Philip Townsend
Professor
A111 Russell Labs
1630 Linden Drive
Madison, WI 53706
Google Voice:608.622.7445
E-mail: ptownsend -at- wisc.edu
Expertise
Forest Remote Sensing and Spatial Analysis
Education/Experience
Education
Degree | Institution | Major Field | Granted |
B.A. | University of Virginia | History | 1989 |
Ph.D. | University of North Carolina | Geography | 1997 |
Professional Experience
Institution | Title | Specialization | Years |
Texas A&M | Asst. Prof. | Remote Sensing/Biogeography | 1997-1998 |
U.of Maryland Cntr for Envir Sci Appalachian Lab. |
Asst. Prof. | Remote Sensing/Landscape Ecology | 1998-2003 |
U.of Maryland Cntr for Envir Sci Appalachian Lab. |
Asst. Prof. | Remote Sensing/Landscape Ecology | 2003-2005 |
UW-Madison | Assoc. Prof. | Remote Sensing/Spatial Analysis | 2005-2009 |
UW-Madison | Assoc. Prof. | Remote Sensing/Spatial Analysis | 2009-present |
Society/Professional Memberships
American Geophysical Union (AGU)
Ecological Society of America (ESA, Life Member)
Institute of Electrical and Electronics Engineers, Geoscience and Remote Sensing Society (IEEE, GRS)
International Association for Landscape Ecology (U.S. Regional Association) (IALE)
Research
Courses Taught
FWE 550 Forest Ecology – Introduction to major abiotic and biotic factors that influence forest ecosystem composition, structure, and function. Reviews important processes that influence structure and function of forest ecosystems. Uses basic ecosystem concepts to elucidate influence of anthropogenic (including forest management) and natural disturbances on forest ecosystem structure and function.
FWE 551 Forest Ecology Lab – Forest Ecology laboratory is the companion course for the lecture-based Forest Ecology (F&W ECOL 550). The objective of the forest ecology laboratory is to review concepts that are presented in the classroom by exposing students to the key concepts and processes discussed in lecture that can best be seen in the field or illustrated with the use of ecosystem models.
FWE 711 Multivariate Analysis of Ecological and Community Data – This course will examine some common methods of multivariate data analysis in ecology and environmental science. Often called “community data analysis,” this class will cover methods for the analysis of complex, multidimensional datasets that are collected in the study of plant, invertebrate, fish, and bird communities. We will also address the concurrent analysis of the environmental factors that may drive community distributions. All of this provides the basis for predictive modeling of distributions across landscapes. General methods we will cover include ordination (PCA, DCA, NMDS, CCA), clustering (or classification), and other comparative analyses of data matrices (ANOSIM, Mantel tests). The “class” (better called a “workshop”) is designed to be applied, meaning that the objective is for students to learn in a “hands-on” way how to use these tools, and the circumstances under which their uses are either appropriate or inappropriate.
Publications
Selected Publications
A list of his publications on Google Scholar can be found here.