Lab 16 - Zonal Statistics | Remote Sensing Course

What You'll Learn

Define an Area of Interest (AOI) using geometry tools
Use the reduceRegion() function to calculate zonal statistics
Understand different reducers (mean, min, max, stdDev)
Create and customize histograms with ui.Chart
Handle maxPixels errors for large regions

Building On Previous Learning

This lab extends Lab 15 where you loaded and visualized SRTM elevation data. Now you'll extract meaningful statistics from that data.

Why This Matters

Zonal statistics are fundamental for quantitative analysis:

Watershed analysis: Calculate average elevation, relief, etc.
Land management: Summarize vegetation indices by parcel or county
Climate studies: Extract mean temperature for regions
Impact assessment: Compare before/after statistics

Before You Start

Prerequisites: Complete Lab 15 and ensure your SRTM data is loaded.
Estimated time: 60 minutes
Materials: Earth Engine access, Lab 15 code, and a spreadsheet for recording statistics.

Key Terms

Zonal Statistics: Summary statistics (mean, sum, etc.) calculated for pixels within a defined zone or region.
Reducer: A GEE object that aggregates data (e.g., ee.Reducer.mean(), ee.Reducer.max()).
reduceRegion(): A method that applies a reducer to all pixels within a geometry and returns a dictionary of results.
Histogram: A chart showing the frequency distribution of values in a dataset.

Step 1: Load SRTM Data

Start with your Lab 15 code or use this starter:

// Load SRTM elevation data
var srtm = ee.Image("USGS/SRTMGL1_003");

// Center on Grand Canyon
Map.setCenter(-112.8598, 36.2841, 9);

// Display the elevation
Map.addLayer(srtm, {min: 0, max: 3000, palette: ['blue', 'green', 'yellow', 'red']}, 'Elevation');

Step 2: Define Your Area of Interest

Use the geometry tools to draw an AOI:

Open the Geometry Tools panel in the Code Editor
Click on the rectangle tool
Draw a rectangle on the map
Rename the geometry to 'aoi' in the imports section

What is an AOI? An Area of Interest is a specific geographic area that defines the focus of your analysis. All statistics will be calculated only for pixels within this boundary.

Step 3: Calculate Zonal Statistics

Use reduceRegion() with different reducers:

// Calculate maximum elevation
var max = srtm.reduceRegion({
  reducer: ee.Reducer.max(),
  geometry: aoi,
  scale: 90
});

// Calculate mean elevation
var mean = srtm.reduceRegion({
  reducer: ee.Reducer.mean(),
  geometry: aoi,
  scale: 90
});

// Calculate minimum elevation
var min = srtm.reduceRegion({
  reducer: ee.Reducer.min(),
  geometry: aoi,
  scale: 90
});

// Calculate standard deviation
var stdd = srtm.reduceRegion({
  reducer: ee.Reducer.stdDev(),
  geometry: aoi,
  scale: 90
});

// Print all statistics
print('Max:', max, 'Min:', min, 'Mean:', mean, 'StdDev:', stdd);

reduceRegion() Parameters

Parameter	Description	Example
`reducer`	The aggregation method to use	ee.Reducer.mean()
`geometry`	The region to analyze	aoi (your drawn polygon)
`scale`	Resolution in meters	90
`maxPixels`	Maximum pixels to process (optional)	1e13

Common Reducers

Reducer	What It Calculates
`ee.Reducer.mean()`	Average value
`ee.Reducer.min()`	Minimum value
`ee.Reducer.max()`	Maximum value
`ee.Reducer.stdDev()`	Standard deviation
`ee.Reducer.sum()`	Sum of all values
`ee.Reducer.count()`	Number of pixels
`ee.Reducer.median()`	Median value

Step 4: Create a Histogram

Visualize the distribution of elevation values:

// Create histogram
var histogram = ui.Chart.image.histogram({
  image: srtm,
  region: aoi, 
  scale: 90,
  minBucketWidth: 10
});

// Customize the histogram appearance
histogram.setOptions({
  title: 'Histogram of Elevation (meters)', 
  vAxis: {title: 'Frequency'},
  hAxis: {title: 'Elevation (m)'},
  legend: {position: 'none'},
  colors: ['#1a73e8']
});

// Print the histogram to the console
print(histogram);

Check Your Understanding

What does the scale parameter control in reduceRegion()?
If your AOI contains 10,000 pixels and you use ee.Reducer.mean(), how many values will be returned?
What would a histogram with two peaks (bimodal distribution) suggest about your landscape?
How would you calculate the total relief (max - min) in your AOI?

Troubleshooting

Problem: "User memory limit exceeded" or maxPixels error

Solution: Add maxPixels: 1e13 to your reduceRegion() call, or increase the scale value to sample fewer pixels.

Problem: Histogram doesn't appear

Solution: Check that your AOI is defined and has the correct variable name. Make sure you're using print(histogram) not Map.addLayer().

Problem: Statistics return null

Solution: Your AOI may not overlap with the image, or the scale is too coarse. Try a smaller scale value or check your geometry.

Pro Tips

Combine reducers: Use ee.Reducer.mean().combine() to calculate multiple stats in one call
Use reduceRegions(): For multiple zones, use reduceRegions() with a FeatureCollection
Export to CSV: Use Export.table.toDrive() to save statistics
Best scale: Use the native resolution of your data when possible

Key Takeaways

reduceRegion() calculates statistics for all pixels within a geometry
Different reducers provide different summary statistics
Histograms help visualize the distribution of values
Use maxPixels to handle large regions or reduce scale

📋 Lab Submission

Subject: Lab 16 - Zonal Statistics - [Your Name]

Submit:

A shareable URL to your GEE code that includes:

SRTM data loaded and displayed
AOI defined with geometry tools
All four statistics calculated (min, max, mean, stdDev)
Histogram created and customized
Comments explaining your code