Kansas Academy of Science

Spatial and temporal patterns of vegetation within the Flint Hills

John M. Briggs1, Donna R. Rieck1, Clarence L. Turner2, Geoffrey M. Henebry3, Douglas G. Goodin4 and M. Duane Nellis4

  1. Division of Biology Ackert Hall, Kansas State University, Manhattan, KS 66506.
  2. Minnesota Department of Natural Resources, Office of Planning, 500 Lafayette Road, Box 10, St. Paul, MN 55155.
  3. Department of Biological Sciences, Smith Hall 135, Rutgers University, 101 Warren Street, Newark, NJ 07102.
  4. Department of Geography, Dickens Hall, Kansas State University Manhattan, KS 66506.

This article is published in the Transactions of the Kansas
Academy of Science, vol. 100, no. 1/2, p. 10-20 (1997).

Table of Contents
Introduction Summary
Methods Acknowledgements
Results and Discussion Literature


In tallgrass prairie, complex interactions among multiple limiting resources in combination with a variety of land-use practices can lead to a heterogeneous landscape. We used remote sensing data (AVHRR) coupled with abiotic factors to explore spatial and temporal patterns of vegetation within the Flint Hills of Kansas and Oklahoma. This information should enable us to detect both natural (e.g., interannual variation in precipitation and temperature) and anthropogenic (e.g., climate change, overgrazing, land-use practices) stresses on this grassland ecosystem. We have correlated shifts in the spatial and temporal patterns of vegetation (as measured from NDVI by AVHRR) with meteorological data (from 117 weather stations) to identify key abiotic variables that determined vegetation patterns across this region. In all four years, the combination of annual precipitation and growing degree days was useful to detect spatial and temporal patterns of vegetation within the Flint Hills. However, it is imperative that land use patterns are known in order to adequately assess spatial and temporal patterns of vegetation in this area.


Grasslands represent the largest vegetative province in North America (Samson and Knopf, 1994), and the tallgrass prairie is the most mesic and productive grassland type in this biome. The tallgrass prairie once covered 67.6 million ha, extending from Kansas to Ohio and Texas to Canada, but Samson and Knopf (1994) report that 90% of all grasslands has been turned by the plow. The Flint Hills region of Kansas and Oklahoma has the largest extent of remaining tallgrass prairie. The region extends in a north-south orientation from northeast Kansas to northern Oklahoma, with its widest extent being about 70 km--see Fig. 1. The Flint Hills are dominated by C4 grasslands with a typical midwestern continental climate characterized by warm, wet summers and dry, cold winters. Mean annual precipitation in this area is sufficient to support forest or savanna vegetation, but drought, fire and grazing play important roles in maintaining this grassland (Axelrod, 1985; Anderson, 1990). In the Flint Hills, spring burning of grasslands is common, and grazing by domestic livestock is the dominant land use because the relatively steep slopes and rocky soils prevent extensive establishment of row-crop agriculture. In Kansas these grasslands support a livestock grazing industry second only to Texas in animal-unit-months.

Seastedt and Knapp (1993) concluded that in tallgrass prairie, complex interactions among multiple limiting resources in combination with a variety of land use practices, can lead to a heterogeneous landscape. Thus, any attempt to examine this landscape must consider these factors. In an attempt to quantify the tallgrass prairie landscape of the Flint Hills and to determine what is responsible for these patterns, we used remote sensing data of the Advanced Very High Resolution Radiometer (AVHRR) to calculate Normalized Difference Vegetation Index (NDVI).

One of the most common applications of remote sensing is monitoring and evaluating vegetation over the land surface. Normalized Difference Vegetation Index is an important tool in this endeavor. NDVI is based on the fact that growing vegetation has a low red reflectance due to absorption by chlorophyll and other plant pigments and a high near-infrared reflectance due to internal reflectances involving the structure of green leaves. Thus, vegetated areas should yield high values of NDVI. Many researchers have demonstrated that NDVI is related to a variety of vegetative parameters including percent cover, leaf-area index, and green biomass. (e.g. Goward et al., 1985; Justice et. al. 1985; Box et al. 1989). Early in the 1990 growing season the U.S. Geological Survey's EROS Data Center (EDC) started acquiring NOAA-11 AVHRR 1-km resolution daily observations to produce weekly and biweekly maximum NDVI composites of the conterminous United States. The objective of the vegetation mapping program is to "compile, annually, a comprehensive series of calibrated, georegistered, daily observations and biweekly maximum NDVI composites" (Eidenshink 1992). As stated by EDC, these data sets provide a comprehensive growing season profile of ecosystems and are extremely useful for assessing seasonal variations in vegetation condition and provide a foundation for studying long-term changes resulting from human or natural factors. We used these NDVI values coupled with abiotic factors and land cover types to explore spatial and temporal patterns of vegetation within the Flint Hills of Kansas and Oklahoma.

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We obtained AVHRR NDVI data from 1990-1994, which was collected and distributed by the U.S. Geological Survey's EROS Data Center (EDC) in Sioux Falls, South Dakota. NDVI is the difference of near-infrared (AVHRR channel 2) and visible(AVHRR channel 1) reflectance values divided by total reflectance.

           IR(channel 2) - Visible(channel 1)
    NDVI = ----------------------------------
           IR(channel 2) + Visible(channel 1)

The equation produces NDVI values in the range of -1.0 to +1.0, where negative values typically represent clouds, snow, water, and other non-vegetated surfaces, and positive values represent vegetated surfaces. NDVI was calculated from calibrated data that were scaled to byte range and geometrically registered.

In order to scale the computed NDVI results to byte data range, we added 1 to the computed value to the NDVI value and multiplied that by 100. As a result, NDVI values less than 100 now represent clouds, snow, water, and other non-vegetative surfaces and values greater than 100 represent vegetative surfaces.

Temperature and precipitation data were derived from a network of measurement stations operated by the National Weather Service Cooperative Observer Network (NWSCON). The cooperative observer network consists of volunteer observers who agree to maintain a station, as well as record, tabulate, and report the data. All NWSCON stations are equipped with a cylindrical gauge for recording precipitation in liquid form. In addition, calibrated measurement probes are used to record snowfall amounts. Some NWSCON stations are also equipped with temperature measurement equipment. These consist of a standard mercury-in-glass thermometer equipped with bulb constriction for measuring maximum temperature, and an alcohol index thermometer for recording the daily minimum. Thermometers at NWSCON stations are mounted within a protective screen to shield them from direct incoming solar radiation

Total annual precipitation (TAP) and growing degree days (GDD) were calculated using data from these stations for each of the years. GDD was calculated according to the following.

          daily maximum temperature + daily minimum temperature
    GDD = ----------------------------------------------------- - threshold temperature
Values were summed on a daily basis. Values below zero were set to zero. The threshold temperature was set at 10°C. This temperature was used since it best describes optimal growing temperature for C4 grasses. For our analysis with GDD and TAP, we created annual integrated NDVI images from the biweekly maximum NDVI composites. GDD was used since Goodin and Henebry (1996) determined it to be sensitive to NDVI curves when they are calculated using close-range sensing data.

In an attempt to assess the importance of land cover on NDVI values, a land cover map from the Kansas Land Cover Mapping Project (Kansas Applied Remote Sensing Program) was obtained for the Kansas portion of our study site--see Flint Hills land cover classification. This land cover map, which was based upon Landsat Thematic Mapper data in 1990, produced 10 distinct cover types--see Table 1. Data were compared against manually interpreted 1985 National High Altitude Photography (NHAP) and 1986 Kansas reappraisal air photos. Coordinates of known homogenous (at least 2 AVHRR pixels) land cover areas (grassland, cropland and woodland) were placed into a geographical information system (GIS). The GIS coverages were then used to obtain sub-samples from the bi-weekly maximum NDVI composites for each year (19 in 1990 and 21 in 1991-1993) to determine how these cover types contributed to the variance in the NDVI over time.

Table 1. Land cover types of the Flint Hills study area (Kansas only). Data from the KANSAS LAND COVER MAPPING PROJECT, Kansas Applied Remote Sensing Program, 2291 Irving Hill Drive, Lawrence, KS 66045-2969, based upon Landsat Thematic Mapper data in 1990.
Land Cover Class Percentage of Area
Residential 0.66%
Commercial/Industrial 0.29%
Urban Grasslands 0.26%
Urban Woodlands 0.01%
Urban Water 0.01%
Cropland 24.33%
Grasslands 65.30%
Woodlands 6.72%
Water 2.07%
Other (mostly highways) 0.36%

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During our four year study period, which included years of below average precipitation (1990) to near record precipitation (1993), NDVI values varied across the Flint Hill landscape. In every year, NDVI tended to be highest in the southeast section, which corresponds to known precipitation patterns. The 30-year mean of precipitation in the southeast is around 1110 mm (Pawhuska, Oklahoma) while in the northwest, the 30-year mean is around 835 mm (Manhattan, Kansas). Multiple linear regression models using a stepwise procedure determined that a combination of GDD or TAP had r² varying from 0.39 to 0.88--see Table 2 and Fig. 2. When all four years were combined, GDD was the only significant variable (r² = 0.32, Table 2). Thus, other factors (i.e. land use) are important in the spatial and temporal patterns measured in this landscape. Other abiotic variables (e.g. growing season precipitation, temperature or GDD based upon different threshold temperatures) did not improve any of the relationships.

Table 2. Regression models of growing degree days (GDD) and total annual precipitation (TAP) with annual integrated NDVI values for each of the four years and with all years combined.
1990 NDVI = 119.19 -3.003 * (GDD/TAP) 0.49 0.002
1991 NDVI = 82.69 + 0.00000714 * (GDD *TAP) 0.83 0.001
1992 NDVI = 69.33 + 0.009 * GDD 0.61 0.001
1993 NDVI = 42.0 + 0.168 * GDD 0.88 0.001
All Years NDVI = 75.52 + 0.008 * GDD 0.32 0.001

Seasonal Variation in NDVI

A two-way ANOVA of the slopes of the 'greenup' and 'senescence' phases of the NDVI curves was conducted to determine the effects of cover types on the NDVI slopes. Greenup phase equals the points from day of year 0 on the NDVI curve to the maximum value while senescence phase equals points from the maximum value on the NDVI curve to the last composite of that year. The slopes were then compared using a GLM with year and cover type as class variables.

Greenup phase

Year was not significant, but cover type was significant (P < 0.05). The slope of the greenup phase (NDVI units/day) on grasslands and woodlands was not significantly different from each other (P > 0.05), but was different from cropland (P < 0.05). Similarly the slope of the greenup phase on woodlands and croplands was not significantly different from each other (P > 0.05) but was significantly different from grasslands--see Fig. 3.

Senescence phase

Cover type had no significant effect on the slope, but significant differences occurred from year to year. 1990 and 1991 were not different from each other but were different from 1992 and 1993. In addition, 1992 and 1993 were significantly different from other years--see Fig. 4.

These differences between the greenup-phase and the senescense phase suggest that croplands are more sensitive to variation in temperature and precipitation, especially early in the growing season. This difference may be explained simply by the fact that farmers can exercise some option in planting time, crop-type, etc. However, during the senescence phase all cover types are more sensitive to seasonal precipitation rather than temperature.

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At a single tallgrass prairie site (Konza Prairie; northwest section of the Flint Hills) over a 19-year time period, abiotic factors were used in predicting peak above ground biomass (Briggs and Knapp, 1995). They reported that although some factors were statistically significant, a large percentage of the variation was not explained. They concluded that no single factor is responsible for the interannual variability in peak aboveground biomass. It appears from our study using AVHRR that this is also true over a larger spatial area. Although a significant relationship between GDD and NDVI in the Flint Hills occurs in most years, overall a large percentage of the variance remains unexplained.

In contrast with many other North American grasslands where shortages of a single resource often dominate system responses, humid tallgrass is best viewed as being limited by multiple resources such as water, light, nitrogen (Knapp and Seastedt, 1986; Chapin et al., 1987; Seastedt and Knapp, 1993). The combination of a highly variable continental climate and various management practices (i.e., plowing, grazing and fire) can lead to a very heterogeneous landscape where the relative importance of limiting factors, and the structural and functional responses of the ecosystem, vary both in space and time. Thus, many ecological patterns and processes in tallgrass prairie are best considered from a non-equilibrium perspective, where frequent shifts in the relative importance of key multiple resources are crucial for maintaining both the diversity and productivity of these ecosystems (Seastedt and Knapp, 1993). Any attempt to examine the spatial and/or temporal patterns in this landscape should incorporate these ideas with an understanding of the non-equilibrium of this system.

We conclude that understanding land cover type in the Flint Hills is critical in defining the spatial and temporal patterns in this relatively homogenous vegetation cover (grassland). Management of the area (mostly grazing and burning) that produces these various land cover types is also very important and any land coverage analysis should try to incorporate land management practices. For example, in our analysis, over 65% of the area was simply classified as grassland (Table 1). We know from research conducted on Konza Prairie, that land management practices such as burning and grazing alter the spectral signatures of the landscape (Briggs and Nellis 1991; Turner et al., 1992; Goodin and Henebry, 1996). Thus any further research plans for examining the spatial patterns of vegetation in the Flint Hills must involve developing a land coverage, which incorporates land management practices such as grazing and burning history.

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We would like to thank J. S. Aber and E. J. Finck for reviewing and improving an earlier version of this manuscript. This research was funded by EPA EMAP grant #R823605-01-0 and NSF LTER grant #BSR-9011662. Konza Prairie is owned by the Nature Conservancy and managed by the Division of Biology, Kansas State University.


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Comments to JMB@lter-konza.konza.ksu.edu