Mapping Grasslands Suitable for Cellulosic Biofuels in the 

Greater Platte River Basin, United States

Introduction 

Biofuels are an important component in the development 

of alternative energy supplies, which is needed to achieve 

national energy independence and security in the United States. 

The most common biofuel product today in the United States 

is corn-based ethanol; however, its development is limited 

because of concerns about global food shortages, livestock and 

food price increases, and water demand increases for irrigation 

and ethanol production. Corn-based ethanol also potentially 

contributes to soil erosion, and pesticides and fertilizers affect 

water quality. Studies indicate that future potential production 

of cellulosic ethanol is likely to be much greater than grain- or 

starch-based ethanol. As a result, economics and policy incentives could, in the near future, encourage expansion of cellulosic biofuels production from grasses, forest woody biomass, 

and agricultural and municipal wastes. If production expands, 

cultivation of cellulosic feedstock crops, such as switchgrass 

(Panicum virgatum L.) and miscanthus (Miscanthus species), is 

expected to increase dramatically.

The main objective of this study is to identify grasslands 

in the Great Plains that are potentially suitable for cellulosic 

feedstock (such as switchgrass) production. Producing ethanol 

from noncropland holdings (such as grassland) will minimize 

the effects of biofuel developments on global food supplies. 

Our pilot study area is the Greater Platte River Basin, which 

includes a broad range of plant productivity from semiarid 

grasslands in the west to the fertile corn belt in the east. The 

Greater Platte River Basin was the subject of related U.S. Geological Survey (USGS) integrated research projects (Thormodsgard, 2009).

Methods

In this study, we applied the dynamic modeling of ecosystem performance (DMEP) method (Wylie and others, 2008) to 

identify grasslands that are potentially suitable for cellulosic 

feedstock development in the GPRB. DMEP monitors and 

models ecosystem performance (EP), a surrogate approach for 

measuring ecosystem productivity. EP accounts for current and 

future ecosystem services, site conditions, and projected climate 

changes. We used remotely sensed vegetation condition information from the archival records of satellite data (expedited 

Moderate Resolution Imaging Spectroradiometer (eMODIS) 

time series Normalized Difference Vegetation Index (NDVI) 

data with a 250-meter spatial resolution), site geophysical and 

biophysical features (such as elevation, slope and aspect, and 

soils), and weather and climate drivers to build site potential 

and EP models to estimate grassland site potentials (long-term 

grass productivities) and range conditions (Gu and others, 

2012). In this study, Growing Season averaged NDVI (GSN) 

was used as a proxy for grassland productivity (Gu and others, 

2013).

We presumed areas that had consistently high grassland 

productivity and that varied from fair-to-good rangeland condition (that is with multiyear persistent ecosystem overperformance or normal performance relative to site conditions and 

weather-based productivity estimates) were potentially suitable 

for cellulosic feedstock development.

On the other hand, we assumed that the following grassland conditions were not appropriate for cellulosic feedstock 

development: unproductive, degraded, or highly vulnerable to 

erosion. Unproductive conditions include grasslands with poor 

soils, dry climate conditions, or other conditions not conducive 

to vegetation growth. Degraded grasslands have multiyear 

persistent ecosystem underperformance with poor rangeland 

conditions caused by wildfire, insect infestation, or heavy grazing. Grasslands that are highly vulnerable to erosion include the 

Sand Hills ecoregion in Nebraska, where removal of biomass 

may lead to sand dune reactivation and migration.

Furthermore, we used regression analyses of eMODIS 

GSN and rangeland productivity, derived from the Soil Survey 

Geographic (SSURGO) Database (http://soildatamart.nrcs.

usda.gov), to verify the reliability of the use of GSN as a proxy 

for grassland productivity (Gu and others, 2013). 

Results

The scatterplot of the eMODIS long-term mean GSN 

(MGSN) and SSURGO rangeland productivity for the Greater 

Platte River Basin is shown in figure 1. A strong relationship 

between MGSN and SSURGO productivity (coefficient of 

determination (R2

) = 0.74; 8,000 samples) supports the validity 

of using GSN as a proxy for grassland productivity. 

Grassland areas that are potentially suitable for cellulosic 

feedstock production in the Greater Platte River Basin are 

shown in figure 2. Pixels that either over-performed or performed normally for at least 7 of 9 years from 2000 to 2008 

(derived from the EP model) and had either moderate (green, 

productivity between 2,750 to 3,600 kilogram per hectare per 

year (kg ha-1 year-1)) or high (blue, productivity greater than 

3,600 kg ha-1 year-1) grassland long-term site potential were 

identified as potentially suitable. Most of the Sand Hills ecoregion was excluded from the identified suitable areas to avoid 

ecologically hazardous land use and land cover changes.

U.S. Department of the Interior

U.S. Geological Survey Printed on recycled paper

Fact Sheet 2012–3126

October 2012
Figure 1. Relation between expedited Moderate Resolution 

Imaging Spectroradiometer (eMODIS) Mean Growing Season 

averaged Normalized Difference Vegetation Index (NDVI) and Soil 

Survey Geographic (SSURGO) Database rangeland productivity in 

the Greater Platte River Basin. NDVI represents the photosynthetic 

capacity of vegetation canopy and the unit is a dimensionless ratio. SSURGO productivity (kilogram per hectare per year) 8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

0 0.2 0.4 0.6 0.8

SSURGO productivity = 9,936.5 × MGSN − 1,554

Coefficient of determination (R2

) = 0.74

Mean growing season averaged NDVI

Figure 2. Map delineates potentially suitable areas (in green and 

blue) for cellulosic feedstock production within the Greater Platte 

River Basin. The spatial resolution of the map is 250 meter.

Grassland biomass greater than 3,600 kilogram per hectare per year (kg ha-1 year-1)

Grassland biomass between 2,750 to 3,600 kg ha-1 year-1

EXPLANATION

WYOMING SOUTH DAKOTA

COLORADO

NEBRASKA

Sand Hills ecoregion

GREATER PLATTE RIVER BASIN

Summary

This approach demonstrates the power of ecosystem models and biophysical information extracted from the extensive 

satellite image archives to monitor grasslands and to project 

future potential biofuel feedstock source areas. This study 

provides a new monitoring and modeling approach that can contribute to decision support tools to help land managers and decision makers make optimal land use decisions regarding possible 

conversion to cellulosic feedstock production. This research 

represents a first step in identifying grassland areas suitable for 

sustainable production of cellulosic feedstock. Further studies 

are needed to evaluate environmental and climate effects (such 

as carbon sequestration) caused by possible expansion of cellulosic feedstock production in the potentially suitable areas, and 

quantitatively compare future climate-based projections of corn 

and grassland productivity in these areas. 

Acknowledgments

This work was performed under USGS contract 

G08PC91508 and funded by the USGS Geographic Analysis 

and Monitoring Program in support of Renewable EnergyBiofuels. The authors thank Ronald B. Zelt, Stephen Boyte, and 

Thomas Adamson for their comments and suggestions.

References

Gu, Y., Boyte, S.P., Wylie, B.K., and Tieszen, L.L., 2012, Identifying grasslands suitable for cellulosic feedstock crops in the 

Greater Platte River Basin—Dynamic modeling of ecosystem 

performance with 250 m eMODIS: Global Change Biology 

Bioenergy, v. 4, p. 96–106.

Gu, Y., Wylie, B.K., and Norman, B.B., 2013, Mapping grassland productivity with 250-m eMODIS NDVI and SSURGO 

database over the Greater Platte River Basin, USA: Ecological Indicators, v. 24, p. 31–36. (Also available at http://dx.doi.

org/10.1016/j.ecolind.2012.05.024).

Soil Survey Geographic Database, Soil Survey Staff 2009, 

accessed January 2012, at http://soildatamart.nrcs.usda.gov.

Thormodsgard, J., 2009, Greater Platte River Basins—Science 

to sustain ecosystems and communities: U.S. Geological 

Survey Fact Sheet 2009–3097, 6 p.

Wylie, B.K., Zhang, L., Bliss, N., Ji, L., Tieszen, L.L., and Jolly, 

M., 2008, Integrating modeling and remote sensing to identify ecosystem performance anomalies in the boreal forest, 

Yukon River Basin, Alaska: International Journal of Digital 

Earth, v. 2, p. 196–220.

Web page: http://lca.usgs.gov/lca/biofuels_platte/index.php

By Bruce K. Wylie1and Yingxin Gu2

1

U.S. Geological Survey (USGS) Earth Resources Observation and Science 

(EROS) Center. 47914 252nd Street, Sioux Falls, SD 57198, (wylie@usgs.gov). 2

ASRC Research and Technology Solutions, contractor to the USGS EROS 

Center. 47914 252nd Street, Sioux Falls, SD 57198, (ygu@usgs.gov).

North Platte River Valley as seen from Scottsbluff National Monument, Nebraska.