Preliminary Comparison of Landscape Pattern–Normalized Difference Vegetation Index (NDVI) Relationships to Central Plains Stream Conditions

Abstract

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.

Combining Logistic Models with Multivariate Methods for the Rapid Biological Assessment of Rivers Using Macroinvertebrates

“…Correlation analysis showed that first axis of DCA and conductivity was strongly correlated in both the ecotypes ( p < 0.05). This supports the assumption that such axis represents the main gradient of eutrophication, since conductivity is generally an adequate water quality variable expressing nutrient concentration (Webb and Walling, 1996;Ladson et al, 1999;Griffith et al, 2002). Therefore, we can apply the proposed methodology that fits the species scores along this axis according to the logit model.…”

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

“…(Ritter, 1986;Battaglin and Goolsby, 1997;McFarland and Hauck, 1999;Arheimer and Liden, 2000;Liu et al, 2000;Ometo et al, 2000;Sliva and Williams, 2001;Smith et al, 2001;Donner, 2003;Meador and Goldstein, 2003;Santos-Roman et al, 2003;Turner and Rabalais, 2003;Carle et al, 2005;Xian et al, 2007;Zampella et al, 2007;Chang, 2008;Coats et al, 2008;Amiri and Nakane, 2009;Atkinson et al, 2009). Understandings of such empirical relationships can help assess conditions of unmonitored water bodies, identify human activities that significantly contribute to pollution as well as critical areas that are at risk, and promote management practices to reduce nonpoint source pollution (McFarland and Hauck, 1999;Gergel et al, 2002;Griffith et al, 2002;Baker, 2003).…”

Characterizing Landscape Spatial Heterogeneity Using Semivariogram Parameters Derived From Ndvi Images

“…A total of 25% of its minimize the effects of shadows caused by topography (VOROVENCII, 2014). Although NDVI is sensitive to soil and atmospheric effects, it is a good indicator of the total amount of vegetation (HENEBRY, 1993) and is considered important for the analysis of land cover structure and temporal changes (GRIFFITH et al, 2002;SADER, 2003).…”

“…Several works have investigated environmental changes, using spatial heterogeneity derived from various types of remote sensing data (WU et al, 2000;HENEBRY, 2009 In some studies, the Normalized Difference Vegetation Index (NDVI) has been used to detect and analyse spatial heterogeneity using semivariograms (GARRIGUES et al, 2006;BALAGUER-BESER et al, 2013). NDVI, which represents an especially informative vector for landscape structure and temporal change analyses (GRIFFITH et al, 2002), has been used in numerous studies of vegetation dynamics because of its simplicity and close relationship to variables of ecological interest such as land cover change and disturbance propagation at multiple scales (ZURLINI et al, 2006;ZACCARELLI et al, 2008).…”