HR: 14:25h
AN: B23F-04    [Abstracts]
TI: Scaling effects on forest area estimation in the three Lake States of USA
AU: * Zheng, D
EM: daolan.zheng@unh.edu
AF: University of New Hampshire, 215 James Hall Dept. of Natural Resources, Durham, NH 03824, United States
AU: Heath, L S
EM: lheath@fs.fed.us
AF: USDA Forest Service, 271 Mast Rd., Durham, NH 03824, United States
AU: Ducey, M J
EM: mjducey@cisunix.unh.edu
AF: University of New Hampshire, 215 James Hall Dept. of Natural Resources, Durham, NH 03824, United States
AB: While fine resolution land-cover datasets (e.g. 30-m Landsat data) are appropriate and verifiable for local land use planning, coarse land characterization datasets (1-km resolution) are more suitable for large scale ecological analysis. A better understanding of scaling-up effects on estimating some important landscape characteristics (e.g. forest cover percentage) is critical for improving ecological applications at large scales. This study illustrated scaling-up effects on regional forest cover estimates in Minnesota, Wisconsin, and Michigan of the USA using 30-m land-cover maps (1992 and 2001) produced by the National Land Cover Dataset. The 30-m land-cover maps were scaled up to 1-km resolution and the forest cover percentages before and after the scaling process were compared at the county level. The mean difference of forest area estimates at county level was 8% ranging from 0 to 17% within a 95% confidence interval. A simple empirical model allowed prediction of the scaling effect from data at either resolution. Mean difference between observed and predicted scaling effects at 1-km resolution for a spatial cross-validation test of the model was 2.5% (Std. = 1.9%, standard error = 3.1%), compared to 2.8%, 2.2%, and 3.6%, for a temporal test. Cross-validation of the empirical model indicates that uncertainties in forest area estimates caused by the scaling-up process could be quantified in a simple and fast way with a standard error of 7.6%. Furthermore, scaling-up effects on forest area estimates appear to be both spatially and temporally consistent as well as projection independent. The identified empirical relationship may have broad applicability for large-scale ecological applications using coarse resolution data.
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting