U.S. Geological Survey
Matthew Rigge
Collin Homer
Hua Shi
Debra K Meyer
20191218
Temporal and Spatio-Temporal High-Resolution Satellite Data for the Validation of a Landsat Time-Series of Fractional Component Cover Across Western United States (U.S.) Rangelands
spreadsheet
https://doi.org/10.5066/P90Q8BCP
Western U.S. rangelands have been quantified as six fractional cover (0-100%) components over the Landsat archive (1985-2018) at 30-m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. We leverage field data observed concurrently with HRS imagery over multiple years and locations in the Western U.S. to dramatically expand the spatial extent and sample size of validation analysis relative to a direct comparison to field observations and to previous work. We compare HRS and BIT data in the corresponding space and time. Our objectives were to evaluate the temporal and spatio-temporal relationships between HRS and BIT data, and to compare their response to spatio-temporal variation in climate. We hypothesize that strong temporal and spatio-temporal relationships will exist between HRS and BIT data and that they will exhibit similar climate response. We evaluated a total of 42 HRS sites across the western U.S. with 32 sites in Wyoming, and 5 sites each in Nevada and Montana. HRS sites span a broad range of vegetation, biophysical, climatic, and disturbance regimes. Our HRS sites were strategically located to collectively capture the range of biophysical conditions within a region. Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, “base” maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time.
Two tabular data presented here correspond to a temporal and spatio-temporal validation of the BIT data. First, the temporal data are HRS and BIT component cover and climate variable means by site by year. Second, the spatio-temporal data are HRS and BIT component cover and associated climate variables at individual pixels in a site-year.
The goal of this project is to provide the Nation with complete, current, and consistent public domain information on its land use and land cover.
1985
2018
ground condition
As needed
-119.9472
-104.2109
46.8470
39.7848
Author Defined
ISO 19115 Topic Category
biota
farming
biota
geoscientificInformation
USGS Thesaurus
shrubland ecosystems
terrestrial ecosystems
time-series
fractional components
validation
remote sensing
rangelands
Alexandria Digital Library Feature Type Thesaurus
shrublands
None
shrub
sagebrush
big sagebrush
herbaceous
annual herbaceous
litter
grass
vegetation
bare ground
rangeland
shrubland
back-in-time
USGS Metadata Identifier
USGS:5de7de8fe4b02caea0eb9917
Common geographic areas
United States
Wyoming
Nevada
Montana
None
WY
MT
NV
Central Basin and Range
Northern Basin and Range
Northwestern Great Plains
Southern Rockies
Wyoming Basin
Any downloading and use of these data signifies a user's agreement to comprehension and compliance of the USGS Standard Disclaimer. Insure all portions of metadata are read and clearly understood before using these data in order to protect both user and USGS interests.
Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. User specifically agrees not to misrepresent the data, nor to imply that changes made were approved or endorsed by the U.S. Geological Survey. Please refer to https://www.usgs.gov/privacy.html for the USGS disclaimer.
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mailing address
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Microsoft Excel worksheets
Though we geometrically correct each image, spatial registration of HRS data may vary among years, and when compared to BIT data. To account for this potential variation, we compare the HRS and BIT datasets temporally and spatio-temporally. The temporal analysis removes essentially all impacts of spatial mis-registration by analyzing means across large HRS sites. While the spatio-temporal analysis could be impacted by registration issues, it is made more robust through direct comparison of a very large number of individual HRS and BIT pixel values. Moreover, the spatio-temporal analysis is not subject to the inflation of regression statistics that can occur when regressing means against means in the temporal analysis. HRS predictions have high, but not perfect accuracy, but still provide an invaluable validation reference. Some discrepancies exist in the summation of the primary components of bare ground, herbaceous, litter, and shrub cover. These components were modeled to sum to 100%, however do to modelling error some discrepancy from 100% does exist.
First, for each HRS site-year (n = 77) we averaged both the HRS and BIT prediction based on a sample of 10,000 random points within each site. We excluded areas identified as non-rangeland in either the HRS or BIT dataset and areas identified as cloud/cloud shadow/snow in
the HRS dataset. We then plotted the site average HRS and BIT component covers against each other to quantify temporal accuracy. Regression (n = 77) statistics; R2, root-mean square error (RMSE), and p-value were calculated for each component. Some (n = 17) HRS site-years were used to train the base map, which itself is a major input to the BIT process. To test for the influence of potential circularity, we also plot regression statistics for 2015 used to train base predictions separately from all other data (Table 3). Next for the spatio-temporal analysis, we plotted individual pixel values of corresponding HRS and BIT predictions against each other, aggregated across space and time at 770,000 points. Data from all HRS sites and years were included. Again, we calculated regression statistics; R2, root-mean square error (RMSE), and p-value were calculated for each component. Finally, we compared the spatio-temporal response of both datasets to Daymet climate variables resampled to 30-m. We evaluated four climate variables; water year total precipitation (WYPRCP), mean maximum temperature (WYTMAX), mean minimum temperature (WYTMIN), and growing season (April-September) total precipitation (GSPRCP).
This data represents the version dated 20191204. Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
A formal accuracy assessment of the horizontal positional information in the data set has not been conducted.
A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable.
Field data were used to train 2-m predictions of fractional component cover at each HRS site and year. The 2-m predictions were degraded to 30-m, and some were used to train regional Landsat-scale, 30-m, “base” maps of fractional component cover representing circa 2016 conditions. A Landsat-imagery time-series spanning 1985-2018, excluding 2012, was analyzed for change through time. Pixels and times identified as changed from the base were trained using the base fractional component cover from the pixels identified as unchanged. Changed pixels were labeled with the updated predictions, while the base was maintained in the unchanged pixels. The resulting BIT suite includes the fractional cover of the six components described above for 1985-2018. We compare the two datasets, HRS and BIT, in space and time.
20191202
Vector
G-polygon
1
Albers Conical Equal Area
29.5
45.5
-96.0
23.0
0.0
0.0
coordinate pair
0.6096
0.6096
meters
WGS_1984
WGS_84
6378137.0
298.257223563
spatio_temporal_data_singlesheet.xlsx (partial_list)
Excel Worksheet
Producer Defined
POINT_X
Latitude
Producer Defined
Latitude
meters in UTM
Producer defined
-1102519.387
example
Producer defined
POINT_Y
Longitude
Producer Defined
Longitude
meters in UTM
Producer defined
2104476.067
example
Producer defined
site
High Resolution Satellite (HRS) site. Refer to Figure 1 of corresponding manuscript for site locations.
Producer Defined
wy51
example of an area in Wyoming
Producer defined
HRS
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
bare ground
Producer Defined
% cover
percent coverage
Producer defined
9
example
Producer defined
HRS
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
herbaceous
Producer Defined
% cover
percent coverage
Producer defined
1
example
Producer defined
HRS.1
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
litter
Producer Defined
% cover
percent coverage
Producer defined
22
example
Producer defined
HRS.2
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
sagebrush
Producer Defined
% cover
percent coverage
Producer defined
84
example
Producer defined
HRS.3
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
shrubland
Producer Defined
% cover
percent coverage
Producer defined
71
example
Producer defined
BIT
Back in Time, Landsat based (30m) prediction of fractional component cover.
bare ground
Producer Defined
% cover
percent coverage
Producer defined
24
example
Producer defined
BIT.1
Back in Time, Landsat based (30m) prediction of fractional component cover.
herbaceous
Producer Defined
% cover
percent coverage
Producer defined
18
example
Producer defined
BIT.2
Back in Time, Landsat based (30m) prediction of fractional component cover.
litter
Producer Defined
% cover
percent coverage
Producer defined
22
example
Producer defined
BIT.3
Back in Time, Landsat based (30m) prediction of fractional component cover.
sagebrush
Producer Defined
% cover
percent coverage
Producer defined
24
example
Producer defined
BIT.4
Back in Time, Landsat based (30m) prediction of fractional component cover.
shrubland
Producer Defined
% cover
percent coverage
Producer defined
38
example
Producer defined
Climate
Water Year (October- September) total precipitation
WYPRCP
Producer Defined
mm
millimeter
Producer defined
312.604248
example
Producer defined
Climate.1
Growing Season (April - September) total precipitation
GSPRCP
Producer Defined
mm
millimeter
Producer defined
218.4744568
example
Producer defined
Climate.2
Water Year (October- September) average daily maximum temperature
WYTMAX
Producer Defined
C
Degrees Celsius
Producer defined
12.6289883
example
Producer defined
Climate.3
Water Year (October- September) average daily minimum temperature
WYTMIN
Producer Defined
C
Degrees Celsius
Producer defined
-2.2485173
example
Producer defined
temporal_data_singlesheet.xlsx (partial_example)
Excel Worksheet
Producer Defined
site
High Resolution Satellite (HRS) site. Refer to Figure 1 of corresponding manuscript for site locations.
Producer Defined
WY01
example of an area in Wyoming
Producer defined
year
Year of data
Producer Defined
2008
example
Producer defined
HRS
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
Bare ground
Producer Defined
% cover
percent coverage
Producer defined
59.95193378959096
example
Producer defined
HRS
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
Herbaceous
Producer Defined
% cover
percent coverage
Producer defined
11.99840840362884
example
Producer defined
HRS.1
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
Litter
Producer Defined
% cover
percent coverage
Producer defined
15.924133906308027
example
Producer defined
HRS.2
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
Sagebrush
Producer Defined
% cover
percent coverage
Producer defined
6.571621836702212
example
Producer defined
HRS.3
High Resolution Satellite prediction of fractional component cover. Produced at 2m resolution and degraded to 30m.
Shrub
Producer Defined
% cover
percent coverage
Producer defined
10.414159902382089
example
Producer defined
BIT
Back in Time, Landsat based (30m) prediction of fractional component cover.
Bare ground
Producer Defined
% cover
percent coverage
Producer defined
58.493925407183404
example
Producer defined
BIT.1
Back in Time, Landsat based (30m) prediction of fractional component cover.
Herbaceous
Producer Defined
% cover
percent coverage
Producer defined
14.381134277680513
example
Producer defined
BIT.2
Back in Time, Landsat based (30m) prediction of fractional component cover.
Litter
Producer Defined
% cover
percent coverage
Producer defined
11.973499920420181
example
Producer defined
BIT.3
Back in Time, Landsat based (30m) prediction of fractional component cover.
Sagebrush
Producer Defined
% cover
percent coverage
Producer defined
10.21430845137673
example
Producer defined
BIT.4
Back in Time, Landsat based (30m) prediction of fractional component cover.
Shrub
Producer Defined
% cover
percent coverage
Producer defined
14.10416998249244
example
Producer defined
Climate
Water Year (October- September) total precipitation
WYPRCP
Producer Defined
mm
millimeters
Producer defined
242.67954624943752
example
Producer defined
Climate.1
Growing Season (April - September) total precipitation
GSPRCP
Producer Defined
mm
millimeters
Producer defined
130.9342683060126
example
Producer defined
Climate.2
Water Year (October- September) average daily maximum temperature
WYTMAX
Producer Defined
C
Degrees Celsius
Producer defined
11.69292439053305
example
Producer defined
Climate.3
Water Year (October- September) average daily minimum temperature
WYTMIN
Producer Defined
C
Degrees Celsius
Producer defined
-3.4066861758154072
example
Producer defined
Full excel worksheets available as: temporal_data.xlsx and spatio_temporal_data.xlsx
See Science Base page at https://doi.org/10.5066/P90Q8BCP.
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sciencebase@usgs.gov
Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
Digital Data
https://doi.org/10.5066/P90Q8BCP
None
20200818
U.S. Geological Survey
Customer Service Representative
mailing address
47914 252nd Street
Sioux Falls
SD
57198-0001
United States
(605) 594-6151
custserv@usgs.gov
FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
FGDC-STD-001.1-1999