Using the Sentinel-2 Satellite to monitor Deforestation

Real-Time Deforestation Monitoring in the Congo Basin with Sentinel-2 Satellite Data

Comment

Monitoring Deforestation in the Congo Basin Using Sentinel-2 Satellite Imagery

Background

The Congo Basin, one of the world’s largest carbon sinks, held over 197 million hectares of natural forest as of 2010. This invaluable ecosystem is home to countless species and is critical for carbon storage and climate regulation. However, ongoing deforestation—largely due to logging, agriculture, and illegal activities—poses severe environmental threats, resulting in significant greenhouse gas emissions and biodiversity loss.

Our project aims to use Sentinel-2 satellite data from the Copernicus Programme to systematically monitor and analyze deforestation patterns across critical areas in the Congo Basin, leveraging high-resolution optical imagery to track changes over time.

Project Objective

The primary goal of this project is to leverage Sentinel-2’s 12-band imagery to capture, analyze, and visualize forest cover changes within the Congo Basin’s national parks and reserves. We focus on critical areas where conservation efforts are underway, providing valuable insights to environmental stakeholders.

Dataset Used

  • Sentinel-2 L1C: A high-resolution dataset capturing optical imagery at spatial resolutions ranging from 10 m to 60 m, perfect for tracking land cover changes across large, forested areas like the Congo Basin.

Forest Reserves and National Parks Covered

The project specifically targets two major reserves within the Congo Basin:

  • Salonga National Park
  • Virunga National Park

These parks are selected for their critical roles in biodiversity conservation and climate regulation.

How It Works

1. Data Preparation

  • Define Areas of Interest: Using the coordinates obtained via bboxfinder.com, we establish xMin, yMin, xMax, and yMax values for targeted areas within the Congo Basin, specifying them in search_bbox for precise area boundaries.
  • Set Time Interval: The search_time_interval function is used to select relevant time periods, enabling comparative analysis of historical and current satellite data for deforestation trends.

2. Tile Selection and Retrieval

  • Identify Unique Tiles: We use the wfs_iterator function to identify unique tile IDs for each time period, ensuring comprehensive coverage of the area of interest.
  • Select Optimal Tiles: For the best image quality, tiles with the lowest cloud cover are chosen from the AWS S3 buckets, allowing for the clearest possible visualization of forest changes.

3. Band Selection and Visualization

  • Band Selection: Key Sentinel-2 bands—B01, B02, B03, B04, B07, B08, B8A, B10, B11, B12—are utilized to highlight vegetation health and density.
  • NDVI Calculation: Bands 4 and 8 are used specifically for NDVI (Normalized Difference Vegetation Index) calculations to quantify vegetation density. This index helps pinpoint deforested areas, as it highlights areas of low vegetation.

4. Data Processing and Image Plotting

  • Image Plotting: Using Python libraries such as matplotlib and rasterio, we generate time-sequenced plots that illustrate the extent of deforestation.
  • Highlight Affected Areas: By overlaying images from different time frames, we can visually highlight regions where forest cover has declined significantly, aiding in targeted conservation efforts.

Challenges

  • Data Quality: High cloud cover and occasional broken tile downloads affected the clarity of certain images, complicating analysis.
  • Limited Satellite Passes: Certain areas had limited satellite coverage, making it challenging to capture frequent, high-quality images.

Accomplishments

  • Successfully processed and visualized high-resolution images revealing extensive deforestation in targeted areas of the Congo Basin.
  • Demonstrated Sentinel-2’s capability as a tool for real-time environmental monitoring, which is crucial for conservation and intervention planning.

Lessons Learned

  • Acquired hands-on experience using Sentinel-2 datasets to monitor forest cover over time and evaluate deforestation impact with vegetation indices.
  • Gained insight into challenges and solutions for processing large-scale satellite data for environmental applications.

Future Directions

  1. Developing an API

    • An API will be created to automatically retrieve Sentinel-2 imagery and datasets from the AWS bucket, enabling regular updates for ongoing monitoring.
    • The API will serve real-time data to relevant stakeholders, including the Forestry Commission and other environmental agencies, to inform rapid response and conservation measures.

  2. Expanding Coverage

    • We plan to include additional national parks and forest reserves in the Congo Basin, expanding the monitoring network for better regional coverage.

  3. Enhanced Analysis Tools

    • Integrate advanced data visualization and machine learning models to further analyze and predict deforestation patterns.

Conclusion

Through Sentinel-2 imagery, this project offers a critical tool for real-time deforestation tracking in the Congo Basin, helping stakeholders understand and address the alarming rate of forest loss. By making timely information available to conservationists and policymakers, we can support actions to protect and restore these vital ecosystems.

Built With

Share this project:

Updates