Quantitative assessment of fire occurrence Dead Fuel Index threshold and spatio-temporal variation in different grassland types of China-Mongolia border area

doi:10.1007/s11442-023-2146-2

Abstract

Abstract:

Climate change is manifesting rapidly in the form of fires, droughts, floods, resource scarcity, and species loss, and remains a global risk. Owing to the disaster risk management, there is a need to determine the Dead Fuel Index (DFI) threshold of the fire occurrence area and analyze the spatio-temporal variation of DFI to apply prevention measures efficiently and facilitate sustainable fire risk management. This study used the MODIS Burned Area Monthly L3 (MCD64A1), Landsat Global Burned Area (BA) products, and MODIS Surface Reflectance 8-Day L3 (MOD09A1) data from 2001 to 2020 to calculate the values of the DFI in the study area before the occurrence of fire. The results showed that: (1) The inversion of the meadow steppe DFI values in the fire area was distributed in the range of 14-26, and the fire rate was the highest in the range of 20-22. The inversion of the typical steppe DFI values in the fire area was distributed in the range of 12-26, and the fire rate was the highest in the range of 16-22. (2) Areas with high fire DFI values included Khalkhgol, Matad, Erdenetsagaan, Bayandun, Gurvanzagal, Dashbalbar in Mongolia, and scattered areas of the Greater Khingan Mountains (forest edge meadow steppe area), East and West Ujumqin Banner, and Xin Barag Right Banner. The highest fire probability of fire occurred during October and April. (3) The DFI values were sensitive to changes in altitude. The results of this study may provide useful information on surface energy balance, grassland carbon storage, soil moisture, grassland health, land desertification, and grazing in the study area, especially for fire risk management.

Key words: Dead Fuel Index (DFI), grassland fire, withered grass, China-Mongolia border

CHAO Lumen, BAO Yulong, ZHANG Jiquan, BAO Yuhai, MEI Li, YUAN Zhihui. Quantitative assessment of fire occurrence Dead Fuel Index threshold and spatio-temporal variation in different grassland types of China-Mongolia border area[J].Journal of Geographical Sciences, 2023, 33(8): 1631-1659.

Figures/Tables 21

Figure 1

Table 1

Table 2

Table 3

Burned area information statistics

Year	Area	Date of fire	Month	DFI date	Vegetation type
2001	East Ujimqin Banner	269	September	257	Meadow steppe
2001	Xin Barag Right Banner	256-259	September	249	Typical steppe
2002	Prairie Chenbarhu banner	132-133	May	129	Typical steppe
2004	Erdenetsagaan border	110-115	April	105	Typical steppe
2005	Khalkhgol border	284-288	October	281	Meadow steppe and broadleaf forest
2005	East Ujimqin Banner	289-290	October	281	Meadow steppe
2006	East Ujimqin Banner	267-268	September	265	Meadow steppe and typical steppe
2006	Ewenki Autonomous Banner	136-138	May	129	Meadow steppe
2006	Khalkhgol border	150	May	145	Meadow steppe and typical steppe
2007	Xin Barag Left Banner	121-127	May	113	Typical steppe
2007	Khalkhgol border	160-170	June	153	Meadow steppe
2008	Xin Barag Left Banner border	92-102	April	89	Typical steppe
2009	Prairie Chenbarhu Banner border	149-151	May	145	Meadow steppe and typical steppe
2009	Khalkhgol border	305-309	November	297	Meadow steppe and broadleaf forest
2010	Khalkhgol	247-248	September	241	Meadow steppe and typical steppe
2012	Khalkhgol	107-114	April	105	Meadow steppe and typical steppe
2013	Xin Barag Right Banner border	279-280	October	273	Typical steppe
2013	East Ujimqin Banner	271-273	September	265	Typical steppe
2013	Khalkhgol and Matad	133-136	May	129	Meadow steppe and typical steppe
2014	Khalkhgol and Matad	86-89	March	81	Typical steppe
2015	Khalkhgol	80-82	March	73	Meadow steppe
2015	Khalkhgol	83	March	81	Meadow steppe
2016	Khalkhgol border	188	July	185	Typical steppe
2016	Khalkhgol	112-113	April	105	Meadow steppe
2017	Ewenki Autonomous Banner	169-170	June	161	Typical steppe
2017	Choibalsan	176-181	June	169	Typical steppe
2017	Choibalsan	177-180	June	169	Typical steppe
2018	Xin Barag Left Banner border	123-129	May	121	Typical steppe
2019	Manzhouli border	110-113	April	105	Typical steppe
2019	Xin Barag Left Banner border	110-113	April	105	Typical steppe
2019	Khalkhgol border	271-276	October	265	Typical steppe
2020	Tsagaan-Ovoo	289-292	October	281	Typical steppe
2020	East Ujimqin Banner	265-267	September	257	Typical steppe

Table 3

Figure 2

Table 4

The MCD64A1 burned area validation with Landsat burned area results

Year	Date	Test dataset	Validation method	Result	Year		Date		Test dataset	Validation method	Result
2001	256-259	TP	Precision	0.95	2007		121-127		TP	Precision	0.71
		TN	Recall	0.95					TN	Recall	0.75
		FP	Specially	0.97					FP	Specially	0.88
		FN	Accuracy	0.96					FN	Accuracy	0.85
			AOC	0.96						AOC	0.82
2001	269	TP	Precision	0.77	2007		160-170		TP	Precision	0.83
		TN	Recall	0.47					TN	Recall	0.87
		FP	Specially	0.83					FP	Specially	0.85
		FN	Accuracy	0.64					FN	Accuracy	0.86
			AOC	0.65						AOC	0.86
2002	132-133	TP	Precision	0.78	2008		92-102		TP	Precision	0.91
		TN	Recall	0.79					TN	Recall	0.91
		FP	Specially	0.74					FP	Specially	0.87
		FN	Accuracy	0.77					FN	Accuracy	0.89
			AOC	0.77						AOC	0.89
2004	110-115	TP	Precision	0.80	2009		149-151		TP	Precision	0.80
		TN	Recall	0.95					TN	Recall	0.88
		FP	Specially	0.87					FP	Specially	0.83
		FN	Accuracy	0.90					FN	Accuracy	0.85
			AOC	0.91						AOC	0.85
2005	284-288	TP	Precision	0.93	2009		305-309		TP	Precision	0.87
		TN	Recall	0.99					TN	Recall	0.95
		FP	Specially	0.95					FP	Specially	0.93
		FN	Accuracy	0.97					FN	Accuracy	0.94
			AOC	0.97						AOC	0.94
2005	289-290	TP	Precision	0.90	2010		247-248		TP	Precision	0.80
		TN	Recall	0.98					TN	Recall	0.82
		FP	Specially	0.97					FP	Specially	0.76
		FN	Accuracy	0.97					FN	Accuracy	0.79
			AOC	0.97						AOC	0.79
2006	136-138	TP	Precision	0.88	2012		107-114		TP	Precision	0.73
		TN	Recall	0.93					TN	Recall	0.75
		FP	Specially	0.83					FP	Specially	0.82
		FN	Accuracy	0.89					FN	Accuracy	0.79
			AOC	0.88						AOC	0.78
2006	150	TP	Precision	0.71	2013		133-136		TP	Precision	0.78
		TN	Recall	0.65					TN	Recall	0.79
		FP	Specially	0.78					FP	Specially	0.86
		FN	Accuracy	0.72					FN	Accuracy	0.83
			AOC	0.71						AOC	0.86
2006	267-268	TP	Precision	0.93	2013		271-273		TP	Precision	0.90
		TN	Recall	0.85					TN	Recall	0.80
		FP	Specially	0.96					FP	Specially	0.99
		FN	Accuracy	0.92					FN	Accuracy	0.95
			AOC	0.90						AOC	0.96
2013	279-280	TP	Precision	0.89	2017	177-180		TP		Precision	0.74
		TN	Recall	0.96				TN		Recall	0.75
		FP	Specially	0.95				FP		Specially	0.87
		FN	Accuracy	0.95				FN		Accuracy	0.83
			AOC	0.95						AOC	0.81
2014	86-89	TP	Precision	0.77	2018	123-129		TP		Precision	0.73
		TN	Recall	0.94				TN		Recall	0.77
		FP	Specially	0.83				FP		Specially	0.77
		FN	Accuracy	0.82				FN		Accuracy	0.77
			AOC	0.81						AOC	0.77
2015	80-82	TP	Precision	0.94	2019	110-113 Manzhouli		TP		Precision	0.85
		TN	Recall	0.96				TN		Recall	0.87
		FP	Specially	0.94				FP		Specially	0.92
		FN	Accuracy	0.94				FN		Accuracy	0.91
			AOC	0.94						AOC	0.90
2015	83	TP	Precision	0.86	2019	110-113 East Chenbaerhu		TP		Precision	0.94
		TN	Recall	0.96				TN		Recall	0.94
		FP	Specially	0.88				FP		Specially	0.94
		FN	Accuracy	0.92				FN		Accuracy	0.94
			AOC	0.92						AOC	0.94
2016	188	TP	Precision	0.77	2019	271-276		TP		Precision	0.93
		TN	Recall	0.85				TN		Recall	0.83
		FP	Specially	0.73				FP		Specially	0.72
		FN	Accuracy	0.76				FN		Accuracy	0.79
			AOC	0.76						AOC	0.78
2016	112-113	TP	Precision	0.94	2020	289-292		TP		Precision	1.00
		TN	Recall	0.94				TN		Recall	1.00
		FP	Specially	0.92				FP		Specially	1.00
		FN	Accuracy	0.88				FN		Accuracy	1.00
			AOC	0.88						AOC	1.00
2017	169-170	TP	Precision	0.77	2020	265-267		TP		Precision	0.93
		TN	Recall	0.94				TN		Recall	0.96
		FP	Specially	0.91				FP		Specially	0.95
		FN	Accuracy	0.92				FN		Accuracy	0.95
			AOC	0.92						AOC	0.96
2017	176-181	TP	Precision	0.79
		TN	Recall	0.79
		FP	Specially	0.74
		FN	Accuracy	0.77
			AOC	0.77

Table 4

Figure 3

Figure 4

Figure 5

Figure 6

Table 5

Figure 7

Table 6

Figure 8

Figure 9

Figure 10

Figure 11

Table 7

Figure 12

Figure 13

Table 8

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Bands	Wavelength (nm)	Resolution (m)	Bands	Wavelength (nm)	Resolution (m)
Band1	620-670	500	Band5	1230-1250	500
Band2	841-876	500	Band6	1628-1652	500
Band3	459-479	500	Band7	2105-2155	500
Band4	545-565	500

Month Area	Level
Month Area	March	April	May	June	September	October	November
(14, 16)	1095.64	11767.36	26000.39	11669.39	36650.02	4085.66	172.35
(16, 18)	1377.54	19295.78	16757.01	338.70	1053.62	12208.74	561.43
(18, 20)	1932.75	15686.02	3281.13	24.87	40.30	23546.51	1594.91
(20, 22)	3230.54	8809.07	326.48	11.58	18.22	19606.19	3424.12
(22, 24)	4604.22	4539.69	65.38	8.15	9.86	8929.97	3300.85
(24, 26)	5083.33	1568.97	7.29	4.07	7.07	1275.50	2917.13

Month Area	Level
Month Area	March	April	May	June	September	October	November
(12, 14)	10295.07	44575.89	66123.62	93450.52	116313.19	23861.41	113.19
(14, 16)	12305.64	49211.83	45419.00	29100.59	60913.38	38803.79	584.80
(16, 18)	12161.37	34989.52	12417.97	1875.94	1794.27	62280.20	2831.81
(18, 20)	10899.16	14609.68	1784.41	160.56	284.04	53837.06	8052.56
(20, 22)	10983.41	3237.19	466.47	60.88	97.97	19267.70	11030.14
(22, 24)	12779.18	750.72	220.37	36.66	52.95	3275.34	14193.80
(24, 26)	16259.03	289.18	68.81	16.51	34.73	334.42	16869.77

DFI values	Area (km²)	Accuracy
In threshold	893.275623	0.983
Not in threshold	15.43	0.983