Fig 2 - uploaded by Litao Wang
Content may be subject to copyright.
The fire danger rating maps for the study region from 2003 to 2004. Those maps are produced by the fire danger rating assessment, which show the distribution of fire danger in the study region. a Map of fire danger categories for the last 10 days of April 2003, b Map of fire danger categories in last 10 days of October 2003, c Map of fire danger categories in middle 10 days of January 2004, d Map of fire danger categories in last 10 days of January 2004, e Map of fire danger categories in first 10 days of February 2004, f Map of fire danger categories in middle 10 days of February 2004
Source publication
Every year several million hectares of forest and grassland in China are affected by wildfires. The majority of wildfires occur in the northern part of China, where grasslands and forests are ubiquitous. A critical step toward the protection of life, property, and natural resources from wildfires is the development of a fire danger rating system. T...
Similar publications
Residual stress in high velocity oxy-fuel (HVOF) thermally sprayed WC-10Co-4Cr coating was studied based on design of experiment (DOE) with five factors of oxygen flow, fuel gas hydrogen flow, powder feed rate, stand-off distance, and surface speed of substrate. In each DOE run, the velocity and temperature of in-flight particle in flame, and subst...
Citations
... The authors of Ceccato, et al. [90] developed the Global Vegetation Monitoring Index (GVMI) and demonstrated that the index can directly measure vegetation moisture content regardless of species composition. Wang, et al. [91] incorporated GVMI for fire-danger assessment. Higher fuel moisture may decrease the likelihood of fuel ignition. ...
... An increase in land surface temperature (LST) indicates a reduction in fuel moisture content, thus increasing the likelihood of fire ignition. Hence, LST has been used as a driver for fire-danger assessment in numerous studies, e.g., [37,91,[117][118][119]. The LST was extracted from MODIS MOD11A2 version 6.1. ...
Grasslands are key to the Earth’s system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa’s grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread.
... For VWC, it determines the fuel flammability and changes fire probability (Arganaraz et al., 2016); meanwhile affects fire energy release and fuel consumption. In such cases, most existing operational fire behavior models and fire danger mapping are mainly based on weather variables like the Global Wildfire Information System (Pettinari and Chuvieco, 2017) and the fire behavior model-BehavePlus (Andrews et al., 2005), or rely on VWC (Wang et al., 2013;Argañaraz et al., 2016). ...
Fires greatly affect terrestrial ecosystems, climate, human health, and economy. Under the threat of global warming, assessing fire occurrence frequency and fire danger is critical to mitigate fire’s negative impacts. The state-of-the-art satellite microwave observation provides the unique opportunity to monitor vegetation water content (VWC) for multiple vegetation strata, yet still lacks associated investigation in fire dynamics. Here, we applied the multiple microwave Emissivity Difference Vegetation Indexes (EDVIs) as VWC proxies of different vegetation strata to explore their influence on fire occurrence frequency (given by the number of fires, NOF) and the cumulative fire radiant energy (FRE) in mainland Southeast Asia during 2015–2019; and compared to that with the commonly used fire weather indices from the Canadian Forest Fire Weather Index system. Then, we identified the optimal regression models to depict the relationship of fires with EDVIs and fire weather indices, respectively; and incorporated both kinds of indices in models to retrospectively predict NOF and FRE. Results show that: 1) the monthly mean NOF and FRE nonlinearly decrease with EDVI in mainland Southeast Asia, and 40–60% of the variances are explained by EDVI, which further improve to 60–70% by incorporating EDVIs of two different vegetation layers. 2) In comparison, NOF and FRE nonlinearly increase with fire weather indices, and connections are stronger than that with EDVI; nevertheless, combing fire weather indices with EDVI, especially with EDVIs of two vegetation layers provides almost the highest explanation for fires. 3) The log-linear and/or log-quadratic models are identified to outperform the other models in depicting the fire-EDVI and fire-fire weather indices relationships; and incorporating EDVI and fire weather indices in models generally improve the prediction accuracy. These findings reveal the differentiated VWC effects on NOF and FRE among vegetation strata, and quantify the strong nonlinear response of fires to VWC and weather conditions in mainland Southeast Asia, which could provide new insight for fire prediction and fire danger assessments with the utility of microwave observations.
... In addition, the GWLR model gave consistent predictions with the actual fire data distribution, indicating that the GWLR model has immense predictive ability. This research is consistent with other studies that showed the best prediction performance of the GWLR model (Wang et al., 2013;Su et al., 2021). ...
Introduction
Forest fires seriously threaten the safety of forest resources and human beings. Establishing an accurate forest fire forecasting model is crucial for forest fire management.
Methods
We used different meteorological and vegetation factors as predictors to construct forest fire prediction models for different fire prevention periods in Heilongjiang Province in northeast China. The logistic regression (LR) model, mixed-effect logistic (mixed LR) model, and geographically weighted logistic regression (GWLR) model were developed and evaluated respectively.
Results
The results showed that (1) the validation accuracies of the LR model were 77.25 and 81.76% in spring and autumn fire prevention periods, respectively. Compared with the LR model, both the mixed LR and GWLR models had significantly improved the fit and validated results, and the GWLR model performed best with an increase of 6.27 and 10.98%, respectively. (2) The three models were ranked as LR model < mixed LR model < GWLR model in predicting forest fire occurrence of Heilongjiang Province. The medium-and high-risk areas of forest fire predicted by the GWLR model were distributed in western and eastern parts of Heilongjiang Province in spring, and western part in autumn, which was consistent with the observed data. (3) Driving factors had strong temporal and spatial heterogeneities; different factors had different effects on forest fire occurrence in different time periods. The relationship between driving factors and forest fire occurrence varied from positive to negative correlations, whether it’s spring or autumn fire prevention period.
Discussion
The GWLR model has advantages in explaining the spatial variation of different factors and can provide more reliable forest fire predictions.
... Apart from curing degree, the total organic matter available for ignition and combustion is another inescapable parameter in fire research, which is commonly defined as fuel in many studies. In 2012, Wang et al. [161] proposed a fire danger index based on the analytical hierarchy process and five estimated parameters, including curing degree and fuel weight, which was derived from Landsat images. Then, Bian et al. [162] created a grassland fire risk index that combined fuel, fire climate, accessibility, human-social factors, and topography data into an MLR model in which the NDVI was applied to represent the annual continuity of grassland fuels. ...
... Martin et al. [157] curing degree MLR NDVI,GVMI Chaivaranont et al. [158] curing degree MLR NDVI,VOD Li [159] curing degree MLR NDVI, GVMTI Li [160] curing degree MLR NDVI, GVMTI Bian et al. [162] fuel biomass NDVI average curve NDVI Sesnie et al. [163] fuel biomass RF vegetation indices Wang et al. [161] fuel biomass linear regression NDVI Jurdao et al. [164] LFMC MLR NDVI, surface temperature Arganara et al. [165] LFMC linear regression EVI Luo et al. [166] LFMC PROSAIL satellite products Yebra et al. [167] LFMC PROSAIL satellite products Second, for dynamic monitoring, the effective and accurate monitoring of a burned area can assist in damage assessment, understanding the extent of ecological changes caused by the fire, and making restoration policies. In 2010, Dubinin et al. [168] performed a long time series of burned area reconstruction in the southern Russian arid grasslands by the NDVI and a decision-tree model. ...
The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite.
... The NDVI, land use type and vegetation water content were selected as factors to reveal vegetation status. Though the NDVI has been used in many current studies to characterize fuel loads [35,[47][48][49], litter loads in combustibles (hay/withered grass) may be more accurate than the NDVI in reflecting the status of combustibles. However, due to the complexity of litter spectral characteristics, the inversion of litter loads on a large scale by remote sensing is not easy to obtain. ...
Inner Mongolia, as a fragile ecological zone in northern China, is prone to severe fires due to natural forces and intensive human disturbances. The development of a fire risk assessment system at the finer spatial scale is not sufficient in this region. In this study, we obtained the data of burned areas and fire hotspots numbers for Inner Mongolia from the Terra/Aqua Moderate-resolution Imaging Spectroradiometer data (MCD45A1 and MOD14A1/MYD14A1, 2002~2016). These fire maps were used to determine the fire spatial and temporal variability, as well as the interactions with environmental controls (climatic, vegetation, topography, and anthropic characteristics) derived in geographic information system (GIS) layers. Based on this, the fire-causing variables were selected as the dependent variables for model building, whereas data on burned area and number of fire hotspots were used for model validation. The fire risk assessment map was then generated in a 500 × 500 m grid cell using an analytic hierarchy process approach and a GIS technique. This work could be easily used for the ultimate aim of supporting fire management.
... Gitelson et al., 2002) (Schneider et al., 2008); (Stow et al., 2005); (Huete et al., 1984);; (Mildrexler et al., 2007); (Bisquert et al., 2014) Remote Sensing meteorological variables such as Surface Temperature (Ts), Air Temperature (Ta) and Relative Humidity (RH) on the other hand are used as indicators in monitoring and analysis of fire risk conditions. Ts computed from thermal infrared bands have been found to provide valuable information on fuel temperature, vegetation moisture status and air humidity over the surface (Leblon et al., 2012, Wang et al., 2013. Manzo-Delgado et al. (2004) used Ts variable as the fire risk indicator over the temperate forest in Central Mexico. ...
... In general, quantification of vegetation/fuel cover moisture has been conducted through the measure of fuel moisture content or the Equivalent Water Thickness (EWT) defined as ratio between the quantity of water and the leaf area (Leblon et al., 2012) and Relative Water Content (RWC) compares the water content of a leaf with the maximum water content at full turgor (Ceccato et al., 2002, Wang et al., 2013. It is regarded as an extremely essential vegetation condition parameter since it ha as an inverse relation with ignition probability owing to the fact that the energy necessary to start a fire is used up in the process of evaporation even before the fire starts to burn (Dimitrakopoulos and Bemmerzouk, 2003). ...
... ( Wang et al., 2013); (Ceccato et al., 2002) Normalized Differences Infrared Index Hunt and Rock, 1989); (Chuvieco et al., 2002) Moisture Stress Index ( ...
... Analytic hierarchy process (AHP) decision-making method was applied in the estimation of FPI. AHP has been widely applied as the decision-support in fire risk (Chavan et al. 2012;Mahdavi et al. 2012;Malik et al. 2013;Wang et al. 2013;Ajin et al. 2016). AHP is a mathematical method which analyses complex decision problems under multi criteria and helps to set priorities and make best decision (Saaty 1996(Saaty , 2008. ...
... September is also the month that coincides with spring warming, a condition that promote fuel desiccation and more severe fire (Arganaraz et al. 2016). According to Dasgupta et al. (2006) and Wang et al. (2013), LST increase in drier areas due to evapotranspiration. Moreover, LST lead to reduction of fuel moisture content thus making fuel more prone to consumption by fire in the event of ignition. ...
Wildfires are a recurrent and serious challenge in mountainous regions. Improved understanding of fire risk is important for future fire management plans. Fire potential index (FPI) derived from remotely sensed, meteorological variables and elevation from Advanced Spaceborne Thermal Emission Reflection Radiometer- Digital Elevation Model (ASTER-DEM) was used to provide useful index of fire risk in the study area from 2011 – 2014. The mean inter-annual area coverage of FPI shows that most of the study area fell between low (42%) and insignificant (34%) fire danger categories. Areas vulnerable to greatest fire danger include valleys and low lying plains towards the eastern portion of the study area. The model revealed an overall accuracy of 89% ranging from 33%-100% indicating that maximum of fires fell under low to moderate danger classes. The model performance indicates the potential of Moderate-Resolution Imaging Spectroradiometer (MODIS) derived indices to predict fire danger in mountainous terrains with limited accessibility.
... Our central purpose was to evaluate and compare the relative contribution of fuel composition, topography, and human activity to fire occurrence, size, and BP in a subtropical coniferous forest region in East China. The study area is a county on the border of Zhejiang and Fujian Provinces [19,[29][30][31]. Zhejiang and Fujian are among the top three provinces with the highest forest coverage rate in China, the top eight provinces with the largest plantation, and also among the provinces with the highest forest productions output [32]. ...
The contribution of factors including fuel type, fire-weather conditions, topography and human activity to fire regime attributes (e.g. fire occurrence, size distribution and severity) has been intensively discussed. The relative importance of those factors in explaining the burn probability (BP), which is critical in terms of fire risk management, has been insufficiently addressed. Focusing on a subtropical coniferous forest with strong human disturbance in East China, our main objective was to evaluate and compare the relative importance of fuel composition, topography, and human activity for fire occurrence, size and BP. Local BP distribution was derived with stochastic fire simulation approach using detailed historical fire data (1990–2010) and forest-resource survey results, based on which our factor contribution analysis was carried out. Our results indicated that fuel composition had the greatest relative importance in explaining fire occurrence and size, but human activity explained most of the variance in BP. This implies that the influence of human activity is amplified through the process of overlapping repeated ignition and spreading events. This result emphasizes the status of strong human disturbance in local fire processes. It further confirms the need for a holistic perspective on factor contribution to fire likelihood, rather than focusing on individual fire regime attributes, for the purpose of fire risk management.
... This conclusion matched the results obtained through remote sensing data and meteorological data. In the study of fire risk rating assessment for forest and grassland in northern China, Wang et al. (2013) proposed the potential fire risk index. This potential fire risk index was calculated based on 5 fire risk assessment indexes which include LST, degree of vegetation curing, equivalent water thickness, degree of vegetation continuity and fuel weight. ...
Lightning-caused forest fires can cause serious damage to the social economy and public property and even threaten human life. Therefore, lightning-caused forest fire risk rating assessment is very important for forest management agency, because the risk rating assessment results could provide important information to prevent fires and allocate extinguishing resources. The existing forest fire risk rating assessment methods are more difficult for the area with sparse meteorological stations, imperfect lightning monitoring systems and complex terrain conditions. Based on remote sensing data and case-based reasoning principle, this paper proposed a method to overcome the limitations of existing forest fire risk rating assessment methods. The proposed method uses three dynamic and two static indexes to characterize the potential fire environment. The dynamic indexes are temperature condition index, vegetation condition index and water condition index. The static indexes are terrain fluctuation and LIS/OTD lightning density. In DaXingAn Mountains of China, the fire risk rating spatial distribution maps with 8-day cycle before the occurrences of historical lightning-caused fires were produced by using the lightning-caused forest fire risk rating assessment method during 2000–2006 in this paper. The results showed that most of the historical lightning-caused fires occurred in the region with high fire risk rating, and the spatial–temporal distribution changes of the lightning-caused fire risk rating followed the same trend as the changes in the number of lightning-caused fires. Therefore, the lightning-caused forest fire risk rating assessment method proposed in this paper could assess the fire risk rating effectively, and this method could also provide a reference for other countries and regions with sparse meteorological stations and imperfect lightning monitoring systems.
... Forest fire is a natural disaster which could destroy forest resource and could be a great danger to human well-being through air quality and economic loss (Cheng et al. 2013). Fires affect several hundred million hectares of forest and grassland each year in the world (Wang et al. 2013). Fire is also an extremely important ecological process in more than 50 % of the world's terrestrial ecosystems (Shlisky et al. 2009). ...
The accurate extraction of burned area is important for biomass burning monitoring and loss evaluation. Environment and Disasters Monitoring Microsatellite Constellation put forward by China has two satellites of HJ-1A and HJ-1B in orbit. Each satellite has two CCD cameras with four bands to meet the need of mapping burned area. In order to evaluate the capability for mapping the burned area using HJ satellite’s CCD data, a forest fire occurring in Yuxi, Yunnan Province of Southwest China, was selected to analyze the spectral characteristic in the range of visible and near infrared in this paper. The research of mapping burned area was carried out based on the HJ satellites using three spectral indices (NDVI, GEMI and BAI). The color composite images including NIR band could reflect the spectral change in post-fire vegetation with a higher repetition cycle (2 days, or 1 day in some region) and higher spatial resolution (30 m). Through the comparison with the discrimination index M and extraction accuracy, the BAI has higher discrimination capability than NDVI and GEMI, and the highest M value is 2.1943. The extraction of burned area based on BAI showed higher accuracy, and the highest kappa value is 0.8957. Using HJ satellites, the map of burned area with higher temporal–spatial resolution and higher accuracy could provide the potential for dynamic monitoring and analyzing fire behavior.
