This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

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.

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to, or the know how to, take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the storage and sharing of a broad class of hydrologic data including time series, geographic features and rasters, multidimensional space-time data and structured collections of data representing river geometry. Web service tools and a python client library provide researchers with access to high performance computing resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This talk will illustrate web and client based use of data services that support the delineation of watersheds to define a modeling domain, then extract terrain and land use information to automatically configure the inputs required for hydrologic models. These services support the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation and generation of hydrology-based terrain information such as wetness index and stream networks. These services also support the derivation of inputs for the Utah Energy Balance snowmelt model used to address questions such as how climate, land cover and land use change may affect snowmelt inputs to runoff generation. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Presentation at Kansas University GIS Days November 18, 2015

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.

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.

This map provides an elevation tinted hillshade surface generated dynamically using a chain of server-side functions on a Terrain layer. A tinted hillshade is a combination of a hillshade applied to the Terrain, fused to a colormap applied to the same Terrain to represent elevation. The hillshading is based on a solar altitude angle of 45 degrees, and solar aspect angle of 315 degrees. The z factor is varied based on scale so that a suitable hillshade is visible at all scales. What can you do with this layer?Use for Visualization: Yes. This is appropriate for visualizing the shape and height of the terrain at a range of map scales. The image service can be added to applications or maps to enhance a users’ contextual understanding. Use for Analysis: No. To learn more about the technique used in this map to fuse the elevation tint with hillshade, refer NAGI fusion method.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 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.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

The dataset was developed as part of the Western Economic Partnership Agreement (WEPA) project covering all of NTS 73M, southern three-quarters of 74D and southeast part of 84A. It contains all the linear landform features such as eskers, flutings and melt-water channels, etc. Part of the dataset was complied by air photo interpretations and followed by random ground-truthing (NTS73M) by AGS geologists. Dataset was then merged with other existing surficial geology maps (NTS 74D and 84A). Analysis of surficial geological materials, aspects of local relief, and morphological characteristics of surface landforms form an integral component in the evaluation of recharge fluxes to regional groundwater flow systems. To assist in the evaluation of groundwater recharge, terrain analysis maps were constructed in GIS format at a scale of 1:50 000 and 1:250 000 for most of the study area, including all of map NTS 73M (Winefred), the southern three-quarters of map NTS 74D (Waterways), and the southeast part of NTS 84A (Algar). Surficial geology maps of the portion of the study area that lies within map area NTS 83P (Pelican) were published by the surficial geology group in the Minerals Section of the Alberta Geological Survey. The terrain analysis maps in NTS 73M and NTS 84A were constructed almost entirely from the interpretation of 1:60 000 scale aerial photographs, supplemented with only a minor amount of ground verification. Terrain analysis maps in the area defined by NTS 74D were constructed from both aerial photograph analysis as well as from published surficial geology information (Bayrock, L. and Reimchen, T., 1973). Classification of the terrain was based on interpretations of landform types, tonal reflections of surface materials, differences in vegetative cover, and differences in drainage patterns and characteristics, all of which can be identified on aerial photographs. It is for this reason that the maps are referred to as aerial photograph terrain analysis maps, rather than surficial geology maps, which generally have a greater amount of ground verification. The reader is therefore cautioned that a higher degree of uncertainty exists regarding the information depicted on the terrain analysis map, compared to that on a surficial geology map.

Attribute reclassification for fixed amplitude and varying Cmin.

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to or the know-how to take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the upload, storage, and sharing of a broad class of hydrologic data including time series, geographic features and raster datasets, multidimensional space-time data, and other structured collections of data. Web service tools and a Python client library provide researchers with access to HPC resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This presentation will illustrate the use of web based data and computation services from both the browser and desktop client software. These web-based services implement the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation, generation of hydrology-based terrain information, and preparation of hydrologic model inputs. They allow users to develop scripts on their desktop computer that call analytical functions that are executed completely in the cloud, on HPC resources using input datasets stored in the cloud, without installing specialized software, learning how to use HPC, or transferring large datasets back to the user's desktop. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Slides for AGU 2015 presentation IN51C-03, December 18, 2015

This dataset is the 2018 Digital Elevation Model (DEM) of the key area of Qilian Mountain,spatial resolution 5m. This dataset is based on the high-definition survey satellite ZY-3. Through the three-lines CCD with spatial resolution of 2.1m and 3.5m, combined with the basic data such as high-precision topographic maps. The dem is generated by the front-view stereo relative and adjustment model. Finally, the 5m×5m Qilian Mountain key area DEM data set was spliced by the Mosaic tool of GIS software. The data can be applied to three-dimensional spatial data processing, hydrological analysis, terrain analysis, disaster monitoring, and human activity monitoring in key areas of Qilian Mountain.

Comparison of modelling approaches in palaeo-terrain research with geoarchaeological issues.

Compilation of compound-specific buffer widths.

This layer provides a hillshaded surface (single band grayscale image) generated dynamically using the hillshade server-side function on the Terrain layer. The hillshading is based on a solar altitude angle of 45 degrees, and solar aspect angle of 315 degrees. The z factor is varied based on scale so that a suitable hillshade is visible at all scales. This layer is useful for simple visualization of the Terrain because it is easy to interpret and use as a base layer in applications and maps. Update 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. Hillshade provides a quick indication of the shape of the terrain at a range of map scales. The image service can be added to web applications or other maps to enhance contextual understanding. Use for Analysis: No. A hillshade is generally not used for analysis. 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.

The Topographic Wetness Index (TWI) is a terrain analysis technique used to quantify the potential for wetness of a particular area. The TWI can provide information about the amount of water that is likely to accumulate in a location and the flow patterns of water across the terrain. TWI quantifies the potential wetness of a landscape based on topography and slope, it is not a direct measure of wetness, it is a measure of potential wetness based on an analysis of terrain data. The formula for TWI involves two components: slope and contributing area. The slope is computed from the DTM. It represents the change in elevation between neighboring cells. Flow accumulation identifies the flow direction of water across the landscape. It accumulates the contributing area for each cell. The contributing area represents the upstream area that contributes flow to a specific cell. Contributing area is only considered up to a maximum of 180 meters away from an analysis pixel (areas in the landscape for which no Lidar data has been collected are not considered). Cells with higher contributing area are more likely to be wetter due to water accumulation. The TWI combines slope and contributing area. Wetlands, depressions, and valleys typically have high TWI values. Uplands, ridges, and steep slopes have lower TWI values.

This data is a 12.5m digital elevation model data from the northeast edge of the Qinghai Tibet Plateau. The digital elevation model map can express the results of remote sensing data research and is an important method for studying geomorphology. It plays an important role in the continuous development of research on the characteristics of debris flow development. The data is sourced from geospatial data cloud websites（ https://www.gscloud.cn/ ）. Crop the original digital elevation model data in ArcGIS software according to the scope of the study area and create a map. The resolution of the data is 12.5m, that is, the ground area represented by each pixel is 12.5m×12.5m. The data set is provided in TIFF format and can be directly used in GIS software for terrain analysis and FLO-2D simulation. This study can provide data and scientific basis for in-depth research on the influencing factors of debris flow disasters, and also provide relevant information for the study of debris flows in the study area, providing a basis for related disaster prevention and reduction work.

Two hydro-DEM versions available. Hydro elevation surface derived from Iowa lidar collected during 2007-2010. S-LibraryThe Iowa S-Library series of elevation data are watershed-scale, high-resolution rasters that have been hydro-conditioned and include some hydrologic enforcement. These data should be considered 'bare earth data' and are suitable for large scale examination of the landscape using a variety of terrain analysis methods and visualization techniques. Full metadata available here: https://acpfdata.gis.iastate.edu/ACPF/SLib2m_metadata.pdf

P-LibraryThe Iowa P-Library series of elevation data are watershed-scale, high-resolution rasters that have been hydro-conditioned to remove artifacts of the LiDAR collection process. These data should be considered 'bare earth data' and are suitable for large scale examination of the landscape using a variety of terrain analysis methods and visualization techniques. Full metadata available here: https://acpfdata.gis.iastate.edu/ACPF/PLib2m_metadata.pdf

Python Scripting for ArcGIS Pro stars with the fundamentals of Python programming and then dives into how to write useful Python scripts that work with spatial data in ArcGIS Pro. Leam how to execute geoprocessing tools, describe, create and update data, as well as execute a number of specialized tasks. See how to write simple, Custom scripts that will automate your ArcGIS Pro workflows.Some of the key topics you Will learn include:Python fundamentalsSetting up a Python editorAutomating geoprocessing tasksExploring and manipulating spatal and tabular dataWorking With geometriesMap scriptingDebugging ard error handlingHelpful "points to remember," key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Corresponding data and exercises are available online.Whether want to learn python or already have some experience, Python Scripting for ArcGlS Pro is comprehensive, hands-on book for learning versatility of Python coding as an approach to solving problems and increasing your productivity in ArcGlS Pro. Follow the step-by-step instruction and common workflow guidance for automating tasks and scripting with Python.Don't forget to also check out Esri Press's other Python title:Advanced Python Scripting for ArcGIS ProAUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPaul A Zandbergen is an associate professor of geography at the University of New Mexico in Albuquerque. His areas of expertise include geographic information science; spatial and statistical analysis techniques using GIS; error and uncertainty in spatial data; GIS applications in criminology, economics, health, and spatial ecology; terrain analysis and modeling; and community-based mapping using GIS and GPS.Pub Date: Print 7/7/2020 Digital: 7/7/2020ISBN: Print 9781589484993 Digital: 9781589485006 Price: Print: $79.99 USD Digital: $79.99 USD Pages: 420 Trim: 8 x 10 in.Table of ContentsPrefaceAcknowledgmentsChapter 1. Introducing Py%onChapter 2. Working with Python editorsChapter 3. Geoprocessing in ArcGIS ProChapter 4. Leaming Python language fundamentalsChapter 5. Geoprocessing using PythonChapter 6. Exploring spatial dataChapter 7. Debugging and error handlingChapter 8. Manipulating spatial and tabular dataChapter 9. Working with geometriesChapter 10. Working with rastersChapter 11. Map scriptingIndexPython Scripting and Advanced Python Scripting for ArcGIS Pro | Official Trailer | 2020-07-12 | 01:04Paul Zandbergen | Interview with Esri Press | 2020-07-10 | 25:37 | Link.

This is a collection of bare-Earth digital elevation models covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. Bare-Earth DEMs, also commonly called Digital Terrain Models (DTM), represent the ground topography after removal of persistent objects such as vegetation and buildings, and therefore show the natural terrain.The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for Earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols whereas on an orthoimage all details appear just as in original aerial or satellite imagery.Tribal lands have been masked from this public service in accordance with Tribal agreements.