Sunday, July 30, 2023

Google Earth Engine Code for Mapping Land Use Classifications

Mapping land use classifications is a fundamental task in environmental monitoring, urban planning, and natural resource management. With the advent of advanced geospatial technologies and the availability of satellite imagery, Google Earth Engine has emerged as a powerful platform for conducting land use analysis at regional and global scales. By harnessing its vast collection of satellite data and machine learning capabilities, researchers and policymakers can gain valuable insights into the distribution and dynamics of land use patterns. Below is the process of using Google Earth Engine to map land use classifications, providing step-by-step guidelines and a working example code to facilitate the creation of accurate and informative land use maps.

 // Define the region of interest (ROI) for the land use classification
// Change the polygon coordinates for your study area
var roi = ee.Geometry.Polygon(
        [[[-122.475, 37.768], [-122.483, 37.759], [-122.490, 37.768]]]);

// Load the satellite imagery (Landsat) for the region and time period of interest
// Change the dates of preference
var image = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
             .filterDate('2022-01-01', '2022-12-31')

// Define the land use classes (e.g., urban, agriculture, forest, water)
var landUseClasses = [
  {name: 'Urban', value: 1},
  {name: 'Agriculture', value: 2},
  {name: 'Forest', value: 3},
  {name: 'Water', value: 4}

// Create training data by selecting sample points for each land use class
var trainingData = urban.merge(agriculture).merge(forest).merge(water);

// Perform land use classification using Random Forest algorithm
var classifier = ee.Classifier.randomForest(10).train(trainingData, 'class');
var classifiedImage = image.classify(classifier);

// Display the land use classification on the map
Map.centerObject(roi, 12);
Map.addLayer(classifiedImage, {min: 1, max: 4, palette: ['blue', 'green', 'red', 'cyan']}, 'Land Use Classification');

// Export the land use classification image to Google Drive
  image: classifiedImage,
  description: 'Land_Use_Classification',
  scale: 30,
  region: roi

Please note that this example assumes you have already prepared the training data (sample points) for each land use class. The code uses the Landsat 8 Surface Reflectance product as input imagery and applies the Random Forest algorithm for land use classification. Additionally, it exports the classified image to Google Drive for further analysis or visualization.

For a more complete and accurate land use classification, you will need to customize the code based on your specific study area and the complexity of land use classes you want to map. You may also need to adjust the parameters of the classifier and pre-process the imagery to suit your analysis requirements.


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