<del>MANGROVE_COVER_3577_13_-43

creation_dt 2019-05-02T00:31:00.658195 creation_time 2019-05-02 00:31:00 format GeoTIFF id e51cbdd1-f47c-4d11-ba04-d95949f94dad instrument TM,ETM+,OLI label • lat -38.636 to -37.626 lon 146.695 to 147.955 platform LANDSAT_5,LANDSAT_7,LANDSAT_8 product_type mangrove_extent_cover time 1989-01-01 00:00:00 to 1989-01-01 00:00:00

Location 🔗

file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER_3577_13_-43_19890101.yaml

canopy_cover_class	file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER/x_13/y_-43/1989/MANGROVE_COVER_3577_13_-43_19890101_canopy_cover_class.tif
extent	file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER/x_13/y_-43/1989/MANGROVE_COVER_3577_13_-43_19890101_extent.tif

Metadata Document 🔗

RAW As Stac

id: e51cbdd1-f47c-4d11-ba04-d95949f94dad

product_type: mangrove_extent_cover

creation_dt: 2019-05-02T00:31:00.658195

platform:

code: LANDSAT_5,LANDSAT_7,LANDSAT_8

instrument:

format:

extent:

coord:

ll:

lat: -38.635935316993795

lon: 146.82549698475336

lr:

lat: -38.52668812630366

lon: 147.95451524670497

ul:

lat: -37.73427470027869

lon: 146.69490426389746

ur:

lat: -37.62634568385196

lon: 147.814174888289

to_dt: 1989-01-01T00:00:00

from_dt: 1989-01-01T00:00:00

center_dt: 1989-01-01T00:00:00

grid_spatial:

projection:

valid_data:

type: Polygon

coordinates: [

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1300000.0

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1300000.0

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geo_ref_points:

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lr:

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y: -4300000.0

ul:

x: 1300000.0

y: -4200000.0

ur:

x: 1400000.0

y: -4200000.0

spatial_reference: EPSG:3577

image:

bands:

extent:

path: file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER/x_13/y_-43/1989/MANGROVE_COVER_3577_13_-43_19890101_extent.tif

layer: 1

canopy_cover_class:

path: file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER/x_13/y_-43/1989/MANGROVE_COVER_3577_13_-43_19890101_canopy_cover_class.tif

layer: 1

lineage:

algorithm:

parameters:

configuration_file:

storage:

crs: EPSG:3577

driver: GeoTIFF

chunking:

x: 512

y: 512

time: 1

tile_size:

x: 100000.0

y: 100000.0

resolution:

x: 25

y: -25

dimension_order: [

time

]

location: /g/data/fk4/datacube/002/MANGROVE_COVER/

computation:

date_ranges:

end_date: 1989-12-31

step_size: 1y

start_date: 1989-01-01

stats_duration: 1y

input_region:

tiles: [

[

-43

]

var_attributes:

extent:

long_name: Mangrove Canopy Cover Extent

standard_name: mangrove_canopy_cover_extent

coverage_content_type: qualityInformation

canopy_cover_class:

long_name: Mangrove Canopy Cover Class

standard_name: mangrove_canopy_cover_class

coverage_content_type: qualityInformation

output_products: [

crs: EPSG:3577

recipe:

input:

juxtapose: [

input:

q: [

]

input:

juxtapose: [

input:

juxtapose: [

collate: [

input:

juxtapose: [

product: ls8_fc_albers

measurements: [

]

source_filter:

product: ls8_level1_scene

gqa_iterative_mean_xy: [

]

dataset_predicate: datacube.virtual.generate_product.ls8_on

flags:

cloud_acca: no_cloud

contiguous: True

cloud_fmask: no_cloud

nir_saturated: False

red_saturated: False

blue_saturated: False

green_saturated: False

swir1_saturated: False

swir2_saturated: False

cloud_shadow_acca: no_cloud_shadow

cloud_shadow_fmask: no_cloud_shadow

input:

product: ls8_pq_albers

fuse_func: datacube.helpers.ga_pq_fuser

transform: make_mask

mask_measurement_name: pixelquality

]

transform: apply_mask

mask_measurement_name: pixelquality

input:

juxtapose: [

product: ls7_fc_albers

measurements: [

]

source_filter:

product: ls7_level1_scene

gqa_iterative_mean_xy: [

]

dataset_predicate: datacube.virtual.generate_product.ls7_on

flags:

cloud_acca: no_cloud

contiguous: True

cloud_fmask: no_cloud

nir_saturated: False

red_saturated: False

blue_saturated: False

green_saturated: False

swir1_saturated: False

swir2_saturated: False

cloud_shadow_acca: no_cloud_shadow

cloud_shadow_fmask: no_cloud_shadow

input:

product: ls7_pq_albers

fuse_func: datacube.helpers.ga_pq_fuser

transform: make_mask

mask_measurement_name: pixelquality

]

transform: apply_mask

mask_measurement_name: pixelquality

input:

juxtapose: [

product: ls5_fc_albers

measurements: [

]

source_filter:

product: ls5_level1_scene

gqa_iterative_mean_xy: [

]

dataset_predicate: datacube.virtual.generate_product.ls5_on

flags:

cloud_acca: no_cloud

contiguous: True

cloud_fmask: no_cloud

nir_saturated: False

red_saturated: False

blue_saturated: False

green_saturated: False

swir1_saturated: False

swir2_saturated: False

cloud_shadow_acca: no_cloud_shadow

cloud_shadow_fmask: no_cloud_shadow

input:

product: ls5_pq_albers

fuse_func: datacube.helpers.ga_pq_fuser

transform: make_mask

mask_measurement_name: pixelquality

]

transform: apply_mask

mask_measurement_name: pixelquality

]

flags:

cloud: False

cloud_shadow: False

noncontiguous: False

water_observed: False

terrain_or_low_angle: False

input:

product: wofs_albers

fuse_func: digitalearthau.utils.wofs_fuser

transform: make_mask

mask_measurement_name: water

]

transform: apply_mask

mask_measurement_name: water

input:

flags:

cloud: False

cloud_shadow: False

noncontiguous: False

terrain_or_low_angle: False

input:

product: wofs_albers

fuse_func: digitalearthau.utils.wofs_fuser

transform: make_mask

mask_measurement_name: water

output:

clear_observ:

formula: water

transform: formula

]

group_by: year

aggregate: datacube.virtual.aggregation.Percentile

quality_band: clear_observ

not_sure_mark: -2

minimum_valid_observations: 3

transform: datacube.virtual.external.MangroveCC

shape_file: maximum_extent_of_mangroves_Apr2019.shp

thresholds: [

]

input:

q: [

]

input:

collate: [

input:

juxtapose: [

product: ls8_nbart_albers

measurements: [

blue

green

red

nir

swir1

swir2

]

source_filter:

product: ls8_level1_scene

gqa_iterative_mean_xy: [

]

dataset_predicate: datacube.virtual.generate_product.ls8_on

flags:

cloud_acca: no_cloud

contiguous: True

cloud_fmask: no_cloud

nir_saturated: False

red_saturated: False

blue_saturated: False

green_saturated: False

swir1_saturated: False

swir2_saturated: False

cloud_shadow_acca: no_cloud_shadow

cloud_shadow_fmask: no_cloud_shadow

input:

product: ls8_pq_albers

fuse_func: datacube.helpers.ga_pq_fuser

transform: make_mask

mask_measurement_name: pixelquality

]

transform: apply_mask

mask_measurement_name: pixelquality

input:

juxtapose: [

product: ls7_nbart_albers

measurements: [

blue

green

red

nir

swir1

swir2

]

source_filter:

product: ls7_level1_scene

gqa_iterative_mean_xy: [

]

dataset_predicate: datacube.virtual.generate_product.ls7_on

flags:

cloud_acca: no_cloud

contiguous: True

cloud_fmask: no_cloud

nir_saturated: False

red_saturated: False

blue_saturated: False

green_saturated: False

swir1_saturated: False

swir2_saturated: False

cloud_shadow_acca: no_cloud_shadow

cloud_shadow_fmask: no_cloud_shadow

input:

product: ls7_pq_albers

fuse_func: datacube.helpers.ga_pq_fuser

transform: make_mask

mask_measurement_name: pixelquality

]

transform: apply_mask

mask_measurement_name: pixelquality

input:

juxtapose: [

product: ls5_nbart_albers

measurements: [

blue

green

red

nir

swir1

swir2

]

source_filter:

product: ls5_level1_scene

gqa_iterative_mean_xy: [

]

dataset_predicate: datacube.virtual.generate_product.ls5_on

flags:

cloud_acca: no_cloud

contiguous: True

cloud_fmask: no_cloud

nir_saturated: False

red_saturated: False

blue_saturated: False

green_saturated: False

swir1_saturated: False

swir2_saturated: False

cloud_shadow_acca: no_cloud_shadow

cloud_shadow_fmask: no_cloud_shadow

input:

product: ls5_pq_albers

fuse_func: datacube.helpers.ga_pq_fuser

transform: make_mask

mask_measurement_name: pixelquality

]

transform: apply_mask

mask_measurement_name: pixelquality

]

transform: datacube.virtual.external.TCIndex

group_by: year

aggregate: datacube.virtual.aggregation.Percentile

minimum_valid_observations: 3

transform: datacube.virtual.external.MaskByValue

greater_than: -1850

mask_measurement_name: TCW_PC_10

]

transform: apply_mask

mask_measurement_name: TCW_PC_10

metadata:

format:

platform:

code: LANDSAT_5,LANDSAT_7,LANDSAT_8

instrument:

product_type: mangrove_extent_cover

output_params:

predictor: 2

resampling: nearest

overview_level: 5

file_path_template: MANGROVE_COVER/x_{x}/y_{y}/{epoch_start:%Y}/MANGROVE_COVER_3577_{x}_{y}_{epoch_start:%Y%m%d}_{var_name}.tif

]

global_attributes:

title: Mangrove Canopy Cover - Annual

source: Annual summary of Fractional Cover and WOfS

authurs: Leo Lymburner, Peter Bunting, Richard Lucas, Peter Scarth, Imam Alam, Claire Phillips, Catherine Ticehurst, Alex Held

license: CC BY Attribution 4.0 International License

summary: The mangrove canopy cover product provides valuable information about the extent and canopy density of mangroves for each year between 1987 and 2016 for the entire Australian coastline. The canopy cover classes are 20-50% (pale green), 50-80% (mid green), 80-100% (dark green). The product consists of a sequence (one per year) of 25 meter resolution maps that are generated by analysing the Landsat fractional cover (https://doi.org/10.6084/m9.figshare.94250.v1) developed by the Joint Remote Sensing Research Program and the Global Mangrove Watch layers (https://doi.org/10.1071/MF13177) developed by the Japanese Aerospace Exploration Agency.

keywords: AU/GA,NASA/GSFC/SED/ESD/LANDSAT,REFLECTANCEE,TM+,TM,OLI,EARTH SCIENCE

platform: LANDSAT-5,LANDSAT-7,LANDSAT-8

instrument: TM,ETM+,OLI

references: Scarth, P., Roder, A. and Schmidt, M., 2010. Tracking grazing pressure and climate interaction - the role of Landsat fractional cover in time series analysis, Proceedings of the 15th Australasian Remote Sensing & Photogrammetry Conference, viewed 4 January 2011, www.scribd.com/doc/37455672/15arspc-Submission-140. Schmidt, M., Denham, R. and Scarth, P., 2010. Fractional ground cover monitoring of pastures and agricultural areas in Queensland, Proceedings of the 15th Australasian Remote Sensing & Photogrammetry Conference, viewed 4 January 2011, www.scribd.com/doc/37455826/15arspc-Submission-119. N. Mueller, A. Lewis, D. Roberts, S. Ring, R. Melrose, J. Sixsmith, L. Lymburner, A. McIntyre, P. Tan, S. Curnow, A. Ip, Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia, Remote Sensing of Environment, Volume 174, 1 March 2016, Pages 341-352, ISSN 0034-4257, http://dx.doi.org/10.1016/j.rse.2015.11.003. (http://www.sciencedirect.com/science/article/pii/S0034425715301929).

institution: Commonwealth of Australia (Geoscience Australia)

cdm_data_type: Grid

publisher_url: http://www.ga.gov.au

acknowledgment: 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 & ABARES and State and Territory governments to collect additional calibration data has enabled the calibration to extend to the entire Australian continent. FC_25 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. WOfS is being developed in parallel with the National Flood Studies Database system which wil provide Flood Study documentation and reports to a wide range of users.

publisher_name: Section Leader, Operations Section, NEMO, Geoscience Australia

publisher_type: institution

product_version: Version 1.0.0, July 2018

publisher_email: earth.observation@ga.gov.au

keywords_vocabulary: GCMD

machine:

uname: Linux r3704 3.10.0-862.14.4.el6.x86_64 #1 SMP Thu Sep 27 07:25:17 AEST 2018 x86_64

hostname: r3704

software_versions:

python:

version: 3.6.2 (default, Aug 14 2017, 13:37:56) [GCC 4.4.7 20120313 (Red Hat 4.4.7-18)]

datacube:

version: 1.7rc1+57.g7d66985.dirty

repo_url: https://github.com/opendatacube/datacube-core.git

source_datasets: