The National Park Service (NPS) Vegetation Inventory Program (VIP) is an effort to classify, describe, and map existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VIP is managed by the NPS Inventory and Monitoring Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The USGS Upper Midwest Environmental Sciences Center, NatureServe, and NPS Mississippi National River and Recreation Area (MISS) have completed vegetation classification and mapping of MISS for the NPS VIP.

Mappers, ecologists, and botanists collaborated to identify and describe vegetation types within the U.S. National Vegetation Classification (USNVC) and to determine how best to map them by using aerial imagery. The team collected data from 132 vegetation plots within MISS to develop detailed descriptions of USNVC associations. Data from 52 verification sites were also collected to test both the dichotomous key to vegetation associations and the application of vegetation types to a sample set of map polygons. Furthermore, data from 776 accuracy assessment (AA) sites were collected (of which 757 were used to test accuracy of the vegetation map layer). These data sets led to the identification of 45 vegetation association in the USNVC at MISS.

A total of 45 map classes were developed to map the vegetation and open water of MISS, including the following: 35 map classes represent natural (including ruderal) vegetation in the USNVC, 7 map classes represent cultural vegetation (agricultural and developed) in the USNVC, and 3 map classes represent non-vegetative open-water bodies (non-USNVC). Features were interpreted from viewing color-infrared digital aerial imagery dated September and October 2012 (during peak leaf-phenology change of trees) via digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems (GIS). The interpreted data were digitally and spatially referenced, thus making the spatial database layers usable in GIS. Polygon units were mapped to either a 0.5 ha or 0.25 ha minimum mapping unit, depending on vegetation type.

A geodatabase containing various feature-class layers and tables shows the locations of USNVC vegetation types (vegetation map), vegetation plot samples, verification sites, AA sites, project boundary extent, and aerial image centers. The feature-class layer and relate tables for the vegetation map provides 4,498 polygons of detailed attribute data covering 21,771.6 ha of area, with an average polygon size of 4.8 ha; the vegetation map covers the entire administrative boundary for MISS.

Summary reports generated from the vegetation map layer show map classes representing USNVC natural (including ruderal) vegetation associations apply to 4,012 polygons (89.2% of polygons) and cover 8,938.7 ha (41.1%) of the map extent. Of these polygons, the map layer shows MISS to be 27.5% forest and woodland (5,986.2 ha), 1.6% shrubland (353.6 ha), 11.2% herbaceous vegetation (2,431.8 ha), and 0.8% sparse vegetation (163.9 ha). Map classes representing USNVC cultural types apply to 415 polygons (9.2% of polygons) and cover 7,628.5 ha (35.0%) of the map extent. Map classes representing non-vegetative open-water bodies (non-USNVC) apply to 71 polygons (1.6% of polygons) and cover 5,204.4 ha (23.9%) of the map extent.

For a full report on the National Park Service Vegetation Inventory Program mapping effort, see: National Park Service Vegetation Inventory Program (pdf, 54 MB)

This map overlay service includes data from a collaboration between the California Native Plant Society (CNPS) and the California Dept of Fish and Wildlife (CDFW).The CNPS Vegetation Program has worked for over 15 years to provide standards and tools for identifying and representing vegetation, as an important feature of California's natural heritage and biodiversity. Many knowledgeable ecologists and botanists support the program as volunteers and paid staff. Through grants, contracts, and grass-roots efforts, CNPS collects field data and compiles information into reports, manuals, and maps on California's vegetation, ecology and rare plants in order to better protect and manage them. We provide these services to governmental, non-governmental and other organizations, and we collaborate on vegetation resource assessment projects around the state. CNPS is also the publisher of the authoritative Manual of California Vegetation, you can purchase a copy HERE. To support the work of the CNPS, please JOIN NOW and become a member!The CDFW Vegetation Classification and Mapping Program develops and maintains California's expression of the National Vegetation Classification System. We implement its use through assessment and mapping projects in high-priority conservation and management areas, through training programs, and through working continuously on best management practices for field assessment, classification of vegetation data, and fine-scale vegetation mapping.HOW THESE OVERLAYS WERE CREATED:CNPS / CDFW / National Park Service Sources: Unlike the Landsat-based raster modelling of the Natureserve and Gap national ecological systems, (see below) the CNPS/CDFW/NPS data date back to the origin of the National Vegetation Classification effort to map the US national parks in the mid 1990's.
These mapping efforts are a hybrid of photo-interpretation, satellite and corollary data to create draft ecological land units, which are then sampled by field crews and traditional vegetation plot surveys to quantify and analyze vegetation composition and distribution into the final vector boundaries of the formal NVC classes identified and classified. As such these are much more accurate maps, but the tradeoff is they are only done on one field project area at a time so there is not yet a national or even statewide coverage of these detailed maps.
However, with almost 2/3d's of California already mapped, that time is approaching. The challenge in creating standard map layers for this wide diversity of projects over the 2 decades since NVC began is the extensive evolution in the NVC standard itself as well as evolution in the field techniques and tools. To create a consistent set of map layers, a master crosswalk table was built using every different classification known at the time each map was created and then crosswalking each as best as could be done into a master list of the currently-accepted classifications. This field is called the "NVC_NAME" in each of these layers, and it contains a mixture of scientific names and common names at many levels of the classification from association to division, whatever the ecologists were able to determine at the time. For further precision, this field is split out into scientific name equivalents and common name equivalents.Nserve and GapLC Sources: Early shortcomings in the NVC standard led to Natureserve's development of a mid-scale mapping-friendly "Ecological Systems" standard roughly corresponding to the "Group" level of the NVC, which facilitated NVC-based mapping of entire continents. Current scientific work is leading to the incorporation of Ecological Systems into the NVC as group and macrogroup concepts are revised. Natureserve and Gap Ecological Systems layers differ slightly even though both were created from 30m landsat data and both follow the NVC-related Ecological Systems Classification curated by Natureserve. In either case, the vector overlay was created by first enforcing a .3ha minimum mapping unit, that required deleting any classes consisting of fewer than 4 contiguous landsat cells either side-side or cornerwise. This got around the statistical problem of numerous single-cell classes with types that seemed improbable given their matrix, and would have been inaccurate to use as an n=1 sample compared to the weak but useable n=4 sample. A primary goal in this elimination was to best preserve riparian and road features that might only be one pixel wide, hence the use of cornerwise contiguous groupings. Eliminated cell groups were absorbed into whatever neighboring class they shared the longest boundary with. The remaining raster groups were vectorized with light simplification to smooth out the stairstep patterns of raster data and hopefully improve the fidelity of the boundaries with the landscape. The resultant vectors show a range of fidelity with the landscape, where there is less apparent fidelity it must be remembered that ecosystems are normally classified with a mixture of visible and non-visible characteristics including soil, elevation and slope. Boundaries can be assigned based on the difference between 10% shrub cover and 20% shrub cover.SUBLAYER NAMING: The data sublayers in this map overlay service are all based on the US National Vegetation Classification, a partnership of the USGS GAP program, US Forest Service, Ecological Society of America and Natureserve, with adoption and support from many federal & state agencies and nonprofit conservation groups. The USNVC grew out of the US National Park Service Vegetation Mapping Program, a mid-1990's effort led by The Nature Conservancy, Esri and the University of California. The classification standard is now an international standard, with associated ecological mapping occurring around the world. NVC is a hierarchical taxonomy of 8 levels, from top down: Class, Subclass, Formation, Division, Macrogroup, Group, Alliance, Association LAYER NAMING: Layer names follow this pattern: Source - Program - Level - Scale - RegionSource - Program = who created the data: Nserve = Natureserve, GapLC = USGS Gap Program Landcover Data PADUS = USGS Gap Protected Areas of the USA program Cnps/Cdfw = California Native Plant Society/Calif Dept of Fish & Wildlife, often followed by the project name such as: SFhill = Sierra Foothills, Marin Open Space, MMWD = Marin Municipal Water District etc. National Parks are included and may be named by their standard 4-letter code ie YOSE = Yosemite, PORE = Point Reyes.Level: The level in the NVC Hierarchy which this layer is based on: Base = Alliances and Associations Mac = Macrogroups Sub = SubclassesScale: One of 3 basic scales at which this layer will appear: Base = base scale, approx 1:1k up to 1:36k Mid = 72k to about 500k Out = 1m to 10mRegion: The region that this layer covers, ie USA=USA, WEST= western USA,
Marin = Marin County. May not appear if redundant to the Source-Program text.LABEL & COLOR: These overlays utilize a separate labelling layer to make it easy to include or not include labels, as needed. These are named the same as the layer they label, with "LABEL" added, and often the color used for that label layer in order to help tell them apart on the map. Note there can be multiple different label layers for the same set of polygons, depending upon the attribute or naming style desired, ie scientific names or common names. Finally the order of these services in the sublayers of a map service is normally designed so that ALL of the label services appear above ANY/ALL of the vector services they refer to, to prevent a vector service writing on top of a label and obscuring it.SUBLAYER CATALOG Point Labels BROWN (0) Vegetation Field Plot Survey points for Point Reyes/Golden Gate National Parks, LABEL layer Veg Points SF Parks (1) Vegetation Field Plot Survey points for Point Reyes/Golden Gate National Parks MMWD Base labels TAN (2) NVC Vegetation Community LABELS for Marin Municipal Water District PORE Veg Mac 288k (3) NVC MACROGROUP Vegetation Community Boundaries for Point Reyes/Golden Gate Nat. Park MarinOpenSPace Base labels (4) NVC Vegetation Community Boundary LABELS for Marin Open Space District MMWD Veg Base 36k (5) NVC Vegetation Community Boundaries for Marin Municipal Water District MarinOpenSpace VegBase 36k (6) NVC Vegetation Community Boundaries for Marin Open Space District PORE Base labels TAN (7) NVC Vegetation Community Boundary LABELS for for Point Reyes/Golden Gate Nat. Park PORE Veg Base 36k (8) NVC Vegetation Community Boundaries for for Point Reyes/Golden Gate Nat. ParkATTRIBUTES CATALOG: PORE Veg Base 36k nserve_id String Natureserve/National Veg Classification web link ID, mainly for associations

vegebank_id String Vegbank web link ID, mainly for alliances

eolid String Encyclopedia of Life web link ID for first dominant species name

usdaid String USDA Plants Database web link ID for first dominant species name

nvc_elcode String Natureserve/National Veg Classification System Element Code

association String length: 300 Field name use for NVCS names in this dataset nvccommonname String length: 150 NVCS name translated into common

nservevegbanknotes String length: 1024 Summary Ecological Notes from Natureserve/Vegbank entries in case those services are nonresponsive. group_ String length: 56 -NVCS Group Name where knownnvcsmg String length 255 -NVCS Macrogroup name where knowncons_status String length: 17 - IUCN Global Conservation Status, 5 levels (G1=critically imperiled, to G5=secure) mcvid String length: 3 - CNPS Manual of California Vegetation web link IDmcvhabitats String length: 255 -CNPS Manual of

Residential yards are a significant component of urban socio-ecological systems; residential land covers 11% of the United States and is often the dominant land use within urban areas. Residential yards also play an important role in the sustainability of urban socio-ecological systems, affecting biogeochemical cycles, water, and the climate via individual- and household-level behaviors. Vegetation, such as trees and grasses, are unevenly distributed across front and back yards, and we sought to understand how similar yards are to each other when compared to their neighboring yards and neighborhoods using aerial imagery. There are many ways to measure yard similarity, and we compared several measures to account for different definitions of ‘neighborness’. We examined the spatial autocorrelation of several yard vegetation characteristics in both front and backyards in Boston, MA, USA. Our study area included 1,027 Census block groups (sub-neighborhood areas) and 175,576 parcels with matched front-backyard pairings (n = 351,152 yards in total) across Boston’s metropolitan area. This data package contains 1) 351,152 yard spatially-referenced yard polygons with five measures of vegetation summarized, 2) the containing block groups, and 3) and *.R script that replicates the analyses reported in Locke, D. H., Ossola, A., Minor, E., & Lin, B. B. (2021). Spatial contagion structures urban vegetation from parcel to landscape. People and Nature, 00, 1–15. https://doi.org/10.1002/pan3.10254

Methods

1. Study Area This study focused on the Boston, MA, metropolitan region (42°21′29″N 71°03′49″W), an area of approximately 703 km2. The region has a humid continental climate (mean annual temperature = 9.6 °C; mean annual precipitation = 1233 mm) (PRISM Climate Group 2015) and was historically covered with mesic forests. Forty-four percent of the land area is residential (Ossola et al., 2019a), which is consistent with other urban areas in western countries such as Baltimore, MD (Avolio et al., 2020), Chicago, IL (Lewis et al., 2019), Adelaide, (Australia)(Ossola et al., 2021), Edinburgh (Scotland), Belfast (Northern Ireland), Cardiff (Wales), and Leicester and Oxford (England) (Loram et al., 2007), and represents more than twice as much land area as parks and open spaces (18.43%) (Ossola et al., 2019b). Backyards compose 14% of all urban land area and contain ~21% of all tree canopy cover; front yards cover ~8% of the area and have ~8% of the study area’s tree canopy cover (Ossola et al., 2019b).

2. Open Data Classified LiDAR point cloud data (year 2014) were obtained from the US Geological Survey (“MA Post-Sandy CMPG 2013–14”, NPS = 0.7 m, vertical and horizontal accuracy = 0.05 m and 0.35 m, respectively). High-resolution RBG-NIR imagery (1 m ground resolution, year 2014) were obtained from the National Agriculture Imagery Program (NAIP, USDA). Residential parcel polygons, building footprints, and road centerline data were downloaded from the open data portals of the Commonwealth of Massachusetts (2017) and the City of Boston (2017).

3. Geospatial analyses All front, corner, and backyards contained in all residential parcels with a house were located and classified in ArcGIS Desktop 10.5 (ESRI, Redlands, CA) by using the workflow described in Ossola and others (2019a, 2019b). Briefly, each house centroid was identified to fit an offset line perpendicular to the closest street centerline. Front and backyards were then located by splitting each parcel polygon with a dividing segment, perpendicular to the offset line, passing through the house centroid, and extending to the parcel’s border. Yards were classified by attributing the front yard as the closest unit to the respective road centerline. Corner yards, which lack clear front/back sides, were assigned to all parcels located within 15 m from street intersections and were excluded from analyses. The workflow used to locate and classify yards exceeded 98% accuracy (Ossola et al., 2019a). Vegetation maps detailing tree height, canopy volume, and tree and grass covers were modelled and validated for their accuracy based on the LiDAR and RBG-NIR imagery as detailed in previous papers (Ossola et al., 2019a, 2019b). Briefly, tree canopy height was extracted from a canopy height model (1.5 m ground resolution) interpolated from the LiDAR data in ArcGIS Desktop 10.5 (ESRI, Redlands, CA). Tree and grass covers were modelled at 1.5 m resolution by using maximum likelihood supervised classification of ~100,000 pixels manually attributed to one of three land cover classes (i.e., tree, grass and non-vegetated cover), and based on the tree canopy height map and the RGB-NIR imagery (Singh et al., 2012). The average vertical accuracy of the tree height data, as recorded by the LiDAR point cloud, is 5.3 cm. The accuracy of the grass and tree canopy cover classification is 91.7% and 98.9%, respectively (Ossola & Hopton, 2018a). Canopy volume was calculated as the product of tree canopy cover and height within each pixel, assuming this volume to be completely occupied by vegetation (Ossola & Hopton, 2018a, 2018b), which overestimates total volume. Because these remotely sensed data view the earth from above, and tree canopy overhangs turf, the turf estimates are plausibly underestimates (Akbari et al., 2003).

References

Akbari, H., Rose, L. S., & Taha, H. (2003). Analyzing the land cover of an urban environment using high-resolution orthophotos. Landscape and Urban Planning, 63(1), 1–14. https://doi.org/10.1016/S0169-2046(02)00165-2 Avolio, M. L., Blanchette, A., Sonti, N. F., & Locke, D. H. (2020). Time Is Not Money: Income Is More Important Than Lifestage for Explaining Patterns of Residential Yard Plant Community Structure and Diversity in Baltimore. Frontiers in Ecology and Evolution, 8(April), 1–14. https://doi.org/10.3389/fevo.2020.00085 Lewis, A. D., Bouman, M. J., Winter, A. M., Hasle, E. A., Stotz, D. F., Johnston, M. K., Klinger, K. R., Rosenthal, A., & Czarnecki, C. A. (2019). Does nature need cities? Pollinators reveal a role for cities in wildlife conservation. Frontiers in Ecology and Evolution, 7(JUN), 1–8. https://doi.org/10.3389/fevo.2019.00220 Loram, A., Tratalos, J., Warren, P. H., & Gaston, K. J. (2007). Urban domestic gardens (X): The extent & structure of the resource in five major cities. Landscape Ecology, 22(4), 601–615. https://doi.org/10.1007/s10980-006-9051-9 Ossola, A., & Hopton, M. E. (2018a). Climate differentiates forest structure across a residential macrosystem. Science of the Total Environment, 639, 1164–1174. https://doi.org/10.1016/j.scitotenv.2018.05.237 Ossola, A., & Hopton, M. E. (2018b). Measuring urban tree loss dynamics across residential landscapes. Science of The Total Environment, 612, 940–949. https://doi.org/10.1016/j.scitotenv.2017.08.103 Ossola, A., Jenerette, G. D., McGrath, A., Chow, W., Hughes, L., & Leishman, M. R. (2021). Small vegetated patches greatly reduce urban surface temperature during a summer heatwave in Adelaide, Australia. Landscape and Urban Planning, 209. https://doi.org/10.1016/j.landurbplan.2021.104046 Ossola, A., Locke, D. H., Lin, B., & Minor, E. (2019a). Greening in style: Urban form, architecture and the structure of front and backyard vegetation. Landscape and Urban Planning, 185(November 2018), 141–157. https://doi.org/10.1016/j.landurbplan.2019.02.014 Ossola, A., Locke, D. H., Lin, B., & Minor, E. S. (2019b). Yards increase forest connectivity in urban landscapes. Landscape Ecology, 7(12). https://doi.org/10.1007/s10980-019-00923-7 Singh, K. K., Vogler, J. B., Shoemaker, D. A., & Meentemeyer, R. K. (2012). LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy. ISPRS Journal of Photogrammetry and Remote Sensing, 74(November), 110–121. https://doi.org/10.1016/j.isprsjprs.2012.09.009

This zip file contains 21 raster layers representing data from a variety of landscape metrics used to analyze the landscape context of Bighorn Canyon National Recreation Area (BICA). Their names, descriptions and categorization are as follows: Housing This raster dataset contains sixteen layers named in the format "bhc1950us," with the name of each layer containing a year representing the decades from 1950 through 2100 (ex. bh1960us, bhc1970us, bhc1980us, etc.). The layers depict housing density classes for the area around the 30 km buffer around and including BICA’s managed lands for each decade. These housing density estimates come from a Spatially Explicit Regional Growth Model (SERGoM, Theobald 2005) based on U.S. Census data from 2010 and depict the location and density of private land housing unit classes around BICA. SERGoM methods combined housing data with information on land ownership and density of major roads (interstates, state highways, and county roads) to provide a more accurate allocation of the location of housing units over the landscape. Details on how SERGoM was used for NPS data can be found in the NPScape Standard Operating Procedure (SOP): Housing Measure at https://irma.nps.gov/DataStore/Reference/Profile/2221576 The SERGoM used historical and current housing density patterns as data inputs to develop a simulation model to forecast future housing density patterns based on county-level population projections. Further details about the methodology of SERGoM can be found at https://www.jstor.org/stable/26267722?seq=2 SERGoM_bhc_metrics: Value CLASSNAME 0 Private undeveloped 1 2,470 units / square km 12 Commercial/industrial Land Cover This raster dataset depicts land cover and contains four layers from the National Land Cover Database (NLCD). The names and descriptions of each layer are as follows: NLCD2001. The National Land Cover Database 2001 land cover layer for mapping was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. This is a single layer for 2001 landcover data at all levels. Level 2 data for 2001 can be derived from this layer by collapsing level 1 features into level 2 categories. This level 1 layer contains seventeen classes: Value Land Cover 0 Unknown 11 Open Water 12 Perennial Snow/Ice 21 Developed, Open Space 22 Developed, Low Intensity 23 Developed, Medium Intensity 24 Developed, High Intensity 31 Barren Land 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 52 Shrub/Scrub 71 Herbaceous 81 Hay/Pasture 82 Cultivated Crops 90 Woody Wetlands 95 Emergent Herbaceous Wetlands NLCD2001 land cover class descriptions: Open Water - All areas of open water, generally with less than 25% cover or vegetation or soil. Perennial Ice/Snow - All areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover. Developed, Open Space - Includes areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20 percent of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes. Developed, Low Intensity - Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20-49 percent of total cover. These areas most commonly include single-family housing units. Developed, Medium Intensity - Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50-79 percent of the total cover. These areas most commonly include single-family housing units. Developed, High Intensity - Includes highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80 to100 percent of the total cover. Barren Land - Rock/Sand/Clay; Barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover. Deciduous Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species shed foliage simultaneously in response to seasonal change. Evergreen Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species maintain their leaves all year. Canopy is never without green foliage. Mixed Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75 percent of total tree cover. Shrub/Scrub - Areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions. Herbaceous - Areas dominated by graminoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling but can be utilized for grazing. Hay/Pasture - Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20 percent of total vegetation. Cultivated Crops - Areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20 percent of total vegetation. This class also includes all land being actively tilled. Woody Wetlands - Areas where forest or shrub land vegetation accounts for greater than 20 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for greater than 80 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. Landcover_NaturalConverted_NLCD2011. This layer depicts natural vs. converted land cover circa 2011 and was extracted from the map package NLCD2011_LNC.mpk. This layer contains two classes: Value Class Name 1 Converted 2 Natural Natural vs. Converted class descriptions: Converted - Developed areas, cultivated crops, and hay/pasture lands. Natural - All other major cover types. Landcover_Level1_NLCD2011. This layer was extracted from the map package NLCD2011_Level1.mpk and contains nine classes: Value Class Name 1 Open Water 2 Developed 3 Barren/Quarries/Transitional 4 Forest 5 Scrubs/Shrub 6 Perennial Ice/Snow 7 Grassland/Herbaceous 8 Agriculture 9 Wetlands Landcover_Level2_NLCD2011. This layer was extracted from the map package NLCD2011_Level2.mpk and contains fifteen classes: Value Class Name 11 Open Water 12 Perennial Ice/Snow 21 Developed, Open Space 22 Developed, Low Intensity 23 Developed, Medium Intensity 24 Developed, High Intensity 31 Barren Land 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 52 Shrub/Scrub 71 Herbaceous 81 Hay/Pasture 82 Cultivated Crops 90 Woody Wetlands 95 Emergent Herbaceous Wetlands Road Density - Road_Density. This raster layer depicts road density (km/km2) calculated for all roads in and around the study area (30 km buffer around BICA) as of 2005. This layer was extracted from the map package AllRoads_rdd.mpk. The map packages mentioned above can be found in the DataStore reference: Bighorn Canyon National Recreation Area Landscape Context, Map Packages. National Park Service. https://irma.nps.gov/DataStore/Reference/Profile/2306146>https://irma.nps.gov/DataStore/Reference/Profile/2306146

The Cooperative Land Cover Map is a project to develop an improved statewide land cover map from existing sources and expert review of aerial photography. The project is directly tied to a goal of Florida's State Wildlife Action Plan (SWAP) to represent Florida's diverse habitats in a spatially-explicit manner. The Cooperative Land Cover Map integrates 3 primary data types: 1) 6 million acres are derived from local or site-specific data sources, primarily on existing conservation lands. Most of these sources have a ground-truth or local knowledge component. We collected land cover and vegetation data from 37 existing sources. Each dataset was evaluated for consistency and quality and assigned a confidence category that determined how it was integrated into the final land cover map. 2) 1.4 million acres are derived from areas that FNAI ecologists reviewed with high resolution aerial photography. These areas were reviewed because other data indicated some potential for the presence of a focal community: scrub, scrubby flatwoods, sandhill, dry prairie, pine rockland, rockland hammock, upland pine or mesic flatwoods. 3) 3.2 million acres are represented by Florida Land Use Land Cover data from the FL Department of Environmental Protection and Water Management Districts (FLUCCS). The Cooperative Land Cover Map integrates data from the following years: NWFWMD: 2006 - 07 SRWMD: 2005 - 08 SJRWMD: 2004 SFWMD: 2004 SWFWMD: 2008 All data were crosswalked into the Florida Land Cover Classification System. This project was funded by a grant from FWC/Florida's Wildlife Legacy Initiative (Project 08009) to Florida Natural Areas Inventory. The current dataset is provided in 10m raster grid format.Changes from Version 1.1 to Version 2.3:CLC v2.3 includes updated Florida Land Use Land Cover for four water management districts as described above: NWFWMD, SJRWMD, SFWMD, SWFWMDCLC v2.3 incorporates major revisions to natural coastal land cover and natural communities potentially affected by sea level rise. These revisions were undertaken by FNAI as part of two projects: Re-evaluating Florida's Ecological Conservation Priorities in the Face of Sea Level Rise (funded by the Yale Mapping Framework for Biodiversity Conservation and Climate Adaptation) and Predicting and Mitigating the Effects of Sea-Level Rise and Land Use Changes on Imperiled Species and Natural communities in Florida (funded by an FWC State Wildlife Grant and The Kresge Foundation). FNAI also opportunistically revised natural communities as needed in the course of species habitat mapping work funded by the Florida Department of Environmental Protection. CLC v2.3 also includes several new site specific data sources: New or revised FNAI natural community maps for 13 conservation lands and 9 Florida Forever proposals; new Florida Park Service maps for 10 parks; Sarasota County Preserves Habitat Maps (with FNAI review); Sarasota County HCP Florida Scrub-Jay Habitat (with FNAI Review); Southwest Florida Scrub Working Group scrub polygons. Several corrections to the crosswalk of FLUCCS to FLCS were made, including review and reclassification of interior sand beaches that were originally crosswalked to beach dune, and reclassification of upland hardwood forest south of Lake Okeechobee to mesic hammock. Representation of state waters was expanded to include the NOAA Submerged Lands Act data for Florida.Changes from Version 2.3 to 3.0: All land classes underwent revisions to correct boundaries, mislabeled classes, and hard edges between classes. Vector data was compared against high resolution Digital Ortho Quarter Quads (DOQQ) and Google Earth imagery. Individual land cover classes were converted to .KML format for use in Google Earth. Errors identified through visual review were manually corrected. Statewide medium resolution (spatial resolution of 10 m) SPOT 5 images were available for remote sensing classification with the following spectral bands: near infrared, red, green and short wave infrared. The acquisition dates of SPOT images ranged between October, 2005 and October, 2010. Remote sensing classification was performed in Idrisi Taiga and ERDAS Imagine. Supervised and unsupervised classifications of each SPOT image were performed with the corrected polygon data as a guide. Further visual inspections of classified areas were conducted for consistency, errors, and edge matching between image footprints. CLC v3.0 now includes state wide Florida NAVTEQ transportation data. CLC v3.0 incorporates extensive revisions to scrub, scrubby flatwoods, mesic flatwoods, and upland pine classes. An additional class, scrub mangrove – 5252, was added to the crosswalk. Mangrove swamp was reviewed and reclassified to include areas of scrub mangrove. CLC v3.0 also includes additional revisions to sand beach, riverine sand bar, and beach dune previously misclassified as high intensity urban or extractive. CLC v3.0 excludes the Dry Tortugas and does not include some of the small keys between Key West and Marquesas.Changes from Version 3.0 to Version 3.1: CLC v3.1 includes several new site specific data sources: Revised FNAI natural community maps for 31 WMAs, and 6 Florida Forever areas or proposals. This data was either extracted from v2.3, or from more recent mapping efforts. Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes. The Dry Tortugas have been reincorporated. The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough reviewChanges from Version 3.1 to Version 3.2:CLC v3.2 includes several new site specific data sources: Revised FNAI natural community maps for 43 Florida Park Service lands, and 9 Florida Forever areas or proposals. This data is from 2014 - 2016 mapping efforts. SITE level class review: Wet Coniferous plantation (2450) from v2.3 has been included in v3.2. Non-Vegetated Wetland (2300), Urban Open Land (18211), Cropland/Pasture (18331), and High Pine and Scrub (1200) have undergone thorough review and reclassification where appropriate. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.2.5 to Version 3.3: The CLC v3.3 includes several new site specific data sources: Revised FNAI natural community maps for 14 FWC managed or co-managed lands, including 7 WMA and 7 WEA, 1 State Forest, 3 Hillsboro County managed areas, and 1 Florida Forever proposal. This data is from the 2017 – 2018 mapping efforts. Select sites and classes were included from the 2016 – 2017 NWFWMD (FLUCCS) dataset. M.C. Davis Conservation areas, 18331x agricultural classes underwent a thorough review and reclassification where appropriate. Prairie Mesic Hammock (1122) was reclassified to Prairie Hydric Hammock (22322) in the Everglades. All SITE level Tree Plantations (18333) were reclassified to Coniferous Plantations (183332). The addition of FWC Oyster Bar (5230) features. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com, including classification corrections to sites in T.M. Goodwin and Ocala National Forest. CLC v3.3 utilizes the updated The Florida Land Cover Classification System (2018), altering the following class names and numbers: Irrigated Row Crops (1833111), Wet Coniferous Plantations (1833321) (formerly 2450), Major Springs (4131) (formerly 3118). Mixed Hardwood-Coniferous Swamps (2240) (formerly Other Wetland Forested Mixed).Changes from Version 3.4 to Version 3.5: The CLC v3.5 includes several new site specific data sources: Revised FNAI natural community maps for 16 managed areas, and 10 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2019 – 2020 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. This version of the CLC is also the first to include land identified as Salt Flats (5241).Changes from Version 3.5 to 3.6: The CLC v3.6 includes several new site specific data sources: Revised FNAI natural community maps for 11 managed areas, and 24 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2018 – 2022 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.6 to 3.7: The CLC 3.7 includes several new site specific data sources: Revised FNAI natural community maps for 5 managed areas (2022-2023). Revised Palm Beach County Natural Areas data for Pine Glades Natural Area (2023). Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. In this version a few SITE level classifications are reclassified for the STATE level classification system. Mesic Flatwoods and Scrubby Flatwoods are classified as Dry Flatwoods at the STATE level. Upland Glade is classified as Barren, Sinkhole, and Outcrop Communities at the STATE level. Lastly Upland Pine is classified as High Pine and Scrub at the STATE level.

The Cooperative Land Cover Map is a project to develop an improved statewide land cover map from existing sources and expert review of aerial photography. The project is directly tied to a goal of Florida's State Wildlife Action Plan (SWAP) to represent Florida's diverse habitats in a spatially-explicit manner. The Cooperative Land Cover Map integrates 3 primary data types: 1) 6 million acres are derived from local or site-specific data sources, primarily on existing conservation lands. Most of these sources have a ground-truth or local knowledge component. We collected land cover and vegetation data from 37 existing sources. Each dataset was evaluated for consistency and quality and assigned a confidence category that determined how it was integrated into the final land cover map. 2) 1.4 million acres are derived from areas that FNAI ecologists reviewed with high resolution aerial photography. These areas were reviewed because other data indicated some potential for the presence of a focal community: scrub, scrubby flatwoods, sandhill, dry prairie, pine rockland, rockland hammock, upland pine or mesic flatwoods. 3) 3.2 million acres are represented by Florida Land Use Land Cover data from the FL Department of Environmental Protection and Water Management Districts (FLUCCS). The Cooperative Land Cover Map integrates data from the following years: NWFWMD: 2006 - 07 SRWMD: 2005 - 08 SJRWMD: 2004 SFWMD: 2004 SWFWMD: 2008 All data were crosswalked into the Florida Land Cover Classification System. This project was funded by a grant from FWC/Florida's Wildlife Legacy Initiative (Project 08009) to Florida Natural Areas Inventory. The current dataset is provided in 10m raster grid format.Changes from Version 1.1 to Version 2.3:CLC v2.3 includes updated Florida Land Use Land Cover for four water management districts as described above: NWFWMD, SJRWMD, SFWMD, SWFWMDCLC v2.3 incorporates major revisions to natural coastal land cover and natural communities potentially affected by sea level rise. These revisions were undertaken by FNAI as part of two projects: Re-evaluating Florida's Ecological Conservation Priorities in the Face of Sea Level Rise (funded by the Yale Mapping Framework for Biodiversity Conservation and Climate Adaptation) and Predicting and Mitigating the Effects of Sea-Level Rise and Land Use Changes on Imperiled Species and Natural communities in Florida (funded by an FWC State Wildlife Grant and The Kresge Foundation). FNAI also opportunistically revised natural communities as needed in the course of species habitat mapping work funded by the Florida Department of Environmental Protection. CLC v2.3 also includes several new site specific data sources: New or revised FNAI natural community maps for 13 conservation lands and 9 Florida Forever proposals; new Florida Park Service maps for 10 parks; Sarasota County Preserves Habitat Maps (with FNAI review); Sarasota County HCP Florida Scrub-Jay Habitat (with FNAI Review); Southwest Florida Scrub Working Group scrub polygons. Several corrections to the crosswalk of FLUCCS to FLCS were made, including review and reclassification of interior sand beaches that were originally crosswalked to beach dune, and reclassification of upland hardwood forest south of Lake Okeechobee to mesic hammock. Representation of state waters was expanded to include the NOAA Submerged Lands Act data for Florida.Changes from Version 2.3 to 3.0: All land classes underwent revisions to correct boundaries, mislabeled classes, and hard edges between classes. Vector data was compared against high resolution Digital Ortho Quarter Quads (DOQQ) and Google Earth imagery. Individual land cover classes were converted to .KML format for use in Google Earth. Errors identified through visual review were manually corrected. Statewide medium resolution (spatial resolution of 10 m) SPOT 5 images were available for remote sensing classification with the following spectral bands: near infrared, red, green and short wave infrared. The acquisition dates of SPOT images ranged between October, 2005 and October, 2010. Remote sensing classification was performed in Idrisi Taiga and ERDAS Imagine. Supervised and unsupervised classifications of each SPOT image were performed with the corrected polygon data as a guide. Further visual inspections of classified areas were conducted for consistency, errors, and edge matching between image footprints. CLC v3.0 now includes state wide Florida NAVTEQ transportation data. CLC v3.0 incorporates extensive revisions to scrub, scrubby flatwoods, mesic flatwoods, and upland pine classes. An additional class, scrub mangrove – 5252, was added to the crosswalk. Mangrove swamp was reviewed and reclassified to include areas of scrub mangrove. CLC v3.0 also includes additional revisions to sand beach, riverine sand bar, and beach dune previously misclassified as high intensity urban or extractive. CLC v3.0 excludes the Dry Tortugas and does not include some of the small keys between Key West and Marquesas.Changes from Version 3.0 to Version 3.1: CLC v3.1 includes several new site specific data sources: Revised FNAI natural community maps for 31 WMAs, and 6 Florida Forever areas or proposals. This data was either extracted from v2.3, or from more recent mapping efforts. Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes. The Dry Tortugas have been reincorporated. The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough reviewChanges from Version 3.1 to Version 3.2:CLC v3.2 includes several new site specific data sources: Revised FNAI natural community maps for 43 Florida Park Service lands, and 9 Florida Forever areas or proposals. This data is from 2014 - 2016 mapping efforts. SITE level class review: Wet Coniferous plantation (2450) from v2.3 has been included in v3.2. Non-Vegetated Wetland (2300), Urban Open Land (18211), Cropland/Pasture (18331), and High Pine and Scrub (1200) have undergone thorough review and reclassification where appropriate. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.2.5 to Version 3.3: The CLC v3.3 includes several new site specific data sources: Revised FNAI natural community maps for 14 FWC managed or co-managed lands, including 7 WMA and 7 WEA, 1 State Forest, 3 Hillsboro County managed areas, and 1 Florida Forever proposal. This data is from the 2017 – 2018 mapping efforts. Select sites and classes were included from the 2016 – 2017 NWFWMD (FLUCCS) dataset. M.C. Davis Conservation areas, 18331x agricultural classes underwent a thorough review and reclassification where appropriate. Prairie Mesic Hammock (1122) was reclassified to Prairie Hydric Hammock (22322) in the Everglades. All SITE level Tree Plantations (18333) were reclassified to Coniferous Plantations (183332). The addition of FWC Oyster Bar (5230) features. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com, including classification corrections to sites in T.M. Goodwin and Ocala National Forest. CLC v3.3 utilizes the updated The Florida Land Cover Classification System (2018), altering the following class names and numbers: Irrigated Row Crops (1833111), Wet Coniferous Plantations (1833321) (formerly 2450), Major Springs (4131) (formerly 3118). Mixed Hardwood-Coniferous Swamps (2240) (formerly Other Wetland Forested Mixed).Changes from Version 3.4 to Version 3.5: The CLC v3.5 includes several new site specific data sources: Revised FNAI natural community maps for 16 managed areas, and 10 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2019 – 2020 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. This version of the CLC is also the first to include land identified as Salt Flats (5241).Changes from Version 3.5 to 3.6: The CLC v3.6 includes several new site specific data sources: Revised FNAI natural community maps for 11 managed areas, and 24 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2018 – 2022 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com.Changes from Version 3.6 to 3.7: The CLC 3.7 includes several new site specific data sources: Revised FNAI natural community maps for 5 managed areas (2022-2023). Revised Palm Beach County Natural Areas data for Pine Glades Natural Area (2023). Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. In this version a few SITE level classifications are reclassified for the STATE level classification system. Mesic Flatwoods and Scrubby Flatwoods are classified as Dry Flatwoods at the STATE level. Upland Glade is classified as Barren, Sinkhole, and Outcrop Communities at the STATE level. Lastly Upland Pine is classified as High Pine and Scrub at the STATE level.

Commonage GIS Dataset. Published by Department of Housing, Local Government and Heritage. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Commonage Framework Planning was a joint initiative between the National Parks and Wildlife Service and the Department of Agriculture and Food. Teams combining agricultural and ecological skills to assess the sustainable use of these areas have surveyed all known commonage areas in Ireland.

To date in excess of 4,400 plans have been prepared, covering more than 440,000 hectares. Where necessary, destocking (removal of some of the stock kept on commonage) was prescribed to ensure recovery of the vegetation. These plans have been implemented through REPS, AEOS and the NPWS Farm Plan Scheme, as relevant, from 1999 - 2012.

A commitment has been made to monitor the condition of commonages to demonstrate, in particular, that initiatives are delivering recovery in overgrazed areas and that undergrazing is not becoming a problem. Ireland also has obligations to monitor the state of SACs containing uplands and peatlands in non-commonage areas. This involves a reassessment of habitats in commonage areas, some of which were assessed as early as 1999, and also non-commonage areas.

Planning teams comprising both agriculturalists and environmentalists have been trained and re-surveys have been completed in commonage blocks in Counties Mayo, Galway, Cork, Kerry, Donegal, Sligo, Leitrim, Tipperary, Limerick and Louth between 2004 and 2010. Monitoring reports have been forwarded to the EU Commission highlighting the findings and trends. Additional survey work in 2007 focussed on Counties Mayo, Donegal and Kerry. In 2008, all commonage that had a destocking of greater than 50% were re-assessed.

In this context GIS files were set up to describe: - Destocking rates assigned to Agricultural Units - Habitat types and damage categories assigned to Agricultural Sub-Units and - Locations of Base-Stations and habitat types / damage categories recorded at these stations

A review of all the Commonage Framework Plans, setting sustainable stocking rates, will conclude in 2012 and will be communicated to all shareholders by the Department of Agriculture, Food and the Marine. This information is not contained here....

This cooperative program builds on the extensive knowledge base generated by the many decades of research conducted by the Kruger National Park Scientific Services, and the decade-long Kruger National Park River Research Program and more recent River Savanna Boundaries Program in collaboration with several South African and American universities, government departments, and research agencies. The Kruger Park has a long history of interest in the possibility of erection of formal research exclosures (rather than the “incidental exclosures” which have become available because of for instance, enclosures being built to breed up rare antelope). This interest relates 6 particularly relating to fire and elephant effects. Fortunately this historical interest, and the more recent interest of other groups in riparian and riparian-upland issues in the Kruger Park, has provided an opportunity for unified structures dealing with all these interests, to be put in place. In February 2000 the Kruger National Park was presented with a unique opportunity to develop a long-term experiment designed to examine the effects of herbivory. The largest flood since 1925 for the Sabie (7,000 to 8,000 m3 s-1) and Letaba rivers removed most of the vegetation along these rivers, primary research sites in the park, resetting the system to bedrock and sand. The vegetation before the flood was a mature riparian forest including stately fig and other majestic riparian trees. The Sabie River catchment is 7,086 km2 with a river length of 230 km and a mean discharge of 633 million m3/a. The Letaba catchment is 13400 km2 with a length of 490 km and a mean discharge of 631 million m3/a. Construction of large exclosures subsequent to this major flood event will allow us to follow the successional development and pattern formation of vegetation along riparian zones. Riparian zones are recognized as “hotspots” of activity because they integrate terrestrial and aquatic systems (Naiman and Décamps 1997). We believe the research we are conducting (viewed across aquatic, riparian and upland zones) will generate a novel understanding of savannas as integrated yet heterogeneous ecological systems. The systems approach to ecosystem study requires ecologists to expose the connections and fluxes between the elements of heterogeneity, and the feedbacks between heterogeneity and ecosystem function (Risser 1995). Therefore, boundaries in the landscape that define this heterogeneity are of particular ecological interest, and riparian corridors are perhaps the most obvious expression of boundaries in savanna regions. The emerging view of system heterogeneity emphasizes that biological richness has many facets (Noss and Cooperrider 1994). The first facet highlights the kinds of ecological systems that are present in a region. The second facet indicates the relative abundance of each kind of entity present in the area. The third facet indicates that the 7 various ecological components are dynamic in time, and that they are functionally connected with one another. Finally, the three facets of kind, relative abundance, and function are expressed in all ecological realms. Therefore, genes, species and populations, communities and ecosystems, and landscapes all can be considered as components of system heterogeneity (Kolasa and Pickett 1991, Collins and Benning 1996). Riparian systems, and their connection to in-stream processes and to interchanges with the upland components of savanna landscapes, provide a powerful test of the functional significance of spatial and temporal ecosystem heterogeneity. The development of this understanding is the overarching mission of our scientific programme.