CHAO Lumen; BAO Yulong; ZHANG Jiquan; BAO Yuhai; MEI Li; YUAN Zhihui

doi:10.1007/s11442-023-2146-2

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地理学报(英文版) ›› 2023, Vol. 33 ›› Issue (8) : 1631-1659. DOI: 10.1007/s11442-023-2146-2

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

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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

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CHAO Lumen, BAO Yulong, ZHANG Jiquan, BAO Yuhai, MEI Li, YUAN Zhihui. [J]. Journal of Geographical Sciences, 2023, 33(8): 1631-1659 https://doi.org/10.1007/s11442-023-2146-2

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 https://doi.org/10.1007/s11442-023-2146-2

1 Introduction

Climate change is manifesting rapidly in the form of droughts, fires, floods, resource scarcity, and species loss, and remains a global risk (Zahidi, 2022). Without proper disaster risk management, the number of poor people worldwide will increase by 100 million in 2040. In particular, fires have a significant impact on the production and function of terrestrial ecosystems, carbon storage (Balshi et al., 2007; Dai et al., 2016; Yu, 2020), habitat and biodiversity (He et al., 2019), vegetation replacement patterns (Wang et al., 2015), and nutrient cycling. Fires also greatly influence local society, and the economy, and can cause significant ecological losses in ecosystems (Reid et al., 2016). “Grassland fire” is the phenomenon of grassland burning, referring to the rapid combustion of grasslands that occurs at certain temperatures. Grassland fire risk is a quantitative analysis of grassland fire hazards, and the consequences of them becoming a reality. The risk of grassland fires is quantified based on four factors: the danger of grassland fires occurrence, risk of exposure to grassland fires, vulnerability to grassland fires, and the ability to prevent and mitigate disasters. Grassland fire hazards can be understood as the degree of fire risk in a certain area, or the possibility of fire (Zhang et al., 2006; 2007; Carlson and Burgan, 2010). The level of potential danger of grassland fires depends on the fire sources (human and lightning strikes), fuel characteristics, meteorological factors (wind speed, temperature, number of sunny days, dryness, humidity, and precipitation), and terrain (slope, aspect, and position) (Zhang et al., 2007; 2013). Grassland fuel is the material basis and a prerequisite for the occurrence and development of grassland fires. The spread of fire can only occur when there is fuel on the ground, weather conducive to fire, or a source of fire (Zhou and Zhang, 1996).

The main characteristics of fuels include fuel type, fuel biomass, fuel water content, fuel height, fuel continuity, and fuel coverage (Seyin, 2002; Bao et al., 2011; French et al., 2011; Bao, 2013). Fuels are roughly divided into live and dead. Dead fuels are those that appear light to dark brown or gray with no remaining green chlorophyll pigment and stalks that break easily. Many dead fuel (non-photosynthetic vegetation) areas have the highest fire risk (Roberts et al., 2015). However, in recent decades, the frequency of fires has increased as a result of increasing temperatures linked to climate change, the increasing frequency of human activities (Kirchmeier Young et al., 2019; Li et al., 2021), and the significant increase in the amount of withered grass (Bao et al., 2014; Hong, 2016; Tong et al., 2018). The Dead Fuel Index (DFI) is an estimate of Dead Fuel (DF) coverage or coverage of non-photosynthetic vegetation, such as withered grass (Cao et al., 2010). The more favorable the combustible DFI, the more likely it is that grassland fires will occur under favorable weather conditions. The DFI affects the fire risk and influences fire behaviors. Therefore, an accurate assessment of the DFI index is vital.

Dead Fuel Index is a good indicator to represent combustibles. In the past, NDVI was used to represent combustibles (Liu et al., 2012). However, Dead Fuel Index can better express the material basis required for fire occurrence and combustibles in spring and autumn, when fire occurrence probability is high. DFI represents dead combustibles and therefore does not include fresh grass and plants, which are represented by NDVI. Ignition sources of fire are random and not easy to predict. Moreover, a variety of weather conditions is conducive to the occurrence of fire, but fire cannot occur in the absence of combustibles. Therefore, the accurate expression of combustibles is crucial for fire warning, and the accurate calculation of combustibles can provide data to support the real-time monitoring of fire risk. The distribution map of burnt areas on the Mongolian Plateau indicates that there are significant differences between the two sides of the border between China and Mongolia (Li et al., 2018) and that combustibles are the basis for the occurrence and development of fires. At present, there is a lack of real-time monitoring of combustibles and reliable data support. In order to build fire warning capabilities, it is necessary to quantify combustibles, which requires basic data. Most existing research focuses on weather-related fire risk warnings; however, research on combustible-related fire risk warnings is scarce (Claudia et al., 2019; Miguel et al., 2020; Nikolaos et al., 2022). Accurate calculation of DFI is crucial for grassland fire warnings (Chai et al., 2020). DFI-related studies focus primarily on the spatial and temporal distribution of DFI and the inversion of NPV (Wang et al., 2020; Bai et al., 2021), with few studies attempting to elucidate a relationship between DFI and fires. This study attempts to fill the gap by evaluating the relationship between DFI and fire.

In recent decades, remote sensing technology has made significant progress in many aspects of geography (Abera et al., 2022). Multispectral and hyperspectral indices for calculating non-photosynthetic vegetation have been established using spectral reflectance, such as the Normalized Difference Index (NDI), Normalized Difference Tillage Index (NDTI), Normalized Difference Senescent Vegetation Index (NDSVI) (Qi et al., 2002), Simple Tillage Index (STI), Short Wave Infrared Red (SWIR32) (Guerschman et al., 2009), Modified Soil-Adjusted Crop Residue Index (MSACRI), Dead Fuel Index (DFI), and Normalized Difference Vegetation Index-Cellulose Absorption Index (NDVI-CAI). However, considering the limitations of hyperspectral data, multispectral indices have been proposed and widely used in various satellite platforms. Cao et al. (2010) used multispectral MODIS data to estimate withered grass using DFI, which was better than NDI, Crop Residue Index Multiband (CRIM), Soil Adjusted Corn Residue Index (SACRI), and CAI Index (Nagler et al., 2003), and is considered to be the best index for estimating withered grass (Guo et al., 2021).

The Earth’s surface consists of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil. Several studies have been conducted on photosynthetic vegetation (Tucker, 2001; Jeong et al., 2011; John et al., 2013; Bao et al., 2014; Chen et al., 2014). The spectral characteristics of non-photosynthetic vegetation and bare soil are similar and difficult to distinguish. Therefore, non-photosynthetic vegetation has not been studied as extensively as photosynthetic vegetation. In recent years, research on non-photosynthetic vegetation in sparse arid grasslands has gradually increased (Yue et al., 2020; Guo et al., 2021). Researchers of non-photosynthetic vegetation mainly use near-infrared wavelengths of 841-876 nm and short-wave infrared wavelengths of 1100-2500 nm. The 2100 nm SWIR absorption band, which is the lignocellulose absorption band of dry litter, may be caused by cellulose, hemicellulose, lignin, and/or other structural compounds (Elvidge, 2007). Therefore, the CAI was proposed using the 2000-2050, 2080-2130, and 2190-2240 nm bands, based on the 2100 nm absorption band. The CAI can be used to retrieve DF. However, because of the frequent use of hyperspectral images, a large range of CAI areas cannot be retrieved. The narrow wavelength ranges of 2.1, 2.0 and 2.2 mm in the CAI calculation are not available for multi-band satellite sensors, such as the MODIS and Landsat (Ren et al., 2018). Pigments (chlorophyll a, chlorophyll b, carotene, and lutein) in green vegetation near the 660 nm band, between 620 and 670 nm, have strong absorption, and the spectrum in the short-wave infrared 1100-2500 nm region is important for estimating vegetation water content or fuel water content (Ceccato et al., 2002). In the SWIR region, the reflectivity of the Dead Fuel (DF) is generally higher than that of green vegetation, except for a peak at 1600 nm. The reflectivity of the DF in the visible and near-infrared bands increases monotonically, parallel to and higher than that in the soil (Aase and Tanaka, 1991). In the 650-850 nm band, changes in DF and soil reflectance were more stable than those in photosynthetic vegetation (PV). In MODIS bands 6 and 7, the high and low orders of PV, DF, and soil were reversed, and the slope of DF was between those of PV and soil. In the samples (PV, DF, and soil), the spectral changes in bands 6 and 7 were almost synchronous and proportional (Cao et al., 2010). Therefore, DFI was calculated using 1 (visible: 620-670 nm), 2 (NIR: 841-876 nm), 6 (SWIR: 1628-1652 nm), and 7 (SWIR: 2105-2155 nm) band data. MODIS is better used in regional studies with long and relatively large time scales (Guerschman et al., 2009). Spectral unmixing of vegetation is better suited to arid regions greater, but less so in the arid and semi-arid Mongolian Plateau.

From 1994 to 1997, Mongolian cross-border witnessed over 20 grassland fires in China. In the last two decades, the frequency and scale of fires have increased globally (Li et al., 2018). Mongolia is sparsely populated, with weak firefighting capabilities, less precipitation in spring and autumn, dry weather, and frequent wind. The spatial distribution of the burned areas in the China-Mongolia border area from 2000 to 2014 showed that the total burned area of the Mongolian Plateau area was 8. 09 × 10³ km², 95% of which falls in Mongolia (7.68 × 10³ km²). The burned area in China amounted to 4.09 × 10² km², or 5% of the total burned area (Li et al., 2016). On May 21, 2009, large areas of forest resource were lost in the spread of the fire (Yang and Dong, 2020). On April 7, 2012, strong winds affected high voltage lines, forming a short circuit. The East Ujumqin Mandubaolige town’s Erenbaolige, Taosennuoer, Aershanbaoli, Erengaobi and Mandubaolige Gacha in Xilingol league of Inner Mongolia autonomous region, including 120 households were affected, burned grassland area increased to 7.66 × 10⁴ ha. In recent years, one or two fires have occurred in the study area annually. Few studies have been conducted on non-photosynthetic vegetation in the grassland areas of the China-Mongolia border area. In the meadow steppe, because of good vegetation conditions and abundant fuel, species renewal and biomass increase occur rapidly after the fires, fuel accumulates again rapidly and causes another fire. Therefore, the burnt area and burning frequency are highest in the meadow steppe (Qu et al., 2010). Nevertheless, there are many studies on the real-time monitoring of grassland fires in the border areas of China and Mongolia (Li et al., 2018) and their spatio-temporal distributions (Bao et al., 2013; Li et al., 2017; Liu et al., 2017; Zhang et al., 2017). However, few studies have been conducted on DFI using MODIS images.

The objectives of this study were to: (1) determine the grassland types in the fire occurrence area of DFI threshold in the China-Mongolia border area and provide research basis for other regions, (2) analyze the spatio-temporal variation in DFI, and (3) provide parametric fuel index data for fire risk analysis. Work-related to the DFI in the China-Mongolia border area is part of the foundational research required for fire risk management. Moreover, it is important to study DFI well for fire risk management.

2 Materials and methods

2.1 Study area

The Mongolian Plateau (Inner Mongolia Autonomous Region of China and Mongolia) is a unique arid and semi-arid region, and various economic activities affect its ecosystem (Neupert, 1999; Feng et al., 2007; Liu et al., 2008; Zhang et al., 2009). The natural ecological environment is fragile and sensitive to global climate change (Bao et al., 2016). It is one of the largest grassland ecosystems worldwide, covering an area of 2.76 million square kilometers, with unique landscape and climate characteristics (Bao et al., 2021). Inner Mongolia accounts for 68% of the total of the China-Mongolia border, with a length of 3193 km (Li et al., 2018). The long-term changes in biomass in the Mongolian Plateau indicate great geographic differences, with non-significant changes in vegetation, which accounted for 35% in Inner Mongolia and 44% in Mongolia (Zhao et al., 2021). The Mongolian Plateau is located in the transition zone from the Gobi Desert in South Asia to the coniferous forests in northern Siberia (Hilker et al., 2014; Chen et al., 2018). It has a typical continental climate with hot summers and cold winters. Under the influence of climate differentiation, the vegetation showed obvious horizonal and vertical zonation. Most vegetation types in arid and semi-arid regions, such as forests, meadows, shrubs, typical desert steppes, Gobi vegetation, and grasslands, account for approximately 70% of the plateau (Bao et al., 2014). Fire protection is difficult in the border areas of China and Mongolia, and grasslands are widely distributed, Khalkgol, Matad, and Erdenetsagaan in Mongolia and East Ujumqin Banner in China have the highest frequency of grassland fires, and there are more fuels on the ground (Li et al., 2018). The study area of the boundary line of the 200 km buffer zone and elevation (a), land cover type (b), vegetation type (d), and the same-level administrative divisions in China’s banner (county) and Mongolia’s sumu (c) (Figure 1) are required in the border area between East Ujimqin Banner and Hulunbuir City in eastern Inner Mongolia, where fire rates are frequent. The study area happens to be the typical grassland and meadow grassland area with high vegetation biomass and vegetation coverage, the west is the desert grassland and where is with few fires (Qu et al., 2010). The fires in the region of the China-Mongolia border are often blown into Inner Mongolia (Li et al., 2016), the 200 km buffer zone border area on both sides of the border is chosen and the fact that most of the grassland on both sides of the border is covered, also are sheltered from agricultural and agro-pastoral ecotone. Mongolia has the lowest population density in the world at 2.2 people per square kilometer, and because of long-term nomadism, humans have less impact on the ecosystem (http://1212.mn/2021).

Figure 1 Location of the study area (China-Mongolia border area) and elevation (a); Land cover type (b); banner, county (c); grassland types of the study area (d)

Full size|PPT slide

2.2 Data

The MCD64A1 Burned Area Product is a monthly, Level-3 gridded 500 m product containing per-pixel burning and quality information, and tile-level metadata. Burn grid data show different parameters. Each 500-m grid cell has ordinal days of burn (1-366) (Table 1), with 0 = unburned land, -1 = unmapped due to insufficient data, and -2 = water (Louis, 2018), (https://ladsweb.modaps.eosdis.nasa.gov/search/) and an automated pipeline for generating 30 m resolution global-scale annual burned area maps utilizing Google Earth Engine was proposed the global 30 m spatial resolution Landsat Burned Area (BA) product (Long et al., 2019) (https://vapd.gitlab.io/post/gabam/) data from 2001 to 2020 were used; there were no fire track data in 2003 and 2011. The MCD64A1 is binary data classified by fire occurrence date and non-fire occurrence date, and Landsat burned area data is binary data classified by fire occurrence and non-fire occurrence. These data are used to determine and verify the statistics and occurrence of fire in the study area. MCD64A1 has a high usage rate and Landsat global Burned Area (BA) product has a high resolution. MOD09A1 provides MODIS band 1-7 surface reflectance at 500 m resolution. It is a level-3 composite of 500 m resolution MOD09GA. Each product pixel contains the best possible L2G observation during an 8-day period as selected on the basis of high observation coverage, low view angle, absence of clouds or cloud shadow, and aerosol loading (Table 2) (Vermote et al., 2015) (https:// ladsweb. modaps.eosdis.nasa. gov/search/) data from 2001 to 2020, including bands 1, 2, 6, and 7, were used to inverse the DFI. The DFI data were calculated using these bands.

Table 1 Day-of-year (DOY) of the first day of each calendar month

Month	Non-leap year start DOY	Leap year start DOY	Month	Non-leap year start DOY	Leap year start DOY
January	1	1	July	182	183
February	32	32	August	213	214
March	60	61	September	244	245
April	91	92	October	274	275
May	121	122	November	305	306
June	152	153	December	335	336

Table 2 MOD09A1 band information

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

The vegetation type map and vector map of the Mongolian Plateau were provided by the Key Laboratory of Remote Sensing and Geographic Information System of Inner Mongolia Autonomous Region. The 30 m DEM was obtained from https://appeears.Earthdatacloud.nasa.gov/. Livestock and population data were obtained from the Inner Mongolia Statistical Yearbook website (http://tj.nmg.gov.cn) for China and from http://1212.mn/ for Mongolia. In October 2021, WorldCover project team according to the European space agency (ESA) of Sentinel-1 and Sentinel-2 data of 2020 was released the first resolution of the global land cover map for 10 m, and we used it (https://worldcover2021.esa.int/). Temperature and precipitation data are available from http://www.nmic.cn/data/ and Mongolian Weather Bureaus.

The products of MCD64A1 and MOD09A1 were further processed using the MODIS reprojection tool (MRT Version 4.0) to convert the data format from HDF to GeoTIFF and projection from sinusoidal projection to WGS84/Albers projection, and to perform mosaicking, resampling, and clipping. The fire area vector is Visual interpretation from the MCD64A1 data. From 2001 to 2020, the burned area after the occurrence of fire was found every year on average, at least once per year, and at most three times per year in the China-Mongolia border area. Therefore, data on 33 fire-burned areas were collected at different dates and locations (Table 3 and Figure 2). Livestock and population data were statistically analyzed according to the banner (county) and sumu. The land cover type data was mosaicking and clipping. Vegetation type map and 30 m DEM were clipped from the study area. These data operations used the Python and MATLAB, and the Environmental Systems Research Institute (ESRI) software ArcGIS10.3 and ENVI5.2.

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

Figure 2 Burned area information of 2001-2020

Full size|PPT slide

2.3 Method

2.3.1 DFI

The DFI was summarized according to the MODIS band range and the spectral characteristics of PV, DF, and soil. The formula used is as follows:

D F I = 100 \times (1 - \frac{S W I R 2}{S W I R 1}) \times \frac{R e d}{N I R}

(1)

where SWIR1, SWIR2, Red, and NIR represent bands 6, 7, 1, and 2 of the MODIS, respectively. The DFI has good potential for estimating DF coverage in grassland areas (Yue and Tian, 2020).

2.3.2 Accuracy verification

Landsat data were used the base data, and MODIS data as the predicted data. Binary classification data divided instances into positive (P) and negative (N) classes and subdivided them into true positive (TP), false positive (FP), true negative (TN), and false negative (FN) classes. Precision, accuracy, recall, F1, over prediction rate (OPR), unpredicted presence (UPR), Matthews correlation coefficient (MCC), sensitivity, and specificity were selected to verify data accuracy (Rong et al., 2020). The binary classification data were verified using precision, recall (sensitivity), specificity, accuracy, and AUC for comprehensive verification. The higher the precision value, the better the discriminative ability of the positive sample. The F1 value was more sensitive to negative samples; therefore, the validation for the non-fire areas was better. The accuracy value is the result of the validation of the entire sample. The AUC value can be used as an objective area to indicate the proportion of accurate classification, and the closer the value is to 1, the higher the recognition degree.

Accuracy, also known as the correct rate, is the proportion of the total number of correct classifications.

P r e c i s i o n = \frac{T P}{T P + F P}

(2)

A c c u r a c y = \frac{T P + T N}{T P + T N + F P + F N}

(3)

Recall rate is the ratio of TP examples to all positive examples or sensitivity.

R e c a l l = \frac{T P}{T P + F N}

(4)

The receiver operating characteristic (ROC) curve is a commonly used method to evaluate landslide prediction models. It is plotted based on “Sensitivity” and “1—Specificity.” The sensitivity and specificity were calculated as follows:

S e n s i t i v i t y = \frac{T P}{T P + F N}

(5)

S p e c i f i c i t y = \frac{T N}{T N + F P}

(6)

The validation can be determined by calculating the area under the curve (AUC) (Luis et al., 2011). The threshold for AUC values is 0.5-1; the closer it is to 1, the more accurate the model.

2.3.3 Mann-Kendall test

The Mann-Kendall statistical test (MK) is a non-distribution (also known as non-parametric statistical) test (Douglas et al., 2000; Partal and Kahya, 2006), in which the dataset does not need to be in a particular order and is not affected by outliers. The MK test of the time series was computed using Equations 7-10:

S = \underset{i = 1}{\overset{n - 1}{\sum^{}}} \underset{j = i + 1}{\overset{n}{\sum^{}}} sgn (x_{j} - x_{i})

(7)

sgn (θ) = {\begin{matrix} 1, & θ > 0 \\ 0, & θ = 0 \\ - 1, & θ < 0 \end{matrix}}

(8)

V a r (S) = \frac{n (n - 1) (2 n + 5) - \underset{i = 1}{\overset{n}{\sum^{}}} t_{i} (i - 1) (2 i + 5)}{18}

(9)

where x_i and x_j are the time-series data values at times i and j, respectively; n is the length of the time series; and t is the number of ties for the value. In cases where the sample size is n>10, the standard normal variable Z_c is computed using Equation 10:

Z_{c} = {\begin{matrix} \frac{s - 1}{\sqrt{V a r (S)}}, & S > 0 \\ 0, & S = 0 \\ \frac{s + 1}{\sqrt{V a r (S)}}, & S < 0 \end{matrix}}

(10)

Positive values of Z_c indicate increasing trends, whereas negative values of Z_c indicate decreasing trends.

3 Result

3.1 Fire statistics and validation

The MCD64A1 fire site data were used to determine the date of occurrence and area, the DFI value of the date before the fire occurrence was calculated based on the study area range.

To verify and determine the fire occurrence along the China-Mongolia border from 2001 to 2020 (no data in 2003 and 2011), the MCD64A1 fire data were used to verify the Landsat global burned area (BA) data. The precision, recall (sensitivity), specificity, accuracy, and AUC values of 74,632 sample points in 33 study areas were all high (Table 4 and Figure 3), reaching the accuracies required for data verification and proving that the image of fire areas obtained represented a realistic fire zone.

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

Figure 3 ROC curve of the sampling area

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3.2 Determination of DFI thresholds for fire occurrence areas

The MOD09A1 data before the occurrence of fire in the China-Mongolia border area from 2001 to 2020 were selected to invert the DFI values. The average and standard deviation of the DFI histogram in each fire occurrence area were calculated. Grassland fires occurred on the meadow and typical steppes. The meadow steppe DFI minimum value for the fire occurrence area was 13.97, with a maximum value of 25.89, and a 95% confidence level obtained from 59979 sample points in 33 sample areas over 20 years. The lower limit of the meadow steppe DFI was 15.59, with an upper limit of 24.88, and a mean of 20.40. Therefore, it can be concluded that the DFI values of the fire occurrence area were distributed between 14 and 26. The DFI values were classified into the following grades: 14-16, 16-18, 18-20, 20-22, 22-24, and 24-26. The distribution of fire occurrence probability from highest to lowest was as follows: 20-22, 22-24, 18-20, 16-18, 14-16, and 24-26. These probabilities, given as percentages, were 31.0%, 21.5%, 17.5%, 13.1%, 6.2%, and 5.7%, respectively (Figure 4a).

Figure 4 Histogram of DFI values distribution in fire occurrence area (a. meadow steppe; b. typical steppe)

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The typical steppe DFI minimum value of the fire occurrence area was 11.93, with a maximum value of 25.89. The data had a 95% confidence level obtained from 13034 sample points in 33 sample areas over 20 years. The data had a lower limit of 12.23, an upper limit of 24.16, and a mean of 18.03. Therefore, the DFI value of the fire occurrence area was distributed between 12 and 26. The classification grades of the typical steppe DFI values were 12-14,14-16, 16-18, 18-20, 20-22, 22-24, and 24-26. The distribution of fire occurrence probability from highest to lowest was as follows: 16-18, 18-20, 20-22, 14-16, 22-24, 12-14, and 24-26. These probabilities as percentages were 20.5%, 19.4%, 18.4%, 15.8%, 10.2%, 5.2%, and 4.9%, respectively (Figure 4b).

3.3 DFI value distribution characteristics

3.3.1 Spatial variation features

Among the 6 grades of the meadow steppe DFI distribution, the 20-22 grade (which represented approximately 15,112.16 km²) had the highest probability of fire occurrence and was mainly distributed in Khalkhgol, Erdenetsagaan, Bayandun, Gurvanzagal, Dashbalbar in Mongolia and scattered in the Greater Khingan Mountains (forest edge meadow steppe area). The 22-24 and 24-26 (approximately 18,342.48 km² and 13,333.11 km², respectively) were similar in distribution in Khalkhgol, the border area of Arxan in China, the northeast and southeast of East Ujumqin Banner, the sporadic part of West Ujimqin Banner, and Xin Barag Left Banner. Other grades of DFI, 14-16 (approximately 1033.47 km²), 16-18 (approximately 4330.90 km²), and 18-20 (approximately 8498.66 km²) were all spread around the grades of 20-22. In addition, Arxan, Horqin Right Front Banner, Jarud Banner, Horqin Right Middle Banner, Jalaid Banner, Zhalantun, and West Ujimqin Banner (Figure 5).

Figure 5 Spatial distributions of annual mean DFI for the meadow steppe of the China-Mongolia border area between 2001 and 2020

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Among the 7 grades of the typical steppe DFI distribution, the 16-18 grade (covering approximately 22,622.15 km²) had the highest probability of fire occurrence and was mainly distributed in Matad, Khalkhgol, Erdenetsagaan, Suhbaatar, Asgant, Dariganga, Khalzan, and Bulgan in Mongolia and scattered in Manzhouli, Xin Barag Right Banner, Xin Barag Left Banner, East Ujumqin Banner, West Ujimqin Banner, southern Xilinhot, and northern Abaga Banner. The 18-20 and 20-22 grades (approximately 39,376.58 km² and 55,707.22 km², respectively) were similar in distribution in comparison to the 16-18 grade, and were distributed in Holonbuir, Tsagaan-Ovoo, Bayantumen, Sergelen, Choibalsan, Gurvanzagal and Chuluunkhoroot in Mongolia, East Ujumqin Banner, West Ujimqin Banner, Xin Barag Right Banner, Xin Barag Left Banner, Prairie Chenbarhu Banner, and Ewenki Autonomous Banner in China Inner Mongolia. Other grades of DFI, 14-16 and 12-14 (approximately 22,191.70 km² and 3330.00 km², respectively), were mainly distributed in Naran, Ongon, Abaga Banner, Xilinhot and Western East Ujumqin Banner, Southwest Xin Barag Right Banner. The 22-24 and 24-26 (approximately 39,160.50 km² and 16,147.56 km², respectively) were spread in the Prairie Chenbarhu Banner, and Ewenki Autonomous Banner (Figure 6).

Figure 6 Spatial distributions of annual mean DFI for the typical steppe of the China-Mongolia border area between 2001 and 2020

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The meadow steppe DFI group with the highest fire probability (20-22) mainly occurred in April and October. The largest area that this group of DFI values were distributed in, occurred in October, (covering approximately 20-22, 19,606.19 km²) (Table 5) in Khalkhgol, Erdenetsagaan, Bayandun, Gurvanzagal, Dashbalbar in Mongolia and scattered in the Greater Khingan Mountains (forest edge meadow steppe area). After April, however, the DFI value group 20-22 was mainly distributed in the forest margin meadow steppe area of the Greater Khingan Mountains, Khalkhgol in Mongolia on both sides of the China-Mongolia border, northeastern Ujumqin in East China, the Khingan Arxan, and Bayandun and Dashbalbar in Mongolia. The high DFI values (20-22) in March (covering approximately 3230.54 km²) were mainly distributed in the east of the Greater Khingan Mountains, scattered in Xilinhot, and in Dashbalbar, Mongolia. The distribution of DFI values in group 20-22 in May and September (covering approximately 326.48 km² and 18.22 km², respectively) were similar to, but slightly lower than, the value distribution in April. The higher value distribution in November (group 20-22, approximately 3424.12 km²) was the result of an adjustment to snow. In June, the DFI value group 20-22, was distributed, covering approximately 11.58 km² (Table 5 and Figure 7).

Table 5 The meadow steppe area of each grade in each month (per km²)

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

Figure 7 Spatial distributions of meadow steppe monthly mean DFI during the fire prevention period in the China-Mongolia border area between 2001 and 2020

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The typical steppe DFI groups with the highest fire probability (16-18), (18-20), (20-22) were mainly distributed throughout October. The largest area that each of these three DFI groups were occurred in September (group 16-18, approximately 62,280.20 km²) and October (group 18-20, approximately 53,837.06 km² and group 20-22, approximately 19,267.70 km²) (Table 6). These distributions largely in the typical steppe, except the Ongon, Naran, Bayandelger and little of Matad in Mongolia, and Abaga Banner, Xilinhot, West of East Ujumqin Banner, Northwest West Ujimqin Banner, Southwest Xin Barag Right Banner, West of Xin Barag Left Banner, scattered in the typical steppe area. In April the 16-18, 18-20, and 20-22 DFI groups covered approximately 34,989.52 km², 14,609.68 km², and 20-22, 3237.19 km², respectively. These DFI groups were mainly distributed in the Tsagaan-Ovoo, Holonbuir, Bayantumen, Sergelen, Choibalsan, Gurvanzagal, Chuluunkhoroot, Matad and Erdenetsagaan in Mongolia, Middle Northeast Ujumqin Banner, Southeast West Ujumqin Banner, Northwest Xin Barag Right Banner, Prairie Chenbarhu Banner, and Ewenki Autonomous Banner in Inner Mongolia. The high DFI value groups in March, covering approximately 12,161.37 km², 10,899.16 km² and 10,983.41 km², respectively, were mainly distributed in the middle of Matad, Northern Erdenetsagaan, Naran, Ongon and Asgat in Mongolia, Middle of Abaga Banner and scattered in East Ujumqin Banner and West Ujumqin Banner. The DFI groups 16-18, 18-20, and 20-22 covered 12,417.97 km², 1794.27 km², and 466.47 km²) in May, and approximately 1794.27 km², 284.04 km², and 97.97 km² in September, respectively. These groups were distributed in the middle of Matad, Tsagaan-Ovoo, and Choibalsan in Mongolia, scattered in East Ujumqin Banner and West Ujumqin Banner. The higher values in November 2831.81 km², 8052.56 km², and 11,030.14 km², respectively, were mainly observed in Naran, Ongon in Mongolia, Abaga Banner, Xilinhot and West of East Ujumqin Banner. In June, these groups had the smallest coverage, with approximately 1875.94 km², 160.56 km², and 60.88 km², respectively (Table 6 and Figure 8).

Table 6 The typical steppe area of each grade in each month (per km²)

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

Figure 8 Spatial distributions of typical steppe monthly mean DFI during the fire prevention period in the China-Mongolia border area between 2001 and 2020

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The meadow steppe DFI values were sensitive to a change in altitude because the DFI values varied gently with altitude changes between 250 and 500 m, and between 800 to 1650 m, while varying rapidly between 550 and 800 m. At altitudes greater than 700 m, there were higher fire probability values (Figure 9).

Figure 9 The relationship between DFI and elevation

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The typical steppe DFI values were sensitive to a change in altitude because the DFI values varied gently with altitude changes between 250 and 500 m, and varied rapidly between 550 and 1650 m. With DFI values between 600 and 1100 m, and between 1400 and 1600 m, fire occurrences were more likely (Figure 9).

3.3.2 Temporal variation features

The temporal variation in the meadow steppe DFI showed a significant increase of 7.90% (approximately 5595.89 km²) at a significance level of 0.05, distributed in the central and northern parts of East Ujumqin Banner, the central part of Xilinhot City, Horqin Right Middle Banner, Tuquan County, Central Matad, and small Central and Southeast parts of Khalzan in Mongolia. Further, there was a significant decrease of 15.05% (approximately 10,700.66 km²), distributed in the central and northeastern parts of Arxan in Inner Mongolia, the southern part of the Ewenki Autonomous Banner, the sporadic parts of Zhalantun, Central Erdenetsagaan in Mongolia, and northern Chuluunkhoroot. A non-significant increase of 30.55% (21,726.94 km²) was observed in Dashbalbar in northern Mongolia, and there was sporadic distribution in Gurvanzagal and Khalkhgol, and Ewenki Autonomous Banner and Horqin Right Front Banner. A non-significant decrease of 46.53% (33,093.21 km²) was observed mostly in Khalkhgol, Gurvanzagal, Chuluunkhoroot, Erdenetsagaan, Dashbalbar, Bayandun, and a small section of Xin Barag Left Banner (Figure 10a). The DFI values were highest in November, decreasing to their lowest in June and September. There was an increase, from September to November, and a decrease from November to June. This was consistent with the increase and decrease observed in the withered grass (Figure 11).

Figure 10 Temporal distribution of DFI in the China-Mongolia border area between 2001 and 2020: (a) meadow steppe (b) typical steppe

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Figure 11 Monthly distribution of DFI fire prevention period in the China-Mongolia border area during 2001-2020

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The temporal variation of the typical steppe of DFI showed a significant increase of 11.37% (approximately 24,235.49 km²) at a 0.05 level and was distributed in the central and northern parts of East Ujumqin Banner, the central part of Xilinhot City, Horqin Right Middle Banner, Tuquan County, Central Xin Barag Right Banner, Manzhouli, Northern Xin Barag Left Banner, Central Matad and Suhbaater, parts of Khalzan, Ongon and borders of Asgat, and Erdenetsagaan in Mongolia. At 0.05, there was a significant decrease of 5.39% (approximately 11,483.96 km²), which was distributed in the central and northeastern parts of Arxan in Inner Mongolia, the southern part of the Ewenki Autonomous Banner, Central Erdenetsagaan, scattered Naran and Dariganga, and northern Chuluunkhoroot in Mongolia. A non-significant increase of 42.46% (90,495.23 km²) was observed in central Sergelen, Bulgan, Choibalsan, and other places with significantly increased area. A non-significant decrease of 40.78% (86,914.20 km²) was observed in the central-eastern and northern parts of Matad in Mongolia, and highest decreases were in Khalkhgol, Tsagaan-Ovoo, Gurvanzagal, Holonbuir, Bayan-Uul, Bayandun, Dashbalbar Abaga Banner, and this was small in Xin Barag Right Banner (Figure 10b). The DFI value was the highest in November, decreased to its lowest in June, increased from September to November, and decreased from November to June. This was consistent with the increase and decrease observed in the withered grass (Figure 11).

3.3.3 Influence of DFI value distribution

The above observations are consistent with the DFI values with population and livestock partial correlation coefficients of -0.158 and 0.108, respectively (Table 7). There are many coal mines in sumu in southern Mongolia, and the DFI distribution of a high fire occurrence probability is low, indicating that human activities have a considerable impact on DFI distribution in the grassland area; therefore, the population and number of livestock may influence the DFI distribution (Table 7 and Figure 12). The distribution of DFI values was low in semi-agricultural and semi-pastoral banners (counties). Climatic factors and human activities influence global fires (Aldersley et al., 2011). The DFI value is generally higher in some years than in others, which may be related to the amount of precipitation and temperature of that year. A correlation was observed between the DFI values and precipitation and temperature, with correlation coefficients of 0.225 and -0.614, respectively (Table 7 and Figure 13). Dead fuel moisture and available water capacity showed weak correlations (Sesnie et al., 2018), whereas temperature showed a greater correlation.

Table 7 Influencing factors of DFI value partial correlation coefficient

	Temperature	Precipitation	Livestock	Population
DFI values	-0.641	0.225	0.108	-0.158

^*Significance at the 0.01 level.

Figure 12 Population and livestock densities in the China-Mongolia border area

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Figure 13 Spatial distribution of the average annual air temperature (a) and precipitation (b) in the China-Mongolia border area

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4 Discussion

4.1 Determination of DFI thresholds for fire occurrence areas

Compared with hyperspectral CDI index, multi-spectral DFI was more applicable. Compared with NDI, NDTI, NDSVI, STI, SWIR32, CRIM and SACRI, DFI had better inversion of non-photosynthetic vegetation and could a better express dry grass and provide data support for fire warning. The distribution of fire occurrence area (DFI values of 12-26 and 14-26) in the meadow steppe and typical steppe areas, respectively, and were similar to the distribution of the non-photosynthetic vegetation index (NPV) values in the range of 2.67-27.2 (Chai et al., 2020) and 7.05-26.5 (Cao et al., 2010). Moisture content has a significant effect on the spectral characteristics of Bare Soil (BS) and NPV, especially in the SWIR spectral region, and can help distinguish between BS and NPV (Quemada and Daughtry, 2016). In the 400-2400 nm spectral region, moisture content not only reduces the spectral reflectance of BS and NPV but also changes the spectral curve shape (Xie et al., 2016). A larger DFI value is related to moisture content. Normalization-corrected data attenuates the effects of moisture (Jacques et al., 2014). The DFI values of the water bodies were very high (greater than 26); therefore, the water bodies were all masked. However, the values around the water bodies were also relatively high, reflecting the fires that occur in the meadow steppe area, in the 20-22 range. The typical steppe mainly occurs in the 16-22 range. The range of values is related to the moisture content of the water bodies. The DFI value of the typical steppe area was lower than that of the meadow steppe area, because the meadow steppe area consist of rich vegetation types and superior water and heat conditions (Ol et al., 2013; Ren et al., 2018). Plateau forests and meadow steppes have the latest withering periods, which may be related to their productivity. The typical steppes are relatively late (Zhao et al., 2021). The results of the aforementioned study are consistent with those of the present study, in terms of the distribution of DFI values for meadow and typical steppes.

The fire in Khalkhgol on the southern China-Mongolia on October 25th, 2021 (DOY 298) midday was reported by http://news.cctv.com/china/. Using MCD64A1 data the occurrence and DFI of the fire area was quantified using MOD09A1 data (DOY 297). The range of the DFI values was with in the DFI fire occurrence thresholds, and its accuracy was 98.3% (Table 8). The meadow steppe DFI values and typical steppe DFI values of these areas are distributed in the ranges of 20-22 and 16-22, respectively, which are consistent with our results that showed a high probability of fire. The fire is categorized as an emergency hazard, the DFI is calculated from past fire occurrences, therefore, field data validation is more difficult except during the fire prevention period, where the study area is monitored. However, the DFI quantified for the area where the fire occurred indicates potential fire hazards, showing the reliability of the statistics of this study.

Table 8 Accuracy of DFI thresholds

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

4.2 DFI value distribution characteristics

Mongolia Matad Sum, Erdenetsagaan Sum, and Khalkhgol Sum are located in the Dornod Mongol Strictly Protected Areas of the Eastern Mongolian Grassland Nature Reserve in Mongolia, mainly on hills and plains (Hong, 2016). The Daur National Nature Reserve in Haorotu Sumu of Dornod Province is sparsely populated (Sukhbaatar and Odgerel, 2006; Ol et al., 2013), and its natural and geographical environment has not been polluted or damaged by human activities. It has maintained its original natural appearance and abundant pasture resources. Other protected areas include the Nomrog strictly protected area, Daguuriin Mongol - A Strictly Protected Area, Daguuriin Mongol - B Strictly Protected Area, Nomrog Mnt. Strictly Protected Area, the Ugtam Mnt. Nature Reserves, Toson Khulstai Nature Reserves, and Lkhachinvandad Mnt. Nature Reserves. Hulunbuir City, Khingan League, Xilingol League, and other areas in Inner Mongolia have continuous grassland distributions, long withers period, and a dry climate. Grassland fires occur frequently due to drought, and with the implementation of ecological projects, such as Restoring Farmland to Grassland in recent years, some areas that used to be ecologically degraded, with low vegetation coverage and low amounts of fuel, now show increased vegetation coverage and availability, with an increasing content of combustible materials, creating new fire zones (Huang et al., 2021). The Mongolian Plateau of the China-Mongolia border has distributions of the easy to burn fuel vegetation type meadow steppe and typical steppe. Abaga Banner has a relatively high population and stocking density, and its grassland fire rate is relatively low. Mongolia’s Dornod and Suhbaatar provinces have relatively low populations and stocking densities, and the grassland fire rate is significantly high (Qu et al., 2010). Grazing and mowing management can affect the distribution of DFI values (Li et al., 2016). The area burned by fires decreases when the population density exceeds a certain threshold, and these thresholds vary with location (Bistinas et al., 2013).

The probability of occurrence of large fires in study area in April was higher than that in March because of the impact of snow and frost in the northern region in March, a value above 26 was calculated in meadow and typical steppes. Alternatively, drought in April caused green grass to turn yellow. The probability of fire occurrences in October was higher than that in September because the vegetation withered and yellowed on DOY 290. In September, it was not completely withered or yellowed, and green vegetation was still present (Ren et al., 2019). The high values in November and March were due to snow, and the moisture in the soil severely reduced the reflectance in all ranges. The snow coverage rate of the Mongolian Plateau reached its maximum in December and January of the following year, and the snow began to melt from March to April of the following year (Liu et al., 2011; Li et al., 2020). Therefore, the DFI values for December, January, and February were not calculated (Zhang et al., 2016). The DFI in July and August were not selected for use because there were fewer fires in July and August (Liu et al., 2010; Liu et al., 2017; Chen et al., 2019).

The DFI values around Hulun Lake were generally low, and the DFI values in the Greater Khingan and Khentii Mountains were high. The vegetation phenology is delayed with increasing altitude (Bao et al., 2021; Wu et al., 2021), and the DFI value is related to increasing altitude. Elevation is an important factor in the vertical distribution of vegetation in mountainous areas because it influences precipitation and air temperature (Jin et al., 2009; Zhang et al., 2011; Mu et al., 2013; Zhao et al., 2016), and may also be influenced by the population and livestock (Zhang et al., 2022).

Many standard fire data products are currently available, such as MCD54A1, MCD64A1, MCD14ML, GFED and Fire_CCI. The MCD64A1 burned area mapping algorithm was developed in 2009(Louis, 2009). The product was generated using surface reflectance and active fire input data. General improvement (reduced omission error) was detected in the burned area. There were significantly better detection of small burns. Expanded per-pixel quality assurance (QA) product layer. The MCD54A1 product was then discontinued and replaced with MCD64A1. The MCD14ML resolution was 1000 m (Louis, 2016). GFED4 burned area data provided the global monthly burned area at 0.25° spatial resolution from 1997 to 2015 (Louis, 2013). The most recent BA algorithm from the ESA was designed for MERIS sensor of ENVISAT, which offers spectral bands in the visible and near infrared spectra at a spatial resolution of 300 m. The higher spatial resolution of the Fire_CCI product (300 m) enabled better detection of smaller fires; however, the results proved the opposite. The pixel size analysis showed a tendency for large commission errors due to confusion between burned land and other disturbances. Fire_CCI is a recent product and its validation is still at its initial stage; preliminary comparisons showed good agreement with other global products, but greater omission and commission errors. Further validation is in progress (Davide, 2017). The MCD64A1 product was used for data quality and resolution.

This study provides some reference basis for the later research on the relationship between DFI and fire, but there are still some limitations. Combustibles are the basis for the occurrence and development of fires, but there is a lack of real-time monitoring of combustibles and reliable data. Fire warnings necessitate determining the basic data required for combustibles. However, a good indicator of non-photosynthetic vegetation is DFI, which was previously indicated by NDVI for combustibilities (Bao, 2013; Liu et al., 2014), there are many reasons for why the complete presence or absence of NDVI during the fire protection period is related to local grazing and mowing. Although DFI can better express combustible materials, it uses an index to represent the amount of dead grass cover rather than the actual amount of dead grass. The actual amount of dead grass, degree of fuel continuity, and water content of fuel provide more accurate data for determining fire risk. The fire source is random, and the fire risk weather is relatively large, but no fire will occur when in the absence of combustibles. Therefore, an accurate expression of combustibles is very important in fire risk warnings, and the accurate calculation of combustibles can provide data support for real-time monitoring of fire risk warnings. Currently, most research is focused on weather warnings of fire risk rather than on the warnings of combustible fire risk (Fox et al., 2018; Puneet et al., 2020; Zhuang et al., 2021). Research on the relationship between DFI and fire can provide reliable scientific data support and knowledge for fire warnings. The varying threshold ranges of DFI in the study area may be different.

The fire areas were all in the grassland areas of the China-Mongolia border. Water and snow have certain influence on the DFI inversion. Verification of the DFI retrieved by Sentinel-2 and non-photosynthetic vegetation cover measured in the field will be our future work. In the future, the red-edge bands (705, 740, and 783) of Sentinel-2 will be used to improve the estimation accuracy of vegetation combustibles (Sesnie et al., 2018). However, because the fire occurred suddenly and the DFI was collected in the area where the fire occurred, monitoring the field DFI in the area before the fire was difficult.

5 Conclusion

The meadow steppe DFI threshold for fire occurrence areas was 14-26, of which 20-22 had the highest fire occurrence rate, accounting for 31.0%. The typical steppe DFI threshold for fire occurrence areas was 12-26. This can be used for real-time fire risk warning and to provide data on basic flammable materials. The result also showed that 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 DFI is higher on the Mongolian side of the border in the specified fire months. Therefore, it is better to focus on fire management in Mongolia and on defense in China. 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.

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https://doi.org/10.3390/rs11050489

http://www.mdpi.com/2072-4292/11/5/489

本文引用 [1] 摘要

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

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https://www.ncbi.nlm.nih.gov/pubmed/30158718

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The two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, on-board NASA's Terra and Aqua satellites, have provided more than a decade of global fire data. Here we describe improvements made to the fire detection algorithm and swath-level product that were implemented as part of the Collection 6 land product reprocessing, which commenced in May 2015. The updated algorithm is intended to address limitations observed with the previous Collection 5 fire product, notably the occurrence of false alarms caused by small forest clearings, and the omission of large fires obscured by thick smoke. Processing was also expanded to oceans and other large water bodies to facilitate monitoring of offshore gas flaring. Additionally, fire radiative power (FRP) is now retrieved using a radiance-based approach, generally decreasing FRP for all but the comparatively small fraction of high intensity fire pixels. We performed a Stage-3 validation of the Collection 5 and Collection 6 Terra MODIS fire products using reference fire maps derived from more than 2500 high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. Our results indicated targeted improvements in the performance of the Collection 6 active fire detection algorithm compared to Collection 5, with reduced omission errors over large fires, and reduced false alarm rates in tropical ecosystems. Overall, the MOD14 Collection 6 daytime global commission error was 1.2%, compared to 2.4% in Collection 5. Regionally, the probability of detection for Collection 6 exhibited a similar to 3% absolute increase in Boreal North America and Boreal Asia compared to Collection 5, a similar to 1% absolute increase in Equatorial Asia and Central Asia, a similar to 1% absolute decrease in South America above the Equator, and little or no change in the remaining regions considered. Not unexpectedly, the observed variability in the probability of detection was strongly driven by regional differences in fire size. Overall, there was a net improvement in Collection 6 algorithm performance globally. (C) 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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https://www.mdpi.com/2225-1154/10/10/143

本文引用 [1] 摘要

Forest fires are of critical importance in the Mediterranean region. Fire weather indices are meteorological indices that produce information about the impact as well as the characteristics of a fire event in an ecosystem and have been developed for that reason. This study explores the spatiotemporal patterns of the FWI system within a study area defined by the boundaries of the Greek state. The FWI has been calculated and studied for current and future periods using data from the CFSR reanalysis model from the National Centers for Environmental Protection (NCEP) as well as data from NASA satellite programs and the European Commission for Medium-Range Weather Forecasts (ECWMF) in the form of netCDF files. The calculation and processing of the results were conducted in the Python programming language, and additional drought- and fire-related indices were calculated, such as the standardized precipitation index (SPI), number of consecutive 50-day dry periods (Dry50), the Fosberg fire weather index (FFWI), the days where the FWI exceeds values of 40 and 50 days (FWI > 40) and (days FWI > 50). Similar patterns can easily be noted for all indices that seem to have their higher values concentrated in the southeast of the country owing to the higher temperatures and more frequent drought events that affect the indices’ behavior in both the current and future periods.

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https://www.ncbi.nlm.nih.gov/pubmed/27082891

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Wildfire activity is predicted to increase in many parts of the world due to changes in temperature and precipitation patterns from global climate change. Wildfire smoke contains numerous hazardous air pollutants and many studies have documented population health effects from this exposure.We aimed to assess the evidence of health effects from exposure to wildfire smoke and to identify susceptible populations.We reviewed the scientific literature for studies of wildfire smoke exposure on mortality and on respiratory, cardiovascular, mental, and perinatal health. Within those reviewed papers deemed to have minimal risk of bias, we assessed the coherence and consistency of findings.Consistent evidence documents associations between wildfire smoke exposure and general respiratory health effects, specifically exacerbations of asthma and chronic obstructive pulmonary disease. Growing evidence suggests associations with increased risk of respiratory infections and all-cause mortality. Evidence for cardiovascular effects is mixed, but a few recent studies have reported associations for specific cardiovascular end points. Insufficient research exists to identify specific population subgroups that are more susceptible to wildfire smoke exposure.Consistent evidence from a large number of studies indicates that wildfire smoke exposure is associated with respiratory morbidity with growing evidence supporting an association with all-cause mortality. More research is needed to clarify which causes of mortality may be associated with wildfire smoke, whether cardiovascular outcomes are associated with wildfire smoke, and if certain populations are more susceptible.Reid CE, Brauer M, Johnston FH, Jerrett M, Balmes JR, Elliott CT. 2016. Critical review of health impacts of wildfire smoke exposure. Environ Health Perspect 124:1334-1343; http://dx.doi.org/10.1289/ehp.1409277.

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https://www.mdpi.com/2073-4441/12/11/3066

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Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects.

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本文引用 [2] 摘要

Airborne laser scanning (ALS) data is increasingly distributed freely for ever larger territories, albeit usually in only low resolution. This data source is extensively used in archaeology; however, various remains of past human activities are not recorded in sufficient detail, or are missing completely. The main purpose of this paper is to present a cost-effective approach providing reliable and accurate 3D documentation of the deserted medieval settlement of Hound Tor, a complex site consisting of preserved stone building walls and field system remains. The proposed procedure integrates ALS data with structure from motion (SfM) photogrammetry into a single data source (point cloud). Taking advantage of the benefits of both techniques (reclassified ALS data documents the hinterland, while SfM records the residential area in high detail), an enhanced 3D model has been created surpassing the available ALS data and reflecting the actual state of preserved features. The final outputs will help with the management of the site, its presentation to the general public, and also to enrich understanding of it. As both data sources are currently easily accessible and the proposed procedure has only limited budget requirements, it can be easily adopted and applied extensively (e.g., for virtual preservation of threatened complex sites and areas).

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This study analyzed the spatial and temporal variations in the Normalized Difference Vegetation Index (NDVI) on the Mongolian Plateau from 1982-2013 using Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data and explored the effects of climate factors and human activities on vegetation. The results indicate that NDVI has slight upward trend in the Mongolian Plateau over the last 32 years. The area in which NDVI increased was much larger than that in which it decreased. Increased NDVI was primarily distributed in the southern part of the plateau, especially in the agro-pastoral ecotone of Inner Mongolia. Improvement in the vegetative cover is predicted for a larger area compared to that in which degradation is predicted based on Hurst exponent analysis. The NDVI-indicated vegetation growth in the Mongolian Plateau is a combined result of climate variations and human activities. Specifically, the precipitation has been the dominant factor and the recent human effort in protecting the ecological environments has left readily detectable imprints in the NDVI data series.

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Normalized difference vegetation index data from the polar-orbiting National Oceanic and Atmospheric Administration meteorological satellites from 1982 to 1999 show significant variations in photosynthetic activity and growing season length at latitudes above 35 degrees N. Two distinct periods of increasing plant growth are apparent: 1982-1991 and 1992-1999, separated by a reduction from 1991 to 1992 associated with global cooling resulting from the volcanic eruption of Mt. Pinatubo in June 1991. The average May to September normalized difference vegetation index from 45 degrees N to 75 degrees N increased by 9% from 1982 to 1991, decreased by 5% from 1991 to 1992, and increased by 8% from 1992 to 1999. Variations in the normalized difference vegetation index were associated with variations in the start of the growing season of -5.6, +3.9, and -1.7 days respectively, for the three time periods. Our results support surface temperature increases within the same period at higher northern latitudes where temperature limits plant growth.

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Global warming has led to significant vegetation changes especially in the past 20 years. Hulun Buir Grassland in Inner Mongolia, one of the world's three prairies, is undergoing a process of prominent warming and drying. It is essential to investigate the effects of climatic change (temperature and precipitation) on vegetation dynamics for a better understanding of climatic change. NDVI (Normalized Difference Vegetation Index), reflecting characteristics of plant growth, vegetation coverage and biomass, is used as an indicator to monitor vegetation changes. GIMMS NDVI from 1981 to 2006 and MODIS NDVI from 2000 to 2009 were adopted and integrated in this study to extract the time series characteristics of vegetation changes in Hulun Buir Grassland. The responses of vegetation coverage to climatic change on the yearly, seasonal and monthly scales were analyzed combined with temperature and precipitation data of seven meteorological sites. In the past 30 years, vegetation coverage was more correlated with climatic factors, and the correlations were dependent on the time scales. On an inter-annual scale, vegetation change was better correlated with precipitation, suggesting that rainfall was the main factor for driving vegetation changes. On a seasonal- interannual scale, correlations between vegetation coverage change and climatic factors showed that the sensitivity of vegetation growth to the aqueous and thermal condition changes was different in different seasons. The sensitivity of vegetation growth to temperature in summers was higher than in the other seasons, while its sensitivity to rainfall in both summers and autumns was higher, especially in summers. On a monthly-interannual scale, correlations between vegetation coverage change and climatic factors during growth seasons showed that the response of vegetation changes to temperature in both April and May was stronger. This indicates that the temperature effect occurs in the early stage of vegetation growth. Correlations between vegetation growth and precipitation of the month before the current month, were better from May to August, showing a hysteresis response of vegetation growth to rainfall. Grasses get green and begin to grow in April, and the impacts of temperature on grass growth are obvious. The increase of NDVI in April may be due to climatic warming that leads to an advanced growth season. In summary, relationships between monthly-interannual variations of vegetation coverage and climatic factors represent the temporal rhythm controls of temperature and precipitation on grass growth largely.

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J Q

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https://doi.org/10.11821/yj2007040012

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With the development of disaster research and economy, disaster risk assessment has become a new research area, which will help decision makers to choose optimal technical policies to manage disaster and to set up disaster mitigation strategies. As one of the important natural disasters, the grassland disaster has greatly influenced the development of stockbreeding. So it is necessary to research on grassland fire risk. The grassland in the western Jilin province is an important one in China, which belongs to Songnen grassland in Northeast China, and is also a region susceptible to fire. According to the statistics, the fire disaster in the grassland in the western Jilin province occurred 142 times and affected an area of 7095.5ha during the period 2001-2004, thus seriously threatening and restricting sustainable stockbreeding development in the region. In terms of natural disaster generating mechanism and risk analysis formula, the grassland fire disaster risk index (GFDRI) is set up based on the analysis of grassland fire disaster, exposure, vulnerability and emergency response and recovery ability by using linear weighting model, analytic hierarchy process (AHP) and grassland disaster risk evaluation model. The risk values of grassland fire disaster are calculated respectively in the western Jilin province. The study area was divided into four risk regions, namely, extremely heavy, heavy, moderate and light.The results are proved to be higher reliable.This study is can provide reference and guidance for grassland fire disaster insurance, managing grassland fire disaster and developing strategies to mitigate grassland fire disaster and reduce losses from it. Because grassland fire disaster was influenced by many factors, the index such as vegetation and terrain should be taken into account when the study is carryied out in other place where the terrain changes sharply and the vegetation varies. At the same time, the index value should be changed correspondingly. In the further research the spatial data should be applied to describe the distribution and variety of grassland fire risk.

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http://www.publish.csiro.au/?paper=WF16026

本文引用 [1] 摘要

\n\nGrassland fires are major disturbances to ecosystems and economies around the world. Therefore, research on the spatial patterns of grassland fires is important for understanding the dynamics of fire occurrence and providing evidence for fire prevention and management. One of the problems in grassland fire risk analysis is that historically observed fire data are generally in the point format, with imprecise positions, whereas other influencing factors are often expressed in continuous areal units. To minimise the influences of inaccurate locations and grid size, density estimates can be produced using kernel density estimation (KDE) – a nonparametric statistical method for estimating probability densities. This method has been widely used to convert historical fire data into continuous surfaces. In this study, KDE was applied to grassland fire events in the eastern Inner Mongolia of China, based on Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily active fire data from 2001 to 2014. The bandwidth choice was based on the mean random distance method. Annual and seasonal kernel density maps were produced, showing that the spatial patterns of grassland fire events remained temporally consistent. These results were used to create grassland fire risk zones on the basis of the mean density values in the study area. Grassland fire prevention and planning may focus on high-risk areas identified using this method.\n

[97]

Zhao

H Y

, Gong

L J

, Qu

H H

et al., 2016. The climate change variations in the northern Greater Khingan Mountains during the past centuries. Journal of Geographical Sciences, 26(5): 585-602.

https://doi.org/10.1007/s11442-016-1287-y

本文引用 [1] 摘要

The Greater Khingan Mountains (Daxinganling) are China’s important ecological protective screen and also the region most sensitive to climate changes. To gain an in-depth understanding and reveal the climate change characteristic in this high-latitude, cold and data-insufficient region is of great importance to maintaining ecological safety and corresponding to global climate changes. In this article, the annual average temperature, precipitation and sunshine duration series were firstly constructed using tree-ring data and the meteorological observation data. Then, using the climate tendency rate method, moving-t-testing method, Yamamoto method and wavelet analysis method, we have investigated the climate changes in the region during the past 307 years. Results indicate that, since 1707, the annual average temperature increased significantly, the precipitation increased slightly and the sunshine duration decreased, with the tendency rates of 0.06℃/10a, 0.79 mm/10a and -5.15 h/10a, respectively (P≤0.01). Since the 21st century, the period with the greatest increase of the annual average temperature (also with the greatest increase of precipitation) corresponds to the period with greatest decrease of sunshine duration. Three sudden changes of the annual average temperature and sunshine duration occurred in this period while two sudden changes of precipitation occurred. The strong sudden-change years of precipitation and sunshine duration are basically consistent with the sudden-change years of annual average temperature, suggesting that in the mid-1860s, the climatic sudden change or transition really existed in this region. In the time domain, the climatic series of this region exhibit obvious local variation characteristics. The annual average temperature and sunshine duration exhibit the periodic variations of 25 years while the precipitation exhibits a periodic variation of 20 years. Based on these periodic characteristics, one can infer that in the period from 2013 to 2030, the temperature will be at a high-temperature stage, the precipitation will be at an abundant-precipitation stage and the sunshine duration will be at an less-sunshine stage. In terms of spatial distribution, the leading distribution type of the annual average temperature in this region shows integrity, i.e., it is easily higher or lower in the whole region; and the second distribution type is more (or less) in the southwest parts and less (or more) in the northeast parts. Precipitation and sunshine duration exhibit complex spatial distribution and include four spatial distribution types. The present study can provide scientific basis for the security investigation of homeland, ecological and water resources as well as economic development programming in China’s northern borders.

[98]

Zhao

, Shen

H H

, Geng

X Q

et al., 2021. Three-decadal destabilization of vegetation activity on the Mongolian Plateau. Environmental Research Letters, 16(3): 034049.

https://doi.org/10.1088/1748-9326/abd81d

本文引用 [2] 摘要

Steppes on the Mongolian Plateau, mainly within the Republic of Mongolia and the Inner Mongolia Autonomous Region (IMAR) of China, have been subjected to widespread degradation as a result of climate change and human utilization. Field experiments and long-term observations suggest that the productivity of degraded grassland ecosystems might show greater instability, i.e. stronger interannual variation in vegetation activities, when driven by climate change. However, it remains unknown whether this hypothesized destabilization of steppe vegetation activity has occurred in the past three decades and how this destabilization has fed back to livestock production on the plateau. Herein, we define temporal instability of vegetation activity using three indicators, the start and end of the growing season as indicated by the normalized difference vegetation index (NDVI) and the mean growing-season NDVI, and examine their trends between 1983 and 2015. Our results show a significant destabilization of vegetation activity over a large proportion of the total steppe area. Compared with the IMAR, vegetation destabilization has occurred to a significantly higher extent in Mongolia. Climate warming, drying and interannual climate variability accounted for approximately 60%–80% of the vegetation destabilization. The destabilization of steppe productivity was significantly associated with the interannual variability of livestock production in Mongolia, while the interannual variability of steppe productivity and livestock production were decoupled in the IMAR. Our findings highlight the need to improve livestock production systems and conserve degraded grasslands for sustainable development in view of the destabilization of steppe productivity on the Mongolian Plateau.

[99]	Zhou D W, Zhang Z S, 1996. Grassland fire factors and their ecological effects. Grassland of China, (2): 73-76. (in Chinese) 本文引用 [1]

[100]

Zhuang

Y Z

, Fu

, Benjamin

D S

et al., 2021. Quantifying contributions of natural variability and anthropogenic forcings on increased fire weather risk over the western United States. PNAS, 118(45): e2111875118.

https://doi.org/10.1073/pnas.2111875118

https://pnas.org/doi/full/10.1073/pnas.2111875118

本文引用 [1] 摘要

The western United States (WUS) has experienced a rapid increase of fire weather (as indicated by vapor pressure deficit, VPD) in recent decades, especially in the warm season. However, the extent to which an increase of VPD is due to natural variability or anthropogenic warming has been unclear. Our observation-based estimate suggests ∼one-third of the VPD trend is attributable to natural variability of atmospheric circulation, whereas ∼two-thirds is explained by anthropogenic warming. In addition, climate models attribute ∼90% of the VPD trend to anthropogenic warming. Both estimates suggest that anthropogenic warming is the main cause for increasing fire weather and provide a likely range for the true anthropogenic contribution to the WUS trend in VPD.

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Abstract
Key words
引用本文
1 Introduction
2 Materials and methods
2.1 Study area
Figure 1 Location of the study area (China-Mongolia border area) and elevation (a); Land cover type (b); banner, county (c); grassland types of the study area (d)
2.2 Data
Table 1 Day-of-year (DOY) of the first day of each calendar month
Table 2 MOD09A1 band information
Table 3 Burned area information statistics
Figure 2 Burned area information of 2001-2020
2.3 Method
2.3.1 DFI
2.3.2 Accuracy verification
2.3.3 Mann-Kendall test
3 Result
3.1 Fire statistics and validation
Table 4 The MCD64A1 burned area validation with Landsat burned area results
Figure 3 ROC curve of the sampling area
3.2 Determination of DFI thresholds for fire occurrence areas
Figure 4 Histogram of DFI values distribution in fire occurrence area (a. meadow steppe; b. typical steppe)
3.3 DFI value distribution characteristics
3.3.1 Spatial variation features
Figure 5 Spatial distributions of annual mean DFI for the meadow steppe of the China-Mongolia border area between 2001 and 2020
Figure 6 Spatial distributions of annual mean DFI for the typical steppe of the China-Mongolia border area between 2001 and 2020
Table 5 The meadow steppe area of each grade in each month (per km2)
Figure 7 Spatial distributions of meadow steppe monthly mean DFI during the fire prevention period in the China-Mongolia border area between 2001 and 2020
Table 6 The typical steppe area of each grade in each month (per km2)
Figure 8 Spatial distributions of typical steppe monthly mean DFI during the fire prevention period in the China-Mongolia border area between 2001 and 2020
Figure 9 The relationship between DFI and elevation
3.3.2 Temporal variation features
Figure 10 Temporal distribution of DFI in the China-Mongolia border area between 2001 and 2020: (a) meadow steppe (b) typical steppe
Figure 11 Monthly distribution of DFI fire prevention period in the China-Mongolia border area during 2001-2020
3.3.3 Influence of DFI value distribution
Table 7 Influencing factors of DFI value partial correlation coefficient
Figure 12 Population and livestock densities in the China-Mongolia border area
Figure 13 Spatial distribution of the average annual air temperature (a) and precipitation (b) in the China-Mongolia border area
4 Discussion
4.1 Determination of DFI thresholds for fire occurrence areas
Table 8 Accuracy of DFI thresholds
4.2 DFI value distribution characteristics
5 Conclusion
参考文献

收稿日期	接受日期	出版日期
2022-06-13	2023-03-22	2023-08-25
发布日期
2023-08-29

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Abstract

Key words

引用本文

1 Introduction

2 Materials and methods

2.1 Study area

Figure 1 Location of the study area (China-Mongolia border area) and elevation (a); Land cover type (b); banner, county (c); grassland types of the study area (d)

2.2 Data

Table 1 Day-of-year (DOY) of the first day of each calendar month

Table 2 MOD09A1 band information

Table 3 Burned area information statistics

Figure 2 Burned area information of 2001-2020

2.3 Method

2.3.1 DFI

2.3.2 Accuracy verification

2.3.3 Mann-Kendall test

3 Result

3.1 Fire statistics and validation

Table 4 The MCD64A1 burned area validation with Landsat burned area results

Figure 3 ROC curve of the sampling area

3.2 Determination of DFI thresholds for fire occurrence areas

Figure 4 Histogram of DFI values distribution in fire occurrence area (a. meadow steppe; b. typical steppe)

3.3 DFI value distribution characteristics

3.3.1 Spatial variation features

Figure 5 Spatial distributions of annual mean DFI for the meadow steppe of the China-Mongolia border area between 2001 and 2020

Figure 6 Spatial distributions of annual mean DFI for the typical steppe of the China-Mongolia border area between 2001 and 2020

Table 5 The meadow steppe area of each grade in each month (per km2)

Figure 7 Spatial distributions of meadow steppe monthly mean DFI during the fire prevention period in the China-Mongolia border area between 2001 and 2020

Table 6 The typical steppe area of each grade in each month (per km2)

Figure 8 Spatial distributions of typical steppe monthly mean DFI during the fire prevention period in the China-Mongolia border area between 2001 and 2020

Figure 9 The relationship between DFI and elevation

3.3.2 Temporal variation features

Figure 10 Temporal distribution of DFI in the China-Mongolia border area between 2001 and 2020: (a) meadow steppe (b) typical steppe

Figure 11 Monthly distribution of DFI fire prevention period in the China-Mongolia border area during 2001-2020

3.3.3 Influence of DFI value distribution

Table 7 Influencing factors of DFI value partial correlation coefficient

Figure 12 Population and livestock densities in the China-Mongolia border area

Figure 13 Spatial distribution of the average annual air temperature (a) and precipitation (b) in the China-Mongolia border area

4 Discussion

4.1 Determination of DFI thresholds for fire occurrence areas

Table 8 Accuracy of DFI thresholds

4.2 DFI value distribution characteristics

5 Conclusion

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参考文献

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脚注

Table 5 The meadow steppe area of each grade in each month (per km²)

Table 6 The typical steppe area of each grade in each month (per km²)