Mapping Light Pollution for Wildlife Corridors

ISEF Category: Environmental Engineering

Subcategory: Pollution Control  ·  Difficulty: Intermediate  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

Artificial light at night does more than waste energy. It can steer animals off safe routes and change when they move. You can measure that problem with tools you already know, like maps and a phone camera. The cool part is that different data sources may disagree, and that gap can become your research question.

What Is It?

Light pollution mapping means measuring how bright the night sky or the ground environment is across different places. In this project, you compare three ways to estimate that brightness. VIIRS satellite radiance gives you a wide overhead view. Globe-at-Night citizen data gives you human sky observations from many locations. A phone-camera Bortle-class classifier gives you a local, ground-level estimate from your own images.

Think of it like comparing a satellite photo, a crowd-sourced weather report, and your own window view of a storm. Each one sees the same world, but each one has blind spots. VIIRS can miss small bright spots under tree cover or near roads. Citizen data depends on who reports, where they live, and how clear the sky was that night. A phone-camera classifier adds detail at street level, which can help you estimate what wildlife in a corridor might actually experience.

Why This Is a Good Topic

This is a strong science fair topic because you can test real measurement differences, not just describe light pollution. You can compare data sources, map them in GIS, and ask whether each method tells the same story about a wildlife corridor. That connects to habitat fragmentation, animal movement, and urban planning. You can build solid analysis skills without needing a wet lab.

Research Questions

• How does VIIRS radiance compare with Globe-at-Night sky brightness scores across sites near a wildlife corridor?
• What is the effect of distance from roads on phone-camera Bortle-class estimates?
• Does canopy cover change the agreement between satellite light data and ground-based observations?
• To what extent do urban edge sites show higher light exposure than interior corridor sites?
• Which data source best predicts local nighttime brightness for habitat risk mapping?
• How does seasonal timing affect the match between citizen reports and phone-camera measurements?

Basic Materials

• Smartphone with a low-light camera mode or manual camera controls.
• Tripod or stable phone mount.
• Laptop with spreadsheet software.
• Free map viewer such as Google Earth or QGIS.
• Access to Globe-at-Night public data.
• Access to VIIRS night-light data.
• Printed field sheet for site notes.
• Compass or phone GPS app.
• Headlamp with red-light mode, if you collect photos in the field.

Advanced Materials

• Camera with manual exposure settings.
• DSLR or mirrorless camera with a wide-angle lens.
• Light meter or sky quality meter.
• GPS receiver.
• Tablet or laptop for field data entry.
• GIS software with spatial analysis tools.
• External battery pack for long field sessions.
• Calibration targets for camera consistency.
• Optional canopy cover measurement tool or hemispherical photography setup.

Software & Tools

• QGIS: Maps satellite, citizen, and phone-based light data on the same landscape.
• ImageJ: Measures brightness patterns in phone images and helps standardize your classifier inputs.
• Excel: Organizes field observations, calculates summary statistics, and makes charts.
• Google Earth Pro: Helps you place sampling sites along roads, trails, and habitat edges.
• R: Supports correlation tests, spatial summaries, and figure creation for stronger analysis.

Experiment Steps

1. Define the corridor you will study and decide how you will sample sites across roads, edges, and darker habitat interior.
2. Choose one brightness metric for each data source so you can compare them on the same scale.
3. Design a phone-photo protocol that keeps your camera position, viewing direction, and exposure settings consistent.
4. Build a comparison table that matches each site to VIIRS values, Globe-at-Night reports, and your own photo-based score.
5. Plan a map-based analysis that tests whether disagreement between methods changes with land cover, road density, or distance from development.
6. Select statistics that fit your question, such as correlation, regression, or grouped comparison, before you collect data.

Common Pitfalls

• Mixing data from different dates, which can hide real changes in seasonal lighting patterns.
• Comparing raw VIIRS pixels to phone brightness scores without rescaling them to a common metric.
• Taking phone photos with different exposure settings, which makes the classifier output inconsistent.
• Choosing sites only from bright urban areas, which gives you too little contrast to test corridor effects.
• Ignoring cloud cover or moon phase, which can change night-sky brightness and distort citizen observations.

What Makes This Competitive

A stronger project goes past simple mapping and tests where the methods agree, where they fail, and why. You can add spatial controls like canopy cover, road distance, and land use to explain differences between satellite, citizen, and phone-based measures. That kind of comparison turns a descriptive map into a real validation study. If you use careful sampling and clear statistics, your project starts to look like environmental monitoring research, not a class demo.

Project Variations

• Compare light exposure in protected parks, suburban edges, and downtown corridors to see how land use changes method agreement.
• Focus on one wildlife route, then test whether brightness spikes near crossings, fences, or trailheads.
• Replace Bortle-class scoring with a phone-based lux estimate and compare how well each ground method matches VIIRS.

Learn More

• NASA Earthdata: Search for VIIRS night lights datasets and user guides through NASA Earthdata Learn.
• NOAA Night-Time Lights: Find background on nighttime remote sensing and global light data through NOAA resources.
• Globe at Night: Use the project’s public observing database and protocol pages to access citizen science sky-brightness reports.
• USGS EarthExplorer: Search for satellite and land-cover layers that help you compare light exposure with habitat features.
• QGIS Documentation: Read the free user guide to map and analyze your spatial data.
• PubMed: Search for review articles on artificial light at night, wildlife behavior, and corridor fragmentation.