DEA Geometric Median and Median Absolute Deviation - Landsat 8 and 9 v4.0.0

projects/geoscience-aus-cat/assets/ga_ls8cls9c_gm_cyear_3
info

This dataset is part of a Publisher Catalog, and not managed by Google. Contact Geoscience Australia for bugs or view more datasets from the Geoscience Australia Catalog. Learn more about Publisher datasets.

Catalog Owner
Geoscience Australia
Dataset Availability
2013-01-01T00:00:00Z–2023-01-01T00:00:00Z
Dataset Provider
Contact
https://www.ga.gov.au/contact-us
Earth Engine Snippet
ee.ImageCollection("projects/geoscience-aus-cat/assets/ga_ls8cls9c_gm_cyear_3")
Tags
australia ga geoscience-aus-cat landsat-derived publisher-dataset

Description

This product provides statistical tools to exploit the time series of Landsat 8 and 9 data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year. For calendar years 2022 onwards, Landsat 8 and 9 are combined to offer improved performance than the standalone Landsat 8, due to using a larger number of observations.

The geometric median part of the product provides an "average" cloud-free image over the given year. The geometric median image is calculated with a multi-dimensional median, using all the spectral measurements from the satellite imagery at the same time in order to maintain the relationships among the measurements.

The median absolute deviation part of the product uses three measures of variance, each of which provides a "second order" high dimensional statistical composite for the given year. The three variance measures show how much an area varies from the "average" in terms of "distance" based on factors such as brightness and spectra:

  • Euclidean distance (EMAD)
  • Cosine (spectral) distance (SMAD)
  • Bray Curtis dissimilarity (BCMAD)

Together, they provide information on variance in the landscape over the given year and are useful for change detection applications.

For more information, please see the DEA Geometric Median and Median Absolute Deviation Landsat

More information on what has changed between the versions can be found in the changelog

This product is part of the Digital Earth Australia Program

Bands

Resolution
25 meters

Bands

Name Min Max Wavelength Description
nbart_blue 0* 10000* 0.452-0.512 μm

Band blue surface reflectance geometric median.
nbart_green 0* 10000* 0.533-0.590 μm

Band green surface reflectance geometric median.
nbart_red 0* 10000* 0.636-0.673 μm

Band red surface reflectance geometric median.
nbart_nir 0* 10000* 0.851-0.879 μm

Band near infrared surface reflectance geometric median.
nbart_swir_1 0* 10000* 1.566-1.651 μm

Band shortwave infrared 1 surface reflectance geometric median.
nbart_swir_2 0* 10000* 2.107-2.294 μm

Band shortwave infrared 2 surface reflectance geometric median.
edev 0* 10000*

The Median Absolute Deviation using Euclidean distance (EMAD). EMAD is more sensitive to changes in target brightness.
sdev 0* 10000*

The Median Absolute Deviation using Cosine (spectral) distance (SMAD). SMAD is more sensitive to change in target spectral response.
bcdev 0* 10000*

The Median Absolute Deviation using Bray Curtis dissimilarity (BCMAD). BCMAD is more sensitive to the distribution of the observation values through time.
count 0* 400*

The number of the available pixels used for calculation per calendar year.
* estimated min or max value

Terms of Use

Terms of Use

CC-BY-4.0

Citations

Citations:

  • Roberts, D., Mueller, N., & Mcintyre, A. (2017). High-dimensional pixel composites from earth observation time series. IEEE Transactions on Geoscience and Remote Sensing, 55(11), 6254-6264. doi:10.1109/TGRS.2017.2723896. Roberts, D., Dunn, B., & Mueller, N. (2018). Open data cube products using high-dimensional statistics of time series. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 8647-8650. doi:10.1109/IGARSS.2018.8518312.

DOIs

Explore with Earth Engine

Code Editor (JavaScript)

var geomedian_ls8ls9 = ee.ImageCollection(
    'projects/geoscience-aus-cat/assets/ga_ls8cls9c_gm_cyear_3');

var geometry =
    /* color: #98ff00 */
    /* displayProperties: [
      {
        "type": "rectangle"
      }
    ] */
    ee.Geometry.Polygon(
        [[
          [121.15880998755823, -15.010654451073695],
          [121.15880998755823, -18.377531570740548],
          [125.81701311255823, -18.377531570740548],
          [125.81701311255823, -15.010654451073695]
        ]],
        null, false);

var composite = geomedian_ls8ls9.filterBounds(geometry)
                    .filterDate('2018-01-01', '2019-01-01')
                    .mosaic();

var visualization = {
  bands: ['nbart_red', 'nbart_green', 'nbart_blue'],
  min: 0,
  max: 3000
};

Map.centerObject(geometry, 10);
Map.addLayer(composite, visualization, '2018 True Color Composite');
Open in Code Editor
This product provides statistical tools to exploit the time series of Landsat 8 and 9 data available in Digital Earth Australia, providing annual images of general conditions and how much an area changes for a given year. For calendar years 2022 onwards, Landsat 8 and 9 are combined to offer …
projects/geoscience-aus-cat/assets/ga_ls8cls9c_gm_cyear_3, australia,ga,geoscience-aus-cat,landsat-derived,publisher-dataset
2013-01-01T00:00:00Z/2023-01-01T00:00:00Z
-44.41 108.81 -9.13 157.82
Google Earth Engine