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Metadata : Mangrove extent mapping across the Darwin Harbour Integrated Marine Monitoring and Research Program (DHIMMRP) |
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Metadata Details:
Name:AS/NZS ISO 19115 Geographic Information - Metadata, ANZLIC Metadata Profile
Version:1.0
Date Metadata Extracted:2024-10-28
Date Metadata Last Updated:2024-10-08
Current URL (HTML format) : http://www.ntlis.nt.gov.au/metadata/export_data?type=html&metadata_id=23EC4E21E4412A1DE0632144CD9BD709
Current URL (XML format) : http://www.ntlis.nt.gov.au/metadata/export_data?type=xml&metadata_id=23EC4E21E4412A1DE0632144CD9BD709
Citation
ANZLIC Identifier:23EC4E21E4412A1DE0632144CD9BD709
Title: Mangrove extent mapping across the Darwin Harbour Integrated Marine Monitoring and Research Program (DHIMMRP)
Citation Date:2024-10-08
Date Type:creation
Custodian:Department of Environment, Parks and Water Security
Description
Mangrove extent mapping is a dataset that consists of mangrove extent classification derived from annual and seasonal composites. The dataset intend for applications like mapping mangrove monitoring, modelling mangrove prioritization, vegetation change detection analysis during the specified periods, and other scientific uses.
This dataset provides mangrove extent mapping by using machine learning random forest classification. Auxiliary data such as vegetation indices, Digital elevation models and Woody Vegetation Canopy Height Models also provide support to the decision trees classifying to the mangrove areas. The tiles captured to perform the mangrove extent classification were 52lfm 52lgm 52lfl 52lgl then mosaic in a single raster.
- biota
- environment
Bounding Coordinates:
- North Bounding Coordinate:-10
- South Bounding Coordinate:-26
- East Bounding Coordinate:138
- West Bounding Coordinate:129
Dataset Status
Dataset ID:
Language:English
Character Set: Latin 1
Progress:completed
Maintenance and Update Frequency:annually
Data Currency Start Date:2016-01-01
Data Currency End Date:2023-12-31
Access Constraint:You are licensed to use the NTG products on the terms and conditions set out in: Creative Commons Attribution 4.0 International Public License (CC BY 4.0) at: creativecommons.org/licenses/by/4.0/legalcode
Data Quality
Lineage:The mangrove extent mapping involved mangrove classification since baseline in 2016 by the methodology in Sun and Staben, 2019. Reviewed in 2023 (Salum and Roach, 2024).
Data Acquisition
Copernicus Annual and seasonal Sentinel 2 satellite imagery for June to August, January to December for 2016 to 2023. Tiles 52lfm, 52lgm, 52lfl, 52lgl. AUSLIG Northern Territory coastline.
Digital Elevation Model DEM S v1.0.
Extent of the Darwin Harbour by Darwin Harbour Integrated Marine Monitoring and Research Program
Preprocessing
Annual and seasonal datasets were processed to a spatial resolution of 10m, including TM like bands (Blue, Green, Red, NIR, SWIR1, and SWIR2). Two bands originally captured at 20m resolution were resampled using cubic convolution. (Flood, 2017; Sun and Staben, 2019)
Distance to coastline was extracted with a 10m spatial resolution from the AUSLIG and the DEM S v1.0.
DEM S v1.0 was resampled to 10-meter spatial resolution.
Vegetation indices were extracted from Red and NIR bands and stored in temporary memory to support mangrove extent classification (Sun and Grant, 2019).
Woody vegetation structural parameters predicted from Sentinel imagery, were stored in temporary memory (Staben, 2018).
All the above data were stacked.
Training data was created and saved in a pickle file format.
Classification
Stacked layers were classified using a Random Forest machine learning algorithm of mangroves in Darwin Harbour. Merged 4 tiles of mangrove extent classification. Clipped mangrove extent to DHMMRP study site boundary.
Manual editing was conducted to reclassify false classification for the extent in 2023. Pixel clusters larger than 3 by 3 were considered as mangrove during the manual editing process (Salum and Roach, 2024).
Validation
The mangrove extent were mapped, audited, and validated through fieldwork and Aerial photography from the Department of Infrastructure Planning and Logistics.
Positional Accuracy:Mangrove extent classification maps has a geolocation accuracy one pixel dislocation or 10-meter (Queenland Government, 2024). No geo dislocation was observed on the overlay imagery product.
Attribute Accuracy:Accuracy of the mangrove classification was assessed by ground truthing at 3992 sample points against imagery. 95.02% of sample points returned coherent (Sun and Grant, 2019).
Logical Consistency:The logical consistency is maintained from the Copernicus parent data
Completeness:100% Completed
Contacts
Name Organisation Position Role Phone Fax Data Requests Officer Department of Environment, Parks and Water Security Geospatial Services Branch (on behalf of department) distributor datarequests.depws@nt.gov.au
Data Dictionary
No data dictionary defined for this dataset
Supplementary Information
DLRM. (2014). Darwin Harbour Water Quality Protection Plan. Department of Land Resource Management. URL: denr.nt.gov.au/__data/assets/pdf_file/0011/254855/Water-Quality-Protection-Plan-Feb2014-web.pdf.
Gallant, J.C., Dowling, T.I., Read, A.M., Wilson, N., Tickle, P., Inskeep, C. (2011) 1 second SRTM Derived Digital Elevation Classifications User Guide. Geoscience Australia www.ga.gov.au/topographic-mapping/digital-elevation-data.html.
Geoscience Australia. (2015). Digital Elevation Classification (DEM) of Australia derived from LiDAR 5 Metre Grid.
Queenland Government. (2024). Statewide Landcover and Trees Study Methodology Overview v1.2. Brisbane Queensland Department of Environment, Science and Innovation.
Salum, R.; Roach, C. (2024). Darwin Harbour Integrated Marine Monitoring and Research Program: Mangrove Monitoring Report 2023 2024: Mangrove Health 2023. Mangrove Health 2023. Technical Report N. 22 2024, Department Northern Territory Government, Palmerston.
Staben, G., Lucieer, A., and Scarth, P. (2018). Modelling LiDAR derived tree canopy height from Landsat TM, ETM plus and OLI satellite imagery A machine learning approach. International journal of applied earth observation and geoinformation, 73, 666 to 681.
Sun, J.; Staben, G. (2019). Development of an integrated long-term mangrove monitoring program for Darwin Harbour. DENR Technical Report n. 41 2019.
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