Characterising vegetative biomass burning in China using MODIS data

Qin, Xizhong; Huang, Yongwen; Zhan, Zihui; Li, Zengyuan

doi:10.1071/wf12163

Cited by 19 publications

(12 citation statements)

References 15 publications

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“…The region Harbin belonged was renowned for many times due to fertile land and well-developed agriculture (Chen et al, 2017). Agricultural residues burning during harvesting season is accepted as one of the most effective ways to reduce straw accumulation (Cao et al, 2016;Qin et al, 2014). Given the significant contribution of biomass combustion to air pollution, some efforts and new emission standards are required to reduce biomass fuel boilers and agriculture waste burning.…”

Section: Nox Sources Apportionmentmentioning

confidence: 99%

The importance of coal combustion and heterogeneous reaction for atmospheric nitrate pollution in a cold metropolis in China: Insights from isotope fractionation and Bayesian mixing model

Sun

Zong

Wang

et al. 2020

Atmospheric Environment

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Section: Nox Sources Apportionmentmentioning

confidence: 99%

The importance of coal combustion and heterogeneous reaction for atmospheric nitrate pollution in a cold metropolis in China: Insights from isotope fractionation and Bayesian mixing model

Sun

Zong

Wang

et al. 2020

Atmospheric Environment

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“…The prominent topographic relief amplifies the regional climatic variation, represented by an obvious dry season typically starting from December to the following May [41]. Concerned by global climate change scenarios, wildfires are now widely monitored across China as well as in the Indo-China Peninsula and its surrounding regions, which are dominated by the IOM and POM [1,42]. The weak capacity of MODIS fire products to detect wildfire occurrence in this region, as clearly shown by Giglio et al [25], therefore strengthens the impetus for estimating the omission error in the data using confirmed ground records, and for exploring the influence of potential factors on this margin of error.…”

Section: Study Areamentioning

confidence: 99%

Wildfire Detection Probability of MODIS Fire Products under the Constraint of Environmental Factors: A Study Based on Confirmed Ground Wildfire Records

et al. 2019

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The Moderate Resolution Imaging Spectroradiometer (MODIS) has been widely used for wildfire occurrence and distribution detecting and fire risk assessments. Compared with its commission error, the omission error of MODIS wildfire detection has been revealed as a much more challenging, unsolved issue, and ground-level environmental factors influencing the detection capacity are also variable. This study compared the multiple MODIS fire products and the records of ground wildfire investigations during December 2002–November 2015 in Yunnan Province, Southwest China, in an attempt to reveal the difference in the spatiotemporal patterns of regional wildfire detected by the two approaches, to estimate the omission error of MODIS fire products based on confirmed ground wildfire records, and to explore how instantaneous and local environmental factors influenced the wildfire detection probability of MODIS. The results indicated that across the province, the total number of wildfire events recorded by MODIS was at least twice as many as that in the ground records, while the wildfire distribution patterns revealed by the two approaches were inconsistent. For the 5145 confirmed ground records, however, only 11.10% of them could be detected using multiple MODIS fire products (i.e., MOD14A1, MYD14A1, and MCD64A1). Opposing trends during the studied period were found between the yearly occurrence of ground-based wildfire records and the corresponding proportion detected by MODIS. Moreover, the wildfire detection proportion by MODIS was 11.36% in forest, 9.58% in shrubs, and 5.56% in grassland, respectively. Random forest modeling suggested that fire size was a primary limiting factor for MODIS fire detecting capacity, where a small fire size could likely result in MODIS omission errors at a threshold of 1 ha, while MODIS had a 50% probability of detecting a wildfire whose size was at least 18 ha. Aside from fire size, the wildfire detection probability of MODIS was also markedly influenced by weather factors, especially the daily relative humidity and the daily wind speed, and the altitude of wildfire occurrence. Considering the environmental factors’ contribution to the omission error in MODIS wildfire detection, we emphasized the importance of attention to the local conditions as well as ground inspection in practical wildfire monitoring and management and global wildfire simulations.

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“…Due to space constraints, detailed comments on data uncertainty are provided in Appendix B. Despite the uncertainties identified in those datasets, which inevitably introduce some degree of uncertainty in any analysis, they are the most widely used for global scale research addressing fire and land cover issues, and have been extensively employed in studies dealing with fire in China (Huang, Li, Li, & Song, 2012a;Huang, Song, Li, Li, & Zhu, 2012b;Qin, Yan, Zhan, & Li, 2014;Yi, Bao, & Zhang, 2017). Appendix B also includes a discussion of the impact of cloud cover on the active fire and burned area data used in the present study.…”

Section: Satellite Datamentioning

confidence: 99%

“…long fire season with a peak in spring) and North China Plain (i.e. short fire season with a peak in summer) (Qin et al, 2014). Fuel, topography, weather, land use management and fire suppression efforts tend to contribute significantly to the inequality in fire size distributions through controlling fire behavior.…”

Section: Fire Regimes In China and Their Potential Driversmentioning

confidence: 99%