Estimating water content from hyperspectral measurements for fire risk mapping

Authors

  • M.E. Herrera
  • E. Chuvieco Universidad de Alcalá

DOI:

https://doi.org/10.18172/cig.2518

Keywords:

fuel moisture content, equivalent water thickness, MODIS.

Abstract

This paper shows the interest of laboratory hyperspectral measurements to estimate fuel moisture content for fire risk assessment. Measurements were obtained for two Mediterranean forest species over a controlled water stress experiment. The hyperspectral measurements were performed with a Specim camera, sensible to spectral reflectance from 900 to 1700 nm. We calculated the spectral correlation with variations in the moisture content, both for the camera raw bands as well as for those adjusted to the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. High correlation values were obtained for moisture content measurements and the Normalized Difference Water Index (NDWI), both for Q. suber (r=0.803 with the FMC and r=0.846 for EWT), and for the Q. robur (r=0.705 with the FMC and r=0.802 for EWT). Our work asserts that both parameters can be estimated with appropriate accuracy using satellite images.

Downloads

Download data is not yet available.

References

Bowyer, P. y Danson, F.M. (2004). Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level. Remote Sensing of Environment, 92, 297-308.

Boyer, J.S. (1995). Measuring the water status of plants and soils: Academic Press, INC.

Burgan, R.E. y Rothermel, R.C. (1984). BEHAVE: Fire Behavior Prediction and Fuel Modeling System. Fuel Subsystem. En. Ogden, Utah: USDA Forest Service.

Camia, A., Leblon, B., Cruz, M., Carlson, J.D. y Aguado, I. (2003). Methods Used to Estimate Moisture Content of Dead Wildland Fuels. En E. Chuvieco (Ed.), Wildland Fire Danger Estimation and Mapping. The Role of Remote Sensing Data, Singapore: World Scientific Publishing, pp. 91-117.

Carter, G.A. (1991). Primary and secondary effects of water content on the spectral reflectance of leaves. American Journal of Botany, 78, 916-924.

Castro, F.X., Tudela, A. y Sebastiá, M.A. (2003). Modeling moisture content in shrubs to predict fire risk in Catalonia (Spain). Agricultural and Forest Meteorology, 116, 49–59.

Ceccato, P., Leblon, B., Chuvieco, E., Flasse, S. y Carlson, J.D. (2003). Estimation of Live Fuel Moisture Content. En E. Chuvieco (Ed.), Wildland Fire Danger Estimation and Mapping. The Role of Remote Sensing Data, Singapore: World Scientific Publishing, pp. 63-90.

Chuvieco, E., Cocero, D., Riaño, D., Martín, M.P., Martínez-Vega, J., de la Riva, J. y Pérez, F. (2004). Combining NDVI and Surface Temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92, 322–331.

De Santis, A., Vaughan, P. y Chuvieco, E. (2006). Foliage moisture content estimation from 1-D and 2-D spectroradiometry for fire danger assessment. Journal of Geophysical Research - Biosciences, 111, doi:10.1029/2005JG000149.

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.

Gratani, L., Covone, F. y Larcher, W. (2006). Leaf plasticity in response to light of three evergreen species of the Mediterranean maquis. Trees-Structure and Function, 20, 549-558.

Jurdao, S., Oliva, P., Yebra, M. y Chuvieco, E. (2013). Leaf and canopy response to plant drying: implications to estimate live fuel moisture content from Radiative Transfer Models. Photogrammetric Engineering and Remote Sensing, in review.

Knipling, E.B. (1970). Physical and Physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment, 1, 155-159.

Ruiz de la Torre, J. (Ed.) (1990-1999). Mapa Forestal de España. Madrid: Ministerio de Medio Ambiente.

Vogelman, T.C. y Björn, L.O. (1984). Measurement of light gradients and espectral regime in plant tissue with a fiber optic probe. Physiologia Plantarum, 60, 361-368.

Westman, W.E. y Price, C.V. (1988). Spectral changes in conifers subjected to air pollution and water stress: experimental studies. IEEE Transactions on Geoscience and Remote Sensing, 26, 11-20.

Yebra, M., de Santis, A. y Chuvieco, E. (2005). Estimación del peligro de incendios a partir de teledetección y variables meteorológicas: variación temporal del contenido de humedad del combustible". Recursos Rurais, 1, 9-19.

Yebra, M., Chuvieco, E. y Riaño, D. (2008). Estimation of live Fuel Moisture Content from MODIS images for fire risk assessment. Agricultural and Forest Meteorology, 148, 523-536.

Yebra, M. y Chuvieco, E. (2009). Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem. Remote Sensing of Environment, 113, 2403-2411.

Yebra, M., Dennison, P., Chuvieco, E., Riaño, D., Zylstra, P., Hunt, E.R., Danson, F.M., Qi, Y. y Jurdao, S. (2013). A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products Remote Sensing of Environment, 136, 455-468.

Published

07-03-2014

How to Cite

1.
Herrera M, Chuvieco E. Estimating water content from hyperspectral measurements for fire risk mapping. CIG [Internet]. 2014 Mar. 7 [cited 2024 Mar. 29];40(2):295-310. Available from: https://publicaciones.unirioja.es/ojs/index.php/cig/article/view/2518