Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2025-01, Vol.17 (1), p.140
• Main Authors: Ling, Xintao, Zhang, Gui, Zheng, Ying, Xiao, Huashun, Yang, Yongke, Zhou, Fang, Wu, Xin
• Format: Article
• Language: English
• Subjects: Accuracy; burned areas; Decision trees; Error analysis; Forest & brush fires; forest fire impact factors; Forest fires; Humidity; Ignition; Independent variables; Machine learning; machine learning models; Mathematical models; Multilayer perceptrons; Neural networks; Normalized difference vegetative index; Predictive control; Regions; Relative humidity; Remote sensing; Root-mean-square errors; Satellite imagery; Simulation; Simulation models; Support vector machines; Surveillance; Temperature perception; Topography; Trends; Vegetation; Vegetation type; Wind speed
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17010140

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 (R2). 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.