Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images
The formation of forest fire burned area, influenced by a variety of factors such as meteorology, topography, vegetation, and human intervention, is a dynamic process of fire line burning that develops from the point of ignition to the boundary of the burned area. Accurately simulating and predictin...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| author | Xintao Ling Gui Zhang Ying Zheng Huashun Xiao Yongke Yang Fang Zhou Xin Wu |
| author_facet | Xintao Ling Gui Zhang Ying Zheng Huashun Xiao Yongke Yang Fang Zhou Xin Wu |
| author_sort | Xintao Ling |
| collection | DOAJ |
| description | The formation of forest fire burned area, influenced by a variety of factors such as meteorology, topography, vegetation, and human intervention, is a dynamic process of fire line burning that develops from the point of ignition to the boundary of the burned area. Accurately simulating and predicting this dynamic process can provide a scientific basis for forest fire control and suppression decisions. In this study, five typical forest fires located in different regions of China were used as the study object. The straight path distances from the ignition point grid to each grid on fire line in Sentinel-2 imageries for each forest fire were used as the target variables. We obtained the values of 11 independent variables for each pathway, including wind speed component, Temperature, Relative Humidity, Elevation, Slope, Aspect, Degree of Relief, Normalized Difference Vegetation Index, Vegetation Type, Fire Duration, and Gross Domestic Product reflecting human intervention capacity for fires. The value of each target variable and that of its corresponding independent variable constituted a sample. Four machine learning models, such as Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained using 80% effective samples from four forest fires, and 20% used to verify the above models. The hyper-parameters of each model were optimized using grid search method. After analyzing the validation results of models which showed temperature as a non-significant variable, the training and validation process of models above was repeated after excluding temperature. The results show that RF is the optimal model with 49.55 m for root mean square error (RMSE), 29.19 m for mean absolute error (MAE) and 0.9823 for coefficient of determination (R<sup>2</sup>). This study used the RF model to construct the shape of burned areas by predicting lengths of all straight path distances from the ignition point to the fire line. The study can dynamically capture the development of forest fire scenes. |
| format | Article |
| id | doaj-art-694fbbb09f794b778ac1f1f71b4ada30 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-694fbbb09f794b778ac1f1f71b4ada302025-01-10T13:20:22ZengMDPI AGRemote Sensing2072-42922025-01-0117114010.3390/rs17010140Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing ImagesXintao Ling0Gui Zhang1Ying Zheng2Huashun Xiao3Yongke Yang4Fang Zhou5Xin Wu6College of Forestry, Central South University of Forestry and Technology, Changsha 410004, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha 410004, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha 410004, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha 410004, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha 410004, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha 410004, ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha 410004, ChinaThe formation of forest fire burned area, influenced by a variety of factors such as meteorology, topography, vegetation, and human intervention, is a dynamic process of fire line burning that develops from the point of ignition to the boundary of the burned area. Accurately simulating and predicting this dynamic process can provide a scientific basis for forest fire control and suppression decisions. In this study, five typical forest fires located in different regions of China were used as the study object. The straight path distances from the ignition point grid to each grid on fire line in Sentinel-2 imageries for each forest fire were used as the target variables. We obtained the values of 11 independent variables for each pathway, including wind speed component, Temperature, Relative Humidity, Elevation, Slope, Aspect, Degree of Relief, Normalized Difference Vegetation Index, Vegetation Type, Fire Duration, and Gross Domestic Product reflecting human intervention capacity for fires. The value of each target variable and that of its corresponding independent variable constituted a sample. Four machine learning models, such as Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained using 80% effective samples from four forest fires, and 20% used to verify the above models. The hyper-parameters of each model were optimized using grid search method. After analyzing the validation results of models which showed temperature as a non-significant variable, the training and validation process of models above was repeated after excluding temperature. The results show that RF is the optimal model with 49.55 m for root mean square error (RMSE), 29.19 m for mean absolute error (MAE) and 0.9823 for coefficient of determination (R<sup>2</sup>). This study used the RF model to construct the shape of burned areas by predicting lengths of all straight path distances from the ignition point to the fire line. The study can dynamically capture the development of forest fire scenes.https://www.mdpi.com/2072-4292/17/1/140remote sensingforest fire impact factorsburned areasmachine learning models |
| spellingShingle | Xintao Ling Gui Zhang Ying Zheng Huashun Xiao Yongke Yang Fang Zhou Xin Wu Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images Remote Sensing remote sensing forest fire impact factors burned areas machine learning models |
| title | Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images |
| title_full | Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images |
| title_fullStr | Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images |
| title_full_unstemmed | Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images |
| title_short | Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images |
| title_sort | research on the simulation model of dynamic shape for forest fire burned area based on grid paths from satellite remote sensing images |
| topic | remote sensing forest fire impact factors burned areas machine learning models |
| url | https://www.mdpi.com/2072-4292/17/1/140 |
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