Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires

Abstract Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to...
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Autor*in:	McNamara, Derek [verfasserIn] Mell, William

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Wildland-Urban interface WUI Wildland Fire Remote sensing Tubbs fire Light detection and ranging

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Fire technology - New York, NY [u.a.] : Springer Science + Business Media B.V., 1965, 58(2021), 1 vom: 26. Aug., Seite 641-672
Übergeordnetes Werk:	volume:58 ; year:2021 ; number:1 ; day:26 ; month:08 ; pages:641-672

Links:	Volltext

DOI / URN:	10.1007/s10694-021-01170-6

Katalog-ID:	SPR045985677

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allfields	10.1007/s10694-021-01170-6 doi (DE-627)SPR045985677 (SPR)s10694-021-01170-6-e DE-627 ger DE-627 rakwb eng McNamara, Derek verfasserin (orcid)0000-0002-1029-6234 aut Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to ground-based assessments from the 2017 California Tubbs Fire to classify building damage, destruction, and defensive actions in an intermix and interface community. We also geolocate defensive action information from active fire images, videos, and eyewitness accounts. We utilize both manual and object-based classification approaches. Both types of overhead imagery using manual classifications had high kappa statistics ranging from 0.81 to 0.96, indicating almost perfect agreement with ground-based assessments for primary building destruction (e.g., homes). Object-based classifications of destruction had kappa statistics ranging from 0.63 to 0.88 for primary buildings, indicating substantial agreement. Additionally, manual and object-based classifications identified many destroyed secondary buildings (e.g., sheds) missed by ground-based assessments. Occlusions due to canopy cover contribute to lower classification results in the intermix community. All imagery missed significant damage identified in the ground-based assessment. Remotely piloted aircraft systems imagery was superior to satellite imagery in identifying defensive action indicators. Nonetheless, all image types are valuable additions to ground-based assessments of damage, destruction, and defensive actions. Finally, we demonstrate the importance of accounting for defensive actions in assessing building response at wildland-urban interface fires. Wildland-Urban interface (dpeaa)DE-He213 WUI (dpeaa)DE-He213 Wildland Fire (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Tubbs fire (dpeaa)DE-He213 Light detection and ranging (dpeaa)DE-He213 Mell, William aut Enthalten in Fire technology New York, NY [u.a.] : Springer Science + Business Media B.V., 1965 58(2021), 1 vom: 26. Aug., Seite 641-672 (DE-627)325609861 (DE-600)2037915-8 1572-8099 nnns volume:58 year:2021 number:1 day:26 month:08 pages:641-672 https://dx.doi.org/10.1007/s10694-021-01170-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 58 2021 1 26 08 641-672
spelling	10.1007/s10694-021-01170-6 doi (DE-627)SPR045985677 (SPR)s10694-021-01170-6-e DE-627 ger DE-627 rakwb eng McNamara, Derek verfasserin (orcid)0000-0002-1029-6234 aut Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to ground-based assessments from the 2017 California Tubbs Fire to classify building damage, destruction, and defensive actions in an intermix and interface community. We also geolocate defensive action information from active fire images, videos, and eyewitness accounts. We utilize both manual and object-based classification approaches. Both types of overhead imagery using manual classifications had high kappa statistics ranging from 0.81 to 0.96, indicating almost perfect agreement with ground-based assessments for primary building destruction (e.g., homes). Object-based classifications of destruction had kappa statistics ranging from 0.63 to 0.88 for primary buildings, indicating substantial agreement. Additionally, manual and object-based classifications identified many destroyed secondary buildings (e.g., sheds) missed by ground-based assessments. Occlusions due to canopy cover contribute to lower classification results in the intermix community. All imagery missed significant damage identified in the ground-based assessment. Remotely piloted aircraft systems imagery was superior to satellite imagery in identifying defensive action indicators. Nonetheless, all image types are valuable additions to ground-based assessments of damage, destruction, and defensive actions. Finally, we demonstrate the importance of accounting for defensive actions in assessing building response at wildland-urban interface fires. Wildland-Urban interface (dpeaa)DE-He213 WUI (dpeaa)DE-He213 Wildland Fire (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Tubbs fire (dpeaa)DE-He213 Light detection and ranging (dpeaa)DE-He213 Mell, William aut Enthalten in Fire technology New York, NY [u.a.] : Springer Science + Business Media B.V., 1965 58(2021), 1 vom: 26. Aug., Seite 641-672 (DE-627)325609861 (DE-600)2037915-8 1572-8099 nnns volume:58 year:2021 number:1 day:26 month:08 pages:641-672 https://dx.doi.org/10.1007/s10694-021-01170-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 58 2021 1 26 08 641-672
allfields_unstemmed	10.1007/s10694-021-01170-6 doi (DE-627)SPR045985677 (SPR)s10694-021-01170-6-e DE-627 ger DE-627 rakwb eng McNamara, Derek verfasserin (orcid)0000-0002-1029-6234 aut Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to ground-based assessments from the 2017 California Tubbs Fire to classify building damage, destruction, and defensive actions in an intermix and interface community. We also geolocate defensive action information from active fire images, videos, and eyewitness accounts. We utilize both manual and object-based classification approaches. Both types of overhead imagery using manual classifications had high kappa statistics ranging from 0.81 to 0.96, indicating almost perfect agreement with ground-based assessments for primary building destruction (e.g., homes). Object-based classifications of destruction had kappa statistics ranging from 0.63 to 0.88 for primary buildings, indicating substantial agreement. Additionally, manual and object-based classifications identified many destroyed secondary buildings (e.g., sheds) missed by ground-based assessments. Occlusions due to canopy cover contribute to lower classification results in the intermix community. All imagery missed significant damage identified in the ground-based assessment. Remotely piloted aircraft systems imagery was superior to satellite imagery in identifying defensive action indicators. Nonetheless, all image types are valuable additions to ground-based assessments of damage, destruction, and defensive actions. Finally, we demonstrate the importance of accounting for defensive actions in assessing building response at wildland-urban interface fires. Wildland-Urban interface (dpeaa)DE-He213 WUI (dpeaa)DE-He213 Wildland Fire (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Tubbs fire (dpeaa)DE-He213 Light detection and ranging (dpeaa)DE-He213 Mell, William aut Enthalten in Fire technology New York, NY [u.a.] : Springer Science + Business Media B.V., 1965 58(2021), 1 vom: 26. Aug., Seite 641-672 (DE-627)325609861 (DE-600)2037915-8 1572-8099 nnns volume:58 year:2021 number:1 day:26 month:08 pages:641-672 https://dx.doi.org/10.1007/s10694-021-01170-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 58 2021 1 26 08 641-672
allfieldsGer	10.1007/s10694-021-01170-6 doi (DE-627)SPR045985677 (SPR)s10694-021-01170-6-e DE-627 ger DE-627 rakwb eng McNamara, Derek verfasserin (orcid)0000-0002-1029-6234 aut Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to ground-based assessments from the 2017 California Tubbs Fire to classify building damage, destruction, and defensive actions in an intermix and interface community. We also geolocate defensive action information from active fire images, videos, and eyewitness accounts. We utilize both manual and object-based classification approaches. Both types of overhead imagery using manual classifications had high kappa statistics ranging from 0.81 to 0.96, indicating almost perfect agreement with ground-based assessments for primary building destruction (e.g., homes). Object-based classifications of destruction had kappa statistics ranging from 0.63 to 0.88 for primary buildings, indicating substantial agreement. Additionally, manual and object-based classifications identified many destroyed secondary buildings (e.g., sheds) missed by ground-based assessments. Occlusions due to canopy cover contribute to lower classification results in the intermix community. All imagery missed significant damage identified in the ground-based assessment. Remotely piloted aircraft systems imagery was superior to satellite imagery in identifying defensive action indicators. Nonetheless, all image types are valuable additions to ground-based assessments of damage, destruction, and defensive actions. Finally, we demonstrate the importance of accounting for defensive actions in assessing building response at wildland-urban interface fires. Wildland-Urban interface (dpeaa)DE-He213 WUI (dpeaa)DE-He213 Wildland Fire (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Tubbs fire (dpeaa)DE-He213 Light detection and ranging (dpeaa)DE-He213 Mell, William aut Enthalten in Fire technology New York, NY [u.a.] : Springer Science + Business Media B.V., 1965 58(2021), 1 vom: 26. Aug., Seite 641-672 (DE-627)325609861 (DE-600)2037915-8 1572-8099 nnns volume:58 year:2021 number:1 day:26 month:08 pages:641-672 https://dx.doi.org/10.1007/s10694-021-01170-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 58 2021 1 26 08 641-672
allfieldsSound	10.1007/s10694-021-01170-6 doi (DE-627)SPR045985677 (SPR)s10694-021-01170-6-e DE-627 ger DE-627 rakwb eng McNamara, Derek verfasserin (orcid)0000-0002-1029-6234 aut Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to ground-based assessments from the 2017 California Tubbs Fire to classify building damage, destruction, and defensive actions in an intermix and interface community. We also geolocate defensive action information from active fire images, videos, and eyewitness accounts. We utilize both manual and object-based classification approaches. Both types of overhead imagery using manual classifications had high kappa statistics ranging from 0.81 to 0.96, indicating almost perfect agreement with ground-based assessments for primary building destruction (e.g., homes). Object-based classifications of destruction had kappa statistics ranging from 0.63 to 0.88 for primary buildings, indicating substantial agreement. Additionally, manual and object-based classifications identified many destroyed secondary buildings (e.g., sheds) missed by ground-based assessments. Occlusions due to canopy cover contribute to lower classification results in the intermix community. All imagery missed significant damage identified in the ground-based assessment. Remotely piloted aircraft systems imagery was superior to satellite imagery in identifying defensive action indicators. Nonetheless, all image types are valuable additions to ground-based assessments of damage, destruction, and defensive actions. Finally, we demonstrate the importance of accounting for defensive actions in assessing building response at wildland-urban interface fires. Wildland-Urban interface (dpeaa)DE-He213 WUI (dpeaa)DE-He213 Wildland Fire (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Tubbs fire (dpeaa)DE-He213 Light detection and ranging (dpeaa)DE-He213 Mell, William aut Enthalten in Fire technology New York, NY [u.a.] : Springer Science + Business Media B.V., 1965 58(2021), 1 vom: 26. Aug., Seite 641-672 (DE-627)325609861 (DE-600)2037915-8 1572-8099 nnns volume:58 year:2021 number:1 day:26 month:08 pages:641-672 https://dx.doi.org/10.1007/s10694-021-01170-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 58 2021 1 26 08 641-672
language	English
source	Enthalten in Fire technology 58(2021), 1 vom: 26. Aug., Seite 641-672 volume:58 year:2021 number:1 day:26 month:08 pages:641-672
sourceStr	Enthalten in Fire technology 58(2021), 1 vom: 26. Aug., Seite 641-672 volume:58 year:2021 number:1 day:26 month:08 pages:641-672
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Wildland-Urban interface WUI Wildland Fire Remote sensing Tubbs fire Light detection and ranging
isfreeaccess_bool	false
container_title	Fire technology
authorswithroles_txt_mv	McNamara, Derek @@aut@@ Mell, William @@aut@@
publishDateDaySort_date	2021-08-26T00:00:00Z
hierarchy_top_id	325609861
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author	McNamara, Derek
spellingShingle	McNamara, Derek misc Wildland-Urban interface misc WUI misc Wildland Fire misc Remote sensing misc Tubbs fire misc Light detection and ranging Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires
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topic_title	Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires Wildland-Urban interface (dpeaa)DE-He213 WUI (dpeaa)DE-He213 Wildland Fire (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 Tubbs fire (dpeaa)DE-He213 Light detection and ranging (dpeaa)DE-He213
topic	misc Wildland-Urban interface misc WUI misc Wildland Fire misc Remote sensing misc Tubbs fire misc Light detection and ranging
topic_unstemmed	misc Wildland-Urban interface misc WUI misc Wildland Fire misc Remote sensing misc Tubbs fire misc Light detection and ranging
topic_browse	misc Wildland-Urban interface misc WUI misc Wildland Fire misc Remote sensing misc Tubbs fire misc Light detection and ranging
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title	Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires
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title_full	Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires
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title_sort	towards the use of remote sensing for identification of building damage, destruction, and defensive actions at wildland-urban interface fires
title_auth	Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires
abstract	Abstract Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to ground-based assessments from the 2017 California Tubbs Fire to classify building damage, destruction, and defensive actions in an intermix and interface community. We also geolocate defensive action information from active fire images, videos, and eyewitness accounts. We utilize both manual and object-based classification approaches. Both types of overhead imagery using manual classifications had high kappa statistics ranging from 0.81 to 0.96, indicating almost perfect agreement with ground-based assessments for primary building destruction (e.g., homes). Object-based classifications of destruction had kappa statistics ranging from 0.63 to 0.88 for primary buildings, indicating substantial agreement. Additionally, manual and object-based classifications identified many destroyed secondary buildings (e.g., sheds) missed by ground-based assessments. Occlusions due to canopy cover contribute to lower classification results in the intermix community. All imagery missed significant damage identified in the ground-based assessment. Remotely piloted aircraft systems imagery was superior to satellite imagery in identifying defensive action indicators. Nonetheless, all image types are valuable additions to ground-based assessments of damage, destruction, and defensive actions. Finally, we demonstrate the importance of accounting for defensive actions in assessing building response at wildland-urban interface fires. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to ground-based assessments from the 2017 California Tubbs Fire to classify building damage, destruction, and defensive actions in an intermix and interface community. We also geolocate defensive action information from active fire images, videos, and eyewitness accounts. We utilize both manual and object-based classification approaches. Both types of overhead imagery using manual classifications had high kappa statistics ranging from 0.81 to 0.96, indicating almost perfect agreement with ground-based assessments for primary building destruction (e.g., homes). Object-based classifications of destruction had kappa statistics ranging from 0.63 to 0.88 for primary buildings, indicating substantial agreement. Additionally, manual and object-based classifications identified many destroyed secondary buildings (e.g., sheds) missed by ground-based assessments. Occlusions due to canopy cover contribute to lower classification results in the intermix community. All imagery missed significant damage identified in the ground-based assessment. Remotely piloted aircraft systems imagery was superior to satellite imagery in identifying defensive action indicators. Nonetheless, all image types are valuable additions to ground-based assessments of damage, destruction, and defensive actions. Finally, we demonstrate the importance of accounting for defensive actions in assessing building response at wildland-urban interface fires. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Post-fire remote sensing provides a promising tool for assessing building damage, destruction, and defensive actions from wildland fire. However, limited studies exist to guide image acquisitions. Consequently, we compare remotely piloted aircraft systems and satellite post-fire imagery to ground-based assessments from the 2017 California Tubbs Fire to classify building damage, destruction, and defensive actions in an intermix and interface community. We also geolocate defensive action information from active fire images, videos, and eyewitness accounts. We utilize both manual and object-based classification approaches. Both types of overhead imagery using manual classifications had high kappa statistics ranging from 0.81 to 0.96, indicating almost perfect agreement with ground-based assessments for primary building destruction (e.g., homes). Object-based classifications of destruction had kappa statistics ranging from 0.63 to 0.88 for primary buildings, indicating substantial agreement. Additionally, manual and object-based classifications identified many destroyed secondary buildings (e.g., sheds) missed by ground-based assessments. Occlusions due to canopy cover contribute to lower classification results in the intermix community. All imagery missed significant damage identified in the ground-based assessment. Remotely piloted aircraft systems imagery was superior to satellite imagery in identifying defensive action indicators. Nonetheless, all image types are valuable additions to ground-based assessments of damage, destruction, and defensive actions. Finally, we demonstrate the importance of accounting for defensive actions in assessing building response at wildland-urban interface fires. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	1
title_short	Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires
url	https://dx.doi.org/10.1007/s10694-021-01170-6
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author2	Mell, William
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doi_str	10.1007/s10694-021-01170-6
up_date	2024-07-03T19:35:18.337Z
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Towards the use of Remote Sensing for Identification of Building Damage, Destruction, and Defensive Actions at Wildland-Urban Interface Fires

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