Using AHS hyper-spectral images to study forest vegetation recovery after a fire

M. Huesca Martinez, Silvia Merino-de-Miguel*, Federico Gonzalez-Alonso, Sergio Martinez, Jose Miguel Cuevas, Abel Calle

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Recent advances in sensor technology have led to the development of new hyper-spectral instruments capable of measuring reflected radiation over a wide range of wavelengths. These instruments can be used to assess the diverse characteristics of vegetation recovery that are only noticeable in certain parts of the electromagnetic spectrum. In this research, such instruments were used to study vegetation recovery following a forest fire in a Mediterranean ecosystem. The specific event occurred in an area called El Rodenal of Guadalajara (in Central Spain) between 16 and 21 July 2005. Remotely sensed hyper-spectral multitemporal data were used to assess the forest vegetation response following the fire. These data were also combined with remotely sensed fire severity data and satellite high temporal resolution data. Four Airborne Hyperspectral Scanner (AHS) hyper-spectral images, 361 Moderate Resolution Imaging Spectroradiometer (MODIS) images, field data, and ancillary information were used in the analysis. The total burned area was estimated to be 129.4 km2. AHS-derived fire severity level-of-damage assessments were estimated using the normalized burn ratio (NBR). Post-fire vegetation recovery was assessed according to a spectral unmixing analysis of the AHS hyper-spectral images and the normalized difference vegetation index (NDVI), as calculated from the MODIS time series. Combining AHS hyper-spectral images with field data provides reliable estimates of burned areas and fire severity levels-of-damage. This combination can also be used to monitor post-fire vegetation recovery trends. MODIS time series were used to determine the types and rates of vegetation recovery after the fire and to support the AHS-based estimates. Data and maps derived using this method may be useful for locating priority intervention areas and planning forest restoration projects.
Original languageEnglish
Pages (from-to)4025-4048
Number of pages24
JournalInternational journal of remote sensing
Issue number11
Publication statusPublished - 25 Feb 2013
Externally publishedYes


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