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CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
Polygon features marking proposed woodlands, published by Galway City Council. Each feature records a proposed woodland area with its computed area and perimeter length.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
Boundaries of Kentucky Department of Fish and Wildlife Resources Public Hunting Areas. These are both Wildlife Management Areas (WMA), Hunting Access Areas, Outdoor Recreation Area, and areas for which there is some kind of public hunting allowed (liek state forests and state parks). The current hunting and trapping guide should be referred to for specific regulations. Tracts of different ownership are merged into a single polygon for each of the public hunting areas. Not all WMAs are in public ownership. Some are privately owned and have short term agreements to allow public hunting access. There are two other feature classes, " Wildlife Management Areas" and "Non_WMA_Hunting_Area_tracts" that are broken down by tract. That Wildlife Management Area feature class includes the ownership for each tract of the WMA.
Overview Carotenoid Content Index (Car) from the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI). Car describes the canopy content of carotenoids in terrestrial vegetation. Car is calculated as ((1/R495)-(1/R705))*R800, where R495, R705, and R800 are the surface reflectance at 495, 705 and 800 nanometers (nm), respectively (Gitelson et al. 2002). Data provided as global, 8-day averages mapped to a standard, equal-angle, latitude-longitude grid of 0.02 degrees, which equates to ~2 kilometers (km) at the equator. Data has been corrected for atmospheric scattering and absorption. Pixels with no data values are masked (excluded) due to presence of water, cloud cover, or other features marked by quality control flags as described below. Data Layer Summary Name, units Carotenoid Content Index (Car) Measure of terrestrial canopy carotenoid content, a phytosynthetic and photoprotective pigment. Car is unitless. Spatial resolution 2 km2 Level-3, mapped to standard grid, global coverage Time Resolution 8 day average Each pixel represents an average of the valid, daily values observed within that grid element during the 8-day interval (approx 1 week). Note: 8 day average data layer is calculated from daily, "refined" (archive-quality) Level 3 PACE Car products available here from NASA Earthdata in NetCDF format. Earthdata account required. Each NetCDF file includes all 10 PACE vegetation indexes, including Car. Data Version Version 3.1 Data versions change as improved calibration, validation, algorithm refinements, etc are applied over time. See Version 3.1 Release Notes for details. Latency ~1 month This imagery service provides “refined” (archive-quality) PACE data products from March 2024-present, which have an approx 1 month latency. Note: Lower latency “near real time (NRT)” Level 3 PACE Car products available daily from NASA Earthdata at this link in NetCDF format. Latency typically Earthdataaccount required. Each NetCDF file includes all 10 PACE vegetation indexes, including Car. Intended Use: What can I do with this layer? There are three major classes of plant pigments: chlorophylls, carotenoids, and anthocyanins. Car provides information on changes in vegetation carotenoid content and photophysiological status. Carotenoids are structural components of the photosynthetic antenna and reaction center complexes and serve both photosynthetic and photoprotective functions (Blackburn 2007). The time (8 day average) and space (2 km2) resolution of this data layer enables weekly to seasonal and annual insight at local to global scales. Limitations of Use: What should I know about the data? Data Status (Maturity): This product is provisional, which means that product results have been reviewed and are in family with heritage data products or other basis of expectation, but have not yet been extensively validated and so may still contain significant errors. Data layer is sufficient for qualitative determinations of product fitness-for-purpose, for testing by operational users, and may be suitable for scientific publication. Data Masking: This is a Level 3, globally-binned product from NASA’s Ocean Biology Processing Group (OBPG). By default, OBPG Level 3 data values are masked (value is set to null, or no data) according to a standard set of quality control parameters. Please see Level 3 Algorithm Usage Constraints for masking parameter details. Algorithm Theoretical Basis Document (ATBD): See ATBD for algorithm details including scientific theory, calculations, etc. https://doi.org/10.5067/8L77O0Y8GHV8. Where do I go with further questions? Visit NASA’s Earthdata Forum where users can post questions that are answered by the personnel working at NASA with the PACE mission and/or the Ocean Biology Distributed Active Archive Center (OB.DAAC) that processes and distributes PACE data. First, follow this link to view only “PACE” and “OBDAAC” tagged questions on the Earthdata Forum. Then search and read through previous posts for your question. If it has not yet been asked please post your question in the Forum and be sure "PACE" and "OBDAAC" filter tags are applied. Mission and Product Background PACE Mission Overview: Launched in February 2024, NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observatory uniquely provides near-daily, hyperspectral coverage of Earth’s surface, plus hyperspectral and hyperangular polarimetry. PACE is a comprehensive Earth System mission, delivering all new, space-based information on the health and diversity of aquatic and terrestrial life (algae, plants), aerosol properties & modes (e.g. smoke, dust), cloud types, and more. The Ocean Color Instrument (OCI), PACE's primary sensor, is a hyperspectral (5 nm resolution) imaging spectrometer that measures properties of light from the ultraviolet (UV, 340 nm) through visible and near infrared (NIR, 895 nm) portions of the electromagnetic spectrum in 1.25 to 2.5 nm steps, plus 7 shortwave infrared (SWIR) bands (Werdell et al. 2019). Observations from OCI are moderate spatial resolution (1.2 km2), enabling a relatively frequent revisit time of 1-2 days. This combination of continuous, global coverage and spectral capabilities is unmatched by any other satellite platform, public or private. PACE benefits society by expanding our foundational knowledge of Earth and enabling novel, space-based science and applications tools across water resources (e.g. fisheries), air quality, land monitoring & management, and more. Terrestrial Remote Sensing from an Ocean Color Sensor? Designed and optimized to operate across a broad dynamic range, from the dark target of the ocean to bright, highly reflective cloud tops, PACE’s OCI also collects surface reflectance data from the world's terrestrial environments across 122 bands, resolving spectral properties of the Earth more finely than previous multispectral missions could achieve. Work thus far has demonstrated viability of OCI’s land data products for terrestrial investigations (Caplan et al. 2025). OCI’s coverage and capabilities enable provision of both novel, hyperspectral-enabled land observations as well as maintenance and extension of certain historical multispectral data records. Advanced vegetation indexes (VIs) enabled by OCI’s narrow-wavelength surface reflectance measurements provide continuous, synoptic insights into foliar pigment amounts (chlorophylls, carotenoids, and anthocyanins), water content, and photosynthetic light use efficiency across the globe each day. Heritage VIs (e.g., normalized difference vegetation index, NDVI; reviewed in Huang et al. 2021) can also be calculated by aggregating (averaging) OCI’s narrow bandwidths across ranges that approximate the multi-spectral, broadband reflectances of instruments such as MODIS, see the Mathematical Theory section of the ATBD for details. See the full list of heritage and hyperspectral-enabled VIs produced by PACE here. What other PACE data layers are available? This data layer is 1 of 11 PACE products initially selected for the Living Atlas catalog from the mission’s comprehensive aquatic, atmospheric, and terrestrial product catalog. Click here to view all 11 PACE Living Atlas imagery services. The Level 3, 122-band hyperspectral surface reflectance data (SFREFL) used to derive all VIs is available from NASA Earthdata to download at 2 kilometer resolution in NetCDF format. To access PACE mission data products available from NASA, first learn about PACE data access here, next decide which PACE product(s) you would like to use, then go to NASA Earthdata to find PACE products in NetCDF format, which can be downloaded and then imported into ArcGIS. An Earthdata account is needed to download data from NASA, sign up here. To learn about using Earthdata Search to find data, watch this tutorial. How can I stay up to date with the PACE mission? Join the PACE Community for mission news, events, new product release announcements, and to receive our newsletters. Would you like to see imagery services for more PACE products? Please let us know here. References: Algorithm Theoretical Basis Document: https://doi.org/10.5067/8L77O0Y8GHV8 Blackburn, G. A. (2007) Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany, 58(4), 855-867. https://doi.org/10.1093/jxb/erl123 Caplan, S. and Huemmrich, K. F. (2025) Unveiling PACE OCI’s hyperspectral terrestrial data products. Remote Sensing Letters, 16(4), 422–433. https://doi.org/10.1080/2150704X.2025.2470905 Gitelson, A. A., Zur, Y., Chivkunova, O. B., and Merzlyak, M. N.(2002) Assessing Carotenoid Content in Plant Leaves with Reflectance Spectroscopy. Photochemistry and Photobiology, 75(3): 272–281. https://doi.org/10.1562/0031-8655(2002)0750272ACCIPL2.0.CO2 Huang, S., Tang, L., Hupy, J. P., Wang, Y. and Shao, G. (2021) A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1-6. https://doi.org/10.1007/s11676-020-01155-1 Werdell, P. J. et al. (2019) The Plankton, Aerosol, Cloud, Ocean Ecosystem Mission: Status, Science, Advances. Bulletin of the American Meteorological Society, 100, 1775–1794. https://doi.org/10.1175/BAMS-D-18-0056.1
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
Below are the 8 definition queries in order to filter out the geographies in the tree canopy data. Geography = 'Block Group' Geography = 'City of Charlotte' Geography = 'Council Districts' Geography = 'County' Geography = 'Jurisdiction' Geography = 'Land Ownership' Geography = 'NPA' Geography = 'Watershed'
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
NOTICE: This is a beta service without operational support. It will be expanded in a future release to cover the historical and ongoing temporal record of ATL08. This map service is a GIS-ready version of the ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7 (ATL08) data set. ATL08 provides along-track estimates of terrain height, canopy height, and canopy cover, as well as beam and reference parameters, acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory. The complete ATL08 source data formatted as HDF5 files are available from the NASA NSIDC DAAC, along with user documentation and resources. This beta map service displays ATL08 data as a series of georeferenced points arranged in linear "ground tracks." These ground tracks represent the 100 m land segments output from the algorithms applied to the photon return data collected by the ATLAS instrument as ICESat-2 orbits the Earth over its 91-day repeat cycle. As shown in the figure below, the ATLAS instrument measures data along six ground tracks (GT) arranged in 3 pairs (one "weak" beam and one "strong" beam in each pair). Each GT is numbered according to the relative location of the laser that generates it, along with L (left) or R (right). GT1L and GT1R form pair one, GT2L and GT2R form pair two, and GT3L and GT3R form pair three, with GT1L on the far left and GT3R on the far right relative to the satellite flight direction. Left/right beams within each pair are approximately 90 m apart with the pairs separated by 3.3 km in the across-track direction. The along-track laser spacing is ~70 cm. Algorithms produce parameters representing 100 m land segments in the along-track direction for each beam. Image source: Smith et al., 2019 - https://doi.org/10.1016/j.rse.2019.111352 Additional information about the ATLAS instrument, ICESat-2 satellite, and ground track naming conventions and geometry can be found in Appendix A of the ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7 User Guide. Available Parameters A selection of "core" parameters was chosen from the full suite of parameters available in the complete ATL08 source data. A list of the available core parameters, including canopy and terrain height, can be found in the technical specification document. When accessing the data through the web-based Map Viewer, these parameters are visible as a popup window when a single data point is selected. Note: where canopy data is absent, these parameters will not have values. Spatial Coverages and Resolution Spatial coverage is nearly global (approximately 88°N to 88°S). However, data are not produced for orbital segments that cross open ocean only (i.e., do not cross a land surface). The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons but may in some cases contain less. Temporal Coverage and Resolution 9 December 2025 to 11 March 2026 Data are re-acquired along each of ATLAS/ICESat-2's 1,387 reference ground track every 91 days. This beta service presents a single 91-day satellite repeat cycle. Recommended Data Access Guidelines If you would like to access these data using ArcGIS Pro or QGIS, we recommend using the Feature Service, rather than the Map Service. When accessing the Map Service data using the web-based Map Viewer, we suggest the following settings be applied:Time Slider: Set temporal range to the full 91-day extent Visibility Range: Within the map properties window, ensure the current map scale (black triangle) is within the selected visibility range Recommended settings when using Map Viewer. For in-depth data users who wish to perform customized data processing, we suggest using the full ATL08 source data, available from the NASA NSIDC DAAC as ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7.
NOTICE: This is a beta service without operational support. It will be expanded in a future release to cover the historical and ongoing temporal record of ATL08. This map service is a GIS-ready version of the ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7 (ATL08) data set. ATL08 provides along-track estimates of terrain height, canopy height, and canopy cover, as well as beam and reference parameters, acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the ICESat-2 observatory. The complete ATL08 source data formatted as HDF5 files are available from the NASA NSIDC DAAC, along with user documentation and resources. This beta map service displays ATL08 data as a series of georeferenced points arranged in linear "ground tracks." These ground tracks represent the 100 m land segments output from the algorithms applied to the photon return data collected by the ATLAS instrument as ICESat-2 orbits the Earth over its 91-day repeat cycle. As shown in the figure below, the ATLAS instrument measures data along six ground tracks (GT) arranged in 3 pairs (one "weak" beam and one "strong" beam in each pair). Each GT is numbered according to the relative location of the laser that generates it, along with L (left) or R (right). GT1L and GT1R form pair one, GT2L and GT2R form pair two, and GT3L and GT3R form pair three, with GT1L on the far left and GT3R on the far right relative to the satellite flight direction. Left/right beams within each pair are approximately 90 m apart with the pairs separated by 3.3 km in the across-track direction. The along-track laser spacing is ~70 cm. Algorithms produce parameters representing 100 m land segments in the along-track direction for each beam. Image source: Smith et al., 2019 - https://doi.org/10.1016/j.rse.2019.111352 Additional information about the ATLAS instrument, ICESat-2 satellite, and ground track naming conventions and geometry can be found in Appendix A of the ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7 User Guide. Available Parameters A selection of "core" parameters was chosen from the full suite of parameters available in the complete ATL08 source data. A list of the available core parameters, including canopy and terrain height, can be found in the technical specification document. When accessing the data through the web-based Map Viewer, these parameters are visible as a popup window when a single data point is selected. Note: where canopy data is absent, these parameters will not have values. Spatial Coverages and Resolution Spatial coverage is nearly global (approximately 88°N to 88°S). However, data are not produced for orbital segments that cross open ocean only (i.e., do not cross a land surface). The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons but may in some cases contain less. Temporal Coverage and Resolution 9 December 2025 to 11 March 2026 Data are re-acquired along each of ATLAS/ICESat-2's 1,387 reference ground track every 91 days. This beta service presents a single 91-day satellite repeat cycle. Recommended Data Access Guidelines This Feature Service is best accessed using ArcGIS Pro or QGIS, and may be slow to load if viewed using the web-based Map Viewer. If you wish to access the data using the Map Viewer, we recommend using the Map Service instead. When using Map Viewer, we suggest the following settings be applied:Time Slider: Set temporal range to the full 91-day extent Visibility Range: Within the map properties window, ensure the current map scale (black triangle) is within the selected visibility range Recommended settings when using Map Viewer. For in-depth data users who wish to perform customized data processing, we suggest using the full ATL08 source data, available from the NASA NSIDC DAAC as ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 7.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
Dates of Images: Post-Event: 5/6/2018; 5/15/2024 Pre-Event: 4/8/2018; 4/21/2024 Date of Next Image: Unknown Summary: NDVI: The dynamically generated Normalized Difference Vegetation Index (NDVI) layer is an index for quantifying green vegetation. It reflects the state of vegetation health based on how vegetation reflects light at certain wavelengths. Suggested Use: NDVI: Dark green colors are areas with a lot of green leaf growth which indicates the presence of chlorophyll. Chlorophyll reflects more infrared light and less visible light. Areas with some green leaf growth are in light greens, and areas with little to no vegetation growth are even lighter greens. Satellite/Sensor: Landsat 9 Operational Land Imager (OLI) Resolution: 30 meters Credits: NASA/MSFC, USGS Esri REST Endpoint: See URL section on right side of page WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_EX2602_202405_FLOOD_BRAZIL/ex2602_landsat_ndvi/ImageServer/WMSServer
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
Overview This layer shows North America classified into "anthropogenic biomes," also known as "anthromes" or "human biomes." Biomes are major global ecosystem types, characterized by particular climate and soil conditions and distinctive forms of life adapted to these conditions. Incorporating the significant role of humans in altering ecosystems, this layer offers a new way of classifying the parts of the earth in which life exists. Geographic Coverage The coverage spans the entire North American continent, including Canada, Mexico, and the United States. Data Sources and Licensing Commission for Environmental Cooperation (CEC), based on the anthropogenic biomes classification developed by Erle C. Ellis and Navin Ramankutty in 2008, Putting people in the map: Anthropogenic biomes of the world. Frontiers in Ecology and the Environment 6, doi:10.1890/070062. The data is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing redistribution and adaptation, including for commercial purposes, with appropriate attribution. Update frequency: Not planned Limitations of use: None. Acknowledgment of the Commission for Environmental Cooperation and/or Ellis & Ramankutty (2008) would be appreciated in products derived from these data. Vintage: 2008 Applications (Intended Use) Research on human influence on terrestrial ecosystems and biodiversity. Land use and land cover analysis integrating human population patterns. Conservation planning that accounts for the degree of human alteration of natural systems. Integration with other thematic layers in the North American Environmental Atlas for comprehensive continental analysis. Files Download
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
The identification of species is one of the most basic, and yet critically important, issues in biology with far-reaching potential implications for fields such as biodiversity conservation, population ecology and epidemiology. Morphology has long been the primary tool biologists have used to categorize life. However, we now know that a significant portion of natural diversity is morphologically hidden, and therefore, we must integrate nonmorphological tools into the description of biodiversity. Here, we demonstrate the utility of multilocus population genetic data for identifying and characterizing cryptic species complexes, even when species share large amounts of genetic variability. Specifically, we have used DNA sequence data from 12 genomic regions to characterize two widespread species complexes in the coral genus Acropora: A. cytherea and A. hyacinthus. These two morphospecies have each been sampled from 5 to 7 locations throughout their Indo-Pacific distributions, and with the use of structure and hierarchical clustering, we demonstrate the presence of at least six widespread cryptic species within these two morphospecies complexes. After identifying cryptic lineages, we then utilize the genetic data to examine the history of introgressive hybridization within and between these morphospecies complexes. Our data indicate that these two complexes form a global syngameon with consistent patterns of introgression between species across large geographic distributions. Citation: Southwestern Pacific OBIS (2015). Extensive sympatry, cryptic diversity and introgression throughout the geographic distribution of two coral species complexes. Southwestern Pacific OBIS, National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand, 20 records. Online: http://nzobisipt.niwa.co.nz/resource.do?r=smpatry_of_coral Released on April 7, 2015. Bibliography: LADNER, J. T. and PALUMBI, S. R. (2012), Extensive sympatry, cryptic diversity and introgression throughout the geographic distribution of two coral species complexes. Molecular Ecology, 21: 2224–2238. doi: 10.1111/j.1365-294X.2012.05528.x_________________________________________________________________________________________________________________ Item Page Created: 2016-06-09 02:30 Item Page Last Modified: 2025-04-05 18:55 Owner: NIWA_OpenData Sympatry_of_two_coral_species_complexes No data edit dates available Fields: id,modified,language,bibliographicCitation,institutionCode,collectionCode,datasetName,basisOfRecord,dynamicProperties,catalogNumber,occurrenceRemarks,individualCount,sex,lifeStage,occurrenceStatus,associatedTaxa,eventID,samplingProtocol,eventDate,startDayOfYear,year,month,day,fieldNumber,waterBody,country,stateProvince,county,locality,minimumDepthInMeters,maximumDepthInMeters,decimalLatitude,decimalLongitude,geodeticDatum,coordinateUncertaintyInMeters,footprintWKT,identifiedBy,typeStatus,scientificNameID,scientificName,kingdom,phylum,class,order_,family,genus,subgenus,specificEpithet,infraspecificEpithet,scientificNameAuthorship,FID
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
Identifies notable trees as set out in the policy framework of D13 Notable Trees Overlay and Schedule 10 of the Plan.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
This dataset shows the boundaries of the province's six fire management zones that existed prior to 2014 in which most forest fires received the same type of response. These management zones were based on: Common forest and forest fire management objectives land use density of values at risk fire load forest ecology The 2014 Wildland Fire Management Strategy moved from a zone-based approach to one where each wildland fire is assessed and receives an appropriate response according to the circumstances and condition of the fire. Additional DocumentationFire Management Zone - Data Description (PDF) Fire Management Zone - Documentation (Word) StatusCompleted: Production of the data has been completed Maintenance and Update FrequencyAs needed: Data is updated as deemed necessary ContactAviation, Forest Fire and Emergency ServicesAFFES.GIS@ontario.ca
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
Researchers working with the Global Land Analysis & Discovery (GLAD) at University of Maryland, College Park, Maryland and Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory, Pasadena, California, created maps of surface disturbance due to ongoing wildfires in Los Angeles County in January 2025 using the OPERA Disturbance Alert from Harmonized Landsat Sentinel-2 (DIST-ALERT-HLS) products. The results posted here are preliminary results, primarily intended to aid the field response and people who wanted to have a rough first look at the area impacted by wildfire. The ARIA-share website has always focused on posting preliminary results as fast as possible for urgent response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk. OPERA DIST-ALERT-HLS The Disturbance product (DIST) maps per pixel vegetation disturbance (specifically, vegetation cover loss) from the Harmonized Landsat Sentinel-2 (HLS) scenes. We provide the (1) maximum vegetation anomaly value (VEG-ANOM-MAX) layer and (2) vegetation disturbance status (VEG-DIST-STATUS). All provided layers have been filtered to include only disturbance detected following the start of the Greater Los Angeles wildfires (starting Jan. 7, 2025). Images are provided from January 12, 2025. The images are provided as GeoTIFF files. VEG-ANOM-MAX: Difference 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. VEG-DIST-STATUS: Indication 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 with 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. Satellite/Sensor: MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/B satellites and Operational Land Imager 2 (OLI-2) on NASA’s Landsat 8/9 satellite. Resolution: 30 meters The DIST-ALERT-HLS products included here have these flags: 255 represents ''No Data'' and is based on the Fmask layer of the source HLS granule. OPERA DIST-ALERT-HLS data availability: The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20250113-GreaterLosAngeles_Fires/DIST-HLS/ 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 GLAD project, visit https://glad.umd.edu/ 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/ Suggested Use for DIST-HLS: VEG-DIST-STATUS: Layer values: 0: No disturbance (suggested color: #121212) 1: first 2: provisional 3: confirmed 4: first >=50% (suggested color: #008888) 5: provisional >=50% (suggested color: #e48727) 6: confirmed >=50% (suggested color: #e01b07) 7: confirmed 8: confirmed >=50%, finished (suggested color: #dddddd) 255: No data (suggested color: transparent) VEG-ANOM-MAX Layer values: 0-100: Maximum loss of percent vegetation (suggested color: white to red gradient) 255: No data (suggested color: transparent) Product POCs: Alexander L. Handwerger (alexander.handwerger@jpl.nasa.gov) Zhen Song (zhensong@umd.edu) Matt Hansen (mhansen@umd.edu) Steven Chan (steventsz.k.chan@jpl.nasa.gov) David Bekaert (david.bekaert@jpl.nasa.gov) Credits: The 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. NASA JPL-Caltech-UMD ARIA/OPERA Team ================== Files: OPERA-DIST-ALERT-HLS-VEG-ANOM-MAX_20250112.tif: January 12, 2025 showing the maximum vegetation anomaly. OPERA-DIST-ALERT-HLS-VEG-DIST-STATUS_20250112.tif: January 12, 2025 showing the status anomaly. Last Update 14 January 2025 WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_CA_WILDFIRES_202501/202501_opera_dist_hls_gl/ImageServer/WMSServer
This image service provides one-time estimates of 30-meter resolution global mangrove distribution, aboveground biomass, and canopy height that represent conditions of mangrove-forested wetlands in the 2000s based on remotely sensed data and in situ field measurements and region-specific allometric models. The underlying multidimensional image layer contains three variables. Users should use the multidimensional settings to switch between variables.agb (Mg ha-1): mangrove aboveground biomass measured in megagrams per hectare hmax (meters): maximum canopy maximum height (height of the tallest tree) measured in meters hba (meters): basal-area weighted height (individual tree heights weighted in proportion to their basal area). The maximum canopy height and basal-area weighted height values were capped at the 95th percentile of the maximum value in each country to minimize the impact of canopy height error reported from a potentially small number of misclassified pixels. Data sources used include the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data collected in February 2000, lidar heights collected from 2003 to 2009 by the ICESat/Geoscience Laser Altimeter System (GLAS) Spaceborne Lidar mission, and in situ field measurements collected from 2004 to 2014. Details about the underlying data can be found at https://doi.org/10.3334/ORNLDAAC/1665 Legend:Variable Units Symbol Pixel Value Description agb Mg ha-1 High: 910.476 Mangrove aboveground biomass Low: 0.284 hmax meters High: 62.789 Mangrove canopy maximum height (height of the tallest tree) Low: 0.849 hba meters High: 39.79 Mangrove basal-area weighted height (individual tree heights weighted in proportion to their basal area) Low: 0.538 Acknowledgement:Creation of this dataset was funded by NASA"s Carbon Monitoring System (CMS) program. Dataset Citation:Simard, M., T. Fatoyinbo, C. Smetanka, V.H. Rivera-monroy, E. Castaneda, N. Thomas, and T. Van der stocken. 2019. Global Mangrove Distribution, Aboveground Biomass, and Canopy Height. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1665 Contact:For inquiries about this service, please contact support@earthdata.nasa.gov or post/view questions on the Earthdata Forum.
Status and trends on U.S. forest location, health, growth, mortality, and production, from the U.S. Forest Service's Forest Inventory and Analysis (FIA) program. The Forest Inventory and Analysis (FIA) dataset is a nationwide survey of the forest assets of the United States. The FIA research program has been in existence since 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and use of trees on the nation's forest land. Domain: continental U.S., 1928-2018 Resolution: plot-level (irregular polygon) This dataset was curated and brought to Azure by CarbonPlan.
Polygon features marking existing woodlands, published by Galway City Council. Each feature records a woodland area with its computed area and perimeter length.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.
A research point is a feature representing single coordinate or point based locations, where some form of research observation, test, trial, measure, or monitoring activity has, or will take place. The mapped features are generally small in extent (for example, soil water table monitor, tree cavity nest), and can be used to represent the locations of sampling techniques (for example, basal area prism sweep). This data class is one of three primitive data classes: Research Point (RESPOINT) Research Line (RESLINE) Research Polygon (RESPOLY) Status: On going: data is continually being updated Maintenance and Update Frequency: As required Contact Calvin Julie, Natural Resources Information Unitcalvin.julie@ontario.ca Note Additional features licensed under the Ontario Geospatial Data Exchange (OGDE) Agreement are available to members of the OGDE for professional, non-commercial use only. To find out more about the OGDE visit Geospatial Ontario on Ontario.ca.
This page provides data for the Tree and Shade Canopy performance measure. Sustainable growth for the City of Tempe is influenced by many factors, including the development and maintenance of our tree canopy. An adequate canopy provides shade, cooling, reduces water evaporation, and is an indication of a healthy and well-maintained environment. As per the Urban Forestry Master Plan, this data represents tree canopy in parks, street right-of-ways, and municipally owned and managed facilities. The performance measure dashboard is available at 4.11 Tree Coverage Data Dictionary Additional Information Source: Contact: Craig Hayton Contact E-Mail: Craig_Hayton@tempe.gov Data Source Type: CSV Preparation Method: Tracked by staff quarterly Publish Frequency: Annual Publish Method: Manual
Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 50 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 104 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2005). Level IV ecoregions are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Data Download: https://app.fw.ky.gov/kfwis/kygeonet/ecoregion2012.zip Literature cited: Bailey, R.G., Avers, P.E., King, T., and McNab, W.H., eds., 1994, Ecoregions and subregions of the United States (map): Washington, D.C., USFS, scale 1:7,500,000. Bryce, S.A., Omernik, J.M., and Larsen, D.P., 1999, Ecoregions—a geographic framework to guide risk characterization and ecosystem management: Environmental Practice, v. 1, no. 3, p. 141-155. Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A. L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Griffith, G., Omernik, J., Azevedo, S., 1998, Ecoregions of Tennessee (text, map, summary tables, and photographs): Reston, Virginia, U.S. Geological Survey, map scale 1:940,000. McMahon, G., Gregonis, S.M., Waltman, S.W., Omernik, J.M., Thorson, T.D., Freeouf, J.A., Rorick, A.H., and Keys, J.E., 2001, Developing a spatial framework of common ecological regions for the conterminous United States: Environmental Management, v. 28, no. 3, p. 293-316. Omernik, J.M., 1987, Ecoregions of the conterminous United States (map supplement): Annals of the Association of American Geographers, v. 77, p. 118-125, scale 1:7,500,000. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. U.S. Department of Agriculture–Soil Conservation Service, 198, Land resource regions and major land resource areas of the United States: Agriculture Handbook 296, 156 p. U.S. Environmental Protection Agency, 2002, Level III ecoregions of the continental United States (revision of Omernik, 1987): Corvallis, Oregon, USEPA–National Health and Environmental Effects Research Laboratory, Map M-1, various scales. Wiken, E., 1986, Terrestrial ecozones of Canada: Ottawa, Environment Canada, Ecological Land Classification Series no. 19, 26 p. Woods, A.J., Omernik, J.M., Brockman, C.S., Gerber, T.D., Hosteter, W.D., and Azevedo, S.H., 1998, Ecoregions of Indiana and Ohio: Reston, USGS, map scale 1:500,000 Comments and questions regarding the Level III and IV Ecoregions should be addressed to Glenn Griffith, USGS, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, (541)-754-4458, email:omernik.james@epa.gov This was first downloaded by KDFWR in 2005 for the State Wildlife Action Plan and then updated in 2012 again to reflect an updated national shapefile. Kentucky Department of Fish and Wildlife Resources downloaded first the Kentucky Data set https://www.epa.gov/eco-research/ecoregion-download-files-state-region-4#pane-15 , but saw that the boundary did match well the Kentucky state boundary maintained by KY Geonet. We then downloaded the national data set obtained from https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-statesand intersected to the KY DGI state boundary, For those slivers assigned to unexpected ecoregions (55d Pre-Wisconsinan Drift Plains, 67h Southern Sandstone Ridges, 71m Northern Shawnee Hills, 71n Southern Shawnee Hills, 73c St. Francis Lowlands) we merged to the geographically nearest polygon so that the ecoregions found in this dataset match that in the poster Ecoregions of Kentucky.
Furbish’s Lousewortis an Endangered plant species in New Brunswick. An updated draft Recovery Strategy was created for the species in 2025, and Survival & Recovery Habitat were delineated. For more information, please refer to the Species at Risk Public Registry. La pédiculaire de Furbish est une espèce végétale en voie de disparition au Nouveau-Brunswick. Une version préliminaire du programme de rétablissement a été mise à jour pour l'espèce en 2025, et des habitats de survie et de rétablissement ont été délimités. Pour en savoir plus, veuillez consulter le Registre public des espèces en péril.
A derived spatial layer generated from research plot source data identifying features and attributes with active temporal protection requirements within a designated management area or polygon. The spatial features and attributes in this layer are sourced from the following data classes: Research Point (RESPOINT) Research Line (RESLINE) Research Poly (RESPOLY) The temporal constraint assesses the active status of source features and facilitates automated lifecycle management of the features included in this derived layer. Temporal constraint PROTECTION_END_DATE is greater than the date this layer was generated. The spatial extents of features in this derived layer are based on source feature extents, to which specified buffer distances has been added. Applied buffer PROTECTION_BUFFER_DIST field value (meters). This layer is regenerated at scheduled intervals, with the previous version of this layer archived and replaced by the newly generated version. Status: On going: data is continually being updated Maintenance and Update Frequency: As required Contact Calvin Julie, Natural Resources Information Unitcalvin.julie@ontario.ca Note Additional features licensed under the Ontario Geospatial Data Exchange (OGDE) Agreement are available to members of the OGDE for professional, non-commercial use only. To find out more about the OGDE visit Geospatial Ontario on Ontario.ca.