Environmental Management

, Volume 50, Issue 5, pp 914–928 | Cite as

Mixed-Grass Prairie Canopy Structure and Spectral Reflectance Vary with Topographic Position

  • Rebecca L. Phillips
  • Moffatt K. Ngugi
  • John Hendrickson
  • Aaron Smith
  • Mark West


Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of canopy height in autumn to ensure conservation of grassland structure for wildlife and forage for livestock. However, more comprehensive assessment of vegetation structure could be achieved for mixed-grass prairie by integrating field survey, topographic position (summit, mid and toeslope) and spectral reflectance data. Thus, we examined the variation of mixed-grass prairie structural attributes (canopy leaf area, standing crop mass, canopy height, nitrogen, and water content) and spectral vegetation indices (VIs) with variation in topographic position at the Grand River National Grassland (GRNG), South Dakota. We conducted the study on a 36,000-ha herbaceous area within the GRNG, where randomly selected plots (1 km2 in size) were geolocated and included summit, mid and toeslope positions. We tested for effects of topographic position on measured vegetation attributes and VIs calculated from Landsat TM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data collected in July 2010. Leaf area, standing crop mass, canopy height, nitrogen, and water content were lower at summits than at toeslopes. The simple ratio of Landsat Band 7/Band 1 (SR71) was the VI most highly correlated with canopy standing crop and height at plot and landscape scales. Results suggest field and remote sensing-based grassland assessment techniques could more comprehensively target low structure areas at minimal expense by layering modeled imagery over a landscape stratified into topographic position groups.


Landsat ASTER Remote sensing Biomass VOR Canopy height 



This work would not have been possible without the gracious cooperation of GRNG Field Crew and Dakota Prairie Grasslands managers (Phil Sjursen, Dan Svingen, and staff at the Lemmon, SD field office). The authors heartily thank data collectors Justin Feld, Jonathan Rolfson, Sarah Waldron, Cari Ficken, Marla Striped-Face Collins, Mary Kay Tokach for their outstanding teamwork and attention to data quality. Special thanks to Cari Ficken, Duane Pool, Nick Saliendra, Brad Rundquist, and the anonymous reviewers for their comments. Support for this project was funded by the USFS Co-operative Agreement No. 5445-21310-001-07.


Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. USDA is an equal opportunity provider and employer.


  1. Beeri O, Phillips P, Carson P, Liebig M (2005) Alternate satellite models for estimation of sugar beet residue nitrogen credit. Agriculture, Ecosystems and Environment 107:21–25CrossRefGoogle Scholar
  2. Beeri O, Phillips R, Hendrickson J, Frank AB, Kronberg S (2007) Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment 110(2):216–225CrossRefGoogle Scholar
  3. Benkobi L, Uresk DW, Schenbeck C, King RM (2000) Protocol for monitoring standing crop in grasslands using visual obstruction. Journal of Range Management 53:627–633CrossRefGoogle Scholar
  4. Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58(3–4):239–258CrossRefGoogle Scholar
  5. Biodini ME, Manske L (1996) Grazing frequency and ecosystem processes in a northern mixed grass prairie, USA. Ecological Applications 6:239–256CrossRefGoogle Scholar
  6. Bittencourt HR, Clarke RT (2003) Logistic discrimination between classes with nearly equal spectral response in high dimensionality. In: IEEE international geoscience and remote sensing symposium, Toulouse, France, pp 3748–3750Google Scholar
  7. Burke IC, Lauenroth WK, Riggle R, Brannen P, Madigan R, Beard S (1999) Spatial variability of soil properties in the shortgrass steppe: the relative importance of topography, grazing, microsite, and plant species in controlling spatial patterns. Ecosystems 2:422–438Google Scholar
  8. Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S (2002) Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1. Theoretical approach. Remote Sensing of Environment 82:188–197CrossRefGoogle Scholar
  9. Chen JM (1996) Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal conifer forests. IEEE Transactions on Geoscience and Remote Sensing 34(6):1353–1368CrossRefGoogle Scholar
  10. Daubenmire R (1959) A canopy-coverage method of vegetational analysis. Northwest Science 33:43–64Google Scholar
  11. Dauwalter DC, Fisher WL, Belt KC (2006) Mapping stream habitats with a global positioning system: accuracy, precision, and comparison with traditional methods. Environmental Management 37:271–280CrossRefGoogle Scholar
  12. Feldesman MR (2002) Classification trees as an alternative to linear discriminant analysis. American Journal of Physical Anthropology 119:257–275CrossRefGoogle Scholar
  13. Fisher RJ, Davis SK (2010) From Wiens to Robel: a review of grassland-bird habitat selection. Journal of Wildlife Management 74:264–273Google Scholar
  14. Frank AB, Dugas WA (2001) Carbon dioxide fluxes over a northern, semiarid, mixed-grass prairie. Agricultural and Forest Meteorology 108:317–326CrossRefGoogle Scholar
  15. Fujisada H (1998) ASTER Level-1 data processing algorithm. IEEE Transactions on Geoscience and Remote Sensing 36:1101–1112CrossRefGoogle Scholar
  16. Gesch DM, Oimoen S, Greenlee S, Nelson M, Steuck M, Tyler D (2002) The national elevation dataset. Photogrammetric Engineering and Remote Sensing 68:5–11Google Scholar
  17. Hall K, Johansson LJ, Sykes MT, Reitalu T, Larsson K, Prentice HC (2010) Inventorying management status and plant species richness in seminatural grasslands using high spatial resolution imagery. Applied Vegetation Science 13:221–233CrossRefGoogle Scholar
  18. Hansen K (2008) Plants of the Grand River and Cedar River national grasslands. US Department of Agriculture Dakota Prairie Grasslands, Washington, DC. Accessed 20 June 2010
  19. Hardinsky MA, Klemas V, Smart RM (1983) The influence of soil-salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogrammetric Engineering and Remote Sensing 49:77–83Google Scholar
  20. Holifield Collins CD, Emmerich WE, Moran MS, Hernandez M, Scott RL, Bryant RB, King DM, Verdugo CL (2008) A remote sensing approach for estimating distributed daily net carbon dioxide flux in semiarid grasslands. Water Resources Research 44:W05S17–W05S18. doi: 10.1029/2006WR005699 Google Scholar
  21. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83:195–213CrossRefGoogle Scholar
  22. Hunt RE, Jr, Everitt JH, Ritchie JC, Moran MS, Booth DT, Anderson GL, Clark PE, Seyfried MS (2003) Applications and research using remote sensing for rangeland management. Photogrammetric Engineering & Remote Sensing 69:675–693Google Scholar
  23. Hunt ER, Rock BN (1989) Detection of changes in leaf water content using near- and middle-infrared reflectance. Remote Sensing of Environment 40:33–54Google Scholar
  24. Jenness J (2006) Topographic position index (tpi_jen.avx) extension for ArcView 3.x, v 1.2. Jenness Enterprises. Accessed 3 June 2010
  25. Knapp AK, Fahnestock JT, Hamburg SP, Statland LB, Seastedt TR, Schimel DS (1993) Landscape patterns in soil–plant water relations and primary production in tallgrass prairie. Ecology 74:549–560CrossRefGoogle Scholar
  26. Larivière S (2003) Edge effects, predator movements, and the travel-lane paradox. Wildlife Society Bulletin 31:315–320Google Scholar
  27. Lawrence RL, Ripple WJ (1998) Comparisons among vegetation indices and bandwise regression in a highly disturbed, heterogeneous landscape: Mount St Helens, Washington. Remote Sensing of Environment 64:91–102Google Scholar
  28. Luoto M, Virkkala R, Heikkinen RK, Rainior K (2004) Predicting bird species richness using remote sensing in boreal agricultural-forest mosaics. Ecological Applications 14:1946–1962CrossRefGoogle Scholar
  29. MacMillan RA, Pettapiece WW, Nolan SC, Goddard TW (2000) A generic procedure for automatically segmenting landforms into landform elements using DEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems 113:81–109CrossRefGoogle Scholar
  30. Markham BL, Barker JL (1987) Radiometric properties of U.S. processed Landsat MSS data. Remote Sensing of Environment 22:39–71CrossRefGoogle Scholar
  31. Marsett RC, Qi J, Heilman P, Biedenbender SH, Watson MC, Amer S, Weltz M, Goodrich D, Marsett R (2006) Remote sensing for grassland management in the arid southwest. Range Ecology and Management 59:530–540CrossRefGoogle Scholar
  32. Milchunas DG, Lauenroth WK (1993) Quantitative effects of grazing on vegetation and soils over a global range of environments. Ecological Monographs 63(4):327–366CrossRefGoogle Scholar
  33. Milchunas DG, Laenroth WK, Chapman PL, Kazempour MK (1989) Effects of grazing, topography, and precipitation on the structure of a semiarid grassland. Vegetatio 80:11–23CrossRefGoogle Scholar
  34. Moran MS, Inoue Y, Barnes EM (1997) Opportunities and limitations for Image-based remote sensing in precision crop management. Remote Sensing of Environment 61:319–346CrossRefGoogle Scholar
  35. NDAWN hourly data (2000) NDSU-NDAWN, Fargo. Accessed 20 Aug 2011
  36. NRCS Official Soil Series Descriptions. United States Department of Agriculture. Accessed 10 February 2011
  37. Omernik JM (1987) Ecoregions of the conterminous United States. Annals of Association of American Geographers 77(1):118–125CrossRefGoogle Scholar
  38. Qi J, Chehbouni A, Huete A, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sensing of Environment 48:119–126CrossRefGoogle Scholar
  39. Qin CZ, Zhu AX, Shi X, Li BL, Pei T, Zhou CH (2009) Quantification of spatial gradation of slope positions. Geomorphology 110:152–161CrossRefGoogle Scholar
  40. R Development Core Team (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. Accessed 3 Sept 2010
  41. Robel RJ, Briggs JN, Dayton AD, Hulbert LC (1970) Relationships between visual obstruction measurements and weight of grassland vegetation. Journal of Range Management 23:296–297CrossRefGoogle Scholar
  42. Roberts DA, Batista G, Pereira JLG, Waller E, Nelson B (1998) Change identification using multitemporal spectral mixture analysis: application in eastern Amazonia. In: Elvidge C, Lunetta R (eds) Remote sensing change detection: environ monitoring applications and methods. Ann Arbor Press, Ann Arbor, pp 137–161Google Scholar
  43. Singh JS, Milchunas DG, Lauenroth WK (1998) Soil water dynamics and vegetation patterns in a semiarid grassland. Plant Ecology 134:77–89CrossRefGoogle Scholar
  44. Sjursen P (2009) Grand River National Grassland Robel pole inventory. U.S. Forest Service, BismarckGoogle Scholar
  45. Svingen D (2009) Grassland bird management on public lands in the United States: an example from the northern Great Plains. In: Proceedings of the fourth international partners in flight conference: tundra to tropics, pp 590–593Google Scholar
  46. Tonooka H, Sakuma F, Kudoh M, Iwafune K (2003) ASTER/TIR onboard calibration status and user-based recalibration. In: Proceedings of the Society of Photo-Optical Instrumentation Engineers, pp 191–201Google Scholar
  47. Uresk DW, Benson TA (2007) Monitoring with a modified Robel pole on meadows in the central Black Hills of South Dakota. West North American Naturalist 67:46–50CrossRefGoogle Scholar
  48. Uresk DW, Juntti TM (2008) Monitoring Idaho fescue grasslands in the Big Horn Mountains, Wyoming, with a modified Robel pole. West North American Naturalist 68:1–7CrossRefGoogle Scholar
  49. Vermiere LT, Ganguli AC, Gillen RL (2002) A robust model for estimating standing crop across vegetation types. Journal of Range Management 55:494–497CrossRefGoogle Scholar
  50. Vermiere LT, Gillen RL (2001) Estimating herbage standing crop with visual obstruction in tallgrass prairie. Journal of Range Management 54:57–60Google Scholar
  51. Washington-Allen RA, West NE, Ramsey RD, Efroymson RA (2006) A protocol for retrospective remote sensing-based ecological monitoring of rangelands. Range Ecology and Management 59(1):19–29CrossRefGoogle Scholar
  52. Wood J (1996) Scale-based characterization of digital elevation models. In: Parker D (ed) Innovations in GIS 3. Taylor and Francis, London, pp 163–175Google Scholar

Copyright information

© Springer Science+Business Media, LLC (outside the USA) 2012

Authors and Affiliations

  • Rebecca L. Phillips
    • 1
  • Moffatt K. Ngugi
    • 1
  • John Hendrickson
    • 1
  • Aaron Smith
    • 2
  • Mark West
    • 3
  1. 1.United States Department of Agriculture (USDA)Agricultural Research Service (ARS)MandanUSA
  2. 2.Ducks UnlimitedBismarckUSA
  3. 3.United States Department of Agriculture (USDA)Agricultural Research Service (ARS)Fort CollinsUSA

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