017). Trend in the Normalized Difference Vegetation Index (NDVI) in the Southern Part of Baja California Peninsula. Investigaciones Geográficas, 94, 82–90.

SMN (Sistema Meteorologico Nacional). (2018). Ciclones tropicales: información historica. [Accessed Dec 2018]:

Tucker, C. J., Newcomb, W. W., Los, S. O., & Prince, S. D. (2001). Mean and inter-year variation of growing-season normalized difference vegetation index for the Sahel 1981–1989. International Journal of Remote Sensing, 12, 1133–1135.

Willis, K. S. (2015). Remote sensing change detection for ecological monitoring in United States protected areas. Biological Conservation, 182, 233–242.