MANGROVE_COVER_3577_17_-39_20090101

dataset of product mangrove_cover
Indexed by al9577 , created
Archived
for 1st January 2009

Fields 🔗

creation_time 2019-04-30 23:10:53 🔍 format GeoTIFF id da135a8f-500d-48d8-b092-b6681cd30179 🔍 instrument TM,ETM+,OLI label • 🔍 lat -34.582 to -33.561 🔍 lon 150.508 to 151.747 🔍 platform LANDSAT_5,LANDSAT_7,LANDSAT_8 product_type mangrove_extent_cover time 2009-01-01 00:00:00 to 2009-01-01 00:00:00 🔍

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LS5_TM_PQ_3577_17_-39_2009_v1501875574.nc#part=13
LS5_TM_NBART_3577_17_-39_2009_v1493741131.nc#part=2
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... 226 more
MANGROVE_COVER_3577_17_-39_20090101

Location 🔗

file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER_3577_17_-39_20090101.yaml

canopy_cover_class file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER/x_17/y_-39/2009/MANGROVE_COVER_3577_17_-39_20090101_canopy_cover_class.tif
extent file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER/x_17/y_-39/2009/MANGROVE_COVER_3577_17_-39_20090101_extent.tif

Metadata Document 🔗

RAW As Stac
id: da135a8f-500d-48d8-b092-b6681cd30179
product_type: mangrove_extent_cover
creation_dt: 2019-04-30T23:10:53.858080
platform:
code: LANDSAT_5,LANDSAT_7,LANDSAT_8
instrument:
name: TM,ETM+,OLI
format:
name: GeoTIFF
extent:
coord:
ll:
lat: -34.58192386328815
lon: 150.66638928114975
lr:
lat: -34.44744739844929
lon: 151.74747781363052
ul:
lat: -33.694281737623484
lon: 150.5084340261626
ur:
lat: -33.56118022963672
lon: 151.58065572831362
to_dt: 2009-01-01T00:00:00
from_dt: 2009-01-01T00:00:00
center_dt: 2009-01-01T00:00:00
grid_spatial:
projection:
valid_data:
type: Polygon
coordinates: [
[
[
1700000.0
-3800548.537651134
]
[
1700000.0
-3800000.0
]
[
1800000.0
-3800000.0
]
[
1800000.0
-3900000.0
]
[
1700000.0
-3900000.0
]
[
1700000.0
-3800548.537651134
]
]
]
geo_ref_points:
ll:
x: 1700000.0
y: -3900000.0
lr:
x: 1800000.0
y: -3900000.0
ul:
x: 1700000.0
y: -3800000.0
ur:
x: 1800000.0
y: -3800000.0
spatial_reference: EPSG:3577
image:
bands:
extent:
path: file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER/x_17/y_-39/2009/MANGROVE_COVER_3577_17_-39_20090101_extent.tif
layer: 1
canopy_cover_class:
path: file:///g/data/fk4/datacube/002/MANGROVE_COVER/MANGROVES_2_0_2/MANGROVE_COVER/x_17/y_-39/2009/MANGROVE_COVER_3577_17_-39_20090101_canopy_cover_class.tif
layer: 1
lineage:
algorithm:
name: virtual-product
parameters:
configuration_file:
storage:
crs: EPSG:3577
driver: GeoTIFF
chunking:
x: 512
y: 512
time: 1
predictor: 2
tile_size:
x: 100000.0
y: 100000.0
resampling: nearest
resolution:
x: 25
y: -25
overview_level: 5
dimension_order: [
time
y
x
]
location: /g/data/fk4/datacube/002/MANGROVE_COVER/
computation:
date_ranges:
end_date: 2009-12-31
step_size: 1y
start_date: 2009-01-01
stats_duration: 1y
input_region:
tiles: [
[
17
-39
]
]
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
name: mangrove_extent_cover_albers
recipe:
input:
juxtapose: [
input:
q: [
10
]
input:
juxtapose: [
input:
juxtapose: [
collate: [
input:
juxtapose: [
product: ls8_fc_albers
measurements: [
PV
]
source_filter:
product: ls8_level1_scene
gqa_iterative_mean_xy: [
0
1
]
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: [
PV
]
source_filter:
product: ls7_level1_scene
gqa_iterative_mean_xy: [
0
1
]
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: [
PV
]
source_filter:
product: ls5_level1_scene
gqa_iterative_mean_xy: [
0
1
]
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: [
15
40
62
]
input:
q: [
10
]
input:
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: [
0
1
]
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: [
0
1
]
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: [
0
1
]
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:
name: GeoTIFF
platform:
code: LANDSAT_5,LANDSAT_7,LANDSAT_8
instrument:
name: TM,ETM+,OLI
product_type: mangrove_extent_cover
output_params:
zlib: True
fletcher32: True
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
repo_url: https://github.com/opendatacube/datacube-core.git
source_datasets: