The abiotic and biotic conditions in forest ecosystems can be significantly influenced by forest fires. However, difficulties in policy decisions for restoration inevitably occur in the absence of information on the damaged forests, such as location, area, and burn severity. In this study, eight spectral indices calculated from Sentinel 2 MSI imagery and machine learning algorithms (Random Forest (RF) and Support Vector Machine (SVM)) were used for mapping burned areas and severity. Two study sites with similar meteorological environment (dry season) and species (coniferous vegetation) were tested, and dataset (EMSR448) from Copernicus Emergency Management Service (CEMS) was used as the reference truth. RF showed better performance for classifying pixels from classes with similar properties than SVM. Normalized Burn Ratio (NBR) and Green Normalized Difference Vegetation Index (GNDVI) showed high importance in assessing fire severity suggesting that it may be effective for identifying senescent plants. The results also confirmed that the CEMS dataset has transferability as a reference truth for fire damage classification in other regions. Implementation of this method enables fast and accurate mapping of the area and severity of destructive damage by forest fires, and also has applicability for other disasters.

Forest fires result in significant damage, including the loss of critical ecosystems and individuals that depend on forests. Remote sensing provides efficient and reliable information for forest fire detection on various scales. The purposes of this study were to produce burned-area maps and to identify the applicability of transfer learning. We produced a burned-area (BA) maps using single post-fire PlanetScope images and a deep learning (DL)-based algorithm for three cases in the Republic of Korea and Greece. Publicly accessible Copernicus Emergency Management Service and land cover maps were used as reference data for classification and validation. The DL model was trained using six schemes, including three vegetation indicators, and the data were split into training, evaluation, and validation sets based on a specified ratio. In addition, the model was applied to another site and assessed for transferability. The performance of the model was assessed using its overall accuracy. The U-Net model used in this study produced an F1-score of 0.964–0.965 and an intersection-over-union score of 0.938–0.942 for BAs. When compared with other satellite images, unburned and non-forested areas were accurately identified using PlanetScope imagery with a spatial resolution of approximately 3 m. The structure and seasonality of the vegetation in each target area were also more accurately reflected because of the higher resolution, potentially lowering the transferability. These results indicate the possibility of efficiently identifying Bas using a method based on DL with single satellite images.

Sungkyu Jeong, Byeongcheol Kim, Seonyoung Park, Eugene Chung, Soyoung Lee. Korean J. Remote Sens. 2024;40:1409-19. https://doi.org/10.7780/kjrs.2024.40.6.3.3

Accurate, detailed, and up-to-date tree species distribution information is essential for effective forest management and environmental research. However, existing tree species maps face limitations in resolution and update cycle, making it difficult to meet modern demands. To overcome these limitations, this study proposes a novel framework that utilizes existing medium-resolution national tree species maps as ‘weak labels’ and fuses multi-temporal Sentinel-2 and PlanetScope satellite imagery data. Specifically, a super-resolution (SR) technique, using PlanetScope imagery as a reference, was first applied to Sentinel-2 data to enhance its resolution to 2.5 m. Then, these enhanced Sentinel-2 bands were combined with PlanetScope bands to construct the final multi-spectral, multi-temporal input data. Deep learning (DL) model training data was constructed by strategically sampling information-rich pixels from the national tree species map. Applying the proposed methodology to Sobaeksan and Jirisan National Parks in South Korea, the performance of various machine learning (ML) and deep learning (DL) models was compared, including traditional ML (linear regression, random forest) and DL architectures (multilayer perceptron (MLP), spectral encoder block (SEB)—linear, and SEB-transformer). The MLP model demonstrated optimal performance, achieving over 85% overall accuracy (OA) and more than 81% accuracy in classifying spectrally similar and difficult-to-distinguish species, specifically Quercus mongolica (QM) and Quercus variabilis (QV). Furthermore, while spectral and temporal information were confirmed to contribute significantly to tree species classification, the contribution of spatial (texture) information was experimentally found to be limited at the 2.5 m resolution level. This study presents a practical method for creating high-resolution tree species maps scalable to the national level by fusing existing tree species maps with Sentinel-2 and PlanetScope imagery without requiring costly separate field surveys. Its significance lies in establishing a foundation that can contribute to various fields such as forest resource management, biodiversity conservation, and climate change research.

Sungkyu Jeong, Byeongcheol Kim, Seonyoung Park, Eugene Chung, Soyoung Lee. Korean J. Remote Sens. 2025;41:241-2. https://doi.org/10.7780/kjrs.2025.41.1.19

Forest fires pose significant threats to both human safety and the natural ecosystem. Detecting and accurately estimating the extent of the burned area is crucial for effective response planning. Remote sensing emerges as a valuable solution for estimating the burned area, with various satellites employed in previous studies. Unlike these satellites, microsatellites offer a promising alternative with higher spatiotemporal resolution. In this study, we utilized PlanetScope imagery and implemented the U-Net model. PlanetScope provides images at a 3m spatial resolution and revisits the same area every day, offering a distinct advantage in accurately estimating burned areas across different scales of fire events. However, PlanetScope lacks a Shortwave Infrared (SWIR) band commonly used in forest fire studies. To address this limitation, a virtual SWIR band was introduced in this study. To enhance accuracy in specific regions, a virtual SWIR band was created using machine learning techniques using the SWIR images from Landsat and Sentinel-2. Our approaches were tested in four study regions. The U-Net model was employed to generate burned area prediction maps, and each model's performance was evaluated using several metrics, including intersection-over-union (IoU), mean IoU, recall, precision, F1-Score, and the Kappa coefficient. In this study, we not only validated the effectiveness of our proposed methods but also identified the potential to enhance the accuracy of burned area estimations, particularly for microsatellites lacking a SWIR band.