Professor
Office Location
W-523 Turner Hall, MC-047
1102 South Goodwin Avenue
Urbana, Illinois 61801
Phone: 217-333-9346
Email: gertner@uiuc.edu
Education
Ph.D., Forest Biometrics, University of Washington, Seattle, 1979
M.S., Forest Biometrics, University of Wisconsin, Madison, 1976
B.S., Forest Science, University of Illinois at Urbana-Champaign, 1974
Areas of Expertise
Statistics & Experimental Design, Modeling, Remote Sensing, GIS, & Spatial Analysis
Faculty member since 1979
How I got interested in this field
Developing mathematical models has become an integral part of research in many areas of ecology, environmental sciences and natural resources. These models can range from very simple empirical models to very complicated mechanistic process models. Models are now frequently used to answer many questions. What is the potential damage to forests due to acid rain? How will a particular forest look 50 years from now under different environmental scenarios? What is the influence of climate on sustainability of tree species in a mountainous environment? Models are being used by everyone, from small landowners managing their woodlots in Illinois using a growth and yield model to international agencies determining environmental policy relating to global warming using complex ecosystem models. Needless to say, for real world systems, projections made with the simplest to the most complicated model have statistical errors and uncertainties. For many ecological and environmental models, there can be hundreds of sources of uncertainties due to measurements, sampling, knowledge gaps, parameter estimates, multiple temporal and spatial scales, stochasticity, etc. If one doesn't account for error and uncertainty, the outcomes from models have little or no value. Assessing consequences of the propagation of errors and uncertainties becomes particularly complex as scientists make spatially explicit projections forward in time, or when they test complex hypotheses based on models. For the last twenty years my primary area of research has been to develop a comprehensive framework to statistically identify and manage error and uncertainty. By accounting for errors and uncertainty, a so called "error budget" can be developed. An error budget shows the overall precision of estimates/predictions made with a system, divided according to different types of error sources within and outside of the system. There are benefits to having an error budget for users of these models. The primary benefit is the acknowledgment that errors and uncertainties exist; and it is reported. When using the model, the user will know that there is uncertainty and will use the model with full knowledge of the uncertainty and the risk of making the correct predictions (or incorrect predictions). The second most important benefit is that in a systematic fashion the decision-maker will understand the sources of errors and uncertainties in the predictions. If they understand that there are errors and uncertainties, they can attempt to manage and possibly reduce the errors and uncertainties if feasible. Finally, they can assess the risk of making mistakes due to model uncertainties versus the costs for improving the models.
Curriculum Vitae (PDF)
Uncertainty Analysis and Sensitivity Analysis ToolBox Download - Executable File
Research Interests
My research team in the Spatial Methods and Dynamic Modeling Laboratory have been developing error budgets for both non-spatial and geospatial large scale natural resource monitoring and projection systems. Emphasis for the last ten year has been given to geospatial systems, where both ground and remote sensed monitoring systems have been used for inputs for large scale landscape natural resource modeling systems. Spatially identifying the sources of uncertainties, modeling their accumulation and propagation, and finally, quantifying them locally and globally for current and future landscape maps has been the focus of the research. Primary funding has come from cooperative environmental research program of the Department of Defense, Department of Energy and the Environmental Protection Agency. A variety of non-spatial and geospatial natural resource systems have been studied including avalanche hazard models for Swiss Alps, carbon sequestration models, fragmentation assessment models, forest growth process models, land disturbance and restoration models, nutrient models for large water sheds (Mississippi River Basin), plant biodiversity models, spatially explicit animal and plant population viability assessment models, spatially explicit soil erosion models, sustainability models, topographic models, urban sprawl models, etc. Much of the research has been published in a wide range of refereed journals that span many disciplines.
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