Google Earth Engine: мощный инструмент для геоинформационного анализа

Google Earth Engine (GEE)

Google Earth Engine (GEE) is a cloud-based platform and geospatial data processing system developed by Google for analyzing and visualizing geographic information. It offers powerful tools for performing complex data operations such as land cover classification, climate monitoring, deforestation analysis, and more.

GEE provides a versatile environment for working with geospatial data in various formats, including raster and vector data. It offers a rich set of tools for accessing, processing, analyzing, and visualizing data.

Programming in GEE

Programming in GEE is done using the JavaScript language. Let's take a look at some code examples to better understand the capabilities of this platform.

Example 1: Building a NDVI (Normalized Difference Vegetation Index) time series to analyze vegetation dynamics over a specific area for several years.

// Loading a static Landsat dataset
var dataset = ee.ImageCollection('LANDSAT/LC08/C01/T1');

// Defining the region of interest (ROI)
var roi = ee.Geometry.Rectangle([-122.45, 37.74, -122.4, 37.8]);

// Filtering the data by ROI and time period
var filtered = dataset.filterBounds(roi)
                      .filterDate('2010-01-01', '2020-01-01');

// Calculating NDVI and building the time series
var getNDVI = function(image) {
  return image.normalizedDifference(['B5', 'B4']);
};

var ndviTimeSeries = filtered.map(getNDVI);

// Visualizing the results
print(ndviTimeSeries);

Example 2: Building a numerical terrain elevation model using SRTM (Shuttle Radar Topography Mission) data.

// Loading SRTM data
var srtm = ee.Image('CGIAR/SRTM90_V4');

// Defining the region of interest (ROI)
var roi = ee.Geometry.Rectangle([120.0, 30.0, 122.0, 32.0]);

// Clipping the data within the ROI
var elevation = srtm.clip(roi);

// Visualizing the terrain elevation model
var visParams = {
  min: 0,
  max: 3000
};

Map.addLayer(elevation, visParams, 'Elevation');

Example 3: Building a forest index time series to monitor changes in forest cover.

// Loading forest defoliation index (FRI) dataset
var dataset = ee.ImageCollection('ESA/CCI/FIRECCI/5_1');

// Defining the region of interest (ROI)
var roi = ee.Geometry.Point(-80.0, 25.0);

// Filtering the data by ROI and time period
var filtered = dataset.filterBounds(roi)
                      .filterDate('2000-01-01', '2022-01-01');

// Calculating the average forest defoliation index
var meanFRI = filtered.mean();

// Visualizing the results
var visParams = {
  min: 0,
  max: 100
};

Map.addLayer(meanFRI, visParams, 'Mean FRI');