AndreaBozzo/ceres-open-data-index huggingface dataset:
Vegetation - Santa Clara River Update - 2016 [ds2961]

Western Foundation of Vertebrate Zoology contracted Stillwater Sciences in 2018 to create a fine-scale vegetation map of portions of the Santa Clara River. The mapping study area, consists of approximately 16,370 acres of Ventura County. Work was performed on the project during the summer and fall of 2018. The projects main goal was to address the need for detailed up-to-date vegetation information in support of identifying and modeling habitat for southwestern willow flycatcher, yellow-billed cuckoo, and least Bell's vireo. Funding for the project was provided by an Endangered Species Act Section 6 grant from the United States Fish and Wildlife Service to the California Department of Fish and Wildlife. This project builds off a prior mapping project that was conducted by Stillwater Sciences and URS, which was funded by the California State Coastal Conservancy and the Santa Clara River Trustee Council, in 2007. Species composition data collected in the field was compiled and reviewed in the office to assign the appropriate MCV alliance to each sampled location. In cases where the species present were best described by an MCV association (a sub-category of the broader MCV alliance), one was assigned. For field sampled locations with unique species composition not currently represented by an existing MCV alliance or association, a provisional alliance or association was created. In addition, some areas were classified into broader land cover types (e.g., agriculture, developed, riverwash). The vegetation map was produced applying digital aerial imagery (natural color, 2-foot resolution) from the National Agricultural Imagery Program (NAIP) (USDA-FSA 2016) flown in May, June, and July 2016. The minimum mapping unit (MMU) is 0.5 acres for most types and 0.1 for more unusual types that were discernable from areal photography and/or documented in the field. Once the map was made photointerpretation of the NAIP imagery took place in order to identify vegetation types. Field mapping took place after to refine the vegetation type definitions, CNPS vegetation reconnaissance field forms were used in the field. There was a total of 91 mapping classes. There was no accuracy assessment was done for this project. More information can be found in the project report, which is bundled with the vegetation map published for BIOs here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/2900_2999/ds2961.zip
Vegetation - San Clemente Island [ds2962]

The first of two objectives for the study was to develop and test efficient and effective methods for semi-automated vegetation mapping of the recovering plant communities on SCI, specifically using hierarchical object-based classification with a rule-based expert system, ultra-high spatial resolution (UHSR) multispectral imagery, and lidar-derived digital canopy height data.A secondobjective was to map the distribution and quantify the cover of vegetation alliances over the entirety of SCI. The final vegetation map portrays the distribution of 19 vegetation communities across SCI, with the largest areas comprised of California Annual and Perennial Grassland (35 percent) and three types of coastal sage scrub and maritime succulent scrub, comprising a combined 53 percent of the area. Map accuracy was assessed to be 79 percent based on fuzzy methods and 61 percent with a traditional accuracy assessment. The accuracy of tree identification was assessed to be 81 percent, but species-level tree accuracy was 45 percent.
Vegetation - Suisun Marsh - 2018 [ds2963]

Vegetation delineations based on photo interpretation and formal vegetation classification plus change detection. This update by the Geographical Information Center (GIC) of the North State Planning and Development Collective at California State University, Chico is part of an ongoing triennial vegetation monitoring program for the Suisun Marsh. The project tracks changes in the Suisun Marsh vegetation over time to fulfill specific permit requirements of the Suisun Marsh Plan of Protection of 1984, the Suisun Marsh Preservation Agreement of 1986, and the 2015 Suisun Marsh Preservation Agreement. This is the seventh update using the current mapping standards originally implemented in 1999. All of the previous vegetation maps from 1999 to 2015 can be viewed and downloaded using the online California Department of Fish and Wildlife (CDFW) Biogeographic Information and Observation System (BIOS); the links to the associated reports are included in the map metadata. Minimum mapping unit (MMU): Typically, the minimum mapping size is 0.25 acres. However, the photo interpreters use their best judgment to determine if a stand below 0.25 acre should be separately delineated. For example, a smaller polygon would be appropriate for any new visible occurrence of a non-native species of concern, such as Phragmites australis, Arundo donax, Carpobrotus edulis, Eucalyptus spp., and Lepidium latifolium. Minimum mapping width: There are many long and narrow polygons within the Suisun Marsh study area, most of which are roads, ditches, levees, and sloughs. The minimum mapping width is typically 10 feet; however, if small sections of a stand fell below the minimum width, the polygon was not split. More information can be found in the project report, which is bundled with the vegetation map published for BIOS here: [https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/2900_2999/ds2963.zip], and the report is available for download separately at: [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=218048].
Vegetation - Western San Diego County [ds2964]

The purpose of this project was to describe each of the native and naturalized vegetation types known to occur within western San Diego County and to provide the user a means to determine each type through direct observations of species composition. The classification presented herein is the result of a detailed analysis of data collected throughout the western San Diego County study area. Under contract to the San Diego Association of Governments (SANDAG), Biologists from AECOM, Conservation Biology Institute, and the California Department of Fish and Game (CDFG) Vegetation Classification and Mapping Program (VegCAMP) collaborated on these analyses, the definition of the classifications, and preparation of this manual. This classification study was conducted in a manner consistent with the recommendations for standardized data collection and analysis by CDFG VegCAMP ([https://wildlife.ca.gov/Data/VegCAMP]) and the methods used in the preparation of A Manual of California Vegetation, 2nd ed. (Sawyer; Keeler-Wolf; Evens 2009), published by the California Native Plant Society (CNPS). This projects classifications is in accordance with this larger work. A Manual of California Vegetation (MCV) is intentionally consistent within the larger context of the National Vegetation Classification System (NVCS), which has been adopted by federal agencies and nongovernmental organizations such as the US Geological Survey, National Park Service, and NatureServe. Thus each of these classifications can be compared in context with the others nationwide.
Vegetation - Gaviota Coast Dangermond Preserve [ds2957]

WRA, Inc (WRA) created a fine-scale vegetation map of portions of the Cojo-Jalama Ranches. WRA conducted field reconnaissance assistance for this project, as well as accuracy assessment (AA) field data collection. The primary purpose of the project was to provide a comprehensive overview of habitats, plants, and wildlife to inform planning for land-use, conservation, and ranch-specific activities.The mapping study area, consists of approximately 24,400 acres of the Cojo-Jalama ranches in unicoporated coastal Santa Barbara County, California. Work was performed on the project between 2012 and 2017. Ranch-wide floristic surveying was conducted from April 2012 to October 2014 to address natural communities and sensitive plant and wildlife species. WRA botanists documented vegetation alliances while on-foot. Site-specific surveys were conducted between 2015 and 2017 collected natural community and sensitive species data to be incorporated into WRA’s long-term geodatabase as supplementary data. WRA botanists then further refined vegetation alliance mapping by conducting accuracy assessment where site-specific surveys were conducted; when terrain made sites inaccessible, field botanists used binoculars to observe plant communities from an appropriate vantage point.Field maps generated by WRA used high-quality aerial photographs from 2010 to 2012 overlain with 10-foot contour lines. vegetation polygons were then hand-drawn by field biologists and later digitized using ArcGIS software. Trimble Geo XH GPS units with sub-meter accuracy were used by the biologists to map especially small-scale landscape features and rare plant point-occurrences. The minimum mapping unit (MMU) is variable from 1.0 acres to point depending on the map feature type. There was a total of 50 mapping classes. The overall Fuzzy Accuracy Assessment rating for the final vegetation map was not calculated, but much of the map was field checked.
B118_Basins_Prioritized_Joined_Components

[DWR Basin Prioritization]. Prioritization table of components used to prioritize basins was joined to this basin layer, for use in the SNMP web application. The dataset is a feature class showing the boundaries of 515 groundwater basins and subbasins as defined by the California Department of Water Resources as last modified by the Basin Boundary Emergency Regulation adopted on October 21, 2015 and subsequent modifications requested through the Basin Boundary Modification Request Process. The file is in ESRI geodatabase format and is intended for use with compatible GIS software. Groundwater basins are represented as polygon features and designated on the basis of geological and hydrological conditions - usually the occurrence of alluvial or unconsolidated deposits. When practical, large basins are also subdivided by political boundaries, as in the Central Valley. Basins are named and numbered per the convention of the Department of Water Resources. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR GIS Spatial Data Standards. DWR makes no warranties or guarantees, either expressed or implied, as to the completeness, accuracy or correctness of the data, nor accepts or assumes any liability arising from or for any incorrect, incomplete or misleading subject data. The official DWR GIS Data Steward for this dataset is Brett Wyckoff, who may be contacted at 916-902-6779, or at brett.wyckoff@water.ca.gov. Comments, problems, improvements, updates, or suggestions should be forwarded to the official GIS Data Steward as available and appropriate.
Vegetation - Springtown Alkali Sink [ds2965]

The University of California Berkeley Herbarium contracted Aerial Information Systems, Inc. in 2008 to create a baseline inventory of wetlands and associated upland vegetation for approximately 38 square miles of land north of the city of Livermore, California, including and adjacent to the Springtown Alkali Sink Preserve. The vegetation map adheres to the 2008 National Vegetation Classification Standard (NVCS) and the Manual of California Vegetation. One-foot natural color imagery flown in May 2005 was used as a base for the delineated polygons and photo interpretation signature in the focus study areas. Additional online digital imagery was deemed necessary as supplementary information and included the National Agricultural Inventory Program (NAIP) imagery flown in the summer of 2005 which was used as a base for areas outside of the focus studies.*Note: It is important to understand that the interpretation in the focus areas is georeferenced to the 1-foot 2005 imagery and will not line up precisely to the NAIP imagery in all cases. Therefore it is not advisable to view the delineations over the NAIP imagery, especially at a fine-scale level in the focus studies. The complete mapping effort is divided into two phases. The first phase is the detailed mapping of several focus study areas which total approximately 4200 acres in size and include the Springtown Preserve and adjacent areas along with Brushy Peak and the upper Altamont Creek drainage. The Phase II portion involves the creation of a more generalized vegetation map for the remaining thirty square miles including much of the remaining Altamont Creek watershed in the northern portion of the Livermore Valley.
California Recreational Fisheries Survey, Catch Per Unit Angler for All Species and All Effort - R7 - 2004-2024 - CDFW [ds3186]

The data is reported in Catch Per Unit Angler (CPUA) which is a simple calculation of the number of fish caught divided by the number of anglers targeting the fishery type within a particular 1' block in a given period. CRFS Catch records for all caught species were aggregated into 5-year bins from 2004-2024 as well as the entire period. All catch of these species were summed and attributed to the blocks (1' x 1' area) which were recorded during the survey interview. If multiple blocks were recorded, then the catch data was divided across all blocks indicated. Catch data is further divided into 'kept' and 'released' fish which is used to calculate a CPUA for only kept fish and a CPUA for both kept and released fish. The total number of anglers targeting any species in each block was calculated for each time frame. An angler represents a single person fishing in a block on a single day. The data is limited to blocks that had 3 or more reported fishing trips over the entire time period. Effort under the 'All' trip type is an aggregation of all anglers surveyed. This includes anglers from specific trip types such as highly migratory, salmon, bottomfish, coastal migratory, inshore, other anadromous, invertebrates and anything. Raw data from 2004 - 2015 was extracted from the Pacific States Marine Fisheries Commission (PSMFC) legacy RecFIN database. Data from 2016 - 2024 was extracted from the CDFW's CRFS data system. The data is divided into reported catch (includes reported kept, reported released alive, and reported released dead), observed kept catch, effort and location tables. Attributes: * Block Box: 1' grid where the data was reported. * Catch: The species or the name of the species group whose individual catch records were aggregated. * Trip Type: Trip type category that includes the primary target species for the angler that was aggregated. This layer is an aggregation of all angler effort. * All_04_09: CPUA for both kept and released catch for 2004-2009. * All_10_15: CPUA for both kept and released catch for 2010-2015. * All_16_20: CPUA for both kept and released catch for 2016-2020. * All_21_24: CPUA for both kept and released catch for 2021-2024. * Kept_04_09: CPUA for only kept catch for 2004-2009. * Kept_10_15: CPUA for only kept catch for 2010-2015. * Kept_16_20: CPUA for only kept catch for 2016-2020. * Kept_21_24: CPUA for only kept catch for 2021-2024. * CPUA_All: CPUA for both kept and released catch for the entire 2004-2024 period. * CPUA_Kept: CPUA for only kept catch for the entire 2004-2024 period. This field is used as the source of the symbology for this layer. Gray blocks represent where effort occurred, but no catch was reported. * Samples: The number of individual survey samples that reported fishing in block for the entire period.
Peregrine Falcon Observations Generalized [ds2968]

The Peregrine Falcon Observations Database is a product of the California Department of Fish and Wildlife's (CDFW's) Wildlife Branch and Biogeographic Data Branch. It was created by the Santa Cruz Predatory Bird Research Group ([https://pbrg.pbsci.ucsc.edu/]) then managed privately for a time. CDFW took ownership in 2011. The records generally represent peregrine falcons (PEFA, Falco peregrinus) nesting, perching, and flying. Additional information about the falcon, the database, and submitting data can be found on CDFW’s American Peregrine Falcons in California Webpage: [https://www.wildlife.ca.gov/Conservation/Birds/Peregrine-Falcon]. This dataset represents observed nest locations for peregrine falcons, generalized to a USGS 7.5 minute topographic quad level. If you need more specific location information, please contact the listed point of contact in the metadata.
Mule Deer Migration Corridors - Blue Canyon - 2018-2020 [ds2971]

The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (5 adult females) from the Blue Canyon herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2020. GPS fix rates were between 4-13 hours. The Blue Canyon herd migrates from winter ranges in the western foothills of the Sierra Nevada range, south of Interstate 80, eastward along the Forest Hill Divide to higher altitude terrain near Soda Springs and The Cedars. To improve the quality of the data set as per Bjorneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 3 migrating deer, including 9 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 36 days and 32.82 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to the majority of BBMMs producing variance rates greater than 8000, a fixed motion variance of 1000 was set per migration sequence. Winter range analyses were based on data from 3 individual deer and 6 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. This collar project was not specifically designed to pinpoint precise migration routes or winter range designations, hence the low sample size. Additional migration routes and winter range areas likely exist beyond what was modeled in our output. Corridor tiers (low, medium, high) could not be computed with such a small dataset. Therefore, all corridors were given the same weight and designation in this analysis. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.
Mule Deer Migration Corridors - Bucks Mountain-Mooretown - 2018-2020 [ds2972]

The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (4 adult females) from the Bucks Mountain-Mooretown herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2020. GPS fixes were between 12-13 hours. The Bucks Mountain-Mooretown herd migrates from winter ranges in the American Valley, Crescent Hill, and various high elevation locations of the Sierra Nevada range east and south to even higher altitude terrain in Plumas National Forest. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 4 migrating deer, including 11 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 13.09 days and 27.77 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to the majority of BBMMs producing variance rates greater than 8000, a fixed motion variance of 1000 was set per migration sequence. Winter range analyses were based on data from 4 individual deer and 6 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. This collar project was not specifically designed to pinpoint precise migration routes or winter range designations, hence the low sample size. Additional migration routes and winter range areas likely exist beyond what was modeled in our output. Corridor tiers (low, medium, high) could not be computed with such a small dataset. Therefore, all corridors were given the same weight and designation in this analysis. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.