This dataset is one of many used in the development of the manuscript 'Advancing Cave Detection using Terrain Analysis Techniques and Thermal Imagery' by Wynne et al. 2021. Manuscript Abstract: Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) determine the utility of methods designed for terrain analysis and applied to thermal imagery; (2) analyze the usefulness of predawn and midday imagery for detecting caves; and, (3) determine which imagery type (predawn, midday, or the difference between the two) was most useful. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis for model selection, and ...

Digital Elevation Models (DEM) are widely used to derive information for the modeling of hydrologic processes. The basic model for hydrologic terrain analysis involving hydrologic conditioning, determination of flow field (flow directions) and derivation of hydrologic derivatives is available in multiple software packages and GIS systems. However as areas of interest for terrain analysis have increased and DEM resolutions become finer there remain challenges related to data size, software and a platform to run it on, as well as opportunities to derive new kinds of information useful for hydrologic modeling. This presentation will illustrate new functionality associated with the TauDEM software (http://hydrology.usu.edu/taudem) and new web based deployments of TauDEM to make this capability more accessible and easier to use. Height Above Nearest Drainage (HAND) is a special case of distance down the flow field to an arbitrary target, with the target being a stream and distance measured vertically. HAND is one example of a general class of hydrologic proximity measures available in TauDEM. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in TauDEM to determine the height of each grid cell above the nearest stream along the flow path from that cell to the stream. With this information, and the depth of flow in the stream, the potential for, and depth of flood inundation can be determined. Furthermore, by dividing streams into reaches or segments, the area draining to each reach can be isolated and a series of threshold depths applied to the grid of HAND values in that isolated reach catchment, to determine inundation volume, surface area and wetted bed area. Dividing these by length yields reach average cross section area, width, and wetted perimeter, information that is useful for hydraulic routing and stage-discharge rating calculations in hydrologic modeling. This presentation will describe the calculation of HAND and its use to determine hydraulic properties across the US for prediction of stage and flood inundation in each NHDPlus reach modeled by the US NOAA’s National Water Model. This presentation will also describe two web based deployments of TauDEM functionality. The first is within a Jupyter Notebook web application attached to HydroShare that provides users the ability to execute TauDEM on this cloud infrastructure without the limitations associated with desktop software installation and data/computational capacity. The second is a web based rapid watershed delineation function deployed as part of Model My Watershed (https://app.wikiwatershed.org/) that enables delineation of watersheds, based on NHDPlus gridded data anywhere in the continental US for watershed based hydrologic modeling and analysis.

Presentation for European Geophysical Union Meeting, April 2018, Vienna. Tarboton, D. G., N. Sazib, A. Castronova, Y. Liu, X. Zheng, D. Maidment, A. Aufdenkampe and S. Wang, (2018), "Hydrologic Terrain Analysis Using Web Based Tools," European Geophysical Union General Assembly, Vienna, April 12, Geophysical Research Abstracts 20, EGU2018-10337, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-10337.pdf.

Taudem is awesome!. Visit https://dataone.org/datasets/sha256%3A81541df9e076c9da83de8a18e11e1a09c6fba33767e88919616bcbe2aad006d4 for complete metadata about this dataset.

Rasters of positive openness for the 53 hydrologic unit code (HUC) 8 watersheds in the state of North Carolina. Positive openness uses a line-of-sight approach to measure the surrounding eight zenith angles viewed above the landscape surface out to a specified distance. The central cells gets and average of the eight angles. An angle of 90 degrees would indicate a flat surface, while angles less than 90 degrees indicate a concave surface. Positive openness was calculated with the Relief Visualization Toolbox (https://iaps.zrc-sazu.si/en/rvt#v, Kokalji et al., 2011; Zakšek et al., 2011) using light detection and ranging (lidar) derived digital elevation models (DEM) with a resolution of 10 ft. (~3m). A length scale of 60 ft.(6 pixels) was used to search surrounding terrain elevations in the eight cardinal directions.

A 1/3 arc second digital elevation model from the National Elevation dataset. This DEM has had a flow direction conditioning procedure applied to it to remove barriers along high resolution NHD flowlines. The outlet.shp shapefile is the location where this Onion Creek enters the Colorado River of Texas and is used to specify the point upstream of which watersheds should be delineated.

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.

This publication contains spatial data, tabular data and scripts used to analyze the spatial patterns of refugia and associated plant communities following each of several fires in northern New Mexico. Four of the geotiff files were derived during the project (*Kernel.tif) using dNBR (delta Normalized Burn Ratio) or dNDVI (delta Normalized Difference Vegetation Index). The kernel raster data represent density of unburned/low severity grid cells in approximately 10-hectare neighborhoods following the Cerro Grande, Dome, La Mesa, and Las Conchas fire events in 2000, 1996, 1977, and 2011, respectively. The data were produced using a kernel smooth process, with output values range from 0 to 1, representing a gradient in neighborhood density of refugia. In addition, geotiff files of the dNBR for Las Conchas (this version is not available at mtbs.gov, but was provided for the study by S. Howard, USGS-EROS), the dNDVI for La Mesa and the La Mesa footprint (both developed for the Fire atlas for the Gila and Aldo Leopold Wilderness Areas project; https://doi.org/10.2737/RDS-2015-0023) are also included. Finally, the archive contains a digital elevation model (developed by USGS-EROS), cropped to the study area; the DEM was used to derive terrain metrics describing topographic heterogeneity at local and catchment scales. The text files contain R scripts and associated tabular data that can be used to repeat the analysis presented in the publication by performing the following functions: 1) generate the kernel rasters (kernel geotiffs described, above); 2) generate terrain metrics from DEM (geotiff included), 3) sample the kernel rasters, terrain metric outputs and 1 kilometer resolution bioclimatic data (downloaded from https://adaptwest.databasin.org/pages/adaptwest-climatena); 4) develop environmental models from the raster sample data (text file included); and 5) conduct a multivariate analysis of species and communities using species data recorded in the field (text file included).

This dataset includes ArcGIS files for the Etched Terrain analysis from Warner et al. (2022) Stratigraphy of InSight landing site.

This resource contains the data and scripts used for: Garousi-Nejad, I., D. G. Tarboton, M. Aboutalebi and A. F. Torres-Rua, (2019), "Terrain Analysis Enhancements to the Height Above Nearest Drainage Flood Inundation Mapping Method," Water Resources Research, http://doi.org/10.1029/2019WR024837.

Abstract from the paper: Flood inundation remains challenging to map, model, and forecast because it requires detailed representations of hydrologic and hydraulic processes. Recently, Continental‐Scale Flood Inundation Mapping (CFIM), an empirical approach with fewer data demands, has been suggested. This approach uses National Water Model forecast discharge with Height Above Nearest Drainage (HAND) calculated from a digital elevation model to approximate reach‐averaged hydraulic properties, estimate a synthetic rating curve, and map near real‐time flood inundation from stage. In 2017, rapid snowmelt resulted in a record flood on the Bear River in Utah, USA. In this study, we evaluated the CFIM method over the river section where this flooding occurred. We compared modeled flood inundation with the flood inundation observed in high‐resolution Planet RapidEye satellite imagery. Differences were attributed to discrepancies between observed and forecast discharges but also notably due to shortcomings in the derivation of HAND from National Elevation Dataset as implemented in CFIM, and possibly due to sub optimal hydraulic roughness parameter. Examining these differences highlights limitations in the HAND terrain analysis methodology. We present a set of improvements developed to overcome some limitations and advance CFIM outcomes. These include conditioning the topography using high‐resolution hydrography, dispersing nodes used to subdivide the river into reaches and catchments, and using a high‐resolution digital elevation model. We also suggest an approach to obtain a reach specific Manning's n from observed inundation and validated improvements for the flood of March 2019 in the Ocheyedan River, Iowa. The methods developed have the potential to improve CFIM.

The file Readme.md describes the contents and steps for reproducing the analyses in the paper.

Classification of Mars Terrain Using Multiple Data Sources Alan Kraut1, David Wettergreen1 ABSTRACT. Images of Mars are being collected faster than they can be analyzed by planetary scientists. Automatic analysis of images would enable more rapid and more consistent image interpretation and could draft geologic maps where none yet exist. In this work we develop a method for incorporating images from multiple instruments to classify Martian terrain into multiple types. Each image is segmented into contiguous groups of similar pixels, called superpixels, with an associated vector of discriminative features. We have developed and tested several classification algorithms to associate a best class to each superpixel. These classifiers are trained using three different manual classifications with between 2 and 6 classes. Automatic classification accuracies of 50 to 80% are achieved in leave-one-out cross-validation across 20 scenes using a multi-class boosting classifier.

This dataset depicts lines of constant elevation. It can be used for terrain analysis and cartography purposes.

Additional tables and supporting documentation are available in the Data Dictionary and User Manual.

This map provides a colorized representation of slope, generated dynamically using server-side slope function on the Terrain layer. The degree of slope steepness is depicted by light to dark colors - flat surfaces as gray, shallow slopes as light yellow, moderate slopes as light orange and steep slopes as red-brown. A scaling is applied to slope values to generate appropriate visualization at each map scale. This service should only be used for visualization, such as a base layer in applications or maps. Note: If access to non-scaled slope values is required, use the Slope Degrees or Slope Percent functions, which return values from 0 to 90 degrees, or 0 to 1000%, respectively.Units: DegreesUpdate 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: Yes. This colorized slope is appropriate for visualizing the steepness of the terrain at all map scales. This layer can be added to applications or maps to enhance contextual understanding. Use for Analysis: No. 8 bit color values returned by this service represent scaled slope values. For analysis with non-scaled values, use the Slope Degrees or Slope Percent functions.For more details such as Data Sources, Mosaic method used in this layer, please see the Terrain layer. 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 export image request.

This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

This DEM dataset comes from Ross et al., 2016 (ES&T) and represents a pre-mining DEM for much of west-virginia. The majority of the map was generated before 1970.

The dataset was used to estimate the relevant range of spatial scales with multi-scale contextual spatial modelling. The modelled soil properties were cation exchange capacity, pH, and water content at field capacity. The soil quality indicator data was modelled and predicted with partial least squares regression models based on NIR and MIR spectroscopy (Pangaea DOI (doi:10.1594/PANGAEA.938522): “Soil spectroscopy data from 130 soil profiles in Lora del Rio, Andalusia, Spain”). The soil samples were taken in an area of 1000 km² around Lora del Rio, Andalusia, Spain, in the Sierra Morena mountain range (Palaeozoic granite, gneiss, and slate), at the Guadalquivir river flood plain (Pleistocene marl, calcarenite, coarse sand, and Holocene sands and loams), and southern tertiary terraces (coarse gravel and cobble with sands and loams). Present soil types according to USDA Soil Taxonomy are Alfisols, Entisols, Inceptisols, and Vertisols. The basis for the multi-scale terrain analysis was a digital terrain model by the Centro Nacional de Information Geográfica (CNIG) of the Spanish government. The digital terrain model was published under the CC-BY 4.0 license via the Centro de Descargas del CNIG (IGN; doi:10.7419/162.09.2020) with the title Digital Terrain Model - DTM05 (EPSG: 25830) and last accessed on March, 31st 2020. The study area is covered by the MTN50 map sheets 0941, 0942, 0963, 0964, 0985, and 0986. The multi-scale contextual spatial modelling and the derivation of the scaled terrain covariates was based on the Gaussian pyramid (doi:10.1016/j.geoderma.2017.09.015 and doi:10.1038/s41598-018-33516-6) and the estimation of the relevant range of scales was based on exhaustive additive and subtractive machine learning sequences (doi:10.1038/s41598-019-51395-3). The models were trained with the multi-scale terrain covariates at each soil profile location extracted from the digital terrain model derivatives. For each soil depth of the soil dataset (0-10, 10-20, 20-30, 40-60, and 70-100 cm) two model sequences (additive and subtractive) were trained.

Workshop Materials Directory Overview

This directory contains a collection of workshop materials and resources for training sessions focused on planetary photogrammetric techniques. It includes step-by-step guides, exercises, installation instructions, presentations, and supplementary data files to support participants in utilizing software for photogrammetry.

Contents:

• Workshop Materials
  Includes resources on NASA’s Ames Stereo Pipeline (ASP), structure-from-motion (SfM) techniques, photogrammetry basics, and HiRISE DTM analysis in QGIS. Key files:

  • ASP: Installation notes, introductory slides, support files, command guide, Docker setup, and stereopipeline documentation.
  • SfM: Resources and guides on GCPs, surveying, exercise materials, and installation/setup instructions.
  • Additional Materials: Workshop agenda, QGIS HiRISE DTM analysis guide, and introduction presentations.

• Elysium Planitia Lava Preprocessing Data
  A zip file containing preprocessing data for analyzing the Elysium Planitia region on Mars, useful for DTM and geospatial applications.

Each sub-directory provides targeted resources designed to aid participants in learning digital elevation modeling for planetary surfaces. This structured, multi-session dataset supports both beginners and advanced users in photogrammetry, geospatial analysis, and terrain modeling applications, with a focus on Martian data.

This is a collection of results derived using hydrologic terrain analysis from the Logan River Digital elevation model.

Geospatial data for New Zealand from Landcare Research. Export to CAD, GIS, PDF, KML and CSV, and access via API.

The Java codes for all the drainage direction algorithms used in the study entitled “A New Multiple Flow Direction Algorithm for Determining Limited Dispersive Domain Based on Flow Aggregation Technique” are provided as an Eclipse Java project. Additionally, the test data and the output of iFAD∞ are also included for further experimentation and validation of the algorithms discussed in this study.Pengfei Wu: Work phone: +86 18351936707; E-mail: wpf@hhu.edu.cnJintao Liu: Work phone: +86 13327836738; E-mail: jtliu@hhu.edu.cn

Results from Hydrologic terrain analysis performed on Logan River Basin Digital Elevation model using TauDEM

The input digital elevation model (DEM) is Logan.tif.

The sequence in the script script.py performs a TauDEM analysis that does the following - Remove pits (by filling them) - D8 Flow direction - D8 Contributing area - Peuker Douglas Valley skeleton - Weighted D8 contributing area on Peuker Douglas valley skeleton - Drop analysis to determine objective channel threshold - Threshold to map stream indicator raster - Streamnet to produce shapefile of the stream network

Dinfinity analysis for wetness index and height above the nearest drainage (HAND) - Dinfinity flow direction - Dinfinity contributing area - Topographic wetness index - Distance down to stream in the vertical direction

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.