The Digital Geologic-GIS Map of Great Basin National Park and Vicinity, Nevada is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (grba_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (grba_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (grba_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (grba_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (grba_geology_metadata_faq.pdf). Please read the grba_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Stanford University and the Stanford Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (grba_geology_metadata.txt or grba_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This webmap features the USGS GAP application of the vegetation cartography design based on NVCS mapping being done at the Alliance level by the California Native Plant Society (CNPS), the California Dept of Fish and Game (CDFG), and the US National Park Service, combined with Ecological Systems Level mapping being done by USGS GAP, Landfire and Natureserve. Although the latter are using 3 different approaches to mapping, this project adopted a common cartography and a common master crossover in order to allow them to be used intercheangably as complements to the detailed NVCS Alliance & Macrogroup Mapping being done in Calif by the California Native Plant Society (CNPS) and Calif Dept of Fish & Wildlife (CDFW). A primary goal of this project was to develop ecological layers to use as overlays on top of high-resolution imagery, in order to help interpret and better understand the natural landscape. You can see the source national GAP rasters by clicking on either of the "USGS GAP Landcover Source RASTER" layers at the bottom of the contents list.Using polygons has several advantages: Polygons are how most conservation plans and land decisions/managment are done so polygon-based outputs are more directly useable in management and planning. Unlike rasters, Polygons permit webmaps with clickable links to provide additional information about that ecological community. At the analysis level, polygons allow vegetation/ecological systems depicted to be enriched with additional ecological attributes for each polygon from multiple overlay sources be they raster or vector. In this map, the "Gap Mac base-mid scale" layers are enriched with links to USGS/USNVC macrogroup summary reports, and the "Gap Eco base scale" layers are enriched with links to the Naturserve Ecological Systems summary reports.Comparsion with finer scale ground ecological mapping is provided by the "Ecol Overlay" layers of Alliance and Macrogroup Mapping from CNPS/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 CDFG 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 THE OVERLAY LAYERS WERE CREATED: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. Often large landscape areas would create "godzilla" polygons of more than 50,000 vertices, which can affect performance. These were eliminated using SIMPLIFY POLYGONS to reduce vertex spacing from 30m down to 50-60m where possible. Where not possible DICE was used, which bisects all large polygons with arbitrary internal divisions until no polygon has more than 50,000 vertices. To create midscale layers, ecological systems were dissolved into the macrogroups that they belonged to and resymbolized on macrogroup. This was another frequent source for godzillas as larger landscape units were delineate, so simplify and dice were then run again. Where the base ecol system tiles could only be served up by individual partition tile, macrogroups typically exhibited a 10-1 or 20-1 reduction in feature count allowing them to be assembled into single integrated map services by region, ie NW, SW. CNPS / CDFW / National Park Service Sources: (see also base service definition page) Unlike the Landsat-based raster modelling of the Natureserve and Gap national ecological systems, 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.MAP LAYER NAMING: The data sublayers in this webmap 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. The layers in this webmap represent 4 distinct programs: 1. The California Native Plant Society/Calif Dept of Fish & Wildlife Vegetation Classification and Mapping Program (Full Description of these layers is at the CNPS MS10 Service Registration Page and Cnps MS10B Service Registration Page . 2. USGS Gap Protected Areas Database, full description at the PADUS registration page . 3. USGS Gap Landcover, full description below 4. Natureserve Ecological Systems, full description belowLAYER NAMING: All 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 =

The Digital Geologic-GIS Map of Great Sand Dunes National Park, Colorado is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (grsa_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (grsa_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (grsa_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (grsa_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (grsa_geology_metadata_faq.pdf). Please read the grsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (grsa_geology_metadata.txt or grsa_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:35,000 and United States National Map Accuracy Standards features are within (horizontally) 17.8 meters or 58.3 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Network Mapping Software Market Outlook

According to our latest research, the global network mapping software market size reached USD 2.1 billion in 2024, reflecting robust adoption across diverse industries. The market is projected to expand at a CAGR of 11.2% from 2025 to 2033, reaching an estimated USD 5.5 billion by 2033. This growth trajectory is primarily driven by the increasing complexity of enterprise networks, heightened demand for proactive network monitoring, and the growing need for real-time visibility into network infrastructure. The proliferation of cloud computing, hybrid IT environments, and the rising threat landscape further accentuate the necessity for advanced network mapping tools, positioning the market for significant expansion over the coming decade.

A critical growth factor for the network mapping software market is the escalating complexity of modern enterprise networks. As organizations increasingly embrace digital transformation, their network topologies become more intricate, encompassing physical, virtual, and cloud-based resources. This complexity necessitates sophisticated network mapping solutions capable of providing comprehensive visibility and real-time insights into network performance, device connectivity, and data flows. Enterprises are prioritizing investments in network mapping software to streamline network management, reduce downtime, and improve overall operational efficiency. The ability to automate network discovery and maintain up-to-date network diagrams is becoming indispensable for IT teams striving to manage dynamic environments, thus fueling the adoption of advanced network mapping tools.

Another pivotal driver is the heightened focus on cybersecurity and regulatory compliance. With the surge in cyberattacks and the increasing sophistication of threat actors, organizations are compelled to deploy robust network security measures. Network mapping software plays a vital role in identifying vulnerabilities, monitoring unauthorized access, and ensuring compliance with industry regulations such as GDPR, HIPAA, and PCI DSS. The integration of network mapping tools with security information and event management (SIEM) systems enhances threat detection and incident response capabilities. This synergy not only supports compliance initiatives but also strengthens the overall security posture of organizations, further propelling the demand for network mapping software across multiple sectors.

The rapid adoption of cloud computing and hybrid IT environments is also catalyzing market growth. As businesses migrate workloads to the cloud and embrace remote work models, the need for visibility across on-premises, cloud, and hybrid networks becomes paramount. Network mapping software enables IT teams to map, monitor, and manage distributed network assets, ensuring seamless connectivity and performance optimization. The shift towards software-defined networking (SDN) and network function virtualization (NFV) is amplifying the requirement for agile, scalable, and automated network mapping solutions. This trend is particularly pronounced among large enterprises and managed service providers seeking to deliver uninterrupted services and maintain competitive differentiation in an increasingly digital landscape.

From a regional perspective, North America continues to lead the network mapping software market owing to its advanced IT infrastructure, high penetration of cloud technologies, and early adoption of innovative network management solutions. The region is characterized by a strong presence of major technology vendors, a mature cybersecurity ecosystem, and stringent regulatory frameworks. Europe follows closely, driven by digital transformation initiatives, data privacy regulations, and growing investments in network automation. The Asia Pacific region is witnessing the fastest growth, fueled by rapid industrialization, expanding enterprise IT footprints, and increasing awareness of network security best practices. Emerging markets in Latin America and the Middle East & Africa are also exhibiting steady adoption, supported by government-led digitalization programs and the proliferation of connected devices.

The Ministry of Natural Resources and Forestry’s Make a Topographic Map is a mapping application that features the best available topographic data and imagery for Ontario. You can: * easily toggle between traditional map backgrounds and high-resolution imagery * choose to overlay the topographic information with the imagery * turn satellite imagery on or off * customize your map by adding your own text * print your custom map Data features include: * roads * trails * lakes * rivers * wooded areas * wetlands * provincial parks * municipal, township and other administrative boundaries You don’t need special software or licenses to use this application. Technical information Using cached imagery and topographic data, the application provides a fast, seamless display at pre-defined scales. The caches are updated annually.

Context

I would like to write a quest scraper. A Tool that takes a look at an image of a Heroquest quest map and can derive all symbols with their positions correctly; turning the "dead" image once again into an editable quest file. On Heroscribe.org a great java-based tool for editing quest files can be downloaded. In ideal case, my tool can take an image and output the Heroscribe format.

That's a task for later. Today, we just want to do the recognition.

I took around 100 Maps from the ancient game Heroquest, cut them down to single square images and used them as training data set for a neural net. The incredible imbalance in the data set made it necessary that I made 100 more maps, to boost the underrepresented symbol appearances. All of the maps have been made in Heroscribe (downloadable at Heroscribe.org) and exported as png; like that they have the same size.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1711994%2F9050fb998965fcf24ef4b76d4c9fe4d7%2F11-BastionofChaos_EU.png?generation=1570256920345210&alt=media" alt="EU format Heroquest map">

Now I have 13 thousand snippets of Heroquest Quest Maps, in three cut out factors (78, 42 and 34 pixel). In each sample, there can be one or more of the following things: Monsters, Furniture, Doors, and rooms. For each snippet, the position information is already preserved in the data set: It was taken during the cropping process. You know where you cut the image right now, so why not keeping that information right away?

In the easiest case, there is just one symbol in a square. In some cases there are two or three of them at the same time; like there can be one or more door, one monster, and the square itself is discolored because the room is a special room. So here we have do recognize several symbols at the same time.

The first (roughly half) of the dataset contains real data from real maps, in the second half I've made up data to fill gaps in the data coverage.

The Y-Labels

Y-Data is provided in an excel-formatted spreadsheet. One column is for single-square-items and furniture; four for doors and one for rooms. If there were too many items in one square, or sometimes when I was tired from labelling all the data, it could happen that I was putting a label in the wrong column or even put the wrong label. I guess that currently, around 0.5% of the data is mislabelled; except for the room symbol column; which is not at all well labeled.

I tried to train a resnet to recognize the Y-Data given and it was surprisingly difficult. The current best working solution has four convolutional layers and one dense layer; has nothing to do with the current state-of-the-art deep learning. The advantage is, it is trainable under an hour on any laptop; the disadvantage is does not yet always work as intended.

See some examples for the images and the difficulties: The "center pic" of a "table" symbol: It is difficult to recognize anything here.

https://i.imgur.com/yCP4pF9.png" alt="Table, small cutout">

And the same square in the "pic" cutout:

https://i.imgur.com/9a9scVN.png" alt="Table, big cutout">

"center pic" of a Treasure Chest: Sufficient to recognize it; easily!

https://i.imgur.com/KjX1QUV.png" alt="Treasure Chest, small cutout">

Big cutout of the same Treasure Chest: Distracting details in the surrounding.

https://i.imgur.com/OPBlWHV.png" alt="Treasure Chest, big cutout">

For each symbol, I also extracted the two main colors. There are maps in the EU format, which are completely black and white (see above picture). The other half of the maps is in US format: Monsters are green, furniture is dark red, traps and trapped furniture have a orange or turquoise background instead of white; Hero symbols are bright red. There is real information in those colors.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1711994%2F3900bd109f86618a48e619ec00ce892d%2F11-BastionofChaos_US.png?generation=1570257035192555&alt=media" alt="US format Heroquest map">

The symbols in the data set are black and white, all of them. The columns 'min_color' and 'max_color' preserve the color information. I planned to give it as an auxiliary input to the neural net, but didn't yet get round to do it. The color information can be distracting, too: In the US map format, sometimes otherwise normal furniture symbols are marked with trap colors when they thought about some special event for it.

Target acceptance rates

Those are quite easy images on one side. Noiseless, size-fixed, no skew or zoom coming from photography... I even bootstrapped my data set by using K-Means to bulk-label some images. Yes, K-Means. It is easy to classify this data beyond the 95% recognition. So what's the catch?

First of all, the number of classes. It's not a single-class recognition problem; in this data set we have around 100 class...

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The digital vegetation map was produced using a combination of machine processing and visual interpretation. We used two primary image sources. These included 2006 1:12,000-scale infrared aerial photography for the areas west of the Sangre de Cristo Mountain range that was subsequently processed by the USFWS and 2006 National Agricultural Imagery Program (NAIP) imagery, and ground-truthing to interpret the complex patterns of vegetation and landuse at GRSA. Other referenced imagery included 2006 and 2007 Quickbird imagery which covered portions of the project area. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcInfo© software. Draft maps created from the vegetation classification were field-tested and revised before independent ecologists completed an assessment of the map‘s accuracy during 2008. During the summer of 2008 we sampled 1,537 accuracy assessment points to establish a final overall accuracy of 73.7%. This metric is subject to considerable interpretation and is discussed in detail in the results section.

These data provide an accurate high-resolution shoreline compiled from imagery of CRUZ BAY TO GREAT CRUZ BAY, ST. JOHN, USVI . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartogr...

This webmap is a collaboration between the National Park Service, California Native Plant Society (CNPS) and the California Dept of Fish and Game (CDFG).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 CDFG 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 THE OVERLAY LAYERS WERE CREATED: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. Often large landscape areas would create "godzilla" polygons of more than 50,000 vertices, which can affect performance. These were eliminated using SIMPLIFY POLYGONS to reduce vertex spacing from 30m down to 50-60m where possible. Where not possible DICE was used, which bisects all large polygons with arbitrary internal divisions until no polygon has more than 50,000 vertices. To create midscale layers, ecological systems were dissolved into the macrogroups that they belonged to and resymbolized on macrogroup. This was another frequent source for godzillas as larger landscape units were delineate, so simplify and dice were then run again. Where the base ecol system tiles could only be served up by individual partition tile, macrogroups typically exhibited a 10-1 or 20-1 reduction in feature count allowing them to be assembled into single integrated map services by region, ie NW, SW. CNPS / CDFW / National Park Service Sources: (see also base service definition page) Unlike the Landsat-based raster modelling of the Natureserve and Gap national ecological systems, 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.MAP LAYER NAMING: The data sublayers in this webmap 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. The layers in this webmap represent 4 distinct programs: 1. The California Native Plant Society/Calif Dept of Fish & Wildlife Vegetation Classification and Mapping Program (Full Description of these layers is at the CNPS MS10 Service Registration Page and Cnps MS10B Service Registration Page . 2. USGS Gap Protected Areas Database, full description at the PADUS registration page . 3. USGS Gap Landcover, full description below 4. Natureserve Ecological Systems, full description belowLAYER NAMING: All 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.MAP LAYER CATALOGThis map includes a test segment of Natureserve Ecological Systems in the US Southwest, with the following layers and sublayers:GapNsUSA BoundaryMasksALB2: A grid showing the boundaries that define each partition tile of the national vegetation map services, with regional and state boundaries in the USGS Gap US Albers projectionPadus Gap13 WM Base Scale plus Label: (Full PADUS FGDC Metadata here) Overlay vectors at 1k to 288k scale with separate 1k-288k Labelling services for one of 3 different attributes: --Landowner Name: Land owner and primary entity responsible for managing parcel when ‘Manager Name’ is not attributed (e.g.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The Digital Geomorphic-GIS Map of the Great Swash to Quork Hammock Area (1:10,000 scale 2006 mapping), North Carolina is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (gsqh_geomorphology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (gsqh_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (gsqh_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (caha_fora_wrbr_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (caha_fora_wrbr_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (gsqh_geomorphology_metadata_faq.pdf). Please read the caha_fora_wrbr_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: East Carolina University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (gsqh_geomorphology_metadata.txt or gsqh_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:10,000 and United States National Map Accuracy Standards features are within (horizontally) 8.5 meters or 27.8 feet of their actual _location as presented by this dataset. Users of this data should thus not assume the _location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

These data provide an accurate high-resolution shoreline compiled from lidar and imagery of GREAT EGG HARBOR INLET, NJ . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

5-Digit and 3-Digit ZIP Code data for Maptitude mapping software are from Caliper Corporation and contain boundaries and demographic data.

TanDEM-X Forest/Non-Forest (FNF) map(s) have been one such data focusing on the status of global forest coverage, which has played an essential role in combating climate change. Although the producers have carried out verification and comparison analyses, the need for accuracy assessments in a broader sense creates uncertainties for the users to approve the new data. For this purpose, TanDEM-X 50 m FNF maps were exclusively examined visually through 66,000 test grids within 30 geocells selected from temperate, boreal, and tropical forest zones. Thus, it was aimed to provide product accuracy utilizing visual inspections to the end users of TanDEM-X FNF maps. In addition, Collect Earth (CE) software was used to evaluate the dataset visually, and its advantages or disadvantages were compared with similarly designed studies. Consequently, even though the producers’ data sets were found to have an accuracy of around 85 percent, it was observed that there were some issues, especially in the definition of the “non-forest” class. CE software was found to be helpful in such studies. However, the dependence of the analyses on geo-browser supplied imagery had some limitations in estimating the accuracy of a new dataset.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

Chapter 3: Multiuser Concepts and Workflow Replicability in sUAS Applications Jason A. Tullis, Katie Corcoran, Richard Ham, Bandana Kar, and Malcolm Williamson Advances in high spatial resolution mapping capabilities and the new rules established by the Federal Aviation Administration in the United States for the operation of Small Unmanned Aircraft Systems (sUAS) have provided new opportunities to acquire aerial data at a lower cost and more safely versus other methods. A similar opening of the skies for sUAS applications is being allowed in countries across the world. Also, sUAS can access hazardous or inaccessible areas during disaster events and provide rapid response when needed. Applications of Small Unmanned Aircraft systems: Best Practices and Case Studies is the first book that brings together the best practices of sUAS applied to a broad range of issues in high spatial resolution mapping projects. Very few sUAS pilots have the knowledge of how the collected imagery is processed into value added mapping products that have commercial and/or academic import. Since the field of sUAS applications is just a few years old, this book covers the need for a compendium of case studies to guide the planning, data collection, and most importantly data processing and map error issues, with the range of sensors available to the user community. Written by experienced academics and professionals, this book serves as a guide on how to formulate sUAS based projects, from choice of a sUAS, flight planning for a particular application, sensors and data acquisition, data processing software, mapping software and use of the high spatial resolution maps produced for particular types of geospatial modeling.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

These data provide an accurate high-resolution shoreline compiled from imagery of BARNEGAT INLET TO GREAT EGG HARBOR INLET, NJ . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Carto...