Boundary polygons for Alabama counties.

The 2022 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. In MCD states where no MCD exists or no MCD is defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county subdivisions. The generalized boundaries of legal MCDs are based on those as of January 1, 2022, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CCDs, delineated in 21 states, are based on those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

County Boundaries of the State of Alabama published by the National Weather Service in Birmingham to utilize for severe weather and precipitation mapping applications and maps.

This data set is tiled lidar point cloud LAS files v1.4, for the 2016 Alabama 25 County lidar area of interest (AOI).

USGS NGTOC task order G17PD00243 required Spring 2017 LiDAR surveys to be collected over 18,845 square miles covering part or all of 25 counties in Alabama. These counties are Autauga, Baldwin, Barbour, Bullock, Butler, Chambers, Cherokee, Clarke, Conecuh, Covington, Cre...

The Project data set consists of the Classified Point Cloud. The Geographical Extent of this dataset extends to the AL 17County_2020_B20 UTM Zone 16N D1 boundary and the AL 17County_2020_B20 UTM Zone 16N D2 boundary. These areas are in northern and central Alabama.

Block 1 - AL_17Co_1_2020, Work Unit 226776 The data covers 6440.76 square miles in the following counties: Blount, Colbert, DeK...

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). In addition to the preceding, required text, the Abstract should also describe the projection and coordinate system as well as a general statement about horizontal accuracy.

This dataset consists of a map depicting the landcover of the Natchez Trace Parkway. The mapping output was created using mosaiced color infrared aerial photography of the Parkway. The map shows the distribution of 18 landcover classes based on the National Vegetation Classification Standard. Ground-based vegetation classification was provided by the National Park Service (NPS). The mapping output delineates grasses, road-developed areas, scrub-shrub, shrubland, plantation, water bodies, areas of white oak, oak, pine-oak, pine-cedar, pine-sweetgum, sweetgum (including sweetgum-oak), scattered trees, swamp forest, irregular classes, aquatic vegetation, invasive species, canopy gaps, and clouds.

Total mapped area includes a 100 m buffer outside the park boundary. 235 digital orthophoto quarter quadrangles (DOQQs) were required to cover the entire 715 km long Parkway. For ease of use, the DOQQs were grouped into 11 mosaics, each covering a section of the Parkway. At the request of the NPS, each mosaic was divided into ten tiles to allow for efficient loading on less robust computers.

The 2020 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. In MCD states where no MCD exists or no MCD is defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county subdivisions. The generalized boundaries of legal MCDs are based on those as of January 1, 2020 as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CCDs, delineated in 21 states, are those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

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

This dataset defines the symbology for the landcover map of the Natchez Trace Parkway. The map shows the distribution of 18 landcover classes based on the National Vegetation Classification Standard. Ground-based vegetation classification was provided by National Park Service (NPS). The mapping output and layer delineate grasses, road-developed areas, scrub-shrub, shrubland, plantation, water bodies, areas of white oak, oak, pine-oak, pine-cedar, pine-sweetgum, sweetgum (including sweetgum-oak), scattered trees, swamp forest, irregular classes, aquatic vegetation, invasive species, canopy gaps, and clouds. Mapped classes that have been digitized are noted with an asterisk (*) in the legend.

2017 TIGER/Line® Shapefiles: Roads

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the Alabama West (FIPS 2703) State Plane projection and coordiante system The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

The Floodplain Mapping/Redelineation study deliverables depict and quantify the flood risks for the study area. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The Floodplain Mapping/Redelineation flood risk boundaries are derived from the engineering information Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).

2017 TIGER/Line® Shapefiles: Roads

2017 TIGER/Line® Shapefiles: Roads

This dataset represents aerial photography of the Natchez Trace Parkway that was taken in late September and early October of 2004. Images were acquired during leaf-on conditions as required by the National Park Service (NPS) to accomplish their ground-based vegetation classification. Nearly 400 aerial photography frames were scanned and orthorectified to generate digital orthophoto quarter quads (DOQQs). These were cut to match existing DOQQs creating a total of 235 DOQQs for the entire 715 km long Parkway. For ease of use, the DOQQs were grouped into 11 mosaics, each covering a section of the Parkway. At the request of the NPS, each mosaic was divided into ten tiles to allow for efficient loading on less robust computers.

2017 TIGER/Line® Shapefiles: Roads

2017 TIGER/Line® Shapefiles: Roads

2017 TIGER/Line® Shapefiles: Roads

Alabama county boundaries, 2010