This geospatial dataset depicts ownership patterns of forest land across Michigan, circa 2019. The data sources are listed below. The first seven sources of data supersede the final data source. The final data source is modeled from Forest Inventory and Analysis points from 2012-2017 and the most up-to-date publicly available boundaries of federal, state, and tribal lands.1.MI_State_Boundary_Census_Gov_2019.shp (State of MI boundary) clipped from cb_2019_us_state_500k from https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html2.NPS_Land_Resources_Division_MI.shp clipped from NPS_-_Land_Resources_Division_Boundary_and_Tract_Data_Service-shp taken from https://public-nps.opendata.arcgis.com/datasets/nps-land-resources-division-boundary-and-tract-data-service/data?layer=1Published December 12, 2019This service depicts National Park Service tract and boundary data that was created by the Land Resources Division. NPS Director's Order #25 states: "Land status maps will be prepared to identify the ownership of the lands within the authorized boundaries of the park unit. These maps, showing ownership and acreage, are the 'official record' of the acreage of Federal and non-federal lands within the park boundaries. While these maps are the official record of the lands and acreage within the unit's authorized boundaries, they are not of survey quality and not intended to be used for survey purposes." As such this data is intended for use as a tool for GIS analysis. It is in no way intended for engineering or legal purposes. The data accuracy is checked against best available sources which may be dated and vary by location. NPS assumes no liability for use of this data. The boundary polygons represent the current legislated boundary of a given NPS unit. NPS does not necessarily have full fee ownership or hold another interest (easement, right of way, etc...) in all parcels contained within this boundary. Equivalently NPS may own or have an interest in parcels outside the legislated boundary of a given unit. In order to obtain complete information about current NPS interests both inside and outside a unit’s legislated boundary tract level polygons are also created by NPS Land Resources Division and should be used in conjunction with this boundary data. To download this data directly from the NPS go to https://irma.nps.gov/App/Portal/Home Property ownership data is compiled from deeds, plats, surveys, and other source data. These are not engineering quality drawings and should be used for administrative purposes only. The National Park Service (NPS) shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on a computer system at the National Park Service, no warranty expressed or implied is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data. Terms of UseProperty ownership data is compiled from deeds, plats, surveys, and other source data. These are not engineering quality drawings and should be used for administrative purposes only. The National Park Service shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on a computer system at the National Park Service, no warranty expressed or implied is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data.3.Isle Royale.shp only Isle Royale clipped from MI_State_Boundary_Census_Gov_2019.shp4.FWSInterest_MI.shp (U.S. Fish and Wildlife Service) clipped from FWSInterest from FWSInterest_Apr2020.zipfrom https://www.fws.gov/gis/data/CadastralDB/index_cadastral.html (being moved on 6/26/2020)Use inttype1 = OThis data layer depicts lands and waters administered by the U.S. Fish and Wildlife Service (USFWS) in North America, U.S. Trust Territories and Possessions. It may also include inholdings that are not administered by USFWS. The primary source for this information is the USFWS Realty program.5.surfaceownership_MI.shp (U.S. National Forest Service) clipped from S_USA.SurfaceOwnership.gdb and downloaded fromhttps://data.fs.usda.gov/geodata/edw/datasets.phphttps://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=surfaceownershiprefreshed May 26, 2020Used NFSLandU_4 field and surfaceO_3 and surfaceO_3 to identify NFS parcelsAn area depicting ownership parcels of the surface estate. Each surface ownership parcel is tied to a particular legal transaction. The same individual or organization may currently own many parcels that may or may not have been acquired through the same legal transaction. Therefore, they are captured as separate entities rather than merged together. This is in contrast to Basic Ownership, in which the surface ownership parcels having the same owner are merged together. Basic Ownership provides the general user with the Forest Service versus non-Forest Service view of land ownership within National Forest boundaries. Surface Ownership provides the land status user with a current snapshot of ownership within National Forest boundaries.6.MichiganDNR_02062020.shp (State of Michigan) from the State of MI delivered @ email on 5/14/2020Has State forests, State Wildlife areas, and State parks.7.The previous public ownership layers supersede this Sass et al. (2020) layer.In Sass et al. (2020), the nonforest areas are masked out.Identification_Information:Citation:Citation_Information:Originator: Sass, Emma M.Originator: Butler, Brett J.Originator: Markowski-Lindsay, Marla Publication_Date: 2020Title:Estimated distribution of forest ownership across the conterminous United States – geospatial datasetGeospatial_Data_Presentation_Form: raster digital dataPublication_Information:Publication_Place: Fort Collins, COPublisher: Forest Service Research Data ArchiveEight values of ownership type:1 = Family (Private): Owned by families, individuals, trusts, estates, family partnerships, and other unincorporated groups of individuals that own forest land. FIACode 45.2 = Corporate (Private): Owned by corporations. FIA Code 41.3 = TIMO/REIT (Private): Owned by Timber Investment Management Organizations or Real Estate Investment Trusts. Included in FIA Code 414 = Other Private (Private): Owned by conservation and natural resource organizations, unincorporated partnerships and associations. FIA Codes 42-43.5 = Federal (Public): Owned by the federal government. FIA Codes 11-13, 21-25.6 = State (Public): Owned by a state government. FIA Code 31.7 = Local (Public): Owned by a local government. FIA Code 32.8 = Tribal: Owned by Native American tribes. FIA Code 44.8.FIA inventory units developed by FIA, 2020

Forest type group layer was developed using data from over 213,000 national forest inventory plots measured during the period 2014-2018 from the USDA Forest Service Forest Inventory and Analysis (FIA) program, in conjunction with other auxiliary information. Roughly 4,900 Landsat 8 Operational Land Imager scenes, collected during the same time period, were processed to extract information about vegetation phenology. This information, along with climatic and topographic raster data, were used in an ecological ordination model of tree species. The model produced a feature space of ecological gradients that was then used to impute FIA plots to pixels. The plots imputed to each pixel were then used to assign values of forest type groups. Data source: FIA BIGMAP General Layer Catalog, Forest Type Groups of the Continental United States at https://usfs.maps.arcgis.com/apps/LayerShowcase/index.html?appid=22cc1bd57eb84b46a89a2d9935325e0fFor more information about the methods used to produce this dataset please see the following references:Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E. 2018. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data. ISPRS Journal of Photogrammetry and Remote Sensing. 137: 29-46.Wilson, Barry Tyler; Woodall, Christopher W.; Griffith, Douglas M. 2013. Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance and Management. 8:1. doi:10.1186/1750-0680-8-1Wilson, B. Tyler; Lister, Andrew J.; Riemann, Rachel I. 2012. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. Forest Ecology and Management. 271: 182-198.Ohmann, Janet L.; Gregory, Matthew J. 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, U.S.A. Canadian Journal of Forest Research. 32: 725-741A detailed description of FIA Forest Types can be found in the Forest Atlas of the United States HERE. Code Forest type group100 White / red / jack pine group120 Spruce / fir group140 Longleaf / slash pine group150 Tropical softwoods group160 Loblolly / shortleaf pine group170 Other eastern softwoods group180 Pinyon / juniper group200 Douglas-fir group220 Ponderosa pine group240 Western white pine group260 Fir / spruce / mountain hemlock group280 Lodgepole pine group300 Hemlock / Sitka spruce group320 Western larch group340 Redwood group360 Other western softwoods group370 California mixed conifer group380 Exotic softwoods group390 Other softwoods group400 Oak / pine group500 Oak / hickory group600 Oak / gum / cypress group700 Elm / ash / cottonwood group800 Maple / beech / birch group900 Aspen / birch group910 Alder / maple group920 Western oak group940 Tanoak / laurel group960 Other hardwoods group970 Woodland hardwoods group980 Tropical hardwoods group988 Cloud forest990 Exotic hardwoods group999 Nonstocked

In this project we established a network of forest inventory plots to gather the data needed to forecast future forest performance under global change. Data collected from forest inventory plots, i.e., size and location of individual trees from all ages and species, have been shown to be particularly useful to link tree species demographic rates (survival, growth, age at maturity, fecundity) with community characteristics (assemblages and species turnovers), and are also widely used to estimate biomass removal (logging) and biomass production (carbon sequestration).

Map of Nordhouse Dunes Wilderness Area to be used in the Nordhouse Dunes Wilderness Storymap. The map shows the location of the wilderness area within the Huron-Manistee National Forests. It also shows trails and ownership within the Forest. Huron-Manistee National Forests are located in the lower peninsula of Michigan and is part of USDA Forest Service.

This dataset provides a land cover map focused on peatland ecosystems in the upper peninsula of Michigan. The map was produced at 12.5-m resolution using a multi-sensor fusion (optical and L-band SAR) approach with imagery from Landsat-5 TM and ALOS PALSAR collected between 2007 and 2011. A random forest classifier trained with polygons delineated from field data and aerial photography was used to determine pixel classes. Accuracy assessment based on field-sampled sites show high overall map accuracy (92%).

Recent and previous disturbance of forest canopy, persisting forest cover, persisting nonforest, and water were mapped within Lake Michigan basin, USA, 1985-2008. Landsat time series stacks and a regionally optimized vegetation change tracker algorithm were used for modeling a 30-meter spatial resolution dataset with a minimum mapping unit of 4 pixels (0.88 acres).The dataset was designed and produced to support Great Lakes Restoration Initiative (GLRI) assessments of water quality. Possible additional purposes include, but are not limited to: monitoring areal forest change across time, forest succession monitoring, forest fragmentation monitoring, and land management planning at a scale of 1:24000 scale or coarser.A corresponding Research Map cartographically portrays this dataset. A companion dataset and map have been produced for Lake Superior basin, USA and Canada. See references below.

Original metadata date was 08/01/2014. Minor metadata updates on 12/13/2016 and 10/03/2019.

The purpose of "Ecological Diversity of the Huron-Manistee National Forests" is to determine the ecological diversity of the Huron-Manistee National Forests of northern Lower Michigan by determining the occurence of landscape ecosystems at several hierachical spatial scales to determine the major wetland ecosystems, provide examples of local wetland ecosystems, and map their pattern of occurence to determine how well the areas designated as potential old growth forests represent the ecological diversity of the forests, and to provide and overall conceptual model for the study of ecological diversity of large landscapes.

The overall goal is to characterize the ecological diversity of the Huron-Manistee National Forest, an area of over 800,000 acres in the northern part of lower Michigan. In particular, to examine the extent to which the 173,000 acres of old-growth lands tentatively set aside respresent the full range of ecological diversity.

Collection Organization: University of Michigan, School of Natural Resources & Environment

Collection Methodology: The approach is to study the existing regional and local classifications of the area and determine kinds and patterns of landscape ecosystems within selected levels of these classifications, to determine the amount of old growth forest in each classification unit and unique ecosystems that should be set aside as old growth, to examine and sample dryland and wetland ecosystems throughout the forests and develop a classification of wetland ecosystems for the area, map local areas to illustrate the fine-scale diversity of landscape ecosystems. In the field transects were run and sampled, and sample plots were established and sampled.

Collection Frequency: Data collected at different places in field seasons: 1992, 1993, 1994.

Update Characteristics: No data update. Analyses have been performed on field data.

LANGUAGE:

English ACCESS/AVAILABILITY:

Data Center: University of Michigan Dissemination Media: Hard copy (notes/data taken in field); also exists on plot forms, and student computer disks Access Instructions: Contact the data center. Access Restrictions: None

These geospatial data portray early successional forest (ESF) and other land cover in Michigan, Wisconsin, and most of Minnesota. Forest canopy disturbance between 1990 and 2009 was mapped using 42 Landsat time series stacks (LTSS) and a modified version of the vegetation change tracker algorithm (VCTw). Corresponding winter imagery was used to reduce commission errors of forest disturbance in densely vegetated nonforest tracts by identifying areas of persistent snow cover and assigning those areas to nonforest class. The resulting disturbance age map was classed into four 5-year age classes and persisting cover classes, then used to attribute age to forested pixels within the National Land Cover Database of 2011 (NLCD2011). Additional post processing was conducted to reduce misregistration, and a minimum mapping unit of 4 30-meter pixels was applied to comply with the USDA Forest Service, Forest Inventory and Analysis (FIA) definition of forest. A small percentage of NLCD2011 Shrub/Scrub and Grassland/Herbaceous pixels were also reclassified as forest based on VCTw data (see processing steps).These data were produced to identify early successional forest for wildlife habitat analyses at a regional scale. Other possible uses include coarse scale analysis of regional or statewide forest change and succession monitoring, erosion and water quality modeling, carbon accounting, forest fragmentation monitoring, and land management planning.Original metadata dates was 01/14/2016. On 04/07/2016 the layer file was slightly modified by updating a path which now points to a relative location so that it works for all users, and the metadata updated accordingly. Minor metadata updates on 12/19/2016 and 04/12/2019.

Treatments are managerially desirable activities that further management objectives. Treatments include any activity that affects the vegetative composition and attributes recorded in Stands layer such as timber harvest, prescribed burning or tree planting. Treatments may be part of a sequence, or multiple activities that occur over time. There is only one active treatment at a time with older treatments recorded in Treatment Lineage and future treatments described in Next Step Treatments. Approval status is used to identify phases of the inventory, approval and implementation process. Treatments usually originate from a Stand but may be larger or smaller for various business reasons. Treatment-level attributes may include, but are not limited to: Treatment type Treatment method Cover type objective, Age structure, and Acceptable regeneration Prescription specifications and other comments

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

Basal Area (BA). 30 meter pixel resolution. Data represents forest conditions circa 2002.These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

The National Park Service (NPS), in conjunction with the Biological Resources Division (BRD) of the U.S. Geological Survey (USGS), has implemented a program to "develop a uniform hierarchical vegetation methodology" at a national level. The program will also create a geographic information system (GIS) database for the parks under its management. The purpose of the data is to document the state of vegetation within the NPS service area during the 1990's, thereby providing a baseline study for further analysis at the Regional or Service-wide level. The vegetation units of this map were determined through stereoscopic interpretation of aerial photographs supported by field sampling and ecological analysis. The vegetation boundaries were identified on the photographs by means of the photographic signatures and collateral information on slope, hydrology, geography, and vegetation in accordance with the Standardized National Vegetation Classification System (October 1995). The mapped vegetation reflects conditions that existed during the specific year and season that the aerial photographs were taken (spring - 1996 and fall - 1994). There is an inherent margin of error in the use of aerial photography for vegetation delineation and classification.

The purpose of this spatial data is to provide the National Park Service the necessary tools to wisely manage the natural resources within this park system. Several parks, representing different regions, environmental conditions, and vegetation types, were chosen by BRD to be part of the prototype phase of the program. The initial goal of the prototype phase is to "develop, test, refine, and finalize the standards and protocols" to be used during the production phase of the project. This includes the development of a standardized vegetation classification system for each park and the establishment of photointerpretation, field, and accuracy assessment procedures. Isle Royale National Park was initially identified as one of the prototypes within the National Park System for the USGS-NPS Vegetation Mapping Program. Isle Royale National Park was established March 3, 1931 and was also designated as an International Biosphere Reserve in 1980. The park contains approximately 571,790 acres of land and water (893 square miles) of which 133,782 acres is land and the rest is open water of Lake Superior as well as inland lakes and ponds. Isle Royale National Park is an archipelago of islands located in the northwestern region of Lake Superior close to the United States-Canada border. The main island, Isle Royale, consists of a series of ridges and valleys running the length of the island and is surrounded by approximately 200 smaller islands. The primary methods of transportation on the island are hiking and boating. Isle Royale National Park was authorized on March 3, 1931; it was formally established in 1940, and officially dedicated in 1946. Most of the park's land area (98%) was designated as a Wilderness area in October 1976, and later additions increased the total Wilderness to 99% of the park. The park was designated an International Biosphere Reserve in 1980.

Isle Royale National Park is an archipelago of islands located in the northwestern region of Lake Superior close to the United States-Canada border. The park is located about 60 miles northwest of Michigan.s Keweenaw Peninsula, about 22 miles east of Grand Portage, Minnesota, and about 35 miles southeast of Thunder Bay, Ontario.

Information for this metadata was obtained from the site "http://biology.usgs.gov/npsveg/isro/metaisrospatial.html" and converted to NASA Directory Interchange Format.

Campgrounds on State Forest Land are public campgrounds located in the Michigan State Forest System. This layer shows the point locations of state forest campgrounds located throughout Michigan's Upper and northern Lower Peninsulas. This layer contains the location, name, and phone number for each campground.

These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This is one piece of the R9 HMNF Wild and Scenic Rivers story map. The purpose of map is to be used in one of the side cars showing a close up view of each river. The map displays the HMNF boundary and ownership, National Wild and Scenic Rivers, River Corridors, and recreation opportunities along the rivers. Relevant extent is the Huron-Manistee National Forests in the Lower Peninsula of Michigan.

Data created using ORS Z-score algorithm using MODIS satellite imagery from 2016, 2017, julian date range 152 - 200 (June 1 - July 19). Baseline imagery from 2010 to 2014. NDVI was the spectral index used for this analysis. Aerial Disease Survey data collected with the Digital Mobile Survey Mapper (DMSM) in late June/early July 2017 is included in the map for reference. The satellite-based model agrees with the ADS data in many cases. It also delineates areas not identified by the aerial surveyors. One use of this map will be to ground-check some areas to see if they identify real damage/change or if they are identifying noise.

As of July 26, ADS data have not been collected on the Hiawatha National Forest, so this classification may serve as a guide to field check those areas. Drummond Island shows little damage in the MODIS model; the ADS polygons indicate moderate-density damage (11-29%) which may be below the detection threshold of the sensor and computer algorithm.

Raster layers (satellite models) in this map include the unfiltered layer (Unsieved), Sieve1, in which some noise has been filtered and clumped, and Sieve2, in which a greater degree of sieving and clumping has been done.

Michigan land-use change by plot, 2019. This dataset includes per-plot land use class during current (Time 2, T2) and previous (Time 1, T1) time periods, and land-use change between two time periods (T1-T2), for Michigan, USA. Measurements are based on USDA Forest Service, Forest Inventory and Analysis (FIA; https://www.fia.fs.fed.us/) permanent, remeasured sample plots within Michigan (NRS; https://www.nrs.fs.fed.us/fia/). Land-use classes include: Cropland, Developed, Forest, Other, Pasture/Rangeland, Water, and Wetland. Land-use change classes include: Forest gain, Forest loss, Remained forest, and Remained nonforest. Nonsampled and partially nonsampled plots were excluded; thus, FIA plots in this dataset may comprise a subset of all plots included in standard FIA data and tools (https://www.fia.fs.fed.us/tools-data/index.php). Basemap: World Topographic .Map, Esri, HERE, Garmin, FAO, NOAA, USGS, © OpenStreetMap contributors, and the GIS User Community. Cartographer: Scott A. Pugh, U.S. Forest Service, Dec. 2020.

Data for the Fuelwood/Firewood Collection map. More information at www.michigan.gov/fuelwood

Modeled distribution is taken from Tree species distribution in the United States Part 1 in the Journal of Maps by Rachel Riemann, Barry T. Wilson, Andrew J. Lister, Oren Cook & Sierra Crane-Murdoch.Rachel Riemann, Barry T. Wilson, Andrew J. Lister, Oren Cook & Sierra Crane-Murdoch (2018) Tree species distribution in the United States Part 1, Journal of Maps, 14:2, 561-566, DOI: https://doi.org/10.1080/17445647.2018.1513383PDF available at https://usfs.maps.arcgis.com/sharing/rest/content/items/0a7e5c26ecbd46eda2068dca071920f6/data