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.

This map provides a colorized representation of slope, generated dynamically using server-side slope function on Terrain service. 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. 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.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.

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

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 includes the code (written in Python 3.6) and the documentation of a technique which is presented for adjusting the slopes of a Digital Elevation Model (DEM) derived drainage network where the slope is zero. The procedure uses the stream river network delineated from the grid-based DEM using Terrain analysis using Digital Elevation Models (TauDEM) software and re-compute the slopes considering the length and slope of all the upstream, downstream, and side entrance reaches. The results of this procedure is that all of the DEM-derived drainage network will have a positive (“downhill”) slope which are constrained to be greater than 0 m/m even when the elevation smoothing process produces equal upstream and downstream elevations on a flow line.

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.

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.

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.

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.

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.

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.

The use of remote sensing techniques to identify (geo)archaeological features is wide spread. For archaeological prospection and geomorphological mapping, Digital Terrain Models (DTMs) on based LiDAR (Light Detection And Ranging) are mainly used to detect surface and subsurface features. LiDAR is a remote sensing tool that scans the surface with high spatial resolution and allows for the removal of vegetation cover with special data filters. Archaeological publications with LiDAR data in issues have been rising exponentially since the mid-2000s. The methodology of DTM analyses within geoarchaeological contexts is usually based on “bare-earth” LiDAR data, although the terrain is often significantly affected by human activities. However, “bare-earth” LiDAR data analyses are very restricted in the case of historic hydro-engineering such as irrigation systems, mills, or canals because modern roads, railway tracks, buildings, and earth lynchets influence surface water flows and may dissect the terrain. Consequently, a "natural" pre-modern DTM with high depth accuracy is required for palaeohydrological analyses. In this study, we present a GIS-based modelling approach to generate a pre-modern and topographically purged DTM. The case study focuses on the landscape around the Early Medieval Fossa Carolina, a canal constructed by Charlemagne and one of the major medieval engineering projects in Europe. Our aim is to reconstruct the pre-modern relief around the Fossa Carolina for a better understanding and interpretation of the alignment of the Carolingian canal. Our input data are LiDAR-derived DTMs and a comprehensive vector layer of anthropogenic structures that affect the modern relief. We interpolated the residual points with a spline algorithm and smoothed the result with a low pass filter. The purged DTM reflects the pre-modern shape of the landscape. To validate and ground-truth the model, we used the levels of recovered pre-modern soils and surfaces that have been buried by floodplain deposits, colluvial layers, or dam material of the Carolingian canal. We compared pre-modern soil and surface levels with the modelled pre-modern terrain levels and calculated the overall error. The modelled pre-modern surface fits with the levels of the buried soils and surfaces. Furthermore, the pre-modern DTM allows us to model the most favourable course of the canal with minimal earth volume to dig out. This modelled pathway corresponds significantly with the alignment of the Carolingian canal. Our method offers various new opportunities for geoarchaeological terrain analysis, for which an undisturbed high-precision pre-modern surface is crucial.

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

Grid digital elevation models having resolution of 1 m or less are increasingly available to scientists and engineers interested in describing current state and evolution of Earth and space topography. Significant information loss is, however, clearly observed when existing terrain analysis methods are used in geophysical modeling, especially when coarse meshes are needed for computational efficiency. The present study shows how thalweg and ridge networks can be extracted automatically from any high-resolution grid digital elevation model without the need to alter the observed topographic data, and how these networks can be used as landmarks for terrain partitioning. The slopeline network extracted in grid digital elevation models is used to determine ridge points, related average rejunction lengths of slopelines extending from ridge points on opposite slopes, exorheic and endorheic basins. Exorheic and endorheic basins are connected through the spilling saddles from endorheic basins to form the thalweg network, and the related ridge network is identified. The obtained thalweg and ridge networks are characterized by using the known concept of drainage area and the new concept of spread area to provide physically meaningful landmarks for terrain partitioning at the desired level of detail. Although the developed methods are inspired by the observation of gravity-driven processes, they support any investigation in Earth and space science where thalweg and ridge networks are relevant topographic features. Potential impacts are exemplified by quantifications of preserved depressions over a mountain area and benefits from physically meaningful unstructured terrain partitioning in surface flow propagation over a complex floodplain.

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.

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

This dataset is about: Multi-scale terrain covariates at each soil profile location from Lora del Rio, Andalusia, Spain. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.938774 for more information.