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

PSC In The News

RSS Feed icon

Yang comments on importance of migrant remittances to future of recipient families

Frey says America's black population is changing with recent immigration

Bailey and Danziger's War on Poverty book reviewed in NY Review of Books

Highlights

Hicken wins 2015 UROP Outstanding Research Mentor Award

U-M ranked #1 in Sociology of Population by USN&WR's "Best Graduate Schools"

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

ISR addition wins LEED Gold Certification

Next Brown Bag

Mon, May 18
Lois Verbrugge, Disability Experience & Measurement

Daniel G. Brown photo

Estimating Error in an Analysis of Forest Fragmentation Change Using North American Landscape Characterization (NALC) Data

Publication Abstract

Brown, Daniel, J.D. Duh, and S. Drzyzga. 2000. "Estimating Error in an Analysis of Forest Fragmentation Change Using North American Landscape Characterization (NALC) Data." Remote Sensing of Environment, 71: 106-117.

We describe an approach for estimating measurement error in an analysis of forest fragmentation dynamics. We classified North American Landscape Characterization (NALC) images in four path-row locations in the Upper Midwest to characterize changing patterns of forest cover. To estimate error, we calculated the differences in values of forest fragmentation metrics for overlapping scene pairs from the same time frame (or epoch). The overlapping image areas were subdivided into landscape partitions. We tested the effects of amount of forest cover, landscape phenology, atmospheric variability (e.g., haze and clouds), and alternative processing approaches on the consistency of metric values calculated for the same place and approximate time but from different images. Two of the metrics tested (average patch size and number of patches) were more sensitive to image characteristics and contained more measurement error in a change detection analysis than the others (percent forest cover and edge density). Increasing the landscape partition size moderately reduced the amount of error in landscape change analysis, but at the cost of reduced spatial resolution. Processes used to generalize the forest map, such as small-polygon sieving and majority filtering, were not found to consistently decrease measurement error in metric values and in some cases increased error. Predictive models of error in a forest fragmentation change analysis were developed and significantly explained up to 50% of the variation in error. We demonstrate how, in a change analysis, predicted error can be used to identify locations that exhibit change substantially greater than the error in value estimation.

DOI:10.1016/S0034-4257(99)00070-X (Full Text)

Browse | Search : All Pubs | Next