under
construction Current
courses
ESPM 102B - Natural resource sampling
This
course is about sampling of natural resources and the environment. It covers the
major sampling designs (random, systematic, stratified, cluster,
ratio, regression, and multi-stage) and their estimators used in all types of
natural resource and environmental sampling projects. The labs focus on
collecting and analyzing real data including sampling for grassland and forest
biomass; soil texture and pH and below ground fine root biomass; stream
temperature and dissolved oxygen; insect population levels; rare populations and
a student designed sampling project. There is one all day Saturday lab to Point
Reyes for the annual Tule Elk complete population census and to Domaine Chandon
for vineyard sampling.
The course is a junior level course and is required for all students majoring in
resource management. The course will also provide a foundation in
environmental and resource assessment for ecology, wildlife biology, geography,
landscape architecture, engineering, range management and students in other
related fields.
ESPM 210 - Natural resource spatial data analysis
An introduction to natural resource spatial data analysis. Topics covered include: spatial sampling, quadrat analysis, distance methods, spatial point patterns and Ripley's K-function, spatial autocorrelation, and geostatistics (Kriging). Readings cover applications in various natural resource fields as well as general theory. (From the 1999-2001 General Catalog). The course format is a combination of lectures by the instructor and class discussions in seminar format. There are accompanying computer-based assignments using Splus 2000 for Windows with the accompanying Spatial Statistics module.
Course Prerequisites: One year of upper division probability and statistics, one course in multivariate analysis, or consent of instructor. (One year of matrix algebra and calculus are strongly recommended)
ESPM
272 - Forest Simulation Modeling
Course Objectives:
To present basic tree and forest modelling methodology, and a review of current
literature. This course is designed to provide theoretical and practical
applications of model building in forestry. Sample datasets are used for class
projects that realistically portray relationships found in California forests.
This course is taught in a combination of lecture and seminar format. Topics
selected each year may vary according to the interests of the class.
Prerequisites: One
year of upper division probability and statistics, one course in
multivariate
analysis, or consent of instructor.
