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For Additional Informatio Colin Brooks Senior Research Scientist 734.913.6858 [email protected] David Banach Assistant Research Scientist 734.994.7225 [email protected] Mark Fedora USDA Forest Service Ottawa National Forest Supervisory Hydrologist 906.932.1330 ext 318 [email protected] Using Multi-temporal Imagery to Improve Mapping and Inventory of Forested Roads in Michigan’s Upper Peninsula The purpose of this cooperative project between the US Department of Agriculture Forest Service and the Michigan Tech Research Institute (MTRI) was to develop an updated and improved roads data layer for use in ecological analysis and planning. Preliminary investigations of forested watersheds in Michigan’s Upper Peninsula by the project team had shown that as few as 14% of the roads in these watersheds had been captured by existing GIS layers. It is vital to understand the spatial locations of roads within a watershed since such features can increase stream temperature, non-point source pollution and any receiving features such as streams, lakes, and ponds (Weng, 2012; Jennings and Jarnagin, 2002; Opperman et al. 2005), and cause fish passage impairment (Gibson et al. 2005). Through the use of multi-temporal imagery, previously unidentified road features in four watersheds were digitized and quantified using ESRI ArcGIS software. Statistics concerning the total number of segments, total mileage, levels of confidence, and percent change between the two data sets were calculated. Analysis into potential watershed vulnerability based on the number and spatial location of road-stream intersections was also conducted. Fieldwork Field site verifications were conducted provide an idea into what a forested road looks like from the ground vs. how they appear in imagery and to verify the detection of potentially hydrologically impacted wetlands. Wetland Assessment In order to determine where potential hydrologic impacts may exist for future studies, an example wetland assessment was conducted. The MTRI- produced EPA Great Lakes Coastal Wetland data layer (Bourgeau-Chavez et al, 2015) (http ://mtri.org/coastal_wetland_mapping.html ) was used in conjunction with the updated roads layers to identify potentially impacted wetlands, determined by where an updated road segment was adjacent to or bisected a wetland. Both GIS-based automated and manual image interpretation methods were used to locate potentially impacted wetlands. Statistics concerning the area of wetlands potentially impacted by both the original and updated road networks were calculated. Original 1,313 miles Additiona l 1,770 miles Updated Total 3,083 miles 135% Increase Brule Ontonagon Original 3,854 miles Additiona l 2,875 miles Updated Total 6,729 miles 75% Increase Black-Presque Isle Sturgeon Original 1,461 miles Additional 3,050 miles Updated Total 4,511 miles 208% Increase Original 997 miles Additional 2,271 miles Updated Total 3,269 miles 227% Increase by O riginalR oad N etw ork (ha) by Updated R oad N etw ork (ha) W ater 5,314 5,370 1.05% Aquatic Bed 12 51 325% W etland 1,379 2,476 80% Schoenoplectus 1 7 600% Typha 3 5 67% Phragmites 0 0 0% Bog 0 0 0% O pen Fen 55 91 65% Shrub Fen 135 172 27% Tree Fen 226 339 50% W etland Shrub 14,666 24,494 53% Forested W etland 83,919 99,868 19% TO TAL 105,710 132,873 26% Area ofW etlandsPotentially Im pacted C lass PercentC hange (% ) References Bourgeau-Chavez, L.L, Z.M Laubach, A.J. Landon, E.C. Banda, M.J. Battaglia, S.L. Endres, M.E. Miller, R. Macleod, and C.N. Brooks. 2015. Great Lakes Coastal Wetland Mapping, Chapter 15 in Advances in Wetland Mapping ed by. R. Tiner, V. Klemas, and M. Lang, CRC Press. Weng, Q. 2012. "Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends." Remote Sensing of Environment 117: 34-49. Jennings, David B., and S. Taylor Jarnagin. "Changes in anthropogenic impervious surfaces, precipitation and daily streamflow discharge: a historical perspective in a mid-Atlantic subwatershed." Landscape Ecology 17.5 (2002): 471-489. Opperman, J.J., Lohse, K.A., Brooks, C., Kelly, N.M., Merenlender, A.M., 2005. Influence of land use on fine sediment in salmonid spawning gravels within the Russian River Basin, California. Canadian Journal of Fisheries and Aquatic Sciences 62: 2740-2751. Gibson, R.J., Haedrich, R.L., Wernerhein, M.C., 2005. Loss of Fish Habitat as a Consequence of Inappropriately Constructed Stream Crossings. Fisheries 30(1): 10-17. Hawbaker, T.J. and Radeloff, V.C. 2004. Roads and Landscape Pattern in Northern Wisconsin Based on a Comparison of Four Road Data Sources. Conservation Biology. 18[5]: 1233-1244.

For Additional Information Colin Brooks Senior Research Scientist 734.913.6858 David Banach Assistant Research Scientist 734.994.7225

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Page 1: For Additional Information Colin Brooks Senior Research Scientist 734.913.6858 David Banach Assistant Research Scientist 734.994.7225

For Additional InformationColin BrooksSenior Research [email protected]

David BanachAssistant Research [email protected]

Mark FedoraUSDA Forest ServiceOttawa National ForestSupervisory Hydrologist906.932.1330 ext [email protected]

Using Multi-temporal Imagery to Improve Mapping and Inventory of Forested Roads in Michigan’s Upper PeninsulaThe purpose of this cooperative project between the US Department of Agriculture Forest Service and the Michigan Tech Research Institute (MTRI) was to develop an updated and improved roads data layer for use in ecological analysis and planning. Preliminary investigations of forested watersheds in Michigan’s Upper Peninsula by the project team had shown that as few as 14% of the roads in these watersheds had been captured by existing GIS layers. It is vital to understand the spatial locations of roads within a watershed since such features can increase stream temperature, non-point source pollution and any receiving features such as streams, lakes, and ponds (Weng, 2012; Jennings and Jarnagin, 2002; Opperman et al. 2005), and cause fish passage impairment (Gibson et al. 2005).Through the use of multi-temporal imagery, previously unidentified road features in four watersheds were digitized and quantified using ESRI ArcGIS software. Statistics concerning the total number of segments, total mileage, levels of confidence, and percent change between the two data sets were calculated. Analysis into potential watershed vulnerability based on the number and spatial location of road-stream intersections was also conducted.

FieldworkField site verifications were conducted provide an idea into what a forested road looks like from the ground vs. how they appear in imagery and to verify the detection of potentially hydrologically impacted wetlands.

Wetland AssessmentIn order to determine where potential hydrologic impacts may exist for future studies, an example wetland assessment was conducted. The MTRI-produced EPA Great Lakes Coastal Wetland data layer (Bourgeau-Chavez et al, 2015) (http://mtri.org/coastal_wetland_mapping.html) was used in conjunction with the updated roads layers to identify potentially impacted wetlands, determined by where an updated road segment was adjacent to or bisected a wetland. Both GIS-based automated and manual image interpretation methods were used to locate potentially impacted wetlands. Statistics concerning the area of wetlands potentially impacted by both the original and updated road networks were calculated.

Original1,313 miles

Additional 1,770 miles

Updated Total 3,083 miles

135% Increase

BruleOntonagon

Original 3,854 miles

Additional 2,875 miles

Updated Total6,729 miles

75% Increase

Black-Presque Isle Sturgeon

Original1,461 miles

Additional 3,050 miles

Updated Total4,511 miles

208% Increase

Original997 miles

Additional 2,271 miles

Updated Total3,269 miles

227% Increase

by Original Road Network (ha)

by Updated Road Network (ha)

Water 5,314 5,370 1.05%Aquatic Bed 12 51 325%Wetland 1,379 2,476 80%Schoenoplectus 1 7 600%Typha 3 5 67%Phragmites 0 0 0%Bog 0 0 0%Open Fen 55 91 65%Shrub Fen 135 172 27%Tree Fen 226 339 50%Wetland Shrub 14,666 24,494 53%Forested Wetland 83,919 99,868 19%TOTAL 105,710 132,873 26%

Area of Wetlands Potentially ImpactedClass Percent Change (%)

ReferencesBourgeau-Chavez, L.L, Z.M Laubach, A.J. Landon, E.C. Banda, M.J. Battaglia, S.L. Endres, M.E. Miller, R. Macleod, and C.N. Brooks. 2015. Great Lakes Coastal Wetland Mapping, Chapter 15 in Advances in Wetland Mapping ed by. R. Tiner, V. Klemas, and M. Lang, CRC Press.

Weng, Q. 2012. "Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends." Remote Sensing of Environment 117: 34-49.

Jennings, David B., and S. Taylor Jarnagin. "Changes in anthropogenic impervious surfaces, precipitation and daily streamflow discharge: a historical perspective in a mid-Atlantic subwatershed." Landscape Ecology 17.5 (2002): 471-489.

Opperman, J.J., Lohse, K.A., Brooks, C., Kelly, N.M., Merenlender, A.M., 2005. Influence of land use on fine sediment in salmonid spawning gravels within the Russian River Basin, California. Canadian Journal of Fisheries and Aquatic Sciences 62: 2740-2751.

Gibson, R.J., Haedrich, R.L., Wernerhein, M.C., 2005. Loss of Fish Habitat as a Consequence of Inappropriately Constructed Stream Crossings. Fisheries 30(1): 10-17.

Hawbaker, T.J. and Radeloff, V.C. 2004. Roads and Landscape Pattern in Northern Wisconsin Based on a Comparison of Four Road Data Sources. Conservation Biology. 18[5]: 1233-1244.