The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2018, 2020 and 2022. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov). The NAIP imagery from 2018, 2020 and 2022 was classified using Random Forest Modeling to produce land cover maps with three main classifications – bare, vegetation, and shadows. A total of 600 independent reference points were used in the accuracy assessment. The overall accuracy for all classes for each dataset is 98%. See attached ScottsCreek_20XX_AccuracyAssessment.csv files (contained within each LandCoverMap_associated_files_20XX.zip for each year respectively) for details. A preview image of the land cover map for 2018 is attached to this data release as an example (see LandCoverMap_RF_ScottsCreekWatershed_USGS2022_CC0.png). The percentage of bare, vegetation and shadow pixels were calculated for the complete watershed and each individual NHDPlus2.1 catchment basins (slightly modified to support hydrological modeling). These metrics can be used to quantify bare and vegetated areas and detect and quantify vegetation changes over time. Users should be aware of the inherent errors in remote sensing products.

This document provides a map of California and at each county a pie-chart depicts the proportion of utility-scale electrical production capacity coming from small or large hydroelectric facilities. The second page provides the same county-level data except each county's total hydroelectric capacity (megawatts) is also given. The total statewide capacity generated from these sources is depicted as well as the total contribution from each source. All data is from 2018.

This web map displays the California Department of Education's (CDE) core set of geographic data layers. This content represents the authoritative source for all statewide public school site locations and school district service areas boundaries for the 2018-19 academic year. The map also includes school and district layers enriched with student demographic and performance information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.

This data is derived by a 3_class instance segmentation model. Since the model uses zero-mean input of images, the predicted segments have been filtered by a mean range of [50, 105] for 2018 image. Model has been trained with samples of Aliso Creek from 2011, 2020 and 2021 images.All the Arundo predictions have confidence scores greater than 0.6 and are kept to the output.All the Palm predictions have confidence scores greater than 0.6 and are kept to the output.The flowering plant predictions that have confidence scores less than 0.85 are removed from the output.Updated:Version2 used improved algorithm and deleted 6 strong ripple objects near the outlet of the creek.

Reporting requirements for power plants at least 1 MW are in accordance with 20 CA CCR 304 and 1385. Counties without pie symbols had no utility-scale (commercial) electric generation installed. Distributed renewable generation (e.g. rooftop solar) is not included. Map and data from the California Energy Commission. Energy production data is from the Quarter Fuel and Energy Report (QFER) and the Wind Performance Report System (WPRS) databases. Data is from 2018, and is current as of June 2019. Contact Dylan Kojimoto at (916) 651-0477 or John Hingtgen at (916) 657-4046 for questions.

This document provides a map of California and at each county a pie-chart depicts the proportion of electrical generation from small or large hydroelectric facilities. The second page provides the same county-level data except each county's total hydroelectric generation (gigwatt hours) is also given. The total statewide utility-scale electrical generation from these sources is depicted as well as the total contribution from each source. All data is from 2018.

This map of California depicts the amount of solar energy produced in each county (gigawatt hours) as well as the capacity (MW) of each county's utility-scale resources. This data is for 2018 and statewide totals are indicated.

The map depicts the amount of renewable energy production (gigawatt hours) for each county of California. The state-wide total is also given. The table depicts the amount of renewable energy production for each energy type for every county and a total summation is given. All data is for 2018 and for utility-scale purposes.

This part of DS 781 presents fault data for the geologic and geomorphic map of the Offshore of Gaviota map area, California. The vector data file is included in "Faults_OffshoreGaviota.zip," which is accessible from https://doi.org/10.5066/F7TH8JWJ. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Hartwell, S.R., Golden, N.E., Kvitek, R.G., and Davenport, C.W. (S.Y. Johnson and S.A. Cochran, eds.), 2018, California State Waters Map Series—Offshore of Gaviota, California: U.S. Geological Survey Open-File Report 2018–1023, pamphlet 41 p., 9 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20181023. The southwest-striking south strand of the Santa Ynez fault obliquely cuts the shelf in the western part of the map area. As mapped onshore by Dibblee (1950, 1988a) this fault is unique among Santa Barbara fold belt structures in that it obliquely crosses the Santa Ynez Mountains and the dominant east-west structural grain. The fault was difficult to map in the offshore, even with our dense seismic-reflection data coverage, because the pre-LGM section on the shelf includes massive "reflection free" zones, probably associated with gas or steep dips, and the adjacent slope is mainly underlain by massive to chaotic seismic facies of the Conception Fan. References Cited: Dibblee, T.W., 1988a, Geologic map of the Solvang and Gaviota quadrangles, Santa Barbara County, California, edited by H.E. Ehrenspeck (1988): Dibblee Geological Foundation, Map DF-16, scale 1:24,000. Dibblee, T.W., Jr., 1950, Geology of southwestern Santa Barbara County, California, Point Arguello, Lompoc, Point Conception, Los Olivos, and Gaviota quadrangles: California Division of Mines and Geology Bulletin 150, 95 p., scale 1:62,500.

Description for i03_DAU_county_cnty2018 is as follows:Detailed Analysis Unit-(DAU) Convergence via County Boundary cnty18_1 for Cal-Fire, (See metadata for CAL-FIRE cnty18_1), State of California.The existing DAU boundaries were aligned with cnty18_1 feature class.Originally a collaboration by Department of Water Resources, Region Office personnel, Michael L. Serna, NRO, Jason Harbaugh - NCRO, Cynthia Moffett - SCRO and Robert Fastenau - SRO with the final merge of all data into a cohesive feature class to create i03_DAU_COUNTY_cnty24k09 alignment which has been updated to create i03_DAU_COUNTY_cnty18_1.This version was derived from a preexisting “dau_v2_105, 27, i03_DAU_COUNTY_cnty24k09” Detailed Analysis Unit feature class's and aligned with Cal-Fire's 2018 boundary.Manmade structures such as piers and breakers, small islands and coastal rocks have been removed from this version. Inlets waters are listed on the coast only.These features are reachable by County\DAU. This allows the county boundaries, the DAU boundaries and the State of California Boundary to match Cal-Fire cnty18_1.DAU BackgroundThe first investigation of California's water resources began in 1873 when President Ulysses S. Grant commissioned an investigation by Colonel B. S. Alexander of the U.S. Army Corps of Engineers. The state followed with its own study in 1878 when the State Engineer's office was created and filled by William Hammond Hall. The concept of a statewide water development project was first raised in 1919 by Lt. Robert B. Marshall of the U.S. Geological Survey.In 1931, State Engineer Edward Hyatt introduced a report identifying the facilities required and the economic means to accomplish a north-to-south water transfer. Called the "State Water Plan", the report took nine years to prepare. To implement the plan, the Legislature passed the Central Valley Act of 1933, which authorized the project. Due to lack of funds, the federal government took over the CVP as a public works project to provide jobs and its construction began in 1935.In 1945, the California Legislature authorized an investigation of statewide water resources and in 1947, the California Legislature requested that an investigation be conducted of the water resources as well as present and future water needs for all hydrologic regions in the State. Accordingly, DWR and its predecessor agencies began to collect the urban and agricultural land use and water use data that serve as the basis for the computations of current and projected water uses.The work, conducted by the Division of Water Resources (DWR’s predecessor) under the Department of Public Works, led to the publication of three important bulletins: Bulletin 1 (1951), "Water Resources of California," a collection of data on precipitation, unimpaired stream flows, flood flows and frequency, and water quality statewide; Bulletin 2 (1955), "Water Utilization and Requirements of California," estimates of water uses and forecasts of "ultimate" water needs; and Bulletin 3 (1957), "The California Water Plan," plans for full practical development of California’s water resources, both by local projects and a major State project to meet the State's ultimate needs. (See brief addendum below* “The Development of Boundaries for Hydrologic Studies for the Sacramento Valley Region”)DWR subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR), corresponding to the State’s major drainage basins. The next levels of delineation are the Planning Areas (PA), which in turn are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so are the smallest study areas used by DWR.The DAU/counties are used for estimating water demand by agricultural crops and other surfaces for water resources planning. Under current guidelines, each DAU/County has multiple crop and land-use categories. Many planning studies begin at the DAU or PA level, and the results are aggregated into hydrologic regions for presentation.Since 1950 DWR has conducted over 250 land use surveys of all or parts of California's 58 counties. Early land use surveys were recorded on paper maps of USGS 7.5' quadrangles. In 1986, DWR began to develop georeferenced digital maps of land use survey data, which are available for download. Long term goals for this program is to survey land use more frequently and efficiently using satellite imagery, high elevation digital imagery, local sources of data, and remote sensing in conjunction with field surveys.There are currently 58 counties and 278 DAUs in California.Due to some DAUs being split by county lines, the total number of DAU’s identifiable via DAU by County is 782.**ADDENDUM**The Development of Boundaries for Hydrologic Studies for the Sacramento Valley Region[Detailed Analysis Units made up of a grouping of the Depletion Study Drainage Areas (DSA) boundaries occurred on the Eastern Foothills and Mountains within the Sacramento Region. Other DSA’s were divided into two or more DAU’s; for example, DSA 58 (Redding Basin) was divided into 3 DAU’s; 143,141, and 145. Mountain areas on both the east and west side of the Sacramento River below Shasta Dam went from ridge top to ridge top, or topographic highs. If available, boundaries were set adjacent to stream gages located at the low point of rivers and major creek drainages.Later, as the DAU’s were developed, some of the smaller watershed DSA boundaries in the foothill and mountain areas were grouped. The Pit River DSA was split so water use in the larger valleys (Alturas area, Big Valley, Fall River Valley, Hat Creek) could be analyzed. A change in the boundary of the Sacramento Region mountain area occurred at this time when Goose Lake near the Oregon State Line was included as part of the Sacramento Region.The Sacramento Valley Floor hydrologic boundary was at the edge of the alluvial soils and slightly modified to follow the water bearing sediments to a depth of 200 feet or more. Stream gages were located on incoming streams and used as an exception to the alluvial soil boundary. Another exception to the alluvial boundary was the inclusion of the foothills between Red Bluff and the Redding Basin. Modifications of the valley floor exterior boundary were made to facilitate analysis; some areas at the northern end of the valley followed section lines or other established boundaries.Valley floor boundaries, as originally shown in Bulletin 2, Water Utilization and Requirements of California, 1955 were based on physical topographic features such as ridges even if they only rise a few feet between basins and/or drainage areas. A few boundaries were based on drainage canals. The Joint DWR-USBR Depletion Study Drainage Areas (DSA) used drainage areas where topographic highs drained into one drainage basin. Some areas were difficult to study, particularly in areas transected by major rivers. Depletion Study Drainage Areas containing large rivers were separated into two DAU’s; one on each side of the river. This made it easier to analyze water source, water supply, and water use and drainage outflow from the DAU.Many of the DAUs that consist of natural drainage basins have stream gages located at outfall gates, which provided an accurate estimate of water leaving the unit. Detailed Analysis Units based on political boundaries or other criteria are much more difficult to analyze than those units that follow natural drainage basins.]**END ADDENDUM**.............................................................................................................................................cnty18_1 metadata Summary:(*See metadata for CAL-FIRE cnty18_1). CAL-FIRE cnty18_1 boundary feature class is used for cartographic purposes, for generating statistical data, and for clipping data. Ideally, state and federal agencies should be using the same framework data for common themes such as county boundaries. This layer provides an initial offering as "best available" at 1:24,000 scale.cnty18_1 metadata Description:(*See metadata for CAL-FIRE cnty18_1).cnty18_1 metadata Credits:CAL-FIRE cnty18_1 metadata comment:This specific dataset represents the full detailed county dataset with all coding (islands, inlets, constructed features, etc.). The user has the freedom to use this coding to create definition queries, symbolize, or dissolve to create a more generalized dataset as needed.In November 2015, the dataset was adjusted to include a change in the Yuba-Placer county boundary from 2010 that was not yet included in the 14_1 version of the dataset (ord. No. 5546-B). This change constitutes the difference between the 15_1 and 14_1 versions of this dataset.In March 2018, the dataset was adjusted to include a legal boundary change between Santa Clara and Santa Cruz Counties (December 11, 1998) as stated in Resolution No. 98-11 (Santa Clara) and Resolution No. 432-98 (Santa Cruz). This change constitutes the difference between the 18_1 and 15_1 versions of this dataset.(*See metadata for CAL-FIRE cnty18_1). - U.S. Bureau of Reclamation, California Department of Conservation, California Department of Fish and Game, California Department of Forestry and Fire protection

Settlement area boundaries within Grey County. This layer includes Hamlet (Tertiary), Primary Urban and Secondary Urban Communities. Areas extracted from 2018 Official Plan. A Servicing attribute denotes whether the settlement area has water or wastewater services, or no services. Please contact Grey County Planning for more information.

California Department of Fish and Game (DFG) ecologists conducted field reconnaissance for this project and Santa Monica Bay Restoration Commission assisted with field data collection. Under contract to the DFG, GreenInfo Network digitized a fine-scale vegetation map of the Ballona Wetlands Ecological Reserve (BWER). The mapping study area consists of approximately 600 acres within Ballona Wetlands Ecological Reserve of Los Angeles County, California. CNPS under separate contract and in collaboration with California Department of Fish and Wildlife 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).This map study was initiated to assist in restoration planning for the Ballona Wetland Enhancement Project, which aims to restore and enhance native habitats on BWER and provide public access and recreational opportunities. The primary purpose of CDFW’s goal of developing fine-scale digital vegetation maps is part of the California Biodiversity Initiative Roadmap of 2018. The mapping study area consists of approximately 600 acres within Ballona Wetlands Ecological Reserve of Los Angeles County, California. Reconnaissance was conducted by CDFW ecologists on May 9-11, 2006 to collect a preliminary list of vegetation types to accurately represent the study area. On June 13, 2006, I.K. Curtis Aerial Photography took a true color orthophoto at 1-foot pixel resolution (±1:16,800) under contract to the Coastal Conservancy and Brad Henderson (CDFW) combined the preliminary vegetation list to manually draw polygons overlaid the air photo. GreenInfo used the drawing and air photo for a digitized map draft of polygons to be verified and assessed by field crews for Vegetation Type, Percent Cover, Exotics, Development Disturbance, and other attributes on June 19-22, 2007. Field crews noted 8 exotic, invasive species in the map polygons worthy of special interest in regards to restoration planning decisions. Field reconnaissance enhanced map quality. There was a total of 61 mapping classes. No accuracy assessment of this map has been performed because ecologists visited every polygon in the field. For detailed information, please refer to the following report:Vegetation Classification and Mapping Program, California Department of Fish and Game. Vegetation Map of Ballona Wetlands Ecological Reserve, Los Angeles County, California, 2007. California Department of Fish and Game; 2007. Available from: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=16316

The Tamalpais Lands Collaborative (One Tam; https://www.onetam.org/), the network of organizations that manage lands on Mount Tamalpais in Marin County, initiated the countywide mapping project with their interest in creating a seamless, comprehensive map depicting vegetation communities across the landscape. With support from their non-profit partner the Golden Gate National Parks Conservancy (https://www.parksconservancy.org/) One Tam was able to build a consortium to fund and implement the countywide fine scale vegetation map.Development of the Marin fine-scale vegetation map was managed by the Golden Gate National Parks Conservancy and staffed by personnel from Tukman Geospatial (https://tukmangeospatial.com/) Aerial Information Systems (AIS; http://www.aisgis.com/), and Kass Green and Associates. The fine-scale vegetation map effort included field surveys by a team of trained botanists. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the California Native Plant Society (CNPS) Vegetation Program (https://www.cnps.org/vegetation) with support from the California Department of Fish and Wildlife Vegetation Classification and Mapping Program (VegCAMP; https://wildlife.ca.gov/Data/VegCAMP) to develop a Marin County-specific vegetation classification.High density lidar data was obtained countywide in the early winter of 2019 to support the project. The lidar point cloud, and many of its derivatives, were used extensively during the process of developing the fine-scale vegetation and habitat map. The lidar data was used in conjunction with optical data. Optical data used throughout the project included 6-inch resolution airborne 4-band imagery collected in the summer of 2018, as well as 6-inch imagery from 2014 and various dates of National Agriculture Imagery Program (NAIP) imagery.In 2019, a 26-class lifeform map was produced which serves as the foundation for the much more floristically detailed fine-scale vegetation and habitat map. The lifeform map was developed using expert systems rulesets in Trimble Ecognition®, followed by manual editing.In 2019, Tukman Geospatial staff and partners conducted countywide reconnaissance fieldwork to support fine-scale mapping. Field-collected data were used to train automated machine learning algorithms, which produced a fully automated countywide fine-scale vegetation and habitat map. Throughout 2020, AIS manually edited the fine-scale maps, and Tukman Geospatial and AIS went to the field for validation trips to inform and improve the manual editing process. In the spring of 2021, draft maps were distributed and reviewed by Marin County's community of land managers and by the funders of the project. Input from these groups was used to further refine the map. The countywide fine-scale vegetation map and related data products were made public in June 2021. In total, 107 vegetation classes were mapped with a minimum mapping size of one fifth to one acre, varying by class.Accuracy assessment plot data were collected in 2019, 2020, and 2021. Accuracy assessment results were compiled and analyzed in the summer of 2021. Overall accuracy of the lifeformmap is 95%. Overall accuracy of the fine-scale vegetation map is 77%, with an overall 'fuzzy' accuracy of 81%.The Marin County fine-scale vegetation map was designed for a broad audience for use at many floristic and spatial scales. At its most floristically resolute scale, the fine-scale vegetation map depicts the landscape at the National Vegetation Classification alliance level - which characterizes stands of vegetation generally by the dominant species present. This product is useful to managers interested in specific information about vegetation composition. For those interested in general land use and land cover, the lifeform map may be more appropriate. Tomake the information contained in the map accessible to the most users, the vegetation map is published as a suite of GIS deliverables available in a number of formats. Map products are being made available wherever possible by the project stakeholders, including the regional data portal Pacific Veg Map (http://pacificvegmap.org/data-downloads).

When the districts running on a particular election ballot are identical for 2-6 adjacent regular precincts, California Election Code 12241 allows for those precincts to be consolidated. In Sacramento County it is policy that the consolidated precinct will bear the lowest precinct number of the original regular precincts. Through the 2016 elections, consolidated precincts with 250 or more registered voters were assigned a polling place and designated "Polling Place" precincts. Consolidated precincts with less than 250 registered voters were designated "Mail Ballot" precincts. For every Polling Place Precinct there also existed a coextensive "Vote by Mail" precinct for the registered voters of that precinct who voted by mail. Since the 2018 elections, there is no longer a distinction between "Polling Place" precincts and "Mail Ballot" precincts. All Consolidated Precincts also have a corresponding and coextensive "Vote by Mail" precinct. Because the combination of contests on ballot is unique to a particular election, the set of consolidated precincts is unique to that particular election.Sacramento County Voter Registration and Elections

This map contains a brief analysis of USA County Percent Mammogram Rates and Los Angeles County Percent Mammogram Rates. The data includes County Health Rankings and Department of Public Health Service Planning Areas, Locations for Health Screening, Locations for Public Health Programs, and Insurance Coverage by Gender using Census Tracts. The following attributes are included: 2016 Population, Household Income, Percent Unemployed, Percent Adults Uninsured, Percent Mammography, and Percent Female. Contains a bar chart, percent mammography black, percent mammography white. Bookmarks include Alaska, California, Southern California Counties, Imperial County, Los Angeles County, Orange County, San Bernardino County, Riverside County and Ventura County, Hawaii and USA. Data obtained through 2018 County Health Rankings. County Health Rankings 2018

This data shows traffic counts approaching and occurring at signalized intersections within the City of Salinas, Monterey County, California primarily prior to 2018. It can also be found on the Map Gallery within the City of Salinas website.

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extent This subdataset covers the West Coast CONUS, i.e. CA OR WA For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Map of 2018 Utility Scale Renewable Electrical Generation Types by County and 2018 Behind the Meter Solar Photovoltaic in MW.

The Camp Fire rapidly spread across a landscape in Butte County, California, toward the city of Paradise in the early morning hours of 8 November 2018. GEE acquires L8 data products from USGS that include Real-Time Tier 1 DN values, representing scaled, calibrated at-sensor radiance, and Level-1 Precision Terrain (L1TP) processing. We carried out additional processing on the pre- and post-fire images, including an illumination correction to account for effects of steep topography, and radiometric normalization to ensure homogeneity between images. We also identified and processed high-quality images for both the pre-fire condition(25 Oct 2013), and the post-fire burn scar (26 Dec 2018). Using the tools we provided in the paper, we estimate that, over the first hour, the Camp Fire was consuming ~200 ha/minute of vegetation with a linear spread rate of 14 km over the fire’s first 25 minutes, or ~33km/hr. We briefly discuss broader use of remote sensing and geospatial analysis for fire research and risk management. A visualization app (sliderApp) that includes pre-fire, active fire, and post-fire images: https://caralyngorman.users.earthengine.app/view/camp-fire-sliding-map-3-9-19