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Michael Goerndt - Missouri State University. Springfield, MO, US

Michael Goerndt Michael Goerndt

Assistant Professor, Agriculture | Missouri State University

Springfield, MO, UNITED STATES

Dr. Goerndt is a forestry biometrics expert, specializing in spatial estimation, remote sensing and bioenergy research in the Northern U.S.

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Biography

Dr. Michael Goerndt is an Assistant Professor of Forestry and Natural Resources at Missouri State University. During his time at Missouri State, Dr. Goerndt has built a forestry curriculum including courses in forest ecology, forest measurements, silviculture and dendrology.

Prior to MSU, he worked for four years at University of Missouri as a Postdoctoral Fellow. His Postdoctoral research focused on spatial estimation methods for forest biomass availability for co-firing in power plants as part of the Northern Forest Futures Project with the USDA Forest Service.

Dr. Goerndt is currently involved in two primary research projects. The first project involves assessment of different chemical and mechanical treatments for woody understory species and grassland woody invasive species. This project includes the implementation of field-based experimental design and random assignment of selected chemical and mechanical treatments for several species including: buckbrush, bush honeysuckle, Russian olive, gooseberry and several others.

The other project is made possible through seed funding from the Missouri Transect (EPSCor) project. This project includes collection of hyperspectral imagery of forest canopy using unmanned aerial vehicles (UAV). The hyperspectral imagery will be used in conjunction with field measurements to model the correlation between climate variation and phenotypical vegetation changes, such as growth rate, chlorophyll flow and photosynthetic rate in several tree species. Dr. Goerndt is actively working to initiate research for establishment of silvopasture systems in the Ozark Highlands. This research will likely be a collaborative effort between the MSU College of Agriculture and the Missouri Center for Agroforestry.

Industry Expertise (3)

Research Education/Learning Agriculture and Farming

Areas of Expertise (6)

Forestry Natural Resources Biometrics Bioenergy Coal-fired and biomass-fired energy facilities Statistics

Education (4)

Oregon State University: Ph.D., Forest Biometrics and Mensuration 2010

Dissertation: "Comparison and analysis of small-area estimation methods for improving estimates of selected forest attributes."

Oregon State University: M.S., Statistics 2009

Iowa State University: M.S., Forest Biometrics 2005

Iowa State University: B.S., Forest Ecosystem Management 2003

Affiliations (3)

  • Industry Studies Association: Member (2013-present)
  • Society of American Foresters: Member (2013-present)
  • Society of Collegiate Scholars: Member (2001-present)

Event Appearances (6)

Expanding Horizons for Forestry Education and Research Collaboration at Missouri State University

Missouri Natural Resources Conference  Osage Beach, Missouri

2016-02-03

Comparison of Small Area Estimation Methods Applied to Biopower Feedstock Supply in the Northern U.S. Region

Society of American Foresters National Convention  Baton Rouge, Louisiana

2015-11-07

Using Small Area Estimation and LiDAR-derived Variables to Estimate Attributes for Forest Stands

Society of American Foresters National Convention  Charleston, South Carolina

2013-10-23

Small Area Estimation of County-Level Forest Attributes Using Ground Data and Remote Sensed Data

Society of American Foresters National Convention  Charleston, South Carolina

2013-10-23

Potential for Energy Generation from Woody Biomass Co-Firing in the U.S.

Society of American Foresters National Convention  Charleston, South Carolina

2013-10-23

Wood Energy in the U.S. Power Sector: Challenges and Opportunities for Public Policy, Technology and Infrastructure Development

Industry Studies Association Conference  Kansas City, Missouri

2013-05-29

Research Grants (3)

Localized spatial estimation of forest attributes in the North using small area estimation, with applications for supply and economic feasibility of biomass co-combustion

National Institute of Food and Agriculture $153,000

2014

Comparing chemical and mechanical treatments for woody understory species and regeneration of hardwoods in southern Missouri forests

Missouri State University Faculty Research Grant $7500

2014

Monitoring and modeling vegetative response to climate fluctuation and seasonal change through UAS-based hyperspectral sensor and camera system

NSF Missouri Transect (EPSCOR) Seed Funding Grant $49,812

2014

Articles (7)

Potential for Coal Power Plants to Co-Fire with Woody Biomass in the U.S. North, 2010–2030 U.S. Forest Service

2015

Future use of woody biomass to produce electric power in the U.S. North can have an important influence on timber production, carbon storage in forests, and net carbon emissions from producing electric power. The Northern Forest Futures Project (NFFP) has provided regional- and state-level projections of standing forest biomass, land-use change and timber harvest, which all influence forest contributions to global carbon cycles. This study supports the NFFP study of global carbon cycles by estimating potential local woodybiomass supply under alternate procurement regimes and associated delivered costs to coal power plants for co-firing for 2010 and 2030.

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Chapter 4: Wood Energy and Forest Management Earthscan Publishing

2014

This is a book chapter in Wood Energy in Developed Economies, edited by Francisco Aguilar.

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Resource potential for renewable energy generation from co-firing of woody biomass with coal in the Northern US Biomass and Bioenergy

2013

Past studies have established measures of co-firing potential at varying spatial scales to assess opportunities for renewable energy generation from woody biomass. This study estimated physical availability, within ecological and public policy constraints, and associated harvesting and delivery costs of woody biomass for co-firing in selected power plants of the Northern U.S.

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Drivers of biomass co-firing in U.S. coal-fired power plants Biomass and Bioenergy

2013

Substantial knowledge has been generated in the U.S. about the resource base for forest- and other residue-derived biomass for bioenergy including co-firing in power plants. However, a lack of understanding regarding power plant-level operations and manager perceptions of drivers of biomass co-firing remains. This study gathered information from U.S. power plant managers to identify drivers behind co-firing, determine key conditions influencing past and current use, and explore future prospects for biomass in co-firing.

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Small-area estimation of county-level forest attributes using ground data and remote sensed auxiliary information Forest Science

2013

Small-area estimation (SAE) is a concept that has considerable potential for precise estimation of forest ecosystem attributes in partitioned forest populations. In this study, several estimators were compared as SAE techniques for 12 counties in the northern Oregon Coast range.

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Changes in forest habitat classes under alternative climate and land-use change scenarios in the northeast and midwest, USA Mathematical and Computational Forestry & Natural-Resource Sciences

2013

Large-scale and long-term habitat management plans are needed to maintain the diversity of habitat classes required by wildlife species. Planning efforts would benefit from assessments of potential climate and land-use change effects on habitats. We assessed climate and land-use driven changes in areas of closed- and open-canopy forest across the Northeast and Midwest by 2060. Our assessments were made using projections based on A1B and A2 future scenarios developed by the Intergovernmental Panel on Climate Change. Presently, forest land covers 70.2 million ha and is evenly divided between closed- and open-canopy habitats. Projections indicated that total forest land would decrease by 3.8 or 4.5 million ha for A2 and A1B, respectively. Within persisting forest land, the balance between closed- and open-canopy habitats depended on assumed harvest rates of woody biomass. Standard harvest rates led to closed-canopy habitat attaining a slight majority of total forest land area. Intensive harvest rates resulted in the majority of forest land being in open-canopy habitat for A1B or maintained the even split between closed- and open-canopy habitats for A2. Ultimately, managers need to identify benchmark habitat conditions informed by historical conditions and wildlife population dynamics and plan to meet these benchmarks in dynamic forest landscapes.

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A comparison of small-area estimation techniques to estimate selected stand attributes using LiDARderived auxiliary variables Canadian Journal of Forestry Research

2011

One of the challenges often faced in forestry is the estimation of forest attributes for smaller areas of interest within a larger population. Small-area estimation (SAE) is a set of techniques well suited to estimation of forest attributes for small areas in which the existing sample size is small and auxiliary information is available. Selected SAE methods were compared for estimating a variety of forest attributes for small areas using ground data and light detection and ranging (LiDAR) derived auxiliary information. The small areas of interest consisted of delineated stands within a larger forested population. Four different estimation methods were compared for predicting forest density (number of trees/ha), quadratic mean diameter (cm), basal area (m2/ha), top height (m), and cubic stem volume (m3/ha). The precision and bias of the estimation methods synthetic prediction (SP), multiple linear regression based composite prediction (CP), empirical best linear unbiased prediction (EBLUP) via Fay–Herriot models, and most similar neighbor (MSN) imputation) are documented. For the indirect estimators, MSN was superior to SP in terms of both precision and bias for all attributes. For the composite estimators, EBLUP was generally superior to direct estimation (DE) and CP, with the exception of forest density.

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