Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Stafford says exiting down stock market worsened position of low-income households

Bailey's work cited on growing income disparities in college enrollment and graduation

Murphy says mobile sensor data will allow adaptive interventions for maximizing healthy outcomes

Highlights

PSC Fall 2014 Newsletter now available

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Michigan's graduate sociology program tied for 4th with Stanford in USN&WR rankings

Jeff Morenoff makes Reuters' Highly Cited Researchers list for 2014

Next Brown Bag

Monday, Nov 3
Melvin Stephens, Estimating Program Benefits

Propagating error in land-cover-change analyses: impact of temporal dependence under increased thematic complexity

Publication Abstract

Burnicki, A.C., Daniel Brown, and P. Goovaerts. 2010. "Propagating error in land-cover-change analyses: impact of temporal dependence under increased thematic complexity." International Journal of Geographical Information Science, 24(7): 1043-1060.

We examined the impact of temporal dependence between patterns of error in classified time-series imagery through a simulation modeling approach. This research extended the land-cover-change simulation model we previously developed to investigate: (1) the assumption of temporal independence between patterns of error in classified time-series imagery; and (2) the interaction of patterns of change and patterns of error in a post-classification change analysis. In this research, the thematic complexity of the classified land-cover maps was increased by increasing the number of simulated land-cover classes. Simulating maps with increased categorical resolution permitted the incorporation of: (1) higher-order, more complex spatial and temporal interactions between land-cover classes; and (2) patterns of error that better reproduce the complex error interactions that often occur in time-series classified imagery. The overall modeling framework was divided into two primary components: (1) generation of a map representing true change; and (2) generation of a suite of change maps that had been perturbed by specific patterns of error. All component maps in the model were produced using simulated annealing, which enabled us to create a series of map realizations with user-defined spatial and temporal patterns. Comparing the true map of change to the error-perturbed maps of change using accuracy assessment statistics showed that increasing the temporal dependence between classification errors did not improve the accuracy of resulting maps of change when the categorical scale of the land-cover classified maps was increased. The increased structural complexity within the time series of maps effectively inhibited the impact of temporal dependence. However, results demonstrated that there are interactions between patterns of error and patterns of change in a post-classification change analysis. These interactions played a major role in determining the accuracy associated with the maps of change.

DOI:10.1080/13658810903279008 (Full Text)

Country of focus: United States of America.

Browse | Search : All Pubs | Next