Implications of quality filtering of Enhanced Vegetation Index (EVI) for ecosystem functioning monitoring


  • A. Reyes-Díez Centro Andaluz para la Evaluación y Seguimiento del Cambio Global (CAESCG) ; Universidad de Almería (UAL)
  • D. Alcaraz-Segura Universidad de Granada
  • J. Cabello-Piñar Universidad de Almería



MODIS, EVI, vegetation Index, QA, quality, assessment, monitoring ecosystem functioning


The use of MODIS (Moderate Resolution Imaging Spectroradiometer) images for ecosystem monitoring is currently widespread both in research and management. Vegetation indexes (VIs), such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), are broadly extended for monitoring ecosystem functioning. These indexes are linear estimators of the fraction of photosynthetically active radiation intercepted by vegetation (fAPAR), the main control of net primary production. However, VIs are subject to errors. To handle such errors, the MOD13Q1 VI product includes a Quality Assessment (QA) layer with information about pixel quality. This QA layer represents a great advantage for final users, allowing filtering of pixels with VI values influenced by aerosols, clouds, snow, or shadows. However, the use of homogeneous filtering criteria throughout a heterogeneous region may cause the systematic loss of information in particular areas or times of the year. In this paper, we assessed the effect of different filtering criteria on spatiotemporal data of EVI for the period 2001-2010 in the Iberian Southeast. Our results showed no effect of filtering on EVI availability and magnitude values in low altitudes, but strong and significant differences in the mountains depending on the filter applied: aerosol, shadows or snow filters. Such effects of filtering on the EVI revealed that monitoring programs in these regions should include a filtering step before exploring for abrupt changes or longterm trends in the EVI time series.


Download data is not yet available.

Author Biographies

A. Reyes-Díez, Centro Andaluz para la Evaluación y Seguimiento del Cambio Global (CAESCG) ; Universidad de Almería (UAL)

Técnivo investigador en el CAESCG.

Licenciado Ciencias Ambientales (UAL)

Máster oficial Tecnologías de la Información Geográfica de la UAH

D. Alcaraz-Segura, Universidad de Granada

Departamento de Botánica

J. Cabello-Piñar, Universidad de Almería

Departamento de Biología y Geología


Alcaraz-Segura, D., Paruelo, J. M., Cabello, J. 2006. Identification of current ecosystem functional types in the Iberian Peninsula. Global Ecology and Biogeography, 15(2): 200-212.

Alcaraz-Segura, D., Cabello, J., Paruelo, J. M., Delibes, M. 2009. Use of Descriptors of Ecosystem Functioning for Monitoring a National Park Network: A Remote Sensing Approach. Environmental Management, 43(1): 38-48.

Alcaraz-Segura, D., Chuvieco, E., Epstein, H. E., Kasischke, E. S., Trishchenko, A. 2010. Debating the greening vs. Browning of the North American boreal forest: Differences between satellite datasets. Global Change Biology, 16(2): 760-770.

Alexandridis, T. K., Gitas, I. Z., Silleos, N. G. 2008. An estimation of the optimum temporal resolution for monitoring vegetation condition on a nationwide scale using MODIS/Terra data. International Journal of Remote Sensing, 29(12): 3589-3607.

Ali, A., de Bie, C. A. J. M., Skidmore, A. K. 2013. Detecting long-duration cloud contamination in hyper-temporal NDVI imagery. International Journal of Applied Earth Observation and Geoinformation, 24: 22-31.

Beck, P. S. A., Jönsson, P., Høgda, K. A., Karlsen, S. R., Eklundh, L., Skidmore, A. K. 2007. A groundvalidated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola Peninsula. International Journal of Remote Sensing, 28(19): 4311-4330.

Cabello, J., Alcaraz-Segura, D., Ferrero, R., Castro, A. J., Liras, E. 2012. The role of vegetation and lithology in the spatial and inter-annual response of EVI to climate in drylands of Southeastern Spain. Journal of Arid Environments, 79: 76-83.

Chen, X., Vierling, L., Deering, D. 2005. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment, 98(1):63-79.

Chuvieco, E. 2010. Teledetección ambiental. Barcelona. Editorial Ariel.

Colditz, R., Conrad, C., Wehrmann, T., Schmidt, M. y Dech, S. 2006. Generation and Assessment of MODIS Time Series using Quality Information. 2006 IEEE International Symposium on Geoscience and Remote Sensing, 779-782.

Costanza, R., d’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S., O’Neill, R. V., Paruelo, J., Raskin, R. G., Sutton, P., van den Belt, M. 1997. The value of the world’s ecosystem services and natural capital. Nature, 387(6630): 253-260.

De Abelleyra, D., Verón, S. R. 2014. Comparison of different BRDF correction methods to generate daily normalized MODIS 250 m time series. Remote Sensing of Environment, 140: 46-59.

Dionisio, M. A., Alcaraz-Segura, D., Cabello, J. 2012. Satellite-Based Monitoring of Ecosystem Functioning in Protected Areas: Recent Trends in the Oak Forests (Quercus pyrenaica Willd.) of Sierra Nevada (Spain). In S. S. Young & S. E. Silvern (Eds.), International Perspectives on Global Environmental Change, pp. 355-374. InTech.

Dozier, J., Painter, T. H., Rittger, K., Frew, J. E. 2008. Time-space continuity of daily maps of fractional snow cover and albedo from MODIS. Advances in Water Resources, 31(11): 1515-1526.

Foley, J. A., Asner, G. P., Costa, M. H., Coe, M. T., DeFries, R., Gibbs, H. K., Howar, E. A., Olson, S., Patz, J., Ramankutty, N., Snyder, P. 2007. Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin. Frontiers in Ecology and the Environment, 5(1): 25-32.


Fraser, R. S., Kaufman, Y. J. 1985. The relative importance of scattering and absorption in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 23(5): 625-633.

Gao, X., Huete, A. R., Ni, W., Miura, T. 2000. Optical-Biophysical Relationships of Vegetation Spectra without Background Contamination. Remote Sensing of Environment, 74(3): 609-620.

Giner, C., Martínez, B., Gilabert, M. A., Alcaraz-Segura, D. 2012. Tendencias en el verdor de la vegetación y en la producción primaria bruta de las áreas forestales en la España peninsular (2000-2009). Revista de Teledetección, 38: 51-64. Obtenido de http://www. Fecha de acceso: marzo de 2015.

Giorgi, F., Lionello, P. 2008. Climate change projections for the Mediterranean region. Global and Planetary Change, 63(2-3): 90-104.

Gu, J., Li, X., Huang, C., Okin, G. S. 2009. A simplified data assimilation method for reconstructing time-series MODIS NDVI data. Advances in Space Research, 44(4): 501-509.

Hess, L., Ratana, P., Huete, A., Potter, C., Melack, J. 2009. Use of MODIS Enhanced Vegetation Index to detect seasonal patterns of leaf phenology in central Amazon várzea forest. Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, 4: 1007-1010.

Hoare, D., Frost, P. 2004. Phenological description of natural vegetation in southern Africa using remotelysensed vegetation data. Applied Vegetation Science, 7(1): 19-28.

Huete, A., Justice, C. O., van Leeuwen, W. 1999. MODIS Vegetation Index (MOD13). Algorithm theoretical basis document. Obtenido de Fecha de acceso: marzo de 2015.

Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., Ferreira, L. G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2): 195-213.

Huete, A., Didan, K., Shimabukuro, Y. E., Ratana, P., Saleska, S. R., Hutyra, L. R., Yang, W., Nemani, R.R., Myneni, R. 2006. Amazon rainforests greenup with sunlight in dry season. Geophysical Research Letters, 33(6): 2-5.

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.- K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.

Jacquin, A., Sheeren, D., Lacombe, J. P. 2010. Vegetation cover degradation assessment in Madagascar savanna based on trend analysis of MODIS NDVI time series. International Journal of Applied Earth Observation and Geoinformation, 12(S1): S3-S10.

Jolly, W. M., Nemani, R., Running, S. W. 2005. A generalized, bioclimatic index to predict foliar phenology in response to climate. Global Change Biology, 11(4): 619-632.

Justice, C., Townshend, J., Vermote, E., Masuoka, E., Wolfe, R. E., Saleous, N., Roy, D. P., Morisette, J. T. 2002. An overview of MODIS Land data processing and product status. Remote Sensing of Environment, 83(1-2): 3-15.

Kerr, J. T., Ostrovsky, M. 2003 From space to species: ecological applications for remote sensing. Trends in Ecology and Evolution, 18(6): 299-305.

Larcher, W., Bauer, H. 1981. Ecological significance of resistance to low temperature. Encyclopedia of plant physiology, new series, volume 12A. Physiological plant ecology. I. Responses to the physical environment, 403-437.

Li, Z., Wang, Y., Zhou, Q., Wu, J., Peng, J., Chang, H. 2008. Spatiotemporal variability of land surface moisture based on vegetation and temperature characteristics in Northern Shaanxi Loess Plateau, China. Journal of Arid Environments, 72(6): 974-985.

Lloyd, D. 1990. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. International Journal of Remote Sensing, 11(12): 2269-2279.

McNaughton, S. J., Oesterheld, M,. Frank, D. A., Williams, K. J. 1989 Ecosystem-level patterns of primary productivity and herbivory in terrestrial habitats. Nature 341(6238): 142-144.

Milchunas, D. G., Laurenroth, W. K. 1995. Inertia in plant community structure: State changes after cessation of nutrient enrichment stress. Ecological Applications, 5(2): 1195-2005.

Monteith, J. L., Moss, C. J. 1977. Climate and the efficiency of crop production in Britain. Philosophical Transactions of the Royal Society London B, 281(980): 277-294.

Morales-Baquero, R., Pérez-Martínez, C., Reche, I. 2001. Ecosistemas de alta montaña, las atalayas de la troposfera. Revista Ecosistemas, 10(3).

Morton, D. C., Nagol, J., Carabajal, C. C., Rosette, J., Palace, M., Cook, B. D., Vermote E. F., Harding, D. J., North, P. R. J. 2014. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature, 506(7487): 221-224.

Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J. L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G. R., Lotsch, A., Friedl, M., Morisette, J. T., Votava, P., Nemani, R. R., Running, S.W. 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83(1-2): 214-231.

ÖQuist, G. 1983. Effects of low temperature on photosynthesis. Plant, Cell & Environment, 6(4): 281-300.

Oyonarte, C., Alcaraz-Segura, D., Oyarzábal, M., Paruelo, J. M., Cabello, J. 2010. Sistema de apoyo a la gestión de reservas de la biosfera basado en el monitoreo de la productividad primaria: ensayo en Cabo de Gata-Níjar (Almería-España). In P. Araya & M. Clüsener-Godt (Eds.), Reservas de la Biosfera: Su contribución a la provisión de servicios de los ecosistemas. Experiencias exitosas en Iberoamérica (pp. 118-140). Paris: UNESCO.

Paruelo, J. M, Lauenroth, W. K., Epstein, H. E., Burke I. C., Aguiar, M. R., Sala, O. E. 1995. Regional climatic similarities in the temperate zones of North and South America. Journal of Biogeography, 22(4-5): 2689-2699.

Paruelo, J. M., Oesterheld, M., Di Bella, C. M., Arzadum, M., Lafontaine, J., Cahuepé, M., Rebella, C. M. 2000. Estimation of primary production of subhumid rangelands from remote sensing data. Applied Vegetation Science, 3(2): 189-195.

Paruelo, J. M., Jobbágy, E. G., Sala, O. E. 2001. Current distribution of ecosystem functional types in temperate South America. Ecosystems, 4(7): 683-698.

Piñeiro, G., Oesterheld, M., Paruelo, J. 2006. Seasonal Variation in Aboveground Production and Radiationuse Efficiency of Temperate rangelands Estimated through Remote Sensing. Ecosystems, 9(3): 357-373.

Piquer-Rodríguez, M., Kuemmerle, T., Alcaraz-Segura, D., Zurita-Milla, R., Cabello, J. 2012. Future land use effects on the connectivity of protected area networks in southeastern Spain. Journal for Nature Conservation, 20(6): 326-336.

Potter, C. S., Brooks, V. 1998. Global analysis of empirical relations between annual climate and seasonality of NDVI. International Journal of Remote Sensing, 19(15): 2921-2948.

Poulter, B., Cramer, W. 2009. Satellite remote sensing of tropical forest canopies and their seasonal dynamics. International Journal of Remote Sensing, 30(24): 6575-6590.

Prasad, A. K., Singh, R. P., Singh, A. 2006. Seasonal climatology of aerosol optical depth over Indian subcontinent: trend and departures in recent years. International Journal of Remote Sensing, 27(12): 2323-2329.

Querol, X., Alastuey, A., Viana, M. M., Rodríguez, S., Artíñano, B., Salvador, P., Garcia do Santos, S., Fernández-Patier, R., Ruiz, C. R., de la Rosa, J., Sánchez de la Campa, A., Menéndez, M., Gil, J. I. 2004. Speciation and origin of PM10 and PM2.5 in Spain. Journal of Aerosol Science, 35(9): 1151-1172.

Reinart, A., Reinhold, M. 2008. Mapping surface temperature in large lakes with MODIS data. Remote Sensing of Environment, 112(2): 603-611.

Requena-Mullor, J., López, E., Castro, A., Cabello, J., Virgós, E., González-Miras, E., Castro, H. 2014. Modeling spatial distribution of European badger in arid landscapes: an ecosystem functioning approach. Landscape Ecology, 29(5): 843-855.

Rivas-Martínez, S. 1983. Pisos bioclimáticos de España. Lazaroa, 5, 33-43.

Sellers, P. J., Berry, J. A., Collatz, G. J., Field, C. B., Hall, F. G. 1992. Canopy reflectance, photosynthesis, and transpiration. III. A reanalysis using improved leaf models and a new canopy integration scheme. Remote Sensing of Environment, 42(3): 187-216.

Solano, R., Didan, K., Jacobson, A., Huete, A. 2010. MODIS Vegetation Indices (MOD13) C5 User’s Guide. Versión 2, 2010.

Talbot, R. W., Harriss, R. C., Browell, E. V., Gregory, G. L., Sebacher, D. I., Beck, S. M. 1986. Distribution and Geochemistry of Aerosols in the Tropical North Atlantic Troposphere: Relationship to Saharan Dust. Journal of Geophysical Research: Atmospheres.,91(D4): 5173-5182.

Tucker, C. J., Sellers, P. J. 1986. Satellite remote-sensing of primary production. International Journal of Remote Sensing, 7(11): 1395-1416.

Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E., Steininger, M. (2003). Remote sensing for biodiversity science and conservation. Trends in Ecology and Evolution, 18(6): 306-314.

Virginia, R. A., Wall, D. H., Levin, S. A. 2001. Principles of ecosystem function. Encyclopedia of biodiversity. Academic Press, San Diego, 345-352.

Wang, Q., Tenhunen, J., Dinh, N., Reichstein, M., Otieno, D., Granier, A., Pilegarrd, K. 2005. Evaluation of seasonal variation of MODIS derived leaf area index at two European deciduous broadleaf forest sites. Remote Sensing of Environment, 96(3-4): 475-484.

Wiegand, T., Snyman, H. A., Kellner, K., Paruelo, J. M. 2004. Do grasslands have a memory: Modeling phytomass production of a semiarid South African grassland. Ecosystems, 7(3): 243-258.

Zhang, L., Huang, J., Guo, R., Li, X., Sun, W., Wang, X. 2013. Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data. Journal of Zhejiang University SCIENCE B, 14(2): 144-161.

Zhao, M., Running, S. W. 2010. Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009. Science, 329(5994): 940-943.

Zhou, L., Tian, Y., Myneni, R. B., Ciais, P., Saatchi, S., Liu, Y., Piao, S., Chen, H., Vermote, E.F., Song, C., Hwang, T. 2014. Widespread decline of Congo rainforest greenness in the past decade. Nature, 509(7498): 86-90.





Research articles