DEA Mangroves (Landsat)

Version:: 4.0.0
Type:: Derivative, Raster
Resolution:: 30 m
Coverage:: 1987 to 2024
Data updates:: Yearly frequency, Ongoing

Overview

About

Tracking changes in the canopy density of mangroves, Digital Earth Australia (DEA) Mangroves reveals how these extraordinary trees may be responding to sea level rise, severe tropical cyclones, drought, climatic cycles, changing temperatures and large storm events.

This version includes breaking changes

All tile grid references have been changed to refer to a new origin point. Learn more in the Version 4.0.0 changelog.

Access the data

For help accessing the data, see the Access tab.

See it on a map

DEA Maps

Explore data availability

DEA Explorer

Access the data on AWS

Data sources

Access the data on NCI

Data sources

View code examples

Code examples

Web Map Service (WMS)

Web services

Key specifications

For more specifications, see the Specifications tab.

Technical name	Geoscience Australia Mangrove Canopy Cover Collection 3
Bands	canopy_cover_class
Catalogue ID	145497
Currency	See currency and the latest and next update dates
Parent products	DEA Fractional Cover Percentiles (Landsat), DEA Tasseled Cap Percentiles (Landsat)
Collection	Geoscience Australia Landsat Collection 3
Licence	Creative Commons Attribution 4.0 International Licence

Cite this product

Data citation

Lymburner, L., Lucas, R., Scarth, P., Alam, I., Phillips, C., Ticehurst, C., Held, A., Bunting, P. 2021. Geoscience Australia Mangrove Canopy Cover Collection 3. Geoscience Australia, Canberra. https://pid.geoscience.gov.au/dataset/ga/145497

Paper citation

Lymburner, L., Bunting, P., Lucas, R., Scarth, P., Alam, I., Phillips, C., Ticehurst, C. and Held, A., 2020. Mapping the multi-decadal mangrove dynamics of the Australian coastline. Remote Sensing of Environment, 238, p.111185. https://doi.org/10.1016/j.rse.2019.05.004

Publications

Lymburner, L., Bunting, P., Lucas, R., Scarth, P., Alam, I., Phillips, C., Ticehurst, C., & Held, A. (2020). Mapping the multi-decadal mangrove dynamics of the Australian coastline. Remote Sensing of Environment, 238, 111185. https://doi.org/10.1016/j.rse.2019.05.004

Duke, N.C., Mackenzie, J.R., Canning, A.D., Hutley, L.B., Bourke, A.J., Kovacs, J.M., Cormier, R., Staben, G., Lymburner, L. and Ai, E., 2022. ENSO-driven extreme oscillations in mean sea level destabilise critical shoreline mangroves—An emerging threat. PLoS Climate, 1(8), p.e0000037.

Asbridge, E. et al. (2025) Characterising the short- and long-term impacts of tropical cyclones on mangroves using the Landsat archive. Cambridge Prisms: Coastal Futures, 3, p. e4. doi:10.1017/cft.2024.19.

Description

Background

DEA Mangroves was developed to address the critical need for comprehensive, long-term monitoring of Australia’s mangrove ecosystems, which face increasing pressures from climate change, coastal development, and extreme weather events.

Applications

This dataset provides annual mangrove canopy density information across Australia’s entire coastline since 1987, offering valuable insights into ecosystem dynamics. The timeseries capabilities enable effective monitoring and quantification of mangrove canopy cover changes, supporting important conservation and management decisions. The extensive spatial and temporal coverage provides opportunities to understand how mangroves respond to major disturbance events such as severe tropical cyclones. These maps enhance our understanding of the vital ecosystem services that mangroves provide, including coastal protection, carbon storage, nursery grounds for commercially important fish and prawn species, and habitat for migratory and endemic bird species.

Technical Information

The DEA Mangroves product consists of 30 m resolution annual maps of mangrove canopy cover classes. Each annual map contains one layer (band) canopy_cover_class, with values defined as follows:

Not observed (grey) — 0: insufficient observations per year to determine if mangroves exist
Woodland 20-50% (pale green) — 1: mangroves present with Planimetric Canopy Cover Percentage (PCC%) between 20% and 50%
Open forest 50-80% (mid green) — 2: mangroves present with PCC% between 50% and 80%
Closed forest 80-100% (dark green) — 3: mangroves present with PCC% above 80%
No data (transparent) — 255: no mangroves present, either PCC% below 20% or outside mangrove habitat

Lineage

The mangrove footprint is defined by combining the maximum spatial extent from two sources:

Global Mangrove Watch layers developed by the Japanese Aerospace Exploration Agency
State and Territory mangrove mapping products from Queensland, Northern Territory, and South Australia

This potential mangrove habitat is then refined using DEA Tasseled Cap Percentiles (specifically the wet_pc_10 band).

Mangrove canopy classification is performed using DEA Fractional Cover Percentiles bands:

pixels are classified by thresholding pv_pc_10 values (which serve as a proxy for PCC%) when quality assurance flag qa=2
pixels are marked as Not observed when quality assurance flag qa=1

Note: The maximum spatial extent remains consistent across all product versions. However, the mangrove habitat refinement and classification thresholds are updated and recalibrated for each version.

Processing Steps

The product development follows a three-stage methodology:

Stage 1: Determine the maximum spatial extent of mangroves

Establish the potential mangrove area by combining mangrove maps from multiple time periods (1996, 2007-2010, and 2015/16) generated through the Japanese Aerospace Exploration Agency (JAXA) Global Mangrove Watch (GMW).
Refine this combined map using State and Territory mangrove mapping products.

Stage 2: Calibrate thresholding values for mangrove canopy cover classes

Extract Planimetric Canopy Cover Percentage (PCC%) from high-resolution Light Detection and Ranging (LiDAR) data (1 m resolution).
Perform regression analysis between satellite-derived pv_pc_10 values and LiDAR-derived PCC% for corresponding locations and time periods.
Determine the specific pv_pc_10 threshold values that correspond to 20%, 50%, and 80% PCC% levels.

Stage 3: Classify mangrove canopy density

Refine the mangrove footprint by applying wet_pc_10 thresholds within the maximum spatial extent established in Stage 1.
Classify pixels within the refined mangrove footprint by applying the calibrated pv_pc_10 threshold values (from Stage 2) to assign appropriate canopy cover classes.
Assign Not observed classification to pixels where the quality assurance flag qa=1 indicates insufficient data quality.

Note:

Stage 1 is performed only once during initial product development to establish the maximum spatial extent.
Stage 2 is performed during major version upgrades to recalibrate threshold values.
Stage 3 (mangrove canopy density classification) is performed annually using the established thresholds to generate each year’s mangrove map from 1987 to present.

References

Quality

Accuracy

The accuracy limitations reported below are based on the DEA Landsat Collection 2 version 2.0.2 of the mangroves dataset. The current DEA Mangroves version 4.0.0 product uses input datasets from DEA Landsat Collection 3. The similarities and differences between these two foundation dataset collections can be reviewed here.

The primary changes affecting the Collection 3 mangroves product include:

Pixel resolution change from 25 m to 30 m.
Use of the updated Collection 3 Fractional Cover Percentiles data product.
Use of the updated Collection 3 Tasseled Cap Percentiles data product.

Due to the increased pixel spatial resolution, the reported accuracies and errors will be similar to those reported previously but not identical.

Calibration Updates

During the upgrade from Collection 2 to Collection 3, the thresholding values for mangrove canopy classes were recalibrated and adjusted to maintain consistency with the Collection 2 version of the product. The current threshold values are set at (14, 40, 62) for the pv_pc_10 parameter.

Known Limitations

The Global Mangrove Watch (GMW) polygon, created using methods described in Lucas et al. 2014, provides the consistent reference frame for this product. However, this reference dataset has inherent limitations that impact the DEA Mangroves product:
- Omission errors: Areas that were mangrove habitat prior to 2010 but were not identified as mangrove in the 2010 GMW dataset may be excluded from the DEA Mangroves time series maps.
- Commission errors: Some small areas of coastal non-mangrove woody vegetation (such as Casuarina and Melaleuca species) are incorrectly included within the GMW mangrove extent polygons. While efforts have been made to minimize these errors, some non-mangrove vegetation will still be classified as mangrove in the final product.

Quality Assurance

Product quality depends on the underlying Fractional Cover product quality. Quality issues occur during periods of limited satellite coverage.

Problem Periods:

2004-2010: Landsat 5 performance degradation.
2011-2012: Landsat 7 sensor failure.

Impact: Insufficient observations make 10th percentile calculations statistically unreliable, particularly in cloud-prone regions like the Wet Tropics where persistent cloud cover further reduces available data.

User Guidance: Exercise caution when interpreting mangrove classifications from 2004-2012, especially in high cloud frequency areas, as reduced observations may produce unreliable canopy cover estimates.

Specifications

Product ID

The Product ID is ga_ls_mangrove_cover_cyear_3. This ID is used to load data from the Open Data Cube (ODC), for example dc.load(product="ga_ls_mangrove_cover_cyear_3", ...)

Bands

Bands are distinct layers of data within a product that can be loaded using the Open Data Cube (on the DEA Sandbox or NCI) or DEA’s STAC API. Note that the Coordinate Reference System (CRS) of these bands is GDA94 / Australian Albers (EPSG:3577).

	Type	Units	Resolution	No-data	Aliases	Description
canopy_cover_class	uint8	-	30 m	255	-	The canopy cover classes are: Not observed (grey), 20-50% (pale green), 50-80% (mid green), 80-100% (dark green).

For more information on these bands, see the Description tab.

Product information

This metadata provides general information about the product.

Product ID	ga_ls_mangrove_cover_cyear_3	Used to load data from the Open Data Cube.
Short name	DEA Mangroves (Landsat)	The name that is commonly used to refer to the product.
Technical name	Geoscience Australia Mangrove Canopy Cover Collection 3	The full technical name that refers to the product and its specific provider, sensors, and collection.
Version	4.0.0	The version number of the product. See the History tab.
Lineage type	Derivative	Derivative products are derived from other products.
Spatial type	Raster	Raster data consists of a grid of pixels.
Spatial resolution	30 m	The size of the pixels in the raster.
Temporal coverage	1987 to 2024	The time span for which data is available.
Coordinate Reference System (CRS)	GDA94 / Australian Albers (EPSG:3577)	The method of mapping spatial data to the Earth’s surface.
Update frequency	Yearly	The expected frequency of data updates. Also called ‘Temporal resolution’.
Update activity	Ongoing	The activity status of data updates.
Currency	See the Currency Report	Currency is a measure based on data publishing and update frequency.
Latest and next update dates	See the Currency Report	See Table B of the report.
Catalogue ID	145497	The Data and Publications catalogue (eCat) ID.
Licence	Creative Commons Attribution 4.0 International Licence	See the Credits tab.

Product categorisation

This metadata describes how the product relates to other products.

Parent products	DEA Fractional Cover Percentiles (Landsat), DEA Tasseled Cap Percentiles (Landsat)
Collection	Geoscience Australia Landsat Collection 3
Tags	geoscience_australia_landsat_collection_3, forest, woodland, green, coast, coastal_ecosystem

Access

Access the data

DEA Maps	See it on a map	Learn how to use DEA Maps.
DEA Explorer	Explore data availability	Learn how to use the DEA Explorer.
Data sources	Access the data on AWS Access the data on NCI	Learn how to access the data via AWS.
Code examples	View code examples	Learn how to use the DEA Sandbox.
Web services	Web Map Service (WMS)	Learn how to use DEA’s web services.

History

Version history

Versions are numbered using the Semantic Versioning scheme (Major.Minor.Patch). Note that this list may include name changes and predecessor products.

v4.0.0	-	Current version
v3.0.0	of	DEA Mangrove Canopy Cover (Landsat)
v2.0.2	of	DEA Mangrove Canopy Cover (Landsat)

Changelog

30 Apr 2025: The 2024 annual data is now available

The 2024 annual data for this product was published on 30 April 2025. You are now able to access the latest data via DEA Maps and other methods. View the Tech Alert.

Version 4.0.0

Breaking change: Shift in grid origin point — The south-west origin point of the DEA Summary Product Grid has been shifted 18 tiles west and 15 tiles south. All tile grid references have changed (e.g., x10y10 is now x28y25). This affects all summary derivative products, but baseline and scene derivative products remain unaffected.
Enhanced cloud masking to reduce noise — Cloud masking improvements use 6-pixel dilation on cloud and shadows to reduce noise in source datasets. Some high-reflectance areas (sand dunes, ocean) may show increased noise or data gaps, but these occurrences are infrequent with minimal impact.
Additional inputs from Landsat 9 — Landsat 9 data incorporated from 2022 onwards.

Credits

Acknowledgments

Landsat data is provided by the United States Geological Survey (USGS) through direct reception of the data at Geoscience Australias satellite reception facility or download.

The fractional cover algorithm was developed by the Joint Remote Sensing Research Program (JRSRP) and is described in Scarth et al. (2010). While originally calibrated in Queensland, a large collaborative effort between the Department of Agriculture, the Australian Bureau of Agricultural and Resource Economics (ABARES) and State and Territory governments to collect additional calibration data has enabled the calibration to extend to the entire Australian continent. DEA Fractional Cover was made possible by new scientific and technical capabilities, the collaborative framework established by the Terrestrial Ecosystem Research Network (TERN) through the National Collaborative Research Infrastructure Strategy (NCRIS), and collaborative effort between state and Commonwealth governments.

This product is the result of a collaboration between Geoscience Australia, the University of Aberyswyth, CSIRO, the Joint Remote Sensing Research Program and the Terrestrial Ecosystem Research Network.

License and copyright

Released under Creative Commons Attribution 4.0 International Licence.