• Fire Science and Engineering
• /
• v.14 no.3
• /
• pp.27-32
• /
• 2000

The assessment of wildland fire hazard is the first priority to be considered in the prevention, extinction and control of wildland fire. For the standard to measure wildland fire hazard, the wildland fire Warning System is currently being used in Korea which computes the wildland fire occurrence hazard index through a stick weight to moisture conversion formula. It shows the risk of fuel substance being exposed to fire by meteorological factors. For a comprehensive assessment of wildland fire hazards by area, the major factors'hazards need to be measured and the assessment of wildland fire needs to be conducted through historical statistic examination. Therefore, the wildland (ire outbreak frequency and its seriousness of damage are analyzed through historical statistic examination to conduct the assessment of a wildland fire hazard, and then the calorific value of a forest is analyzed through differential scanning calorimeter measurement which assesses the comparative calorific hazard according to the geographical distribution of vegetation.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_1
• /
• pp.1413-1425
• /
• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.