Drought Browning in Parks With NDVI

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Drought Browning in Parks With NDVI

ISEF Category: Plant Sciences

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Subcategory: Ecology  ·  Difficulty: Advanced  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

A forest can look healthy from a trail, then quietly lose green cover across acres you never notice on foot. Satellite data can catch that change before your eyes do. That makes drought browning a strong science fair topic, because you can test it with real data from real parks.

What Is It?

This project studies browning, which means vegetation losing greenness during drought stress. You are not counting leaves by hand. You are using satellite images to measure how green an area looks over time.

The main metric is NDVI, or Normalized Difference Vegetation Index. Think of it like a plant health score made from light reflected by leaves. Healthy plants usually reflect more near-infrared light and absorb more red light, so NDVI rises when vegetation is greener and falls when it is stressed.

You can pair that signal with fire-return interval data, which tells you how often a place tends to burn. That helps you ask whether some parks or habitats recover differently after drought, or whether frequent fire history lines up with stronger browning patterns.

Why This Is a Good Topic

This is a good science fair topic because it uses public data, clear measurements, and a real environmental problem. Drought affects forests, wildlife habitat, and fire risk, so your question connects to something people actually care about. You can build a project around trend detection, comparison between parks, or before-and-after drought events, all without needing a wet lab.

Research Questions

  • How does drought severity affect NDVI browning trends in U.S. national parks?
  • What is the effect of fire-return interval on the strength of NDVI decline during drought periods?
  • Does vegetation browning recover faster in parks with shorter fire-return intervals?
  • To what extent do NDVI trends differ between parks in arid, semi-arid, and humid regions?
  • Which national parks show the largest NDVI drops during the same drought year?
  • How does the timing of browning differ between evergreen-dominated and mixed-vegetation parks?

Basic Materials

  • Computer with internet access.
  • Google Earth Engine account.
  • Spreadsheet software or Google Sheets.
  • U.S. National Park boundary data from a public source.
  • Landsat NDVI time-series data in Google Earth Engine.
  • Fire-return interval data for selected parks.
  • Notebook for planning variables, controls, and hypotheses.
  • Basic statistics reference sheet or class notes on trend lines.

Advanced Materials

  • Computer with internet access and enough memory for geospatial analysis.
  • Google Earth Engine account.
  • Python with pandas, numpy, scipy, matplotlib, and geopandas.
  • QGIS for map checks and visual comparison.
  • Park boundary shapefiles from a public source.
  • Landsat surface reflectance collections in Google Earth Engine.
  • Drought index data from NOAA or USGS sources.
  • Fire-return interval or fire history datasets for comparison.
  • ImageJ or similar image analysis software for validating visual outputs.

Software & Tools

  • Google Earth Engine: Processes Landsat imagery, calculates NDVI time-series, and compares park-scale trends.
  • QGIS: Lets you inspect park boundaries, visualize spatial patterns, and sanity-check outputs.
  • Python: Helps you clean data, run trend tests, and make publication-style graphs.
  • Google Sheets: Works well for quick tables, summaries, and simple plots.
  • ImageJ: Supports spot checks on image-based visual outputs when you want an extra measurement view.

Experiment Steps

  1. Define the park set, the drought periods, and the vegetation outcome you will measure.
  2. Choose one NDVI summary method, such as seasonal average, anomaly, or trend slope, and keep it consistent.
  3. Build a comparison plan that matches parks by region, vegetation type, or fire history so your results are fair.
  4. Decide how you will link fire-return interval data to each park, and how you will handle missing or uneven records.
  5. Plan your statistics before you collect results, so you know whether you need correlations, group comparisons, or regression.
  6. Map your outputs and check whether the patterns make ecological sense, not just numerical sense.

Common Pitfalls

  • Mixing different Landsat products or date ranges, which makes NDVI values hard to compare across years.
  • Using park-wide averages without checking cloud cover or seasonal timing, which can hide real browning signals.
  • Comparing parks with very different vegetation types, which can make climate effects look stronger or weaker than they are.
  • Treating fire-return interval data as exact for every part of a park, which ignores local variation inside the boundary.
  • Running many comparisons without correcting for multiple tests, which can make random noise look like a real pattern.

What Makes This Competitive

A strong version of this project does more than map where browning happened. You can compare multiple drought periods, test whether fire history changes the size of the NDVI drop, and use a clear statistical model instead of eyeballing graphs. You can also improve the project by separating climate stress from seasonal change and by checking whether the same pattern appears across different park types.

Project Variations

  • Compare drought browning in deserts versus forests to test whether biome type changes NDVI response.
  • Use burn severity or recent fire history instead of fire-return interval to ask how disturbance history shifts recovery.
  • Focus on a single park and compare inner zones, edge zones, and riparian areas to look for within-park differences.

Learn More

  • NASA Landsat Science: Find background on Landsat sensors, vegetation indices, and how satellite reflectance works on NASA's public Landsat pages.
  • Google Earth Engine Documentation: Read the official guides for loading imagery, calculating NDVI, and building time-series workflows.
  • USGS Land Remote Sensing Program: Use this for Landsat data background, band definitions, and surface reflectance explanations.
  • NOAA National Centers for Environmental Information: Look up drought indices and climate records that can help you define drought periods.
  • US National Park Service Data and Maps: Find park boundary data, resource reports, and park-specific environmental context.
  • PubMed: Search for review articles on vegetation drought stress, remote sensing, and fire ecology to compare your results with published studies.
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