Peter is an Associate Professor in the Department of Natural Resource Ecology and Management at Iowa State University. His broad interests include forest and landscape ecosystems ecology and monitoring using satellite sensor technologies (FAST Lab: Forests, Agriculture, & Satellite Technology). Currently, he is investigating the combined use of ground and satellite sensor data to calibrate models for estimating burnable forest canopy bulk density in the Arrowhead region of northern Minnesota. The aim is to be able to accurately and efficiently monitor, measure, and map forest biophysical parameters critical for modeling forest fire behavior in this region, which has long eluded traditional modeling strategies using conventional approaches. Successful calibration of these needed forest parameters, using readily available satellite sensor data, will provide a framework for semi-automated, periodic updates to track structural changes and transform the efficiency of forest fire risk assessment in this region.
Peter completed his Ph.D. at UW-Madison with Dr. Phil Townsend in May of 2009. His dissertation work focused on using synoptic polar orbiting sensors (Landsat, SPOT, Radarsat, and PALSAR) to model and map forest biophysical parameters (e.g., tree species abundance, tree size, basal area, crown closure, etc.) in northern Minnesota to support ongoing studies focused on understanding landscape-level insect outbreak dynamics which may lead to the design of more pest-resistant landscapes through adaptive forest management.
Prior to moving to Madison, Peter was a Research Fellow with the Natural Resources Research Institute at the University of Minnesota –Duluth (1991-2004). His work at NRRI focused on using multi-temporal satellite sensor data to map forest cover, monitoring forest change through time, and study the effects of harvesting patterns on landscape structure in the upper Midwest and Canada.
Previously, Peter received a MS in Forest Resources from the University of New Hampshire – Durham in 1990 where he also worked as a Research Scientist at UNH’s former Complex Systems Research Center (Now Earth Systems Research Center) within the Institute for the Study of Earth, Oceans, and Space. His work at Complex Systems centered on using multi-resolution satellite sensor data to link fine-scale forest change dynamics in the Brazilian Amazon to broader scales for global change research and simulation models of biotic influence on atmospheric gas composition.
Fire severity and ecosystem impacts immediately following an extreme fire event in northern Minnesota... Pagami Fire link
A lightning strike on August 18, 2011 started a forest fire in the heart of the Boundary Waters Canoe Area (BWCA) Wilderness in northern Minnesota near Pagami Creek. On September 12, fanned by 35mph winds, this relatively small forest fire rapidly grew into the largest forest fire east of the Mississippi in over 115 years. The Pagami Creek Fire provided an exceptional research opportunity due to the magnitude of the disturbance, the range of fire intensities it contained, and the wealth of mapped pre-fire forest composition and structure information our research team has assembled.
Initial research questions for our project included:
- To what extent do remotely-sensed (hyperspectral) fire severity estimates reflect field-based severity indices in both the overstory and the understory?
- How do overstory and understory fire severities interact to influence soil carbon (C), nitrogen (N), and mercury (Hg) loss immediately after a fire in comparison with those in the first growing season after the fire?
The primary objective of the Pagami Fire Severity Project is to establish a baseline of initial impacts upon which future research into fire-forest interactions may be based.
Satellite-based forest structure mapping in the Boundary Waters Canoe Area Wilderness for ecosystem management and decision support
The Superior National Forest manages a 2 million acres wilderness area. After the 1999 blowdown event that affected over 200,000 acres of the wilderness, fire management activities were primarily oriented towards full suppression strategies. However, in the past 5 years the forest has started managing fires in the wilderness for resource benefits. The forest is looking at expanding that option to other areas of the forest. One area that has been found to be lacking in order to make fire management decision is the availability of accurate spatial data. The two areas identified as lacking the most are:
- Vegetation is not adequately mapped for the BWCAW area. Therefore, when a fire does occur, sometimes extensive aerial reconnaissance is needed to determine the effects of the fire. Structure Regeneration
- Fire behavior modeling has not been accurate due to the relative inaccuracy of forest canopy bulk density models provided by LANDFIRE. Extensive modifications have to be made to these data to get realistic fire behavior outputs.
My lab is tasked with components of the second item, which involves measuring and spatially modeling two of the primary forest structural parameters (at 10 meter resolution) that are needed to more accurately model fire behavior in northern Minnesota:
- Understory coniferous ladder fuels – not currently present in fuel model scenarios.
- Canopy Bulk Density – poorly defined by Landfire and difficult to model using Lidar data
Analysis of spruce budworm outbreak patterns in the Boarder Lakes region of Minnesota in support of alternative land management strategies
Recent work has provided evidence to suggest that the magnitude of spruce budworm outbreak dynamics are also impacted by the abundance and spatial distribution of host trees, the relative abundance of non-host conifers and deciduous species, and coarse-level forest management practices (Robert et al. 2016, 2020). For example, suppression of fire in Minnesota during the twentieth century led to increased abundance of shade tolerant species, which included assemblages of fir and spruce host. Hence, the increase in both frequency and severity of spruce budworm outbreaks in these eastern U.S. boreal forest systems is often closely associated with post-fire suppression fir-spruce assemblages (Blais, 1983). Interestingly, the pervasive mortality of trees after spruce budworm outbreak and while still in red-attack phase (prior to needle drop) only temporarily increases the likelihood of severe fire risk (Simard et al. 2011), which would result in a positive feedback. Nevertheless, feedback effects of insect outbreak on other disturbance like fire depends on factors such as host abundance, severity of outbreak, time since outbreak, vegetation response, and weather (Fleming et al., 2002; Page and Jenkins, 2007; Simard et al. 2011). Spatial and temporal complexities of disturbance interaction effects may increase under the range of IPCC expected climate change scenarios as the likelihood of disturbance by insects and fire will likely increase with increasing temperature and drought pattern especially in Northern US forests. In this context, understanding of the historical distribution of host trees, as well as the spatial and temporal patterns of disturbance will provide valuable inputs for modeling spruce budworm disturbance under a variety of climate scenarios and adaptive management strategies.
Past studies on spruce budworm were based on the dendrochronological records of the host trees (e.g. Fraver et al., 2005) and extrapolation were made to a regional scale. However, there are only a few studies which use the large scale assessment of host trees abundance (using aerial surveys) and defoliation records to model spruce budworm outbreak (Candau and Fleming, 2005). Our study marks the first use of satellite sensor data to map host tree abundance, distribution, and disturbance on a regional scale. This project aims to use historical defoliation records and archived satellite sensor data (Landsat) to:
- Produce the maps of the spatial distribution and abundance of host and non-host tree species.
- Detect and map forest disturbance by spruce budworm.
- Explore spatio-temporal distributions of host species to better understand and predict spruce budworm spatial dynamics using the LANDIS II modeling framework (Scheller et al.2005)
Adaptation of agroecosystems to climate change at the edge of the U.S. Cornbelt―assessing different drivers in a network of infrastructure Cornbelt Link
Recent strengthening of demands for food, feed, energy and ecosystem services have intensified demand for land resources (Lambin and Meyfroidt 2011). Climate change has placed much uncertainty around the underlying capacity of land resources to produce these outputs (Hatfield et al. 2008). The extent and nature of a region’s agriculture is determined largely by the region’s climate and soils. However, there is much more to an efficient production system. An increasingly sophisticated complex of local services supports all land in a locality that is under a given production system. Thus, the presence of such infrastructure as elevators and machinery services a) is the result of significant cropping presence and b) reduces the costs of cropping due to its very presence. Comprehension must precede effective control. It is important to acknowledge that farms in a production system are interdependent through their support of infrastructure when seeking to manage approaches to climate change adaptation. If climate change causes a sufficient number of farms to change production system then a system’s viability in a locality may be precipitously undermined.
We are characterizing the spatial and temporal patterns in climate change relevant to agriculture across the Dakotas with a focus on grass-based agriculture, corn and soybeans, and small grain production. We are striving to identify and characterize metrics relevant to agricultural and ecosystem sustainability. With these data, we are developing methodologies to discern shifts in the location of different cropping activities in the United States, and also shifts in cropping system borders for crops relevant to the Dakotas. Using these outputs, spatio-temoral data on land use, and other relevant data, we are relating location shifts to climate change, technical change, agricultural and environmental policies and events in the external economy.
Understanding factors linked to corn suseptability to wind damage in Iowa
On 10 August 2020, Iowa experienced 100-112 mph straight-line winds of what is now known as the derecho event, which affected ca. 6.6 million acres of land (forest & crop) to some degree across central Iowa. While damage to Iowa's 2020 corn crop was severe, there was an abundance of observational evidence to suggest that some corn fields (often adjacent to damaged fields) possessed far superior wind-defense advantage. In this emerging research we wish to understand factors that may have enabled this apparent wind-defense advantage, including, but not limited to, corn genetics, best management practices, and pesticide application patterns. Outcomes will include recommendations to protect corn crops against extreme wind events in the future should they become more frequent.
A ECL 406, Wildlife Camp
FOR 345, Natural Resource Photogrammetry and Geographic Information Systems
FOR 206, Forestry Camp
NREM 380, Field Ecology Research & Teaching
NREM 446/546, Integrating GPS and GIS for Natural Resource Management
NREM 489/589, Satellite Remote Sensing Laboratory
NREM 496A, Natural History of Cuba
NREM 496A, Natural History of Madagascar
American Geophysical Union
American Society of Photogrammetry and Remote Sensing
Association of Fire Ecology
Ecological Society of America
Society of American Foresters
2021 Iowa Nutrient Reduction Center $101,596 (pending). Riparian Buffer Resurgence: Advancing Foundational Research in Iowa.
2020 USFS $241,812 (pending). Leveraging Google Earth Engine and satellite data to map forest disturbance from unusual weather events and invasive forest stressors in Iowa.
2019 USFS/Iowa DNR $22,1451. Saving Iowa’s native eastern white pine: Mapping distribution and abundance trends in northeastern Iowa.
2017 USDA Forest Service $42,317. Spruce budworm disturbance modeling (Lead PI).
2016 USDA Forest Service $10,537. Analysis of spruce budworm outbreak patterns in the Boarder Lakes region of Minnesota in support of alternative land management strategies. (Lead PI).
2015 USDA Forest Service $7,500. Analysis of spruce budworm outbreak patterns in the Boarder Lakes region of Minnesota in support of alternative land management strategies. (Lead PI).
2015 USDA Forest Service $25,552. Satellite-based forest structure mapping in the Boundary Waters Canoe Area Wilderness for ecosystem management and decision support (Lead PI).
2015 USDA Forest Service $12,506. Analysis of spruce budworm outbreak patterns in the boarder lakes region of Minnesota in support of alternative land management strategies (Lead PI).
2014 USGS NCCSC $292,008. Understanding dynamics of land use switching with satellite and field level data in context of climate variability (Lead PI).
2014 USDA Forest Service $32,070. Satellite-based forest structure mapping in the Boundary Waters Canoe Area Wilderness for ecosystem management and decision support (Lead-PI).
2014 Iowa Nutrient Reduction Strategy grant $70,000. Development of Remote Sensing Protocols for Inventory of Nutrient Management Practices: Permanent Vegetative Practices (Co-PI).
2014 ISU CARD Grant $24,972. Characterizing and Comprehending Land Use Change in the Loess Hills Region (Co-PI).
2013 USDA-NIFA $550,000. Adaptation of agroecosystems to climate change at the edge of the U.S. Cornbelt―assessing different drivers in a network of infrastructure (Lead-PI).
2013 Ohio State University $1000. Relationships between pre-fire forest composition and hardwood regeneration following wildfire in northeast Minnesota (Lead PI).
2011 NSF (DEB-1201146) $14,201, RAPID Collaborative Research: Fire severity and ecosystem impacts immediately following an extreme fire event in northern Minnesota (Lead PI).
2009 U.S. Fish & Wildlife Service $3000.00. Estimating forest composition and basal area in Sherburne National Wildlife Refuge using multi-temporal Landsat data (Lead PI).
2008 The Nature Conservancy $5,500, Forest landscape change analysis, Manitou and Sand Lakes\Seven Beavers: 2000-2005 (lead PI).
2001 EPA STAR/NASA Grant, $600,000, Development of state indicators for Great Lakes ecosystem health using remote sensing technology (Co-PI with Dr. Gerald Niemi and Dr. Carol Johnston).
1999 NASA (LCLUC99-0151-0006), $497,327, Mapping and modeling forest change in a boreal landscape (Co-PI with John Pastor lead PI).
1995 USDA Forest Service, $20,000, Forest classification of northern Minnesota using Landsat data (lead PI).
Peer reviewer for following journals:
Remote Sensing of Environment
Photogrammetric Engineering and Remote Sensing
International Journal of Remote Sensing
Canadian Journal of Remote Sensing
IEEE Geoscience & Remote Sensing
Guest Editor of “Forests,” an international and cross-disciplinary peer-reviewed journal of forestry and forest ecology, conservation, and management.
Thapa, B., Wolter, P.T., Sturtevant, B.R., & Townsend, P.A. (2021). Linking remote sensing and insect defoliation biology – A cross-system comparison. Remote Sensing of Environment (in prep).
Thapa, B., Wolter, P.T., Sturtevant, B.R., Townsend, P.A. & Foster, J.R. (2021). Estimating seasonal defoliation impacts from frass deposition and insect phenology. Methods in Ecology and Evolution (in prep).
Wolter, P.T., Bogert, M.G., Johnson, P., Engelstad, P.S., and Poznanovic, A. (2021). Estimating understory fuel biomass using low-density Lidar. Fire (in prep.)
Wolter, P. T., Olbrich, J. J., & Johnson, P. J. (2021). Modeling sub-boreal forest canopy bulk density in Minnesota, USA, using synthetic aperture radar and optical satellite sensor data. Fire Ecology, 17(1), 1-23.
Robert, L. E., Sturtevant, B. R., Kneeshaw, D., James, P. M., Fortin, M. J., Wolter, P. T., Townsend, P.A., & Cooke, B. J. (2020). Forest landscape structure influences the cyclic‐eruptive spatial dynamics of forest tent caterpillar outbreaks. Ecosphere, 11(8), e03096.
Thapa, B., Wolter, P.T., Sturtevant, B.R., and Townsend, P.A. (2020). Reconstructing past forest composition and abundance by using archived Landsat and national forest inventory data. International Journal of Remote Sensing, 41(10), 4022-4056.
Engelstad, P. S., Falkowski, M., Wolter, P., Poznanovic, A., & Johnson, P. (2019). Estimating Canopy Fuel Attributes from Low-Density LiDAR. Fire, 2(3), 38.
Zlonis, E.J., Walton, N.G., Sturtevant, B.R., Wolter, P.T., & Niemi, G.J. (2019). Burn severity and heterogeneity mediate avian response to wildfire in a hemiboreal forest. Forest Ecology and Management, 439, 70-80.
Arora, G., & Wolter, P.T. (2018). Tracking land cover change along the western edge of the US Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors. Journal of Land Use Science, 13(1-2), 59-80.
Robert, L.-E., Sturtevant, B. R., Cooke, B. J., James, P. M. A., Fortin, M.-J., Townsend, P. A., Wolter, P.T. and Kneeshaw, D. (2018), Landscape host abundance and configuration regulate periodic outbreak behavior in spruce budworm Choristoneura fumiferana. Ecography, 41, 1556-1571.
Grinde, A. R., Niemi, G. J., Sturtevant, B. R., Panci, H., Thogmartin, W., & Wolter, P.T. (2017). Importance of scale, land cover, and weather on the abundance of bird species in a managed forest. Forest Ecology and Management, 405, 295-308.
Dietrick, A., Falls, D., Kern, K., McCreary, H., Nawaz, D.A., Pai, A.M., Wolter, P.T.,... & Bennett, M. (2017). JOURNAL STAFF. Photogrammetric engineering & remote SenSing, 609.
Wolter, P.T., Hilgemann, L.A., and White, M.A. (2017). Quantifying coarse woody debris and residual basal area following retention harvesting in northeast Minnesota using Landsat sensor data. Canadian Journal of Forest Research, 47: 1325–1338.
Kolka, R., Sturtevant, B., Miesel, J., Townsend,P., Wolter, P., Fraver, S., and DeSutter, D. (2017). Emissions of Forest Floor and Mineral Soil Carbon, Nitrogen and Mercury Pools and Relationships with Fire Severity for the Pagami Creek Fire in the boreal forest of northern Minnesota. International J. of Wildland Fire, 26(4), 296-305. http://dx.doi.org/10.1071/WF16128
White, M. A., Cornett, M. W., and Wolter, P. T. (2017). Two scales are better than one: Monitoring multiple-use northern temperate forests. Forest Ecology and Management, 384, 44-53.
Miranda, B. R., Sturtevant, B. R., Schmelzer, I., Doyon, F., and Wolter, P. (2016). Vegetation recovery following fire and harvest disturbance in central Labrador—a landscape perspective. Canadian Journal of Forest Research, 46(8), 1009-1018.
Cooley, R.A., Wolter, P.T., and Sturtevant, B.R. (2016). Quantifying early-seral forest composition with remote sensing. Photogrammetric Engineering & Remote Sensing, 82(11), 853-863.
Arora, G., Wolter, P.T., Hennessy, D.A., and Feng, H (2016). Land use change and policy in Iowa’s Loess Hills. Sustainable Agriculture Research, 5(4), 31-45.
Arora, G., Wolter, P.T., Feng, H., and Hennessy, D.A. (2016). Role of ethanol plants in Dakotas land use change: incorporating flexible trends in the difference-in-difference framework with remotely-sensed data. CARD Working Papers. Paper 583. http://lib.dr.iastate.edu/card_workingpapers/583
Arora, G., Wolter, P.T., Feng, H. and Hennessy, D.A. (2015). Characterizing and comprehending land use change in the Loess Hills region. Agricultural Policy Review. 1, Article 5.
Wolter, P.T., Berkley, E.A., Peckham, S.D., and Singh, A. (2014). Satellite-based management tool for oak savanna ecosystem restoration. Journal of Fish and Wildlife Management, 5(2), 252-269.
Kolka, R., Sturtevant, B., Townsend,P., Miesel, J. Wolter, P., Fraver, S., and DeSutter, D. (2014). Forest floor and upper mineral soil carbon, nitrogen and mercury pools shortly after fire and comparisons utilizing fire severity indices. Soil Science Society of America Journal. 1, 1-8.
Baumann, M., Ozdogan, M., Wolter, P.T., Krylov, A., Vladimirova, N., Radeloff, V.C. (2014). Landsat remote sensing of windfall disturbance. Remote Sensing of Environment. 143, 171-179.
Sturtevant, B.R., Miranda, B.R., Wolter, P.T., James, P., Fortin, M.J., & Townsend, P.A. (2014). Forest recovery patterns in response to divergent disturbance regimes in the Border Lakes region of Minnesota (USA) and Ontario (Canada). Forest Ecology and Management, 313, 199-211. http://www.sciencedirect.com/science/article/pii/S0378112713007263#
Wolter, P.T., Sturtevant, B.R., Miranda, B.R., Lietz, S.M., Townsend, P.A., and Pastor, J. (2012). Greater Border Lakes Region land cover classification and change detection. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. http://dx.doi.org/10.2737/RDS-2012-0007
Wolter, P.T., Berkley, E.A., Peckham, S.D., Singh, A., & Townsend, P.A. (2012). Exploiting tree shadows on snow for estimating forest basal area using Landsat data. Remote Sensing of Environment, 121, 69-79.
Sturtevant, B.R., Miranda, B.R., Shinneman, D.J., Gustafson, E.J., & Wolter, P.T. (2012). Comparing modern and presettlement forest dynamics of a subboreal wilderness: Does spruce budworm enhance fire risk? Ecological Applications, 22(4), 2012, pp. 1278–1296.
James, P.M.A., Sturtevant, B.R., Townsend, P.A., Wolter, P.T., & Fortin, M-J (2011). Two-dimensional wavelet analysis of spruce budworm host basal area in the Border Lakes landscape. Ecological Applications, 21(6), 2197-2209.
Wolter, P.T. & Townsend, P.A. (2011). Estimating forest species composition using a multi-sensor fusion approach. Remote Sensing of Environment, 115, 671–691.
Wolter, P.T., Townsend, P.A., & Sturtevant, B.R. (2009). Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data. Remote Sensing of Environment, 113, 2019-2036.
Johnston, C.A., Brown, T.N., Hollenhorst, T.P., Wolter, P.T., Danz, N.P., Niemi, G.J. (2009). GIS in support of ecological indicator development. Manual of Geographic Information Systems (56):1095-1113.
Wolter, P.T., Townsend, P.A., Kingdon, C.C., & Sturtevant, B.R. (2008). Remote sensing of the distribution and abundance of host species for spruce budworm in Northern Minnesota and Ontario. Remote Sensing of Environment, 112(10), 3971-3982.
Johnston, C.A., Watson, T., & Wolter, P.T. (2008). Sixty-three years of land alteration in Erie Township. International Journal of Great Lakes Research, 33 (3), 253–268.
Wolter, P.T., Johnston, C.A., & Niemi, G.J. (2006). Land use land cover change in the U.S. Great Lakes basin 1992 to 2001. International Journal for Great Lakes Research, 32, 607-628.
Pastor, J., Sharp, A., & Wolter, P. (2005). An application of Markov models to the dynamics of Minnesota’s forests. Canadian Journal of Forest Research, 35, 3011-3019.
Wolter, P.T., Niemi, G.J., & Johnston, C.A. (2005). Mapping SAV in the U.S. Great Lakes Using Quickbird Satellite Data. International Journal of Remote Sensing, 26(23), 5255-5274.
Wolter, P.T. & White, M.A. (2002). Recent forest cover type transitions and landscape structural changes in northeast Minnesota. Landscape Ecology, 17, 133-155.
Hanowski, J.M., Wolter, P.T., & Niemi, G.J. (2002). Effects of prescriptive riparian buffers on Landscape characteristics in northern Minnesota. Journal of the American Water Resources Association, 38(2), 633-639.
Wolter, P.T. & Lane, W.H. (2001). A look at Boreal Owl nesting habitat in northeastern Minnesota using Landsat data. Loon, 73, 192-199.
Wolter, P.T., Mladenoff, D.J., Host, G.E., & Crow, T.R. (1995). Improved forest classification in the northern Lake State using multi-temporal Landsat imagery. Photogrammetric Engineering and Remote Sensing, 61(9), 1129-1143.
Arora, G., Hennessy, D.A., Feng, H., and Wolter, P.T. (2016). Strategic Grasslands Conversions and Conservation Easement Acquisitions in the Dakotas: Analysis using Remotely Sensed Data. In 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts (No. 236016). Agricultural and Applied Economics Association.
Arora, G., Wolter, P.T., Feng, H., and Hennessy, D.A. (2015). Role of Ethanol Plants in Dakotas’ Land Use Change: Analysis Using Remotely Sensed Data. In 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California (No. 205877). Agricultural and Applied Economics Association & Western Agricultural Economics Association.
Kolka, R., Sturtevant, B., Townsend, P., Wolter, P., Fraver, S., and DeSutter, T. (2014). Post-fire comparisons of forest floor and soil carbon, nitrogen, and mercury pools with fire severity indices. Soil Science Society of America Journal, 12th North American Forest Soils Conference, Whitefish, MT, 16–20 June 2013. 8 pp.
Cooley RA, Wolter P.T. (2014). Use of remote sensing to quantify aspen regeneration and study drivers affecting forest response following wildfire in northeast Minnesota. Poster session presented at: Graduate and Professional Student Research Conference, 4 April, Iowa State University, 8; Ames, IA.
Wolter, P.T., Berkley, E.A., Peckham, S.D., Singh, A., & Townsend, P.A. (2013). Using Landsat sensor data as a management tool for oak woodland and savanna ecosystem restoration (poster). The 98th annual symposium of the Ecological Society of America, 4-9 August, Minneapolis, MN.
Wolter, P.T., Berkley, E.A., & Peckham, S.D. (2011). Estimating basal area of oak woodland and savanna using moderate-resolution satellite imagery (Poster). U.S. Fish and Wildlife Service Conference, Conserving the Future: Wildlife Refuges and The Next Generation, 10-13 July, Madison, WI.
Sturtevant, B., Quinn, V., Robert, L-E., Kneeshaw, D., James, P., Fortin, M-J., Wolter, P., Townsend, P., & Cooke, B. (2009). Can landscape-scale management influence insect outbreak dynamics? A natural experiment for eastern spruce budworm (Abs.). Seventh North American Forest Ecology Workshop, Utah State University, 22-26 June, Logan, UT.
Wolter, P.T. & Townsend, P.A. (2009). Estimating forest species composition using a multi-sensor approach (poster). Eos Trans. AGU, 88(54), Fall Meet. Suppl., Abstract B31A-0312.
James, P.M., Sturtevant, B.R., Townsend, P.A., Wolter, P.T., & Fortin, M-J. (2008). Spatial legacies of forest management in spruce budworm host species. Proceedings: 23rd Annual Landscape Ecology Symposium, 6-10 April 2008 Madison, WI.
Wolter, P.T., Townsend, P.A., Kingdon, C.C., & Sturtevant, B.R. (2007). A multivariate approach for using satellite imagery to map the composition and structure of forests susceptible to insect disturbance: Application to the simulation of carbon dynamics in northern Minnesota and Ontario (Poster), Eos Trans. AGU, 88(52), Fall Meet. Suppl., Abstract B43C-1443.
Batzli, S.A., Toyoda, E., Wolter, P., Olsen, T., & Roman, G. (2006). Coupling real-time weather information and satellite imagery in support of emergency management. In Proceedings: American Association of Geographers Annual Meeting, 7-11 March, Chicago, IL.
Wolter, P.T., Johnston, C.A., & Niemi, G.J. (2005). Land use change in the U.S. Great Lakes basin 1992 to 2001 (Oral Presentation), Lake Michigan: State of the Lake/State of the Bay and Annual Great Lakes Beach Association Conference, 2-3 November, Green Bay, WI.
Wolter, P.T., Niemi, G.J., & Johnston, C.A. (2003). Development of environmental indicators for the U.S. Great Lakes basin using QuickBird, Radarsat, and Landsat (poster). 30th International Symposium on Remote Sensing of Environment, Honolulu, HI, 10-14 November.
Wolter, P.T. & Pastor, J. (2002). Mapping and modeling forest change in a boreal landscape (poster). NASA LCLUC Science Team Meeting, Washington D.C., 20-22 November.
Hanowski, J.M., Wolter, P.T., & Niemi, G.J. (2000). Effects of riparian buffers on Landscape characteristics: implications for breeding birds. International Conference: Riparian Ecology and Management in Multi-land use Watersheds, American Water Resources Association, Portland, OR, 28-31 August 2000, p. 523-528.
Host, G.E., White, M.A. Polzer, P.L., & Wolter, P.T. (1997). Great Lakes assessment: Assessing landscape pattern and structure in Lake States forests. Proceedings: 7th Annual Minnesota GIS/LIS Conference, St. Cloud, MN, 1-3 October.
Wolter, P.T. & Mladenoff, D.J. (1995). Forest cover classification of northeastern Minnesota using multi-temporal Landsat data and National Wetlands Inventory data. (Abstract). Poster presented by P.T. Wolter at the 10th Annual U.S. Landscape Ecology Symposium, Minneapolis, MN, 22-26 April.
Polzer, P., Wolter, P.T., Mladenoff, D.J., Host, G.E., & White, M.A. (1994). Quantifying forest change dynamics using a MSS-NDVI composite for a northern Wisconsin landscape. (Abstract). Poster presented by P. Polzer and P.T. Wolter at the Ecosystem Management Strategies Conference for the Lake Superior Region, 16-19 May.
Wolter, P.T., Mladenoff, D.J., Host, G.E., & Crow, T.R. (1993). Improved forest classification in the northern Lake State using multi-temporal Landsat imagery. (Abstract). Poster presented by P.T. Wolter at the 8th Annual U.S. Landscape Ecology Symposium, Oak Ridge, TN, March.
White, M.A., Mladenoff, D.J., Host, G.E., Wolter, P.T., & Crow, T.R. (1993). Analyzing regional landscape structure across ownership categories and ecological land units. (Abstract). Poster presented by M.A. White at the 8th Annual U.S. Landscape Ecology Symposium, Oak Ridge, TN, March.
Niemi, G.J., Solin, J., Watters, D., and Wolter, P.T. (2015). Breeding Bird Inventory of the St. Louis River, Minnesota and Wisconsin, 1999. NRRI Technical Report; NRRI/TR-00-34.
Host, G., White, M.A., Polzer, P.L., and Wolter, P.T. (2015). Great Lakes assessment: Assessing landscape pattern and structure in Great Lakes forests. NRRI Technical Report; NRRI/TR-97-03.
Hanowski, J.M., Danz, N., Lind, J., Niemi, G.J., and Wolter, P.T. (2015). Wildlife species: responses to forest harvesting and management in riparian stands and landscapes. NRRI Technical Report; NRRI/TR-01-02.
Niemi, G.J., Johnston, C.A., and Wolter, P.T. (2015). Development of environmental indicators for the U.S. Great Lakes Basin using remote sensing technology. NRRI Technical Report; NRRI/TR-06-26.