The purpose of this digital elevation model (DEM) is to assist in flood modeling. It was hydro-corrected to allow for a more accurate modeling of water flow in Hampton Roads.Download here: https://arcg.is/1jeS5P To create the hydro-corrected DEM of the AIST watersheds as seen on the right, we followed a method developed by Allen and Howard (2015). Utilizing NHD flowline layer, we first divided flowline into 30m segment, created 5m buffer zone for each segment, and assigned a unique buffer zone identification (ID) number to each zone. Then, the LiDAR point clouds were extracted based on the buffer zones and converted to point data. Spatial join analysis was conducted to assign buffer zone ID to LiDAR point data, and the minimum values within each buffer zone were extracted and joined back to the flowline buffer zones based on the ID numbers. We then converted the buffer zones from vector to raster format with 1m resolution. With the conditional evaluation function (i.e., Con), we combined the rasterized buffer zones with the original DEM, that is, if a cell of the buffer zone raster had a lower elevation value compared to the original DEM, then the lower value replaced the original one.Reference: Allen, T. R., & Howard, R. (2015). Improving low-relief coastal LiDAR DEMs with hydro-conditioning of fine-scale and artificial drainages. Frontiers in Earth Science, 3, 72.Link to meta data xml file and readme here.NOTE: This downloads a geo-database tile package given the large file size. You will need software to read a .gdb, or geo-database.

In this project, we use the Light Detection and Ranging (LiDAR) data to create the Digital Elevation Model (DEM). The LiDAR data can be downloaded through the Data Access Viewer of NOAA ( https://coast.noaa.gov/dataviewer/#/lidar/search/). For Molokai, the majority of the DEM is created using the data of 2013 U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP) Topobathy LiDAR – Local Mean Sea Level (LMSL). For some areas not covered by this data set, we use the LiDAR data from 2006 FEMA LiDAR: Hawaiian Islands and 2007 JALBTCX Hawaii LiDAR: North Coasts of Hawaii (Big Island), Kauai, Maui, Molokai, Oahu, which are accessed in the Data Access Viewer of NOAA. Please read “Description of Digital Elevation Model (DEM) for Molokai, Hawaii.docx” for detailed information.

This data set provides four related spatial data products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include vector data showing (1) roads, (2) rivers, and (3) hypsography and (4) digital elevation model (DEM) images that were encoded from the hypsography vectors. There are 15 data files with this data set which includes 12 compressed *.zip files containing ArcInfo shape files and 3 GeoTIFFS.

This data set contains vector data showing roads, rivers, and hypsography for each study area in ESRI ArcGIS shapefile format. The vectors were hand-digitized by the Images Company in Brazil from paper maps produced by the Brazilian government. Depending on the scale of the original maps, the digitization errors vary. For some maps, some vectors are missing. Data were manually checked for duplicate or extra vectors. These data sets were derived from several map sheets produced from aerial coverages dating from 1974 to 1978.

The DEM images were encoded from the hypsography vectors and are provided in GeoTIFF format. The attribute value associated with each line and point in the vector segment is encoded into the image channel; the image channel is then filled in by interpolating image data between encoded vector data. For each DEM: 1 image channel with pixel resolution = 25m x 25m. DEM images are provided for Manaus, Tapajos National Forest, and Rondonia. The files for Rio Branco were unusable due to a documentation error.

DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products.

KNOWN PROBLEMS:

The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data.

• Any use of the derived data is not recommended because the results have not been validated.

• However, the DEM, vectors, and orthorectified SAR data (related data set) can be used if the user understands how these were produced and accepts the limitations.

HydroEnforced DEM: Hydro-Enforced DEM (HDEM) countywide mosaic representing the bare earth DEM with hydro-connectors burned into the surface. The Hydro-Connectors determine the lowest ground at each end of the DEM and slopes the elevation across the burn for a more realistic connection on the surface. DEM mosaics were created from LiDAR bare earth tiles from Kansas LiDAR projects ranging from 2015 thru 2018. HydroEnforced Geodatabase Products:Breaklines: A spatial Dataset with line features identifying meaningful breaks in the terrain that are important for 2D hydraulic modeling. Breaks captured include Dam, Levee, Lagoons, and Roads. Levees from the NLD or Dams from the NRCS/KDA databases are included with specific Levee/Dam names attributed for reference as well. Not all AG-Dams have been captured in this process, but rather, those lower in the watershed that are more likely to impact contributing waters for downstream flood modeling.HydroConnectors: A spatial dataset consisting of lines that represent underground water connections such as culverts. Drainage area is computed and based on drainage area values, a score is applied to the hydro-connector buffer. The buffer will define how wide the hydro-connector should be for burning into the DEM. Hydro-Connectors representing valves from the NLD are applied and those generated for Dams focus on using the primary spillway. Underground pipe networks provided by respective cities were used to help define flow path for streamlines in urbanized areas.Road Breakline: A spatial dataset consisting of lines that have been topographically corrected for road networks within each county. These serve as additional hydraulic breaklines where the road centerline has been adjusted to the high ground of the road from the DEM as opposed to simply digitizing the centerline.Contour lines were generated at 2 foot intervals by county using Kansas LiDAR Bare Earth DEMs collected from 2015 through 2018. The source LiDAR Bare Earth DEM tiles are 1 meter resolution USGS QL2 data. A contouring script developed by the Iowa Department of Natural Resources was adapted to create Kansas contours at every 2 feet, 10 feet at a time. Contour lines were then simplified using a 2 foot tolerance, and lines less than 100 feet were removed. There is a identifier in the attribute table for 10 foot contour lines. Generalization of the contours lines over water bodies and roads will have a triangular appearance due to their lack of elevation data.The full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/Counties may have multiple years available. Download file may contain streams, ponds or lakes depending on features for that county.

Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map depicts geographic features on the surface of the earth. One intended purpose is to support emergency response at all levels of government. The geospatial data in this map are from selected National Map data holdings and other government sources.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Elevation/MD_USGSTopoQuads/MapServer/0

The DTM is a digital terrain model of the Hong Kong Special Administrative Region. It shows the topography of terrain (including non-ground information such as elevated roads and bridges) in 5-metre raster grid with an accuracy of ±5m. If land area is covered by vegetation, the terrain will be depicted by the height of vegetation.

These DEMs were produced from digitized contours at a cell resolution of 100 meters. Vector contours of the area were used as input to a software package that interpolates between contours to create a DEM representing the terrain surface. The vector contours had a contour interval of 25 feet. The data cover the BOREAS MSAs of the SSA and NSA and are given in a UTM map projection.

Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map depicts geographic features on the surface of the earth. One intended purpose is to support emergency response at all levels of government. The geospatial data in this map are from selected National Map data holdings and other government sources. Please note, data from neighboring states is included in this service in order to capture all of Maryland. Feature Service: https://mdgeodata.md.gov/imap/rest/services/Elevation/MD_USGSTopoQuads/MapServer/1

The USGS Transportation downloadable data from The National Map (TNM) is based on TIGER/Line data provided through U.S. Census Bureau and supplemented with HERE road data to create tile cache base maps. Some of the TIGER/Line data includes limited corrections done by USGS. Transportation data consists of roads, railroads, trails, airports, and other features associated with the transport of people or commerce. The data include the name or route designator, classification, and location. Transportation data support general mapping and geographic information system technology analysis for applications such as traffic safety, congestion mitigation, disaster planning, and emergency response. The National Map transportation data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and structure ...

Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, boundaries, and other selected map features. This map depicts geographic features on the surface of the earth. One intended purpose is to support emergency response at all levels of government. The geospatial data in this map are from selected National Map data holdings and other government sources.

For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). Methods

1. INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limited human intervention. Sloan et al. (2023) present a map indicating the various areas of Equatorial Asia from which these images were sourced.
IMAGE NAMING CONVENTION A common naming convention applies to satellite images’ file names: XX##.png where:

XX – denotes the geographical region / major island of Equatorial Asia of the image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

1. INTERPRETING ROAD FEATURES IN THE IMAGES For each of the 200 input satellite images, its road was visually interpreted and manually digitized to create a reference image dataset by which to train, validate, and test AI road-mapping models, as detailed in Sloan et al. (2023). The reference dataset of road features was digitized using the ‘pen tool’ in Adobe Photoshop. The pen’s ‘width’ was held constant over varying scales of observation (i.e., image ‘zoom’) during digitization. Consequently, at relatively small scales at least, digitized road features likely incorporate vegetation immediately bordering roads. The resultant binary (Road / Not Road) reference images were saved as PNG images with the same image dimensions as the original 200 images.

2. IMAGE TILES AND REFERENCE DATA FOR MODEL DEVELOPMENT

The 200 satellite images and the corresponding 200 road-reference images were both subdivided (aka ‘sliced’) into thousands of smaller image ‘tiles’ of 256x256 pixels each. Subsequent to image subdivision, subdivided images were also rotated by 90, 180, or 270 degrees to create additional, complementary image tiles for model development. In total, 8904 image tiles resulted from image subdivision and rotation. These 8904 image tiles are the main data of interest disseminated here. Each image tile entails the true-colour satellite image (256x256 pixels) and a corresponding binary road reference image (Road / Not Road).
Of these 8904 image tiles, Sloan et al. (2023) randomly selected 80% for model training (during which a model ‘learns’ to recognize road features in the input imagery), 10% for model validation (during which model parameters are iteratively refined), and 10% for final model testing (during which the final accuracy of the output road map is assessed). Here we present these data in two folders accordingly:

'Training’ – contains 7124 image tiles used for model training in Sloan et al. (2023), i.e., 80% of the original pool of 8904 image tiles. ‘Testing’– contains 1780 image tiles used for model validation and model testing in Sloan et al. (2023), i.e., 20% of the original pool of 8904 image tiles, being the combined set of image tiles for model validation and testing in Sloan et al. (2023).

IMAGE TILE NAMING CONVENTION A common naming convention applies to image tiles’ directories and file names, in both the ‘training’ and ‘testing’ folders: XX##_A_B_C_DrotDDD where

XX – denotes the geographical region / major island of Equatorial Asia of the original input 1920x886 pixel image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

A, B, C and D – can all be ignored. These values, which are one of 0, 256, 512, 768, 1024, 1280, 1536, and 1792, are effectively ‘pixel coordinates’ in the corresponding original 1920x886-pixel input image. They were recorded within the names of image tiles’ sub-directories and file names merely to ensure that names/directory were uniquely named)

rot – implies an image rotation. Not all image tiles are rotated, so ‘rot’ will appear only occasionally.

DDD – denotes the degree of image-tile rotation, e.g., 90, 180, 270. Not all image tiles are rotated, so ‘DD’ will appear only occasionally.

Note that the designator ‘XX##’ is directly equivalent to the filenames of the corresponding 1920x886-pixel input satellite images, detailed above. Therefore, each image tiles can be ‘matched’ with its parent full-scale satellite image. For example, in the ‘training’ folder, the subdirectory ‘Bo12_0_0_256_256’ indicates that its image tile therein (also named ‘Bo12_0_0_256_256’) would have been sourced from the full-scale image ‘Bo12.png’.

These data consist of plant species records observed between 2007 and 2019 and used in publications listed on the MIREN website. These are records of vascular plant species along mountain roads, the sampling strategy is explained in the accompanying protocol. The data include plant species records and their locations, and the native/non-native status of the species within the region. The names have been verified through a series of checks in available databases. The cover and abundance metrics are described in the protocol, but note cover was not estimated in the surveys in 2007. Note: this second version (v2) of the data set published Dec 31st 2022, it includes more regions and repeated measure of the same plots. Please contact miren.data@gmail.com with questions.

This HydroShare resource contains the required GIS variables for building and running RHESSys models for any watershed with a valid gage at the Coweeta Hydrologic Laboratory. Contained in the .zip file below are custom datasets that include the gage shape file, 10m DEM, isohyet map, custom LAI map, and roads. Running RHESSys requires climate data which is also provided for the base climate station. For the purpose of demonstrating the accompanying Jupyter NoteBook, observed discharge data is included for WS18.

The associated Jupyter NoteBook resource can be dowloaded here: https://www.hydroshare.org/resource/081cbdb68415450b8ac99a5fe3092b5c/

Ets La Pirogue Basongora Road, Av. Kasindi, Beni Dem. Rep. Congou Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Transportation network locations within the Andrews Experimental Forest. Includes locations of all the roads, trails, and gates within and around the forest. Original road layer was drawn on maps in 1992 and field validated. The road construction history (1952-1990) has been captured as an attribute. Roads were updated in 2004 to include roads that have been abandoned. Gates were field checked in 2004, as well as trail locations. The three data sets were updated after the 2008 LiDAR data was delivered. Roads were digitized on-screen from the bare-earth DEM, and gates were moved to match the new road network. Trails were updated for the 2011 Andrews map update. Many were located through GPS, and new trails were added. The original data is represented, as well as the updated datasets. The road network dataset is in an esri file geodatabase format, and the other datasets are in esri shapefile format, and all are in a zipped file format.

Contour lines were generated at 2 foot intervals by county using Kansas LiDAR Bare Earth DEMs collected from 2015 through 2018. The source LiDAR Bare Earth DEM tiles are 1 meter resolution USGS QL2 data. A contouring script developed by the Iowa Department of Natural Resources was adapted to create Kansas contours at every 2 feet, 10 feet at a time. Contour lines were then simplified using a 2 foot tolerance, and lines less than 100 feet were removed. There is a identifier in the attribute table for 10 foot contour lines. Generalization of the contours lines over water bodies and roads will have a triangular appearance due to their lack of elevation data.In steep terrain, it is possible that the individual contours lines may cross each other. Due to the processing steps of creating individual lines and then merging lines into a single file, overlaps may exist and have not been and will not be fixed.For more detailed information on the Kansas LiDAR project areas: LinkPresentation at the 2023 Kansas Association of Mappers conference: LinkThe full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/

Access API

Metadata Portal Metadata Information

This data set provides four related spatial data products for four study areas across the Brazilian Amazon: Manaus, Amazonas; Tapajos National Forest, Para Western (Santarem); Rio Branco, Acre; and Rondonia, Rondonia. Products include vector data showing (1) roads, (2) rivers, and (3) hypsography and (4) digital elevation model (DEM) images that were encoded from the hypsography vectors. There are 15 data files with this data set which includes 12 compressed *.zip files containing ArcInfo shape files and 3 GeoTIFFS.

This data set contains vector data showing roads, rivers, and hypsography for each study area in ESRI ArcGIS shapefile format. The vectors were hand-digitized by the Images Company in Brazil from paper maps produced by the Brazilian government. Depending on the scale of the original maps, the digitization errors vary. For some maps, some vectors are missing. Data were manually checked for duplicate or extra vectors. These data sets were derived from several map sheets produced from aerial coverages dating from 1974 to 1978.

The DEM images were encoded from the hypsography vectors and are provided in GeoTIFF format. The attribute value associated with each line and point in the vector segment is encoded into the image channel; the image channel is then filled in by interpolating image data between encoded vector data. For each DEM: 1 image channel with pixel resolution = 25m x 25m. DEM images are provided for Manaus, Tapajos National Forest, and Rondonia. The files for Rio Branco were unusable due to a documentation error.

DATA QUALITY STATEMENT: The Data Center has determined that there are questions about the quality of the data reported in this data set. The data set has missing or incomplete data, metadata, or other documentation that diminishes the usability of the products.

KNOWN PROBLEMS:

The data providers note that due to limited resources, these data have been neither validated nor quality-assured for general use. For that reason, extreme caution is advised when considering the use of these data.

• Any use of the derived data is not recommended because the results have not been validated.

• However, the DEM, vectors, and orthorectified SAR data (related data set) can be used if the user understands how these were produced and accepts the limitations.

This data set provides measured and calculated variables describing the carbon pools in river waters, CO2 respired from the water and total amount of CO2 evaded, dissolved oxygen isotopes (delta 18O-O2), and concentration of bacterial cells in river water.

Samples were collected from 10 white-water rivers, two clear-water streams (one each in Amazonas and Acre), and two black-water rivers in Amazonas from July to September 2005, which coincided with a severe drought in the western and southern regions of the Amazon Basin (Zeng et al. 2008). Eight of these sites were resampled during August through September 2006 of the following year (no drought).

This dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology. Data are also provided on the research grids for georeferencing. The map data are from a variety of sources and encompass the period 1970-06-01 to 2015-08-31.