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).

The global network mapping software market size was valued at USD 2,325.4 million in 2025 and is projected to grow at a CAGR of 12.3% during the forecast period (2025-2033). The rapid growth of cloud-based, on-premises, and hybrid IT environments, coupled with the increasing adoption of network management best practices, are some of the key factors driving market growth. Furthermore, the need to enhance network visibility and control, improve performance, and simplify network troubleshooting is also contributing to the growing demand for network mapping software. Cloud-based and on-premises solutions held a significant market share in 2025. However, the cloud-based segment is expected to witness faster growth during the forecast period. The growing adoption of cloud-based services, the need for remote network management, and the cost-effectiveness of cloud-based solutions are driving the growth of this segment. In terms of application, the small and medium enterprises (SMEs) segment dominated the market in 2025, and it is expected to maintain its dominance throughout the forecast period. The increasing number of SMEs, the need for cost-effective network management solutions, and the growing awareness of network security are driving the growth of this segment. Network mapping software is a tool that helps businesses visualize and manage their networks. It can be used to create diagrams of the network, identify potential problems, and track down performance issues. The software can also be used to automate tasks such as device discovery and configuration.

The following of best practices of Software Engineering (SE) is something that provides many advantages for software companies. In this scenario SWEBOK is a guideline that supports these companies with information about the core of knowledge of SE, including a list of Best Practices (BP) to adopt. For small companies, however, some restrictions such as limited budget, short schedule, reduced number of employees, can hinder the advantages of the adoption of these practices. In this scenario, it is necessary to have useful information about which BPs have been adopted in small companies. Therefore, this paper describes the planning and execution of a quasi-systematic mapping study in order to report the adopting scenario of SWEBOK BPs in small companies during the last decade. It was possible to observe that the most prominent BP adopted is “Test application”, followed by the using of “Software Process Model” where the tests’ execution is already contemplated by. On the other hand, “Budget Limitation” and “Staff Size” were cited as motivations for avoid the adoption of BPs in small companies.

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

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.

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.

This is a raster-based suitability map of landfill sites produced after the February 6, 2023, Türkiye earthquakes centred on Kahramanmaraş - Pazarcık and Kahramanmaraş - Elbistan. In this study, a site selection model was developed using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM), one of the Multi-Criteria Decision-Making Methods, to determine the most suitable landfill areas immediately after the earthquake.The suitability map of the landfill sites can be accessed through the Serverless Cloud-GIS based Disaster Management Portal at https://web.itu.edu.tr/metemu/nominal/deprem.htmlThe pairwise comparison matrix, weight calculation, and sensitivity analysis are also provided in the MS Excel file.

What is SEPAL?

SEPAL (https://sepal.io/) is a free and open source cloud computing platform for geo-spatial data access and processing. It empowers users to quickly process large amounts of data on their computer or mobile device. Users can create custom analysis ready data using freely available satellite imagery, generate and improve land use maps, analyze time series, run change detection and perform accuracy assessment and area estimation, among many other functionalities in the platform. Data can be created and analyzed for any place on Earth using SEPAL.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/63a3efa0-08ab-4ad6-9d4a-96af7b6a99ec/download/cambodia_mosaic_2020.png" alt="alt text" title="Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia">

Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia

SEPAL reaches over 5000 users in 180 countries for the creation of custom data products from freely available satellite data. SEPAL was developed as a part of the Open Foris suite, a set of free and open source software platforms and tools that facilitate flexible and efficient data collection, analysis and reporting. SEPAL combines and integrates modern geospatial data infrastructures and supercomputing power available through Google Earth Engine and Amazon Web Services with powerful open-source data processing software, such as R, ORFEO, GDAL, Python and Jupiter Notebooks. Users can easily access the archive of satellite imagery from NASA, the European Space Agency (ESA) as well as high spatial and temporal resolution data from Planet Labs and turn such images into data that can be used for reporting and better decision making.

National Forest Monitoring Systems in many countries have been strengthened by SEPAL, which provides technical government staff with computing resources and cutting edge technology to accurately map and monitor their forests. The platform was originally developed for monitoring forest carbon stock and stock changes for reducing emissions from deforestation and forest degradation (REDD+). The application of the tools on the platform now reach far beyond forest monitoring by providing different stakeholders access to cloud based image processing tools, remote sensing and machine learning for any application. Presently, users work on SEPAL for various applications related to land monitoring, land cover/use, land productivity, ecological zoning, ecosystem restoration monitoring, forest monitoring, near real time alerts for forest disturbances and fire, flood mapping, mapping impact of disasters, peatland rewetting status, and many others.

The Hand-in-Hand initiative enables countries that generate data through SEPAL to disseminate their data widely through the platform and to combine their data with the numerous other datasets available through Hand-in-Hand.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/868e59da-47b9-4736-93a9-f8d83f5731aa/download/probability_classification_over_zambia.png" alt="alt text" title="Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia">

Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia

The ADOT Maps Hub Site is the public facing home for spatial data at ADOT. It presents users with the most popular maps in a modern interface that is managed by the technicals teams who create the maps and dashboards. This enables fast updates to the webpage and allows flexibility as new needs are discovered. The hub page leverages standard agency color and font, with icon templates for a consistent look regardless of the number of people updating.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.

Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.

Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.html

As part of the Maine Beach Mapping Program (MBMAP), MGS surveys annual alongshore shoreline positions (see Beach_Mapping_Shorelines). Using these shoreline positions and guidance from the USGS Digital Shoreline Analysis System (DSAS). DSAS is referenced as Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan, 2009, Digital Shoreline Analysis System (DSAS) version 4.0— An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. For more information on DSAS and the methodology DSAS employs, please see: https://woodshole.er.usgs.gov/project-pages/DSAS/. The supporting DSAS User Guide which describes how DSAS works and how statistics are calculated is available here: http://www.maine.gov/dacf/mgs/hazards/beach_mapping/DSAS_manual.pdf. MGS wrote a database procedure following protocols outlined in DSAS that allows for the calculation of different shoreline change rates and supporting statistics. This was done so that MGS no longer needed to depend on USGS updates to the DSAS software to keep current with ArcGIS software updates. The script casts shoreline-perpendicular transects at a set spacing (in this case, 10-m intervals along the shoreline), from a preset baseline (located landward of the monitored shorelines), and calculates a range of shoreline change statistics, including: Process Time: The time when the statistics were calculated. TransectID: The ID of the transect (including the group or line section ID; for example, 1-1, is line 1, transect 1) SCE: Shoreline Change Envelope. The distance, in meters, between the shoreline farthest from and closests to the baseline at each transect. NSM: Net Shoreline Movement. The distance, in meters, between the oldest and youngest shorelines for each tranect. EPR: End Point Rate. A shoreline change rate, in meters/year, calculated by dividing the NSM by the time elapsed between the oldest and youngest shorelines at each transect. LRR: Linear Regression Rate. A shoreline change rate, in meters/year, calculated by fitting a least-squares regression line to all of the shoreline points for a particular transect. The distance from the baseline, in meters, is plotted against the shoreline date, and slope of the line that provides the best fit is the LRR. LR2: The R-squared statistic, or coefficient of determination. The percentage of variance in the data that is explained by a regression, or in this case, the LRR value. It is a dimensionless index that ranges from 1.0 (a perfect fit, with the best fit line explaining all variation) to 0.0 (a bad fit, with the best fit line explaining little to no variation) and measures how successfully the best fit line (LRR) accounts for variation in the data. LCI95: Standard error of the slope at the 95% confidence interval. Calculated by muliplying the standard error, or standard deviation, of the slope by the two-tailed test statistic at the user-specified confidence percentage. For example if a reported LRR is 1.34 m/yr and a calculated LCI95 is 0.50, the band of confidence around the LRR is +/- 0.50. In other words, you can be 95% confidence that the true rate of change is between 0.84 and 1.84 m/yr. LRR_ft: The Linear Regression Rate, converted to feet/year. LCI95_ft: The LCI95, converted to feet. EPR_ft: The End Point Rate converted to feet.

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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).

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).

The Customer Journey Mapping Software market is an increasingly vital segment within the broader field of customer experience management. As organizations strive to enhance their interactions with consumers, these software solutions provide valuable insights into the entire customer journey-from initial awareness th

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

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)

As part of the Maine Beach Mapping Program (MBMAP), MGS surveys annual alongshore shoreline positions (see Beach_Mapping_Shorelines). Using these shoreline positions and guidance from the USGS Digital Shoreline Analysis System (DSAS). DSAS is referenced as Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan, 2009, Digital Shoreline Analysis System (DSAS) version 4.0— An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. For more information on DSAS and the methodology DSAS employs, please see: https://woodshole.er.usgs.gov/project-pages/DSAS/. The supporting DSAS User Guide which describes how DSAS works and how statistics are calculated is available here: http://www.maine.gov/dacf/mgs/hazards/beach_mapping/DSAS_manual.pdf. MGS wrote a database procedure following protocols outlined in DSAS that allows for the calculation of different shoreline change rates and supporting statistics. This was done so that MGS no longer needed to depend on USGS updates to the DSAS software to keep current with ArcGIS software updates. The script casts shoreline-perpendicular transects at a set spacing (in this case, 10-m intervals along the shoreline), from a preset baseline (located landward of the monitored shorelines), and calculates a range of shoreline change statistics, including: Process Time: The time when the statistics were calculated. TransectID: The ID of the transect (including the group or line section ID; for example, 1-1, is line 1, transect 1) SCE: Shoreline Change Envelope. The distance, in meters, between the shoreline farthest from and closests to the baseline at each transect. NSM: Net Shoreline Movement. The distance, in meters, between the oldest and youngest shorelines for each tranect. EPR: End Point Rate. A shoreline change rate, in meters/year, calculated by dividing the NSM by the time elapsed between the oldest and youngest shorelines at each transect. LRR: Linear Regression Rate. A shoreline change rate, in meters/year, calculated by fitting a least-squares regression line to all of the shoreline points for a particular transect. The distance from the baseline, in meters, is plotted against the shoreline date, and slope of the line that provides the best fit is the LRR. LR2: The R-squared statistic, or coefficient of determination. The percentage of variance in the data that is explained by a regression, or in this case, the LRR value. It is a dimensionless index that ranges from 1.0 (a perfect fit, with the best fit line explaining all variation) to 0.0 (a bad fit, with the best fit line explaining little to no variation) and measures how successfully the best fit line (LRR) accounts for variation in the data. LCI95: Standard error of the slope at the 95% confidence interval. Calculated by muliplying the standard error, or standard deviation, of the slope by the two-tailed test statistic at the user-specified confidence percentage. For example if a reported LRR is 1.34 m/yr and a calculated LCI95 is 0.50, the band of confidence around the LRR is +/- 0.50. In other words, you can be 95% confidence that the true rate of change is between 0.84 and 1.84 m/yr. LRR_ft: The Linear Regression Rate, converted to feet/year. LCI95_ft: The LCI95, converted to feet. EPR_ft: The End Point Rate converted to feet.

Contents: This is an ArcGIS Pro zip file that you can download and use for creating map books based on United States National Grid (USNG). It contains a geodatabase, layouts, and tasks designed to teach you how to create a basic map book.Version 1.0.0 Uploaded on May 24th and created with ArcGIS Pro 2.1.3 - Please see the README below before getting started!Updated to 1.1.0 on August 20thUpdated to 1.2.0 on September 7thUpdated to 2.0.0 on October 12thUpdate to 2.1.0 on December 29thBack to 1.2.0 due to breaking changes in the templateBack to 1.0.0 due to breaking changes in the template as of June 11th 2019Updated to 2.1.1 on October 8th 2019Audience: GIS Professionals and new users of ArcGIS Pro who support Public Safety agencies with map books. If you are looking for apps that can be used by any public safety professional, see the USNG Lookup Viewer.Purpose: To teach you how to make a map book with critical infrastructure and a basemap, based on USNG. You NEED to follow the steps in the task and not try to take shortcuts the first time you use this task in order to receive the full benefits. Background: This ArcGIS Pro template is meant to be a starting point for your map book projects and is based on best practices by the USNG National Implementation Center (TUNIC) at Delta State University and is hosted by the NAPSG Foundation. This does not replace previous templates created in ArcMap, but is a new experimental approach to making map books. We will continue to refine this template and work with other organizations to make improvements over time. So please send us your feedback admin@publicsafetygis.org and comments below. Instructions: Download the zip file by clicking on the thumbnail or the Download button.Unzip the file to an appropriate location on your computer (C:\Users\YourUsername\Documents\ArcGIS\Projects is a common location for ArcGIS Pro Projects).Open the USNG Map book Project File (APRX).If the Task is not already open by default, navigate to Catalog > Tasks > and open 'Create a US National Grid Map Book' Follow the instructions! This task will have some automated processes and models that run in the background but you should pay close attention to the instructions so you also learn all of the steps. This will allow you to innovate and customize the template for your own use.FAQsWhat is US National Grid? The US National Grid (USNG) is a point and area reference system that provides for actionable location information in a uniform format. Its use helps achieve consistent situational awareness across all levels of government, disciplines, and threats & hazards – regardless of your role in an incident.One of the key resources NAPSG makes available to support emergency responders is a basic USNG situational awareness application. See the NAPSG Foundation and USNG Center websites for more information.What is an ArcGIS Pro Task? A task is a set of preconfigured steps that guide you and others through a workflow or business process. A task can be used to implement a best-practice workflow, improve the efficiency of a workflow, or create a series of interactive tutorial steps. See "What is a Task?" for more information.Do I need to be proficient in ArcGIS Pro to use this template? We feel that this is a good starting point if you have already taken the ArcGIS Pro QuickStart Tutorials. While the task will automate many steps, you will want to get comfortable with the map layouts and other new features in ArcGIS Pro.Is this template free? This resources is provided at no-cost, but also with no guarantees of quality assurance or support at this time. Can't I just use ArcMap? Ok - here you go. USNG 1:24K Map Template for ArcMapKnown Limitations and BugsZoom To: It appears there may be a bug or limitation with automatically zooming the map to the proper extent, so get comfortable with navigation or zoom to feature via the attribute table.FGDC Compliance: We are seeking feedback from experts in the field to make sure that this meets minimum requirements. At this point in time we do not claim to have any official endorsement of standardization. File Size: Highly detailed basemaps can really add up and contribute to your overall file size, especially over a large area / many pages. Consider making a simple "Basemap" of street centerlines and building footprints.We will do the best we can to address limitations and are very open to feedback!