These Soil Mapping Data Packages include 1. a Soil Map dataset which includes the equivalents to Soil Project Boundaries, Soil Survey Spatial View mapping polygons with attributes from the Soil Name and Layer Files, plus + A Soil Site dataset which includes soil pit site information and detailed soil pit descriptions and any associated lab analyses, and + The Soil Data Dictionary which documents the fields and allowable codes within the data. The Soil Map geodatabase contains the 'best available' data ranging from 1:20,000 scale to 1:250,000 scale with overlapping data removed. The choice of the datasets that remain is based on connectivity to the soil attributes (soil name and layer files), map scale and survey date. (Note: the BC Soil Landscapes of Canada (BCSLC) 1:1,000,000 data has not been included in the Soil_Map or SIFT, but is available from: CANSIS. (A complete soils data package with overlapping soil survey mapping and BCSLC is available on request. Note that the soil survey data with attributes can also be viewed interactively in the [Soil Information Finder Tool](The Soil Map dataset is also available for interactive map viewing or as KMZs from the Soil Information Finder Tool website.

The data were derived from scanned soil maps. Attributes include a soil number (2-180), corresponding to runoff coefficient values in a Hydrology Manual, provided by the Los Angeles County Department of Public Works, Water Resources Division.Purpose: For use in DPW’s Modified Rational Method Hydrology Model.Supplemental Information:Stormwater Engineering is a Division of the Los Angeles County Department of Public Works. Please visit their website for posted publications, including the above mentioned Hydrology Manual.

Get consolidated soil data mapped on a county basis in a digitally stitched and standardized product.

This soil survey data was mapped by a number of soil surveyors from the 1920s to the 1990s. The product incorporates soil information from a variety of map scales. The project has brought the individual county or regional municipality surveys together to reveal inconsistencies in soil data across county boundaries.

The soil complex database contains other descriptive information including:

• slope class
• Canada Land Inventory (CLI) ranking
• stoniness
• drainage class
• texture

(Link to Metadata) This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. Survey Dates - https://www.nrcs.usda.gov/wps/portal/nrcs/surveylist/soils/survey/state/?stateId=VT

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S-map is the new national soils database for New Zealand. When completed, it will provide a seamless digital soil map coverage for New Zealand. S-map is designed to be applied at any scale from farm to region to nation.

Existing soil databases are patchy in scale, age and quality. Many maps do not adequately describe the underlying properties of the soil types they represent. S-map integrates existing reports and digital information and updates soil maps where existing data are of low quality. Our goal is to provide comprehensive, quantitative soil information to support sustainable development and scientific modelling.

S-map terms of use / More about S-map / Paper on S-map

Soil Survey polygons contain soils spatial and attribute information utilized by the Ministry of Environment online Soil Information Finder Tool (SIFT). The information pertains to soil survey data in the Province of British Columbia, as well as links to a variety of reports and maps. Polygons related to soil surveys can be made up of a complex of up to 3 components representing 3 different soil types. These components are aspatial and represent the amount in the polygon indicated by the PERCENT_x field for the component x. Each unique soil type has a soil name and generalized attributes such as drainage, texture and coarse fragment percent. The Soil Name and Layer Files can be joined and related to each of the 3 components of the Soil Survey Spatial View polygons using the SoilSymbol field in the name and layer tables and the SOILSYM_x fields in the Soil Survey Spatial View.

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A soil survey is an inventory of soils and their spatial distribution over a landscape. Soil survey reports contain two parts. The first part is a soil map or series of maps at a particular scale with coding for each soil. Soil survey reports also include a supporting document that contains background information such as how the soil survey was conducted, and an explanation of interpretive criteria and a summary of the area occupied by various soil types. The detailed soil surveys identify more of the variation in soil types across smaller landscapes, as compared to Generalized (1:100 000, i.e. provincial overview) and Reconnaissance or General (1:125 000, or 1/2 inch to 1 mile.) soil surveys. Detailed soil survey information is much more accurate and reliable for making decisions at the farm-level. Soil surveys have been published for most of the agricultural areas, and many surrounding areas, across Canada. Data from these surveys comprise the most detailed soil inventory information in the National Soil Database (NSDB). Version 3 was created by Agriculture and Agri-Food Canada in the 2010's by amalgamating version 2 data. It introduced some minor refinements to the version 2 data structure to provide closer alignment with the Soil Landscapes of Canada data structure.

Soil is essential to human survival. We rely on it for the production of food, fibre, timber and energy crops. Together with climate, the soil determines which crops can be grown, where and how much they will yield. In addition to supporting our agricultural needs, we rely on the soil to regulate the flow of rainwater and to act as a filter for drinking water. With such a tremendously important role, it is imperative that we manage our soils for their long-term productivity, sustainability and health.

The first step in sustainable soil management is ensuring that the soil will support the land use activity. For example, only the better agricultural soils in Manitoba will support grain and vegetable production, while more marginal agricultural soils will support forage and pasture-based production. For this reason, agricultural development should only occur in areas where the soil resource will support the agricultural activity. The only way to do this is to understand the soil resource that is available. Soil survey information is the key to understanding the soil resource.

Soil survey is an inventory of the properties of the soil (such as texture, internal drainage, parent material, depth to groundwater, topography, degree of erosion, stoniness, pH and salinity) and their spatial distribution over a landscape. Soils are grouped into similar types and their boundaries are delineated on a map. Each soil type has a unique set of physical, chemical and mineralogical characteristics and has similar reactions to use and management. The information assembled in a soil survey can be used to predict or estimate the potentials and limitations of the soils’ behaviour under different uses. As such, soil surveys can be used to plan the development of new lands or to evaluate the conversion of land to new uses. Soil surveys also provide insight into the kind and intensity of land management that will be needed.

The survey scale of soils data for Manitoba ranges from 1:5,000 to 1:126,720, as identified in the 'SCALE' column.1:5,000. The survey objective at this scale is to collect high precision field scale data and it is mostly used in research plots and other highly intensive areas. It is also applicable to agricultural production and planning such as precision farming, agriculture capability, engineering, recreation, potato/irrigation suitability and productivity indices. Profile descriptions and samples are collected for all soils. At least one soil inspection exists per delineation and the minimum size delineation is 0.25 acres. The soil taxonomy is generally Phases of Soil Series. The mapping scale is 1:5,000 or 12.7 in/ mile.

This file also contains soils data that has been collected in Manitoba at a survey intensity level of the second order. This includes data collected at a scale of 1:20,000. The survey objective at this scale is to collect field scale data and it is mostly used in agricultural production and planning such as precision farming, agriculture capability, engineering, recreation, potato/irrigation suitability and productivity indices. Soil pits are generally about 200 metres apart and are dug along transects which are about 500 metres apart. This translates to about 32 inspections sites per section (640 acres). The soils in each delineation are identified by field observations and remotely sensed data. Boundaries are verified at closely spaced intervals. Profile descriptions are collected for all major named soils and 10 inspection sites/section and 2 to 3 horizons per site require lab analyses. At least one soil inspection exists in over 90% of delineations and the minimum size delineation is generally about 4 acres at 1:20,000. The soil taxonomy is generally Phases of Soil Series. The mapping scale is 1:20,000 or 3.2 inch/ mile.

This file also contains data that has been collected at the third order. This includes scales of 1:40,000 and 1:50,000. The survey objective at this scale is to collect field scale or regional data. If the topography is relatively uniform, appropriate interpretations include agriculture capability, engineering, recreation, potato/irrigation suitability and productivity indices. Soil pits are generally dug adjacent to section perimeters. This translates to about 16 inspection sites per section (640 acres). Soil boundaries are plotted by observation and remote sensed data. Profile descriptions exist for all major named soils and 2 inspection sites/section and 2 to 3 horizons per site require lab analyses. At least one soil inspection exists in 60-80% of delineations and the minimum size delineation is generally in the 10 to 20 acre range. The soil taxonomy is generally Series or Phases of Soil Series. The mapping scale is 1:40,000 or 2 inch/ mile; 1:50,000 or 1.5 inch/mile.

This file also contains soils data that has been collected at a survey intensity level of the fourth order. This includes scales of 1:63,360, 1:100,000, 1:125,000, and 1:126,720. The survey objective is to collect provincial data and to provide general soil information about land management and land use. The number of soil pits dug averaged to about 6 inspections per section (640 acres). Soil boundaries are plotted by interpretation of remotely sensed data and few inspections exist. Profile descriptions are collected for all major named soils. At least one soil inspection exists in 30-60% of delineations and the minimum size delineation is 40 acres (1:63,360), 100 acres (1:100,000), 156 acres (126,700) and 623 acres (250,000). The soil taxonomy is generally phases of Subgroup or Association.

As of 2022, soil survey field work and reports are still currently being collected in certain areas where detailed information does not exist. This file will be updated as more information becomes available. Typically, this is conducted on an rural municipality basis.

In some areas of Manitoba, more detailed and historical information exists than what is contained in this file. However, at this time, some of this information is only available in a hard copy format. This file will be updated as more of this information is transferred into a GIS format.

This file has an organizational framework similar to the original SoilAID digital files and a portion of this geographic extent was originally available on the Manitoba Land Initiative (MLI) website.

Domains and coded values have also been integrated into the geodatabase files. This allows the user to view attribute information in either an abbreviated or a more descriptive manner. Choosing to display the description of the coded values allows the user to view the expanded information associated with the attribute value (reducing the need to constantly refer to the descriptions within the metadata). To change these settings in ArcCatalog, go to Customize --> ArcCatalog Options --> Tables tab --> check or uncheck 'Display coded value domain and subtype descriptions'. To change these settings in ArcMap, go to Customize --> ArcMapOptions --> Tables tab --> check or uncheck 'Display coded value domain and subtype descriptions'. This setting can also be changed by opening the attribute table, then Table Options (top left) --> Appearance --> check or uncheck 'Display coded value domain and subtype descriptions'. The file also contains field aliases, which can also be turned on or off under Table Options.

The file - "Manitoba Municipal Boundaries" - from Manitoba Community Planning Services was used as one of the base administrative references for the soil polygon layer.

Also used as references were the hydrological features mapped in the 1:20,000 and 1:50,000 NTS topographical layers (National Topographic System of Canada). Typically this would relate to larger hydrological features such as those designated as perennial lakes and perennial rivers.

This same capability is available in ArcGIS Pro.

For more info:

https://www.gov.mb.ca/agriculture/soil/soil-survey/importance-of-soil-survey-mb.html#

Brief Methods: In version 2 of the Sierra Nevada Multi-source Meadow Polygons Compilation, polygon boundaries from the original layer (SNMMPC_v1 - https://meadows.ucdavis.edu/data/4) were updated using ‘heads-up’ digitization from high-resolution (1m) NAIP imagery. In version 1, only polygons larger than one acre were retained in the published layer. In version 2, existing polygon boundaries were split, reduced in size, or merged, and additional polygons not captured in the original layer were digitized. If split, original IDs from version 1 were retained for one half and a new ID was created for the other half. In instances where adjacent meadows were merged together, only one ID was retained and the unused ID was “decommissioned”. If digitized, a new sequential ID was assigned. AcknowledgementsTim Lindemann, Dave Weixelman, Carol Clark, Stacey Mikulovsky, Qiqi Jiang, Joel Grapentine, Kirk Evans - USDA Forest Service, Pacific Southwest Region Wes Kitlasten - U.S. Geological Survey Sarah Yarnell, Ryan Peek, Nick Santos - UC Davis, Center for Watershed Sciences Anna Fryjoff-Hung - UC Merced Meadow Polygon Attributes Field DescriptionAREA_ACRE Meadow area in acresSTATE State in which the meadow is located (CA or NV)ID* Unique meadow identifier UCDSNMxxxxxx*Note: IDs are non-sequential* HUC12 Unique identifier for the Hydrologic Unit Code (HUC), level 12, in which the meadow is locatedOWNERSHIP Land ownership status (multiple sources)EDGE_COMPLEXITY Gives an indication of the meadow's exposure to external conditions EDGE COMPLEXITY = (MEADOWperimeter/EAC perimeter) [EAC = Equal Area Circle]DOM_ROCKTYPE Dominant rock type on which the meadow is located based on the USGS layerVEG_MAJORITY Vegetation majority based on the LANDFIRE layer (GROUPVEG attribute)SOIL_SURVEY Soil survey from which SOIL_COKEY, MAPUNIT_Kf, MAPUNIT_ClayTot_r, SOIL_MUKEY, and SOIL_COMP_NAME were assigned to each meadow (SSURGO or STATSGO depending on layer coverage)SOIL_MUKEY Mapunit Key: Unique identifier for the Mapunit in which the meadow is locatedSOIL_COKEY Component Key: Unique identifier for the major component of the mapunit in which the meadow is located SOIL_COMP_NAME Component Name: Name of the soil component with the highest representative value in the mapunit in which the meadow is located MAPUNIT_Kf K factor: A soil erodibility factor that quantifies the susceptibility of soil particles to detachment by water. Low: 0.05-0.2 Moderate: 0.25-0.4, High: >0.4MAPUNIT_ClayTot_r Representative value (%)of total clayCATCHMENT_AREA The approximate area of the upstream catchment exiting through the meadow(sq. m)ELEV_MEAN Mean elevation (m)ELEV_RANGE Elevation range (m) across each meadowED_MIN_FStopo_ROADS Minimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Roads ED_MIN_FStopo_TRAILS Minimum Euclidean Distance (m) to Forest Service Topographic Map Data Transportation Trails ED_MIN_LAKE Minimum Euclidean Distance (m) to lake edges ED_MIN_FLOW Minimum Euclidean Distance (m) to NHD Streams/Rivers ED_MIN_SEEP Minimum Euclidean Distance (m) to NHD Seeps/Springs MDW_DEM_SLOPE Median DEM based slope (in degrees)STRM_SLOPE_GRADE Length-weighted average slope of all NHD flowline segments in each meadow. Given for meadows with flowlines. Meadows without flowlines are null for this attribute.POUR_POINT_LAT Latitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) POUR_POINT_LON Longitude of the lowest point along a flowline at which water flows out of the meadow in decimal degrees(meadow with no flowline has null value) HGM_Type Dominant meadow hydrogeomorphic (HGM) type LAT_DD Latitude of polygon centroid in decimal degreesLONG_DD Longitude of polygon centroid in decimal degreesShape_Length Meadow perimeter in metersShape_Area Meadow area in sq. meters Detailed Attribute Descriptions:GeologyField: DOM_ROCKTYPEData Source: USGS - https://pubs.usgs.gov/of/2005/1305/Dominant rock type was attributed to the meadow polygons based on available state geology layers. Using Zonal Statisitics in ArcGIS, the most abundant lithology in the map unit (ROCKTYPE1) was identified for each meadow. VegetationField: VEG_MAJORITYData Source: LANDFIRE - https://www.landfire.gov/version_comparison.php?mosaic=YUsing Zonal Statisitics in ArcGIS, the 2014 LANDFIRE dataset was used to attribute generalized vegetation (GROUPVEG) to the meadow polygons. SoilsFields: SOIL_SURVEY, SOIL_MUKEY, SOIL_COKEY, SOIL_COMP_NAME, MAPUNIT_Kf, MAPUNIT_ClayTot_rData Source: USDA, Natural Resources Conservation ServiceSSURGO: https://gdg.sc.egov.usda.gov/STATSGO: https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htmSSURGO (1:24,000 scale) datasets were compiled for the entirety of the study area. Gaps were filled with compiled STATSGO data (1:250,000 scale). Components were assigned based on the soil component with the highest representative value in the map unit in which the meadow was located. For each component, the clay and Kf values from the top-most horizon were assigned to each meadow polygon using Zonal Statistics. Note: MAPUNIT_Kf may be null if the mapunit dominant condition is a miscellaneous area component such as Rock outcrop. Also, forested components with organic litter surface horizons will also return a null K-factor when the surface horizon K-factor is used.STATSGO does not have the detail for approximation of soil properties in the mountain meadows. The polygons are so big (Order 4) that they do not recognize the soils in the meadows as unique components, so there are no data for the meadows anywhere in those map units. As for the K and clay values for CA790 (Yosemite NP), because it is a new survey, O horizons were populated for those components. There may be a similar issue with the Tahoe Basin. NRCS does not populate the K factor for O horizons. And, at least at the time, NRCS is not populating any mineral material in the O horizons. Many NRCS national interpretations have been edited to look at the first mineral horizon and exclude the O. There is also a lot of Rock Outcrop and no horizon data are populated for those components.Slope Field: MDW_DEM_SLOPE Data Source: USGS 10m DEMThe median Digital elevation model (DEM) based slope (in degrees) was assigned via Zonal Statistics to each meadow.All meadows have a value for this attribute. Field: STREAM_SLOPE_GRADEData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlA length-weighted average slope of all NHD flowline segments was calculated within each meadow polygon. Meadows with no NHD flowline will have a NULL value for this attribute. Catchment AreaField: MDW_CATCHMENT_AREA (sq meters)Data Source: USGS NHDPlus V2, NHDPlusHydrodem- http://www.horizon-systems.com/NHDPlus/NHDPlusV2_home.phpScript Source: USGS, Wes Kitlasten; USFS, Kirk Evans, Carol ClarkUsing python scripting and the Watershed tool in ArcGIS, the area of the upstream catchment exiting through the meadow was obtained using a flow direction raster created from the NHDPlusHydrodem.Euclidean Distance Fields: ED_MIN_SEEP, ED_MIN_LAKE, ED_MIN_FLOW, ED_MIN_FSTopo_ROADS, ED_MIN_FSTopo_TRAILSData Source: USGS National Hydrograpy Dataset (NHD) - https://nhd.usgs.gov/data.htmlFSTopo - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FSTopoUsing the Euclidean Distance (Spatial Analyst) tool in ArcGIS, the minimum distance to each meadow was calculated for NHD Springs/Seeps, NHD Streams/Rivers (flow), NHD Waterbodies (lakes), and FS Topographic Transportation Trails and Roads. HGM Type During the mapping process, the dominant Hydrogeomorphic (HGM) type (Weixelman et al 2011) was estimated for each meadow larger than one acre. Visual inspection of NAIP 1-m resolution imagery was used in this process. DEM layers were used to estimate the landform position. The USGS hydrographic layer was used to determine locations of flowlines. Google Earth imagery was used to estimate greenness during the summer months. Meadows are often composed of more than one HGM type. In this effort, the dominant type was estimated. HGM types have not yet been estimated for Yosemite and Sequoia Kings Canyon National Parks. Types were mapped according to the following visual interpretation. 1. Meadows adjacent to lakes or reservoirs and at nearly the same elevation as the Water bodyLacustrine Fringe (LF)1’. Not as above22. Meadow sites located in an obvious topographic depression. 32’. Not as above43. Sites with obvious standing water after mid-summer or vegetation remaining dark green after mid-summer. Depressional Perennial (DEPP)3’. Not as above. Sites with no standing water after mid-summer or apparently not remaining dark green after mid-summer.Depressional Seasonal (DEPS)4. Meadows with a flow line (using the USGS hydrographic layer) entering from above the meadow and exiting below the meadow, or meadows located in a swale or drainway ………………………………Riparian (RIP)4’. Not as above55. Meadows fed by a spring or seep. No flowline entering from above the meadow. Typically occurring on hillslopes or toeslopes. In addition, the USGS DEM layer was used to look for the text label “Springs” and/or a symbol indicating a spring. Discharge Slope (DS)5’. Dry meadows without a visible flowline entering from above the meadow, vegetation greenness disappears by mid-summer. No apparent groundwater inputs from springs or seeps. May occur in a swale, drainageway, gentle hillslope, or crest. Dry (Dry)OwnershipField: OWNERSHIPData Sources by priority:1. USDA Forest Service Basic Ownership (OWNERCLASSIFICATION) - https://data.fs.usda.gov/geodata/edw/datasets.php?dsetCategory=boundaries1. National Parks Service (UNIT_NAME) - https://irma.nps.gov/DataStore/1. California Protected Areas Database – CPAD (LAYER) - http://www.calands.org/1. Protected Area Database-US (CBI Edition) Version 2.1 (OWN_NAME) -

Soil Survey Project Boundaries (soil mapping study areas) contains the soil survey project area and attributes describing each project (project level metadata), plus links to the locations of other data associated with the project (e.g., soil survey reports, polygon datasets, plotfiles, scanned maps, legends). Soil Mapping divides the landscape into units according to soil association, name, type, drainage, parent material, and texture. This layer is derived from the STE_TEI_PROJECT_BOUNDARIES_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: SOIL, TIMSOI, and SOILSW. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01) The Soil Survey dataset contains project boundaries as well as the soil survey polygons which are available in a variety of formats including: 1) via the Soil Information Finder Tool Mapping App (interactive app), 2) Soil Survey Spatial data with Soil Name and Layer Files (for download or viewing via iMapBC), or as 3) Soil Mapping Data Packages with geodatabase or shape files, and a data dictionary.

Analysis of ‘Vegetation - Mendocino Cypress and Related Vegetation [ds2805]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2261c310-9408-4b75-9cc3-90c7cf4d4114 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Mendocino Pygmy Forest is one of the best-known examples of a rare natural community in California. The unique soil and climatic attributes and the resulting vegetation of the Mendocino coastal terraces described by Jenny et al (1969), Westman (1975), Westman and Whittaker (1975), Sholars (1979), Sholars (1982), Sholars (1984) and others are well- known in the scientific and conservation literature.The mapping and classification process assumed that the unique and biologically significant elements of the pygmy forest ecosystem were definable without a complete inventory of the surrounding regional vegetation and land-use patterns. The boundary of the mapped areas was created using existing geographic information on soils, topography, land use, along with fieldwork from previous efforts. Within that area, an array of vegetation samples were collected and classified representing the full array of vegetation patterns within it. The boundary was refined as part of the mapping process. It was later expanded to include property owned by the Mendocino Coast Park and Recreation District after receiving permission to conduct surveys as part of this project. (Polygons that would not have been mapped for the original project but are within the MCPRD property are marked “MCPRD Additional” in the Notes field.)The map was produced using a classification based on an analysis of surveys taken throughout the range of the oligotrophic areas supporting Pygmy Forest vegetation. This classification has been incorporated into the Manual of California Vegetation Online Database. The map classification is mostly at the Association Level of the NVCS hierarchy (12 types), with some at the Alliance Level (5 types) and Group Level (3 types), and 4 land use and water classes. It was hand-digitized using photointerpretation based on the 2014 NAIP Imagery, with other ancillary data used to help with the identification of vegetation types. The minimum mapping unit was 1 acre for vegetation types, and 0.25 acres for water, developed and agricultural type. The total area mapped was 9782 acres.An accuracy assessment performed on the map. The overall accuracy of each of the 5 most reliably sampled types was between 82 and 92 % accuracy, meeting minimum accuracy standards.For more information, see the supplemental information below and the report for the map cited in the references.ReferencesCalifornia Department of Fish and Wildlife, Vegetation Classification and Mapping Program. Classification and Mapping of Pygmy Forest and Related Mendocino Cypress (Hesperocyparis pygmaea) Vegetation, Mendocino and Sonoma Counties, California. CDFW; 11/2018. https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=161736A Manual of California Vegetation, Online Edition. http://www.cnps.org/cnps/vegetation/. California Native Plant Society, Sacramento, CA.USNVC [United States National Vegetation Classification]. http://usnvc.org/. 2017. United States National Vegetation Classification Database, V2.01. Federal Geographic Data Committee, Vegetation Subcommittee, Washington DCJenny, H. R.J. Arkley, and A.M. Schultz. 1969. The pygmy forest-podsol ecosystem and its dune associates of the Mendocino coast. Madroño20:60-74.Westman, W.E. 1975. Edaphic climax pattern of the pygmy forest region of California. Ecological Monographs30:279-338.Westman, W.E. and R.H. Whittaker. 1975. The pygmy forest region of northern California: studies on biomass and primary productivity. Journal of Ecology63:493-520.Sholars, R.E. 1979. Water relations in the pygmy forest of Mendocino County. Ph.D. diss. University of California, Davis.Sholars, R.E. 1982. The pygmy forest and associated plant communities of coastal Mendocino County, California; genesis, soils, vegetation. Black Bear Press, Mendocino, CA.Sholars, R.E. 1984. The pygmy forest of Mendocino. Fremontia12(3): 3-8.Bowles, C.J. and E. Cowgill. 2012. Discovering marine terraces using airborne LiDAR along the Mendocino-Sonoma coast, northern California. Geosphere8(2):386''402.Soil Survey Staff, Natural Resources Conservation Service (NRCS), United States Department of Agriculture. Web Soil Survey. Available online at https://websoilsurvey.nrcs.usda.gov/. Accessed [October 13, 2014].National Agriculture Imagery Program (NAIP), United States Department of Agriculture. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/index

--- Original source retains full ownership of the source dataset ---

This is the first soil survey report to deal solely with an area that is irrigated or proposed for irrigation development. The three soil maps that form part of this report show the kind and location of the various soil and topography types. The report describes the soil types, gives analytical data of representative profiles, and gives the average rating of each soil type. Three appendices cover problems related to land levelling, to salinity and drainage, and to fertility and management.

This data package include GIS layers stored in Geodatabase. The layers describe the characteristics of the catchments along the southern coast of Santa Barbara County used in the article Aguilera, R., & Melack, J. M. (2018). Relationships among nutrient and sediment fluxes, hydrological variability, fire, and land cover in coastal California catchments. Journal of Geophysical Research: Biogeosciences, 123, 2568– 2589. https://doi.org/10.1029/2017JG004119. Catchment characteristics include: Land cover and land use based on hyperspectral imagery obtained by the Airborne Visible/Infrared Imaging Spectrometer; number of inhabitants based on population counts by block from the 2010 census spatial database; relief and slopes estimated from a 30 m digital elevation model; geological substrata obtained from geologic maps of California; soil textural types based on the Soil Survey Geographic data, and fire perimeters for the Gaviota, Gap, Tea and Jesusita fires.

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Soils and soil moisture greatly influence the water cycle and have impacts on runoff, flooding and agriculture. Soil type and soil particle composition (sand, clay, silt) affect soil moisture and the ability of the soil to retain water. Soil moisture is also affected by levels of evaporation and plant transpiration, potentially leading to near dryness and eventual drought.Measuring and monitoring soil moisture can ensure the fitness of your crops and help predict or prepare for flash floods and drought. The GLDAS soil moisture data is useful for modeling these scenarios and others, but only at global scales. Dataset SummaryThe GLDAS Soil Moisture layer is a time-enabled image service that shows average monthly soil moisture from 2000 to the present at four different depth levels. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. The GLDAS soil moisture data is useful for modeling, but only at global scales. Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Depth: This layer has four depth levels. By default they are summed, but you can view each using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter. It is also possible to toggle between depth layers using raster functions, accessed through the Image Display tab.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available. This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the chemical soil variable organic carbon density (ocd) which measures carbon mass in proportion to volume of soil (mass divided by volume.)From Agriculture Victoria: Soil carbon provides a source of nutrients through mineralisation, helps to aggregate soil particles (structure) to provide resilience to physical degradation, increases microbial activity, increases water storage and availability to plants, and protects soil from erosion.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for organic carbon density are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Organic carbon density in kg/m³Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for ocd were used to create this layer. You may access organic carbon density values in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

The Agriculture Capability mapping dataset is the digitized equivalent of the legacy Agriculture Capability Scanned Maps, which date from the 1960's to the 1990s. Agriculture Capability mapping is also known as 'Soil Capability for Agriculture' and 'Agricultural Capability' mapping. Agricultural Capability is an interpreted mapping product based on soil and climate information. In general, climate determines the range of crops possible in an area and the soils determine the type and relative level of management practices required. This is legacy data and changes in climate are not reflected. For more information about the classification system see: Land Capability Classification for Agriculture. Use caution utilizing these legacy maps as the classifications were based on common land management practices and typical crops of the 1960s-1990s era, and subsequent site specific land management practices (e.g. installation of drainage) may have modified the soil conditions since the mapping was completed. This Agriculture Capability legacy mapping is included in the Soil Information Finder Tool (SIFT) mapping application. The SIFT application provides more detailed climate data (e.g. Growing Degree Days, Frost Free Period (5 C), (1960-1990 climate normals). The SIFT 'Soil query tools' may be useful for identifying areas with specific 'growing conditions' of interest based on soils present (soil name), soil texture, drainage, coarse fragment content, slope, elevation, growing degree days and frost free period. Note: This Agriculture Capability Mapping dataset is based on soil mapping at 1:100,000, 1:50,000 or 1:20,000 scale, and is more detailed than the 1:250,000 scale Canada Land Inventory (CLI) Agricultural Capability mapping (available here).