Impacts of Flooding on Vegetation: A Case Study of the 2025 Xinglong Mountain Flood
Description
This dataset was developed to investigate the topographic–hydrodynamic controls on vegetation responses to mountain flood disturbances in arid and semi-arid environments. The underlying research hypothesis is that terrain conditions modulate hydrological processes during flood events, which in turn drive spatial heterogeneity in vegetation dynamics. Specifically, terrain-derived hydrological indices (e.g., flow accumulation, topographic wetness) are expected to influence vegetation resistance and recovery patterns following flood disturbances. The dataset integrates multi-source geospatial data, including satellite-derived vegetation indices, digital elevation model (DEM)-derived topographic variables, and land cover information. Vegetation conditions were primarily characterized using the Normalized Difference Vegetation Index (NDVI), derived from Sentinel-2 imagery. Topographic and hydrological variables (e.g., elevation, slope, aspect, topographic wetness index) were extracted from DEM data. Land cover data were used to classify surface types and assist in stratified analysis. All data were preprocessed following standard procedures, including atmospheric correction, cloud masking, geometric correction, and spatial resampling to a consistent resolution. Terrain variables were calculated using GIS-based spatial analysis methods. The study area was further divided into terrain zones using a classification approach (e.g., Jenks natural breaks), and all raster pixels were assigned corresponding zone labels to facilitate statistical comparison. The dataset reveals clear spatial differentiation in vegetation response patterns under varying terrain conditions. Areas characterized by higher moisture accumulation potential (e.g., valley bottoms and concave slopes) tend to exhibit stronger vegetation recovery, whereas steep slopes and well-drained areas show weaker or delayed responses. These findings support the hypothesis that terrain-driven hydrological processes play a critical role in regulating vegetation dynamics in flood-affected arid mountain regions. Users of this dataset should interpret the variables in a spatially explicit context. Each raster layer represents a specific environmental factor, and pixel values correspond to measured or derived quantities at a given spatial resolution. The terrain zone classification layer can be used as a categorical variable for comparative or statistical analysis. The dataset is suitable for applications such as ecological modeling, hazard assessment, vegetation resilience analysis, and machine learning-based environmental prediction. To ensure reproducibility and correct usage, users are advised to consider the spatial resolution, temporal coverage, and preprocessing steps applied. The dataset can be directly used in GIS or remote sensing software and supports further analysis such as regression modeling, classification, or spatial statistics.
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Institutions
- Lanzhou UniversityGansu, Lanzhou