{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T21:09:30Z","timestamp":1776287370191,"version":"3.50.1"},"reference-count":55,"publisher":"CSIRO Publishing","issue":"2","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":7617,"URL":"https:\/\/doi.org\/10.1071\/journalslicense"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2004,6,30]]},"abstract":"<jats:p>Using pre- and post-fire satellite imagery from SPOT2, we examined the fire severity and intensity of the Christmas 2001 wildfires in the greater Sydney Basin, Australia. We computed a Normalised Difference Vegetation Index (NDVI) from the two satellite images captured before (November 2001) and after (January 2002) the wildfires, then subtracted the later from the former to produce a difference image (NDVIdiff) which was subsequently classified into six fire severity classes (unburnt, low, moderate, high, very high and extreme severity). We then tested the fire severity classification on 342 sample sites within the 225 000ha fire affected area using a qualitative visual assessment guide. We found that the NDVIdiff classification produced an accuracy of at least 88% (Khat = 0.86), with the greatest discrepancy being between the low and moderate classification. Knowledge of rate of spread over some of the affected area, coupled with a complete knowledge of fuel loads, was used to retrospectively model fire intensity, which in areas of extreme fire intensity, produced heat energy levels exceeding 70 000 kW m\u20131. Importantly, we found no positive effect of topography on fire severity, in fact finding an inverse relationship between slope and fire severity and no effect due to aspect. Further analysis showed that flat to moderate slopes less than 18\u00b0 across all aspects suffered the greatest vegetal destruction, and there was no relationship between north-westerly aspects and fire severity. We also introduce a relatively simple method for estimating fuel load biomass using a combination of satellite image and rapid field assessment. We found 79% accuracy for this method based on 125 sample sites. It is postulated that this type of analysis can greatly improve our understanding of the spatial impact of fire, how natural areas within the fire ground were impacted, and how remote sensing and GIS technologies can be efficiently used in fire management planning and post-fire analysis.<\/jats:p>","DOI":"10.1071\/wf03041","type":"journal-article","created":{"date-parts":[[2004,6,29]],"date-time":"2004-06-29T23:12:29Z","timestamp":1088550749000},"page":"227-240","source":"Crossref","is-referenced-by-count":158,"title":["The post-fire measurement of fire severity and intensity in the Christmas 2001 Sydney wildfires"],"prefix":"10.1071","volume":"13","author":[{"given":"Chris J.","family":"Chafer","sequence":"first","affiliation":[{"name":"ASydney Catchment Authority, PO Box 50, Appin, NSW 2502, Australia.Telephone: +61 2 4640 1018; fax: +61 2 4640 1006; email: chris.chafer@sca.nsw.gov.au"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Noonan","sequence":"additional","affiliation":[{"name":"BSydney Catchment Authority, PO Box 323, Penrith, NSW 2751, Australia. Telephone: +61 2 4725 4727; fax: +61 2 4725 2523; email: mark.noonan@sca.nsw.gov.au"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eloys","family":"Macnaught","sequence":"additional","affiliation":[{"name":"ASydney Catchment Authority, PO Box 50, Appin, NSW 2502, Australia.Telephone: +61 2 4640 1018; fax: +61 2 4640 1006; email: chris.chafer@sca.nsw.gov.au"},{"name":"CPresent address: ESRI Australia, Level 1, 414 Kent St, Sydney 2000, Australia. Telephone: +61 2 9285 5509; fax: +61 2 9267 2099; email: emacnaught@esriau.com.au"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"67","published-online":{"date-parts":[[2004,6,30]]},"reference":[{"key":"2025112621135663200_R1"},{"key":"2025112621135663200_R2"},{"key":"2025112621135663200_R3"},{"key":"2025112621135663200_R4","unstructured":"Beck         JI                                       Alexander         ME                                       Harvey         SD                                       Beaver         AK                           2001            Forecasting diurnal variation in fire intensity for use in wildland fire management.      \u2018Fourth Symposium on Fire and Forest Meteorology, Section 2.1\u2019.                                           Reno, Nevada"},{"key":"2025112621135663200_R5"},{"key":"2025112621135663200_R6","unstructured":"Bertolette         D                                       Spotskey         D                           2001            Remotely sensed burn severity mapping.      \u2018Crossing boundaries in park management: Proceedings of the 11th conference on research and resource management in parks and on public lands\u2019.                               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Queensland"},{"key":"2025112621135663200_R34"},{"key":"2025112621135663200_R35"},{"key":"2025112621135663200_R36"},{"key":"2025112621135663200_R37","unstructured":"Rose         RL                                       Chafer         CJ                           1997            GIS and decision-tree modelling as a framework for the preparation of fire management plans in conservation reserves.      \u2018Proceedings of \u201897Australian Bushfire Conference, July 1997.\u2019                                           Winnellie, Northern Territory"},{"key":"2025112621135663200_R38","unstructured":"Rose         RL                                       Wiltshire         G                                       Lang         S                           1999            The importance and application of spatial patterns in the management of fire regimes for the protection of life and property and the conservation of biodiversity.      \u2018Proceedings of the Bushfire \u201999 Australian 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