CDFW BIOS GIS Dataset, Contact: John Radke, Description: This modeled data represents inundation location and depth (meters) for the California Coast resulting from 1.41 m sea level rise coupled with extreme storm events. This research is unique and innovative in its dynamic spatial detail and the fact that it incorporates real, time series water level data from past (near 100 year) storm events to capture the dynamic effect of storm surges in modeling inundation using 3Di.

The Neighborhood Segregation typologies identify which groups have more than 10% representation within the given tract. This function is extracted from this paper — Hall, Matthew, Kyle Crowder, and Amy Spring. 2015. “Neighborhood Foreclosures, Racial/Ethnic Transitions, and Residential Segregation.” American Sociological Review 80:526–549. ACS, 2019.Maps of other typologies regarding segregation and displacement and data downloads can be found here:https://www.urbandisplacement.org/san-francisco/sf-bay-area-gentrification-and-displacementOriginal Data received by HCD from UC Berkeley Urban Displacement Project on 03/15/2021

The Urban Displacement Project (UDP) is a research and action initiative of UC Berkeley. UDP conducts community-centered, data-driven, applied research toward more equitable and inclusive futures for cities. Our research aims to understand and describe the nature of gentrification, displacement, and exclusion, and also to generate knowledge on how policy interventions and investment can respond and support more equitable development.The goal of UDP is to produce rigorous research and create tools to empower advocates and policymakers, to reframe conversations, and to train and inspire the next generation of leaders in equitable development.The Urban Displacement Project's Housing Precarity Risk Model measures the risk of displacement and eviction impacted by 2020 unemployment across 53 metropolitan areas. The goal of this study is to identify where community vulnerabilities exist so that local, state, and federal agencies can direct resources appropriately. Explore the interactive map, learn more about this study, and read our anti-displacement policy recommendations for post-pandemic recovery.Source: https://www.urbandisplacement.org/

Solar Footprints in CaliforniaThis GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy https://www.blm.gov/style/medialib/blm/wo/MINERALS_REALTY_AND_RESOURCE_PROTECTION_/energy/solar_and_wind.Par.70101.File.dat/Public%20Webinar%20Dec%203%202014%20-%20Solar%20and%20Wind%20Regulations.pdfBLM CA Renewable Energy Projects | BLM GBP Hub (arcgis.com)Metadata: (5) Quarterly Fuel and Energy Report (QFER) California Power Plants - Overview (arcgis.com)

The Napa County Watersheds were generated from two elevation datasets. The Napa River Watershed was generated from LIDAR data processed by NCALM at UC Berkeley (http://calm.geo.berkeley.edu/ncalm/index.html). The eastern side of the county was delineated from DTM data which was generated from aerial photography (2002). The watersheds are intended to be used for hydrologic modeling and planning.

Data last synced 08-04-2025 06:05. Data synced on a Monthly interval.

Collection of background and resource data for ePublication on the Sasanian Seal Collection at UC Berkeley

This dataset contains a 30-year rolling average of annual average precipitation from the four models and two greenhouse gas (RCP) scenarios included in the four model ensemble for the years 1950-2099. The year identified is the mid-point of the 30-year average. eg. The year 2050 includes the values from 2036 to 2065. The downscaling and selection of models for inclusion in ten and four model ensembles is described in Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis (Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are: HadGEM2-ES (warm/dry), CanESM2 (average), CNRM-CM5 (cooler/wetter), and MIROC5 the model least like the others to improve coverage of the range of outcomes. These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff. Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/ Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

This dataset contains 30-year rolling average of annual average precipitation across all four models and two greenhouse gas (RCP) scenarios in the four model ensemble. The year identified for a 30 year rolling average is the mid-point of the 30-year average. eg. The year 2050 includes the values from 2036 to 2065. The downscaling and selection of models for inclusion in ten and four model ensembles is described in Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis (Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are: HadGEM2-ES (warm/dry),CanESM2 (average), CNRM-CM5 (cooler/wetter),and MIROC5 the model least like the others to improve coverage of the range of outcomes. These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff. Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/ Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

The First International Workshop on Historical GIS was held on Aug 23rd - 24th, 2001 at Fudan University, Shanghai, China. The Workshop was hosted by the Center for Historical Geographical Studies at Fudan, and organized by: Jianxiong GE (Fudan University), Peter Bol (Harvard University), Ruth Mostern (U.C. Berkeley) , and Lex Berman (Harvard University). RELATED WEBSITE: https://sites.fas.harvard.edu/~chgis/pages/agendas/shanghai_2001.html

Data Access: The imagery is available as a dynamic image service (thanks to the UC Berkeley GIF for hosting) as well as for download as a lossy compressed Mr. SID mosaic (for use as visual reference only). Table 1 below shows download information. This imagery is best displayed as a color composite (also known as color infrared) for assessing fire effects.

Download information for the 2022 postfire imagery is shown in Table 1.

Table 1. Download information

Dataset

Description

Download Location

Mr. SID Lossy Compressed Mosaic

Image mosaic in Mr. SID format for use in desktop GIS software (for use as visual reference only)

https://vegmap.press/czu_postfire_imagery_sid

Dynamic, 4-band, Image Service

Image service hosted on ArcGIS Server at UC Berkeley GIF

https://zenith.cnr.berkeley.edu/arcgis/rest/services/CZU_IMAGERY/ImageServer

Dataset Summary:

This 4-band (Red, Green, Blue, and Near Infrared) imagery was collected in early summer, 2022 for the area that burned in the 2020 CZU Lighning Complex Fire. The data is 6-inch spatial resolution imagery and is in the California State Plane Zone 3 coordinate system.

These data were collected under a CAL FIRE grant to study the effects of the 2020 CZU Lightning Complex Fires. The data, along with accompanying post-fire QL1 lidar data, were collected to assess and map forest canopy damage and to assess the landscape variables most highly correlated to forest canopy damage.

The technical report that describes the post fire imagery, its collection, and its horizontal accuracy, is available here: https://vegmap.press/czu_postfire_imagery_report

This dataset contains annual average precipitation from the four models and two greenhouse gas (RCP) scenarios included in the four model ensemble for the years 1950-2099. The downscaling and selection of models for inclusion in ten and four model ensembles is described in Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis (Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are: HadGEM2-ES (warm/dry),CanESM2 (average), CNRM-CM5 (cooler/wetter),and MIROC5 the model least like the others to improve coverage of the range of outcomes. These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff. Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/ Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

The Eel River CZO operates on several spatial scales from a zero order hillslope to the entire Eel River on the north coast of California. Rivendell, Angelo, Sagehorn, South Fork, and Eel River GIS boundaries. GIS polygon shapefiles. All files are in geographic projection (Lat/Long) with a datum of WGS84.

The watershed boundaries are from USGS Watershed Boundary Dataset (WBD) http://nhd.usgs.gov/wbd.html. Rivendell and Angelo boundaries are created from LiDAR by the CZO. Sagehorn Ranch is a privately held, active commercial ranch with no public access. Please contact the CZO if you are interested in data from Sagehorn Ranch.

Shapefiles

Eel River Watershed (drainage area 9534 km^2): Entire eel river. Greatest extent of CZO research.

South Fork Eel Watershed (drainage area 1784 km^2).

Angelo Reserve Boundary (30.0 km^2): Angelo Coast Range Reserve is a University of California Natural Reserve System protected land. It is the central focus of CZO research. http://angelo.berkeley.edu

Sagehorn Ranch Boundary (21.1 km^2): Sagehorn Ranch is a private ranch with active cattle raising. The owners have allowed the CZO to place instrumentation on their lands. Access is only by explicit agreement by owners.

Rivendell Cachement (0.0076 km^2): Rivendell is a small, heavily instrumented hillslope within the Angelo Reserve. It has roughly 700 instruments deployed as of 2016. Data is online at http://sensor.berkeley.edu

Indicator Description

Wildfire, Baseline Annual hectares burned, 30-year average for 1976-2005

Wildfire, RCP 4.5 Mid-Century Annual hectares burned, 30-year average for 2036-2065

Wildfire, RCP 8.5 Mid-Century Annual hectares burned, 30-year average for 2036-2065

Wildfire, RCP 4.5 Late-Century Annual hectares burned, 30-year average for 2066-2095

Wildfire, RCP 8.5 Late-Century Annual hectares burned, 30-year average for 2066-2095 Source: Cal-AdaptData: Wildfire Simulations for California’s Fourth Climate Change Assessment, University of California, Merced + Wildfire Simulations Derived Products, Geospatial Innovation Facility - University of California, Berkeley.

This dataset contains a 30-year average of annual average minimum and maximum temperatures across all ten models and two greenhouse gas (RCP) scenarios in the ten model ensemble. Three named time periods are included “Historic Baseline (1961-1990)”, “Mid-Century (2035-2064)”, and “End of Century (2070-2099).” The downscaling and selection of models for inclusion in ten and four model ensembles is described in Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis (Table 1, Pierce et al. 2018) and to form a ten model ensemble. These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff. Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/ Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

This layer was created for the redistricting project map. BUSD provided a powerpoint file that showed the boundaries since they could not locate the original shapefile that was used. The core information used the generate the boundaries are the image in the powerpoint file and the 2020 census block boundaries. The source of image used is described below by the original contractor Bruce Wicinas. I was drafted to help BUSD around 1991. At that time they used planning software authored by a San Jose company, "Educational Data Systems." This was long before ESRI was known to the likes of school districts or acknowledged by the Census Bureau. "Educational Data Systems," which had many school district clients around the U.S., performed their own particle-ization of school district geography. They divided districts into rectangles of approximately 4 - 8 city blocks. These they called "planning areas." They were convenient. BUSD they divided into 445, a number neither too fine nor too coarse.Many years later, .shp files became widely available. Alas, not all Planning Area perimeters coincide with line segments of .shp files. In the Berkeley flatlands the discrepancies are not so bad. But in the hills, there aren't "blocks" but meandering strips. "Planning Areas" have line segments which don't correspond to streets or perimeters of .shp files.About 15 years ago I enhanced my custom software to read shp files. Thus I could superimpose Planning Areas and .shp files, observing the overlap discrepancies. I'll omit for now the rest of this story; what I did about the discrepancy between census Block Groups and Planning Areas. I could go into that if you are interested.I got "Planning Areas" into my custom software from the ancient EdSys data, somehow ,decades ago. I may have read a file containing polygon coordinates. At that time I could export the planning area polygons via DXF. But they have no relationship to .shp. I could provide a representation of GIS planning areas in coordinates such as "State Plane" but this probably does you no good. I have never written an ".shp" file exporter. The .shp file format is mind-boggling; archaic compared to modern methods.About 25 years ago I wrote an on-line means by which staff at BUSD can type in a Berkeley address and get the corresponding socio-ec category number. It does this by determining the "planning area number" - 1 through 445 - containing the address. That on-line software could provide the attendance zone as well but no one ever asked for that. The student assignment software used by the high school and by admissions performs that function internally. Every student has an attendance zone number as soon as they get added to the database.

NOTE: The authors believe that this dataset is likely to be LESS accurate than using a clip of the larger San Francisco Bay groundwater dataset, here: https://dash.berkeley.edu/stash/dataset/doi:10.6078/D1W01Q. The reason is that additional datapoints and editing were used to improve the San Francisco Bay groundwater map after this Alameda County map was produced. We expect that the most recent San Francisco Bay map is the most accurate, even for smaller geographic scales.

This dataset contains a comparison of four interpolation methods used to estimate a minimum depth to groundwater surface for Alameda County, within one kilometer/0.6 mi of San Francisco Bay. The interpolation is based on well data from the CA State Water Board GAMA GeoTracker database, and the depth to water was calculated using a 2m USGS Digital Elevation Model.

Methods Well data - California State Water Control Board: GAMA GeoTracker http://geotracker.waterboards.ca.gov/data_download_by_county. Ground elevation data - U.S. Geological Survey https://topotools.cr.usgs.gov/coned/sanfrancisco.php. 2 meter DEM. SF Bay extent (includes open water and tidal wetlands) San Francisco Estuary Institute (SFEI): Bay Area Aquatic Resource Inventory (BAARI) http://www.sfei.org/baari. GIS FILE PROPERTIES: File format: ESRI Layer Package. Cellsize: 6.56. Linear unit: Feet. Z unit: Feet. Projected Coordinate System: NAD_1983_2011_StatePlane_California_III_FIPS_0403_Ft_US. Geographic Coordinate System: GCS_NAD_1983_2011.

METHODS: We subtracted the minimum depth to water at each well point from the ground elevation (extracted from the 2m DEM) to determine groundwater elevation at each well point. These elevations represent the maximum measured groundwater table height in the past 20 years. We then performed the interpolation on this groundwater elevation dataset, a total of 3,183 individual well points. Wells within one mile of the coast were included in the interpolation; results are shown within one kilometer (0.6 miles) of the coast, a distance used in previous studies of sea level rise-induced groundwater inundation. Wells within one-half mile north and south of the county borders were included in the interpolation to ensure continuity, but results are shown only for area within Alameda County.

We tested a variety of methods available in ArcGIS and used cross-validation to determine which method minimized prediction error most. We compared root mean square error (RMSE) to see how accurately each model predicted values at non-sampled locations, and examined mean error (ME), or the averaged difference between actual and predicted values, to see if each model was skewed in one direction or another. For each interpolation technique, we chose the input parameters (e.g. power, number of neighbors included) that minimized RMSE most.

After performing the groundwater table interpolation, we subtracted the output from the original elevation surface to display estimated minimum depth to water values. The interpolation and subtraction method we used produced some negative values for depth to water, indicating water above the ground surface, especially in areas where there were no well sample points at the base of a slope or in a valley. In the provided data files, we have changed these negative values to zero for clarity.

This data package contains the minimum depth to water results obtained by using each of the four interpolation methods, as well as files showing the minimum and maximum of the four methods for comparison. Also included are files showing the bay edge file used and no data areas (greater than 1km/0.6mi from the nearest well point).

Total annual precipitation is shown along with elevation hillshade using the NAGI method. Hillshade is from Esri Elevation Service, and precipitation data is taken from WMO and FAO rain gages in addition to a number of national datasets. The annual and monthly averages for the period 1950-2000 was calculated and interpolated by WorldClim.org, a collaboration between the University of California, Berkeley, the International Cetner for Tropical Agrilculture, and the Cooperative Research Centre for Tropical Rainforest Ecology and Management.

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.

【Courtesy of the C. V. Starr East Asian Library University of California, Berkeley】 Wood block print. In Japanese. Relief shown pictorially. Oriented with north to the left. Title from Mitsui Map collection in U.C.B.

【Courtesy of the C. V. Starr East Asian Library University of California, Berkeley】 Col. wood block print. In Japanese. East Asian Library call number: Ca 24.5 Rare-Map.