Efficient large-scale land cover change detection using Google Earth Engine: Climate-driven vegetation dynamics in...

Area of Science:

• Ecology
• Climate Science
• Remote Sensing

Background:

• Monitoring land cover dynamics and vegetation response to climate change is crucial for dryland ecosystems.
• Drylands are sensitive to climate variability and land use, requiring detailed assessment.

Purpose of the Study:

• To analyze land cover dynamics, vegetation transitions, and climatic drivers in Asian drylands (2001-2022).
• To provide a scalable framework for assessing dryland ecosystem stability under climate change.

Main Methods:

• Integrated MODIS land cover data, TerraClimate climate data, and Google Earth Engine (GEE).
• Employed land cover dynamic indices, transition probability, transfer matrix analyses, and climate attribution.

Main Results:

• Observed expansion of grasslands, savannas, croplands, and water/snow/ice; contraction of shrublands, mixed forests, wetlands, and barren land.
• Identified barren land to grassland and grassland to cropland as dominant transitions.
• Climate attribution revealed distinct vegetation responses to temperature, soil moisture, precipitation, and radiation changes.

Conclusions:

• Land cover and vegetation in Asian drylands are significantly changing due to climate variability and land use.
• Findings enhance understanding of dryland ecosystem dynamics and support ecological assessment in data-scarce regions.