Phase 1: Data Acquisition & Preprocessing (Google Earth Engine)

Objective: Create clean, analysis-ready satellite data

Process:

1. Boundary Definition: Loaded Sundarbans mangrove forest polygon

2. Satellite Collection: Accessed Sentinel-2 surface reflectance data (2016-2024)

3. Cloud Masking: Implemented advanced Cloud Score+ algorithm (60% threshold)

4. NDVI Calculation: Computed vegetation index using Red (B4) and NIR (B8) bands

5. Monthly Composites: Generated median values for each month to reduce noise

6. Data Export: Created multiband GeoTIFF with 87 monthly layers

Technical Innovation: Used Cloud Score+ for superior cloud detection over traditional QA60 band

Phase 2: Advanced Spatial-Temporal Analysis (Python)

Objective: Extract meaningful ecological insights from satellite data

Process:

1. Data Quality Control: Filtered to complete 2016-2022 period (84 months)

2. Trend Analysis: Linear regression to quantify annual mangrove health changes

3. Seasonal Decomposition: Identified monthly growth patterns and stress periods

4. Spatial Analysis: Mapped health distribution and variability across the forest

5. Change Detection: Pixel-wise comparison between 2016 vs 2022

6. Statistical Validation: R² scoring and confidence interval assessment

KEY OUTCOMES & INTERPRETATION

The Central Finding:

"Broad conservation success masks localized ecological crises"

Statistical Evidence:

• Positive Trend: +0.0063 NDVI/year (general improvement)

• Spatial Reality: 23% of mangroves declined significantly vs 12% improved

• Critical Insight: Widespread modest gains overwhelmed by concentrated severe losses

Ecological Interpretation:

"Think of it as a hospital where average patient health is improving, but the ICU is filling up with critical cases"

Seasonal Intelligence:

• Optimal Growth: March (NDVI: 0.333) - pre-monsoon peak

• Critical Stress: April (NDVI: 0.229) - 31% drop from peak

• Management Implication: Target interventions pre-April to mitigate stress

REAL-WORLD TRANSLATION & IMPACT

For Conservation Managers:

1. Resource Allocation: Focus on 2.4 million declining pixels (eastern sectors)

2. Timed Interventions: Schedule activities before April stress period

3. Success Replication: Study the 1.2 million improved pixels

4. Monitoring Framework: Implement early warning for decline hotspots

For Policy Makers:

1. Evidence-Based Funding: Direct resources to areas showing actual decline

2. Climate Adaptation: Develop species resilient to April dry conditions

3. International Reporting: Quantify conservation progress for climate agreements

For Local Communities:

1. Ecosystem Services: Protect mangrove benefits (storm buffering, fisheries)

2. Livelihood Security: Maintain crab and fish habitats

3. Climate Resilience: Preserve natural coastal protection

   GitHub Link: https://github.com/mohammadoney/Sundarbans-7-Year-Satellite-Monitoring/tree/main