Quantifying Spatial Heterogeneity in Streams

Cooper, Scott D.; Barmuta, Leon A.; Sarnelle, Orlando; Kratz, Kim W.; Diehl, Sebastian

doi:10.2307/1468250

Cited by 161 publications

(139 citation statements)

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“…The range (a) defines the distance from a point beyond which there is no further correlation of a biophysical property associated with that point. It has also been described as the average patch size of the landscape (Cooper et al, 1997); i.e., a region that differs from its surroundings, but is not necessarily internally homogeneous. The sill (c) is the ordinate value of the range at which the variogram levels off to an asymptote.…”

Section: Estimating Variogram Model Parameters From Etm+ Datamentioning

confidence: 99%

“…A number of theoretical, empirical, and statistical approaches have been presented by previous studies with the common goal to characterize the spatial dependency of landscape patterns and processes (Cooper et al, 1997;Dent & Grimm, 1999;Isaaks & Srivastava, 1989;Journel & Huijbregts, 1978). Analogously, the spatial representativeness of tower albedo measurements can be characterized by producing a number of geostatistical attributes that describe the overall variability, spatial extent, strength of the spatial correlation, and spatial structure of surface albedo patterns for a given measurement site (Fig.…”

Section: Relative Coefficient Of Variation -R CVmentioning

confidence: 99%

“…Another good indicator of spatial variability is the strength of spatial dependence (or autocorrelation) over the range (SD) (Cooper et al, 1997):…”

Section: Relative Strength Of the Spatial Correlation -R Stmentioning

confidence: 99%

“…By standardizing the semivariance before calculating the slope, a measure that depends on the range (a) and the nugget variance (c 0 ) (Cooper et al, 1997;Dent & Grimm, 1999) can be obtained. In using SD as an indicator of spatial representativeness, this measure can be used to assess changes in surface albedo over short separation distances:…”

Section: Relative Strength Of the Spatial Correlation -R Stmentioning

confidence: 99%

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The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes

Román

Schaaf

Woodcock

et al. 2009

Remote Sensing of Environment

232

161

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A new methodology for establishing the spatial representativeness of tower albedo measurements that are routinely used in validation of satellite retrievals from global land surface albedo and reflectance anisotropy products is presented. This method brings together knowledge of the intrinsic biophysical properties of a measurement site, and the surrounding landscape to produce a number of geostatistical attributes that describe the overall variability, spatial extent, strength of the spatial correlation, and spatial structure of surface albedo patterns at separate seasonal periods throughout the year. Variogram functions extracted from Enhanced Thematic Mapper Plus (ETM+) retrievals of surface albedo using multiple spatial and temporal thresholds were used to assess the degree to which a given point (tower) measurement is able to capture the intrinsic variability of the immediate landscape extending to a satellite pixel. A validation scheme was implemented over a wide range of forested landscapes, looking at both deciduous and coniferous sites, from tropical to boreal ecosystems. The experiment focused on comparisons between tower measurements of surface albedo acquired at local solar noon and matching retrievals from the MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V005) Bidirectional Reflectance Distribution Function (BRDF)/albedo algorithm. Assessments over a select group of field stations with comparable landscape features and daily retrieval scenarios further demonstrate the ability of this technique to identify measurement sites that contain the intrinsic spatial and seasonal features of surface albedo over sufficiently large enough footprints for use in modeling and remote sensing studies. This approach, therefore, improves our understanding of product uncertainty both in terms of the representativeness of the field data and its relationship to the larger satellite pixel.

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Section: Estimating Variogram Model Parameters From Etm+ Datamentioning

confidence: 99%

Section: Relative Coefficient Of Variation -R CVmentioning

confidence: 99%

“…Another good indicator of spatial variability is the strength of spatial dependence (or autocorrelation) over the range (SD) (Cooper et al, 1997):…”

Section: Relative Strength Of the Spatial Correlation -R Stmentioning

confidence: 99%

Section: Relative Strength Of the Spatial Correlation -R Stmentioning

confidence: 99%

See 2 more Smart Citations

The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes

Román

Schaaf

Woodcock

et al. 2009

Remote Sensing of Environment

232

161

View full text Add to dashboard Cite

show abstract

“…Streams are highly variable ecosystems over space and time (Cooper et al, 1997), and this applies to the physical environment and to the structure and dynamics of stream-dwelhng communities. Moreover, abiotic and biotic variation depends on the scale of observation, as has been demonstrated both in temperate (Downes et al, 1993;Boyero, 2003a) and tropical (Boyero & Bailey, 2001;Boyero & Bosch, 2002, 2004 streams.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Riffle-Scale Environmental Variability on Macroinvertebrate Assemblages in a Tropical Stream

Boyero

Bosch

2004

Hydrobiologia

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In a tropical stream (at the Soberanía National Park, Panama), different environmental factors were quantified in riffle habitats (water characteristics: velocity, depth, turbulence, and direction; stone characteristics: surface area, sphericity, and degree of burial; and others: substrate type, and canopy cover). Characteristics of macroinvertebrate assemblages (mean density of individuals, mean taxon richness, and cumulative taxon richness in three stones at each riffle) were related to both mean values and variability of these environmental factors at riffle scale. Macroinvertebrate density was higher in shallow, fast flowing, stony riffles, with low variabihty in dominant substrate type. Taxon richness was also higher in shallow rilfles with loose, not buried stones, and water direction more or less parallel to the bank. Environmental variability resulted as important as mean values of environmental factors to explain variation in macroinvertebrate assemblages. This is the first study, to our knowledge, that quantifies substratum variability and demonstrates its influence on macroinvertebrate assemblages in a tropical stream.

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