Urban Green Space Analysis with NDVI and DeepSeek

This course introduces the fundamentals of analyzing urban green space using remote sensing data and Google Earth Engine (GEE). We'll focus on calculating the Normalized Difference Vegetation Index (NDVI) from Landsat 8 imagery to assess vegetation health in urban areas of North Morocco. Additionally, we'll explore how to leverage AI tools like DeepSeek to assist in code generation for GEE.

You need two browser tabs open side by side throughout this course.

The prompt is the core skill of this course. A good prompt includes the exact dataset ID, band names, study area coordinates, dates, preprocessing steps, and desired outputs.

Paste the following code into your GEE code editor and click Run. Read the beginner notes below to understand what each line does.

// Define the region of interest (Rif Mountains and Middle Atlas)
var roi = ee.Geometry.Polygon([
    [-5.5, 35.0], [-3.5, 35.0], [-3.5, 33.0], [-5.5, 33.0]
]);

// Define the date range
var startDate = '2022-01-01';
var endDate = '2022-12-31';

// Load Landsat 8 surface reflectance data
var landsat8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
    .filterDate(startDate, endDate)
    .filterBounds(roi);

Add this block after the previous code. It continues the analysis with additional processing and visualization.

// Function to mask clouds using the pixel_qa band
var maskClouds = function(image) {
  var qa = image.select('pixel_qa');
  var cloudBitMask = 1 << 5;
  var cloudShadowBitMask = 1 << 3;
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
      .and(qa.bitwiseAnd(cloudShadowBitMask).eq(0));
  return image.updateMask(mask);
};

// Apply cloud masking
var landsat8_masked = landsat8.map(maskClouds);

// Function to calculate NDVI
var calculateNDVI = function(image) {
  return image.normalizedDifference(['B5', 'B4']).rename('NDVI');
};

// Calculate NDVI for each image
var ndviCollection = landsat8_masked.map(calculateNDVI);

Add this block at the end of your script to export the final map as a GeoTIFF to your Google Drive. After clicking Run, go to the Tasks tab in GEE and click Run next to each export task.

// Composite the NDVI collection and export the mean NDVI image.
var ndvi_composite = ndviCollection.median();

Export.image.toDrive({
  image: ndvi_composite,
  description: 'NDVI_North_Morocco_2022',
  folder: 'GEE_Exports',
  region: roi,
  scale: 30,
  maxPixels: 1e13
});

Use DeepSeek to extend the script for each exercise below. Write a follow-up prompt describing what you want to add, then copy the generated code and paste it into your GEE script.

To get the best results from DeepSeek for GEE tasks, always include these elements in your prompts: