Terrain data, as defined in FEMA Guidelines and Specifications, Appendix N: Data Capture Standards, describes the digital topographic data that was used to create the elevation data representing the terrain environment of a watershed and/or floodplain. Terrain data requirements allow for flexibility in the types of information provided as sources used to produce final terrain deliverables. Once this type of data is provided, FEMA will be able to account for the origins of the flood study elevation data. (Source: FEMA Guidelines and Specifications, Appendix N, Section N.1.2).

Terrain data, as defined in FEMA Guidelines and Specifications, Appendix N: Data Capture Standards, describes the digital topographic data that was used to create the elevation data representing the terrain environment of a watershed and/or floodplain. Terrain data requirements allow for flexibility in the types of information provided as sources used to produce final terrain deliverables. Once this type of data is provided, FEMA will be able to account for the origins of the flood study elevation data. (Source: FEMA Guidelines and Specifications, Appendix N, Section N.1.2). NAD83 State Plane Kentucky Single Zone FIPS 1600 is the projection and coordinate system for this project.

Fugro EarthData Company furnished the collection, processing, and development of LiDAR for 825 square miles in Washington (805 square miles of Thurston County and 20 square miles of Gray's Harbor County) to support the FEMA Region 10 RiskMap. All data was acquired between June and July 2011. (Source: FEMA Guidelines and Specifications, Appendix N, Section N.1.2).

Terrain data, as defined in FEMA Guidelines and Specifications, Appendix N: Data Capture Standards, describes the digital topographic data that was used to create the elevation data representing the terrain environment of a watershed and/or floodplain. Terrain data requirements allow for flexibility in the types of information provided as sources used to produce final terrain deliverables. Once this type of data is provided, FEMA will be able to account for the origins of the flood study elevation data. (Source: FEMA Guidelines and Specifications, Appendix N, Section N.1.2). NAD 83 UTM Zone 11 is the projection and coordinate system for this project.

The 2006 OSIP bare-earth Digital Elevation Model (DEM) was derived from digital LiDAR data was collected during the months of March and May (leaf-off conditions). The DEM data covers the entire land area of the northern tier of Ohio (approximately 23,442 square miles. The DEM is delivered in county sets, consisting of 5,000' x 5,000' size tiles that correspond to the tile sizes for the OSIP 1FT imagery products. Where the State borders other states (land only), the entire border of the State is buffered by at least 1,000-feet. Along the Lake Erie Shoreline ortho coverage is buffered beyond the shoreline a minimum distance of 2,500-feet. The file naming convention is as follows: Nxxxxyyy = 5,000' x 5,000' Tiles located in the Ohio State Plane Coordinate System (North Zone). Sxxxxyyy = 5,000' x 5,000' Tiles located in the Ohio State Plane Coordinate System (South Zone). Please note that xxxx and yyy represent the easting and northing coordinates (respectively) in state plane feet, The naming convention for each tile is based upon (the bottom most-left pixel). The full county mosaic is an aggregation of the tiles by county. The mosaic is devloped in the Ohio State Plane Coordinate System (North Zone).. The DEM tiles were provided in ESRI ArcINFO GRID raster and ASCII grid formats, with only the LiDAR in LAS Format containing the above ground and bare-earth LiDAR features. Ownership of the data products resides with the State of Ohio. Orthophotography and ancillary data products produced through this contract are public domain data. The LiDAR used to generate the DEM was acquired Statewide to provide a solid and very accurate base to use during the image rectification process. This same LiDAR can be supplemented with 3D breaklines to generate 2-foot and/or 4/5-foot contours. The average post spacing between LiDAR points is 7-feet. The flying altitude was 7,300-feet AMT, with the targeted flying speed at 170 knots.

Global U.S. Utility Terrain Vehicle Market size is set to expand from $ 5.76 Billion in 2023 to $ 9.23 Billion by 2032, with an anticipated CAGR of around 5.38%.

This 2' gravity anomaly grid for the conterminous United States is NOT the input data set used in development of the GEOID96 model. This gravity grid models the 1.7 million terrestrial and marine gravity data held in the National Geodetic Survey gravity data base in July 1996. The data used in this grid have NOT been augmented by gravity data contributions from NGA (former National Imagery and Mapping Agency (former Defence Mapping Agency)). Please note that the GEOID96 model itself does contain the NGA (former National Imagery and Mapping Agency) contributions. These gravity values are based on the International Gravity Standardization Net 1971 (IGSN71). Terrain corrected, atmospherically corrected, Bouguer anomalies were computed with respect to the Geodetic Reference System 1980 (GRS80) using NAD83 and NAVD88 coordinates. Terrain corrections were computed by FFT integration of 30" digital elevation data throughout the United States. These complete Bouguer anomalies were gridded by splines in tension. The free air anomaly grid was obtained by restoring the Bouguer plate with a 2' mean elevation grid. Additional information is available at http://www.ngs.noaa.gov/GEOID/geoid.html

The statistic shows U.S. sales of all-terrain vehicles (ATVs) from the first quarter of 2014 to the first quarter of 2017. Just under ****** all-terrain vehicles were sold to customers in the United States between January and March 2017. ATVs are usually equipped with four wheels.

Terrain data, as defined in FEMA Guidelines and Specifications, Appendix N: Data Capture Standards, describes the digital topographic data that were used to create the elevation data representing the terrain environment of a watershed and/or floodplain. Terrain data requirements allow for flexibility in the types of information provided as sources used to produce final terrain deliverables. Once this type of data is provided, FEMA will be able to account for the origins of the flood study elevation data. (Source: FEMA Guidelines and Specifications, Appendix N, Section N.1.2).

This statistic gives a breakdown of the all-terrain vehicle market in the United States in the first quarter of 2016, by brand. Arctic Cat's market share reached some nine percent during the first quarter of 2016.

Terrain data, as defined in FEMA Guidelines and Specifications, Appendix M: Data Capture Standards, describes the digital topographic data that was used to create the elevation data representing the terrain environment of a watershed and/or floodplain. Terrain data requirements allow for flexibility in the types of information provided as sources used to produce final terrain deliverables. Once this type of data is provided, FEMA will be able to account for the origins of the flood study elevation data. (Source: FEMA Guidelines and Specifications, Appendix M, Section M.1.4).

The Vegetation/Ecosystem Modeling and Analysis Project (VEMAP) is an ongoing multiinstitutional, international effort addressing the response of biogeography and biogeochemistry to environmental variability in climate and other drivers in both space and time domains. The objectives of VEMAP are the intercomparison of biogeochemistry models and vegetationtype distribution models (biogeography models) and determination of their sensitivity to changing climate, elevated atmospheric carbon dioxide concentrations, and other sources of altered forcing. The VEMAP data set includes three georeferencing and three cell area variables. Data Citation: This data set should be cited as follows: Kittel, T. G. F., N. A. Rosenbloom, T. H. Painter, D. S. Schimel, H. H. Fisher, A. Grimsdell, VEMAP Participants, C. Daly, and E. R. Hunt, Jr. 2002. VEMAP Phase I Database, revised. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Bathymetric terrain models of seafloor morphology are an important component of marine geological investigations. Advances in acquisition and processing technologies of bathymetric data have facilitated the creation of high-resolution bathymetric surfaces that approach the resolution of similar surfaces available for onshore investigations. These bathymetric terrain models provide a detailed representation of the Earth's subaqueous surface and, when combined with other geophysical and geological datasets, allow for interpretation of modern and ancient geological processes.

Report Overview The Global Utility Terrain Vehicles Market size is expected to be worth around USD 12.8 Billion by 2034, from USD 7.3 Billion in 2024, growing at a CAGR of 5.8% during the forecast period from 2025 to 2034. This growth is driven by increasing demand for off-road recreational activities, advancements in vehicle performance, and growing adoption across industries such as agriculture and forestry.

Key Takeaways

Global Utility Terrain Vehicles Market size expected to reach USD 12.8 Billion by 2034, growing at a CAGR of 5.8% from 2025 to 2034. Gasoline dominated the By Propulsion Analysis segment in 2024, holding a 70.1% market share. UTVs with engine displacements between 400 CC and 800 CC led the By Displacement Analysis segment in 2024, accounting for 54.9% of the market share. The Utility application dominated the By Application Analysis segment in 2024, with a substantial market share. North America holds 48.1% of the global UTV market share, valued at USD 3.5 Billion

This graph illustrates the value of the all-terrain vehicles and parts manufacturing industry in the United States from 2012 to 2014. In 2013, this industry's product shipment value came to around *** billion U.S. dollars.

United States US: GM: Sales: Vehicle: USA: Year to Date: By Brand: GMC: Terrain data was reported at 55,291.000 Unit in Jun 2018. This records an increase from the previous number of 32,964.000 Unit for Mar 2018. United States US: GM: Sales: Vehicle: USA: Year to Date: By Brand: GMC: Terrain data is updated quarterly, averaging 37,603.000 Unit from Mar 2017 (Median) to Jun 2018, with 4 observations. The data reached an all-time high of 55,291.000 Unit in Jun 2018 and a record low of 22,855.000 Unit in Mar 2017. United States US: GM: Sales: Vehicle: USA: Year to Date: By Brand: GMC: Terrain data remains active status in CEIC and is reported by General Motors Company. The data is categorized under World Trend Plus’s Top Company: Automobile: Non-Asia – Table RA.NA002: General Motors Company (GM): Operational Data: Sales.

The Terrain with Labels (US Edition) web map includes populated places, admin areas, boundary lines and roads overlaying multidirectional hillshade. The minimal features and styling is designed to draw attention to your thematic content.This basemap is available in the United States Vector Basemaps gallery and uses the World Terrain Reference (US Edition) and World Terrain Base vector tile layers and World Hillshade.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

Gain insights into the Indonesia Tire Market size at USD 1.50 in 2023, featuring Market Share & Growth, Market Forecasts & Outlook, and Market Trends.

This data release includes time-series data and qualitative descriptions from a monitoring station on a steep, landslide-prone slope above the City of Sitka, Alaska. On August 18, 2015, heavy rainfall triggered around 60 landslides in and around Sitka. These landslides moved downslope rapidly; several were damaging, and one demolished a home on South Kramer Avenue and killed three people. On September 16-18, 2019, the U.S. Geological Survey installed instrumentation at a site near the initiation zones of these landslides and other previous landslides on the west face of Harbor Mountain. The station consists of an electronics enclosure, a mounted rain gage, and two instrumented soil pits. Instruments record continuous measurements of precipitation, air temperature, volumetric water content, pore-water pressure, soil temperature, and soil matric potential at five-minute intervals. Soil pits were dug as deep as possible into the soil mantle for installation of the hydrologic monitoring instruments. Extensive probing with a 1.2-m-long piece of rebar to the point of refusal confirmed that the bottom of each hole was near the top of bedrock or compact till. The first soil pit (SP1), located at N 57.08551, W 135.35936, is about 1 m downslope from the north rim of the drainage hollow. SP1 is about 60 cm deep with the upper 12-15 cm in dark brown, moist, silty sand with large concentration of plant roots. Below 15 cm, to bottom of hole, consists of abundant gray sandstone clasts in silty sand matrix, which ranges in color from orange-brown, brown, to gray. The SP1 sensor array consists of a water potential sensor and soil moisture sensor at 25 cm depth, a second soil moisture sensor at 50 cm depth, and a pressure transducer near bottom of hole with a port at ~55 cm depth. The second soil pit (SP2), located at N 57.08548, W 135.35933, is about 5 m downslope from the north rim of the drainage hollow and is 65 cm deep. The top of hard material (bedrock or till) was about 70 cm deep, but there was free water at a depth of about 50-55 cm. Material throughout the depth of the hole was moist sandy silty clay of a gelatinous consistency. Color ranged from orange-brown to dark brown. Very few stones were present. These soils were interpreted as transported/mixed, weathered volcanic ash (Jacqueline Foss, USDA Forest Service, personal communication, 2019). The SP2 sensor array consists of soil moisture sensors at 25 and 40 cm depth, and a pressure transducer lying on the bottom of the hole, with a port at about 60 cm depth. A Campbell Scientific CR1000 datalogger is used to collect continuous data from these sensors. The datalogger and modem are contained in a sealed, weather-resistant fiberglass enclosure. The CR1000 datalogger contains an internal thermistor that continuously measures temperature. Additionally, an air temperature sensor was installed to collect continuous air temperature data. A tipping bucket rain gage installed in a clearing about 10 m northwest of the logger enclosure collects precipitation data. The maximum resolution of the rain gauge is 0.2 mm; that is, one tip of the bucket represents 0.2 mm. Four METER ECH20 EC-5 sensors are used to collect soil moisture data. Pore-water pressures are measured using two Campbell Scientific CS-451 pressure transducers. A METER MPS-6 water potential sensor in SP1 is used to collect soil matric potential. This sensor’s measurements range from -100,000 to -9 kPa was exceeded for the duration of the monitoring period. Recorded values appear to hover around the sensor’s upper limit (-9 kPa), with the exception of September 2019 when the station was first installed and a few brief periods in July 2022 when conditions were sufficiently dry for matric potentials to drop below -9 kPa. The water potential sensor and pressure sensors have integrated thermistors and the associated temperature readings are included. Several factors that may influence data consistency and/or quality should be considered when analyzing the dataset. Gaps in data exist from 12/15/2019 – 6/9/2020 and 9/10/2022 – 9/15/2022, when power to the monitoring system was lost due to battery failure. Critically low system voltages preceding these periods as well as during sub-freezing temperatures (e.g., March and April 2021) may have unexpected effects on data quality. During battery replacements on 10/29/2020 and 3/1/2023, the rain gauge was cleaned of debris, resulting in excessive tips of the bucket that do not represent actual precipitation. Data quality may be inconsistent during times of low battery voltage and or freezing temperatures and must be interpreted cautiously.