Wind barriers play a vital role in protecting saplings until maturity when planted as vegetative windbreak forests. Most previous studies have focused on the porosity of wind barriers, but no studies have simultaneously examined the effects of the porosity and inclination, despite the potential of the inclination to decrease wind speed. We tested three wind barrier cases in wind tunnel experiments: (1) Case A (porosity of 0% with inclinations (90°, 80°, and 70°)), (2) Case B (porosity of 25% with inclinations (90°, 80°, and 70°)), and (3) Case C (porosity of 50% with inclinations (90°, 80°, and 70°)). The vertical and horizontal wind velocities were measured at three vertical and seven horizontal points behind the barriers. The results demonstrated a statistically significant decrease in the correlation between the distance and mean wind velocity for all cases, with up to a six-fold wind protection effect. The wind barrier with 0% porosity and a 90° inclination provided the highest degree of wind protection. However, the wind protection range was limited downwind, and recirculation of wind flow could occur in the leeward direction, potentially damaging saplings. A wind barrier with 50% porosity and 70° inclination sufficiently decreased the wind velocity and prevented recirculation of wind flow, demonstrating that both porosity and inclination considerably impacted the wind protection effect by reducing wind velocity. Our findings offer novel insights into the influence of wind barriers with varying porosities and inclinations and can provide valuable guidance for constructing efficient windbreak forests.

Military–civilian interface zones (MCIZs) adjacent to the Korean Demilitarized Zone (DMZ) represent complex wildfire environments shaped by restricted access, intensive military activities, and adjacent civilian land use. This study develops a spatially explicit wildfire ignition risk assessment framework for the DMZ and Civilian Control Zone (CCZ) in Paju, South Korea, employing Random Forest (RF), Generalized Additive Models (GAM), and Geographically Weighted Regression (GWR) in a complementary analytical design. A dataset of 318 wildfire ignition events (2001–2024), including 78 associated with military activities, was analyzed. The RF model achieved high predictive accuracy (AUC = 0.81), identifying proximity to military training zones, relative humidity, wind speed, and proximity to built infrastructure as dominant ignition drivers. GAM revealed narrow nonlinear thresholds—relative humidity at 13.8%–14.0% and wind speed at 13.5–14.0 m/s—corresponding to peak ignition probabilities. GWR demonstrated pronounced spatial heterogeneity, with military proximity exerting a stronger influence in the eastern and northern sectors, while the meteorological effects varied geographically. Based on these outputs, a unified analytical framework was established in which RF-derived ignition probabilities were interpreted alongside GAM- and GWR-based explanatory layers to provide spatially explicit ignition susceptibility assessments without numerical map fusion. The proposed approach provides a scientifically rigorous and operationally applicable method for quantifying ignition risk in politically sensitive, access-restricted landscapes, offering valuable insights for adaptive wildfire prevention and spatially informed governance of transboundary fire risk.