Correcting satellite data to detect vegetation signal for eco-hydrologic analyses

• Published in: Journal of hydrology (Amsterdam) 2007-09, Vol.344 (1), p.135-145
• Main Authors: Groeneveld, David P., Baugh, William M.
• Format: Article
• Language: English
• Subjects: Alkali scrub; Animal, plant and microbial ecology; Biological and medical sciences; Colorado; data analysis; Earth sciences; Earth, ocean, space; ecohydrology; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Hydrology; Hydrology. Hydrogeology; Landsat; Landsat TM; NDVI; Normalized Difference Vegetation Index; Phreatophytes; remote sensing; San Luis Valley; satellites; semiarid zones; shrublands; spatial variation; Teledetection and vegetation maps; thematic maps; vegetation
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2007.07.001

Multispectral satellite data have great potential for study of factors affecting eco-hydrology. Hydrologic response of vegetation is often evaluated using normalized difference vegetation index (NDVI) that contains significant non-systematic variation arising from atmospheric and soil factors that mask hydrologic response. Relative magnitudes of these influences were quantified using mid-summer Landsat TM data assembled for the San Luis Valley, Colorado, encompassing 14 years of a 17-year span (1986–2002). Spectral data were processed to reflectance and corrected for atmospheric variation. Data for calculating NDVI were extracted from 2953 pixels contained in 180 131-m-diameter circles of homogeneous native phreatophyte alkali scrub chosen from the region of homeostatic reference wells, outside influence from drainages, roadways or obvious disturbance. NDVI of these pixel values were pooled and then averaged for each year for comparison of three processing steps: (i) correcting non-systematic variation by calculating NDVI ∗; (ii) subtracting antecedent precipitation effects; and (iii) correcting phenology for satellite overpass date relative to expected mid-summer peak vegetation. Little change was expected from year to year due homeostatic water table and ecology. This afforded the opportunity to judge improvements in NDVI processing by the extent to which an underlying ecological relationship for vegetation promotion by antecedent precipitation was revealed, and by reduction of standard deviation for the 14-year dataset mean. NDVI ∗ improved the predictive power of antecedent precipitation from r 2 = 0.37 to r 2 = 0.77. Detrending for antecedent precipitation cut standard deviation in half. No improvement was noted for application of a polynomial seasonal model to further correct for the effect of data-collection time within a two-month mid-summer window.