Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions

Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine an...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Nádudvari, Ádám [verfasserIn] Abramowicz, Anna [verfasserIn] Fabiańska, Monika [verfasserIn] Misz-Kennan, Magdalena [verfasserIn] Ciesielczuk, Justyna [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Self-heating Coal waste dump Landsat Self-heating intensity index (SHII) Drone

Anmerkung:	© The Author(s) 2020

Übergeordnetes Werk:	Enthalten in: International journal of coal science & technology - Heidelberg : Springer, 2014, 8(2020), 3 vom: 11. Nov., Seite 441-456
Übergeordnetes Werk:	volume:8 ; year:2020 ; number:3 ; day:11 ; month:11 ; pages:441-456

Links:	Volltext

DOI / URN:	10.1007/s40789-020-00375-4

Katalog-ID:	SPR044649371

Internformat


LEADER	01000caa a22002652 4500
001	SPR044649371
003	DE-627
005	20220112033043.0
007	cr uuu---uuuuu
008	210724s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1007/s40789-020-00375-4 \|2 doi
035			\|a (DE-627)SPR044649371
035			\|a (SPR)s40789-020-00375-4-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
082	0	4	\|a 550 \|a 333.7 \|q ASE
100	1		\|a Nádudvari, Ádám \|e verfasserin \|4 aut
245	1	0	\|a Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions
264		1	\|c 2020
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © The Author(s) 2020
520			\|a Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies.
650		4	\|a Self-heating \|7 (dpeaa)DE-He213
650		4	\|a Coal waste dump \|7 (dpeaa)DE-He213
650		4	\|a Landsat \|7 (dpeaa)DE-He213
650		4	\|a Self-heating intensity index (SHII) \|7 (dpeaa)DE-He213
650		4	\|a Drone \|7 (dpeaa)DE-He213
700	1		\|a Abramowicz, Anna \|e verfasserin \|4 aut
700	1		\|a Fabiańska, Monika \|e verfasserin \|4 aut
700	1		\|a Misz-Kennan, Magdalena \|e verfasserin \|4 aut
700	1		\|a Ciesielczuk, Justyna \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t International journal of coal science & technology \|d Heidelberg : Springer, 2014 \|g 8(2020), 3 vom: 11. Nov., Seite 441-456 \|w (DE-627)815914261 \|w (DE-600)2806625-X \|x 2198-7823 \|7 nnns
773	1	8	\|g volume:8 \|g year:2020 \|g number:3 \|g day:11 \|g month:11 \|g pages:441-456
856	4	0	\|u https://dx.doi.org/10.1007/s40789-020-00375-4 \|z kostenfrei \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_SPRINGER
912			\|a SSG-OPC-GGO
912			\|a SSG-OPC-ASE
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 8 \|j 2020 \|e 3 \|b 11 \|c 11 \|h 441-456

Indexfelder

author_variant	á n án a a aa m f mf m m k mmk j c jc
matchkey_str	article:21987823:2020----::lsiiainfieicawseupbsdnadaatrhrabnsntemlaean
hierarchy_sort_str	2020
publishDate	2020
allfields	10.1007/s40789-020-00375-4 doi (DE-627)SPR044649371 (SPR)s40789-020-00375-4-e DE-627 ger DE-627 rakwb eng 550 333.7 ASE Nádudvari, Ádám verfasserin aut Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies. Self-heating (dpeaa)DE-He213 Coal waste dump (dpeaa)DE-He213 Landsat (dpeaa)DE-He213 Self-heating intensity index (SHII) (dpeaa)DE-He213 Drone (dpeaa)DE-He213 Abramowicz, Anna verfasserin aut Fabiańska, Monika verfasserin aut Misz-Kennan, Magdalena verfasserin aut Ciesielczuk, Justyna verfasserin aut Enthalten in International journal of coal science & technology Heidelberg : Springer, 2014 8(2020), 3 vom: 11. Nov., Seite 441-456 (DE-627)815914261 (DE-600)2806625-X 2198-7823 nnns volume:8 year:2020 number:3 day:11 month:11 pages:441-456 https://dx.doi.org/10.1007/s40789-020-00375-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 11 11 441-456
spelling	10.1007/s40789-020-00375-4 doi (DE-627)SPR044649371 (SPR)s40789-020-00375-4-e DE-627 ger DE-627 rakwb eng 550 333.7 ASE Nádudvari, Ádám verfasserin aut Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies. Self-heating (dpeaa)DE-He213 Coal waste dump (dpeaa)DE-He213 Landsat (dpeaa)DE-He213 Self-heating intensity index (SHII) (dpeaa)DE-He213 Drone (dpeaa)DE-He213 Abramowicz, Anna verfasserin aut Fabiańska, Monika verfasserin aut Misz-Kennan, Magdalena verfasserin aut Ciesielczuk, Justyna verfasserin aut Enthalten in International journal of coal science & technology Heidelberg : Springer, 2014 8(2020), 3 vom: 11. Nov., Seite 441-456 (DE-627)815914261 (DE-600)2806625-X 2198-7823 nnns volume:8 year:2020 number:3 day:11 month:11 pages:441-456 https://dx.doi.org/10.1007/s40789-020-00375-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 11 11 441-456
allfields_unstemmed	10.1007/s40789-020-00375-4 doi (DE-627)SPR044649371 (SPR)s40789-020-00375-4-e DE-627 ger DE-627 rakwb eng 550 333.7 ASE Nádudvari, Ádám verfasserin aut Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies. Self-heating (dpeaa)DE-He213 Coal waste dump (dpeaa)DE-He213 Landsat (dpeaa)DE-He213 Self-heating intensity index (SHII) (dpeaa)DE-He213 Drone (dpeaa)DE-He213 Abramowicz, Anna verfasserin aut Fabiańska, Monika verfasserin aut Misz-Kennan, Magdalena verfasserin aut Ciesielczuk, Justyna verfasserin aut Enthalten in International journal of coal science & technology Heidelberg : Springer, 2014 8(2020), 3 vom: 11. Nov., Seite 441-456 (DE-627)815914261 (DE-600)2806625-X 2198-7823 nnns volume:8 year:2020 number:3 day:11 month:11 pages:441-456 https://dx.doi.org/10.1007/s40789-020-00375-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 11 11 441-456
allfieldsGer	10.1007/s40789-020-00375-4 doi (DE-627)SPR044649371 (SPR)s40789-020-00375-4-e DE-627 ger DE-627 rakwb eng 550 333.7 ASE Nádudvari, Ádám verfasserin aut Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies. Self-heating (dpeaa)DE-He213 Coal waste dump (dpeaa)DE-He213 Landsat (dpeaa)DE-He213 Self-heating intensity index (SHII) (dpeaa)DE-He213 Drone (dpeaa)DE-He213 Abramowicz, Anna verfasserin aut Fabiańska, Monika verfasserin aut Misz-Kennan, Magdalena verfasserin aut Ciesielczuk, Justyna verfasserin aut Enthalten in International journal of coal science & technology Heidelberg : Springer, 2014 8(2020), 3 vom: 11. Nov., Seite 441-456 (DE-627)815914261 (DE-600)2806625-X 2198-7823 nnns volume:8 year:2020 number:3 day:11 month:11 pages:441-456 https://dx.doi.org/10.1007/s40789-020-00375-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 11 11 441-456
allfieldsSound	10.1007/s40789-020-00375-4 doi (DE-627)SPR044649371 (SPR)s40789-020-00375-4-e DE-627 ger DE-627 rakwb eng 550 333.7 ASE Nádudvari, Ádám verfasserin aut Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies. Self-heating (dpeaa)DE-He213 Coal waste dump (dpeaa)DE-He213 Landsat (dpeaa)DE-He213 Self-heating intensity index (SHII) (dpeaa)DE-He213 Drone (dpeaa)DE-He213 Abramowicz, Anna verfasserin aut Fabiańska, Monika verfasserin aut Misz-Kennan, Magdalena verfasserin aut Ciesielczuk, Justyna verfasserin aut Enthalten in International journal of coal science & technology Heidelberg : Springer, 2014 8(2020), 3 vom: 11. Nov., Seite 441-456 (DE-627)815914261 (DE-600)2806625-X 2198-7823 nnns volume:8 year:2020 number:3 day:11 month:11 pages:441-456 https://dx.doi.org/10.1007/s40789-020-00375-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 3 11 11 441-456
language	English
source	Enthalten in International journal of coal science & technology 8(2020), 3 vom: 11. Nov., Seite 441-456 volume:8 year:2020 number:3 day:11 month:11 pages:441-456
sourceStr	Enthalten in International journal of coal science & technology 8(2020), 3 vom: 11. Nov., Seite 441-456 volume:8 year:2020 number:3 day:11 month:11 pages:441-456
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Self-heating Coal waste dump Landsat Self-heating intensity index (SHII) Drone
dewey-raw	550
isfreeaccess_bool	true
container_title	International journal of coal science & technology
authorswithroles_txt_mv	Nádudvari, Ádám @@aut@@ Abramowicz, Anna @@aut@@ Fabiańska, Monika @@aut@@ Misz-Kennan, Magdalena @@aut@@ Ciesielczuk, Justyna @@aut@@
publishDateDaySort_date	2020-11-11T00:00:00Z
hierarchy_top_id	815914261
dewey-sort	3550
id	SPR044649371
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR044649371</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220112033043.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210724s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s40789-020-00375-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR044649371</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40789-020-00375-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="a">333.7</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Nádudvari, Ádám</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Self-heating</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Coal waste dump</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Landsat</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Self-heating intensity index (SHII)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Drone</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abramowicz, Anna</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fabiańska, Monika</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Misz-Kennan, Magdalena</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ciesielczuk, Justyna</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of coal science & technology</subfield><subfield code="d">Heidelberg : Springer, 2014</subfield><subfield code="g">8(2020), 3 vom: 11. Nov., Seite 441-456</subfield><subfield code="w">(DE-627)815914261</subfield><subfield code="w">(DE-600)2806625-X</subfield><subfield code="x">2198-7823</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:3</subfield><subfield code="g">day:11</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:441-456</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s40789-020-00375-4</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-ASE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2020</subfield><subfield code="e">3</subfield><subfield code="b">11</subfield><subfield code="c">11</subfield><subfield code="h">441-456</subfield></datafield></record></collection>
author	Nádudvari, Ádám
spellingShingle	Nádudvari, Ádám ddc 550 misc Self-heating misc Coal waste dump misc Landsat misc Self-heating intensity index (SHII) misc Drone Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions
authorStr	Nádudvari, Ádám
ppnlink_with_tag_str_mv	@@773@@(DE-627)815914261
format	electronic Article
dewey-ones	550 - Earth sciences 333 - Economics of land & energy
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	2198-7823
topic_title	550 333.7 ASE Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions Self-heating (dpeaa)DE-He213 Coal waste dump (dpeaa)DE-He213 Landsat (dpeaa)DE-He213 Self-heating intensity index (SHII) (dpeaa)DE-He213 Drone (dpeaa)DE-He213
topic	ddc 550 misc Self-heating misc Coal waste dump misc Landsat misc Self-heating intensity index (SHII) misc Drone
topic_unstemmed	ddc 550 misc Self-heating misc Coal waste dump misc Landsat misc Self-heating intensity index (SHII) misc Drone
topic_browse	ddc 550 misc Self-heating misc Coal waste dump misc Landsat misc Self-heating intensity index (SHII) misc Drone
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	International journal of coal science & technology
hierarchy_parent_id	815914261
dewey-tens	550 - Earth sciences & geology 330 - Economics
hierarchy_top_title	International journal of coal science & technology
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)815914261 (DE-600)2806625-X
title	Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions
ctrlnum	(DE-627)SPR044649371 (SPR)s40789-020-00375-4-e
title_full	Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions
author_sort	Nádudvari, Ádám
journal	International journal of coal science & technology
journalStr	International journal of coal science & technology
lang_code	eng
isOA_bool	true
dewey-hundreds	500 - Science 300 - Social sciences
recordtype	marc
publishDateSort	2020
contenttype_str_mv	txt
container_start_page	441
author_browse	Nádudvari, Ádám Abramowicz, Anna Fabiańska, Monika Misz-Kennan, Magdalena Ciesielczuk, Justyna
container_volume	8
class	550 333.7 ASE
format_se	Elektronische Aufsätze
author-letter	Nádudvari, Ádám
doi_str_mv	10.1007/s40789-020-00375-4
dewey-full	550 333.7
author2-role	verfasserin
title_sort	classification of fires in coal waste dumps based on landsat, aster thermal bands and thermal camera in polish and ukrainian mining regions
title_auth	Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions
abstract	Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies. © The Author(s) 2020
abstractGer	Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies. © The Author(s) 2020
abstract_unstemmed	Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies. © The Author(s) 2020
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	3
title_short	Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions
url	https://dx.doi.org/10.1007/s40789-020-00375-4
remote_bool	true
author2	Abramowicz, Anna Fabiańska, Monika Misz-Kennan, Magdalena Ciesielczuk, Justyna
author2Str	Abramowicz, Anna Fabiańska, Monika Misz-Kennan, Magdalena Ciesielczuk, Justyna
ppnlink	815914261
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1007/s40789-020-00375-4
up_date	2024-07-04T01:43:18.696Z
_version_	1803610906897154048
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR044649371</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220112033043.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210724s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s40789-020-00375-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR044649371</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40789-020-00375-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="a">333.7</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Nádudvari, Ádám</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract A self-heating intensity index (SHII) based on the highest (pixel max.) and lowest (pixel min.) values taken from satellite thermal maps of burning coal waste dumps are proposed. The index enables the classification of such fires in Ukrainian- and Polish coal waste dumps. Both in Ukraine and in Poland, varying thermal intensities during 1985–2019 are revealed, using the SHII and following thermal intensity threshold values, namely, extreme thermal activity (> 7), advanced (3–7), moderate (3–1.5), initial (1.5–1), no activity (< 1). The SHII shows decreasing thermal activity in the selected Ukrainian coal waste dumps during 2017–2019. It aids in reconstructing the thermal history of the dumps. Analysis of satellite images revealed a large number of burning coal waste dumps in the Donetsk Coal Basin (Ukraine) with high thermal activity. Such burning likely reflects large amounts of organic matter and sulphides in the dumped material subjected to self-heating and self-burning processes, lack of compaction of the coal waste and/or high methane contents. Comparison of SHII values calculated from satellite- and drone thermal-camera images were compared to show that SHII from drone thermal images have much higher values than those from satellite images; the former have better resolution. Thus, SHII from Landsat- and drone images should be used separately in dump heating studies.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Self-heating</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Coal waste dump</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Landsat</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Self-heating intensity index (SHII)</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Drone</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Abramowicz, Anna</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Fabiańska, Monika</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Misz-Kennan, Magdalena</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ciesielczuk, Justyna</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of coal science & technology</subfield><subfield code="d">Heidelberg : Springer, 2014</subfield><subfield code="g">8(2020), 3 vom: 11. Nov., Seite 441-456</subfield><subfield code="w">(DE-627)815914261</subfield><subfield code="w">(DE-600)2806625-X</subfield><subfield code="x">2198-7823</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:8</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:3</subfield><subfield code="g">day:11</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:441-456</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s40789-020-00375-4</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-ASE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">8</subfield><subfield code="j">2020</subfield><subfield code="e">3</subfield><subfield code="b">11</subfield><subfield code="c">11</subfield><subfield code="h">441-456</subfield></datafield></record></collection>
score	7.3986673

Nicht das Richtige dabei?

Schreiben Sie uns!

Classification of fires in coal waste dumps based on Landsat, Aster thermal bands and thermal camera in Polish and Ukrainian mining regions

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?