Machine Learning-Based Forest Burned Area Detection with Various Input Variables: A Case Study of South Korea

Abstract

Recently, an increase in wildfire incidents has caused significant damage from economical, humanitarian, and environmental perspectives. Wildfires have increased in severity, frequency, and duration because of climate change and rising global temperatures, resulting in the release of massive volumes of greenhouse gases, the destruction of forests and associated habitats, and the damage to infrastructures. Therefore, identifying burned areas is crucial for monitoring wildfire damage. In this study, we aim at detecting forest burned areas occurring in South Korea using optical satellite images. To exploit the advantage of applying machine learning, the present study employs representative three machine learning methods, Light Gradient Boosting Machine (LightGBM), Random Forest (RF), and U-Net, to detect forest burned areas with a combination of input variables, namely Surface Reflectance (SR), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR). Two study sites of recently occurred forest fire events in South Korea were selected, and Sentinel-2 satellite images were used by considering a small scale of the forest fires. The quantitative and qualitative evaluations according to the machine learning methods and input variables were carried out. In terms of the comparison focusing on machine learning models, the U-Net showed the highest accuracy in both sites amongst the designed variants. The pre and post fire images by SR, NDVI, NBR, and difference of indices as the main inputs showed the best result. We also demonstrated that diverse landcovers may result in a poor burned area detection performance by comparing the results of the two sites.

Single-Temporal Sentinel-2 for Analyzing Burned Area Detection Methods: A Study of 14 Cases in Republic of Korea Considering Land Cover

“…However, in Area N, the detection performance was reduced owing to the mixed terrain comprising urban and forest areas. The findings are similar to those of a previous study [37]. Knopp et al [36] also reported that misclassification occurs in specific LC types, such as dark coastal areas, agricultural lands, and volcanic rocks, which appear as burned areas.…”

“…In this study, we evaluated different spectral bands and indices as well as ML and DL algorithms for automated burned area detection using Sentinel-2 single-temporal satellite imagery. Our results were comparable with those of previous studies on DL input channels [37,38], and it was confirmed that even when single-temporal images were used, high detection performance can be achieved if a high-quality dataset is available. However, the application of SI to a single image to represent forest fire damage showed a relatively low improvement in accuracy.…”

“…Knopp et al [36] analyzed Sentinel-2 images using random forest and U-Net models and determined that a five-band combination (red, green, blue, NIR, and SWIR) is optimal as the input channel. Lee et al [37] confirmed that in U-Net analysis, the addition of dNDVI and dNBR to all bands as input combinations in the pre-and post-periods resulted in the best performance for two burned areas in Republic of Korea. Tonbul et al [38] demonstrated the higher performance of a method that incorporated NBR, dNBR, and NDVI SI into DL with ML models, compared to a method that used only spectral bands.…”

Improving the Accuracy of Random Forest Classifier for Identifying Burned Areas in the Tangier-Tetouan-Al Hoceima Region Using Google Earth Engine

“…Our next step consisted of calculating the NBR for the pre-fire and post-fire periods in each area using Equation (1). To emphasize the burned areas, we computed the differenced dNBR and applied the United States Geological Survey (USGS) wildfire severity classification system based on the dNBR values [64], as shown in the following Table 4.…”

“…Our next step consisted of calculating the NBR for the pre-fire and post-fire periods in each area using Equation (1). To emphasize the burned areas, we computed the differenced dNBR and applied the United States Geological Survey (USGS) wildfire severity classification system based on the dNBR values [64], as shown in the following Table 4. We marked sample points in areas that experienced vegetation changes based on preand post-fire image visualization, as well as FIRMS data, GLAD data, and USGS classification in Tangier.…”