Last Rev. 01/24/08 - E.Foster, P.E. - FSU/BSRCThe Historic Shoreline Database on the Web contains many directories of related types of information about beach changes in Florida over the past 150 or so years. The historic shoreline map images (see the Drawings directory) show precision-digitized approximate mean high water (mhw) shorelines, from the US government coastal topographic maps listed in the associated map bibliography files (see the Sourcebibs directory). These generally show data extending from the mid to late 1800’s to the mid to late 1970’s. The mhw positions have been extracted and tabulated (see the MWHfiles directory) relative to fixed reference “R” points along the beach, spaced approximately 1000 feet (300 meters) apart. Reference points not actually corresponding to actual “in the ground” survey markers are virtual “V” points. Mean high water positions have been and continue to be extracted from FDEP beach profile surveys from the 1970’s through the present and added to the tables. The beach profile data files from which mhw data have been extracted and added into the mhw tables can be found in the ProfileData directory and visually (for many areas) in the ClickOnProfiles directory. The beach profile files include elevation information along the entire length of the profiles. This profile data set has undergone up to fifteen additional quality control checks to ensure accuracy, reliability, and consistency with the historic database coordinate and bearing set. Note that any data deeper than wading depth have not yet undergone any extra quality control checks. Note also that there are *.cod text files of notes associated with the review of the profile data files.The digital historic shoreline map image files are given in a DWG autocad-based format, which should be usable on most versions, as well as many GIS systems. The Florida State Plane 1927/79-adjusted and 1983/90 horizontal coordinate systems are used. These are not metric systems, but with the proper software can be converted to whatever systems you may need. Each map image DWG file contains many layers, documented in an ASCII layer list archived with the DWG file.The database has been maintained and greatly expanded by E. Foster since approximately 1987 and by N. Nguyen since 1995. The initial map digitizing effort was done for FDEP at Florida State University, primarily by S. Demirpolat. Final processing and editing of the original map files to make them user-friendly was performed by N. Nguyen and E. Foster in 1995-7. Extensive quality control and update work has been performed by E. Foster since 1987, and by N. Nguyen since 1995. Field profile surveys have been performed by the FDEP Coastal Data Acquisition section since the early 1970’s, and by a number of commercial surveyors in recent years.The formats of the mhw tables and profile files are explained in text files included in the respective directories.Note that the digitized map image files were originally created in the UTM coordinate system on Intergraph equipment. The translation from UTM to the State Plane coordinate systems has resulted in some minor textual and other visual shifts in the northwest Florida area map image files.The dates in the map legends in the map images are generally composite dates. It is necessary to use the mhw data tables and map bibliographies for accurate dates for any specific location. The date ranges in the data tables relate to specific information given in the map bibliography files.2Generally it may be assumed that the historic shorelines have been digitized as carefully as possible from the source maps. If a historic shoreline does not contain a systematic position error and is feasible in a physical sense, the accuracy of the mhw position is estimated at plus or minus 15 to 50 feet (5 to 15 m), depending on the source and scale. This is as a position in time, NOT as an average mhw position. Data added from field surveys are estimated at plus or minus 10 feet (3 m) or better.It is to be noted that from the 1920’s onward, aerial photographs have usually been the basis of the US government’s coastal topographic maps. Prior to that, the method was plane table surveying. Along higher wave energy coasts, especially the Florida east coast, if there was significant wave activity in the source photography, it is very possible that the mhw was mapped in a more landward location than was probably correct. Alternatively, the use of photography sets with excessive sun glare may have caused the mhw to be mapped in a more seaward location than was probably correct. These effects have been frequently observed in comparisons of close-in-time FDEP controlled aerial photography with FDEP profile surveys. The use of some photography sets containing high wave uprush or sun glare is probable within the historic data. For example, on the east coast the 1940’s series maps tend to show the mhw more seaward than expected, possibly due to sun glare, and the 1960’s series tend to show the mhw more landward than expected. In the latter case, the effect may be due to the 1960’s being a decade of frequent storms. It is recommended that the analyst be aware that some of these effects may exist in the historic data. A questionable historic shoreline is NOT necessarily one to be discarded, just considered with allowance for its’ potential limitations.Using this database, it can readily be observed that the historic trends in shoreline evolution are very consistent with behavior expected from the longshore transport equation, well known to coastal engineers. This is a non-linear equation. Shoreline change can be expected to be linear or constant only in certain situations. It is NOT recommended that any analyst arbitrarily assume constant or linear shoreline change rates over long periods of time, which is often done but not supported by the evidence. The three primary factors controlling shoreline change are sand supply, wave climate, and local geographic features. In some parts of Florida, major storms since 1995 have also become important factors.

This dynamic imagery layer features Landsat 8 and Landsat GLS imagery for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.To view this imagery layer, you'll want to add it to a map that is using the Polar projection of WGS_1984_EPSG_Alaska_Polar_Stereographic, for example the Arctic Ocean Basemap or the Arctic Imagery basemap. Other polar projections may be used within their useful limits. There is no imagery above 82°30’N due to the orbit of the satellite.Geographic CoverageArctic RegionTemporal CoverageThis layer is updated daily with new imagery.Landsat 8 revisits each point on Earth's land surface every 16 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.This layer also includes imagery from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).Product LevelThe Landsat 8 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA).Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created. Available functions on this layer include:Agriculture with DRA – Bands shortwave IR-1, near-IR, blue (6, 5, 2) with dynamic range adjustment applied on apparent reflectance. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.NDSI Colorized – Normalized difference Snow index (NDSI) with color map, computed as (b3-b6)/(b3+b6) on apparent reflectance. Dark blue represents dense snow, yellow and green areas represent clouds.Bathymetric with DRA – Bands red, green, coastal/aerosol (4, 3, 1) with dynamic range adjustment. Useful in bathymetric mapping applications.Color Infrared with DRA – Bands near-IR, red, green (5, 4, 3) with dynamic range adjustment. Healthy vegetation is bright red while stressed vegetation is dull red.Geology with DRA – Bands shortwave IR-1, near-IR, blue (7, 6, 2) with dynamic range adjustment. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Natural Color with DRA – Natural Color bands red, green, blue (4, 3, 2) displayed with dynamic range adjustmentShort-wave Infrared with DRA – Bands shortwave IR-2, shortwave IR-1, red (7, 6, 4) with dynamic range adjustmentAgriculture – Bands shortwave IR-1, near-IR, blue (6, 5, 2) with fixed stretch applied on apparent reflectance. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Bathymetry – Bands red, green, coastal/aerosol (4, 3, 1) with fixed stretch applied on apparent reflectance. Useful in bathymetric mapping applications.Color Infrared – Bands near-IR, red, green (5, 4, 3) with a fixed stretch. Healthy vegetation is bright red while stressed vegetation is dull red.Geology – Bands shortwave IR-1, near-IR, blue (7, 6, 2) with a fixed stretch. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Natural Color – Natural Color bands red, green, blue (4, 3, 2) displayed with a fixed stretch.Short-wave Infrared – Bands shortwave IR-2, shortwave IR-1, red (7, 5, 4) with a fixed stretchNormalized Difference Moisture Index Colorized – Normalized Difference Moisture Index with color map, computed as (b5 - b6)/(b5 + b6). Wetlands and moist areas are blues, and dry areas in deep yellow and brownNDSI Raw – Normalized difference Snow index (NDSI) computed as (b3 - b6) / (b3 + b6)NDVI Raw – Normalized difference vegetation index (NDVI) computed as (b5 - b4) / (b5 + b4)NBR Raw – Normalized Burn Ratio (NBR) computed as (b5 - b7) / (b5 + b7)Multispectral BandsThe table below lists all available multispectral OLI bands. Natural Color with DRA consumes bands 4,3,2

Band

Description

Wavelength (µm)

Spatial Resolution (m)

1

Coastal aerosol

0.43 - 0.45

30

2

Blue

0.45 - 0.51

30

3

Green

0.53 - 0.59

30

4

Red

0.64 - 0.67

30

5

Near Infrared (NIR)

0.85 - 0.88

30

6

SWIR 1

1.57 - 1.65

30

7

SWIR 2

2.11 - 2.29

30

8

Cirrus (in OLI this is band 9)

1.36 - 1.38

30

9

QA Band (available with Collection 1)*

NA

30

*More about the Quality Assessment Band The layer also provides access to TIRS bands as follows: BandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Unlocking Landsat in the Arctic is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information on Landsat 8 images, see Landsat8.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit GLS.

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. Historical traffic is based on the average of observed speeds over the past three years. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

Ecological Land Units (ELUs) are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The ELU map for 2015 was produced by combining the values in four 250m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). These four components resulted in 3,639 different combinations or ELUs.This 2015 map contains updates to the 2014 map in the form of landforms and land cover data, which have greater variety of classes and better spatial coherence (less arbitrary fragmentation).These four component datasets represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the four components. Values for each of the four input layers are listed in the table below. Every point in this map is symbolized by a combination of values for each of these fields.BioclimateLandformsLithologyLand CoverArcticPlainsUndefinedBare AreaCold DryHillsUnconsolidated SedimentSparse VegetationCold Semi-DryMountainsCarbonate Sedimentary RockGrassland, Shrub, or ScrubCold Moist Mixed Sedimentary RockMostly CroplandCold Wet Non-Carbonate Sedimentary RockMostly Needleleaf/Evergreen ForestCool Dry EvaporiteMostly Deciduous ForestCool Semi-Dry PyroclasticsSwampy or Often FloodedCool Moist Metamorphic RockArtificial or Urban AreaCool Wet Acidic VolcanicsSurface WaterHot Dry Acidic PlutonicsUndefinedHot Semi-Dry Non-Acidic Volcanics Hot Moist Non-Acidic Plutonics Hot Wet Warm Dry Warm Semi-Dry Warm Moist Warm Wet Dataset SummaryThis layer provides access to a cached map service created by Esri in partnership with U.S. Geological Survey's Climate and Land Use Change Program. The work from this collaboration is documented in the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. What can you do with this layer?This map is intended to work as an ecological background map in conjunction with the reference layers of various ArcGIS Online base maps, and supports visualization tasks in ArcGIS Online and Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.An image service is available on ArcGIS Online that provides access to a 250m cell-sized raster of the World Ecophysiographic Land Units. The image service provides access to the data underlying this map. The image service can be used as an input to geoprocessing and to support pop-ups that can be used with this map online.A service is available to the data tables associated with this map as well as other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables.Layers providing access to the four input layers used to create this map see the following links:World BioclimatesWorld Landforms Improved Hammond MethodWorld LithologyWorld Land Cover ESA 2010The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - a reintroductionLiving Atlas Discussion Group

This dynamic imagery layer features Landsat 8 and Landsat GLS imagery, rendered on-the-fly as Short-wave Infrared with DRA, for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.To view this imagery layer, you'll want to add it to a map that is using the Polar projection of WGS_1984_EPSG_Alaska_Polar_Stereographic, for example the Arctic Ocean Basemap or the Arctic Imagery basemap. Other polar projections may be used within their useful limits. There is no imagery above 82°30’N due to the orbit of the satellite.

Geographic CoverageArctic RegionTemporal CoverageThis layer is updated daily with new imagery.Landsat 8 revisits each point on Earth's land surface every 16 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.This layer also includes imagery from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).Product LevelThe Landsat 8 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is Short-wave Infrared (bands 7,6,4) with Dynamic Range Adjustment (DRA).Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Multispectral BandsThe table below lists all available multispectral OLI bands. Short-wave Infrared with DRA consumes bands 7,6,4.BandDescriptionWavelength (µm)Spatial Resolution (m)1Coastal aerosol0.43 - 0.45302Blue0.45 - 0.51303Green0.53 - 0.59304Red0.64 - 0.67305Near Infrared (NIR)0.85 - 0.88306SWIR 11.57 - 1.65307SWIR 22.11 - 2.29308Cirrus (in OLI this is band 9)1.36 - 1.38309QA Band (available with Collection 1)*NA30*More about the Quality Assessment BandTIRS BandsBandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Unlocking Landsat in the Arctic app is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information on Landsat 8 images, see Landsat8.

*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit GLS.

Note: This layer has been superseded by the World Ecological Land Units Map 2015 layer. The new map contains updates to landforms and land cover data, which have a greater variety of classes and better spatial coherence (less arbitrary fragmentation). The updated World Ecological Land Units Map 2015 layer contains 3,639 different combinations, which is 284 fewer than the 2014 result. Ecological Land Units (ELUs) are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The ELU map was produced by combining the values in four 250m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). These four components resulted in 3,923 different combinations or ELUs.These four component datasets represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the four components. Values for each of the four input layers are listed in the table below. Every point in this map is symbolized by a combination of values for each of these fields.BioclimateLandformsLithologyLand CoverArcticPlainsUndefinedBare AreaCold DryHillsUnconsolidated SedimentSparse VegetationCold Semi-DryMountainsCarbonate Sedimentary RockGrassland, Shrub, or ScrubCold Moist Mixed Sedimentary RockMostly CroplandCold Wet Non-Carbonate Sedimentary RockMostly Needleleaf/Evergreen ForestCool Dry EvaporiteMostly Deciduous ForestCool Semi-Dry PyroclasticsSwampy or Often FloodedCool Moist Metamorphic RockArtificial or Urban AreaCool Wet Acidic VolcanicsSurface WaterHot Dry Acidic PlutonicsUndefinedHot Semi-Dry Non-Acidic Volcanics Hot Moist Non-Acidic Plutonics Hot Wet Warm Dry Warm Semi-Dry Warm Moist Warm Wet Dataset SummaryThis layer provides access to a cached map service created by Esri in partnership with U.S. Geological Survey's Climate and Land Use Change Program. The work from this collaboration is documented in the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. Available online What can you do with this layer?This map is intended to work as an ecological background map in conjunction with the reference layers of various ArcGIS Online base maps, and supports visualization tasks in ArcGIS Online and Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.An image service is available on ArcGIS Online that provides access to a 250-m cell-sized raster of the World Ecophysiographic Land Units. The image service provides access to the data underlying this map. The image service can be used as an input to geoprocessing and to support pop-ups that can be used with this map online.A service is available of the data tables associated with this map as well as other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables.The following are four input layers used to create this map::World BioclimatesWorld LandformsWorld LithologyWorld Land CoverThe Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Landscape Layers - A ReintroductionLiving Atlas Discussion Group

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisionaldata sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2004 Sutter County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits. The land use boundaries and attributes were digitized and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Northern District (ND). Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and ND, under the supervision of Tito Cervantes, Senior Land and Water Use Scientist. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Butte County conducted by DWR, Northern District Office staff, under the leadership of Tito Cervantes, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2004. ND staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary date was developed using: 1. The aerial photography used for this survey was taken in June of 2004. 9”x9” color photographs were generated from an altitude of about 6,000 feet above ground to produce a 1:24,000 scale photo. 2. The 9”x9” photos were taken to the field and virtually all the areas were visited to positively identify the land use. Site visits occurred July through September 2004. Land use codes were hand written on the photos. 3. Using AUTOCAD, the land use boundaries were digitized from USGS Digital Orthophoto Quarter Quadrangles (DOQQs) and attributes were entered from the field photos (using a standardized digitizing process). 4. After quality control/assurance procedures were completed on each file (DWG), the data was finalized. 5. The linework and attributes from each DWG quad file were brought into ARCINFO and both quad and survey wide coverages were created, and underwent quality checks. The survey wide coverage was then converted to a shapefile using ARCVIEW. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Before final processing, standard quality control procedures were performed jointly by staff at DWR's Northern District, and at DPLA headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the 9' x 9' color photos, is approximately 23 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

PLEASE NOTE: This data product is not available in Shapefile format or KML at https://naturalengland-defra.opendata.arcgis.com/datasets/Defra::living-england-habitat-map-phase-4/about, as the data exceeds the limits of these formats. Please select an alternative download format.This data product is also available for download in multiple formats via the Defra Data Services Platform at https://environment.data.gov.uk/explore/4aa716ce-f6af-454c-8ba2-833ebc1bde96?download=true.The Living England project, led by Natural England, is a multi-year programme delivering a satellite-derived national habitat layer in support of the Environmental Land Management (ELM) System and the Natural Capital and Ecosystem Assessment (NCEA) Pilot. The project uses a machine learning approach to image classification, developed under the Defra Living Maps project (SD1705 – Kilcoyne et al., 2017). The method first clusters homogeneous areas of habitat into segments, then assigns each segment to a defined list of habitat classes using Random Forest (a machine learning algorithm). The habitat probability map displays modelled likely broad habitat classifications, trained on field surveys and earth observation data from 2021 as well as historic data layers. This map is an output from Phase IV of the Living England project, with future work in Phase V (2022-23) intending to standardise the methodology and Phase VI (2023-24) to implement the agreed standardised methods.The Living England habitat probability map will provide high-accuracy, spatially consistent data for a range of Defra policy delivery needs (e.g. 25YEP indicators and Environment Bill target reporting Natural capital accounting, Nature Strategy, ELM) as well as external users. As a probability map, it allows the extrapolation of data to areas that we do not have data. These data will also support better local and national decision making, policy development and evaluation, especially in areas where other forms of evidence are unavailable. Process Description: A number of data layers are used to inform the model to provide a habitat probability map of England. The main sources layers are Sentinel-2 and Sentinel-1 satellite data from the ESA Copericus programme. Additional datasets were incorporated into the model (as detailed below) to aid the segmentation and classification of specific habitat classes. Datasets used:Agri-Environment Higher Level Stewardship (HLS) Monitoring, British Geological Survey Bedrock Mapping 1:50k, Coastal Dune Geomatics Mapping Ground Truthing, Crop Map of England (RPA), Dark Peak Bog State Survey, Desktop Validation and Manual Points, EA Integrated Height Model 10m, EA Saltmarsh Zonation and Extent, Field Unit NEFU, Living England Collector App NEFU/EES, Long Term Monitoring Network (LTMN), Lowland Heathland Survey, National Forest Inventory (NFI), National Grassland Survey, National Plant Monitoring Scheme, NEFU Surveys, Northumberland Border Mires, OS Vector Map District , Priority Habitats Inventory (PHI) B Button, European Space Agency (ESA) Sentinel-1 and Sentinel-2 , Space2 Eye Lens: Ainsdale NNR, Space2 Eye Lens: State of the Bog Bowland Survey, Space2 Eye Lens: State of the Bog Dark Peak Condition Survey, Space2 Eye Lens: State of the Bog (MMU) Mountain Hare Habitat Survey Dark Peak, Uplands Inventory, West Pennines Designation NVC Survey, Wetland Inventories, WorldClim - Global Climate DataFull metadata can be viewed on data.gov.uk.

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

This layer includes Landsat 8 and 9 imagery rendered on-the-fly as NDVI Colorized for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.Geographic CoverageGlobal Land Surface.Polar regions are available in polar-projected Imagery Layers: Landsat Arctic Views and Landsat Antarctic Views.Temporal CoverageThis layer is updated daily with new imagery.Working in tandem, Landsat 8 and 9 revisit each point on Earth's land surface every 8 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.Product LevelThe Landsat 8 and 9 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is NDVI Colorized, calculated as (b5 - b4) / (b5 + b4) with a colormap applied.Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Pre-defined functions: Natural Color with DRA, Agriculture with DRA, Geology with DRA, Color Infrared with DRA, Bathymetric with DRA, Short-wave Infrared with DRA, Normalized Difference Moisture Index Colorized, NDVI Raw, NDVI Colorized, NBR Raw15 meter Landsat Imagery Layers are also available: Panchromatic and Pansharpened.Multispectral BandsThe table below lists all available multispectral OLI bands. NDVI Colorized consumes bands 4 and 5.BandDescriptionWavelength (µm)Spatial Resolution (m)1Coastal aerosol0.43 - 0.45302Blue0.45 - 0.51303Green0.53 - 0.59304Red0.64 - 0.67305Near Infrared (NIR)0.85 - 0.88306SWIR 11.57 - 1.65307SWIR 22.11 - 2.29308Cirrus (in OLI this is band 9)1.36 - 1.38309QA Band (available with Collection 1)*NA30*More about the Quality Assessment BandTIRS BandsBandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Landsat Explorer App is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information, see Landsat 8 and Landsat 9.

The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. This dataset is also available as a layer package and a file geodatabase.The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8)The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels.The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary.The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

This dynamic imagery layer features Landsat 8 and Landsat GLS imagery, rendered on-the-fly as Normalized Difference Moisture Index Colorized, for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.

To view this imagery layer, you'll want to add it to a map that is using the Polar projection of WGS_1984_EPSG_Alaska_Polar_Stereographic, for example the Arctic Ocean Basemap or the Arctic Imagery basemap. Other polar projections may be used within their useful limits. There is no imagery above 82°30’N due to the orbit of the satellite.

Geographic CoverageArctic RegionTemporal CoverageThis layer is updated daily with new imagery.Landsat 8 revisits each point on Earth's land surface every 16 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.This layer also includes imagery from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).Product LevelThe Landsat 8 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is Normalized Difference Moisture Index Colorized, calculated as (b5 - b6)/(b5 + b6) with a colormap applied. Wetlands and moist areas are blues, and dry areas in deep yellow and brown.Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Multispectral BandsThe table below lists all available multispectral OLI bands. Normalized Difference Moisture Index consumes band 5 and 6.BandDescriptionWavelength (µm)Spatial Resolution (m)1Coastal aerosol0.43 - 0.45302Blue0.45 - 0.51303Green0.53 - 0.59304Red0.64 - 0.67305Near Infrared (NIR)0.85 - 0.88306SWIR 11.57 - 1.65307SWIR 22.11 - 2.29308Cirrus (in OLI this is band 9)1.36 - 1.38309QA Band (available with Collection 1)*NA30*More about the Quality Assessment BandTIRS BandsBandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Unlocking Landsat in the Arctic app is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information on Landsat 8 images, see Landsat8.

*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit GLS.

This dynamic imagery layer features Landsat 8 and Landsat GLS imagery for use in visualization and analysis. This layer is time enabled and includes a number of band combinations and indices rendered on demand. The imagery includes eight multispectral bands from the Operational Land Imager (OLI) and two bands from the Thermal Infrared Sensor (TIRS). It is updated daily with new imagery directly sourced from the USGS Landsat collection on AWS.

To view this imagery layer, you'll want to add it to a web map that is using the Polar projection of WGS_1984_Antarctic_Polar_Stereographic, for example, the Antarctic Imagery basemap. Other polar projections may be used within their useful limits. There is no imagery below 82°45’S due to the orbit of the satellite.

Geographic CoverageAntarcticaTemporal CoverageThis layer is updated daily with new imagery.Landsat 8 revisits each point on Earth's land surface every 16 days.Most images collected from January 2015 to present are included.Approximately 5 images for each path/row from 2013 and 2014 are also included.This layer also includes imagery from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).Product LevelThe Landsat 8 imagery in this layer is comprised of Collection 2 Level-1 data.The imagery has Top of Atmosphere (TOA) correction applied.TOA is applied using the radiometric rescaling coefficients provided the USGS.The TOA reflectance values (ranging 0 – 1 by default) are scaled using a range of 0 – 10,000.Image Selection/FilteringA number of fields are available for filtering, including Acquisition Date, Estimated Cloud Cover, and Product ID.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images.Visual RenderingDefault rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA).Raster Functions enable on-the-fly rendering of band combinations and calculated indices from the source imagery.The DRA version of each layer enables visualization of the full dynamic range of the images.This layer is part of a larger collection of Landsat Imagery Layers that you can use to perform a variety of mapping analysis tasks.Other pre-defined Raster Functions can be selected via the renderer drop-down or custom functions can be created. Available functions on this layer include:Agriculture with DRA – Bands shortwave IR-1, near-IR, blue (6, 5, 2) with dynamic range adjustment applied on apparent reflectance. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.NDSI Colorized – Normalized difference Snow index (NDSI) with color map, computed as (b3-b6)/(b3+b6) on apparent reflectance. Dark blue represents dense snow, yellow and green areas represent clouds.Bathymetric with DRA – Bands red, green, coastal/aerosol (4, 3, 1) with dynamic range adjustment. Useful in bathymetric mapping applications.Color Infrared with DRA – Bands near-IR, red, green (5, 4, 3) with dynamic range adjustment. Healthy vegetation is bright red while stressed vegetation is dull red.Geology with DRA – Bands shortwave IR-1, near-IR, blue (7, 6, 2) with dynamic range adjustment. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Natural Color with DRA – Natural Color bands red, green, blue (4, 3, 2) displayed with dynamic range adjustmentShort-wave Infrared with DRA – Bands shortwave IR-2, shortwave IR-1, red (7, 6, 4) with dynamic range adjustmentAgriculture – Bands shortwave IR-1, near-IR, blue (6, 5, 2) with fixed stretch applied on apparent reflectance. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Bathymetry – Bands red, green, coastal/aerosol (4, 3, 1) with fixed stretch applied on apparent reflectance. Useful in bathymetric mapping applications.Color Infrared – Bands near-IR, red, green (5, 4, 3) with a fixed stretch. Healthy vegetation is bright red while stressed vegetation is dull red.Geology – Bands shortwave IR-1, near-IR, blue (7, 6, 2) with a fixed stretch. Vigorous vegetation is bright green, stressed vegetation dull green and bare areas as brown.Natural Color – Natural Color bands red, green, blue (4, 3, 2) displayed with a fixed stretch.Short-wave Infrared – Bands shortwave IR-2, shortwave IR-1, red (7, 5, 4) with a fixed stretchNormalized Difference Moisture Index Colorized – Normalized Difference Moisture Index with color map, computed as (b5 - b6)/(b5 + b6). Wetlands and moist areas are blues, and dry areas in deep yellow and brownNDSI Raw – Normalized difference Snow index (NDSI) computed as (b3 - b6) / (b3 + b6)NDVI Raw – Normalized difference vegetation index (NDVI) computed as (b5 - b4) / (b5 + b4)NBR Raw – Normalized Burn Ratio (NBR) computed as (b5 - b7) / (b5 + b7)Multispectral BandsThe table below lists all available multispectral OLI bands. Natural Color with DRA consumes bands 4,3,2BandDescriptionWavelength (µm)Spatial Resolution (m)1Coastal aerosol0.43 - 0.45302Blue0.45 - 0.51303Green0.53 - 0.59304Red0.64 - 0.67305Near Infrared (NIR)0.85 - 0.88306SWIR 11.57 - 1.65307SWIR 22.11 - 2.29308Cirrus (in OLI this is band 9)1.36 - 1.38309QA Band (available with Collection 1)*NA30*More about the Quality Assessment Band The layer also provides access to TIRS bands as follows: BandDescriptionWavelength (µm)Spatial Resolution (m)10TIRS110.60 - 11.19100 * (30)11TIRS211.50 - 12.51100 * (30)*TIRS bands are acquired at 100 meter resolution, but are resampled to 30 meter in delivered data product.Additional Usage NotesImage exports are limited to 4,000 columns x 4,000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.WCS and WMS compatibility means this imagery layer can be consumed as WCS or WMS services.The Unlocking Landsat in the Antarctic app is another way to access and explore the imagery.This layer is part of a larger collection of Landsat Imagery Layers.Data SourceLandsat imagery is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). Data is hosted by the Amazon Web Services as part of their Public Data Sets program.For information on Landsat 8 images, see Landsat8.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit GLS.

Date of Images:10/2/2024 at 16:11 UTC (12:11 PM EDT)Summary:The Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory and California Institute of Technology derived the disturbance maps using the OPERA Disturbance Alert from Harmonized Landsat Sentinel-2 (DIST-ALERT-HLS) products. The results posted here are preliminary and unvalidated results, primarily intended to aid the field response and people who want to have a rough first look at the surface disturbance extent. The ARIA-share website has always focused on posting preliminary results as fast as possible for disaster response.OPERA DIST-ALERT-HLSThe Disturbance product (DIST) maps per pixel vegetation disturbance (specifically, vegetation cover loss) from the Harmonized Landsat Sentinel-2 (HLS) scenes. We provide the vegetation disturbance status (VEG-DIST-STATUS) and the maximum vegetation anomaly value (VEG-ANOM-MAX) layers. Images are provided from October 2, 2024. Each image consists of multiple MGRS tiles that were merged together for a composite image saved as a GeoTIFF file.VEG-DIST-STATUSIndication of vegetation cover loss (vegetation disturbance). The status label is based on the maximum anomaly value, confidence level, and whether it is ongoing or finished. "First" means the pixel has had an anomaly detection but no subsequent observations whether anomalous or not. "Provisional" means there have been two consecutive disturbance detections but not yet high confidence. "Confirmed" means that vegetation disturbance is detected with high confidence. The label "finished" is applied to confirmed disturbances that have had two consecutive no-anomaly observations or one 15 days or more after the last anomaly detection. If a new disturbance is detected, it will overwrite those in a "finished" state. These labels are reported for both above and below the 50% disturbance threshold based on the maximum anomaly value.VEG-ANOM-MAXDifference between historical and current year observed vegetation cover at the date of maximum decrease (vegetation loss of 0-100%). This layer can be used to threshold vegetation disturbance per a given sensitivity (e.g. disturbance of >20% vegetation cover loss). The sum of the historical percent vegetation and the anomaly value will be the vegetation cover estimate for the current year.The DIST-ALERT HLS products have these flags:255 represents No Data and is based on the Fmask layer of the source HLS granule.Suggested Use:VEG-ANOM-MAX0-100: Maximum loss of percent vegetation 255: No data VEG-DIST-STATUS:0: No disturbance 1: first <50% 2: provisional <50% 3: confirmed <50% 4: first >50% 5: provisional >50% 6: confirmed >50% 7: confirmed <50%, finished 8: confirmed >50%, finished 255: No data Satellite/Sensor:MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A satellitesResolution:30 metersCredits:NASA JPL-Caltech ARIA/OPERA TeamThe product contains modified Copernicus Sentinel data (2024) and is produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group. Managed by NASA's Jet Propulsion Laboratory, OPERA funds and manages the DIST-ALERT-HLS product developed and produced by the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland.Additional Information:OPERA DIST-ALERT-HLS data availabilityThe post-processed products are available to download at https://aria-share.jpl.nasa.gov/20240926-Hurricane_Helene/DIST. The OPERA DIST-HLS products have been in production since January 2022, are freely distributed to the public via NASA's Land Processes Distributed Active Archive Center (LP DAAC), and can be downloaded through NASA's Earthdata search. For more information about the Surface Disturbance product suite, please refer to the DIST Product page: https://www.jpl.nasa.gov/go/opera/products/dist-product-suite/For more information about the Caltech-JPL ARIA project, visit https://aria.jpl.nasa.gov For more information about the JPL OPERA project, visit https://www.jpl.nasa.gov/go/opera/ Data Download:https://aria-share.jpl.nasa.gov/20240926-Hurricane_Helene/DIST. Esri REST Endpoint:See URL section on right side of pageWMS Endpoint:https://maps.disasters.nasa.gov/ags04/services/hurricane_helene_2024/aria_dist/MapServer/WMSServer

AuthorityIn the 1963 general session, the Utah State Legislature charged the Division of Water Resources with the responsibility of developing a State Water Plan. This plan is to coordinate and direct the activities of state and federal agencies concerned with Utah’s water resources. As a part of this objective, the Division of Water Resources collects water-related land use data for the entire state. This data includes the types and extent of irrigated crops as well as information concerning phreatophytes, wet/open water areas, dry land agriculture and urban areas.The data produced by the water-related land use program are used for various planning purposes. Some of these include: determining cropland water use, evaluating irrigated land losses and conversion to urban uses, planning for new water development, estimating irrigated acreages for any area, and developing water budgets. Additionally, the data are used by many other state and federal agencies.Previous MethodsThe land use inventory methods used by the division in conducting water-related land use studies have varied with regard to the procedures used and the precision obtained. During the 1960s and 70s, inventories were prepared using large format vertical-aerial photographs supplemented with field surveys to label boundaries, vegetation types, and other water use information.After identifying crops and labeling photographs, the information was transferred onto a base map and then planimetered or "dot-counted" to determine the acreage. Tables for individual townships and ranges were prepared showing the amount of land in each land use category within each section. Data were then available for use in preparing water budgets.In the early 1980s, the division began updating its methodology for collecting water-related land use data to take advantage of the rapidly growing fields of Remote Sensing and computerized Geographic Information Systems (GIS).For several years during the early 1980’s, the division contracted with the University of Utah Research Institute, Center for Remote Sensing and Cartography (CRSC), to prepare water-related land use inventories. During this period, water-related land use data was obtained by using high altitude color infrared photography and laboratory interpretation, with field checking.In March 1984, several division staff members visited the California Department of Water Resources to observe its methodology for collecting water-related land use data for state water planning purposes.Based on its review of the California methodology and its own experience, the division developed a water-related land use inventory program. This program included the use of 35mm slides, United States Geological Survey (USGS) 7-1/2 minute quadrangle maps, field-mapping using base maps produced from the 35mm photography and a computerized GIS to process, store and retrieve land use data.Areas for survey were first identified from previous land use studies and any other available information. The identified areas were then photographed using an aircraft carrying a high quality 35mm single lens reflex camera mounted to focus along a vertical axis to the earth. Photos were taken between 6,000 and 6,500 feet above the ground using a 24mm lens. This procedure allowed each slide to cover a little more than one square mile with approximately 30 percent overlap on the wide side of the slide and 5 percent on the slide's narrow side.The slides were then indexed according to a flight-line number, slide number, latitude and longitude. All 35mm slides were stored in files at the division offices and cataloged according to township, range and section, and quadrangle map location.Water-related land use areas were then transferred from the slide to USGS 7-1/2 minute quadrangle maps using a standard slide projector with a 100-200mm zoom lens. This step allowed the technician to project the slide onto the back of a quadrangle map. The image showing through the map was adjusted to the map scale with the zoom lens. Field boundaries and other water-use boundaries were then traced on the 7-1/2 minute quadrangle map.Next, a team was sent to use the map in the field to check the boundaries and current year land use field data on the 7-1/2 minute quadrangles.The final step was to digitize and process the field data using ARC/INFO software developed by Environmental Systems Research Institute (ESRI).Starting in 2000 with the land use survey of the Uintah Basin, the division further improved its land use program by using digital data for the purposes of outlining agricultural and other land cover boundaries. The division used satellite data, USGS Digital Orthophoto Quadrangles (DOQs), National Agricultural Imagery Program (NAIP), and other digital images in a heads-up digitizing mode for this process. This allowed the division to use multiple technicians for the digitizing process.Digitizing was done as line and polygon files using ArcView 3.2 with a satellite image, DOQ or NAIP image as a background with other layers added for reference. Boundary files were created in logical groups so that the process of edge-matching along quad lines was eliminated and precision increased. Subsequent inventories were digitized in the ArcMap 9.x software versions. Present MethodologyUsing the latest statewide NAIP Imagery and ArcGIS 10, all boundaries of individual agricultural fields, urban areas, and significant riparian areas are precisely digitized. 2017 marked the first year of using the CDL Method for the whole state of Utah. This method utilizes the Cropland Data Layer from the USDA's National Ag. Statistics Service which provides acreage estimates major commodities and to produce crop-specific geo-referenced products at 30m resolution. The CDL Method utilizes past line work digitized by the division and reconciles changes that may have occurred, including new crop types or ag-to-urban conversions.Once the process of boundary digitizing is done, the polygons are loaded onto tablet PCs. Field crews are then sent to field check the crop and irrigation type for each agricultural polygon and label the shapefiles accordingly. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. This improved process has saved the division much time and money and even greater savings will be realized as the new statewide field boundaries are completed.Once processed and quality checked, the data is filed in the State Geographic Information Database (SGID) maintained by the State Automated Geographic Reference Center (AGRC). Once in the SGID, the data becomes available to the public. At this point, the data is also ready for use in preparing various planning studies.In conducting water-related land use inventories, the division attempts to inventory all lands or areas that consume or evaporate water other than natural precipitation. Areas not inventoried are mainly desert, rangeland and forested areas.Wet/open water areas and dry land agriculture areas are mapped if they are within or border irrigated lands. As a result, the numbers of acres of wet/open water areas and dry land agriculture reported by the division may not represent all such areas in a basin or county.During land use inventories, the division uses 11 hydrologic basins as the basic collection units. County data is obtained from the basin data. The water-related land use data collected statewide covers more than 4.3 million acres of dry and irrigated agricultural land. This represents about 8 percent of the total land area in the state.Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made in each succeeding year of the Water-Related Land Use Inventory. While this improves the data we report, it also makes comparisons to past years difficult. Making comparisons between datasets is still useful; however, increases or decreases in acres reported should not be construed to represent definite trends or total amounts of change up or down. To estimate such trends or change, more analysis is required.

Sentinel-1 Radiometric Terrain Corrected (RTC) 10-meter C-band synthetic aperture radar (SAR) imagery with on-the-fly functions for visualization and unit conversions for analysis. The Sentinel-1 RTC data in this collection is an analysis ready product derived from the Ground Range Detected (GRD) Level-1 products produced by the European Space Agency. Radiometric Terrain Correction accounts for terrain variations that affect both the position of a given point on the Earth's surface and the brightness of the radar return. For more information in the source data, see Sentinel-1 Radiometric Terrain Corrected (RTC) in the Microsoft Planetary Computer data catalog.With the ability to see through cloud and smoke cover, and because it does not rely on solar illumination of the Earth's surface, Sentinel-1 is able to collect useful imagery in most weather conditions, during both day and night. This data is good for wide range of land and maritime applications, from deforestation monitoring to oil spill mapping.Key PropertiesGeographic Coverage: Global - approximately 80° North to 80° SouthTemporal Coverage: 10/10/2014 – PresentSpatial Resolution: 10 x 10 meterRevisit Time*: ~6-days from 10/10/2014 – 12/23/2021; ~12-days from 12/23/2021 – PresentProduct Type: Ground Range Detected (GRD)Product Level: Radiometrically terrain corrected (RTC) and analysis readyFrequency Band: C-bandInstrument Mode: Interferometric Wide Swath Mode (IW)Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)*Prior to Dec 23, 2021, the mission included two satellites, Sentinel-1A and Sentinel-1B. On Dec 23, 2021, Sentinel-1B experienced a power anomaly resulting in permanent loss of data transmission. The mission is currently comprised of a single satellite, Sentinel-1A.ApplicationsThe RTC product can be used for a wide range of applications, including:Land cover classification such as forests, wetlands, water bodies, urban areas, and agricultural landChange detection such as deforestation and urban growthNatural hazard monitoring such as floodsOceanography such as oil spill monitoring and ship detectionAvailable Bands/PolarizationsDynamic Renderings and Data TransformationsThe default rendering is False Color (VV, VH, VV-VH) in dB scale with Dynamic Range Adjustment (DRA)The DRA version of each layer enables visualization of the full dynamic range of the images.Various pre-defined on-the-fly Raster Functions can be selected, or custom functions created. Name Description

Sentinel-1 RGB dB with DRA RGB color composite of VV,VH,VV-VH in dB scale with a dynamic stretch applied for visualization only

Sentinel-1 RGB dB RGB color composite of VV,VH,VV-VH in dB scale for visualization and some numerical analysis

Sentinel-1 RTC VV Power VV data in Power scale for numerical analysis

Sentinel-1 RTC VH Power VH data in Power scale for numerical analysis

Sentinel-1 RTC VV Amplitude VV data in Amplitude scale for numerical analysis

Sentinel-1 RTC VH Amplitude VH data in Amplitude scale for numerical analysis

Sentinel-1 RTC VV dB VV data in dB scale for visualization and some numerical analysis

Sentinel-1 RTC VV dB with DRA VV data in dB scale with a dynamic stretch applied for visualization only

Sentinel-1 RTC VH dB VH data in dB scale for visualization and some numerical analysis

Sentinel-1 RTC VH dB with DRA VH data in dB scale with a dynamic stretch applied for visualization only Image Selection/FilteringA number of fields are available for filtering, including Polarization Type, Sensor, Orbit Direction, Acquisition Date, and Numdate.To isolate and work with specific images, either use the ‘Image Filter’ to create custom layers or add a ‘Query Filter’ to restrict the default layer display to a specified image or group of images. NOTE: Image Filter is currently only available in Map Viewer Classic.Additional Usage NotesImage exports and Raster Analysis are limited to 4000 columns x 4000 rows per request.This dynamic imagery layer can be used in Web Maps and ArcGIS Pro as well as web and mobile applications using the ArcGIS REST APIs.

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