Accurate and rapid delineation of wildfire-affected areas is essential in the era of climate-driven increases in fire frequency. This study compares and analyzes four techniques for identifying wildfire-affected areas using Sentinel-2 satellite imagery: (1) calibrated differenced Normalized Burn Ratio (dNBR); (2) differenced NDVI (dNDVI) with empirically defined thresholds (0.04–0.18); (3) supervised SVM classifiers applying linear, polynomial, and RBF kernels; and (4) unsupervised ISODATA clustering. In particular, this study proposes an SVM-based classification method that goes beyond conventional index- and threshold-based approaches by directly using the SWIR, NIR, and RED band values of Sentinel-2 as input variables. It also examines the potential of the ISODATA method, which can rapidly classify affected areas without a training process and further assess burn severity through a two-step clustering procedure. The experimental results showed that SVM was able to effectively identify affected areas using only post-fire imagery, and that ISODATA enabled fast classification and severity analysis without training data. This study performed a wildfire damage analysis through a comparison of various techniques and presents a data-driven framework that can be utilized in future wildfire response and policy-oriented recovery support.

The rapid decline of Arctic sea ice is increasing the feasibility of trans-Arctic shipping along the Northern Sea Route (NSR)—a considerably shorter and potentially lower-emission alternative to the Suez Canal route—for which the Bering Strait is the first gateway chokepoint for vessels departing the Republic of Korea. Continuous optical monitoring of this region is fundamentally limited by cloud cover and polar night during the ice season; in 2025, no usable Sentinel-2 MultiSpectral Instrument (MSI) scene was available for January (mean cloud cover 56.9% in November). This study therefore used all-weather Sentinel-1 dual-polarization (VV, VH) C-band SAR, acquired monthly through Google Earth Engine for January–December 2025, to classify open water, thin ice (<30 cm), and first-year ice (FYI, ≥30 cm) with a Support Vector Machine (SVM); land was masked prior to classification. Three kernels (linear, radial basis function (RBF), and polynomial) were compared after $5\times 5$ Lee speckle filtering, using VV, VH, and the VV/VH ratio (in dB) as input features. The RBF kernel achieved the highest accuracy (overall accuracy 97.0%, $\kappa =0.954$), and the classification was cross-referenced against the U.S. National Snow and Ice Data Center (NSIDC) Multisensor Analyzed Sea Ice Extent (MASIE) mask. The monthly ice maps were then reclassified into World Meteorological Organization (WMO)/POLARIS ice types and converted into a POLARIS Risk Index Outcome (RIO) cost surface using simplified proxy weights consistent with the POLARIS risk ordering rather than the full Risk Index Value table; least-cost paths and a seasonal navigability assessment were derived for three representative ship ice classes—a non-ice-class vessel, an ice-class 1A merchant vessel (≈PC7), and the icebreaker Araon (≈PC5)—under risk-index, water-depth, and coastal-buffer constraints. For a representative refreezing-onset scene, the routing outcome was strongly ice-class dependent: the non-ice-class vessel and the ice-class 1A vessel were blocked (after 42 and 139 km, respectively), whereas only the icebreaker Araon completed the transit along a 124 km least-cost path within a 2 km safety corridor. The framework demonstrates how an interpretable SAR-based sea-ice classification can be coupled with a recognized international risk standard to support risk-informed navigation through the Bering Strait gateway. By indicating when shorter, lower-emission Arctic transits are feasible while limiting the risk of ice-related accidents in this sensitive polar environment, the framework also contributes to the safety and environmental sustainability of trans-Arctic shipping.