Los índices de vegetación como indicadores del riesgo de incendio con imágenes del sensor TERRA-MODIS

Referencias bibliográficas

• AGUADO, I., CHUVIECO, E., BORÉN, R. & NIETO, H. 2007. Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment. International Journal of Wildland Fire. 16: 390-397.
• ALONSO-BETANZOS, A., FONTENLA-ROMERO, O., GUIJARRO-BERDIÑAS, B., HERNÁNDEZ-PEREIRA, E., PAZ ANDRADE, I., JIMÉNEZ, E., LEGIDO, J.L. & CARBALLAS, T. 2003. An intelligent system for forest fire risk prediction and fire fighting management in Galicia. Expert Systems with Applications. 25: 545-554.
• CASTRO, F.X., TUDELA, A. & SEBASTIÀ, M.T. 2003. Modeling moisture content in shrubs to predict fire risk in Catalonia (Spain). Agricultural and Forest Meteorology. 116: 49-59.
• CECCATO, P., GOBRON, N., FLASSE, S., PINTY, B. & TARANTOLA, S. 2002. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1. Theoretical approach. Remote Sensing of Environment. 82: 188-197.
• CHENG, Y., ZARCO-TEJADA, P.J., RIAÑO, D., RUEDA, C.A. & USTIN, S. L. 2006. Estimating vegetation water content with hyperspectral data for different Canopy scenarios: Relationships between AVIRIS and MODIS indexes. Remote Sensing of Environment. 105: 354-366.
• CHUVIECO, E., COCERO, D., RIAÑO, D., MARTÍN, P., MARTÍNEZ-VEGA, J., DE LA RIVA, J. & PÉREZ, F. 2004. Combining NDVI and surface temperatura for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment. 92: 322-331.
• DENNISON, P.E., ROBERTS, D.A., PETERSON, S.H., & RECHEL, J. 2005. Use of Normalized Difference Water Index for monitoring live fuel moisture. International Journal of Remote Sensing. 26: 1035-1042.
• GAO, B.C. 1996. NDWI — a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment. 58: 257-266.
• GITELSON, A. A., KAUFMAN, Y. J., STARK, R., & RUNDQUIST, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment. 80: 76-87.
• HUETE A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. 25: 295-309.
• HUETE, A., DIDAN, K., MIURA, T., RODRÍGUEZ, 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: 195-213.
• HUNT, E.R., & ROCK, B.N. 1989. Detection of changes in leaf water-content using near-infrared and middle-infrared reflectances. Remote Sensing of Environment. 30: 43-54.
• LOZANO, F. J., SUÁREZ-SEOANE, S. & DE LUIS, E. 2007. Assessment of several spectral indices derived from multi-temporal Landsat data for fire occurrence probability modelling. Remote Sensing of Environment. 107: 533-544.
• LOZANO, F.J., SUÁREZ-SEOANE, S., KELLY, M., LUIS, E. (2008). Amulti-scale approach for modelling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region. Remote Sensing of Environment, 112, 708-719.
• MAKI, M., ISHIAHRA, M. & TAMURA, M. 2004. Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment. 90: 441-450.
• MARTÍN, A., DÍAZ-RAVIÑA, M. & CARBALLAS, T., 2009. Evolution of composition and content of soil carbohydrates following forest wildfires. Biology and fertility of soils. 45, 5: 511-520.
• MASELLI, F., ROMANELLI, S., BOTTAI, L. & ZIZPOLI, G. 2003. Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas. Remote Sensing of Environment. 86: 187-197.
• PALACIOS-ORUETA, A., CHUVIECO, E., PARRA, A. & CARMONA-MORENO, C., 2005. Biomasa burning emissions: a review of models using remote-sensing data.Environmental monitoring and assessment. 104: 189-209.
• PINTY, B. & VERSTRAETE, M. M. 1992. GEMI: a non-linear index to monitor globlal vegetation from satellites. Vegetation. 101: 15-20.
• ROUSE, J. W., HASS, R. H., SCHELL, J. A., & DEERING, D. W. 1974. Monitoring vegetation systems in the Grat Plains with ERTS. Proceedings, third Earth Resources Technology Satellite-1 Symposium, Greenbelt, NASA SP-351: 309-317.
• SÁNCHEZ, J. M., CASELLES, V., BISQUERT, M. M., PAZ ANDRADE, M. I. Y LEGIDO, J. L. 2008. Estimación del riesgo de incendio en Galicia a partir de imágenes EVI del Sensor Terra-MODIS. Revista de Teledetección. 30: 71-84.
• SEBASTIÁN-LÓPEZ, A., SAN-MIGUELAYANZ, J. & BURGAN, R. E., 2002. Integration of satellite sensor data, fuel type maps and meteorological observations for evaluation of forest fire risk at the pan-European scale. International Journal of Remote Sensing. 23: 2713-2719.
• SHNEIDER, P., ROBERTS, D. A. & KYRIAKIDIDS, P. C. 2008. A VARI-based relative greenness from MODIS data for computing the Fire Potential Index. Remote Sensing of Environment. 113: 1151-1167.
• STOW, D., NIPHADKAR, M. & KAISER, J. 2005. MODIS-derived visible atmospherically resistant index for monitoring chaparral moisture content. International Journal of Remote Sensing. 26: 3867-3873.
• VERBESSELT, J., JÖNSSON, P., LHERMITTE, S., VAN AARDT, J. & COPPEN, P. 2006. Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems. IEEE Transactions on Geoscience and Remote Sensing. 44: 1622- 1632.
• WILLMOTT, C. J. 1982. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 63: 1309-1313.
• YEBRA, M., CHUVIECO, E. & RIAÑO, D. 2008. Estimation of live fuel moisture content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology. 148: 523-536.