A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series

R. Salvador; J. Valeriano; X. Pons; R. Diaz-Delgado

doi:https://doi.org/10.1080/014311600210498

A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series

R. Salvador, J. Valeriano, X. Pons, R. Diaz-Delgado

Department of Geography

Research output: Contribution to journal › Article › Research › peer-review

78 Citations (Scopus)

Abstract

This paper presents a semi-automatic methodology for fire scars mapping from a long time series of remote sensing data. Approximately, a hundred MSS images from different Landsat satellites were employed over an area of 32 100 km2 in the north-east of the Iberian Peninsula. The analysed period was from 1975 to 1993. Results are a map series of fire history and frequencies. Omission errors are 23% for burned areas greater than 200 ha while commission errors are 8% for areas greater than 50 ha. Subsequent work based on the resultant fire scars will also help in describing fire regime and in monitoring post-fire regeneration dynamics. © 2000 Taylor & Francis Group, LLC.

Original language	English
Pages (from-to)	655-671
Journal	International Journal of Remote Sensing
Volume	21
DOIs	https://doi.org/10.1080/014311600210498
Publication status	Published - 1 Jan 2000

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title = "A semi-automatic methodology to detect fire scars in shrubs and evergreen forests with Landsat MSS time series",

abstract = "This paper presents a semi-automatic methodology for fire scars mapping from a long time series of remote sensing data. Approximately, a hundred MSS images from different Landsat satellites were employed over an area of 32 100 km2 in the north-east of the Iberian Peninsula. The analysed period was from 1975 to 1993. Results are a map series of fire history and frequencies. Omission errors are 23% for burned areas greater than 200 ha while commission errors are 8% for areas greater than 50 ha. Subsequent work based on the resultant fire scars will also help in describing fire regime and in monitoring post-fire regeneration dynamics. {\textcopyright} 2000 Taylor & Francis Group, LLC.",

author = "R. Salvador and J. Valeriano and X. Pons and R. Diaz-Delgado",

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AU - Salvador, R.

AU - Valeriano, J.

AU - Pons, X.

AU - Diaz-Delgado, R.

PY - 2000/1/1

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N2 - This paper presents a semi-automatic methodology for fire scars mapping from a long time series of remote sensing data. Approximately, a hundred MSS images from different Landsat satellites were employed over an area of 32 100 km2 in the north-east of the Iberian Peninsula. The analysed period was from 1975 to 1993. Results are a map series of fire history and frequencies. Omission errors are 23% for burned areas greater than 200 ha while commission errors are 8% for areas greater than 50 ha. Subsequent work based on the resultant fire scars will also help in describing fire regime and in monitoring post-fire regeneration dynamics. © 2000 Taylor & Francis Group, LLC.

AB - This paper presents a semi-automatic methodology for fire scars mapping from a long time series of remote sensing data. Approximately, a hundred MSS images from different Landsat satellites were employed over an area of 32 100 km2 in the north-east of the Iberian Peninsula. The analysed period was from 1975 to 1993. Results are a map series of fire history and frequencies. Omission errors are 23% for burned areas greater than 200 ha while commission errors are 8% for areas greater than 50 ha. Subsequent work based on the resultant fire scars will also help in describing fire regime and in monitoring post-fire regeneration dynamics. © 2000 Taylor & Francis Group, LLC.

U2 - https://doi.org/10.1080/014311600210498

DO - https://doi.org/10.1080/014311600210498

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JF - International Journal of Remote Sensing

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