Land-Use/Land-Cover Change Analysis with GeoRetina AI (GRAI)

Land-use/land-cover (LULC) change analysis in GeoRetina AI is a bi-temporal analysis that compares land cover conditions between two specific time periods to identify and quantify surface transformations. Users specify two distinct years or periods, and GRAI analyzes the changes that occurred between these timeframes.

What is Land-Use/Land-Cover Change?

Land-use/land-cover change refers to the transformation of the Earth's surface due to both natural processes and human activities. Common types of changes include:

• Urbanization: Conversion of natural or agricultural areas to built-up environments
• Deforestation: Clearing of forest areas for agriculture, logging, or development
• Agricultural Expansion: Conversion of natural habitats to cropland or pasture
• Reforestation/Revegetation: Natural or artificial restoration of vegetated areas
• Water Body Changes: Expansion or contraction of lakes, reservoirs, and rivers

Monitoring these changes is crucial for sustainable development planning, resource management, and understanding environmental impacts.

Bi-Temporal LULC Change Analysis with GRAI

GRAI's bi-temporal approach compares land cover conditions between two specific time periods, providing precise change detection and quantification.

How Bi-Temporal Analysis Works

1. Define Two Time Periods: Specify the "before" and "after" periods for comparison:

2. Automatic Period Processing: GRAI processes each time period:

   • Period 1 (Before): Analyzes land cover conditions during your first specified timeframe
   • Period 2 (After): Analyzes land cover conditions during your second specified timeframe
   • Change Detection: Compares the two periods pixel-by-pixel to identify transformations

3. Flexible Time Specification: You can specify periods in various ways:

   • Year-to-Year: "between 2018 and 2022"
   • Seasonal Specific: "summer 2019 vs summer 2023"
   • Month-Specific: "January 2020 to January 2024"
   • Recent Changes: "changes in the last 3 years"

Intelligent Temporal Selection

When you don't specify exact months, GRAI automatically selects optimal time windows:

• Consistent Seasonality: Uses the same season for both periods to ensure fair comparison
• Cloud-Free Periods: Selects months with minimal cloud cover for your region
• Vegetation Optimal: Chooses periods when vegetation differences are most apparent
• Regional Climate: Considers local weather patterns (e.g., avoiding monsoon seasons)

Understanding the Results

The LULC change analysis by GRAI typically includes:

• Before and After Maps: Visualizations showing land cover classification for both time periods.
• Change Detection Map: A dedicated map highlighting areas where land cover has changed between the two dates.
• Change Statistics: Quantification of changes in area (km²) and percentage for each land cover class.
• Significant Changes: Identification of the most notable transitions (e.g., forest to urban, grassland to agriculture).

The analysis helps identify patterns, hotspots of change, and the dominant land transformation processes in the study area.

Example Bi-Temporal Analysis

Here's how GRAI handles bi-temporal land cover change analysis: