Background and Objective: In this study, a combination of remote sensing data, the Google Earth Engine (GEE) platform, statistical analyses, and Geographic Information System (GIS) was used to examine the trends in land use/land cover changes, vegetation cover, and land surface temperature (LST) over the past decade (2013–2024) in Firouzkouh County.
Methodology: Google Earth Engine (GEE), as a powerful cloud-based processing platform, provides access to a wide range of satellite datasets such as MODIS and Landsat, along with various tools for spatio-temporal analysis. To generate land surface temperature (LST) maps, the MODIS MOD11A2.061 product was used, and annual as well as decadal average LST maps were extracted. The results indicated an increase in land surface temperature in certain areas, particularly around urban zones and agricultural lands. Additionally, to assess changes in vegetation cover, the Normalized Difference Vegetation Index (NDVI) derived from Landsat and MODIS imagery was utilized. Correlation analysis between NDVI and LST was conducted using SPSS software.
Results and Findings: The results of these analyses revealed a generally inverse relationship between NDVI and LST across most areas; regions with denser vegetation cover experienced lower land surface temperatures. This finding highlights the significant role of vegetation in mitigating surface heat through processes such as evapotranspiration and shading. Subsequently, zonation maps and spatial analyses were conducted using ArcGIS, Excel, and SPSS software, allowing for the identification of critical areas experiencing either vegetation decline or temperature increase. It is recommended that future studies incorporate additional indices such as the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI) for a more detailed assessment of drought and vegetation health. Furthermore, the use of higher spatial resolution data, such as Sentinel-2 imagery, along with machine learning algorithms like Random Forest or Support Vector Machine (SVM) within the GEE environment, can enhance the accuracy of analyses and facilitate more precise identification of complex environmental patterns.