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

PSC In The News

RSS Feed icon

Alexander and Massey compare outcomes for children whose parents did and did not take part in Great Migration

Geronimus on pushing past early dismissal of her weathering hypothesis

Thompson: Censoring reading materials in prisons could lead to more, not less rebellion

More News


Remembering Jim Morgan, founding member of ISR and creator of the PSID

1/17/18: ISR screening and discussion of documentary "Class Divide" at Michigan Theater

Bailey et al. find higher income among children whose parents had access to federal family planning programs in the 1960s and 70s

U-M's campus climate survey results discussed in CHE story

More Highlights

Next Brown Bag

Mon, Jan 22, 2018, noon: Narayan Sastry

Multi-dimensional vegetation structure in modeling avian habitat

Archived Abstract of Former PSC Researcher

Bergen, K.M., A.M. Gilboy, and Daniel G. Brown. 2007. "Multi-dimensional vegetation structure in modeling avian habitat." Ecological Informatics, 2(1): 9-22.

The goal of this study was to evaluate the contributions of forest and landscape structure derived from remote sensing instruments to habitat mapping. Our empirical data focused at the landscape scale on a test site in northern Michigan, using radar and Landsat imagery and bird-presence data by species. We tested the contributions of multi-dimensional forest and landscape structure variables using GARP (Genetic Algorithm for Rule-Set Production), a representative modeling methodology used in biodiversity informatics. For our multi-dimensional variables, radar data were processed to derive forest biomass maps and these data were used with a Landsat-derived vegetation type classification and spatial neighborhood analyses. We collected field data on bird species presence and habitat for northern forest birds known to have a range of vegetation habitat requirements. We modeled and tested the relationships between bird presence and 1) vegetation type, 2) vegetation type and spatial neighborhood descriptions, 3) vegetation type and biomass, and 4) all variables together, using GARP, for three bird species. Modeled results showed that inclusion of biomass or neighborhoods improved the accuracy of bird habitat prediction over vegetation type alone, and that the inclusion of neighborhoods and biomass together generally produced the greatest improvement. The maps and model rules resulting from the multiple factor models were interpreted to be more precise depictions of a particular species habitat when compared with the models that used vegetation type only. We suggest that for bird species whose niche requirements include forest and landscape structure, inclusion of multi-dimensional information may be advantageous in habitat modeling at the landscape level. Further research should focus on testing additional variables and species, on further integration of newer radar and lidar remote sensing capabilities with multi-spectral sensors for quantifying forest and landscape multi-dimensional structure, and incorporating these in biodiversity informatics modeling.

DOI:10.1016/j.ecoinf.2007.01.001 (Full Text)

Public Access Link

Browse | Search : All Pubs | Next