Products

Biomass

The Rangeland Analysis Platform’s vegetation biomass product provides annual and 16-day aboveground biomass from 1986 to present of: annual forbs and grasses, perennial forbs and grasses, and herbaceous (combination of annual and perennial forbs and grasses). Estimates represent accumulated new biomass throughout the year or 16-day period and do not include biomass accumulation in previous years. Aboveground biomass was calculated by separating net primary production (paritioned by functional group) to aboveground and converting carbon to biomass (Jones et al. 2021, Robinson et al. 2019). Estimates are provided in United States customary units (lbs/acre) to facilitate use. Although these data were produced across a broad region, they are primarily intended for rangeland ecosystems. Biomass estimates may not be suitable in other ecosystems, e.g., forests., and are not to be used in agricultural lands, i.e., croplands.

16-day biomass estimates for the current year are provisional and will be recalculated at the end of the year. Significant land cover changes (e.g., woodland to grassland, shrubland to grassland) during the current year may or may not be reflected in the partitioning of biomass to functional groups. We urge users to critically inspect current year 16-day biomass estimates and interpret appropriately.

Biomass estimates are calculated using a process-based model and therefore traditional error metrics (e.g., MSE, RMSE) are not available. A comparison of biomass estimates to gSSURGO, the Rangeland Production Monitoring Service, and 16,591 USDA NRCS NRI on-the-ground estimates is reported in Jones et al. (2021).

RAP NRI biomass
Density scatterplot of RAP herbaceous aboveground biomass (HAGB) and 16,591 USDA NRCS NRI plot-level biomass estimates, 1:1 line (black), and Pearson correlation coefficient (r=0.63). Figure from Jones et al. (2021).

RAP biomass comparison
Differences (a-c) between three gridded rangeland production datasets across western U.S. rangelands using the 50th percentile of annual values (2000-2018) from RAP and RPMS data, and ‘normal’ values from gSSURGO. Scatterplots and Pearson correlation coefficients (d-f) of production values sampled from each data set for 5000 randomly selected rangeland locations. Figure from Jones et al. (2021).

The vegetation biomass data and maps are intended to be used alongside local knowledge and on-the-ground data to inform management actions that improve rangelands and wildlife habitat. They should not be used in isolation to quantify rangeland resources, determine or define thresholds, or evaluate the efficacy of management practices or treatments. Data can be used to evaluate resources in concert with site-specific information about the area under investigation, such as past land management practices, vegetation treatments, conservation efforts, or natural disturbances.

Jones, M.O., N.P. Robinson, D.E. Naugle, J.D. Maestas, M.C. Reeves, R.W. Lankston, and B.W. Allred. 2021. Annual and 16-day rangeland production estimates for the western United States. Rangeland Ecology & Management 77:112–117. http://dx.doi.org/10.1016/j.rama.2021.04.003

Robinson, N.P., M.O. Jones, A. Moreno, T.A. Erickson, D.E. Naugle, and B.W. Allred. 2019. Rangeland productivity partitioned to sub-pixel plant functional types. Remote Sensing 11:1427. https://dx.doi.org/10.3390/rs11121427

Cover

The Rangeland Analysis Platform’s vegetation cover product provides annual percent cover estimates from 1986 to present of: annual forbs and grasses, perennial forbs and grasses, shrubs, trees, and bare ground. The estimates were produced by combining 75,000 field plots collected by BLM, NPS, and NRCS with the historical Landsat satellite record. Utilizing the power of cloud computing, cover estimates are predicted across the United States at 30m resolution, an area slightly larger than a baseball diamond.

Vegetation cover estimates are model predictions. When interpreting cover, it is important to consider the model error specific to each functional group. Error metrics measure the average difference between predicted model output and separate on-the-ground measurements. For vegetation cover, 10% (approximately 7,500 plots) of the field plot data were withheld from model training and used for evaluation. The error metrics in the table below represent the average accuracy of the model for each functional group:

Vegetation cover Annual forbs and grasses Perennial forbs and grasses Shrubs Trees Bare ground
Mean absolute error (%) 7.0 10.2 6.2 2.6 6.5
Root mean square error (%) 11.0 14.0 8.8 6.7 9.6

These errors provide a general accuracy assessment. For example, across 7,500 field plots, the average RAP prediction for annual forb and grass pixel was +/- 7.0% (mean absolute error) relative to field plot measurements.

The vegetation cover data and maps are intended to be used alongside local knowledge and on-the-ground data to inform management actions. They should not be used in isolation to quantify rangeland resources, determine or define cover thresholds, or evaluate the efficacy of management practices or treatments. Data can be used to evaluate resources in concert with site-specific information about the area under investigation, such as past land management practices, vegetation treatments, conservation efforts, or natural disturbances.

Allred, B.W., B.T. Bestelmeyer, C.S. Boyd, C. Brown, K.W. Davies, M.C. Duniway, L.M. Ellsworth, T.A. Erickson, S.D. Fuhlendorf, T.V. Griffiths, V. Jansen, M.O. Jones, J. Karl, A. Knight, J.D. Maestas, J.J. Maynard, S.E. McCord, D.E. Naugle, H.D. Starns, D. Twidwell, and D.R. Uden. 2021. Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty. Methods in Ecology and Evolution. http://dx.doi.org/10.1111/2041-210x.13564

Data download

Annual biomass data are available in Google Earth Engine (example script) and as GeoTIFFs from http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-biomass/.

Cover data are available in Google Earth Engine (ImageCollection ‘projects/rangeland-analysis-platform/vegetation-cover-v3’) and as GeoTIFFs from http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-cover/.


Carbon

Partitioned NPP

The Rangeland Analysis Platform provides net primary productivity (NPP) estimates from 1986 to present. Estimates are partitioned into the following functional groups: annual forb and grass, perennial forb and grass, shrub, and tree. NPP is the net increase (i.e., photosynthesis minus respiration) in total plant carbon, including above and below ground.

Partitioned NPP cannot be viewed or analyzed in the RAP web application.

Robinson, N.P., M.O. Jones, A. Moreno, T.A. Erickson, D.E. Naugle, and B.W. Allred. 2019. Rangeland productivity partitioned to sub-pixel plant functional types. Remote Sensing 11:1427. https://dx.doi.org/10.3390/rs11121427

NPP data download

Partitioned NPP is available as GeoTIFFs from http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-npp/ and in Google Earth Engine (ImageCollection ‘projects/rangeland-analysis-platform/npp-partitioned-v3’).


Great Plains conservation

Categorical tree cover

Annual categorical tree cover across the Great Plains biome. Class categorization was performed with the rangeland cover product at 480m resolution. Agriculture, development, and water were masked out according to the NLCD 2016 Land Cover product. Data represents tree canopy cover in the following classes: 0-4% and >=5%.

Cultivation risk

Probabilistic ecoregion-wide model that used soil, topographic, and climatic variables to simulate future conversion. This data can be used to direct grassland conservation efforts and as a metric to assess suitability of future crop expansion. Additionally, when applied to previous conversion (see https://www.worldwildlife.org/pages/plowprint-report-map), the data can be used to evaluate the suitability of those areas to perpetual row crop agriculture.

Olimb, S.K. and B. Robinson. 2019. Grass to grain: Probabilistic modeling of agricultural conversion in the North American Great Plains. Ecological Indicators 102:237-245. http://dx.doi.org/10.1016/j.ecolind.2019.02.042

Woody transitions

This product provides a rapid screening tool for identifying the leading edge of vegetation transitions in Great Plains rangelands and serves as an early warning for the loss of intact rangelands to woody expansion. Alternative ecological states are incapable of coexisting in the same space at a given point in time (Uden et al. 2019). For example, a savanna represents co-dominance of grasses and trees, meaning that grass-tree functional groups relatively coexist in the same space over time. In contrast, grass-tree functional groups fail to coexist in many regions of the world and represent fundamental alternative states in those instances. Transitions from one state to another are known to exhibit strong spatial order (Allen et al. 2016, Roberts et al. 2019); therefore, functional groups that do not coexist should covary negatively in space. This data product maps the geographic boundaries between grassy and woody states at two scales. When the geographic location of these boundaries change over time, rangeland resilience is being eroded (Uden et al. 2019) and the system is increasingly vulnerable to being lost to woody expansion. This is represented in the data product when a signal is present in a given year, persistent over multiple years, and non-stationarity in its geographic location over time.

For planning purposes, two scales were pulled from a multitude of scales in a multi-scale analysis (following Uden et al. 2019) and provided as woody transitions layers. The broad-scale layer is computed and visualized to represent the boundary of the Great Plains grasslands biome, with scale being a product of both moving window (i.e., kernel) dimension and pixel resolution (i.e., grain). The broad-scale layer was computed with a 139 x 139-pixel moving window algorithm at 240-meter resolution. A second, moderate-scale was computed and visualized for regional-scale planning and represents the general spatial boundaries between alternative grassy and woody ecological states at a finer scale. The moderate-scale layer was computed with an 81 x 81-pixel moving window algorithm at 30-meter resolution. Increasingly negative spatial covariance values between grass and tree functional groups represent increasingly severe segregation of grass/tree functional groups in space. Fill value = NA/NoData; corresponds to developed areas, croplands, or water, as classified in the 2011 National Land Cover Database (Homer et al. 2015), or wetlands and their 60-m buffers, as delineated in the National Wetlands Inventory (U.S. Fish and Wildlife Service 2019).

Input data: Perennial forbs/grasses vs. trees percent cover data. Sourced from Rangeland Analysis Platform (RAP) cover v2.0, https://rangelands.app

Data use and interpretation must follow guidelines set forth at https://rangelands.app/products/, Uden et al. (2019), and Allred et al. (2021).

Reminders and limitations

Transition data are meant to be combined with RAP cover data and local expert knowledge to understand vegetation change and is not meant to replace those products.

When mapping spatial covariance data, upper and lower values over which the color ramp is stretched influence the sensitivity of the early warning signal of vegetation change and the display of transition severity. Such optimization of images may be useful for mapping variability over different ranges of transition severity in different locations; however, users should be advised that these adjustments also affect the potential for false positives/negatives in spatial transition detection.

Transition signals were computed for conservation planning at two scales and represent a subset of a more robust multi-scale analysis. Cross-scale considerations of transitions will elucidate patches of intact rangeland vegetation nested within broader regional patterns shown here. Product testing is underway on cross-scale products and outputs.

Only one functional group combination from RAP data is present in this data. Alternative functional group combinations may provide additional insights into vegetation transitions at various scales.

Allen, C.R., D.G. Angeler, G.S. Cumming, C. Folke, D. Twidwell, and D.R. Uden. 2016. Quantifying spatial resilience. Journal of Applied Ecology 53:625–635. https://dx.doi.org/10.1111/1365-2664.12634

Allred, B.W., B.T. Bestelmeyer, C.S. Boyd, C. Brown, K.W. Davies, M.C. Duniway, L.M. Ellsworth, T.A. Erickson, S.D. Fuhlendorf, T.V. Griffiths, V. Jansen, M.O. Jones, J. Karl, A. Knight, J.D. Maestas, J.J. Maynard, S.E. McCord, D.E. Naugle, H.D. Starns, D. Twidwell, and D.R. Uden. 2021. Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty. Methods in Ecology and Evolution. http://dx.doi.org/10.1111/2041-210x.13564

Homer, C., J. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N. Herold, J. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States–representing a decade of land cover change information. Photogrammetric Engineering & Remote Sensing 81:345–354.

Roberts, C.P., C.R. Allen, D.G. Angeler, and D. Twidwell. 2019. Shifting avian spatial regimes in a changing climate. Nature Climate Change 9:562–568. https://dx.doi.org/10.1038/s41558-019-0517-6

U.S. Fish and Wildlife Service. 2019. National Wetlands Inventory website. U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C., U.S.A. Available online at: https://www.fws.gov/wetlands/

Uden, D. R., D. Twidwell, C. R. Allen, M. O. Jones, D. E. Naugle, J. D. Maestas, and B. W. Allred. 2019. Spatial Imaging and Screening for Regime Shifts. Frontiers in Ecology and Evolution 7:407. https://dx.doi.org/10.3389/fevo.2019.00407

Great Plains data download

Great Plains data are available as GeoTIFFs from http://rangeland.ntsg.umt.edu/data/rap/rap-derivatives/great-plains/.


Sagebrush conservation

Annual herbaceous cover

Estimated percent cover of herbaceous annuals at 30m resolution on rangelands across the sagebrush biome. Non-rangeland areas, such as forests, water, crops, and development are excluded. These data are a weighted average of three large-scale datasets, providing land managers with estimates of recent (2016-2018) annuals cover across western rangelands. This data layer was developed by a cheatgrass committee convened by the Western Governors Association-appointed Western Invasive Species Council as part of a new toolkit for invasive annual grass management across the western U.S. Detailed methods are available.

Maestas, J., Jones, M., Pastick, N.J., Rigge, M.B., Wylie, B.K., Garner, L., Crist, M., Homer, C., Boyte, S., and Witacre, B., 2020, Annual Herbaceous Cover across Rangelands of the Sagebrush Biome: U.S. Geological Survey data release, https://doi.org/10.5066/P9VL3LD5

Categorical tree cover

Annual categorical tree cover across the Sagebrush biome. Class categorization was performed with the rangeland cover product at 30m resolution. Agriculture, development, and water were masked out according to the NLCD 2016 Land Cover product. Data represents tree canopy cover in the following classes: 0-1%, 2-10%, 11-20%, and >=21%.

Ecosystem resilience and resistance

This data provides a tool for rapid risk assessment across the range of sage-grouse using an index of sagebrush ecosystem resilience to disturbance and resistance to cheatgrass (“R&R”). Potential ecosystem R&R depends in part on the biophysical conditions an area is capable of supporting and soil temperature and moisture regimes can be used to depict this gradient (Chambers et al. 2014, 2016, 2017; Maestas et al. 2016). Soils data were derived from two primary sources: 1) completed and interim soil surveys available through the Soil Survey Geographic Database (SSURGO), and 2) the State Soils Geographic Database (STATSGO2) to fill gaps where SSURGO data were not available (Fig. 1). Using best available information and expert input, each soil temperature and moisture regime/moisture subclass was placed into one of three categories of relative R&R: high, moderate, and low (see: Chambers et al. 2014, 2016; Maestas et al. 2016). Soils with high water tables, wetlands, or frequent ponding that would not typically support sagebrush were not rated.

Chambers et al. 2014. Using resistance and resilience concepts to reduce impacts of annual grasses and altered fire regimes on the sagebrush ecosystem and sage-grouse– A strategic multi-scale approach. Fort Collins, CO, USA: U.S. Department of Agriculture, Forest Service, RMRS-GTR-326. https://www.fs.fed.us/rm/pubs/rmrs_gtr326.pdf

Chambers et al. 2016. Using resilience and resistance concepts to manage threats to sagebrush ecosystems, Gunnison sage-grouse, and greater sage-grouse in their eastern range: a strategic multi-scale approach. Gen. Tech. Rep. RMRS-GTR-356. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. https://www.fs.fed.us/rm/pubs/rmrs_gtr356.pdf

Chambers et al. 2017. Using Resilience and Resistance Concepts to Manage Persistent Threats to Sagebrush Ecosystems and Greater Sage-grouse. Rangeland Ecology and Management. 70:149-164. https://www.treesearch.fs.fed.us/pubs/53742

Maestas, J. D., and S. B. Campbell. 2014. Mapping Potential Ecosystem Resilience and Resistance across Sage-Grouse Range using Soil Temperature and Moisture Regimes. Fact Sheet. Sage Grouse Initiative. http://www.sagegrouseinitiative.com/wp-content/uploads/2013/07/Soil-Temp-Moist-Data-Fact-Sheet-HIGH-RES-012215.pdf

Maestas et al. 2016. Tapping Soil Survey Information for Rapid Assessment of Sagebrush Ecosystem Resilience and Resistance. Rangelands 38:120-128. https://dx.doi.org/10.1016/j.rala.2016.02.002

Sagebrush data download

Sagebrush data are available as GeoTIFFs from http://rangeland.ntsg.umt.edu/data/rap/rap-derivatives/sagebrush/.