The Terrain Ruggedness Index (TRI) is used to express the amount of elevation difference between adjacent cells of a DEM. This raster function template is used to generate a visual representation of the TRI with your elevation data. The results are interpreted as follows:0-80m is considered to represent a level terrain surface81-116m represents a nearly level surface117-161m represents a slightly rugged surface162-239m represents an intermediately rugged surface240-497m represents a moderately rugged surface498-958m represents a highly rugged surface959-4367m represents an extremely rugged surfaceWhen to use this raster function templateThe main value of this measurement is that it gives a relatively accurate view of the vertical change taking place in the terrain model from cell to cell. The TRI provides data on the relative change in height of the hillslope (rise), such as the side of a canyon.How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. The output is a visual TRI representation of your imagery. This index supports elevation data.References:Raster functionsApplicable geographiesThe index is a standard index which is designed to work globally.

This dynamic World Elevation Terrain layer returns float values representing ground heights in meters and compiles multi-resolution data from many authoritative data providers from across the globe. Heights are orthometric (sea level = 0), and water bodies that are above sea level have approximated nominal water heights.Height units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see World Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select from numerous additional functions, applied on the server, that return rendered data. For visualizations such as multi-directional hillshade, hillshade, elevation tinted hillshade, and slope, consider using the appropriate server-side function defined on this service.Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns.Note: This layer combine data from different sources and resamples the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percent Aspect Ellipsoidal height Hillshade Multi-Directional Hillshade Dark Multi-Directional Hillshade Elevation Tinted Hillshade Slope Map Aspect Map Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 are included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request.This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

These are the calculations used for examining elevation differences between the drone DSMs and conventional survey elevations across terrain types in the Evans et al. Sawyer Mill dam removal reservoir response manuscript. The “Extract Values to Points” tool in ArcGIS Pro extracted the DSM raster values at the XY locations of the surveyed points. Using the surveyed elevations and extracted DSM values across the available areas and flight dates, trends in the drone DSMs’ Z-direction accuracy were examined across different terrain categories: vegetation, dry terrain (e.g. exposed ground or wood), and submerged terrain (e.g. substrate). Elevation values correspond to NAVD88 in meters. The DSMs' and surveyed points' XY were in WGS 84 when used in the “Extract Values to Points” tool. The "Terrain" columns designate the final terrain type categories used in the terrain analysis presented in the manuscript, while the "Terrain/Notes from Field" columns contain transcribed notes from survey field notebooks that were written in the field. Vegetation heights were also from survey field notebooks. Please see the manuscript and spreadsheet for additional information. These materials were made using resources from an NSF EPSCoR funded project “RII Track-2 FEC: Strengthening the scientific basis for decision-making about dams: Multi-scale, coupled-systems research on ecological, social, and economic trade-offs” (a.k.a. "Future of Dams"). Support for this project is provided by the National Science Foundation’s Research Infrastructure Improvement NSF #IIA-1539071. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Methods:This lidar derivative provides information about the bare surface of the earth. The 2-foot resolution hillshade raster was produced from the 2020 Digital Terrain Model using the hillshade geoprocessing tool in ArcGIS Pro.QL1 airborne lidar point cloud collected countywide (Sanborn)Point cloud classification to assign ground points (Sanborn)Ground points were used to create over 8,000 1-foot resolution hydro-flattened Raster DSM tiles. Using automated scripting routines within LP360, a GeoTIFF file was created for each tile. Each 2,500 x 2,500 foot tile was reviewed using Global Mapper to check for any surface anomalies or incorrect elevations found within the surface. (Sanborn)1-foot hydroflattened DTM tiles mosaicked together into a 1-foot resolution mosaiced hydroflattened DTM geotiff (Tukman Geospatial)1-foot hydroflattened DTM (geotiff) resampled to 2-foot hydro-flattened DTM using Bilinear interpolation and clipped to county boundary with 250-meter buffer (Tukman Geospatial)2-foot hillshade derived from DTM using the ESRI Spatial Analyst ‘hillshade’ function The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, Feet and vertical datum of NAVD88 (GEOID18), Feet. Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations: The hillshade provides a raster depiction of the ground returns for each 2x2 foot raster cell across Santa Clara County. The layer is useful for hydrologic and terrain-focused analysis and is a helpful basemap when analyzing spatial data in relief.Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

GEBCO is a global terrain model for ocean and land providing elevation data in meters on a 15 arc-second interval grid. It is accompanied by a Type Identifier (TID) Grid that gives information on the types of source data that the GEBCO_2022 Grid is based. More Info.What can you do with this layer?Determine spot elevations and depths by clicking on the map and viewing the pop-up.Use in analysis within ArcGIS Online or ArcGIS Pro to enrich points, lines, or polygons with associated elevation data. This can be achieved by using the “Sample” tool in ArcGIS Pro or ArcGIS Online.Use for visualization of seafloor features.Layers associated with the GEBCO 2022 product:GEBCO Type Identifier 2022GEBCO Depth Zones 2022GEBCO 500m Contours 2022GEBCO Shaded Relief 2022GEBCO Bathymetry 2022For more GEBCO related layers and maps please visit the GEBCO ArcGIS Online Group.Source: GEBCO Compilation Group (2022) GEBCO_2022 Grid (doi:10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c)

The Southeast Texas Urban Integrated field lab’s Co-design team captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through autonomous flight, and models were processed through the DroneDeploy engine. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point Cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset will support researchers' decision-making processes under uncertainties.

Geospatial (GIS) Data on glacial topography derived from LiDAR elevation data. Contains GIS vector data (in ESRI file geodatabases) that characterize the geometry ofglacial landforms created during the last glaciation (12,000 to 14,000 years ago), such as moraines, ice walled lake plains, doubly breached doughnuts and eskers and is supplementedby online LiDAR derived elevation data. For easy data access, an ArcGIS Pro 3.0 project (aprx) file is provided.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

This World Elevation TopoBathy service combines topography (land elevation) and bathymetry (water depths) from various authoritative sources from across the globe. Heights are orthometric (sea level = 0), and bathymetric values are negative downward from sea level. The source data of land elevation in this service is same as in the Terrain layer. When possible, the water areas are represented by the best available bathymetry. Height/Depth units: MetersUpdate Frequency: QuarterlyCoverage: World/GlobalData Sources: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see Elevation Coverage Map.What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select additional functions, applied on the server, that return rendered data. For visualizations such as hillshade or elevation tinted hillshade, consider using the appropriate server-side function defined on this service. Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. There is a limit of 5000 rows x 5000 columns. NOTE: This image services combine data from different sources and resample the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, it is recommended to filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. The extent is factor of cell size and rows/columns limit. e.g. if cell size is 10 m, the max extent for analysis would be less than 50,000 m x 50,000 m.Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS Pro, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percentage Hillshade Multi-Directional Hillshade Elevation Tinted HillshadeSlope MapMosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: Accuracy will vary as a function of location and data source. Please refer to the metadata available in the layer, and follow the links to the original sources for further details. An estimate of CE90 and LE90 is included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request. This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks. Disclaimer: Bathymetry data sources are not to be used for navigation/safety at sea.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

Mapová aplikace je určena pro základní analýzy výškopisných dat území České republiky. Umožňuje prohlížení výškopisných dat ve formě obarveného stínovaného reliéfu, sklonitosti a orientace svahů, znázornění prostého stínovaného reliéfu nebo stínovaného reliéfu se Z-faktorem 10. Aplikace nabízí rovněž nástroje umožňující provádět analýzy viditelnosti. Analytické funkce mapové aplikace zajišťují image a geoprocessingové služby, které umožňují provádět dynamické prostorové analýzy nad zdrojovými daty přímo na serveru. Aplikace umožňuje znázornění výsledku analýz na pozadí podkladových map (Základní mapa nebo ortofoto) s možností nastavit průhlednost zobrazených vrstev. Výslednou situaci je možné dále prohlížet ve 3D lokální scéně, případně stáhnout ve formátu SHP, DGN, DXF nebo TXT. Služby jsou publikované a provozované na ArcGIS serveru Zeměměřickým úřadem. Zdrojovými daty těchto služeb jsou digitální model reliéfu 4. generace (DMR 4G), digitální model reliéfu 5. generace (DMR 5G) nebo digitální model povrchu 1. generace (DMP 1G) převedené do rastrového formátu v souřadnicovém systému S-JTSK.

A 2D Hydraulic model (HEC-RAS) for below Tuttle Creek Reservoir at the confluence of the Kansas River and the Big Blue River near Manhattan, KS is presented. Model geometry is based on United States Geological Survey (USGS) 3DEP data (2015), with underwater bathymetry “burned” in using cross-sections sampled in the field in April of 2023. The model was calibrated based on water surface measured during data collection. The hydraulic simulations correspond to streamflows during which fish monitoring data were collected by researchers at Kansas State University (L. Rowley and K. Gido, to be published). Results from the hydraulic model, coupled with a sediment transport model, will be used to study fish and macroinvertabrate ecological response to streamflow. Methods The following is a summary of data utilized for developing a bathymetric terrain for 2D hydraulic modeling using HEC-RAS. Data used for model calibration and validation is also discussed.

Available Data Cross-section elevation data were collected by the United States Army Corps of Engineers (USACE) Kansas City District at approximately 200-foot to 1000-foot increments at the confluence of the Big Blue River and the Kansas River near Manhattan, Kansas. The following equipment was used by two complete surveying teams: • Ohmex SonarMite single beam echo sounder SFX @ 200khz, • Ohmex SonarMite single beam echo sounder DFX @ 28kHz & 200kHZ, • Trimble R12i 0096 & 0098, • Trimble R8 1984 & 6282

The cross-section elevation data were collected by boat and supplemented by hand-carried, pole-mounted Trimbles on April 10 to 14, 2023. The USGS gage on the Big Blue River near Manhattan, KS (06887000) had an average discharge of 425 cfs during the field collection time period (Figure 1). A USGS gage downstream of the confluence, Kansas River at Wamego, KS (06887500) shows an average discharge of 780 cfs at the same time period (Figure 2).

Figure 1 (Refer to supplemental information file). USGS gage Big Blue R NR Manhattan, KS – 06887000 discharge data for the week of April 11, 2023 – April 15, 2023. The average flow was taken as 425 cfs.

Figure 2 (Refer to supplemental information file). USGS gage Kansas River at Wamego, KS (06887500) discharge data for the week of April 11, 2023 – April 15, 2023. The average flow was taken as 780 cfs. Wamego, KS is downstream of the Big Blue River and Kansas River confluence and represents combined flow for both tributaries.

Figure 3 (Refer to supplemental information file). Map of bathymetric cross-sections collected in April 2023 near Manhattan, KS. Arrows show flow direction. Inset is the data collection location relative to the state of Kansas.

Terrain The field data collection featured 56 cross-sections. HEC-RAS 6.3.1 was utilized to create a bathymetric surface by interpolating 1-D cross-sections, while a 1-m resolution USGS 3DEP terrain (2015) was used for the floodplain and surrounding areas. A more recent USGS 3DEP (2018) data was available but featured higher stream flow than the 2015 data collection and therefore, more of the channel was submerged. Overall, the difference between 2015 and 2018 had a mean deviation of ~0.04 feet, with a majority of the differences in the channel ranging between +/-0.5 feet. Islands in this reach are unvegetated and prone to movement, and therefore the exact channel form is uncertain. However, it is assumed that relative island areas are consistent throughout the reach, and 2015 LiDAR was used to delineate the most island area as possible.

To build the bathymetric terrain, a similar process as what was discussed in Harris et al. (2023), field collected data were imported into ArcGIS Pro 3.0 as a point shapefile. To preserve georeferencing, the point shapefile was segmented into groups of 3-4 cross-sections and these cross-sections were interpolated into mini-surfaces using the Inverse Distance Weighted (IDW) spatial analysis tool. These mini-surfaces were brought into HEC-RAS and cross-sections were drawn to intersect with these field surveyed locations. The 1-D cross-sections were then used to create a TIFF for the entire channel area. The 1D interpolation captures the channel centerline between measured cross-sections but meanders and channel widening may not be covered by the interpolated channel. The channel raster was broken into its component objects or “exploded”, in ArcGIS Pro using the Raster to Point tool. The points were then interpolated using the Inverse-Distance-Weighted interpolation tool (IDW). This creates a terrain that covers meanders and channel expansion while maintaining fidelity to the original channel raster.

Areas where the terrain was inundated at the time of LiDAR data collection are “flat” and referred to as a hydro-flattened surface. The Slope tool in ArcMap was used to delineate these hydro-flattened areas and a shapefile tracing unsubmerged islands was used. The IDW surface was clipped to the hydro-flattened extents and then mosaicked with the original 3DEP terrain to create a seamless bathymetric and topographic surface.

The field data collected in April 2023 (Figure 3) required supplemental information to cover a fish monitoring instance upstream of the bridge at Pillsbury Drive/177. In September 2021, the USACE Kansas City District collected sediment samples with XY-georeference and depth measurements. The LiDAR hydro-flattened surface was used to estimate the energy grade slope from the new cross-section to the recent field monitoring extents. The model scenario or “plan” on the April 2023 extents was run at a similar flow as was occurring in September 2021. The combination of water surface elevation at that flow (780 cfs), the energy grade slope in the 3DEP data and field measured depth in 2021 were used to estimate the elevation at the channel bed.

Land Cover Land cover was delineated using the Multi-Resolution Land Characteristic (MRLC) Consortium’s 2019 National Land Cover Data (NLCD) (MRLC 2016). Fifteen types of landcover were identified for this study area by the NLCD: Hay-Pasture, Shrub-Scrub, Developed Low Intensity, Developed Medium Intensity, Cultivated Crops, Deciduous Forest, Herbaceous, Develop Open Space, Developed High Intensity, Woody Wetlands, Emergent Herbaceous Wetland, Open Water, Mixed Forest, Barren Land, and Evergreen Forest. Manning’s n values were selected based on a range of n values along with a “Suggested Initial n” provided by Krest Engineers (2021) (Table 1). Table 1. A table representing a range of Manning’s n values, a suggested Manning’s n value, and percent imperviousness for each NLCD land cover type. (Krest Engineers, 2021)

Model Settings The 2D HEC-RAS mesh was set to 40-feet square, with breaklines to orient cell edges along areas of steep elevation change or to support model convergence. Boundary conditions were placed at three locations in the 2D flow area: the inflow of the Big Blue River (boundary condition type: flow hydrograph), the upstream end of the Kanas River (flow hydrograph), and the downstream end of the Kanas River (normal depth). An energy grade slope was given as 0.0005 ft/ft for the Big Blue River and 0.0003 ft/ft for the Kansas River. Advanced time step control adjustments were implemented using Courant’s Criterion, with a minimum Courant of 0.75 and a maximum of 3.

Calibration The suggested value from Krest Engineers (2021) was the initial Manning’s n used for each land cover type (Table 1). The hydraulic model was then run, and the Manning’s n was changed to better conform to water surface elevations observed during field data collection. Flows corresponding to the field collection dates were 415 cfs for the Big Blue River and 360 cfs for the Kansas River. These streamflows were determined by cross-referencing the field collection dates (April 10 to 14, 2023) to continuous monitoring data available from USGS at gages Big Blue R NR Manhattan, KS (06887000) and Kansas R at Fort Riley, KS (06879100). The 2D model simulation results were compared to the field-measured water surface elevations at each channel cross-section with the ArcGIS Zonal Statistics as Table tool. Model improvement was determined by calculating the Root Mean Square Error (RMSE) of the simulated water surface elevation to the field observed water surface elevation, and the Manning’s n values resulting in the lowest error were selected. Following calibration, the model has overall RMSE of 0.29 ft for depth. The final Manning’s n values used for all the following simulations are included in Table 2.

Land Cover

Mannings n

Open Water

0.025

Emergent Herbaceous Wetlands

0.05

Woody Wetlands

0.045

Herbaceous

0.025

Mixed Forest

0.08

Evergreen Forest

0.08

Deciduous Forest

0.1

Scrub-Shrub

0.07

Hay-Pasture

0.025

Cultivated Crops

0.02

Baren Land

0.023

Developed, Open Space

0.03

Developed, Low Intensity

0.06

Developed, Medium Intensity

0.08

Developed, High Intensity

0.12

Table 2. The selected Manning’s n per Landcover classification after calibration

Simulations Apart from the calibration simulations, further simulations were conducted to match additional fish data collection from July 17 – 21, 2023 and October 2- 6, 2023. USGS gages, Big Blue R NR Manhattan, KS (06887000) and Kansas R at Fort Riley, KS (06879100), were used to find the discharge rates (in cfs) during those fish sampling periods. While discharge was consistent throughout the weeks for some gages (Figures 4 and 7), others showed differences greater than 10% or 100 cfs (Figures 5 and 6). The gages that showed significant differences were divided into two sub-simulations for the lower and higher flows during that week.

USGS Streamflow Data for July 17 - 21, 2023

HEC RAS Scenario Description River Simulation Flow (cfs)

July_KS_LF July lower flow Big

Named Landforms of the World version 2 (NLWv2) contains four sub-layers representing geomorphological landforms, provinces, divisions, and their respective cartographic boundaries. The latter supports map making, while the first three represent basic units, such as landforms, which comprise provinces, and provinces comprise divisions. NLW is a substantial update to World Named Landforms in both compilation method and the attributes that describe each landform. For more details, please refer to our paper, Named Landforms of the World: A Geomorphological and Physiographic Compilation, in Annals of the American Association of Geographers. July 2, 2025: We have made Named Landforms of the World v3 (NLWv3) available. Please explore this group containing all of the layers and data. NLWv2 will remain available. Landforms are commonly defined as natural features on the surface of the Earth. The National Geographic Society specifies terrain as the basis for landforms and lists four major types: mountains, hills, plateaus, and plains. Here, however, we define landforms in a richer way that includes properties relating to underlying geologic structure, erosional and depositional character, and tectonic setting and processes. These characteristics were asserted by Dr. Richard E. Murphy in 1968 in his map, titled Landforms of the World. We blended Murphy"s definition for landforms with the work E.M. Bridges, who in his 1990 book, World Geomorphology, provided a globally consistent description of geomorphological divisions, provinces, and sections to give names to the landform regions of the world. AttributeDescriptionBridges Full NameFull name from E.M. Bridges" 1990 "World Geomorphology" Division and if present province and section - intended for labeling print maps of small extents. Bridges DivisionGeomorphological Division as described in E.M. Bridges" 1990 "World Geomorphology" - All Landforms have a division assigned, i.e., no nulls. Bridges ProvinceGeomorphological Province as described in E.M. Bridges" 1990 "World Geomorphology" - Not all divisions are subdivided into provinces. Bridges SectionGeomorphological Section as described in E.M. Bridges" 1990 "World Geomorphology" - Not all provinces are subdivided into sections.StructureLandform Structure as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Alpine Systems: Area of mountains formed by orogenic (collisions of tectonic plates) processes in the past 350 to 500 million years. - Caledonian/Hercynian Shield Remnants: Area of mountains formed by orogenic (collisions of tectonic plates) processes 350 to 500 million years ago. - Gondwana or Laurasian Shields: Area underlaid by mostly crystalline rock formations fromed one billion or more years ago and unbroken by tectonic processes. - Rifted Shield Areas: fractures or spreading along or adjacent to tectonic plate edges. - Isolated Volcanic Areas: volcanic activity occurring outside of Alpine Systems and Rifted Shields. - Sedimentary: Areas of deposition occurring within the past 2.5 million years Moist or DryLandform Erosional/Depositional variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Moist: where annual aridity index is 1.0 or higher, which implies precipitation is absorbed or lost via runoff. - Dry: where annual aridity index is less than 1.0, which implies more precipitation evaporates before it can be absorbed or lost via runoff. TopographicLandform Topographic type variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Karagulle et. al. 2017 - based on rich morphometric characteristics. Coded Value Domain. Values include: - Plains: Areas with less than 90-meters of relief and slopes under 20%. - Hills: Areas with 90- to 300-meters of local relief. - Mountains: Areas with over 300-meters of relief - High Tablelands: Areas with over 300-meters of relief and 50% of highest elevation areas are of gentle slope. - Depressions or Basins: Areas of land surrounded land of higher elevation. Glaciation TypeLandform Erosional/Depositional variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Values include: - Wisconsin/Wurm Glacial Extent: Areas of most recent glaciation which formed 115,000 years ago and ended 11,000 years ago. - Pre-Wisconsin/Wurm Glacial Extent: Areas subjected only to glaciation prior to 140,000 years ago. ContinentAssigned by Author during data compilation. Bridges Short NameThe name of the smallest of Division, Province, or Section containing this landform feature. Murphy Landform CodeCombination of Richard E. Murphy"s 1968 "Landforms of the World" variables expressed as a 3- or 4- letter notation. Used to label medium scale maps. Area_GeoGeodesic area in km2. Primary PlateName of tectonic plate that either completely underlays this landform feature or underlays the largest portion of the landform"s area.Secondary PlateWhen a landform is underlaid by two or more tectonic plates, this is the plate that underlays the second largest area.3rd PlateWhen a landform is underlaid by three or more tectonic plates, this is the plate that underlays the third largest area.4th PlateWhen a landform is underlaid by four or more tectonic plates, this is the plate that underlays the fourth largest area.5th PlateWhen a landform is underlaid by five tectonic plates, this is the plate that underlays the fifth largest area.NotesContains standard text to convey additional tectonic process characteristics. Tectonic ProcessAssigns values of orogenic, rift zone, or above subducting plate. These data are also available as an ArcGIS Pro Map Package: Named_Landforms_of_the_World_v2.0.mpkx.These data supersede the earlier v1.0: World Named Landforms. Change Log:DateDescription of ChangeJuly 20, 2022Corrected spelling of Guiana from incorrect representation, "Guyana", used by Bridges.July 27, 2022Corrected Structure coded value domain value, changing "Caledonian/Hercynian Shield" to "Caledonian , Hercynian, or Appalachian Remnants". Cite as: Frye, C., Sayre R., Pippi, M., Karagulle, Murphy, A., D. Soller, D.R., Gilbert, M., and Richards, J., 2022. Named Landforms of the World. DOI: 10.13140/RG.2.2.33178.93129. Accessed on:

The Digital Geologic-GIS Map of Isle Royale National Park and Vicinity, Michigan is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (isro_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (isro_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (isro_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (isro_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (isro_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (isro_geology_metadata_faq.pdf). Please read the isro_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (isro_geology_metadata.txt or isro_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Ground control points used to ensure Structure from Motion (SfM) terrain models were georectified (Westoby et al. 2012; Wolf 2021) using Emlid R2 RTK (real-time kinematic) GNSS (global navigation satellite system) system consisting of a base station set up over an established known point (established with Canadian Geodetic Survey of Natural Resources Canada (NRCAN) service Canadian Spatial Reference System Precise Point Positioning (CSRS- PPP)) and a rover.

Once the ground control points were surveyed, aerial drone images were acquired. We created flight polygons in Drone Deploy. Pictures were captured with a DJI Mavic II drone with minimum 80 % overlap of photos. Drone Deploy was chosen because it has an option to account for the doming error commonly found in models created from drone imagery and structure from motion (SfM). The doming effect is a systematic error that impacts the DEMs vertical component and can provide errors larger than the usual centimeter level (Sanz-Ablanedo et al. 2020). Generally, each site was flown once in fall of 2020 and once in spring of 2021.

We created orthorectified images and digital terrain models using Agisoft Metashape, a photogrammetric processing software application that uses SfM. We followed the workflow outlined in Bywater-Reyes and Pratt-Sitaula (2022). Once processed, orthorectified imagery and Digital Elevation Models (DEMs) were exported to ArcGIS Pro for additional analysis. Data collection metadata and postprocessing outcomes can be found in this Zenodo repository.

The European geodiversity data provides a novel perspective on the diversity of non-living nature over large spatial extents. These data describe geological, pedological, geomorphological, and hydrological diversity, including 78 different geofeatures. Geofeatures refer to individual features that each component of geodiversity (geology, pedology, geomorphology, and hydrology) consists of, such as soil types in the case of pedology. This standardized and accessible geodiversity dataset facilitates comparability for geodiversity research across Europe and can be used for multiple purposes, from studying geodiversity patterns to geodiversity–biodiversity relationship and more. Moreover, the methodology (described in Toivanen et al. 2024) establishes a grid-based approach for quantifying geodiversity, which is suitable for large extents and can be applied in other regions worldwide. This grid-based geodiversity dataset, available at resolutions of 1-km and 10-km, includes ready-to-use geor..., We used global and continental open-access data as the basis of our European geodiversity data to describe geological (IHME1500 Lithology), pedological (SoilGrids 2.0), geomorphological (Geomorpho90m), and hydrological (EU-Hydro, Corine Land Cover 2018, IHME1500 Aquifer-type) diversity. EEA Reference Grids were used as the basis of our calculations to produce the raster layers of terrestrial geodiversity at two resolutions (1-km and 10-km) through zonal calculations. All analyses were done with ESRI ArcGIS Pro version 2.8. The spatial extent of the data follows Corine Land Cover 2018 landcover data produced by the European Environment Agency (EEA). Please see the related manuscript (Toivanen et al. 2024) for detailed description of the methodology., Data was produced in and exported from ESRI ArcGIS Pro (v. 2.8), but the data use is not limited to it. All data files (GeoTIFF and csv) can be opened and used in various software, such as open-source alternatives QGIS, GRASS GIS, and R Studio. Please see the README document and the related manuscript for more details on data use., # Geodiversity data for Europe at 1-km and 10-km resolutions

This README file provides key information of the 'Geodiversity data for Europe at 1-km and 10-km resolutions' data files. Please see the related manuscript (Toivanen et al. 2024) for full description on data production and data use.

Data from: Toivanen, M., Maliniemi, T., Hjort., J., Salminen, H., Ala-Hulkko, T., Kemppinen, J., Karjalainen, O., Poturalska, A., Kiilunen, P., Snåre, H., Leppiniemi, O., Makopoulou, E., Alahuhta, J. & Tukiainen, H. (2024). Geodiversity data for Europe. Philosophical Transactions of the Royal Society A. (please see full citation details from the journal website)

Dataset citation: Toivanen, M. Maliniemi, T., Hjort., J., Salminen, H., Ala-Hulkko, T., Kemppinen, J., Karjalainen, O., Poturalska, A., Kiilunen, P., Snåre, H., Leppiniemi, O., Makopoulou, E., Alahuhta, J. & Tukiainen, H. (2024). Geodiversity data for Europe at 1-km and 10-km resolutions, Dryad, Dataset.

Data summary: 'Ge...

Supplementary materials used in the following studies:Kwong, I. H. Y., Lai, D. Y. F., Wong, F. K. K., & Fung, T. (Manuscript in preparation). Integrating five decades of Landsat imagery for territory-wide habitat mapping and change detection in Hong Kong. Kwong, I. H. Y. (2025). Spatio-Temporal Changes in Habitat Type and Quality in Hong Kong Using a 50-Year Archive of Remote Sensing Imagery [Doctoral thesis, Department of Geography and Resource Management, The Chinese University of Hong Kong].Kwong, I. H. Y., Lai, D. Y. F., Wong, F. K. K., & Fung, T. (2025). Spatial variations in forest succession rates revealed from multi-temporal habitat maps using Landsat imagery in subtropical Hong Kong. European Geosciences Union (EGU) General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025. https://doi.org/10.5194/egusphere-egu25-2667. [Poster Presentation: https://presentations.copernicus.org/EGU25/EGU25-2667_presentation-h291057.pdf]Disclaimer: All datasets described here are for reference only. No express or implied warranty or representation is given to the accuracy or completeness of the data or its appropriateness for use in any particular circumstances.GIS mapping results:All raster layers (GeoTiff format) have a pixel size of 30 m covering the 1117-km2 terrestrial area in Hong Kong in this study (Hong Kong 1980 Grid coordinate system). The time period of 1973–2022 was divided into 10 five-year periods in the mapping process.HabitatMapHK_6class_yyyy-yyyy.tif: Raster data showing the 6 habitat classes mapped in this study. Pixel values range from 1 to 6 representing woodland, shrubland, grassland, barren land, built-up area, and water respectively.HabitatMapHK_EstimatedArea.csv: Area coverage (km2) of different habitat classes, as well as their confidence intervals, as mapped in this study.HabitatMapHK_6class_ArcGISsymbology.lyrx: Used to apply the suggested symbology in ArcGIS Pro.ClassificationProbability_yyyy-yyyy.tif: The probability values belonging to each class for every pixel. They were the intermediate products generated from the classification workflow and used to determine the final class with the highest probability and compute the forest index in this study. The sum of probabilities for all six classes is equal to 1. A scale factor of 10000 was applied to the GeoTiff files for storage convenience.HabitatMapHK_8class_yyyy-yyyy.tif: Based on the 6-class outputs, two more classes are added in this product, including wetland (pixel value 7) and plantation (pixel value 8), to serve as inputs for the habitat quality model.HabitatQualityHK_yyyy-yyyy.tif: Habitat quality maps produced in this study. The pixel value is a continuous variable ranging from 0 to 1, with 1 meaning the highest habitat quality.GIS supplementary data:All datasets were collected and compiled from January to June 2024 and represent the conditions at that time.Environmental Raster:DistanceFromCoast.tif: Geometric distance (m) from the coastline.Elevation.tif: Terrain height (m) from a LiDAR-based digital terrain model.Hillfire_10periods.tif: Hill fires occurred in each five-year period, based on burn-area products by Chan et al. (2023) and manual digitisation for early years.Insolation.tif: Annual amount of incoming solar radiation (kWh/m2) computed using SAGA GIS.Landslide_10periods.tif: Landslides occurred in each five-year period, based on the Enhanced Natural Terrain Landslide Inventory (Dias et al., 2009).Northness.tif: Terrain aspect from 1 (due north) to -1 (due south) computed from the DTM.Precipitation.tif: Annual precipitation (mm) (average between 1991-2020) from Hong Kong Observatory.Slope.tif: Steepness (°) of the ground surface computed from the DTM.SoilCEC.tif: Cation exchange capacity (CEC) (mmol/kg) of topsoil from Luo et al. (2007).SoilOrganicMatter.tif: Organic matter content (%) of topsoil from Luo et al. (2007).Temperature.tif: Annual mean temperature (°C) from Morgan and Guénard (2019).TopographicWetnessIndex.tif: Amount of water accumulation due to topographic effects computed using SAGA GIS.Typhoon_10periods.tif: Wind speed (km/h) estimated from WindNinja based on maximum hourly mean wind records associated with typhoon events in each five-year period.WindSpeed.tif: Mean wind speed (km/h) estimated from WindNinja based on monthly prevailing wind records.Human Activities:BuiltupAreas_10periods_shp.zip: Shapefile (polygons) of built-up areas, with attributes on the years of construction (estimated from topographic maps) and density (high and low). It was used as a threat factor in habitat quality mapping and variables in habitat changes.CountryParksProtectedAreas_shp.zip: Shapefile (polygons) of protected areas (Country Parks, Special Areas, etc.), with attributes on the years of designation and revision. It was used as a protection factor in habitat quality mapping and variables in habitat changes.PollutionSource_shp.zip: Shapefile (polygons) of pollution sources (landfills, power stations, and incineration plants), with attributes on the years of construction and closure. It was used as a threat factor in habitat quality mapping.Roads_10periods_shp.zip: Shapefile (polylines) of roads, with attributes on the years of construction (estimated from topographic maps) and type (main and secondary). It was used as a threat factor in habitat quality mapping.Mapping Reference:ForestIndex_FieldCollectedReferenceData.csv: Field survey records of habitat types which were used to evaluate the forest index variable in this study.HabitatMapHK_FieldCollectedReferenceData.csv: Field survey records of habitat types which were used to assess the habitat mapping results in this study.HabitatMapHK_OfficeInterpretedReferenceData.csv: Reference points where the habitat class in each period was determined through visual interpretation of the aerial photographs and other historical records. The points were used for both training and validation of the habitat maps in this study.HabitatQualityHK_FieldSurveyedEcologicalValue2008.csv: Field survey records of ecological values in 2008 which were used to evaluate the habitat quality maps in this study.LandsatHK_CrossSensorCalibrationPoints.csv: Selected points that were assumed to remain unchanged over time and used to cross-calibrate different Landsat sensors in this study.LandsatHK_ImageMetadata.csv: Metadata of the Landsat imagery (1,100 downloaded scenes and 607 valid scenes after pre-processing) acquired and processed in this study.Plantation_1975_1990_2008_2019.tif: Pixels that were identified as plantations on four existing maps in different years (1975, 1990, 2008, 2019), as represented by the four layers contained in this raster file respectively. These pixels were used to help extract plantation class on the habitat map (when producing habitat quality) and denote areas with plantation activities (when modelling habitat changes) in this study.SpeciesObsHK_SpeciesChecklist.csv: A species checklist of 7 taxa in Hong Kong (Plants, Butterflies, Birds, Reptiles, Dragonflies, Amphibians, Mammals) compiled from AFCD, Hong Kong Biodiversity Information Hub, and other secondary sources. Species of conservation concern are identified based on local assessments (Corlett et al., 2000; Fellowes et al., 2002), environmental protection laws, and national and global assessments. The checklist was used to match with the iNaturalist observation data to compute biodiversity metrics at grid levels and evaluate habitat quality maps in this study.SpeciesObsHK_SynonymList.csv: A list of species name synonyms for matching names used in iNaturalist and other secondary sources with the species checklist. It was used to pre-process the iNaturalist observation data and unify the species names from different records in this study.Analysis scripts:Part 1: Mapping Vegetation Habitats from a Satellite Image Time-SeriesP1_01_SearchAndDownloadFromGEE.ipynb: Query and download all available Landsat 1-9 imagery covering the study area using Google Earth Engine. Atmospheric correction is performed if necessary.P1_02_Preprocess_part1.py: Some basic pre-processing steps after downloading the images from cloud platform to local computer, such as mosaicking adjacent scenes and reprojecting to local coordinate system.P1_03_TopographicCorrection.R: SCS+C topographic correction based on terrain slope, aspect, sun azimuth and sun elevation angles.P1_04_CrossSensorCal.R: Cross-calibration of different Landsat sensors based on pseudo-invariant features, followed by computing variables for image classification.P1_05_ImageComposite.R: Create image composites (median and standard deviation statistics) by combining all imagery acquired in the same period.P1_06_ExtractPixelValue.R: Extract pixel values at the locations of reference points.P1_07_TrainingDataStat.R: Summarise the characteristics of pixel values (e.g., spectral reflectance) of each habitat class and Landsat sensor.P1_08_TrainRFModel.R: Train the Random Forest model, fuse probability outputs from each image, evaluate the model accuracies with cross-validation, and create the final model for classifying the entire dataset.P1_09_TestProcedures.R: Modify the classification procedures and re-run the Random Forest models to evaluate their impacts on the classification accuracies.P1_10_ApplyModel.R: Apply the Random Forest model and fusion steps to all images to create the habitat map for each period.P1_11_AreaCoverage.R: Obtain the area coverage of each class on the habitat map as well as the confidence interval of the area estimates.P1_12_CompareFieldData.R: Assess the accuracies of the habitat maps by overlaying with field-collected points and LiDAR height information at different times.P1_13_SurvivalAnalysis.R: Analyse the number of years required for transitioning between vegetation classes as well as the correlations between transition times and environmental variables.Part 2: Computing Habitat Quality Maps with Reference to Habitat Type

Dataset Summary:This 3-foot resolution hillshade depicts shaded relief based on the Digital Terrain Model. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. The hillshade represents the state of the landscape when countywide LiDAR data was collected in 2018 and 2020. Figure 1 shows the vintages of LiDAR contained in this raster. Quality level 1 LiDAR (QL1, red areas in figure 1) was collected in 2018. Quality level 2 LiDAR (QL2) was collected in summer, 2020.Figure 1. Recent LiDAR collections, by Quality Level (QL) in Santa Cruz County Details and Methods: This LiDAR derivative provides information about the bare surface of the earth. The 3-foot resolution raster was produced from the 2018 and 2020 Digital Terrain Model using the hillshade geoprocessing tool in ArcGIS Pro.Uses and Limitations:The Hillshade provides a raster depiction of the ground returns for each 3x3 foot raster cell across Santa Cruz County. The layer is useful for hydrologic and terrain-focused analysis and is a helpful basemap when analyzing spatial data in relief.Related Datasets:This dataset is part of a suite of LiDAR of derivatives for Santa Cruz County. See table 1 for a list of all the derivatives.Table 1. LiDAR derivatives for Santa Cruz CountyDatasetDescriptionLink to DatasheetLink to DataCanopy Height ModelThis depicts Santa Cruz County’s woody canopy as a Digital Elevation Model.https://vegmap.press/sc_chm_datasheethttps://vegmap.press/sc_chmNormalized Digital Surface ModelThis depicts the height above ground of objects on the earth’s surface, like trees and buildings.https://vegmap.press/sc_ndsm_datasheethttps://vegmap.press/sc_ndsmDigital Surface ModelThis depicts the elevation above sea level atop of objects on the earth’s surface.https://vegmap.press/sc_dsm_datasheethttps://vegmap.press/sc_dsm HillshadeThis depicts shaded relief based on the Digital Terrain Model. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/sc_hillshade_datasheethttps://vegmap.press/sc_hillshadeDigital Terrain ModelThis depicts topography, while removing all above-ground objects on the earth’s surface, like trees and buildings.https://vegmap.press/sc_dtm_datasheethttps://vegmap.press/sc_dtm

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including: I. Beaumont Soccer Club II. Corps’ Port Arthur Resident Office III. Halbouty Pump Station comprises its vicinity IV. Lamar University V. MLK Boulevard for aerial images of the industry and the ship channel VI. Salt Water Barrier (include some aerial images about the Big Thicket) Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location. The processed data package of the Halbouty pump station has various file types: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This metadata set comprises aerial photos of the above locations, as well as 3D models, point clouds, and the animation video of Halbouty Pump Station. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

The Topography Toolbox has been updated and expanded for ArcGIS Pro. Tools calculate:McCune and Keon (2002) Heat Load IndexSlope Position ClassificationTopographic Convergence/Wetness IndexTopographic Position IndexMultiscale Topographic Position IndexHeight Above Nearest DrainageHeight Above RiverVector Ruggedness MeasureLocalized Vector Ruggedness MeasureWind Exposure/Shelter IndexHypsometric Integral