Landsat 8 & 9 (USGS/NASA)¶

Provider

USGS / NASA

Website

earthexplorer.usgs.gov | GEE

Access Level

🟡 Free Registration (USGS) | 🔵 GEE

Resolution

30m (multispectral), 15m (panchromatic)

Why Landsat Is Unique¶

Landsat's superpower is its long archive. No other free satellite has data going back to 1972. This makes it perfect for:

Tracking urban growth from 1980 to 2024
Measuring lake and reservoir shrinkage over decades
Detecting deforestation going back 30+ years
Analysing agricultural change across policy periods

Landsat Bands (Landsat 8/9 OLI)¶

Band	Name	Wavelength	Key Use
B1	Coastal/Aerosol	443nm	Water, haze
B2	Blue	482nm	True colour
B3	Green	562nm	True colour
B4	Red	655nm	True colour, NDVI
B5	Near Infrared (NIR)	865nm	NDVI, vegetation
B6	SWIR-1	1610nm	Soil moisture, burn
B7	SWIR-2	2200nm	Geology
B8	Panchromatic	591nm	15m resolution sharpening
B10	Thermal Infrared	10.9μm	Land Surface Temperature
B11	Thermal Infrared	12.0μm	Land Surface Temperature

Access Methods¶

Method 1: USGS EarthExplorer (Direct Download) 🟡¶

Register at earthexplorer.usgs.gov
Draw your area of interest on the map
Select dates and cloud cover threshold
Under "Data Sets" → Select "Landsat Collection 2 Level 2" (atmospherically corrected)
View results → Click Download → Choose GeoTIFF

Each Landsat scene is ~900 MB for all bands. Download only your area with spatial subsetting.

Method 2: Google Earth Engine (Best for Trends) 🔵¶

// GEE: Landsat NDVI Trend Analysis 2000–2023 for a Forest Area
var forest_point = ee.Geometry.Point([76.5, 12.0]); // Western Ghats

// Get annual NDVI values (dry season = March)
var years = ee.List.sequence(2000, 2023);

var annualNDVI = years.map(function(year) {
  var l8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
    .merge(ee.ImageCollection("LANDSAT/LC09/C02/T1_L2"))
    .filterBounds(forest_point)
    .filter(ee.Filter.calendarRange(year, year, 'year'))
    .filter(ee.Filter.calendarRange(2, 4, 'month'))  // Feb-Apr (dry season)
    .filter(ee.Filter.lt('CLOUD_COVER', 20))
    .median()
    .multiply(0.0000275).add(-0.2);  // Scale factor for Collection 2

  var ndvi = l8.normalizedDifference(['SR_B5', 'SR_B4']);
  var mean = ndvi.reduceRegion({
    reducer: ee.Reducer.mean(),
    geometry: forest_point.buffer(5000),
    scale: 30
  });
  return ee.Feature(null, {'year': year, 'NDVI': mean.get('nd')});
});

var chart = ui.Chart.feature.byFeature(
  ee.FeatureCollection(annualNDVI), 'year', 'NDVI'
).setOptions({
  title: 'Annual NDVI Trend — Western Ghats (2000-2023)',
  hAxis: {title: 'Year'},
  vAxis: {title: 'Mean NDVI', minValue: 0.3, maxValue: 0.8}
});
print(chart);

✏️ Practice Exercise¶

Exercise 5.4 — Track Urban Expansion 2000 to 2024

Goal: See how much your city has grown using Landsat imagery.

// Compare Landsat images of your city — 2000 vs 2024
var city = ee.Geometry.Point([77.5946, 12.9716]); // Bangalore — change to yours

function getLandsat(year) {
  return ee.ImageCollection("LANDSAT/LE07/C02/T1_L2")
    .merge(ee.ImageCollection("LANDSAT/LC08/C02/T1_L2"))
    .filterBounds(city)
    .filter(ee.Filter.calendarRange(year, year, 'year'))
    .filter(ee.Filter.calendarRange(11, 2, 'month'))  // Winter
    .filter(ee.Filter.lt('CLOUD_COVER', 20))
    .median()
    .multiply(0.0000275).add(-0.2);
}

var img2000 = getLandsat(2000);
var img2024 = getLandsat(2024);

var vis = {bands: ['SR_B4', 'SR_B3', 'SR_B2'], min: 0, max: 0.3};
Map.centerObject(city, 11);
Map.addLayer(img2000, vis, '2000 — True Colour');
Map.addLayer(img2024, vis, '2024 — True Colour');

Toggle the 2000 and 2024 layers to compare — where did the city expand?

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