Hyperspectral remote sensing of fire: State-of-the-art and future perspectives

Sander Veraverbeke, Philip Dennison, Ioannis Gitas, Glynn Hulley, Olga Kalashnikova, Thomas Katagis, Le Kuai, Ran Meng, Dar Roberts, Natasha Stavros

Research output: Contribution to JournalReview articleAcademicpeer-review

Abstract

Fire is a widespread Earth system process with important carbon and climate feedbacks. Multispectral remote sensing has enabled mapping of global spatiotemporal patterns of fire and fire effects, which has significantly improved our understanding of interactions between ecosystems, climate, humans and fire. With several upcoming spaceborne hyperspectral missions like the Environmental Mapping And Analysis Program (EnMAP), the Hyperspectral Infrared Imager (HyspIRI) and the Precursore Iperspettrale Della Missione Applicativa (PRISMA), we provide a review of the state-of-the-art and perspectives of hyperspectral remote sensing of fire. Hyperspectral remote sensing leverages information in many (often more than 100) narrow (smaller than 20 nm) spectrally contiguous bands, in contrast to multispectral remote sensing of few (up to 15) non-contiguous wider (greater than 20 nm) bands. To date, hyperspectral fire applications have primarily used airborne data in the visible to short-wave infrared region (VSWIR, 0.4 to 2.5 μm). This has resulted in detailed and accurate discrimination and quantification of fuel types and condition, fire temperatures and emissions, fire severity and vegetation recovery. Many of these applications use processing techniques that take advantage of the high spectral resolution and dimensionality such as advanced spectral mixture analysis. So far, hyperspectral VSWIR fire applications are based on a limited number of airborne acquisitions, yet techniques will approach maturity for larger scale application when spaceborne imagery becomes available. Recent innovations in airborne hyperspectral thermal (8 to 12 μm) remote sensing show potential to improve retrievals of temperature and emissions from active fires, yet these applications need more investigation over more fires to verify consistency over space and time, and overcome sensor saturation issues. Furthermore, hyperspectral information and structural data from, for example, light detection and ranging (LiDAR) sensors are highly complementary. Their combined use has demonstrated advantages for fuel mapping, yet its potential for post-fire severity and combustion retrievals remains largely unexplored.

Original languageEnglish
Pages (from-to)105-121
Number of pages17
JournalRemote Sensing of Environment
Volume216
DOIs
Publication statusPublished - 1 Oct 2018

Fingerprint

remote sensing
Remote sensing
Fires
fire severity
sensors (equipment)
state of the art
climate
lidar
sensor
Infrared radiation
climate feedback
combustion
Spectral resolution
space and time
Sensors
spectral resolution
temperature
Image sensors
Ecosystems
imagery

Keywords

  • AVIRIS
  • Fire
  • Fire severity
  • Fuel
  • Hyperspectral
  • HyspIRI
  • Imaging spectroscopy

Cite this

Veraverbeke, S., Dennison, P., Gitas, I., Hulley, G., Kalashnikova, O., Katagis, T., ... Stavros, N. (2018). Hyperspectral remote sensing of fire: State-of-the-art and future perspectives. Remote Sensing of Environment, 216, 105-121. https://doi.org/10.1016/j.rse.2018.06.020
Veraverbeke, Sander ; Dennison, Philip ; Gitas, Ioannis ; Hulley, Glynn ; Kalashnikova, Olga ; Katagis, Thomas ; Kuai, Le ; Meng, Ran ; Roberts, Dar ; Stavros, Natasha. / Hyperspectral remote sensing of fire : State-of-the-art and future perspectives. In: Remote Sensing of Environment. 2018 ; Vol. 216. pp. 105-121.
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Veraverbeke, S, Dennison, P, Gitas, I, Hulley, G, Kalashnikova, O, Katagis, T, Kuai, L, Meng, R, Roberts, D & Stavros, N 2018, 'Hyperspectral remote sensing of fire: State-of-the-art and future perspectives' Remote Sensing of Environment, vol. 216, pp. 105-121. https://doi.org/10.1016/j.rse.2018.06.020

Hyperspectral remote sensing of fire : State-of-the-art and future perspectives. / Veraverbeke, Sander; Dennison, Philip; Gitas, Ioannis; Hulley, Glynn; Kalashnikova, Olga; Katagis, Thomas; Kuai, Le; Meng, Ran; Roberts, Dar; Stavros, Natasha.

In: Remote Sensing of Environment, Vol. 216, 01.10.2018, p. 105-121.

Research output: Contribution to JournalReview articleAcademicpeer-review

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AU - Gitas, Ioannis

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