Spatial contaminations in forestry


Understanding relationships among tree species, or between tree diversity, distribution, and underlying environmental gradients, is a central concern for forest ecologists, managers, and management agencies. The spatial processes underlying observed spatial patterns of trees or edaphic variables often are complex and violate two fundamental assumptions—isotropy and stationarity—of spatial statistics.

Commonly, forestry data is not collected without error, and the performance of any statistics when there is noise or measurement error in the data is highly affected. Our research focuses on the exploration of the codispersion coefficient, which measures the spatial association between two sequences, and the effects of several different types of contaminations that are meaningful in forestry. This study also involves some of the contamination techniques widely used in robustness for dependent data.

See one of our papers at https://www.mdpi.com/1999-4907/9/11/679

Researcher AM2V: Ronny Vallejos

Chilean collaborators: Jonathan Acosta (PUCV)

International collaborators: Aaron Ellison (Harvard University, USA), Hanna Buckley and Bradley Case (Auckland University of Technology, NZ).