Dataset for landscape pattern analysis from a climatic perspective

Area of Science:

• Landscape Ecology
• Remote Sensing
• Climatology

Background:

• Understanding landscape pattern changes is vital in landscape ecology.
• Climate variables like temperature and precipitation significantly influence vegetation dynamics.
• A comprehensive dataset linking climate and vegetation is needed for large-scale spatio-temporal analysis.

Purpose of the Study:

• To create a dataset quantifying the relationship between climate variables and vegetation greenness.
• To support the analysis of spatio-temporal vegetation changes using satellite-derived indices and climate data.
• To provide both spatial (maps) and tabular data for Hungary.

Main Methods:

• Bivariate linear regression analysis was performed.
• The Normalized Difference Vegetation Index (NDVI) from MODIS/TERRA was used as the target variable.
• Climatic variables (temperature, aridification index, evapotranspiration, precipitation) from CarpatClim, along with environmental data (terrain height, land cover), served as predictor variables.

Main Results:

• The dataset includes R-squared values from regression analyses, indicating the strength of relationships between NDVI and climate variables.
• Spatial maps and tabular data are provided for Hungary.
• The data facilitates investigation into both statistical and spatial aspects of vegetation-climate interactions.

Conclusions:

• The generated dataset is a valuable resource for landscape ecology and climate change research.
• It enables detailed studies on vegetation dynamics in response to climatic shifts.
• The integrated approach supports understanding of landscape patterns across Hungary.