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Development of the Rangeland Production Monitoring Service could improve rangeland management

Status: 
Start-up
Dates: 
July, 2018

An image of a shortgrass prairie, showing an example of grasslands where RPMS can monitor vegetation production (photo by Steven Olson, USFS)
The RPMS can be used to monitor vegetation production in lands such as shortgrass prairies.

Background

Matt Reeves recently conducted a podcast with Dr. Tip Hudson, author of the podcast series “The Art of The Range,” in partnership with Washington State University. In this podcast, Matt discusses the RPMS and other tools that will be useful for managers and producers alike.

Rangeland managers and livestock producers need timely and consistent tools that produce information to inform grazing strategies, risk management, and allotment management plans. In addition, National Forests are now in various stages of forest plan revisions which require assessments of current rangeland conditions and past vegetation performance in a clear, unbiased manner. On the ground monitoring is extremely expensive and difficult to employ consistently due to limited resources, limited trained staff, and shifting priorities. In response to this need, Matt Reeves, a Research Ecologist with the Rocky Mountain Research Station, worked in partnership with private industry to develop a new data service freely available to all stakeholders and managers. 

 

Approach

The Rangeland Production Monitoring Service (RPMS) consists of two components. First, it includes a retrospective dataset with the objective of mapping and quantifying annual vegetation production of all 662 million acres of U.S. rangelands from 1984 to present. To create this dataset, we leveraged the Thematic Mapper data suite warehoused on the Google Earth Engine and generated Normalized Difference Vegetation Index (NDVI). The NDVI of an area shows the density of vegetation growing there, and is useful in determining the impact of drought or other events that may reduce or increase vegetation yield. These data were originally offered at a 30 meter spatial resolution (meaning that one pixel represented 30 meters), but to reduce processing constraints and file size (to greatly enhance download capability), the final product was re-sampled to 250 meters. 

An image of the Sonoran Desert, another type of rangeland where the RPMS can be used to monitor production.
The RPMS helps to monitor production in places like the Sonoran Desert (pictured above).

These data were converted to show annual production by processing them through the Rangeland Vegetation Simulator (RVS). The RVS is a simulation system that enables quantification of total annual production, standing carbon in shrubs, stems per acre, carbon and biomass in shrubs, 1, 10, 100, 1000 hour timelag fuels (timelag fuels, or dead fuel, are a way of classifying vegetation components by diameter), and species assemblages response to fire and herbivory. This new simulation program was used to calibrate the NDVI to annual production. 

This unprecedented time series enables users of this service to quantify trends in vegetation production through time, evaluate inter-annual variability, and quantify recovery from drought and wildfire. Obtaining this type of information has long been the target of many producers and managers, so development of this innovative service is timely. It will aid efforts aimed at increasing resiliency and creating better grazing management strategies, which can improve economic and ecological resiliency alike.

The second component of the RPMS is a forage projection system that utilizes machine learning to process near real time climate and remote sensing data to estimate the magnitude and timing of annual production across all rangelands in the Northern Region of the USDA, US Forest Service (Region 1). This automated projection system operates between March and July of the growing season and is updated every 2 weeks. This timeframe allows for a new estimate of the total annual yield to be made in concert with an estimate of the timing of the peak of the growing season. To ensure full disclosure and transparency, we also offer a 95% prediction Interval about the estimate. These data are complementary to the GrassCast offered at:  http://grasscast.agsci.colostate.edu/.

Product Overview

There are two main products from this project:

The first is a retrospective dataset from 1984 to 2017 and beyond (updated annually). The dataset includes data for the period from 1984 to present day, which enables estimates of production such as:

  • Trend of production through time
  • Minimum and maximum production values
  • Inter-annual variability (coefficient of variability)
  • Year of minimum (usually the driest year)

The second product is a forage projection system (near real time, showing the current growing season). For the period from 2018 and beyond, the projections provide estimates of future production. There are 6 inter-related products now available for use and evaluation every 2 weeks in the growing season including:

  • Forage yield estimate
  • Lower bound on forage yield estimate (Forage yield estimate – 95% prediction Interval)
  • Upper bound on forage yield estimate (Forage yield estimate + 95% prediction Interval)
  • Peak of growing season estimate (Julian Day of year of peak)
  • Lower bound on peak of growing season (Peak estimate – 95% prediction Interval)
  • Upper bound on peak of growing season (Peak estimate + 95% prediction Interval)

Examples of Use

RPMS uses data ranging from 1984 to present, which enables rangeland managers and producers to make estimates of annual rangeland production. There are many uses of these data for addressing wildlife habitat concerns, enterprise management, insurance underwriting, restoration planning, and allotment management planning, among other uses. Here, we provide three concrete examples of ways to use this data:

Example 1: Evaluating production trends across all USFS grazing allotments

Four maps of production trends characteristic of grazing lands over time. Upper left shows interannual variability, upper right shows trends through time, lower left shows average vegetation production, lower right shows year of lowest production.
Production trends characteristic of grazing lands over time.

In this example, four metrics have been produced from the RPMS across all grazing allotments in the western U.S.. The upper left shows inter-annual variability in vegetation production (as a percent of the 34 year mean). The upper right shows correlation through time. The lower left shows average production values (lbs ac-1) of vegetation on those areas. The lower right shows the least productive (usually the driest) year on record.  As can be seen, one can create a strong understanding of conditions that are unfolding across all allotments in a matter of minutes using the RPMS, which is important information for improving allotment management strategies.

 

Example 2: Evaluating production on BLM lands: Identifying notable trends 

Four maps showing BLM lands where significant trends occurred. The upper left shows interannual variability, upper right shows trends through time, the lower left shows average vegetation production, and the lower right shows year of lowest production.
BLM lands where significant trends have been observed

A line graph showing the annual production on BLM's Cuate Canyon allotment (based on the trend through time map above).
The annual vegetation production of the Cuate Canyon allotment over time.

In this example, by calculating trend, variability, least productive year and average production, we were able to quickly identify important trends unfolding across the extent of BLM rangelands. In this case, we can see the Cuate Canyon allotment has been losing production since 1984, with an estimated lower end of about 450 pounds per acre. As can be seen, climate variability can strongly impact the vegetation amount in an area.

 

Example 3: Quantifying impacts of drought in 2017 while characterizing range of variability and production trends on Ecological Sites of private ranchlands in Northern Arizona

The RPMS evaluates rangelands across all ownerships and can be used with Ecological Sites to improve risk management strategies. The image shows a color-coded map of the U.S. showing 2017 rangeland production compared with the 34 year mean.
The RPMS evaluates rangelands across all ownerships and can be used with Ecological Sites to improve risk management strategies.

 

A picture of a deer standing in a grassland. Understanding production trends in rangelands is important for management of domestic and wild ungulates (such as cows and deer) alike.
Understanding production trends in rangelands is important for management of domestic and wild ungulates (such as cows and deer) alike.

In this final example, the RPMS was used to update Ecological Site information for a ranch in Northern Arizona. Again, with just a glance, the RPMS enables determination of which sites are undergoing the greatest amount of change. In addition, since the RPMS covers all 662 million acres of rangelands, it can also be used to compare between years, helping us to understand just how deep the drought impacts have been for a given region. For example, see the significant declines in eastern Montana in 2017. This was a very difficult time for producers and mangers throughout the regions where annual production fell between 40 to 97 percent compared with the 34 year average. 

With data ranging from 1984 to present day, the RPMS has enabled estimates of annual rangeland production. These unique systems can be used by managers and producers as in the examples above, or for other purposes, including: 

  1. Quantifying current and past range of variability in vegetation.
  2. Comparing production with like-kind vegetation of nearby sites to investigate:
    1. What is the impact of prairie dogs? 
    2. How has land use effected regional production?
    3. How have range recovery efforts changed production?
  3. Identifying encroachment by coniferous trees, as well as pinyon and juniper species. 
  4. Quantifying the effect of drought at various levels and identify any tipping points. 
  5. Backtesting financial and ecological resiliency strategies.

Accessing the Data

With the data access tool (RMRS Raster Utility Toolbar) any user-defined geography (pastures, allotments, National Forests, private parcels, BLM lands, USFS lands, etc.) can be used to derive annual production estimates for rangelands in the coterminous U.S. There are three options for obtaining the full dataset:

  1. USFS employees can access the entire dataset at: https://usfs.app.box.com/s/kevp607y25t5fylhvusxxecg6n0kr2id (Only accessible from USFS devices)
    • USFS employees can also access the entire dataset on the T drive, inside or outside of Citrix, at T:\FS\RD\RMRS\Science\HD\DataArchive\Reeves\RPMS\RPMS8418.tif
  2. Other stakeholders can download the data at: https://www.lankstonconsulting.com/data-warehouse
  3. Use ArcGIS online. Search for "rangeland productivity" at this location: https://www.arcgis.com/home/index.html  
  4. Contact Matt Reeves (mreeves@fs.fed.us; (406) 546-5875)
 


Project Contact: 

Principal Investigators:
Co-Investigators: