Submitted to: Agroforestry Systems
Publication Type: Peer reviewed journal
Publication Acceptance Date: 2/14/2008
Publication Date: 3/4/2008
Citation: Sivanpillai, R., Booth, D.T. 2008. Characterizing rangeland vegetation using Landsat and 1-mm VLSA data in central Wyoming, USA. Agroforestry Systems 73:55-64. Interpretive Summary: Remote sensing of rangelands by Landsat and other satellites provide a time frequency and economy not matched by aerial or ground inventories; however, there is a need to better correlate field-measured vegetation data with satellite data. As an alternative to expensive field-measured vegetation data, we tested the use of vegetation measurements obtained from very large scale aerial (VLSA) photography for calibrating Landsat data. Using a grid-based sampling scheme, 162 VLSA photographs were acquired at 300 ft above ground level and the percent vegetation cover in each photo was measured using SamplePoint (inventory based) and VegMeasure (reflectance based) software. Our comparison showed that Landsat data used with elevation data could account for 67% of the VegMeasure-based measurements but only 20% of the SamplePoint measurements. Additional work is needed to improve the calibration techniques but these results demonstrate the potential for VLSA photography to be used for calibrating Landsat data and for obtaining rangeland vegetation cover measurements.
Technical Abstract: 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 (inventory based) and VegMeasure (reflectance based) protocols. 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. 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 the western US.