Modeling relationship between land surface temperature anomaly and environmental factors using GEE and Giovanni

Area of Science:

• Environmental Science
• Remote Sensing
• Climate Change Studies

Background:

• Land surface temperature (LST) and vegetation cover are key landscape indicators.
• Understanding LST anomalies, elevation, vegetation, and urban growth is crucial for conservation efforts.

Purpose of the Study:

• To investigate the relationship between LST anomalies, elevation, vegetation, and urban growth in Iran from 2001-2017.
• To analyze long-term trends in LST and vegetation indices across different land uses and seasons.

Main Methods:

• Utilized night-time satellite imagery and Google Earth Engine (GEE).
• Applied kernel methods (points aggregation) and trend analysis for quantifying point data from raster maps.
• Derived variables for summer and winter seasons in urban and natural land uses.

Main Results:

• Observed a consistent rise in LST across various elevations, seasons, and land uses.
• LST anomalies showed significant variations, with higher extremes in forests, mountains, and metropolises.
• Positive trends in LST were noted for forest and mountain uses in summer and winter, while EVI increased in mountain and desert areas.

Conclusions:

• The observed warming and increasing LST elevate the risk of natural disasters, including drought, dust storms, floods, avalanches, and fires.
• Increasing Enhanced Vegetation Index (EVI) is linked to green space initiatives.
• There is an urgent need for conservation of urban and natural environments to mitigate natural disasters and manage population growth.