Probability and spatiotemporal dynamics of active fire occurrence in Inner Mongolia, China from 2000 to 2022

doi:10.1007/s40333-025-0027-5

Journal of Arid Land

2025, Vol. 17

Issue (8): 1084-1102 DOI: 10.1007/s40333-025-0027-5

Research article

Probability and spatiotemporal dynamics of active fire occurrence in Inner Mongolia, China from 2000 to 2022

JIA Xu¹, WEI Baocheng²^,³^,^*(

), ZHANG Zhijie⁴, CHEN Lulu², LIU Mengna², ZHAO Yiming², WANG Jing¹

¹College of Resources and Environmental Economics, Inner Mongolia University of Finance and Economics, Hohhot 010051, China
²College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
³Key Laboratory of Mongolian Plateau's Climate System at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot 010022, China
⁴Hohhot Meteorological Observatory, Hohhot 010020, China

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Abstract

Fires are one of the most destructive natural disasters and have serious long-term effects on the environment, economy, and human health. In Inner Mongolia Autonomous Region, China, frequent fire disturbance occurs due to the intensification of climate change and human activities. It is crucial to understand the fire regime and estimate the probability of regional fire occurrence and reducing fire losses. However, most studies have primarily focused on the dynamic changes, probability of occurrence, and driving mechanisms of wildfires in the grassland and forest land ecosystems in Inner Mongolia, while insufficient research has been conducted on the spatiotemporal variations in active fires and their impact on the wildfire risk in forest land and grassland. Therefore, in this study, we analyzed the active fire regime based on Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomalies and burned area products from 2000 to 2022. Combined with climate, topographic, landscape, anthropogenic, and vegetation datasets, logistic regression (LR), support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) models were chosen to estimate the probability of active fire occurrence at the seasonal timescale. The results revealed that: (1) a total of 100,343 active fires occurred in Inner Mongolia and the burned area reached 6.59×10⁴ km². The number of ignition point exhibited a significant increasing trend, while the burned area exhibited a nonsignificant decreasing trend; (2) four active fire belts were detected, namely, the Hetao-Tumochuan Plain fire belt, Xiliao River Plain fire belt, Songnen Plain fire belt, and Hailar River Eroded Plain fire belt. The centroid of the active fires has shifted 456.4 km toward the southwest; (3) RF model achieved the highest accuracy in estimating the probability of active fire occurrence, followed by CNN, and LR and SVM models had lower accuracies; and (4) the distribution of the high and extremely high fire risk areas largely aligned with the four fire belts. The probability of active fire occurrence was the highest in spring, followed by that in autumn, and it gradually decreased in summer and winter. Our results revealed active fires migrated to the southwest and ignition sources increased, despite reduction of the burned area was not significant. The RF model outperformed the other models in predicting the probability of active fire occurrence. These findings contribute to future fire prevention and prediction in Inner Mongolia.

Key words： active fire regime probability prediction machine learning Moderate Resolution Imaging Spectroradiometer (MODIS) random forest model

Received: 01 March 2025 Published: 31 August 2025

Corresponding Authors: ^*WEI Baocheng (E-mail: nsdwbc@126.com)

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	JIA Xu
	WEI Baocheng
	ZHANG Zhijie
	CHEN Lulu
	LIU Mengna
	ZHAO Yiming
	WANG Jing

Cite this article:

JIA Xu, WEI Baocheng, ZHANG Zhijie, CHEN Lulu, LIU Mengna, ZHAO Yiming, WANG Jing. Probability and spatiotemporal dynamics of active fire occurrence in Inner Mongolia, China from 2000 to 2022. Journal of Arid Land, 2025, 17(8): 1084-1102.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0027-5 OR http://jal.xjegi.com/Y2025/V17/I8/1084

Table 1 Detailed descriptions of data

Table 2 List of explanatory variables

Table 3 Specific information about the aspect classification

Fig. 1 Annual dynamics (a) and Manner-Kendall (M-K) test results (b) of ignition point and burned area. UF and UB stand for the forward sequence statistic and backward sequence statistic, respectively.

Table 4 Contribution rates of ignition point and burned area at the seasonal and monthly scales

Table 5 Proportions of ignition point and burned area in different land use types

Fig. 2 Spatial distribution of active fires from 2000 to 2022. (a), kernel density; (b), standard deviational ellipse (SDE) and centroid migration trajectory. The figures are based on the standard map (蒙S(2023)027) of Department of Natural Resources of Inner Mongolia Autonomous Region (https://zrzy.nmg.gov.cn/bsfw/bzdt/nmgzzqbzdt/), and the boundary of the standard map has not been modified.

Table 6 Performance metrics of the four fire prediction models at the seasonal scale

Table 7 Ranking of variables' importance in LR,RF,SVM, and CNN models at the seasonal scale

Fig. 3 Spatial distribution of fire risk level based on random forest (RF) from 2000 to 2022

Fig. 4 Spatial distribution of fire risk level based on RF model at the seasonal scale. (a), spring; (b), summer; (c), autumn; (d), winter.

Fig. S1 Spatial distribution of fire risk level based on logistic regression (LR) model from 2000 to 2022

Fig. S2 Spatial distribution of fire risk level based on LR model at the seasonal scale. (a), spring; (b), summer; (c), autumn; (d), winter.

Fig. S3 Spatial distribution of fire risk level based on support vector machine (SVM) model from 2000 to 2022

Fig. S4 Spatial distribution of fire risk level based on SVM model at the seasonal scale. (a), spring; (b), summer; (c), autumn; (d), winter.

Fig. S5 Spatial distribution of fire risk level based on convolutional neural network (CNN) model from 2000 to 2022

Fig. S6 Spatial distribution of fire risk level based on CNN model at the seasonal scale. (a), spring; (b), summer; (c), autumn; (d), winter.


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