Students learn about the importance of good data management and begin to explore QGIS and RStudio for spatial analysis purposes. Students will explore National Land Cover Database raster data and made-up vector point data on both platforms.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat. Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

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.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

Aim: Effective management decisions depend on knowledge of species distribution and habitat use. Maps generated from species distribution models are important in predicting previously unknown occurrences of protected species. However, if populations are seasonally dynamic or locally adapted, failing to consider population level differences could lead to erroneous determinations of occurrence probability and ineffective management. The study goal was to model the distribution of a species of special concern, Townsend’s big-eared bats (Corynorhinus townsendii), in California. We incorporate seasonal and spatial differences to estimate the distribution under current and future climate conditions. Methods: We built species distribution models using all records from statewide roost surveys and by subsetting data to seasonal colonies, representing different phenological stages, and to Environmental Protection Agency Level III Ecoregions to understand how environmental needs vary based on these factors. We projected species’ distribution for 2061-2080 in response to low and high emissions scenarios and calculated the expected range shifts. Results: The estimated distribution differed between the combined (full dataset) and phenologically-explicit models, while ecoregion-specific models were largely congruent with the combined model. Across the majority of models, precipitation was the most important variable predicting the presence of C. townsendii roosts. Under future climate scnearios, distribution of C. townsendii is expected to contract throughout the state, however suitable areas will expand within some ecoregions. Main conclusion: Comparison of phenologically-explicit models with combined models indicate the combined models better predict the extent of the known range of C. townsendii in California. However, life history-explicit models aid in understanding of different environmental needs and distribution of their major phenological stages. Differences between ecoregion-specific and statewide predictions of habitat contractions highlight the need to consider regional variation when forecasting species’ responses to climate change. These models can aid in directing seasonally explicit surveys and predicting regions most vulnerable under future climate conditions. Methods Study area and survey data The study area covers the U.S. state of California, which has steep environmental gradients that support an array of species (Dobrowski et al. 2011). Because California is ecologically diverse, with regions ranging from forested mountain ranges to deserts, we examined local environmental needs by modeling at both the state-wide and ecoregion scale, using U.S. Environmental Protection Agency (EPA) Level III ecoregion designations and there are thirteen Level III ecoregions in California (Table S1.1) (Griffith et al. 2016). Species occurrence data used in this study were from a statewide survey of C. townsendii in California conducted by Harris et al. (2019). Briefly, methods included field surveys from 2014-2017 following a modified bat survey protocol to create a stratified random sampling scheme. Corynorhinus townsendii presence at roost sites was based on visual bat sightings. From these survey efforts, we have visual occurrence data for 65 maternity roosts, 82 hibernation roosts (hibernacula), and 91 active-season non-maternity roosts (transition roosts) for a total of 238 occurrence records (Figure 1, Table S1.1). Ecogeographical factors We downloaded climatic variables from WorldClim 2.0 bioclimatic variables (Fick & Hijmans, 2017) at a resolution of 5 arcmin for broad-scale analysis and 30 arcsec for our ecoregion-specific analyses. To calculate elevation and slope, we used a digital elevation model (USGS 2022) in ArcGIS 10.8.1 (ESRI, 2006). The chosen set of environmental variables reflects knowledge on climatic conditions and habitat relevant to bat physiology, phenology, and life history (Rebelo et al. 2010, Razgour et al. 2011, Loeb and Winters 2013, Razgour 2015, Ancillotto et al. 2016). To trim the global environmental variables to the same extent (the state of California), we used the R package “raster” (Hijmans et al. 2022). We performed a correlation analysis on the raster layers using the “layerStats” function and removed variables with a Pearson’s coefficient > 0.7 (see Table 1 for final model variables). For future climate conditions, we selected three general circulation models (GCMs) based on previous species distribution models of temperate bat species (Razgour et al. 2019) [Hadley Centre Global Environment Model version 2 Earth Systems model (HadGEM3-GC31_LL; Webb, 2019), Institut Pierre-Simon Laplace Coupled Model 6th Assessment Low Resolution (IPSL-CM6A-LR; Boucher et al., 2018), and Max Planck Institute for Meteorology Earth System Model Low Resolution (MPI-ESM1-2-LR; Brovkin et al., 2019)] and two contrasting greenhouse concentration trajectories (Shared Socio-economic Pathways (SSPs): a steady decline pathway with CO2 concentrations of 360 ppmv (SSP1-2.6) and an increasing pathway with CO2 reaching around 2,000 ppmv (SSP5-8.5) (IPCC6). We modeled distribution for present conditions future (2061-2080) time periods. Because one aim of our study was to determine the consequences of changing climate, we changed only the climatic data when projecting future distributions, while keeping the other variables constant over time (elevation, slope). Species distribution modeling We generated distribution maps for total occurrences (maternity + hibernacula + transition, hereafter defined as “combined models”), maternity colonies , hibernacula, and transition roosts. To estimate the present and future habitat suitability for C. townsendii in California, we used the maximum entropy (MaxEnt) algorithm in the “dismo” R package (Hijmans et al. 2021) through the advanced computing resources provided by Texas A&M High Performance Research Computing. We chose MaxEnt to aid in the comparisons of state-wide and ecoregion-specific models as MaxEnt outperforms other approaches when using small datasets (as is the case in our ecoregion-specific models). We created 1,000 background points from random points in the environmental layers and performed a 5-fold cross validation approach, which divided the occurrence records into training (80%) and testing (20%) datasets. We assessed the performance of our models by measuring the area under the receiver operating characteristic curve (AUC; Hanley & McNeil, 1982), where values >0.5 indicate that the model is performing better than random, values 0.5-0.7 indicating poor performance, 0.7-0.9 moderate performance and values of 0.9-1 excellent performance (BCCVL, Hallgren et al., 2016). We also measured the maximum true skill statistic (TSS; Allouche, Tsoar, & Kadmon, 2006) to assess model performance. The maxTSS ranges from -1 to +1:values <0.4 indicate a model that performs no better than random, 0.4-0.55 indicates poor performance, (0.55-0.7) moderate performance, (0.7-0.85) good performance, and values >0.80 indicate excellent performance (Samadi et al. 2022). Final distribution maps were generated using all occurrence records for each region (rather than the training/testing subset), and the models were projected onto present and future climate conditions. Additionally, because the climatic conditions of the different ecoregions of California vary widely, we generated separate models for each ecoregion in an attempt to capture potential local effects of climate change. A general rule in species distribution modeling is that the occurrence points should be 10 times the number of predictors included in the model, meaning that we would need 50 occurrences in each ecoregion. One common way to overcome this limitation is through the ensemble of small models (ESMs) (Breiner et al. 2015., 2018; Virtanen et al. 2018; Scherrer et al. 2019; Song et al. 2019) included in ecospat R package (references). For our ESMs we implemented MaxEnt modeling, and the final ensemble model was created by averaging individual bivariate models by weighted performance (AUC > 0.5). We also used null model significance testing with to evaluate the performance of our ESMs (Raes and Ter Steege 2007). To perform null model testing we compared AUC scores from 100 null models using randomly generated presence locations equal to the number used in the developed distribution model. All ecoregion models outperformed the null expectation (p<0.002). Estimating range shifts For each of the three GCMs and each RCP scenario, we converted the probability distribution map into a binary map (0=unsuitable, 1=suitable) using the threshold that maximizes sensitivity and specificity (Liu et al. 2016). To create the final maps for each SSP scenario, we summed the three binary GCM layers and took a consensus approach, meaning climatically suitable areas were pixels where at least two of the three models predicted species presence (Araújo and New 2007, Piccioli Cappelli et al. 2021). We combined the future binary maps (fmap) and the present binary maps (pmap) following the formula fmap x 2 + pmap (from Huang et al., 2017) to produce maps with values of 0 (areas not suitable), 1 (areas that are suitable in the present but not the future), 2 (areas that are not suitable in the present but suitable in the future), and 3 (areas currently suitable that will remain suitable) using the raster calculator function in QGIS. We then calculated the total area of suitability, area of maintenance, area of expansion, and area of contraction for each binary model using the “BIOMOD_RangeSize” function in R package “biomod2” (Thuiller et al. 2021).

WORKING VERSION. All layers are visible in this linked webgis app along with estimated error. The layers available in this dataset are in a WGS84 geographic coordinate reference system (EPSG:4326) where latitude and longitude coordinates at 0.0008983 degrees ground sampling distance per cell, which corresponds to about 1 ha, i.e. ~100 m x ~100 m at the equator, but decreases in area with increasing latitude as the coordinate system is not equal-area, e.g. ~70 m at 45° latitude and ~50 m at 60° latitude. Aspect.tif, slope.tif and elevation.tif represent Earth surface morphology biomass2020fireres.tif - Biomass values at year 2020 Mg/ha CanopyBulkDensity.tif - Amount of canopy biomass per volume of canopy (kg/m3) CanopyBaseHeight.tif - Height of lower canopy from the ground (m) CanopyHeight.tif - Total height of canopy from the ground (m) Fuel Model FuelModelClasses_ScottBurgan.tif - the category of Fuel Model according to Scott&Burgan 2005 FuelModelClasses_Aragonese.tif - the category of Fuel Model according to Aragonese et al. 2023 DOI: 10.5194/essd-15-1287-2023 - values are from 1 to 24, with a Look Up Table for correspondence (values are ordered matching the order in table 1 of the article) . FuelModelClasses_ScottBurgan.clr/qml CLR/QML - style file for QGIS FuelModelClasses_Aragonese.clr/qml CLR/QML - style file for QGIS FuelModelPercent - the percent of fuel model category belonging to that pixel, between 0 and 100 FuelModelAllPerc - multi-band raster with percent of each fuel model category to belong to each pixel.

This repo contains a series of datasets connected to training on geoprocessing.Within the zipped folder there are two subfolder, one containing raster data and the second one containing vector data.

Basic Global Dataset for GRASS GIS
This archive contains a QGIS project and a geopackage with raster and vector data for Governor's Island, New York City, USA. The CRS is NAD83 / New York Long Island (ftUS) with the EPSG code 2263.

Data Sources

• https://orthos.dhses.ny.gov/
• https://data.cityofnewyork.us/

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

Aerial Imagery/Photography, Scanned Historical Maps, CCT DRAFT Ground Level Map (GLM), Infrared Imagery, Digital Elevation Models (DEM), etc. All Raster Image Services (WMS):Use URL below to add WMS Server Connection in ArcGIS Desktop, ArcPro, QGIS, AutoCAD, etc.https://cityimg.capetown.gov.za/erdas-iws/ogc/wms/GeoSpatial Datasets?service=WMS&request=getcapabilities&All Raster Image Services (REST):https://cityimg.capetown.gov.za/erdas-iws/esri/GeoSpatial%20Datasets/rest/services/Popular Image Services: 2021 Aerial Imagery , 2020 Aerial Imagery , 2019 Aerial Imagery , DRAFT CCT Ground Level Map (GLM) 2019_5m_ DEM

This dataset is a compilation of geographic rasters from multiple environmental data sources. It aims at making the life of SDM users easier. All rasters cover the metropolitan French territory, but have varying resolutions and projections. Each directory inside the main directory "0_mydata" contain a single environmental raster. Punctual extraction of raster values can be easily done for large sets of WGS84-(longitude,latitude) points coordinates and for multiple rasters at the same time through the R function get_variables of script _functions.R from Github repository: https://github.com/ChrisBotella/SamplingEffort. All data sources are accessible on the web and free of use, at least for scientific purpose. They have various conditions of citations. Anyone diffusing a work using the present data must reference along with the present DOI, the original source data employed. Those source data are described in the paragraphs below. We provide the articles to cite, when required, and webpages for access.

Pedologic Descriptors of the ESDB v2: 1 km × 1 km Raster Library : The library contains multiple soil pedology (physico-chemical properties of the soil) descriptors raster layers covering Eurasia at a resolution of 1 km. We selected 11 descriptors from the library. They come from the PTRDB. The PTRDB variables have been directly derived from the initial soil classification of the Soil Geographical Data Base of Europe (SGDBE) using expert rules. For more details, see [1, 2] and [3]. The data is maintained and distributed freely for scientific use by the European Soil Data Centre (ESDAC) at http://eusoils.jrc.ec.europa.eu/content/european-soil-databasev2-raster. The 11 rasters are in the directories "awc_top", "bs_top", "cec_top", "dimp", "crusting", "erodi", "dgh", "text", "vs", "oc_top", "pd_top".

Corine Land Cover 2012, Version 18.5.1, 12/2016 : It is a raster layer describing soil occupation with 48 categories across Europe (25 countries) at a resolution of 100 m. This data base of the European Union is freely accessible online for all use at http://land.copernicus.eu/pan-european/corine-land-cover/clc-2012. The raster of this variable is in the directory "clc".

Hydrographic Descriptor of BD Carthage v3: BD Carthage is a spatial relational database holding many informations on the structure and nature of the french metropolitan hydrological network. For the purpose of plants ecological niche, we focus on the geometric segments representing watercourses, and polygons representing hydrographic fresh surfaces. The data has been produced by the Institut National de l’information Géographique et forestière (IGN) from an interpretation of the BD Ortho IGN. It is maintained by the SANDRE under free license for non-profit use and downloadable at:
http://services.sandre.eaufrance.fr/telechargement/geo/ETH/BDCarthage/FX
From this shapefile, we derived a raster containing the binary value raster proxi_eau_fast, i.e. proximity to fresh water, all over France.We used qgis to rasterize to a 12.5m resolution, with a buffer of 50m, the shapefile COURS_D_EAU.shp on
one hand, and the polygons of SURFACES_HYDROGRAPHIQUES.shp with attribute NATURE=“Eau douce
permanente” on the other hand.We then created the maximum raster of the previous ones (So the value of 1 correspond to an approximate distance of less than 50m to a watercourse or hydrographic surface of fresh water). The raster is in the directory named "proxi_eau_fast".

USGS Digital Elevation Data : The Shuttle Radar Topography Mission achieved in 2010 by Endeavour shuttle measured elevation at three arc second resolution over most of the earth surface. Raw measures have been post-processed by NASA and NGA in order to correct detection anomalies. The data is available from the U.S. Geological Survey, and downloadable on the Earthexplorer (https://earthexplorer.usgs.gov/). One may refer to https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-void?qt-science_center_objects=0#qt-science_center_objects for more informations. the elevation raster is in the directory named "alti".

Potential Evapotranspiration of CGIAR-CSI ETP : The CGIAR-CSI distributes this worldwide monthly potential-evapotranspiration raster data. It is pulled from a model developed by Antonio Trabucco [4, 5]. Those are estimated by the Hargreaves formula, using mean monthly surface temperatures and standard deviation from WorldClim 1:4 (http://www.worldclim. org/), and radiation on top of atmosphere. The raster is at a 1km resolution, and is
freely downloadable for a nonprofit use at: http://www.cgiar-csi.org/data/global-aridity-and-pet-database#description. This raster is in the directory "etp".

Bioclimatic Descriptors of Chelsea Climate Data 1.1: Those are raster data with worldwide coverage and 1 km resolution. A mechanistical climatic model is used to make spatial predictions of monthly mean-max-min temperatures, mean precipitations and 19 bioclimatic variables, which are downscaled with statistical models integrating historical measures of meteorologic stations from 1979 to today. The exact method is explained in the reference papers [6] and [7]. The data is under Creative Commons Attribution 4.0 International License and downloadable at (http://chelsa-climate.org/downloads/). The 19 bioclimatic rasters are located in the directories named "chbio_X".

ROUTE500 1.1: This database register classified road linkages between cities (highways, national roads, and departmental roads) in France in shapefile format, representing approxi-mately 500,000 km of roads. It is produced under free license (all uses) by the IGN. Data are available online at http://osm13.openstreetmap.fr/~cquest/route500/. For deriving the variable “droute_fast”, the distance to the main roads networks, we computed with qGis the distance raster to the union of all elements of the shapefile ROUTES.shp (segments).

References :

[1] Panagos, P. (2006). The European soil database. GEO: connexion, 5(7), 32–33.

[2] Panagos, P., Van Liedekerke, M., Jones, A., Montanarella, L. (2012). European Soil Data
Centre: Response to European policy support and public data requirements. Land Use Policy,
29(2),329–338.

[3] Van Liedekerke, M. Jones, A. & Panagos, P. (2006). ESDBv2 Raster Library-a set of rasters
derived from the European Soil Database distribution v2. 0. European Commission and the
European Soil Bureau Network, CDROM, EUR, 19945.

[4] Zomer, R., Bossio, D., Trabucco, A., Yuanjie, L., Gupta, D. & Singh, V. (2007). Trees and
water: smallholder agroforestry on irrigated lands in Northern India.

[5] Zomer, R., Trabucco, A., Bossio, D. & Verchot, L. (2008). Climate change mitigation: A
spatial analysis of global land suitability for clean development mechanism afforestation and
reforestation. Agriculture, ecosystems & environment, 126(1), 67–80.

[6] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler,
M. (2016). Climatologies at high resolution for the earth’s land surface areas. arXiv preprint
arXiv:1607.00217.

[7] Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W. & Kessler, M.
(2016). CHELSEA climatologies at high resolution for the earth’s land surface areas (Version
1.1).

Water resources management is of primary importance for better understanding the impact on scenarios of climate change. The mean monthly runoff, soil moisture and aquifer recharge long-run forecast can support decisions to manage water demand, to recover degraded areas, water security, irrigation, electrical energy generation and urban water supply. The integrative and comprehensive analysis considering the spatial and temporal representation of hydrological process such as the distribution of rainfall, land cover and land use, ground elevation is a challenge. Therefore, these input data are important to modeling the water balance. We present the Rainfall-Runoff Balance Enhanced Model (RUBEM) as a grided hydrological model capable to represent the canopy interception, runoff, soil moisture on the non-saturated soil layer, baseflow and aquifer recharge. The RUBEM includes evapotranspiration and the interception based on the leaf area index (LAI), fraction of photosynthetically active radiation (FAPAR) and normalized difference vegetation index (NVDI). The land use and land cover are updated during the simulations. The RUBEM was tested for tree tropical watersheds in Brazil with different hydrological and soil properties zones. The Piracicaba River has 10,701 km² (latitude 22.7o S), Ipojuca River has 3,471 km² (latitude 8.3o S) and Alto Iguaçu River with 2,696 km² (latitude 25.6o S). The input data from 2000 to 2010 was used to calibrate the runoff and the Nash-Sutcliffe indicator (NSI) results in 0.63, 0.48 and 0.60, respectively. The data input from 2011 to 2018 was the validation model period and NSI results in 0.66, 0.43 and 0.77. According to the NSI results, the model had a suitable calibration and validation in different hydrological zones and soils constitutions. The RUBEM is an important grided hydrological model with capabilities to support researchers, policymakers, and decision-makers under spatial and temporal water balance analysis to water managements plans, recovery degradation areas and long-run forecast.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

The dataset is a 10 m-resolution DEM in grid format covering the whole Italian territory. The DEM is encoded as “ESRI ASCII Raster” obtained by interpolating the original DEM in Triangular Irregular Network (TIN) format. The TIN version benefited from the systematic application of the DEST algorithm. The projection is UTM, the World Geodetic System 1984 (WGS 84). To provide the dataset as a single seamless DEM, the sole zone 32 N was selected, although about half of Italy belongs to zone 33 N. The database is arranged in 193 square tiles having 50 km side. Data e Risorse Questo dataset non ha dati ambiente terremoti vulcani

The Hills of Governor's Island Dataset for QGIS
This archive contains a QGIS project and a geopackage with raster and vector data for the Hills region of Governor's Island, New York City, USA. The CRS is NAD83 / New York Long Island (ftUS) with the EPSG code 2263.

Data Sources

• https://orthos.dhses.ny.gov/
• https://data.cityofnewyork.us/

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

This dataset was used to estimate total ephemeral stream length within the Coeur d'Alene, Fort Apache, and Menominee Reservations. It includes data that is publicly available through the USGS "The National Map" (USGS TNM Download v2.0), including NHDPlus High Resolution hydrography data, and Contour (1:24,000-scale) elevation data. It also includes geographic boundaries for the above mentioned Native American Reservations, as well as "eph5ha" raster data (Fesenmyer et al. 2021), which was used to approximate ephemeral stream locations. The remaining layers in the dataset include exported, site-specific NHDPlus hydrography data, and hand-digitized, estimated ephemeral streams, based on the eph5ha raster data. A map PNG of all three reservations is also included, as well as the map file used to create that map image. Lastly, a PDF of the methods used for this mapping project is also attached.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains solar resource data for: direct normal irradiation (DNI), global horizontal irradiation (GHI), diffuse horizontal irradiation data (DIF), and global irradiation for optimally tilted surfaces (GTI), all in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m). Due to the large amount of data, the coverage has been divided into eight segments. Four segments for the North hemisphere: WWN (West-west-north), WN (West-north), EN (East-north), EEN (East-east-north). Analogically four segments for the South hemisphere: WWS, WS, ES, EES. The data is hyperlinked under 'resources' with the following characteristics: DNI LTAy_AvgDailyTotals (GeoTIFF) Data format: raster (gridded), GEOTIFF File size : 343.99 MB For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019. Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar. The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are: - Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM) - Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed. Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range. This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

This GeoTIFF file is a hillshade raster derived from the USGS 10-meter digital elevation model for the Jornada Basin and surrounding area. The source data come from two tiles from the USGS 3D Elevation Program (3DEP) product at 1/3 arcsecond (~10 meters) resolution. The two tiles were USGS_13_n33w107_20220721.tif and USGS_13_n33w108_20130911.tif, and these were merged together in QGIS and then processed to a hillshade raster using R (IIRC).

Accurate coastal wave and hydrodynamic modelling relies on quality bathymetric input. Many national scale modelling studies, hindcast and forecast products, have, or are currently using a 2009 digital elevation model (DEM), which does not include recently available bathymetric surveys and is now out of date. There are immediate needs for an updated national product, preceding the delivery of the AusSeabed program’s Global Multi-Resolution Topography for Australian coastal and ocean models. There are also challenges in stitching coarse resolution DEMs, which are often too shallow where they meet high-resolution information (e.g. LiDAR surveys) and require supervised/manual modifications (e.g. NSW, Perth, and Portland VIC bathymetries). This report updates the 2009 topography and bathymetry with a selection of nearshore surveys and demonstrates where the 2009 dataset and nearshore bathymetries do not matchup. Lineage: All of the datasets listed in Table 1 (see supporting files) were used in previous CSIRO internal projects or download from online data portals and processed using QGIS and R’s ‘raster’ package. The Perth LiDAR surveys were provided as points and gridded in R using raster::rasterFromXYZ(). The Macquarie Harbour contour lines were regridded in QGIS using the TIN interpolator. Each dataset was mapped with an accompanying Type Identifier (TID) following the conventions of the GEBCO dataset. The mapping went through several iterations, at each iteration the blending was checked for inconstancy, i.e., where the GA250m DEM was too shallow when it met the high-resolution LiDAR surveys. QGIS v3.16.4 was used to draw masks over inconstant blending and GA250 values falling within the mask and between two depths were assigned NA (no-data). LiDAR datasets were projected to +proj=longlat +datum=WGS84 +no_defs using raster::projectRaster(), resampled to the GA250 grid using raster::resample() and then merged with raster::merge(). Nearest neighbour resampling was performed for all datasets except for GEBCO ~500m product, which used the bilinear method. The order of the mapping overlay is sequential from TID = 1 being the base, through to 107, where 0 is the gap filled values.

Permissions are required for all code and internal datasets (Contact Julian OGrady).