Comparison of Stocking Rates From Remote Sensing and Geospatial Data

Abstract Remote sensing data from the Advanced Very High Resolution Radiometer (AVHRR) have coarse spatial resolution (1 km2 pixel size) and high temporal resolution, which can be used to estimate net primary production regionally. The normalized difference vegetation index (NDVI) is used to determine the fraction of absorbed photosynthetically active radiation, which is sensitive to differences in growth caused by a large year-to-year variation in precipitation. The 12-year average of net primary production was used to calculate stocking rates in animal-unit months per acre for the state of Wyoming. Stocking rates were also calculated for Wyoming from 1:500 000 scale soil and climate geospatial data layers based on stocking rates from the US Department of Agriculture Natural Resources Conservation Service Technician Guide to Range Sites and Range Condition. In a pixel-by-pixel comparison, there was a weak but significant correlation between the 2 methods based on the spatial distribution of precipitation. There were classes of vegetation type for which the AVHRR data predicted either much lower or much higher stocking rates. More work needs to be done to reduce geospatial data uncertainties for the determination of stocking rates from both NDVI and stocking rate tables. Remote sensing indicates the actual condition of vegetation, so this is an important step in the development of regional forecasting of range condition, trend, and projected stocking rates for decision support tools.

[1]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[2]  J. Monteith.  Climate and the efficiency of crop production in Britain , 1977 .

[3]  P. J. Curran,et al.  Multispectral remote sensing for the estimation of green leaf area index , 1983, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[4]  Jerry L. Hatfield,et al.  Intercepted photosynthetically active radiation estimated by spectral reflectance , 1984 .

[5]  J. C. Price,et al.  Land surface temperature measurements from the split window channels of the NOAA 7 Advanced Very High Resolution Radiometer , 1984 .

[6]  G. Asrar,et al.  Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1 , 1984 .

[7]  J. Holechek,et al.  An approach for setting the stocking rate. , 1988 .

[8]  Paul T. Tueller,et al.  Remote sensing technology for rangeland management applications. , 1989 .

[9]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[10]  S. Prince.  A model of regional primary production for use with coarse resolution satellite data , 1991 .

[11]  P. Tueller.  Overview of remote sensing for range management , 1992 .

[12]  J. Eidenshink.  The 1990 conterminous U. S. AVHRR data set , 1992 .

[13]  S. Running,et al.  Simulated dry matter yields for aspen and spruce stands in the North American boreal forest , 1992 .

[14]  S. Goward,et al.  Vegetation canopy PAR absorptance and the normalized difference vegetation index - An assessment using the SAIL model , 1992 .

[15]  R. H. Haas,et al.  Evaluating Landsat Thematic Mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands , 1993 .

[16]  S. Running,et al.  8 – Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models , 1993 .

[17]  Gérard Dedieu,et al.  Methodology for the estimation of terrestrial net primary production from remotely sensed data , 1994 .

[18]  Liping Di,et al.  Modelling relationships between NDVI and precipitation during vegetative growth cycles , 1994 .

[19]  Board on Agriculture.  Rangeland Health: New Methods to Classify, Inventory, and Monitor Rangelands , 1994 .

[20]  R. Myneni,et al.  On the relationship between FAPAR and NDVI , 1994 .

[21]  C. Daly,et al.  A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain , 1994 .

[22]  Radiation-Use Efficiency in Summer Rape , 1995 .

[23]  S. Goward,et al.  Global Primary Production: A Remote Sensing Approach , 1995 .

[24]  D. Baldocchi,et al.  CO2 fluxes over plant canopies and solar radiation: a review , 1995 .

[25]  C. D. Keeling,et al.  Global net carbon exchange and intra‐annual atmospheric CO2 concentrations predicted by an ecosystem process model and three‐dimensional atmospheric transport model , 1996 .

[26]  W. Liu,et al.  Monitoring regional drought using the Vegetation Condition Index , 1996 .

[27]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[28]  T. Svejcar,et al.  The rangeland carbon dioxide flux project. , 1997 .

[29]  Kenneth L. Driese,et al.  A digital land cover map of Wyoming, USA: a tool for vegetation analysis , 1997 .

[30]  Yoram J. Kaufman,et al.  MODIS NDVI Optimization To Fit the AVHRR Data Series—Spectral Considerations , 1998 .

[31]  Limin Yang,et al.  An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains , 1998 .

[32]  1:500,000-Scale Digital Soils Map of Wyoming , 1998 .

[33]  S. Goetz,et al.  Modelling Terrestrial Carbon Exchange and Storage: Evidence and Implications of Functional Convergence in Light-use Efficiency , 1999 .

[34]  C. R. Tischler,et al.  Radiation use efficiency and leaf CO2 exchange for diverse C4 grasses. , 1999 .

[35]  S. T. Gower,et al.  Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems , 1999 .

[36]  Y. Nouvellon,et al.  Time Course of Radiation Use Efficiency in a Shortgrass Ecosystem: Consequences for Remotely Sensed Estimation of Primary Production , 2000 .

[37]  J. C. Winslow,et al.  A globally applicable model of daily solar irradiance estimated from air temperature and precipitation data , 2001 .

[38]  M. S. Moran,et al.  Coupling a grassland ecosystem model with Landsat imagery for a 10-year simulation of carbon and water budgets , 2001 .

[39]  J. C. Winslow,et al.  Numerical Terradynamic Simulation Group 3-2001 Mapping Weekly Rangeland Vegetation Productivity Using MODIS Algorithms , 2018 .

[40]  G. L. Anderson,et al.  FFT regression and cross-noise reduction for comparing images in remote sensing , 2002 .

[41]  B. Wylie,et al.  Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands a case study , 2002 .

[42]  M. S. Moran,et al.  Applications and Research Using Remote Sensing for Rangeland Management , 2003 .

[43]  L. Ji,et al.  Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices , 2003 .

[44]  E. Raymond Hunt,et al.  Estimation of Carbon Sequestration by Combining Remote Sensing and Net Ecosystem Exchange Data for Northern Mixed-Grass Prairie and Sagebrush–Steppe Ecosystems , 2004 .

[45]  R. B. Jackson,et al.  A global analysis of root distributions for terrestrial biomes , 1996, Oecologia.

[46]  Patrick L. Shaver,et al.  Interpreting indicators of rangeland health , 2005 .