Land use and land cover digital data

Fegeas, Robin G.; Claire, Robert W.; Guptill, Stephen C.; Anderson, Erin; Hallam, Cheryl A.

doi:10.3133/cir895e

Cited by 42 publications

(20 citation statements)

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“…Geological Survey Land Use Data Analysis (LUDA) database of land use and land cover derived from high-altitude aerial photography (Fegeas et al 1983). The selection was inteded to represent a rough transect of landscape patterns across physiographic regions of the United States (Hunsaker et al 1994).…”

Section: Usgs Luda Mapsmentioning

confidence: 99%

A factor analysis of landscape pattern and structure metrics

et al. 1995

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Fifty-five metrics of landscape pattern and structure were calculated for 85 maps of land use and land cover. A multivariate factor analysis was used to identify the common axes (or dimensions) of pattern and structure which were measured by a reduced set of 26 metrics. The first six factors explained about 87% of the variation in the 26 landscape metrics. These factors were interpreted as composite measures of average patch compaction, overall image texture, average patch shape, patch perimeter-area scaling, number of attribute classes, and large-patch density-area scaling. We suggest that these factors can be represented in a simpler way by six univariate metrics -average perimeter-area ratio, contagion, standardized patch shape, patch perimeterarea scaling, number of attribute classes, and large-patch density-area scaling.

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Section: Usgs Luda Mapsmentioning

confidence: 99%

A factor analysis of landscape pattern and structure metrics

et al. 1995

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“…The USGS digital land use and land cover data base (Fegas et al 1983), which provides landscape maps interpreted from NASA U 2 / R 8 -5 7 high altitude aerial photo coverage obtained in 1973, was used for the analyses. The original aerial photographs were hand digitized into 37 land cover categories.…”

Section: Landscape Datamentioning

confidence: 99%

Effects of changing spatial scale on the analysis of landscape pattern

et al. 1989

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The purpose of this study was to observe the effects of changing the grain (the first level of spatial resolution possible with a given data set) and extent (the total area of the study) of landscape data on observed spatial patterns and to identify some general rules for comparing measures obtained at different scales. Simple random maps, maps with contagion (i.e., clusters of the same land cover type), and actual landscape data from USGS land use (LUDA) data maps were used in the analyses. Landscape patterns were compared using indices measuring diversity (H), dominance (D) and contagion (C). Rare land cover types were lost as grain became coarser. This loss could be predicted analytically for random maps with two land cover types, and it was observed in actual landscapes as grain was increased experimentally. However, the rate of loss was influenced by the spatial pattern. Land cover types that were clumped disappeared slowly or were retained with increasing grain, whereas cover types that were dispersed were lost rapidly. The diversity index decreased linearly with increasing grain size, but dominance and contagion did not show a linear relationship. The indices D and C increased with increasing extent, but H exhibited a variable response. The indices were sensitive to the number (m) of cover types observed in the data set and the fraction of the landscape occupied by each cover type (Pk); both m and Pk varied with grain and extent. Qualitative and quantitative changes in measurements across spatial scales will differ depending on how scale is defined. Characterizing the relationships between ecological measurements and the grain or extent of the data may make it possible to predict or correct for the loss of information with changes in spatial scale.

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“…The data in this study are digitized land use/land cover from the Land Use Data Analysis (LUDA) program, compiled by the U.S. Geological Survey (Fegeas et al 1983). These data were the only readily available and consistently classified land cover data for much of the United States at the start of this research.…”

Section: Methodsmentioning

confidence: 99%

Sampling to characterize landscape pattern

et al. 1994

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Current reseach suggests that metrics of landscape pattern may reflect ecological processes operating at different scales and may provide an appropriate indicator for monitoring regional ecological changes. This paper examines the extent to which a 1 / 16 areal subset of the landscape using equally spaced 40-km 2 hexagons can characterize the spatial extent of land cover types and landscape pattern (number of types of edges, patch shape complexity, dominance, and contagion). For 200-m resolution data the hexagon subset gives a reasonable estimate of overall landscape cover but may not be adequate for monitoring uncommon land cover types such as wetlands. For agriculture and forest, their proportion of the full landscape units is only outside the 95% confidence interval of the hexagon estimate 4-8% of the time, whereas the proportions for wetland and barren areas are outside the confidence interval 11-34~ of the time. The hexagon subset also does not appear to be adequate as the sole basis for monitoring landscape pattern. The values for contagion, dominance, and shape complexity calculated on the full landscape units are outside the 95 % confidence interval of the hexagon estimate 27-76% of the time. Other statistical analyses include regressions between full landscape and hexagon subsets, mean differences and standard errors along with tests on number of positive and negative values, and percent relative error of hexagon estimates.

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Land use and land cover digital data

Cited by 42 publications

References 0 publications

A factor analysis of landscape pattern and structure metrics

A factor analysis of landscape pattern and structure metrics

Effects of changing spatial scale on the analysis of landscape pattern

Sampling to characterize landscape pattern

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