India Location-based Services Market size was valued at USD 460 Million in 2024 and is projected to reach USD 1563 Million by 2032, growing at a CAGR of 16.7% from 2026 to 2032.India Location-based Services Market: Definition/ OverviewLocation-based services (LBS) are applications or services that use a user's geographic location to provide personalized content, services, or information. These services typically rely on technologies such as GPS, Wi-Fi, or cellular data to determine the user's position and tailor experiences based on that location. LBS can be offered through mobile apps, websites, or IoT devices, providing users with relevant information or guidance wherever they are.The application of location-based services spans across various industries, from navigation and travel to retail and marketing. For instance, apps like Google Maps or Uber use LBS to offer real-time route guidance, ride-hailing services, and traffic updates. Retailers use LBS for targeted advertising, sending promotional offers to customers when they are near a store. Additionally, LBS are used in healthcare for monitoring patient movement, in logistics for fleet management, and even in social networking apps where users can share their locations with friends.

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2032, growing at a CAGR of 12.10% during the forecast period 2026-2032.Geospatial Solutions Market: Definition/ OverviewGeospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth's surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today's interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

The agricultural mapping software market is experiencing robust growth, driven by the increasing adoption of precision agriculture techniques and the need for enhanced farm management efficiency. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for optimized resource utilization, including water and fertilizers, is pushing farmers to adopt data-driven decision-making. Secondly, technological advancements in GPS, GIS, and sensor technologies are making agricultural mapping software more affordable and accessible. Furthermore, government initiatives promoting digital agriculture and precision farming in various regions are significantly contributing to market growth. Key segments within the market include software solutions for crop monitoring, yield prediction, and soil analysis, each contributing to the overall market expansion. Leading companies like Trimble, CNH Industrial, and Geosys are at the forefront of innovation, continuously developing advanced features and functionalities to meet evolving farmer needs. However, challenges remain, including the initial investment costs associated with adopting new technologies and the need for reliable internet connectivity in rural areas, potentially hindering wider adoption in some regions. Despite these restraints, the long-term outlook for agricultural mapping software remains positive. The increasing availability of affordable drones and remote sensing technologies is further enhancing the capabilities of these software solutions, allowing for more accurate and timely data collection. The integration of artificial intelligence (AI) and machine learning (ML) is also paving the way for predictive analytics and automated decision support systems, which will further transform farm management practices. The market is likely to witness increased consolidation as larger companies acquire smaller players, leading to the development of more comprehensive and integrated solutions. The geographical expansion of the market, particularly in developing economies with a large agricultural sector, represents a significant opportunity for growth in the coming years. The market will continue to evolve as technology matures and more farmers embrace precision agriculture as a means of improving productivity and profitability.

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: Global Positioning System Ground Control Points and Field Site Locations from 1995

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

This data set is in ARC/INFO export format and contains Global Positioning Systems (GPS) ground control points in and around the International Paper Experimental Forest, Howland ME.

A Trimble roving receiver placed on the top of the cab of a pick-up truck and leveled was used to collect position information at selected sites (road intersections) across the FED project study area. The field collected data was differentially corrected using base files measured by a Trimble Community Base Station. The Community Base Station is run by the Forestry Department at the University of Maine, Orono (UMO). The base station was surveyed by the Surveying Engineering Department at UMO using classical geodetic methods. Trimble software was used to produce coordinates in Universal Transverse Mercator (UTM) WGS84. Coordinates were adjusted based on field notes. All points were collected during January 1995 and differentially corrected.

description: The proposed project will hire and train an invasives mapping and monitoring crew for two primary purposes. The first will be to locate and map populations of invasive plants as part of a larger station-sponsored program to describe and treat multiple invasive species on Complex refuges. This larger station-sponsored program involves the formation and support of a weed treatment strike team. The mapping efforts will be used to document and describe infestations and to reduce the strike team s search times, increasing their ability to control targeted species at the best biological time. The second purpose will be to collect efficacy and impact information from past treatments, including past strike team efforts and other station-sponsored projects. Training of the volunteers in the operation of GPS equipment, weed identification, monitoring protocols, and data management will also develop a pool of trained individuals for future invasive plant management activities. GPS/GIS technologies and a customized data dictionary will be used to inventory and map the location of invasive species populations on refuge lands. The combination of the proposed project and the station-sponsored strike team will help document, monitor, and reduce the incidence of invasive species on refuge lands as a step in the recovery of native shrub-steppe, wetland, and riparian habitats, and will help to evaluate and inform refuge management actions.; abstract: The proposed project will hire and train an invasives mapping and monitoring crew for two primary purposes. The first will be to locate and map populations of invasive plants as part of a larger station-sponsored program to describe and treat multiple invasive species on Complex refuges. This larger station-sponsored program involves the formation and support of a weed treatment strike team. The mapping efforts will be used to document and describe infestations and to reduce the strike team s search times, increasing their ability to control targeted species at the best biological time. The second purpose will be to collect efficacy and impact information from past treatments, including past strike team efforts and other station-sponsored projects. Training of the volunteers in the operation of GPS equipment, weed identification, monitoring protocols, and data management will also develop a pool of trained individuals for future invasive plant management activities. GPS/GIS technologies and a customized data dictionary will be used to inventory and map the location of invasive species populations on refuge lands. The combination of the proposed project and the station-sponsored strike team will help document, monitor, and reduce the incidence of invasive species on refuge lands as a step in the recovery of native shrub-steppe, wetland, and riparian habitats, and will help to evaluate and inform refuge management actions.

OPEN Data View service. The Wildland Fire Risk Assessment project was developed by the National Park Service's Fire and Aviation Management program as a response to the devastating 2011 wildfire season. This project developed a consistent assessment method that has been applied to NPS units nationwide regardless of variations in climate, fuels, and topography.The assessment, based on Firewise® assessment forms, evaluates access, surrounding environment, construction design and materials, and resources available to protect facilities from wildland fire. The data collected during the assessment process can be used for:Identifying, planning, prioritizing and tracking fuels treatments at unit, regional and national levels, and Developing incident response plans for facilities and communities within NPS units.The original spatial data for the assessments comes from a variety of sources including the NPS Buildings Enterprise Dataset, WFDSS, NPMap Edits, manually digitized points using Esri basemaps as a reference at various scales, and GPS collection using a multitude of consumer and professional grade GPS devices. The facilities that have been assessed and assigned a facility risk rating have been ground-truthed and field verified. (In some rare occasions, facilities have been verified during remote assessments. Those that have been remotely assessed are marked as such). The resulting data is stored in a centralized geodatabase, and this publicly available feature layer allows the user to view that data.The NPS Facilities feature layer includes the following layers and related tables:Facility - A facility is defined by the NPS as an asset that the NPS desires to track and manage as a distinct identifiable entity. In the case of wildland fire risk assessments, a facility is most often a structure but in special instances, a park unit may wish to identify and assess other at-risk features such as a historic wooden bridge or an interpretive display. The facilities are assessed based on access, the surrounding environment, construction design, and protection resources and limitations, resulting in a numerical score and risk adjective rating for each facility. These ratings designate the likelihood of ignition during a wildland fire. The facilities are symbolized by their respective risk rating.Community - A community is a group of five or more facilities, a majority of which are within 600 feet of each other, that share common access and protection attributes. The community concept was developed to facilitate data collection and entry in areas with multiple facilities and where it made sense to apply treatments and tactics at a scale larger than individual facilities. Most of the community polygons are created using models in ArcMap, but some may have been created or edited in the field using a Trimble GPS unit. *The NPS Facilities layer is updated continually as new wildfire risk assessments are conducted and the Wildland Fire Risk Assessment project progresses. The assessment data contained here is the most current data available.*More information about the NPS Wildland Fire Risk Assessment Project, and the NPS Facilities data itself, can be found at the New Wildland Fire Risk Assessments website. This site provides information on the data collection process, additional ways to access the data, and how to conduct assessments yourself (for both NPS and non-NPS facilities).FACILITY ATTRIBUTES
Unit_ID NWCG Unit ID, Two letter state code and three letter unit abbreviation, for example UTZIP for Zion National Park in Utah.

Fire_Bldg_ID User maintained unique ID for Facility layer.

Building ID Unique Id from the NPS Enterprise Buildings dataset.

FMSS ID Unique ID for the facility in the NPS FMSS database.

Community ID Unique ID linking facility to a community

Assess Scale Indicates if the facility is part of a community/ will be included in a community assessment. Communities are pre-defined by regional GIS staff and visible in this map as a blue perimeter.
Answer "Yes" if you are adding a facility point within a predefined community.

Common Name Name of the structure. In most cases, the name comes from the NPS FMSS database.

Map Label Numerical label used for mapping purposes.

Owner Indicates who owns the structure being assessed.

Facilty Type Indicates the facility type OR if the facility has been REMOVED, DESTROYED, has NO WILDLAND RISK, is PRIVATE - NO SURVEY REQUIRED or DOES NOT REQUIRE A SURVEY (because it is planned for removal).

Facility Use What is the primary use of the facility?

Building Occupied Is the building occupied?

Community Name Name of the community the facility is located within, if any.

Field Crew Field crew completing the assessment.

Last Site Visit Date Date which the facility was visited and assessment data reviewed/updated.

Location General location within the unit – may use FMUs, watersheds, or other identifier. One location may contain multiple communities and individual facilities. Locations are used to filter data for reports and map products.

PrimaryAccess Primary method of accessing the facility.

IngressEgress Number of routes into and away from the facility.

AccessWidth Width of the road or driveway used to access the facility.

AccessCond Grade and surface material of the road or driveway used to access the facility.

BridgeCond Condition, based on load limits and construction.

Turnaround Describes how close can a fire apparatus drive to the facility and once there, whether it can turnaround.

BldgNum Is the facility clearly signed or numbered?

FuelLoad Fuel loading within 300 ft of the facility (see appendix D of the Wildfire Risk Assessment User Guide)

FuelType Predominant fuel type within 300 ft of the facility.

DefensibleSpace Amount of defensible space around the facility, see criteria for evaluating defensible space in the Wildfire Risk Assessment User Guide.

Topography Predominant slope within 300 ft of facility.

RoofMat Roofing material used on the facility.

SidingMat Siding material used on the facility.

Foundation Describes the facility’s foundation.

Fencing Indicates presence of any wooden attachments, fencing, decking, pergola, etc. and fuels clearance around those attachments.

Firewood Firewood distance from facility.

Propane Inidicates if a propane tank exists within 200 feet of a structure and if there is any fuels clearance around the propane tank(s).

Hazmat List of hazmat existing on the site.

WaterSupply Water supply available to the facility.

OverheadHaz Identifies the presence of overhead hazards that will limit aerial firefighting efforts.

SafetyZone Identifies the presence of any potential safety zones.

SZRadius Radius of any potential safety zones.

Obstacles Additional obstacles, not already included in assessment, that will limit firefighting efforts- to include items such as UXO, hazmat,etc. If there are additional obstacles, be sure to comment in Assessment Comments or Tactic descriptions where appropriate.

TriageCategory Refer to IRPG for descriptions of each category. This information will be displayed in the NIFS Structure Triage layer for incident response.

Score Sum of attribute values for all assessment elements including access, environment, structure and protection portions of the assessment.

Rating Wildland fire risk rating based on score. Ratings are No Wildland Risk, Low, Moderate and High. Rating indicates likelihood if facility igniting if a wildland fire occurs.

ProtectionLevel Inidcates structures which are priority for protection during a wildfire. For Alaska Region data, indicates identified protection level for structure. For lower 48, enter ‘Unknown’ unless specified by local unit.

ProtLevelApprovalName Name of person who designated Protection Level

ProtLevelApprovalDate Date Protection Level Designated

ResourcesOfConcern Indicates if it is necessary to contact park staff before engaging in suppression activities because special resources (natural, cultural, historic) of concern are present?

AssessComments Explain any aspects of the assessment that require extra detail.

RegionCode NPS Region Code - AKR, IMR, NER, NCR, MWR, PWR or SER

UnitCode

NPS Unit Code

ReasonIncluded Why is the point in the dataset – NPS owned, Treatment Planning, Protection Responsibility, Planning (other than treatments). Intent of the dataset is to document wildfire risk for NPS owned structures. Other structures or facilities may be included at the discretion of the unit's fire management staff.

Restriction How can the data be shared – Unrestricted, Restricted - No Third Party Release, Restricted – Originating Agency Concurrence, Restricted – Affected Cultural Group Concurrence, Restricted - No Release, Unknown. Only unrestricted data is included in this dataset.

Local_ID Field which can be used to store unique ids linking back to any local datasets.

RevisitInterval How many years will it take for the fuels to change significantly enough to change the score and rating for this facility?

IsVisited Use this field to keep track of what you have done during a field session. Filter on this field to see what has been assessed and what still needs visited during a field data collection session.

DeleteThis Users enter yes if this is this a duplicate or was no facility found.
If you know the facility was REMOVED or DESTROYED, go back to Facility Type and enter that information there.

Data_Source

FirewiseZone1 List of treatments needed to

Drygalski Ice Tongue is the largest ice tongue in Ross Sea. It is 70 kilometers long (43.4 miles) and 20 kilometers wide (12.4 miles) and drains David Glacier. This ice tongue is large enough to show up on maps of the Antarctic continent. Drygalski Ice Tongue has been growing seaward 150 to 900 meters per year (492 to 2952 feet) for the past few decades.

Static GPS measurements were made starting from 1991 on the David Glacier and on its Drygalski Ice Tongue. As reference station was adopted a 3D benchmark installed on a rocky emergence at Hughes Bluff, while on the ice were fixed 8 alluminium pipes with distances to the reference station ranging from 3 to 120 km. During the survey the GPS antennas were fixed, by adapters for classical tribach, directly on the top of the aluminum pipes permitting in this way a solid and reliable autocentering positioning of the antennas. On the basis of the reliable results obtained with this kind of benchmark the GPS measurements were repeated in successive years, to investigate if seasonal or yearly variations occur on the flow speed.

The same approach was used also to obtain information about Priestley and Reeves Glaciers. Currently the numbers of points systematically measured are 11 for the David Glacier and its Drygalski Tongue, 3 for the Priestley Glacier and Nansen Ice Sheet, 2 for the Reeves Glacier. In two successive days during the 1993/1994 expedition, 24-hour-long measurements using the GPS continuous kinematic technique were performed on five points of the David Glacier and its Drygalski Ice Tongue. At the same time two other GPS receivers were located, the first on a fixed reference station , the second on the fast ice close to Terra Nova Bay Station. The purpose of these measurements was to record the response of floating Drygalski Ice Tongue to the ocean tide, in order to individuate the position of the grounding line and the hydrostatic equilibrium of floating area. Measurements of the same points have been repeated in 1992/1993, 1993/1994, 1994/1995 and 1995/1996.Data have been processed by Geotracer v.2.25 software.

For a file geodatabase (.gdb) Click Here (includes files used to create data).For the final report, full documentation, and metadata Click Here.Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. Digitizing is done as Geodatabase feature classes using ArcPro 2.9.2 with USDA NAIP imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process. Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.AttributesLanduse– A general land cover classification differentiating how the land is usedAgriculture: Land managed for crop or livestock purposesOther: A broad classification of wildlandRiparian/Wetland: Wildland influenced by a high water table, often close to surface waterUrban: Developed areas, includes urban greenspace such as parks.Water: Surface water such as wet flats, streams, and lakes. CropGroup– Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc. Description– Attribute that describes/indicates the various crop types and land use types determined by the GIS process. IRR_Method– Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the cropDry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plotNone: Associated with non-agricultural landSprinkler: Water is applied above the crop via sprinklers that generally move across the field.Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian areaAcres– Calculated acreage of the polygon. State– State where the polygons are found. County– County where the polygons are found. Basin– The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes. SubArea– The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe. Label_Class– Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop. LABEL– A shorthand descriptive label for each crop description and irrigation type. Class_Name– The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description). OldLanduse– Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse.LU_Group– These codes represent some in-house groupings that are useful for symbology and other summarizing.SURV_YEAR– Indicates which year/growing season the data represents.

In English: The main activity carried out in the Antarctic campaign in the Marambio Island, consisted of the geo-positioning of relevant topographical and geologic elements of the James Ross Island for support of the geologic mapping.

The positioning of fourteen reference points was carried out with the help of two MAGELLAN 5000-PRO GPS outfits with external antennas and sub-metric kits, and differential GPS methods were used. The registration of the data was carried out in portable computers.

The fixed station of measure was located in the Marambio Island, in a point previously positioned with regard to a geodesic vertex settled by the U. S. Geological Survey. The mobile station was transported by two helicopters of the Argentinean Air Force to the target points in the James Ross Island.

At the same time that GPS measurements were made, a sampling of rocks of the James Ross Volcanic Group was carried out for the petrologic and geochemical study of this volcanic unit.

They were also carried out analysis of olivines, clinopyroxenes, ore minerals and zeolites by means of electronic microprobe. In this first stage of the studies, it is important to highlight the fact that the magnesium content of ilmenites is relatively high and that the contents of titanium of the clinopyroxenes is moderate and similar to the one observed in this mineral in typica] alkaline basalt series.

En Espanol: La actividad fundamental desarrollada en la campana en la Isla de Marambio consistio en la georreferenciacion de elementos topograficos y geologicos en la isla James Ross, para el apoyo de la cartografia geologica. Se realizo el posicionamiento de catorce puntos mediante tecnicas de GPS diferencial, utilizando dos equipos MAGELLAN 5000-PRO, con antenas externas y kits submetricos efectuandose registros de los datos en ordenadores portatiles.

Se establecio una estacion de medida en la Isla de Marambio, georreferenciada con respecto a un vertice geodesico situado en la isla y posicionado por el U.S. Geological Survey, mientras que la estacion movil, era desplazada a los puntos de medida en la isla James Ross en los helicopteros de las Fuerzas Aereas Argentinas de la Base de Marambio, realizandose alli las mediciones correspondientes, asi como los balizamientos y las fotografias aereas para la identificacion precisa de los puntos.

Simultaneamente a la campana de medidas y aprovechando los desplazamientos, se realizo una toma de muestras de rocas volcanicas del Grupo James Ross con objeto de efectuar estudios petrologicos y geoquimicos sobre este grupo volcanico.

Se realizaron analisis mediante microsonda electronica de olivinos, cliropiroxenos, ceolitas y minerales opacos, siendo de destacar en esta primera fase de los estudios, los contenidos relativamente elevados de magnesio en las ilmenitas de las rocas subvolcanicas y el que los contenidos de titanio de los cliropiroxenos es moderado y equivalente al de los otros piroxeno titanados de series basalticas alcalinas tipicas

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: County Soil Survey Data with Attributes

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

Additionally, almost 450 ha of the surrounding area consists of bogs and other wetlands. Generally, the soils throughout the forest are glacial tills, acid in reaction, with low fertility and high organic composition. These soils are classified primarily within three suborders: orthods, orchrepts, and aquepts. The climate is chiefly cold, humid, and continental and the region exhibits a snowpack of up to 2 m from December through March.

The original soil polygons were obtained by digitizing a 1963 USDA General Soil Map of Penobscot County, Maine. All of the soil symbols used were taken directly off of the county soil map. Data from the State Soil Geographic Database (STATSGO) were cross-matched. The county symbol was chosen as the identifier, and a STATGO identifier that best "fits" the county soil identifier was selected. The original maps used for the digitization came in 6 map sheets. All of the sheets were digitized, corrected, edge-matched, and appended. Once this was finished, topology was built, new items and attributes were added.

The data in its current form can be used to delineate basic soil groups. However, because the STATSGO map unit identifier is located in each polygon the user can link any of the other STATSGO data sets depending on the desired information. The identifier is the key for creating a very detailed and thorough soils data set. Once linked the data can be used for ecological modeling, resource management, and many other applications.

Ice sheet elevation data are collected over Greenland with NASA's Airborne Topographic Mapper (ATM). The data are known as the Greenland Airborne Precision Elevation Survey (GRAPES).

The ATM is a laser altimeter flown on NASA aircraft. The Global Positioning System (GPS) of orbiting satellites is used to navigate the aircraft's autopilot in order to provide precise location information for repeat coverage.

The data are collected yearly starting in 1991. The GRAPES data currently available include results from the 1993 mission, with other data to be included soon. Flight trajectory data are available for all years beginning with 1993.

The data collected by ATM form baseline measurements of ice elevation of Greenland. The data will be used in conjunction with the future elevation measurements of the Geoscience Laser Altimeter System (GLAS) instrument onboard the ICESat satellite (to be launched in 2001). Changes in ice sheet elevation measurements provide a better understanding of glacial changes that may be due to global climate change.

For more information, see http://atm.wff.nasa.gov/

[This summary was derived from the pages of the Observational Science Branch at NASA Wallops Flight Facility. The Observational Science Branch is a division of NASA's Goddard Space Flight Center Laboratory for Hydrospheric Processes.]

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This dataset gives the extents of South Australian pastoral lease stations and other properties within the pastoral region of SA. The extents of the properties shown are based on the areas being managed by the leasees and boundaries are defined by fence lines rather than legal lease boundaries. Fences and legal lease boundaries are frequently divergent.

Purpose

This dataset will show the extents of South Australian pastoral lease stations within the pastoral region of SA.

Dataset History

Assessment/Inspection officers visit each station and drive around the property using a mobile device with GPS capability for field data entry including tracking and waypoints of features. The station owner/manager also contribute new information. Maps are often used to explain complex fencing changes. The GIS officer is responsible for adding the changes collected in the field into the database using ESRI ArcGIS software. Imagery and GoogleEarth are often used to verify data collected however often this is based on 2007 or older imagery.Linework is based on data captured from a variety of sources, some of which are not known.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) Pastoral Stations - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/22ce4795-d3a9-432a-89a7-8fe53391d50d.

The data set consists of 1:25,000 topographic maps covering Lutzow-Holm Bukt coast and major bare rock areas and inland mountains. The contour interval is 10 m. Maps of Lutzow-Holm Bukt coast were published in 1965 - 1986, and those of Prince Olav coast in 1974 - 1985. Total number of map sheets for these areas is 61. Maps of Yamato Mountains were published in 1980 with 11 sheets. All maps have been digitized into raster data and are available with TIFF format.

These data include the types and extent of irrigated crops as well as information concerning dry agriculture, wetlands, open water, and urban areas. The annual data published by the program includes a finalized WRLU geospatial information systems (GIS) dataset, geospatial maps, and this report. Once published, the data are used for various planning purposes including approximating cropland water use, evaluating irrigated land losses, conversion to urban lands, planning for new water development, estimating irrigated acreages, and developing water budgets.Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made each subsequent year. While this improves the data we report, it makes comparisons to historical data difficult. Increases or decreases in acres reported should not be construed to represent definitive trends.In 2024 the USDA Cropland Data Layer (CDL) underwent several methodological changes. These changes included switching from a decision tree classifier to using a random forest classifier, implemented the use of Google Earth Engine (GEE) for data processing, and changed several crop classifications assigned values. Analysis was conducted to adjust and normalize these data as close to the prior methodologies as possible. For more detailed information regarding these changes please refer to the USDA CDL metadata.For a file geodatabase (.gdb) Click Here (includes files used to create data).For the final report, full documentation, and metadata Click Here.Feature Classes are loaded onto tablet PCs and Field crews are sent to label the crop or land cover type and irrigation method for a subset of select fields or polygons. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. Digitizing is done as Geodatabase feature classes using ArcPro 3.5.1 with Sentinel imagery as a background with other layers added for reference. Updates to existing field boundaries of individual agricultural fields, urban areas and more are precisely digitized. Changes in irrigation type and land use are noted during this process. Cropland Data Layer (CDL) rasters from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) are downloaded for the appropriate year. https://nassgeodata.gmu.edu/CropScape/Zonal Statistics geoprocessing tools are used to attribute the polygons with updated crop types from the CDL. The data is then run through several stages of comparison to historical inventories and quality checking in order to determine and produce the final attributes.AttributesLanduse– A general land cover classification differentiating how the land is usedAgriculture: Land managed for crop or livestock purposesOther: A broad classification of wildlandRiparian/Wetland: Wildland influenced by a high water table, often close to surface waterUrban: Developed areas, includes urban greenspace such as parks.Water: Surface water such as wet flats, streams, and lakes. CropGroup– Groupings of broader crop categories to allow easy access to or query of all orchard or grain types etc. Description– Attribute that describes/indicates the various crop types and land use types determined by the GIS process. IRR_Method– Crop Irrigation Method carried over from statewide field surveys ending in 2015 and updated based on imagery and yearly field checks.Drip: Water is applied through lines that slowly release water onto the surface or subsurface of the cropDry Crop: No irrigation method is applied to this agricultural land, the crop is irrigated via natural processes.Flood: Water is diverted from ditches or pipes upland from the crop in sufficient quantities to flood the irrigated plotNone: Associated with non-agricultural landSprinkler: Water is applied above the crop via sprinklers that generally move across the field.Sub-irrigated: This land does not have irrigation water applied, but due to a high water table receives more water, and is generally closely associated with a riparian areaAcres– Calculated acreage of the polygon. State– State where the polygons are found. County– County where the polygons are found. Basin– The hydrologic basin where the polygons are found, closely related to HUC 6. These basin boundaries were created by DWRe to include portions of other basins that have inter-basin flows for management purposes. SubArea– The subarea where the polygons are found, closely related to the HUC 8. Subareas are subdivisions of the larger hydrologic basins created by DWRe. Label_Class– Combination of Label and Class_Name fields created during processing that indicates the specific crop, irrigation, and whether the CDL classified the land as a similar crop or an “Other” crop. LABEL– A shorthand descriptive label for each crop description and irrigation type. Class_Name– The majority pixel value from the USDA CDL Cropscape raster layer within the polygon, may differ from final crop determination (Description). OldLanduse– Similar to Landuse, but splits the agricultural land further depending on irrigation. Pre-2017 datasets defined this as Landuse.LU_Group– These codes represent some in-house groupings that are useful for symbology and other summarizing.SURV_YEAR– Indicates which year/growing season the data represents.