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Modeling grain-size dependent bias in estimating forest area: a regional applicationAuthor(s): Daolan Zheng; Linda S. Heath; Mark J. Ducey
Source: Landscape Ecology. 23: 1119-1132.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
PDF: Download Publication (448.89 KB)
DescriptionA better understanding of scaling-up effects on estimating important landscape characteristics (e.g. forest percentage) is critical for improving ecological applications over large areas. This study illustrated effects of changing grain sizes on regional forest estimates in Minnesota, Wisconsin, and Michigan of the USA using 30-m land-cover maps (1992 and 2001) produced by the National Land Cover Datasets. The maps were aggregated to two broad cover types (forest vs. non-forest) and scaled up to 1-km and 10-km resolutions. Empirical models were established from county-level observations using regression analysis to estimate scaling effects on area estimation. Forest percentages observed at 30-m and 1-km land-cover maps were highly correlated. This intrinsic relationship was tested spatially, temporally, and was shown to be invariant. Our models provide a practical way to calibrate forest percentages observed from coarse-resolution land-cover data.
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CitationZheng, Daolan; Heath, Linda S.; Ducey, Mark J. 2008. Modeling grain-size dependent bias in estimating forest area: a regional application. Landscape Ecology. 23: 1119-1132.
Keywordsaggregation, confidence interval, land-cover map, pixel resolution, standard errors, 3 lake states of USA
- Identifying grain-size dependent errors on global forest area estimates and carbon studies
- Satellite detection of land-use change and effects on regional forest aboveground biomass estimates
- Quantifying scaling effects on satellite-derived forest area estimates for the conterminous USA
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