Derived 500 m Grid Dataset for Post-fire Vegetation Recovery Analysis of the 2022 Uljin Wildfire, South Korea

Description

This dataset provides the derived 500 m grid-based variables used for post-fire vegetation recovery analysis of the 2022 Uljin wildfire, South Korea. It includes annual Landsat-derived dNBR values for 2022–2025, time-lagged seasonal meteorological variables, topographic variables, and forest-structure dummy variables for 1,793 analytical grid cells. The dataset supports reproducibility of the associated manuscript on driver-specific temporal shifts in post-fire vegetation recovery.

Steps to reproduce

Annual dNBR values for 2022–2025 were derived from Landsat 8/9 imagery and integrated with meteorological, topographic, and forest-structure variables in a 500 m grid framework. The burned-area boundary was delineated using April 2022 dNBR ≥ 0.1 and expanded with a 2 km buffer. AWS meteorological data were interpolated using IDW and aggregated into seasonal variables with a one-year lag structure. Elevation, slope, and northness were derived from a 30 m DEM, and forest-structure attributes were assigned using spatial overlay based on the dominant category by area. Categorical variables were converted into dummy variables for OLS regression. The final dataset contains 1,793 analytical grid cells.

Institutions

• Kyungpook National University

  Daegu, Daegu
• Kyungil University

  Gyeongsangbuk-do, Gyeongsan-si
• Chosun University

  Gwangju, Gwangju

Categories

Funders

• National Research Foundation of Korea

  Grant ID: RS-2024-00460627

Licence