Characterizing rangeland vegetation using Landsat and 1-mm VLSA data in central Wyoming (USA)

As an alternative to ground-cover data collection by conventional and expensive sampling techniques, we compared measurements obtained from very large scale aerial (VLSA) imagery for calibrating moderate resolution Landsat data. Using a grid-based sampling scheme, 162 VLSA images were acquired at 100 m above ground level. The percent vegetation cover in each photo was derived using SamplePoint (a manual inventory method) and VegMeasure (a reflectance based, automated method). Approximately two-thirds of the VLSA images were used for calibrating Landsat data while the remainder was used for validation. Regression models with Landsat bands accounted for 55% of the VegMeasure-based measurements of vegetation, whereas models that included both Landsat bands and elevation data accounted for 67%. The relationship between the Landsat bands and the percent vegetation cover measured by SamplePoint was lower (R2 = 20%), highlighting the differences between the inventory and reflectance based protocols. Results from the model validation indicated that the model’s predictive power was lower when the vegetation cover was either <20% or >55%. Additional work is needed in these ecosystems to improve the calibration techniques for sites with low and high vegetation cover; however, these results demonstrate the VLSA imagery could be used for calibrating Landsat data and deriving rangeland vegetation cover. By adopting such methodologies the US Federal land management agencies can increase the efficiency of the monitoring programs in Wyoming and in other western states of the US.

[1]  D Terrance Booth,et al.  Precision Measurements from Very-Large Scale Aerial Digital Imagery , 2006, Environmental monitoring and assessment.

[2]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[3]  Robert D. Berryman,et al.  Point Sampling Digital Imagery with ‘Samplepoint’ , 2006, Environmental monitoring and assessment.

[4]  Elizabeth A. Peck,et al.  Introduction to Linear Regression Analysis , 2001 .

[5]  R. M. Hoffer,et al.  Biomass estimation on grazed and ungrazed rangelands using spectral indices , 1998 .

[6]  A. Beetle.  A study of sagebrush. The section Tridentatae of Artemisia. , 1960 .

[7]  N. Saliendra,et al.  Winter CO2 fluxes above sagebrush-steppe ecosystems in Idaho and Oregon , 2004 .

[8]  D. Terrance Booth,et al.  Very Large Scale Aerial Photography for Rangeland Monitoring , 2006 .

[9]  Douglas E. Johnson,et al.  Detection-Threshold Calibration and Other Factors Influencing Digital Measurements of Ground Cover , 2005 .

[10]  J. R. Jensen.  Remote Sensing Education and Workforce Considerations , 2001 .

[11]  John S. Sperry,et al.  DIFFERENCES IN DROUGHT ADAPTATION BETWEEN SUBSPECIES OF SAGEBRUSH (ARTEMISIA TRIDENTATA) , 1999 .

[12]  J. Campbell.  Introduction to remote sensing , 1987 .

[13]  B. Markham,et al.  Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges , 2003, IEEE Trans. Geosci. Remote. Sens..

[14]  Scott L. Powell,et al.  Effect of Alternative Splitting Rules on Image Processing Using Classification Tree Analysis , 2006 .

[15]  C. Dibari,et al.  Satellite estimate of grass biomass in a mountainous range in central Italy , 2003, Agroforestry Systems.

[16]  M. Louhaichi,et al.  Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat , 2001 .

[17]  J. Hill,et al.  Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands , 2003 .

[18]  Raghavan Srinivasan,et al.  Estimation of managed loblolly pine stand age and density with Landsat ETM+ data , 2006 .

[19]  M. Fukuhara,et al.  Estimation of aboveground grassland phytomass with a growth model using Landsat TM and climate data , 1999 .

[20]  Daniel G. Milchunas,et al.  CO2 ENHANCES PRODUCTIVITY, ALTERS SPECIES COMPOSITION, AND REDUCES DIGESTIBILITY OF SHORTGRASS STEPPE VEGETATION , 2004 .

[21]  J. L. Barker,et al.  Landsat MSS and TM post-calibration dynamic ranges , 1986 .

[22]  Samuel E. Cox,et al.  The Accuracy of Ground-Cover Measurements , 2006 .

[23]  Jammalamadaka.  Introduction to Linear Regression Analysis (3rd ed.) , 2003 .

[24]  R. Lawrence,et al.  Soil Phosphorus and Potassium Mapping Using a Spatial Correlation Model Incorporating Terrain Slope Gradient , 2002, Precision Agriculture.

[25]  D. T. Booth,et al.  Measuring Plant Cover in Sagebrush Steppe Rangelands: A Comparison of Methods , 2006, Environmental management.

[26]  R. D. Ramsey,et al.  Landscape Cover-Type Modeling Using a Multi-Scene Thematic Mapper Mosaic , 1997 .

[27]  Ana M. Cingolani,et al.  Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify land-cover units , 2004 .