Preliminary Comparison of Landscape Pattern–Normalized Difference Vegetation Index (NDVI) Relationships to Central Plains Stream Conditions
- Jerry A. Griffith *a,
- Edward A. Martinkob,
- Jerry L. Whistlerc and
- Kevin P. Pricea
- a Kansas Applied Remote Sensing Program and Dep. of Geography, Univ. of Kansas, Lawrence, KS 66045
b Kansas Applied Remote Sensing Program, Kansas Biological Survey and Dep. of Ecology and Evolutionary Biology, Univ. of Kansas, Lawrence, KS 66045
c Kansas Applied Remote Sensing Program, Univ. of Kansas, Lawrence, KS 66045
We explored relationships of water quality parameters with landscape pattern metrics (LPMs), land use–land cover (LULC) proportions, and the advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) or NDVI-derived metrics. Stream sites (271) in Nebraska, Kansas, and Missouri were sampled for water quality parameters, the index of biotic integrity, and a habitat index in either 1994 or 1995. Although a combination of LPMs (interspersion and juxtaposition index, patch density, and percent forest) within Ozark Highlands watersheds explained >60% of the variation in levels of nitrite–nitrate nitrogen and conductivity, in most cases the LPMs were not significantly correlated with the stream data. Several problems using landscape pattern metrics were noted: small watersheds having only one or two patches, collinearity with LULC data, and counterintuitive or inconsistent results that resulted from basic differences in land use–land cover patterns among ecoregions or from other factors determining water quality. The amount of variation explained in water quality parameters using multiple regression models that combined LULC and LPMs was generally lower than that from NDVI or vegetation phenology metrics derived from time-series NDVI data. A comparison of LPMs and NDVI indicated that NDVI had greater promise for monitoring landscapes for stream conditions within the study area.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
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