U.S. Forest Service
20230823
Rangeland Productivity Z-Score 1984-2022
raster digital data
https://www.data.gov/
Production data were generated using the Normalized Difference Vegetation
Index (NDVI) from the Thematic Mapper Suite from 1984 to 2022 at 250 m resolution.
The NDVI is converted to production estimates using two regression formulas
depending on the level of the NDVI; there is one equation for lower values (and thus
lower production values) and one for higher values. This raster dataset yields
estimates of annual production of rangeland vegetation and should be useful for
understanding trends and variability in forage resources. These results were then
converted to Z-scores for easier comparison of annual relative productivity in
coterminous U.S. rangelands, and for rapid display in online time-enabled
applications.
Annual rangeland productivity data for the coterminous U.S., from 1984 through
2022, converted to z-scores
19840101
20221231
Ground Condition
Annually
-127.987797
-65.254277
51.649773
22.765715
ISO 19115 Topic Categories
biota
None
USDA Forest Service
USFS
Office of Sustainability and Climate
OSC
rangelands
rangeland productivity
climate
drought
forage
RPA Assessment
None
The USDA Forest Service makes no warranty, expressed or implied, including the
warranties of merchantability and fitness for a particular purpose, nor assumes any
legal liability or responsibility for the accuracy, reliability, completeness or utility
of these geospatial data, or for the improper or incorrect use of these geospatial data.
These geospatial data and related maps or graphics are not legal documents and are not
intended to be used as such. The data and maps may not be used to determine title,
ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that
may be in place on either public or private land. Natural hazards may or may not be
depicted on the data and maps, and users should exercise due caution. The data are
dynamic and may change over time. The user is responsible to verify the limitations of
the geospatial data and to use the data accordingly.
USFS Chief Information Office, Enterprise Data Warehouse
physical
Washington
DC
20250
US
Please send an e-mail to the address below.
SM.FS.data@usda.gov
Version 6.2 (Build 9200) ; Esri ArcGIS 10.5.1.7333
Validation results indicate that the amount of variability explained in
observed annual production by the regression models developed above was 89%. Within
each individual validation comparison, results were considerably better for the
short-grass steppe sites than the tallgrass prairie site. There are no
inconsistencies to report but there is one issue that may cause some confusion. In
some areas where satellite data are less reliable (for example in areas with
significant cloud cover or anomalous spectral characteristics), some years might
have missing data even if for other years data are present.
Validation results indicate that the amount of variability explained in observed
annual production by the regression models developed above was 89%. Within each
individual validation comparison, results were considerably better for the short-grass
steppe sites than the tallgrass prairie site. There are no inconsistencies to report but
there is one issue that may cause some confusion. In some areas where satellite data are
less reliable (for example in areas with significant cloud cover or anomalous spectral
characteristics), some years might have missing data even if for other years data are
present.
Data is complete as of publication.
Horizontal accuracy is variable.
There are two main steps for developing this dataset including 1) Data
acquisition and cross sensor calibration and 2) Calibrating the NDVI to annual
production in rangelands. Step 1: Data acquisition and cross sensor calibration
The remote sensing data are from the Thematic Mapper (TM) archive and from MODIS
found on the Google Earth Engine. To obtain data from 1984 to 2018, we accessed
TM (Landsat 5), Enhanced Thematic Mapper (ETM) (Landsat 7) and the Operational
Land Imager (OLI) from Landsat 8. The TM data were from 1984 to 1999, ETM data
were from 1999 to 2011, and the OLI data were used from 2013 to 2022.
Henceforth, we refer to data from these three satellite sensors as “TM data”
recognizing that they represent 3 different sensors. The TM data are offered at
a nominal spatial resolution of 30 m and have a repeat time of 16 days, or 16
days before the sensor can view the same piece of land again. These data were
converted to the commonly used Normalized Difference Vegetation Index (NDVI).
The NDVI is formulated as: NDVI = (NIR – Red) / (NIR + Red), where red and NIR
stand for the spectral responses acquired in the red (visible) and near-infrared
regions (band passes), respectively. This simple ratio has probably been the
most widely used vegetation index since satellite remote sensing began. The
annual maximum NDVI for each year from 1984 to 2022 formed the basis for
estimating rangeland annual net primary productivity (ANPP) of the coterminous
US. These data were screened for clouds and snow and from this potential pool of
data in each year the annual maximum was chosen. Data for 2012 are missing from
the time series because quality data were unavailable for that year due to the
Scan Line Corrector problem that is widely known throughout the remote sensing
community (Chen et al. 2011). The 2012 data were filled using NDVI from the
Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m resolution. For
these data we allowed only those pixels with quality control flags representing
the highest quality annual maximum NDVI values. Next the MODIS NDVI data were
calibrated to those of LANDSAT 8 (OLI) by comparing NDVI values from 2013 and
2014 to the MODIS data from the same time in a similar manner as (Ke et al.
2015). The valid range of the MODIS based MOD13Q1 NDVI product is -2000 to 10000
while the range for NDVI derived from Landsat 8, based on surface reflectance,
is -1 to +1. To calibrate the MODIS NDVI values in 2012 to be very like those
from Landsat 8 (OLI), we evaluated the response across 110 vegetation types for
each sensor for the years of 2013 and 2014. In addition, since the MODIS NDVI
has a larger pixel size (here 250 m on a side) compared with the TM suite (here
30 on a side), the OLI pixels were resampled to match the MODIS pixel size using
a cubic convolution. The OLI has spectral channels within the red and
near-infrared wavelengths that are distinct from previous TM sensors and
oftentimes a crosswalk between the two sensors’ data is performed.
Correspondingly we converted the ETM+ NDVI data to those in the OLI using the
coefficients developed by Roy et al. (2016). This transformation is given as OLI
= 0.0029 + 0.9589 (ETM+), where OLI represents the converted NDVI from the OLI
sensor based on NDVI from the ETM+ sensor derived using surface reflectance
data. Step 2: Calibrating the NDVI to annual production in rangelands: To
calibrate maximum NDVI values to ANPP four sequential steps were employed.
First, all NDVI data were spatially subset to the extent of coterminous U.S.
rangelands representing about 662 million acres using the data from Reeves and
Mitchell (2011). Second, Ecological Sites were spatially represented, where they
exist, using the Soil Survey Geographic Database. Ecological Sites generally
have Ecological Site Descriptions (ESDs) which, among other things, contain
information on average, above average, and below average values of ANPP. These
estimates of productivity at each site were developed over decades of field
sampling representing millions of acres across dozens of vegetation types. These
data represent an ideal and unprecedented dataset for calibrating remotely
sensed data in rangeland environments because of their comprehensive coverage.
This wide range of ANPP is useful for calibrating the full range of TM NDVI
values found across the extent of coterminous US rangelands. The ANPP estimates
from Ecological Sites were spatially aggregated to Biophysical Settings (BpS)
from the Landfire Project such that, for each Biophysical Setting evaluated
there were three data points representing the below average, average, and above
average ANPP estimates. Likewise, the TM maximum NDVI values were also
aggregated to these same BpS classes across the extent of U.S. rangelands
enabling direct comparisons with the ANPP data. The mean, minimum and maximum
annual max NDVI values were compared with the average, below average and above
average ANPP observations for each associated Ecological Site yielding 3 points
for each of 110 vegetation types evaluated across the extent of the study area.
Third, TM NDVI data were spatially compared to the production data from
Ecological Sites and empirical relationships were established to estimate ANPP.
Fourth, these relationships were applied to NDVI data in each year from 1984 to
2018 across the extent of U.S. rangelands. Chen, J., Zhu, X., Vogelmann, J. E.,
Gao, F., & Jin, S. (2011). A simple and effective method for filling gaps in
Landsat ETM+ SLC-off images. Remote sensing of environment, 115(4), 1053-1064.
Ke, Yinghai, et al. "Characteristics of Landsat 8 OLI-derived NDVI by comparison
with multiple satellite sensors and in-situ observations." Remote Sensing of
Environment 164 (2015): 298-313. Roy, D. P., Kovalskyy, V., Zhang, H. K.,
Vermote, E. F., Yan, L., Kumar, S. S., & Egorov, A. (2016). Characterization
of Landsat-7 to Landsat-8 reflective wavelength and normalized difference
vegetation index continuity. Remote sensing of Environment, 185, 57-70. Reeves,
M. C., & Mitchell, J. E. (2011). Extent of coterminous US rangelands:
quantifying implications of differing agency perspectives. Rangeland Ecology
& Management, 64(6), 585-597.
20180821
Raster
Grid Cell
11674
18505
NAD 1983 Albers
29.5
45.5
-96.0
23.0
0.0
0.0
coordinate pair
0.0000000037527980722984474
0.0000000037527980722984474
meter
D North American 1983
GRS 1980
6378137.0
298.257222101
Raster
A spatial data model that defines space as an array of equally sized cells
arranged in rows and columns, and composed of single or multiple bands. Each
cell contains an attribute value and location coordinates. Unlike a vector
structure, which stores coordinates explicitly, raster coordinates are contained
in the ordering of the matrix. Groups of cells that share the same value
represent the same type of geographic feature.
Esri GIS Dictionary
Rowid
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
VALUE
Z-score categories, representing annual production estimates in standard
deviations from the mean
U.S. Forest Service
less than -2.0
z-value range
U.S. Forest Service
-2.0 to -1.5
z-value range
U.S. Forest Service
-1.5 to -1.0
z-value range
U.S. Forest Service
-0.5 to 0.5
z-value range
U.S. Forest Service
0.5 to 1.0
z-value range
U.S. Forest Service
1.0 to 1.5
z-value range
U.S. Forest Service
1.5 to 2.0
z-value range
U.S. Forest Service
greater than 2.0
z-value range
U.S. Forest Service
COUNT
Number of cells for this z-score category
U.S. Forest Service
Number
USFS Chief Information Office, Enterprise Data Warehouse
physical
Washington
DC
20250
Please send an e-mail to the address below.
SM.FS.data@usda.gov
The U.S. Forest Service makes no warranty, express or implied, nor assumes any
liability or responsibility for the accuracy, reliability, completeness, or utility of
these geospatial data or for the improper or incorrect use of those data. The data are
dynamic and may change over time. The user is responsible for verifying the limitations
of the geospatial data and for using the data accordingly.
20190819
USFS Chief Information Office, Enterprise Data Warehouse
physical
Washington
DC
20250
Please send an e-mail to the address below.
SM.FS.data@usda.gov
FGDC Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998
local time