Bergen, K.M., A.M. Gilboy, and Daniel 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.