Jump to Main Content
Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland
- Fern, Rachel R., Foxley, Elliott A., Bruno, Andrea, Morrison, Michael L.
- Ecological indicators 2018 v.94 pp. 16-21
- biomass production, cost effectiveness, ecologists, land cover, managers, normalized difference vegetation index, rangelands, reflectance, remote sensing, sandy soils, semiarid zones, surveys, vegetation cover, Texas
- Rangelands are often too large and inaccessible to determine biomass accumulation and vegetation cover by ground surveys alone, particularly in semi-arid regions where productivity per unit area is typically low and highly variable. Thus, the development of remote sensing derived spectral indices have been of particular interest to rangeland managers as a more cost-effective means of measuring the characteristics, biomass, and extent of vegetation. The Normalized Difference Vegetation Index (NDVI) is the most widely used spectral vegetation index (VI) by ecologists and agriculturalists today. However, regions with sparse vegetation or soils that generate high reflectance values (e.g., dry sandy soils) can severely hinder the reliability of the NDVI as an accurate estimator of green biomass, saturate remote sensors or produce biased estimates of green biomass and vegetative cover. The Optimized Soil Adjusted Vegetation Index (OSAVI) is a newly formed alternative that can accommodate greater variability due to high soil background values. We evaluated the suitability of the NDVI and OSAVI as potential estimators of green biomass and vegetative coverage in a semi-arid rangeland in south Texas. We compared coverage estimates of herbaceous, bare-ground, and woody vegetation calculated from classified satellite images stacked with either an NDVI or OSAVI band to those from traditional ground surveys. OSAVI-derived coverage estimates of herbaceous and woody vegetation did not significantly differ from those produced by ground surveys in 2015. However, NDVI-based estimates for woody vegetation, as well as bare ground, did differ significantly from estimates generated from ground surveys (p = 0.012, 0.018). In 2016, the OSAVI-derived estimates for all three land cover classes were not significantly different than those produced by ground surveys. Our results suggest the OSAVI to be the most appropriate VI-based estimator of green biomass and vegetative coverage in the semi-arid regions of southern Texas.