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 drainage is a relatively simple classification of the soil profile that describes the likelihood of seasonal wetness (Webb & Lilburne 2011). It is based on the occurrence within specific depths of redox segregation and low chroma colours indicative of waterlogging and reduction (Milne et al. 1995). This layer is a "dissolved" representation of the soil drainage attribute for S-map soils, where neighbouring S-map polygons have been combined if they have the same value of the attribute. Please refer to document Smap Data Dictionary Dissolved Layers.pdf at https://lris.scinfo.org.nz/document/22129-smap-data-dictionary-dissolved-layers/

The authoritative City of Sioux Falls street map(s).

Soil texture is the dominant texture class in the control zone (usually the upper 60 cm) of the soil profile as defined in Webb & Lilburne (2011). This layer is a "dissolved" representation of the soil texture attribute for S-map, where neighbouring S-map polygons have been combined if they have the same value of the attribute. Refer to document Smap Data Dictionary Dissolved Layers.pdf at https://lris.scinfo.org.nz/document/22129-smap-data-dictionary-dissolved-layers-july-2019/

Index of sewer maps for the City.

Soil is the outside layer of Earth. It is a made up of living organisms, gases, minerals, and organic matter. Knowing what elements are in the soil helps to work out where it came from and how it was made.Deeper soil samples for the northern half of Ireland were collected by Geological Survey of Northern Ireland (2004-2006) and Geological Survey Ireland (2011-2019). 16,800 soil samples were taken from the top 35–50 cm of the soil, in areas such as meadows, fields, parks and pastures. They were sent to a lab to be tested for the chemicals that make up the soil. This was done using two different methods. Knowing the types of elements in the soil can point to where they came from, how the soils were made. pH and loss on ignition were also tested, which tells us how acid or basic the soils are, and the amount of organic carbon in the soil. The results from the tests were given as mg/kg (milligram per kilogram) or % (percent). When we map the data, we can see the spread of elements across the country. This also allows us to map different soil types. Deeper topsoil is worth testing as it is more related to rock beneath than shallow topsoil. This gives us a better idea of how the soil is formed and if there are any useful minerals in the rocks below. The sample locations are shown as points. Each point shows where the sample was collected and the results for that sample. The data is also available as polygons or areas, which show the 2km-by-2km grid square around where the samples were taken. It also includes the number of samples that were taken in that square. The data contains the average value of each element for all soils samples taken within that grid square. Maps of the grid data use colour scales to show the different strengths of the elements. The Tellus survey is a national airborne geophysical and ground geochemical mapping project managed by the Geological Survey Ireland in Ireland and by the Geological Survey of Northern Ireland in Northern Ireland.

This enhanced Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radiometer. This product is a daily composite of SMAP Level-2 (L2) soil moisture which is derived from SMAP Level-1C (L1C) interpolated brightness temperatures. Backus-Gilbert optimal interpolation techniques are used to extract information from SMAP antenna temperatures and convert them to brightness temperatures, which are posted to the 9 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0) in a global cylindrical projection. As of 2021, the data are also posted to the Northern Hemisphere EASE-Grid 2.0, an azimuthal equal-area projection.

Dataset of IS BK 5 Overview of the soil map of NRW 1: 5.000. The data set shows where in NRW large-scale ground maps, usually in scale 1: 5,000, digital (vectorised) or analog (scanned and georeferenced) are available and whether they originate from forest or agricultural site exploration. Each mapping project (“Procedure”) is described with the name, project code, scale of the survey, year of mapping, list and processing date of the available digital evaluations and linked to a process documentation.

This ancillary SMAP product contains three dynamic GMAO GEOS-5 modeled data sets. Each data set contains surface and atmospheric parameters pertinent to SMAP provided in 1) hourly, 2) 3-hour, and 3) averaged over 3-hour intervals.

This digital soil survey information is used by soil scientists, hydrologists, ecologists, planners and other land managers to locate, compare, and select suitable areas for major kinds of land uses; to identify areas that need more intensive investigations; and to evaluate various management alternatives and predict the effects of the particular alternative on the land. Other intended uses of the soil survey include, but are not limited to, providing federal, state, and private organizations with resource information as it relates to activities such as power transmission right-of-way, coastal zone management, forest land management plans, mineral and energy exploration and development, and site suitability for buildings and dwellings. Tongass National Forest soil scientists began mapping soils in the early 1960s. By 1992 mapping was largely completed for approximately 10 million acres of the forest. During mapping, polylines were created using tones and textures on aerial photographs and field-verification. Soil map units were digitized from polygons drawn on 1:31,680 scale Mylar maps. Polygons on aerial photos were traced onto Mylar overlay sheets using a rapidiograph pen, which is accurate to .035 inches of the source data. Polygons were digitized to .001 inches of their location of the digitizing source (Mylar overlay). Accounting for the possibility of cumulative errors during transfer and digitizing, positional accuracy may vary by ± 250 feet. More recent inventories like Yakutat and South Kruzof were pre-mapped using on-screen digitizing with orthophotos and contours as base maps. Historically, the forest was divided into three soil survey areas-Stikine, Chatham, and Ketchikan. These areas are indicated in the FOREST field of the attribute table as follows: 2 = Stikine, 3 = Chatham, 5 = Ketchikan. By the end of the 1990s the digital soil inventory for the three survey areas on the forest were aggregated into one feature class. Beginning in the late 2000s an effort was made to move the soil inventory to Web Soil Survey (WSS). Each survey area was correlated separately. Updates to line work have occurred since 2010 to include areas not previously mapped. In 2020 a fourth area, the Yakutat Forelands was incorporated in WSS and the forest-wide feature class updated with that information. Line work for the southern half of Kruzof Island is included in this feature class but is currently in the correlation process and is not yet available on WSS. The update in 2020 also used all available line work from WSS to make the forest-wide dataset consistent with the data on WSS. Stikine Area (FOREST = 2): All lands within the Stikine Administrative Area have been mapped. This includes all federal, state, and private lands, including wilderness. The soil is mapped at different intensities across the area based on their Land Use Designations (LUDs) in the Tongass Land Management Plan, USDA-FS, 1979. Generally, areas designated for intensive land use (LUD III) are mapped at larger scales (Order 3 level, 1:15,840), while other areas designated for low intensity land use (LUD I&II) are mapped at smaller scales (Order 4 level, 1:31,680). Some areas that are currently LUD I&II were mapped to Order 3 prior to designation. All of the Stikine Area is mapped to an Order 3 level with the exception of the following, which were mapped to Order 4: the Stikine-LeConte Wilderness Area (Farm and Dry Islands are mapped to Order 3), Anan Creek area, and mainland areas designated for semi-remote recreation use. For exact locations, see Preliminary Soil Resource Inventory Report, Stikine Area. Order 3 surveys were mapped on 1:15,840 scale aerial photos. This resulted in map delineations no smaller than approximately 3 acres, ranging up to several hundred acres. The map units in the Order 3 survey area are composed of soil associations, some consociations and some complexes. The Order 4 surveys were mapped on 1:31,680 scale high-altitude infrared aerial photographs. This resulted in map units no smaller than approximately 10 acres and ranged as high as 500 acres in size. The map units in the Order 4 survey area are composed of phases of soil families, or subgroups. Design of initial mapping units in the Stikine area was strongly influenced by soil-vegetation relationships. This is referred to as the "Soil Ecosystem" type of mapping units, which are defined based on natural vegetation types, corresponding soil properties and associated landform types. Map units were also broken out by slope class.Chatham Area (FOREST = 3): The Chatham Area soil survey covers approximately 4.5 million acres of the Tongass National Forest. The inventory occurred in two stages and was done at two levels of detail. An Order 3 survey was conducted from 1981 to 1984, and an Order 4 survey was conducted from 1987 to 1989. Wilderness areas, national monuments, ANILCA additions, state, private and native lands were not mapped. The Order 3 survey is composed primarily of areas referred to as "Land Use Designations (LUDs) III and IV in the Tongass Land Management Plan, USDA-FS, 1979. LUD III were managed for a combination of uses, including recreation and some timber harvest. LUD IV were allocated to intensive resource use and development opportunities, primarily timber harvest and mining. Both LUD III and IV areas required the greater detail of an Order 3 survey. The Order 4 survey is composed primarily of LUD II. LUD II areas were allocated to roadless area management. The lower intensity management of LUD II justified a less detailed Order 4 survey. For exact locations, see Chatham Area Ecological Unit Inventory User Guide, figure 1. The inventory area was pre-mapped on either color aerial photographs at a scale of 1:15,840 (Order 3) or high altitude, color infrared aerial photographs at a scale of 1:63,360 (Order 4). South Kruzof soil survey covers about 60,795 acres of the Tongass National Forest. It represents the soils on the young Mount Edgecumbe volcanic field. The area was initially mapped during the 1981 to 1984 Order 3 Chatham soil survey. A second effort to gather more data began in 1994 but was not completed at that time. The effort to map South Kruzof restarted during 2009 and was completed in 2011. It was mapped digitally at a scale of 1:31,680 on 1998 2-meter black and white Digital Ortho Quads. The Yakutat soil survey covers about 487,758 acres of the Tongass, primarily on the Yakutat Forelands. This survey was also started during the 1981 to 1984 Order 3 soil survey. Additional data was collected in 1987, 1989, 1991, 1992, and 1993. The Yakutat survey was picked up again in 2009 and completed in 2013, although the mountainous areas are still unmapped. Yakutat was mapped digitally at a scale of 1:31,680 on 2008 Color 1 meter Digital Ortho Quarter Quads. The NRCS completed correlation on the Yakutat mapping area in 2020 but has not completed correlation of South Kruzof. The Chatham inventory was strongly influenced by soil-landform relationships. Additionally, vegetation, geology, and soils information was used to stratify the landscape into natural integral units that reflect ecological processes. Map units were also broken out by slope classes. The mapping criteria are based on features that may be either directly observed or inferred from natural landscape and vegetative features viewed on an aerial photograph. The intent of the mapping is to delineate integral ecological units that provide information required to achieve National Forest System management objectives. The Yakutat SMUs are nested in the landtype associations (LTAs) that were mapped in Landtype Associations of the Yakutat Foreland by Michael Shephard and Terry Brock (Technical Publication No. R10-TP-109, 2002). These LTAs were generalized for the soil survey.Ketchikan Area (FOREST = 5): The Ketchikan soil survey area covers approximately 3 million acres. It includes all of the area previously known as the Ketchikan Administrative Area except the following: Misty Fjords National Monument Wilderness and non-wilderness areas, the South Prince of Wales area and large tracts of federal (Bureau of Land Management), state, private borough and municipal lands. These unmapped lands are found on Cleveland Peninsula, Revillagigedo Island, Sukkwan Island, Long Island, Dall Island and Prince of Wales Island. Areas within the Ketchikan Area Soil Survey are mapped at different levels of intensity. Those designated as moderate and intensive development under the 1997 Tongass Land Management Plan (1997 TLMP) Revision, are mapped at an Order 3 level. Most wilderness areas were not included in the soil survey, although some areas now designated as wilderness and National Monument or 'Mostly Natural Setting' were mapped prior to those designations. These areas include: outside islands (Noyes, Lulu and Baker), Mt Calder/Mt. Holbrook Area, Salmon Bay, Coronation Island, Maurelle Islands, Warren Island, and the Karta River. Some other lands identified in the 1997 TLMP Revision under Wilderness and National Monument and 'Mostly Natural' settings were mapped at an Order 4 level. These areas include: Duke, Hotspur and Cat Islands, Cleveland Peninsula (North of Yes Bay), Bell Island, area east of Naha Bay, and area north of Cholmondeley Sound. For exact locations, see Ketchikan Area Soil Survey User Guide, Tongass N.F., p. 13. The Order 3 surveys were mapped on 1:15,840 or 1:40,000 aerial photos. This resulted in map delineations no smaller than approximately 3 acres ranging up to several hundred acres. The Order 3 survey areas are composed approximately of one-third each of map units of soil consociations, associations and complexes. The Order 4 surveys were mapped on 1:15,840 colored aerial photographs. This resulted in map delineations no smaller than approximately 10 acres and ranging as high as 500 acres in size. The map units are composed of phases of series, soil families, or subgroups.The criteria

This data product contains global daily 1 km resolution surface soil moisture derived from the SMAP L-band radiometer. Specifically, MODIS land surface temperature data is used with the SMAP Enhanced L2radiometer Half-Orbit 9 km EASE-Grid Soil Moisture product in a downscaling algorithm to estimate soil moisture. The data set is validated by in situ soil moisture measurements from dense soil moisture networks representing different global land cover types.

The Digital Geologic-GIS Map of San Miguel Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (smis_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (smis_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (smis_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (smis_geology_metadata_faq.pdf). Please read the chis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Digital Soil Mapping explained in detail a publication under review. Briefly, Maps of soil condition were created for the threats of acidification using pH as indicator. Digital soil maps of pH were created applying the scorpan approach (McBratney et al., 2003) fitting quantile regression forest models (Meinshausen & Ridgeway, 2006) for predicting the soil indicators for both depth intervals (0-20 cm, 20-40 cm). Croplands, water and artificial areas were masked because we did not have pH observations from these land uses in the calibration dataset. The thresholds of soil condition were defined by soil monitoring unit (see map of soil monitoring units for the Basque Country (Román Dobarco et al. 202410.5281/zenodo.14674358), using data from semi-natural forest of native species (e.g., beech, oaks, helm oak, etc.) as reference soils. The thresholds were defined with different percentiles (5%, 12.5% and 25%) from the distribution of reference soils. If the pH of the forest plantations was lower than the threshold, then the soil was considered in “poor condition” (coded with the value 0), and if it was equal or greater, in “good condition” (coded with the value 1). The assessment was applied at each pixel.

The environmental covariates included: Relief covariates were derived from a digital elevation model (DEM) at 25 m resolution (Eusko Jaurlaritza / Gobierno Vasco, 2016) using GRASS GIS 8.3 (GRASS Development Team, 2024) or SAGA GIS (9.2.0) (Conrad et al., 2015): elevation, slope (%), northerness, easterness (cosine and sine transformation of aspect respectively) and interaction between northerness and slope informing of topographic exposure and microclimate, SAGA topographic wetness index (TWI) as a proxy for soil moisture, and valley depth and standardised height as relative elevation metrics, profile and tangential curvature; Climate covariates were the mean annual air temperature (°C), temperature seasonality (standard deviation of monthly temperatures), maximum temperature of the warmest month (°C), mean annual precipitation (mm), and precipitation seasonality (coefficient of variation). Climatic variables were downloaded from CHELSA dataset version 2.1 (Brun et al., 2022); Parent material was represented with lithology and regolith maps at scale 1:25,000 for the Basque Country (Eusko Jaurlaritza / Gobierno Vasco, 1999) available in vectorial format. There were 20 lithology classes that were grouped into 7 broad classes. The regolith had 5 thickness categories (< 0.5 m, 0.5 m – 1 m, 1 m – 2 m, 2 m – 4 m, and > 4 m); Organisms (vegetation) was characterised with the median normalised difference vegetation index (NDVI) for the four quarters of the year, and annual standard deviation as proxies for vegetation. NDVI layers were processed with Google Earth Engine with time series of the Landsat Collection 8 Level 2 Tier 1 (2014-2020) (Crawford et al., 2023; USGS, 2024) after masking clouds and shadows, excluding images with more than 15% of land covered by clouds, and averaged by quarter (January-March, April-June, July-September, October-December). The quantile regression forest models were evaluated with 10-fold cross-validation using root mean squared error (RMSE), bias or mean error (ME), coefficient of determination (R2), and Lin’s concordance correlation coefficient (CCC) (Lin, 1989) as validation statistics. Uncertainty estimates for soil condition assessment were calculated by mapping the probability that the threshold was surpassed from the full-conditional probability distributions of the predicted indicators and expressed as percentage.

The QRF models for pH had good performance in cross-validation, with RMSE = 0.79, ME = 0.01, R2 = 0.53 and Lin’s CCC = 0.67 for 0-20 cm. Similarly, the model for 20-40 cm obtained a RMSE = 0.81, ME = 0.02, R2 = 0.53 and Lin’s CCC = 0.67. The maps of soil pH reflected the influence of lithology and climatic variables, with annual precipitation, temperature seasonality, and maximum temperature of the warmest month as the three most important variables for both depths, followed by lithology and NDVI Q3 for 0-20 cm, and elevation and annual mean temperature for 20-40 cm. The pH was lower in the Atlantic basin following the precipitation gradient, and higher in areas dominated by calcareous rocks and superficial deposits. The surface of forest plantations in the Basque Country considered in unhealthy condition, estimated from pH maps, ranged between 1.6% and 42%.

File name

Description

pH_0_20_forest.tif

Soil pH (water extraction ratio 1:2.5 v/v) of forest soils for the depth interval 0-20 cm

pH_20_40_forest.tif

Soil pH (water extraction ratio 1:2.5 v/v) of forest soils for the depth interval 20-40 cm

condition_pH_0_20_p05_plantations.tif

Condition assessment of plantations at the depth interval 0-20 cm using as threshold for “good condition” the 5% percentile of reference soils (semi-natural forests). Good condition = 1, Poor condition = 0.

condition_pH_0_20_p12_plantations.tif

Condition assessment of plantations at the depth interval 0-20 cm using as threshold for “good condition” the 12.5% percentile of reference soils (semi-natural forests). Good condition = 1, Poor condition = 0.

condition_pH_0_20_p25_plantations.tif

Condition assessment of plantations at the depth interval 0-20 cm using as threshold for “good condition” the 25% percentile of reference soils (semi-natural forests). Good condition = 1, Poor condition = 0.

condition_pH_20_40_p05_plantations.tif

Condition assessment of plantations at the depth interval 20-40 cm using as threshold for “good condition” the 5% percentile of reference soils (semi-natural forests). Good condition = 1, Poor condition = 0.

condition_pH_20_40_p12_plantations.tif

Condition assessment of plantations at the depth interval 20-40 cm using as threshold for “good condition” the 12.5% percentile of reference soils (semi-natural forests). Good condition = 1, Poor condition = 0.

condition_pH_20_40_p25_plantations.tif

Condition assessment of plantations at the depth interval 20-40 cm using as threshold for “good condition” the 25% percentile of reference soils (semi-natural forests). Good condition = 1, Poor condition = 0.

pH.0_20_prob_b_05_plantations.tif

Probability that the soil pH is below the threshold selected for “good condition” (5%), i.e., uncertainty estimate of the soil condition assessment, at 0-20 cm.

pH.0_20_prob_b_12_plantations.tif

Probability that the soil pH is below the threshold selected for “good condition” (12.5%), i.e., uncertainty estimate of the soil condition assessment, at 0-20 cm.

pH.0_20_prob_b_25_plantations.tif

Probability that the soil pH is below the threshold selected for “good condition” (25%), i.e., uncertainty estimate of the soil condition assessment, at 0-20 cm.

pH.20_40_prob_b_05_plantations.tif

Probability that the soil pH is below the threshold selected for “good condition” (5%), i.e., uncertainty estimate of the soil condition assessment, at 20-40 cm.

pH. 20_40_prob_b_12_plantations.tif

Probability that the soil pH is below the threshold selected for “good condition” (12.5%), i.e., uncertainty estimate of the soil condition assessment, at 20-40 cm.

pH. 20_40_prob_b_25_plantations.tif

Probability that the soil pH is below the threshold selected for “good condition” (25%), i.e., uncertainty estimate of the soil condition assessment, at 20-40 cm.

• Date of publication: 31/01/2025

• Reference period: 2024

• Date of creation: 19 July ‎2024

• Last modification: 19 July ‎2024

• Type of data: GeoTIFF file

• Purpose of the data: Soil condition assessment using soil pH as indicator.

• Lineage: First version. The sources of the input data will be described in the accompanied peer review publication and Deliverable 2.1.

• Resolution: 25 m

• Location details (geographic extent):

o North: 4,811,750 m

o South: 4,700,850 m

o West: 461,050 m

o East: 606,500 m

• Projected Coordinate system: ETRS 1989 UTM Zone 30N (EPSG: 25830)

• Geographic Coordinate system: ETRS 1989 (EPSG: 4258)

• Creator or author of the data: Mercedes Román Dobarco, Alex McBratney, Sophie Cornu, Jorge Curiel Yuste.

• Contact: Mercedes Román Dobarco (mercetadzio@gmail.com )

• Access and licensing information: These maps are available under the CC-BY 4.0 License.

• DOI: 10.5281/zenodo.14700581

• Associated publications / Conference presentation: Román Dobarco, M., McBratney, A., Cornu, S., Curiel Yuste, J. Monitoring the condition of forest soils in the Basque Country (Spain). An application of pedogenon mapping for policy implementation. Presented at the Centennial of the IUSS (May 19-21, 2024), Florence, Italy.

• Dataset citation: Román Dobarco, M., McBratney, A., Cornu, S., Curiel Yuste, J. 2025 Digital soil maps for assessing the condition of forest soils of the Basque Country using pH as indicator. DOI: 10.5281/zenodo.14700581

• File size: 3.2 MB – 34.7 MB.

• Keywords: Pedogenon, digital soil mapping, soil monitoring, Soil Monitoring and Resilience Law, soil security.

References

Brun, P., Zimmermann, N.E., Hari, C., Pellissier, L., Karger, D.N., 2022. CHELSA-BIOCLIM+ A novel set of global climate-related predictors at kilometre-resolution. http://dx.doi.org/10.16904/envidat.332

Crawford, C.J., Roy, D.P., Arab, S., Barnes, C., Vermote, E., Hulley, G., Gerace, A., Choate, M., Engebretson, C., Micijevic, E., Schmidt, G., Anderson, C., Anderson, M., Bouchard, M., Cook, B., Dittmeier, R., Howard, D., Jenkerson, C., Kim, M., Kleyians, T., Maiersperger, T., Mueller, C., Neigh, C., Owen, L., Page, B., Pahlevan, N., Rengarajan, R., Roger, J.-C., Sayler, K., Scaramuzza, P., Skakun, S., Yan, L., Zhang, H.K., Zhu,

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore Fort Ross map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore Fort Ross map area data layers. Data layers are symbolized as shown on the associated map sheets.

This Level-3 (L3) soil moisture product provides a composite of daily estimates of global land surface conditions retrieved by the Soil Moisture Active Passive (SMAP) radar as well as a variety of ancillary data sources. SMAP L-band soil moisture data are resampled to an Earth-fixed, global, cylindrical 3 km Equal-Area Scalable Earth Grid, Version 2.0 (EASE-Grid 2.0).

This map shows distribution of the time-averaged shear-wave velocity in the upper 30 m (Vs30) for California. Vs30 is used as a proxy for local geologic site condition in ground motion hazard calculations. The map is produced using data provided by Thompson (ver. 2.0, July 2022), which is based on the method described by Thompson and others (2014) with adjustments (see Thompson, 2022 for detail). Vs30 unit is m/s. Data resolution is 3 arcseconds (approximately 90 m).Due to software limitations, symbology cannot be added to this service. To match the symbology used in the MS48 Additional Maps application, use the following configuration: Esri Color Ramp: Yellow to Dark RedNumber of Classes: 5Classes & Hex Codes:176.1 - 300: #F4FE93300 - 450: #F6DC6A450 - 600: #F2B841600 - 725: #BA671A725 - 1,473.3: #810A01

In order to exploit mineral raw materials close to the Earth's surface, experts are working on trans-regional and national planning documents. To do this, they need maps which clearly depict the raw materials close to the surface in Germany. KOR200 displays Germany's national raw material potential in a comparable way, thus forming a basis for future exploration and investigations as well as making a contribution towards the assurance of the supply of raw materials. The map follows the sheet line system of the topographical survey map 1:200.000 (TÜK 200) and consists of 55 sheets, each with an explanatory booklet. There is a review of the current situation, a description, a depiction and documentation of the occurrence and deposits of mineral raw materials which are usually extracted in mines either on or close to the Earth's surface. Such deposits include, in particular, industrial minerals, rocks and soils, peat, lignite, oil shale and brines. Besides the delimited deposits and areas of raw materials coloured according to the raw material in question, the maps also depict "mining areas" (=operations) or "focal points of several mining areas", each marked with a symbol. The map entries are - just as with the topographical basis - recorded in digitalised form in a databank, from which they can be retrieved via a computer using various search criteria. The entries in the map are supplemented by between 40 to 80 pages of textual explanations, which are currently available only in the printed edition of the map. The text is divided into: - introduction - description of the deposits and occurrence of useful rocks - supply and demand assessment of the deposits and occurrence of raw materials close to the Earth's surface in the area covered by the sheet - possible ways of using the useful rocks present in the sheet area - list of publications - appendix (with, amongst other things, a general legend and survey of sheets)

This feature class depicts Forest Service trails where motorized use is allowed. It contains information on the specific type of motor vehicle and their seasons of use. The feature class is consistent with the appropriate National Forest's Motor Vehicle Use Map (MVUM). Non-motorized trails are not included in this data. Trails in this feature class are legal for some motorized use for at least a portion of the year. Any reference to Open or Dates Open refers strictly to when it is legal to use that motor vehicle on the trail. It is not meant to describe when the conditions would be appropriate for that use. As an example, a trail may be designated open to motorcycles all year long but there may be periods of time when snow depth prevents the use of motorcycles on that trail. It is compiled from the GIS Data Dictionary data and tabular data that the administrative units have prepared for the creation of their MVUMs. This data is published and refreshed on a unit by unit basis as needed. Individual unit's data must be verified and proved consistent with the published MVUMs prior to publication in the Enterprise Data Warehouse (EDW).

Files include data for bike lanes, protected bike lanes, trails, bike routes, shared lane markings, cautionary bike routes, and bridge data from the BikePGH Pittsburgh Bike Map. BikePGH developed this map in 2007 and has been publishing it both on paper and online ever since. See: http://bikepgh.org/maps for more info.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within Californiaâ s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map Californiaâ s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of â landsâ from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of Californiaâ s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bayâ s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Serviceâ Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Scott Creek map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these â ground-truthâ surveying data are available from the CSMP Video and Photograph Portal at http://dx.doi.org/10.5066/F7J1015K. The â seafloor characterâ data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The â potential habitatsâ polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Scott Creek map area data layers. Data layers are symbolized as shown on the associated map sheets.