Vegetation Monitoring (NDVI/EVI)

GeoRetina AI provides comprehensive time-series vegetation monitoring capabilities through vegetation indices analysis. Like Urban Heat Island analysis, vegetation monitoring is inherently time-series based, allowing you to track plant health, growth patterns, and ecological changes across multiple years using satellite imagery from Landsat and Sentinel-2 missions.

Overview

Vegetation monitoring uses spectral indices derived from satellite imagery to assess vegetation health, density, and photosynthetic activity. The most commonly used indices are:

• NDVI (Normalized Difference Vegetation Index): Measures vegetation greenness and health
• EVI (Enhanced Vegetation Index): Provides improved sensitivity in high biomass regions and reduces atmospheric influences

Key Capabilities

Vegetation Index Analysis

Monitor vegetation conditions using proven spectral indices:

• NDVI Analysis: Track vegetation health and density changes
• EVI Analysis: Enhanced vegetation monitoring with atmospheric correction
• Temporal Analysis: Compare vegetation conditions across different time periods
• Seasonal Monitoring: Track vegetation cycles and phenological changes

Data Sources

GRAI leverages multiple satellite platforms for comprehensive vegetation monitoring:

• Landsat 8/9: 30-meter resolution imagery with 16-day revisit cycle
• Sentinel-2: 10-meter resolution imagery with 5-day revisit cycle
• Historical Data: Access to decades of archived satellite imagery

Time-Series Vegetation Monitoring with GRAI

GRAI's vegetation monitoring is inherently time-series based, allowing you to track vegetation patterns across multiple years and detect long-term ecological trends. The system automatically optimizes data collection and processing for your specific region and vegetation type.

The vegetation monitoring interface provides both tabular statistics and interactive map visualizations. The left panel displays yearly NDVI statistics including mean, median, minimum, maximum, and quartile values, while the right panel shows your study area with color-coded vegetation health overlays. GRAI automatically generates insights highlighting key trends, such as identifying the most vigorous vegetation periods and drought stress patterns.

Example query for comprehensive area analysis:

How Time-Series Monitoring Works

1. Define Your Analysis Period: Simply specify the timeframe you're interested in:

2. Automatic Seasonal Optimization: GRAI intelligently processes data based on:

   • Growing Season Focus: Automatically identifies optimal months for vegetation analysis in your region
   • Phenological Timing: Captures key growth periods and seasonal variations
   • Cloud-Free Selection: Prioritizes clear-sky observations for accurate measurements
   • Multi-Sensor Integration: Combines Landsat and Sentinel-2 data for comprehensive coverage

3. Multi-Year Analysis: The system processes data across your specified timeframe to show:

   • Vegetation trends over multiple growing seasons
   • Seasonal patterns and their year-to-year consistency
   • Growth anomalies and stress periods
   • Long-term ecosystem changes and recovery patterns

Interactive Time-Series Visualization

GRAI provides interactive charts that allow you to explore vegetation patterns over time in multiple ways:

• Point-based analysis: Click on any specific location to see detailed time-series data for that exact point
• Area-based analysis: Select and draw areas of interest to monitor vegetation trends across larger regions
• Comparative analysis: Monitor multiple areas or points simultaneously to compare vegetation patterns

The time-series visualization shows NDVI values plotted across your analysis period, making it easy to identify:

• Seasonal cycles and peak vegetation periods
• Year-to-year variations in vegetation health
• Drought stress periods with lower NDVI values
• Recovery patterns following disturbances

Intelligent Data Selection

When you don't specify exact periods, GRAI automatically selects optimal timeframes:

• Agricultural Areas: Focuses on growing season months for crop monitoring
• Forest Regions: Emphasizes peak leaf-on periods for biomass assessment
• Grasslands: Targets spring and summer months for maximum vegetation activity
• Tropical Areas: Avoids cloudy seasons while capturing vegetation dynamics

Time-Series Vegetation Monitoring Applications

Agricultural Time-Series Monitoring

Track crop performance across multiple growing seasons:

Forest Health Time-Series Assessment

Monitor forest ecosystems over multiple years:

Environmental Long-term Monitoring

Assess ecosystem changes over extended periods:

Carbon Sequestration Monitoring

Track vegetation carbon storage over time:

Understanding Vegetation Indices

NDVI (Normalized Difference Vegetation Index)

• Range: -1 to +1
• Interpretation:
  • Values near 0: Bare soil, rock, or water
  • Values 0.2-0.4: Sparse vegetation
  • Values 0.4-0.6: Moderate vegetation
  • Values 0.6-0.8: Dense vegetation
  • Values above 0.8: Very dense vegetation

EVI (Enhanced Vegetation Index)

• Range: -1 to +1
• Advantages:
  • Reduced atmospheric influences
  • Better performance in high biomass areas
  • Improved sensitivity to vegetation structure
  • Less saturation in dense vegetation

Data Quality and Considerations

Cloud Masking

GRAI automatically applies cloud masking to ensure data quality:

• Automatic Detection: Clouds and shadows are automatically identified
• Quality Filters: Poor quality pixels are excluded from analysis
• Composite Images: Multi-date composites reduce cloud impact

Temporal Compositing

For robust analysis, GRAI uses temporal compositing:

• Median Composites: Reduce noise and outliers
• Maximum Value Composites: Capture peak vegetation conditions
• Seasonal Composites: Aggregate data over specific time periods

Best Practices

Site Selection

• Consistent Areas: Use the same geographic boundaries for temporal analysis
• Representative Samples: Ensure study areas represent the vegetation type of interest
• Avoid Edge Effects: Consider buffer zones around areas of interest

Temporal Analysis

• Consistent Seasons: Compare vegetation during similar phenological stages
• Account for Weather: Consider precipitation and temperature effects
• Multi-year Trends: Use multiple years of data to identify genuine trends

Validation and Interpretation

• Ground Truth: Validate satellite observations with field measurements when possible
• Local Knowledge: Incorporate understanding of local growing conditions
• Multiple Indices: Use both NDVI and EVI for comprehensive assessment

Getting Started

To begin vegetation monitoring with GRAI:

1. Define Your Study Area: Draw your region of interest on the map
2. Specify Time Period: Choose your analysis timeframe
3. Select Vegetation Index: Choose NDVI, EVI, or both
4. Interpret Results: Use the visualization and statistical outputs to understand vegetation patterns

Interactive Point and Area Analysis

Once your vegetation analysis is complete, you can interactively explore the data by selecting specific locations or areas of interest:

Point-based Analysis: Click on any location within your study area to view a detailed time-series chart showing how NDVI values change over your specified time period at that exact point.

Area-based Analysis: Select and draw areas of interest to monitor vegetation trends across larger regions, perfect for:

• Monitoring entire crop fields or forest stands
• Comparing vegetation health between different land use areas
• Tracking large-scale ecosystem changes
• Analyzing vegetation gradients across landscapes

This combination of spatial and temporal views helps you understand both where vegetation changes occur and when they happen, providing comprehensive insights for your monitoring needs.