The Mapping Control Database (MCPD) is a database of mapping control covering Montana. The control were submitted by registered land surveyors or mapping professionals.

Full metadata available at https://mslservices.mt.gov/Geographic_Information/Data/DataList/datalist_Details.aspx?did=62c565ec-de6e-11e6-bf01-fe55135034f3.

Long-term habitat mapping and change detection are essential for the management of coastal wetlands as well as for evaluating the impact of conservation policies. Earth observation (EO) data and techniques are a valuable resource for long-term habitat mapping. Although the use of EO data is well developed for the automatic production of land cover (LC) maps, this is not the same for habitat maps, which are highly related to biodiversity. In a previous paper, we used the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) environmental attributes (e.g. water quality, lithology, soil surface aspect) for LC-to-habitat class translation. However, these environmental attributes are often not openly available, not updated or are missing. This paper offers an alternative, knowledge-based solution to automatic habitat mapping. When only expert rules and EO data are used, the final overall map accuracy, which is obtained by comparing reference ground truth patches to the ones depicted in the output map, is lower (75·1%) than the accuracy obtained using environmental attributes alone (97·0%). Some ambiguities that still remain in habitat discrimination are resolved by integrating the use of LCCS environmental attributes (if available) and expert rules. In this paper, we use very high-resolution (VHR) satellite data and LIDAR data. LC classes are labelled according to the LCCS taxonomy, which offers a framework to integrate EO data with in situ and ancillary data. Output habitat classes are labelled according to the European Habitats Directive (92/43 EEC Directive) Annex I habitat types and Eunis habitat classification. Two Natura 2000 coastal wetland sites in southern Italy are considered. Synthesis and applications. In this paper, we study the exploitation of ecological rules on vegetation pattern, plant phenology and habitat geometric properties for automatic translation of land cover (LC) maps to habitat maps in coastal wetlands. The methodology is useful for relatively inaccessible sites (e.g. wetlands) as it does not require in-field campaigns (generally costly) but only the elicitation of ecological expert rules. This can support site (e.g. Natura 2000) managers in long-term automatic habitat mapping. Habitat changes can be automatically detected by comparing map pairs, and trends can be quantified. This is particularly useful to satisfy the commitments of the European Habitats Directive (92/43/EEC), which requires Member States to take measures to maintain as, or restore to, favourable conservation status those natural habitat types and species of community interest that are listed in the Annexes to the Directive.

This dataset corresponds to the expert classification results for the 2019 Land-cover classification of the Andean páramo.

This dataset is presented in a raster format, with a pixel resolution of 1 arc second (30 m).

It was obtained by conducting Maximum Likelihood and Random Forest classifications of Landsat 8 Imagery (2018-2019) for the páramo region. The obtained results were contrasted and validated to generate the expert classification, which was then cropped at the Andean forest - páramo treeline.

Classes are coded as such: 1 - water, 2 - shrubland, 3 - forest, 4 - crop, 5 - desert, 6 - rosette, 7 - glacier, 8 - grassland, 9 - meadow, 10 - rock, 11 - periglacial desert, 12 - urban

The Geoproduct Official Surveying Simplified (MOPUBE) contains the most accurate, available data on the land of the Canton of Bern. In particular, land cover and properties are ideally suited as a map background for the presentation of thematic information. The geo-product Official Surveying Simplified is updated weekly; it has no legal effect. It contains a subset of Official Surveying (AV) data, namely the geometric and attributive ground data required by most GIS users. Further information, which is mainly of interest to surveying specialists, is omitted. The spatial data set includes extracts from the information levels fixed points, land cover, individual objects, nomenclature, properties, administrative and technical classifications of official surveying. In terms of structure, the geoproduct corresponds to the Federal Government's data model "MOpublic". However, it has been supplemented with some cantonal requirements. Replaces the geoproduct: Official Measurement Reduced (AVR)

These are products of the Soil and Landscape Grid of Australia Facility generated through disaggregation of the Western Australian soil mapping. There are 9 soil attribute products available from the Soil Facility: Available Water Holding Capacity - Volumetric (AWC); Bulk Density - Whole Earth (BDw); Bulk Density - Fine Earth (BDf); Clay (CLY); Course Fragments (CFG); Electrical Conductivity (ECD); pH Water (pHw); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.

The DSMART tool (Odgers et al. 2014) tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes (Holmes et al. Submitted). The soil class maps were then used to produce corresponding soil property surfaces using the PROPR tool (Odgers et al. 2015; Odgers et al. Submitted). Legacy mapping was compiled for the state of WA from surveys ranging in map scale from 1:20,000 to 1:2,000,000 (Schoknecht et al., 2004). The polygons are attributed with the soils and proportions of soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) and kriging based on site data by Viscarra Rossel et al. (Submitted). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. In Prep).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for Western Australia’s agricultural zone was developed via WA’s Department of Agriculture and Food (Schoknecht et al., 2004). Seventy-three soil classes (termed ‘WA soil groups’ Schoknecht and Pathan, 2013) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

Western Australia’s expert defined typical range of soil properties by soil class was used to provide reference soil properties to PROPR. These estimates were made separately for each physiographic zone across WA, and are based on available profile data and surveyor experience. Uncertainty bounds were determined by the minimum and maximum soil properties at the ‘qualified soil group’ level, and the property value of the most common soil in the map unit was used to define the typical soil property. This methodology was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties. Depth averaging was applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (Submitted).

The Visual Resource Inventory Classes Polygon is a component of the Visual Resources Inventory (VRI) and includes information needed for inventorying for visual values on BLM-managed public lands according to policy direction found in Manual 8400 and related Handbook 8410-1. Current policy requires that every acre of BLM land be inventoried for visual values and be assigned one of four VRI classes (Class I, II, III, or IV). Class I is reserved for those areas where a management decision has been made previously to maintain a natural landscape. This includes areas such as national wilderness areas, the wild section of national wild and scenic rivers, and other congressionally and administratively designated areas where decisions have been made to preserve a natural landscape. The inventory classes provide the basis for considering visual values in the resource management planning (RMP) process and constitute the current state of visual resource values as part of the affected environment sections of environmental analyses. Particularly, this data pertains Visual Resource National, State, District and Field Office leads and any group, program, or organization that is involved with surface disturbance activities or visually altering activities, including: Land Use Planners, Realty Specialists, Recreation Planners, Natural Resource Specialists, Landscape Architects, Cultural Resource Specialists, Fluid Minerals and Renewable Energy.

This dataset is a subset of the official national dataset, containing features and attributes intended for public release and has been optimized for online map service performance. The Schema Workbook represents the official national dataset from which this dataset was derived.

This dataset includes the polygon features representing the spatial extent and boundaries of the Bureau of Land Management (BLM) National Landscape Conservation System (NLCS) Wilderness Areas (WLD), Wilderness Study Areas (WSA), and Other Related Lands with wilderness characteristics (LWC) or managed for wilderness characteristics (MWC).The data standard for these boundaries will assist in the management of all eleven designations within the NLCS. Particularly, NLCS data pertains to the following BLM groups and their purposes: Land Use Planners, GIS Specialists, NLCS team leads, BLM managers, and public stakeholder groups.As early as 1926, the earliest advocates of wilderness preservation had acknowledged the beauty and important ecological values of the desert lands under the BLM’s administration as candidates for wilderness protection. In 1964, Congress established the National Wilderness Preservation System and designated the first Wilderness Areas in passing the Wilderness Act. The uniquely American idea of wilderness has become an increasingly significant tool to ensure long-term protection of natural landscapes. Wilderness protects the habitat of numerous wildlife species and serves as a biodiversity bank for many species of plants and animals. Wilderness is also a source of clean water.The Federal Land Policy and Management Act of 1976 directed the BLM to inventory and study its roadless areas for wilderness characteristics. Here identified areas became WSAs. The establishment of a WSA served to identify areas for Congress to consider for addition to the National Wilderness Preservation System. To be designated as a WSA, an area must have the following characteristics: Size - roadless areas of at least 5,000 acres of public lands or of a manageable size; Naturalness - generally appears to have been affected primarily by the forces of nature; Opportunities - provides outstanding opportunities for solitude or primitive and unconfined types of recreation. In addition, WSAs often have special qualities such as ecological, geological, educational, historical, scientific and scenic values.In June 2000, the BLM responded to growing concern over the loss of open space by creating the NLCS. The NLCS brings into a single system some of the BLM's premier designations. The Wilderness Areas, WSAs, and Other Related Lands represent three of these eleven premier designations. By putting these lands into an organized system, the BLM hopes to increase public awareness of these areas' scientific, cultural, educational, ecological and other values.The BLM's management of all public lands included data within the NLCS is guided by the Federal Land Policy and Management Act (FLPMA). FLPMA ensures that many of BLM's traditional activities such as grazing and hunting, continue on the lands within the NLCS, provided these activities are consistent with the overall purpose of the area.A Wilderness is a special place where the earth and its community of life are essentially undisturbed; they retain a primeval character, without permanent improvements and generally appear to have been affected primarily by the forces of nature. BLM NLCS Other Related Lands are lands not in Wilderness or WSAs that have been determined to have wilderness character through inventory or land use planning. These lands fall into one of two categories. The first category are lands with "wilderness value and characteristics". These are inventoried areas not in Wilderness or WSAs that have been determined to meet the size, naturalness, and the outstanding solitude and/or the outstanding primitive and unconfined recreation criteria. The second category are "wilderness characteristic protection areas". These are former lands with "wilderness value and characteristics" where a plan decision has been made to protect them.To be designated as a WSA, an area must have the following characteristics: Size - roadless areas of at least 5,000 acres of public lands or of a manageable size; Naturalness - generally appears to have been affected primarily by the forces of nature; Opportunities - provides outstanding opportunities for solitude or primitive and unconfined types of recreation. In addition, WSAs often have special qualities such as ecological, geological, educational, historical, scientific and scenic values.There were forty-seven Wilderness Areas established under the Arizona Wilderness Act of 1984 and Arizona Desert Wilderness Act of 1990. These Acts require the BLM to file boundary legal descriptions and maps to Congress for each Wilderness Area. The standards, format, and language for the legal descriptions and boundary maps were developed during regular meetings of the NLCS Coordinator, GIS specialists and the Cadastral Surveyors. Guidance was provided from congressionally-required map and legal boundary descriptions detailed in the NLCS Designation Manual 6120 (March, 2010). All Arizona BLM Wilderness Area boundary legal descriptions and maps have been transmitted to Congress and certified by the Chief of Cadastral Survey and Arizona State Director. There should be no changes to Wilderness Boundary GIS data. Boundary changes can only be made through an amendment to the legal description and this would need to be sent back to Congress.