Identifying individual fires from satellite-derived burned area data

Joana M.P. Nogueira, Julien Ruffault, Emilio Chuvieco, Florent Mouillot, David Frantz, Marion Stellmes, Achim Röder, Joachim Hill, Sander Veraverbeke, Fernando Sedano, Simon J. Hook, James T. Randerson, Yufang Jin, Brendan M. Rogers, Duarte Oom, Pedro C Silva, Ioannis Bistinas, José M C Pereira, Stijn Hantson, Salvador Pueyo & 3 others Emilio Chuvieco, S Archibald, D. P. Roy

Research output: Chapter in Book / Report / Conference proceedingChapterAcademic

Abstract

High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models.Weused the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the timing of burnt area for 16 large wildland fires. For each fire, parameters for the kriging model were defined using variogram analysis. The optimal number of observations used to estimate a pixel’s time of burning varied between four and six among the fires studied. The median standard error from kriging ranged between 0.80 and 3.56 days and the median standard error from geolocation uncertainty was between 0.34 and 2.72 days. For nine fires in the south-western US, the accuracy of the kriging model was assessed using high spatial resolution daily fire perimeter data available from the US Forest Service. For these nine fires, we also assessed the temporal reporting accuracy of the MODIS burnt area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared with 33% for MCD45A1 and 53% for MCD64A1. Systematic application of this algorithm to wildland fires in the future may lead to new information about vegetation, climate and topographic controls on fire behaviour.
Original languageEnglish
Title of host publicationRemote Sensing
Pages160-163
Number of pages4
DOIs
Publication statusPublished - 2016

Publication series

NameRemote Sensing
Volume9

Fingerprint

kriging
fire behavior
MODIS
pixel
variogram
temperature anomaly
interpolation
spatial resolution
vegetation
climate
product

Keywords

  • Burned patch
  • ESA FIRE_CCI
  • Fire
  • Fire ecology
  • Fire size distribution
  • LANDSAT
  • MODIS MCD45A1
  • Patch indices
  • Patch shape
  • Power law
  • Remote sensing
  • SOC
  • Savannas
  • Self-organized criticality
  • Southern Africa
  • Wildland fire
  • active fire
  • algorithm
  • carbon emissions
  • fire event
  • fire growth
  • fire propagation
  • fire regime
  • fire size distribution
  • fire spread
  • gini coefficient
  • ignition point
  • modis
  • time-gap

Cite this

Nogueira, J. M. P., Ruffault, J., Chuvieco, E., Mouillot, F., Frantz, D., Stellmes, M., ... Roy, D. P. (2016). Identifying individual fires from satellite-derived burned area data. In Remote Sensing (pp. 160-163). (Remote Sensing; Vol. 9). https://doi.org/10.1109/IGARSS.2009.5417974
Nogueira, Joana M.P. ; Ruffault, Julien ; Chuvieco, Emilio ; Mouillot, Florent ; Frantz, David ; Stellmes, Marion ; Röder, Achim ; Hill, Joachim ; Veraverbeke, Sander ; Sedano, Fernando ; Hook, Simon J. ; Randerson, James T. ; Jin, Yufang ; Rogers, Brendan M. ; Oom, Duarte ; Silva, Pedro C ; Bistinas, Ioannis ; Pereira, José M C ; Hantson, Stijn ; Pueyo, Salvador ; Chuvieco, Emilio ; Archibald, S ; Roy, D. P. / Identifying individual fires from satellite-derived burned area data. Remote Sensing. 2016. pp. 160-163 (Remote Sensing).
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Nogueira, JMP, Ruffault, J, Chuvieco, E, Mouillot, F, Frantz, D, Stellmes, M, Röder, A, Hill, J, Veraverbeke, S, Sedano, F, Hook, SJ, Randerson, JT, Jin, Y, Rogers, BM, Oom, D, Silva, PC, Bistinas, I, Pereira, JMC, Hantson, S, Pueyo, S, Chuvieco, E, Archibald, S & Roy, DP 2016, Identifying individual fires from satellite-derived burned area data. in Remote Sensing. Remote Sensing, vol. 9, pp. 160-163. https://doi.org/10.1109/IGARSS.2009.5417974

Identifying individual fires from satellite-derived burned area data. / Nogueira, Joana M.P.; Ruffault, Julien; Chuvieco, Emilio; Mouillot, Florent; Frantz, David; Stellmes, Marion; Röder, Achim; Hill, Joachim; Veraverbeke, Sander; Sedano, Fernando; Hook, Simon J.; Randerson, James T.; Jin, Yufang; Rogers, Brendan M.; Oom, Duarte; Silva, Pedro C; Bistinas, Ioannis; Pereira, José M C; Hantson, Stijn; Pueyo, Salvador; Chuvieco, Emilio; Archibald, S; Roy, D. P.

Remote Sensing. 2016. p. 160-163 (Remote Sensing; Vol. 9).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademic

TY - CHAP

T1 - Identifying individual fires from satellite-derived burned area data

AU - Nogueira, Joana M.P.

AU - Ruffault, Julien

AU - Chuvieco, Emilio

AU - Mouillot, Florent

AU - Frantz, David

AU - Stellmes, Marion

AU - Röder, Achim

AU - Hill, Joachim

AU - Veraverbeke, Sander

AU - Sedano, Fernando

AU - Hook, Simon J.

AU - Randerson, James T.

AU - Jin, Yufang

AU - Rogers, Brendan M.

AU - Oom, Duarte

AU - Silva, Pedro C

AU - Bistinas, Ioannis

AU - Pereira, José M C

AU - Hantson, Stijn

AU - Pueyo, Salvador

AU - Chuvieco, Emilio

AU - Archibald, S

AU - Roy, D. P.

PY - 2016

Y1 - 2016

N2 - High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models.Weused the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the timing of burnt area for 16 large wildland fires. For each fire, parameters for the kriging model were defined using variogram analysis. The optimal number of observations used to estimate a pixel’s time of burning varied between four and six among the fires studied. The median standard error from kriging ranged between 0.80 and 3.56 days and the median standard error from geolocation uncertainty was between 0.34 and 2.72 days. For nine fires in the south-western US, the accuracy of the kriging model was assessed using high spatial resolution daily fire perimeter data available from the US Forest Service. For these nine fires, we also assessed the temporal reporting accuracy of the MODIS burnt area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared with 33% for MCD45A1 and 53% for MCD64A1. Systematic application of this algorithm to wildland fires in the future may lead to new information about vegetation, climate and topographic controls on fire behaviour.

AB - High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models.Weused the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the timing of burnt area for 16 large wildland fires. For each fire, parameters for the kriging model were defined using variogram analysis. The optimal number of observations used to estimate a pixel’s time of burning varied between four and six among the fires studied. The median standard error from kriging ranged between 0.80 and 3.56 days and the median standard error from geolocation uncertainty was between 0.34 and 2.72 days. For nine fires in the south-western US, the accuracy of the kriging model was assessed using high spatial resolution daily fire perimeter data available from the US Forest Service. For these nine fires, we also assessed the temporal reporting accuracy of the MODIS burnt area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared with 33% for MCD45A1 and 53% for MCD64A1. Systematic application of this algorithm to wildland fires in the future may lead to new information about vegetation, climate and topographic controls on fire behaviour.

KW - Burned patch

KW - ESA FIRE_CCI

KW - Fire

KW - Fire ecology

KW - Fire size distribution

KW - LANDSAT

KW - MODIS MCD45A1

KW - Patch indices

KW - Patch shape

KW - Power law

KW - Remote sensing

KW - SOC

KW - Savannas

KW - Self-organized criticality

KW - Southern Africa

KW - Wildland fire

KW - active fire

KW - algorithm

KW - carbon emissions

KW - fire event

KW - fire growth

KW - fire propagation

KW - fire regime

KW - fire size distribution

KW - fire spread

KW - gini coefficient

KW - ignition point

KW - modis

KW - time-gap

U2 - 10.1109/IGARSS.2009.5417974

DO - 10.1109/IGARSS.2009.5417974

M3 - Chapter

SN - 9781424433957

T3 - Remote Sensing

SP - 160

EP - 163

BT - Remote Sensing

ER -

Nogueira JMP, Ruffault J, Chuvieco E, Mouillot F, Frantz D, Stellmes M et al. Identifying individual fires from satellite-derived burned area data. In Remote Sensing. 2016. p. 160-163. (Remote Sensing). https://doi.org/10.1109/IGARSS.2009.5417974