AndreaBozzo/ceres-open-data-index huggingface dataset:
Vegetation - Santa Clara and Santa Cruz Counties [ds3116]

Under contract to the Santa Cruz Mountains Stewardship Network with support from the Golden Gate National Parks Conservancy, and staffed by personnel from Tukman Geospatial, Aerial Information Systems (AIS), and Kass Green and Associates, Tukman Geospatial and Aerial Information Systems created a fine-scale vegetation map of portions of Santa Cruz and Santa Clara Counties. CDFW’s Vegetation Classification and Mapping Program (VegCAMP) provided in-kind service to allocate and score the AA. The mapping study area, consists of approximately 1,133,106.8 acres, of Santa Clara and Santa Cruz counties. Work was performed on the project between 2020 and 2023. The Santa Cruz and Santa Clara fine-scale vegetation map was designed for a broad audience for use at many floristic and spatial scales and is useful to managers interested in specific information about vegetation composition and forest health. CNPS under separate contract and in collaboration with CDFW VegCAMP developed the floristic vegetation classification used for the project. The floristic classification follows protocols compliant with the Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS). The vegetation map was produced with countywide vegetation survey data and combined with surveys from CNPS. Trimble® Ecognition® followed by manual image interpretation that was used to map lifeforms. Fine-scale segmentation was conducted using Trimble Ecognition® and relies on summer 2020 4-band NAIP, the 2020 lidar-derived canopy height model, and a suite of spectral indices derived from the NAIP. They utilized a type of algorithmic data modeling known as machine learning to automate the classification of fine-scale segments into one of Santa Cruz and Santa Clara Counties 121 fine-scale map classes. The minimum mapping unit (MMU) is set by feature type. For agricultural classes, the MMU is 1/4 acre, for woody upland classes is 1/2 acre, woody riparian is 1/4 acre, upland herbaceous is 1/2 acre, wetland herbaceous is 1/4 acre. Bare land is 1/2 acre, impervious features is 1000 square feet, while developed is 1/5 acre and water is 400 square feet. Field reconnaissance and accuracy assessment enhanced map quality. There was a total of 121 mapping classes. The overall Fuzzy Accuracy Assessment rating for the final vegetation map, map at the Alliance and Group levels, is 92 percent. 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/3100_3199/ds3116.zip].
Chronic Wasting Disease Lined Landfills for Deer Carcass Disposal - CDFW [ds3156]

This compilation of layers provides a comprehensive view of CWD-related activities, facilitating informed decision-making and strategic planning for wildlife management and disease prevention efforts. To date, there has not yet been any cases of CWD detected in California and surveillance data will be updated regularly. This data is associated with the following layers: * Chronic Wasting Disease Sampling Stations - 2025 - CDFW [ds3154] ([https://apps.wildlife.ca.gov/bios6/?al=3154]), * Participating Meat Processors and Taxidermists - 2025 - CDFW [ds3155] ([https://apps.wildlife.ca.gov/bios6/?al=3155]), * Chronic Wasting Disease Sampling Stations - 2023-2024 - CDFW [ds3182] ([https://apps.wildlife.ca.gov/bios6/?al=3182]), * Participating Meat Processors and Taxidermists - 2023-2024 - CDFW [ds3188] ([https://apps.wildlife.ca.gov/bios6/?al=3188]), * Chronic Wasting Disease Surveillance - CDFW [ds3157] ([https://apps.wildlife.ca.gov/bios6/?al=3157]). These layers include CWD Testing Sites, Participating Meat Processors and Taxidermists (MPT), Carcass Disposal Sites, and comprehensive data on CWD surveillance spanning from 1999 to 2025. This compilation of layers provides a comprehensive view of CWD-related activities, facilitating informed decision-making and strategic planning for wildlife management and disease prevention efforts. For the latest information, please visit [https://wildlife.ca.gov/CWD].
Vegetation - Upper Santa Ana River Wash - SBWCD [ds3212]

The San Bernardino Valley Water Conservation District (SBVWCD) contracted AECOM to produce an association-level vegetation classification and map of lands owned and/or managed by the District in the Upper Santa Ana River Wash. The classification is consistent with the Manual of California Vegetation (MCV) (CNPS 2018), and vegetation sampling methods followed National Park Service (NPS) Vegetation Index protocols. The mapping study area consists of approximately 2,200-acres near Mentone, in San Bernardino, California. Approximately 1,500-acres lie within the Preserve set aside for the Upper Santa Ana River Wash Habitat Conservation Plan. The primary purpose of this project was to provide the District a consistent system for the mapping of vegetation on its lands. Vegetation mapping is conducted periodically by the District to inform various environmental and species management initiatives. All plot data collection took place May 2-5, 2025. Four-band (RGB and color NIR) aerial imagery was collected May 4, 2022. All image acquisition, processing, and ortho-rectification were conducted by GeoTerra, Inc. Polygons were drawn using a minimum mapping unit (MMU) of 0.5 acre, and vegetation attributes assigned using the vegetation key provided in the newly created classification. Field reconnaissance was conducted by the photo-interpreter during the earliest stages of mapping to establish photo-signature recognition for each alliance and association. There were a total of 21 mapping classes. 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/3200_3299/ds3212.zip
Elk Migration Lines - West Goose Lake - 1999-2002, 2018-2020 [ds2900]

The project leads for the collection of this data were Erin Zulliger and Richard Shinn. The winter range of the West Goose Lake Rocky Mountain elk (Cervus canadensis nelsoni) sub-herd is located north of Alturas and west of Highway 395 within the Devil’s Garden Ranger District of the Modoc National Forest. This area is characterized by juniper (Juniperus occidentalis) woodlands, and sagebrush flats with some stands of lodgepole (Pinus contorta) and ponderosa pine (Pinus ponderosa) throughout flat, rocky terrain. From this area, a portion of the herd migrates approximately 50 miles north into Oregon’s Fremont National Forest, habitat that primarily consists of lodgepole and ponderosa pine forests. Minimal barriers exist along this migration route since the corridor primarily occurs on land managed by the US Forest Service. Additionally, although the core migration route does cross Highway 140, little to no impacts are known to exist from this crossing. Elk (12 adult females, 1 adult male, and 3 juvenile males) were captured from 2018 to February 2020 and equipped with Lotek and Vectronic satellite GPS collars. Additional GPS data was collected from elk (2 females and 1 male) in 1999-2002 and included in the analysis to supplement the small sample size of the 2018-2020 dataset. GPS locations were fixed at 4-hour intervals in the 2018-2020 dataset and 6 to 8-hour intervals in the 1999-2002 dataset. Migration lines as symbolized connect GPS data points per elk per seasonal migration. GPS points were extracted only during migrations using net-squared displacement graphs. Five migration sequences from 3 elk, with an average migration time of 6.8 days and an average migration distance of 16.14 km, were used from the 1999-2002 dataset. All three of these elk were used to supplement the eastern members of this herd, which travel shorter distances between summer and winter range than western individuals in the sample. Twenty migration sequences from 9 elk, with an average migration time of 11.2 days and an average migration distance of 57.75 km, were used from the 2018-2020 dataset.
Elk Migration Lines - East Shasta Valley - 1999-2001, 2016-2020 [ds2902]

The project leads for the collection of this data were Erin Nigon and Michael Hunnicutt. The East Shasta Valley sub-herd of Rocky Mountain elk (Cervus canadensis nelsoni) range as far east as the eastern foothills of Shasta Valley, as far north as the Klamath River, and as far south and east as Deer Mountain. In the winter, these elk spend most of their time on private ranchlands in Shasta Valley. This area offers patches of oak woodlands and grasslands on gentle slopes. In the spring, elk migrate to their summer range around Grass Lake, Bull Meadows, and Deer Mountain where the habitat is primarily characterized by mixed conifer timber stands of Ponderosa pine (Pinus ponderosa), lodgepole pine (Pinus contorta) and Douglas fir (Pseudotsuga menziesii). The meadows around Grass Lake seem especially important to this sub-herd, as neonatal calves have been documented in this area since at least 1984 (Fischer 1987). Some animals from this sub-herd have moved into Oregon periodically, while others have moved long distances eastward to other sub-herds, but Highway 97 presents a significant barrier to this movement. Most elk mortality due to vehicle collisions on Highway 97 has been documented to occur between Horsethief Creek, and Grass Lake Summit (California Department of Transportation, unpublished data). Because of this, a wildlife crossing is proposed for this area to promote habitat connectivity and wildlife movement. Elk (5 adult females, 5 juvenile [less than 1 year of age] males, and 8 juvenile females) were captured from 2016 to 2020 and equipped with Lotek satellite GPS collars. Additional GPS data was collected from elk (7 females and 1 male) in 1999-2001 and included in the analysis to supplement the small sample size of the 2016-2020 dataset. GPS locations were fixed at 4-hour intervals in the 2016-2020 dataset and 3 to 8-hour intervals in the 1999-2001 dataset. Migration lines as symbolized connect GPS data points per elk per seasonal migration. GPS points were extracted only during migrations using net-squared displacement graphs. Fifty-one migration sequences from 10 elk, with an average migration time of 4.87 days and an average migration distance of 22.14 km, were used from the 2016-2020 dataset. Fourteen migration sequences from 8 elk, with an average migration time of 7.43 days and an average migration distance of 30.08 km, were used from the 1999-2001 dataset. Average migration distance and time were higher in 1999-2001 dataset due to a single male elk outlier; removing this outlier led to a mean migration distance of 20.36 km and a mean migration time of 5.75 days for the 1999-2001 dataset.
Elk Migration Lines - Egg Lake - 2001-2002, 2017-2020 [ds2907]

The project leads for the collection of this data were Erin Zulliger and Richard Shinn. Elk (6 adult females, 3 juvenile males, and 2 juvenile females) were captured in 2017 and equipped with Lotek satellite GPS collars or VHF tags, transmitting data from 2017-2020. Additional GPS data was collected from elk (6 females) in 2001-2002 and included in the analysis to supplement the small sample size of the 2017-2020 dataset. The Egg Lake elk herd migrates from a winter range surrounding Egg Lake in Modoc County, California eastward into Siskiyou County for the summer. GPS locations were fixed at 4-hour intervals in the 2017-2020 dataset and 3 to 8-hour intervals in the 2001-2002 dataset. Migration lines as symbolized connect GPS data points per elk per seasonal migration. GPS points were extracted only during migrations using net-squared displacement graphs. Nine migration sequences from 5 elk, with an average migration time of 6.78 days and an average migration distance of 26.83 km, were used from the 2000-2001 dataset. Fourteen migration sequences from 6 elk, with an average migration time of 7.79 days and an average migration distance of 42.40 km, were used from the 2017-2020 dataset.
Pronghorn Home Range - Mount Dome - 2019-2020 [ds2935]

The project lead for the collection of this data was Richard Shinn. Pronghorn (9 adult females) were captured and equipped with GPS collars (Sirtrack, Havelock North, NZ) transmitting data from 2019-2020. The Mount Dome herd contains short distance, elevation-based migrants, but this herd does not migrate between traditional summer and winter seasonal ranges. Instead, much of the herd displays a somewhat nomadic migratory tendency, slowly moving up or down elevational gradients. Some individuals used higher elevation areas throughout the summer, though this pattern was not ubiquitous. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. The plateau extending south of Sardine Flat is highly used during winter by many of the collared animals, while the higher elevation flatlands between Klamath Lake Sump and Mount Dome are used in summer. Overall, a much smaller area is used than adjacent pronghorn herds in California, with much of the full home range extent being high use. GPS locations were fixed between 1-2 hour intervals in the dataset. 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 pronghorn 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 the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 9 pronghorn, including 12 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1000. Large water bodies were clipped from the final outputs. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Twelve years of collective movement data from 9 individuals were used to construct home ranges. Home range designations for this herd may expand with a larger sample.
Bald Eagle Observations Generalized [ds2937]

The Bald Eagle Observations Database is a product of the California Department of Fish and Wildlife's (CDFW's) Wildlife Branch and Biogeographic Data Branch. The records generally represent bald eagles (BAEA, Haliaeetus leucocephalus) nesting, perching, and flying. Additional information about the eagle, the database, and submitting data can be found on CDFW's Bald Eagles in California Webpage: [https://www.wildlife.ca.gov/Conservation/Birds/Bald-Eagle]. This dataset represents observed nest locations for Bald Eagles, generalized to a 2.5 mile hexagon grid. If you need more specific location information, please contact the listed point of contact in the metadata.
Vegetation - Canada de los Osos Ecological Reserve [ds2943]

The vegetation map was created in ARCGIS using an orthophoto base layer from the National Agricultural Imagery Program (NAIP) with the Canada de los Osos Ecological Reserve boundary as an overlay. The original database derived for the purpose of creating a vegetation map for CDLO contained an appreciable level of detail and consequent large number of classifications that were not suitable for the mapping process. Therefore, all subsets of a classification were grouped into a single classification while retaining the vegetation labels defined by the Wildlife and Habitat Branch. For example, while there may have initially been four or five different types of Umbellularia californica – Quercus agrifolia habitat based on percent cover, height and DBH classes etc., there is now only one type of Umbellularia californica – Quercus agrifolia habitat based on dominant species indicated on the map. Furthermore, all of the features used to delineate a given subset of the original classification scheme do not apply to the modified classifications used to create the map. As a result, the original database was retained and a copy of that was modified and saved. The modified database was used as an attributes table for the vegetation shape files in the map. Mapping consisted of creating vegetation polygons on the NAIP layer within ARCGIS. Vegetation at the Alliance level is generally more easily mapped by aerial photo than that described at the Association level. Therefore, it was necessary to ground-truth for purposes of quality control. Certain “mapping rules” were created to maintain consistency when creating polygons. * Trees separated from a larger, woodland stand by greater than two times their canopy width were not included within the larger stand. * Similarly, grassland polygons can contain trees that do not appear associated with an adjacent woodland polygon. * Trees within grassland forming an apparent savannah community were mapped as a community separate from the extenuating grassland. If members of that community appeared to merge with adjacent woodland, they were retained in the savannah polygon. * Areas of grassland or scrubland surrounded by woodland and less than five meters in width were incorporated into the woodland polygon. The map was prepared for publication in BIOS by the California Department of Fish and Wildlife Vegetation Classification and Mapping Program.
Vegetation - Carnegie SVRA [ds2946]

Vegetation types in the study area were initially identified using information obtained from previous vegetation mapping efforts in the Carnegie SVRA (HDR 2004, Jones and Stokes 2000, DPR 1980) and the Existing Vegetation(Eveg) maps (USFS 2007) for the study area. Eveg maps were created by the United State Forest Service (USFS) using automated, systematic procedures, remote sensing classification, photo editing and field based observations to efficiently and cost-effectively map large areas with minimal bias. The classification is a provisional system that meets the floristically based level of the National Vegetation Classification Standard hierarchy. Eveg vegetation cover maps are produced at relatively low spatial detail (1:24,000 map scale) to provide a broad overview of existing vegetation in California. The vegetation types were then refined through aerial photograph interpretation using aerial imagery provided to AECOM by DPR (2010) and National Agricultural Imagery Program (NAIP 2009) aerial imagery to identify areas where vegetation cover may have changed since the previous mapping efforts were completed, or where vegetation boundaries may not have been drawn accurately due to the methodology, classification system, or mapping scale used. AECOM botanists mapped vegetation type polygons in a GIS overlay from aerial photograph interpretation during September and October 2011. Vegetation types were mapped at a fine-scale (1:6,000 map scale) in accordance with the Memorandum of Understanding for Cooperative Vegetation and Habitat Mapping and Classification mapping standards (California Biodiversity Council 2002). The minimum mapping unit is 1 acre for all common vegetation types. All discernible sensitive habitats, including wetland and riparian vegetation types and linear aquatic features (e.g., streams, rivers, ditches) were mapped regardless of size. For woodland vegetation types, trees on the edge of the polygon were included when they were within approximately two average tree canopy widths of the polygon (approximately 30 meters). AECOM then conducted a field verification survey of the study area on December 7 and 8, 2011, to verify, refine, and correct the vegetation types mapped in the office. The vegetation cover and linear aquatic feature GIS data layers created during the prefield mapping exercises were printed on 1 inch = 500 feet scale maps and also loaded onto a Trimble Global Positioning System (GPS) device with the 2010 DPR aerial imagery and compared for consistency with conditions observed on the ground. Areas that deviated from vegetation types mapped from aerial photography were corrected and refined in the field on the hard copy maps and digitally using the GPS device. The boundaries of wetlands and drainages were further refined using data from the wetland delineation conducted in the study area in 2010 (DPR 2010). Representative photographs were taken of each vegetation type.
Vegetation - Lassen Volcanic National Park [ds2959]

The Director of the NPS approved the Natural Resource Challenge in 1999 to encourage national parks to focus on the preservation of the nation’s natural heritage through science, natural resource inventories, and expanded resource monitoring. The Natural Resource Challenge provided funding for 12 baseline inventories to be completed in each of 270 parks with significant natural resources including vegetation mapping inventory. The Vegetation Inventory is an effort by the NPS to classify, describe, and map vegetation communities that follows well-established procedures that are compatible with other agencies and organizations. The inventory uses the USNVC, a system that is integrated with the major scientific efforts in the taxonomic classification of vegetation, and is a FGDC standard. Maps are produced in Universal Transverse Mercator (UTM) coordinates (NAD 83) with a 1:24,000 scale and a minimum mapping unit of 0.5 hectares. The vegetation maps must meet the National Map Accuracy Standards for positional accuracy, and the minimum class accuracy goal across all vegetation and land cover classes of 80%. Report available from: [https://irma.nps.gov/DataStore/Reference/Profile/2244126]. The photographic interpretation was based on 1:15,840-scale true-color aerial photography (prints and transparencies) acquired in July and August 2004. The interpreted overlays were orthorectified along with the scanned aerial photography and vectorized using Arc/Info™ (ESRI, Inc.) software. The photointerpreted mapped 26 vegetation classes and seven non-vegetated classes with an overall proportion correct of 72% (based on the most liberal of three accuracy thresholds).