Philip Townsend

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A111 Russell Labs
1630 Linden Drive
Madison, WI 53706
Google Voice:608.622.7445
E-mail: ptownsend -at-

Lab Website


Forest Remote Sensing and Spatial Analysis



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)


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. 


Selected Publications

A list of his publications on Google Scholar can be found here.