The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

If you are interested in obtaining a copy of this data, see LIO Support - Large Data Ordering Instructions. Data can be requested by project area or a set of tiles. To determine which project contains your area of interest or to view single tiles, zoom in on the map above and click. For bulk tile orders follow the link in the Additional Documentation section below to download the tile index in shapefile format. Data sizes by project area are listed below. Data sizes are listed below.

The Ontario Classified Point Cloud (Imagery-Derived) is a classified elevation point cloud based on aerial photography. The point cloud has been classified into Unclassified, Ground and Noise categories and is structured in non-overlapping 1-km by 1-km tiles in a compressed format. For more details about the product see the User Guides linked below.

Raster derivatives have been created from the point clouds for some imagery projects. These products may meet your needs and are available for direct download. See the Ontario Digital Elevation Model (Imagery-Derived) for a representation of bare earth and the Ontario Digital Surface Model (Imagery-Derived) for a model representing all surface features.

Additional Documentation

Ontario Classified Point Cloud (Imagery-Derived) - User Guide (DOCX)

Ontario Classified Point Cloud (Imagery-Derived) - Tile Index (SHP)

Data Package Sizes

SWOOP 2010 - 826 GB SCOOP 2013 - 118 GB DRAPE 2014 - 114 GBSWOOP 2015 - 112 GB COOP 2016 - 45.8 GB NWOOP 2017 - 126 GB

Status On going: Data is continually being updated

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The vector (polygon) map is in digital format within a geodatabase structure that allows for complex relationships to be established between spatial and tabular data, and allows much of the data to be accessed concurrently. Strict nomenclature was enforced for polygons and a unique name was assigned to each polygon. These reflected the verified physiognomic formation type by a prefix of representative letters (e.g., W = Woodland, SS = shrub savanna), followed by a number. Using ArcMap, polygon boundaries were buffered and excluded based on the distance equal to the radius of the selected plot size, positional accuracy of the map, and positional error of the GPS to be used by the assessment crew (Lea and Curtis 2010). The resulting polygons were converted to raster format and points were distributed using the “distribute spatially balanced points” function in ArcToolbox. This function uses the RRQRR algorithm (Theobald et al. 2007) to distribute spatially balanced points throughout the raster. Next, each point was buffered using the radius of the assigned plot size to create a circular area (see Figure 3-1) that was later used as a visual aid to delineate the survey area. These circular plot areas (polygons, essentially) and the plot centroids for all map classes were merged and assigned a unique identifier. All information was removed that could give an assessor any indication as to which class it belonged.

Purpose: To acquire detailed surface elevation data for use in conservation planning, design, research, floodplain mapping, dam safety assessments and elevation modeling, etc. Classified LAS files are used to show the manually reviewed bare earth surface. This allows the user to create intensity images, breaklines and raster DEMs. The purpose of these LiDAR data was to produce high accuracy 3D hydro-flattened digital elevation models (DEMs) with a 1-meter cell size. These raw LiDAR point cloud data were used to create classified LiDAR LAS files, intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.Product: These are Digital Elevation Model (DEM) data for Northern Maine as part of the required deliverables for the Crown of Maine 2018 QL2 LiDAR project. Class 2 (ground) lidar points in conjunction with the hydro breaklines were used to create a 1-meter hydro-flattened raster DEM.This lidar data set includes unclassified swath LAS 1.4 files, classified LAS 1.4 files, hydro and bridge breaklines, hydro-flattened digital elevation models (DEMs), and intensity imagery. Geographic Extent: 4 partial counties in Northern Maine, covering approximately 6,732 total square miles. Dataset Description: The Crown of Maine 2018 QL2 LiDAR project called for the planning, acquisition, processing, and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.71 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LiDAR Specification, Version 1.2. The data were developed based on a horizontal projection/datum of NAD 1983 (2011), UTM Zone 19, meters and vertical datum of NAVD 1988 (GEOID 12B), meters. LiDAR data were delivered as processed Classified LAS 1.4 files formatted to 8,056 individual 1,500-meter x 1,500-meter tiles, as tiled intensity imagery, and as tiled bare earth DEMs; all tiled to the same 1,500-meter x 1,500-meter schema. Continuous breaklines were produced in Esri file geodatabase format. Ground Conditions: LiDAR was collected in spring of 2018 and 2019, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Quantum Spatial, Inc. utilized a total of 150 ground control points that were used to calibrate the LiDAR to known ground locations established throughout the project area. An additional 256 independent accuracy checkpoints, 149 in Bare Earth and Urban landcovers (149 NVA points), 107 in Tall Weeds categories (107 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.

The Diablo Canyon Power Plant (DCPP) Lidar and Imagery datasets are comprised of three separate Lidar surveys: Diablo Canyon (2010), Los Osos (2011), and San Simeon (2013).

The DCPP Los Osos project study area is located in San Luis Obispo County, California, and encompasses approximately 170,000 acres (674.59 square kilometers). Watershed Sciences, Inc. (WSI) collected Light Detection and Ranging (Lidar) data across the project area from 17 March 2011 to 31 March 2011.

The lidar survey utilized a Leica ALS60 sensor in a Cessna Caravan. The Leica system was set to acquire 105,900 laser pulses per second (i.e., 105.9 kHz pulse rate) and flown at 900 meters above ground level (AGL), capturing a scan angle of ±14° from nadir when clouds and terrain permitted. With these flight parameters, the laser swath width is 449m and the laser pulse footprint is 21cm. These settings yield an average native pulse density of ≥8 pulses per square meter over terrestrial surfaces.

This survey was flown as part of the DCPP Long-Term Seismic Program (LTSP). In addition to raster data, Lidar point cloud data are available for this area: Los Osos Point Cloud Data.

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

PÁRAMO SOC MODELING: REPRODUCIBLE WORKFLOW ========================================== Overview -------- This repository contains two R scripts to (1) fit and validate a spatially-aware Random Forest model for soil organic carbon (SOC) in Colombian paramos, and (2) generate national wall-to-wall SOC predictions and sector-level summaries. Scripts ------- 1) soilCmodel.R - Builds land-cover labels (Disturbed, Forest, Paramo). For modeling, the former "Nosoil" class is collapsed into Disturbed. - Extracts rasters to points and clusters points on a 100 m grid to avoid leakage across train/test folds. - Runs grouped v-fold spatial cross-validation, tunes RF by inner OOB RMSE, computes diagnostics (OOB, random 5-fold, spatial CV) in SOC space using Duan smearing for unbiased back-transform. - Saves the finalized model and artifacts for prediction and reporting. 2) soilCprediction.R - Loads the finalized model and the Duan smearing factor. - Assembles the predictor stack, predicts log-SOC, applies smearing, and outputs SOC density in Mg C ha^-1. Pixels flagged as Nosoil are set to 0. - Converts density to Mg per cell using true cell area in hectares. - Aggregates totals and statistics by paramo sector and land-cover class. - Produces figures and CSVs for the paper. Directory layout (edit paths in scripts if different) ----------------------------------------------------- geo_dir = .../Paramo carbon map/GEographic stats_dir = .../Paramo carbon map/stats2 Required inputs --------------- Points (CSV): - carbon_site.csv with columns: Longitude, Latitude, CarbonMgHa Predictor rasters (aligned to land-cover grid, ~100 m): - dem3_100.tif, TPI100.tif, slope100.tif - temp2.tiff (mean T), tempmax2.tiff, precip2.tiff, soilmoist2.tiff - Cobertura100.tif (grid target) Vectors: - corine_paramo2.* (CORINE polygons; fields include corinetext, Clasificac) - paramos.* (paramo sectors; field NOMBRE_COM) - paramos_names.csv (two columns: NOMBRE_COM, Sector) for short plot labels CRS expectations: - Input points in EPSG:4326 - Clustering for spatial CV uses EPSG:3116 (MAGNA-SIRGAS / Bogota) - Rasters are internally aligned to the Cobertura100.tif grid Software requirements --------------------- Tested with R >= 4.3 and packages: terra, sf, dplyr, tidyr, ranger, rsample, yardstick, vip, ggplot2, purrr, forcats, scales, stringr, bestNormalize (optional) Install once in R: install.packages(c( "terra","sf","dplyr","tidyr","ranger","rsample","yardstick","vip", "ggplot2","purrr","forcats","scales","stringr","bestNormalize" )) Each script starts with: suppressPackageStartupMessages({ library(terra); library(sf); library(dplyr); library(tidyr) library(ranger); library(rsample); library(yardstick); library(vip) library(ggplot2); library(purrr); library(forcats); library(scales); library(stringr) }) How to run ---------- 1) Fit + validate the model Rscript soilCmodel.R Outputs (in stats_dir): - rf_full.rds (finalized ranger model) - smear_full.txt (Duan smearing factor) - variable_importance.csv (permutation importance, mean and sd) - diagnostics.txt (OOB, random 5-fold, spatial CV metrics) - OVP_spatialCV.png (observed vs predicted, pooled folds) - imp_bar_RF.png (RF importance with error bars) 2) Predict wall-to-wall + summarize Rscript soilCprediction.R Outputs (in stats_dir): - SOC_pred_final_RF_GAM.tif (SOC density, Mg C ha^-1) - SOC_totals_by_sector.csv (Tg C by sector x land-cover) - SOC_by_sector_LC_Tg_mean_sd.csv (Tg C plus area-weighted mean/sd in Mg C ha^-1) - SOC_national_mean_sd_by_LC.csv (national area-weighted mean/sd in Mg C ha^-1) - sector_bars_TgC.png (stacked bars by sector using short labels) Units ----- - SOC density outputs are in Mg C ha^-1. - Totals are in Mg and reported as Tg (Mg / 1e6). - Cell areas are computed with terra::cellSize(..., unit="m")/10000 to ensure hectares. Modeling notes -------------- - Learner: ranger Random Forest, permutation importance, respect.unordered.factors="partition". - Response transform: log or Yeo-Johnson (when enabled), with Duan smearing to remove retransformation bias when returning to SOC space. - Spatial CV: grouped v-fold using 100 m clusters to prevent leakage. - Land cover: modeling uses three classes (Disturbed includes former Nosoil). In mapping, Nosoil pixels are forced to 0 SOC. Troubleshooting --------------- - If a write fails with "source and target filename cannot be the same", write to a new filename. - If sector labels appear misaligned in plots, normalize strings and join short names via paramos_names.csv. - If national means look ~100x too small, ensure means are area-weighted over valid pixels only (LC present AND SOC not NA), and that areas are in hectares. - If any join fails, confirm the sector name field (NOMBRE_COM) exists in paramos.shp and in paramos_names.csv. Reproducibility --------------- - set.seed(120) is used throughout. - All area computations are in hectares. - Scripts are deterministic given the same inputs and package versions.

The Mahalanobis distance is a raster dataset used to report a reliability measure of the prediction of the land suitability data of the FGARA project (Mahalanobis, 1936). The Mahalanobis distance is a generalised distance function that measures how similar samples are based on their covariate information and has been used to assess prediction reliability in the context of land suitability prediction (adapted from Sanderman et al., 2011). In this application it is used to represent the spatial covariate information at a given point in the landscape. If a point in the catchment is very similar to regions that were sampled then the model predictions for that point will be more reliable. This raster data represents a modelled surface of values representing distance from known and covariate data. The value range is from 4 - 116 with lower values representing a higher confidence and higher values representing a lower confidence in the suitability data. Processing information is contained in Python scripts (not supplied). The project is described in: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO. References: Mahalanobis PC (1936) On the generalised distance in statistics. Proceedings of the National Institute of Sciences of India 2(1), 49-55. Sanderman J, Baldock J, Hawke B, Macdonald L, Massis-Puccini A and Szarvas S (2011) National Soil Carbon Research Programme: Field and laboratory methodologies. CSIRO Sustainable Agriculture Flagship. Lineage: This Mahalanobis reliability data has been created from a range of inputs and processing steps. Below is an overview. For more information refer to the CSIRO FGARA published reports and in particular ‘Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment’. Broadly, the steps were to: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc. of various formats; reports, spatial vector, spatial raster etc.) 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software 5. Create Mahalanobis distance (Mahalanobis, 1936)

Many Ontario lidar point cloud datasets have been made available for direct download by the Government of Canada through the federal Open Government Portal under the LiDAR Point Clouds – CanElevation Series record. Instructions for bulk data download are available in the Download Instructions document linked from that page. To download individual tiles, zoom in on the map in GeoHub and click a tile for a pop-up containing a download link. See the LIO Support - Large Data Ordering Instructions to obtain a copy of data for projects that are not yet available for direct download. Data can be requested by project area or a set of tiles. To determine which project contains your area of interest or to view single tiles, zoom in on the map above and click. For bulk tile orders follow the link in the Additional Documentation section below to download the tile index. Data sizes by project area are listed below. The Ontario Point Cloud (Lidar-Derived) consists of points containing elevation and intensity information derived from returns collected by an airborne topographic lidar sensor. The minimum point cloud classes are Unclassified, Ground, Water, High and Low Noise. The data is structured into non-overlapping 1-km by 1-km tiles in LAZ format. This dataset is a compilation of lidar data from multiple acquisition projects, as such specifications, parameters, accuracy and sensors vary by project. Some projects have additional classes, such as vegetation and buildings. See the detailed User Guide and contractor metadata reports linked below for additional information, including information about interpreting the index for placement of data orders. Raster derivatives have been created from the point clouds. These products may meet your needs and are available for direct download. For a representation of bare earth, see the Ontario Digital Terrain Model (Lidar-Derived). For a model representing all surface features, see the Ontario Digital Surface Model (Lidar-Derived). You can monitor the availability and status of lidar projects on the Ontario Lidar Coverage map on the Ontario Elevation Mapping Program hub page. Additional DocumentationOntario Classified Point Cloud (Lidar-Derived) - User Guide (DOCX) Ontario Classified Point Cloud (Lidar-Derived) - Tile IndexOntario Lidar Project Extents (SHP) OMAFRA Lidar 2016-18 - Cochrane - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Peterborough - Additional Metadata (PDF)OMAFRA Lidar 2016-18 - Lake Erie - Additional Metadata (PDF)CLOCA Lidar 2018 - Additional Contractor Metadata (PDF)South Nation Lidar 2018-19 - Additional Contractor Metadata (PDF)OMAFRA Lidar 2022 - Lake Huron - Additional Metadata (PDF)OMAFRA Lidar 2022 - Lake Simcoe - Additional Metadata (PDF)Huron-Georgian Bay Lidar 2022-23 - Additional Metadata (Word)Kawartha Lakes Lidar 2023 - Additional Metadata (Word)Sault Ste Marie Lidar 2023-24 - Additional Metadata (Word)Thunder Bay Lidar 2023-24 - Additional Metadata (Word)Timmins Lidar 2024 - Additional Metadata (Word) OMAFRA Lidar Point Cloud 2016-18 - Cochrane - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18- Peterborough - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2016-18 - Lake Erie - Lift Metadata (SHP)CLOCA Lidar Point Cloud 2018 - Lift Metadata (SHP)South Nation Lidar Point Cloud 2018-19 - Lift Metadata (SHP)York-Lake Simcoe Lidar Point Cloud 2019 - Lift Metadata (SHP)Ottawa River Lidar Point Cloud 2019-20 - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Huron - Lift Metadata (SHP)OMAFRA Lidar Point Cloud 2022 - Lake Simcoe - Lift Metadata (SHP)Eastern Ontario Lidar Point Cloud 2021-22 - Lift Medatadata (SHP)DEDSFM Huron-Georgian Bay Lidar Point Cloud 2022-23 - Lift Metadata (SHP)DEDSFM Kawartha Lakes Lidar Point Cloud 2023 - Lift Metadata (SHP)DEDSFM Sault Ste Marie Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Sudbury Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Thunder Bay Lidar Point Cloud 2023-24 - Lift Metadata (SHP)DEDSFM Timmins Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Cataraqui Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Chapleau Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Dryden Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Ignace Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Sioux Lookout Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Northeastern Ontario Lidar Point Cloud 2024 - Lift Metadata (SHP)DEDSFM Atikokan Lidar Point Cloud 2024 - Lift Metadata (SHP)GTA 2023 - Lift Metadata (SHP) Data Package SizesLEAP 2009 - 22.9 GBOMAFRA Lidar 2016-18 - Cochrane - 442 GBOMAFRA Lidar 2016-18 - Lake Erie - 1.22 TBOMAFRA Lidar 2016-18 - Peterborough - 443 GBGTA 2014 - 57.6 GBGTA 2015 - 63.4 GBBrampton 2015 - 5.9 GBPeel 2016 - 49.2 GBMilton 2017 - 15.3 GBHalton 2018 - 73 GBCLOCA 2018 - 36.2 GBSouth Nation 2018-19 - 72.4 GBYork Region-Lake Simcoe Watershed 2019 - 75 GBOttawa River 2019-20 - 836 GBLake Nipissing 2020 - 700 GBOttawa-Gatineau 2019-20 - 551 GBHamilton-Niagara 2021 - 660 GBOMAFRA Lidar 2022 - Lake Huron - 204 GBOMAFRA Lidar 2022 - Lake Simcoe - 154 GBBelleville 2022 - 1.09 TBEastern Ontario 2021-22 - 1.5 TBHuron Shores 2021 - 35.5 GBMuskoka 2018 - 72.1 GBMuskoka 2021 - 74.2 GBMuskoka 2023 - 532 GBDigital Elevation Data to Support Flood Mapping 2022-26:Huron-Georgian Bay 2022 - 1.37 TBHuron-Georgian Bay 2023 - 257 GBHuron-Georgian Bay 2023 Bruce - 95.2 GBKawartha Lakes 2023 - 385 GBSault Ste Marie 2023-24 - 1.15 TBSudbury 2023-24 - 741 GBThunder Bay 2023-24 - 654 GBTimmins 2024 - 318 GBCataraqui 2024 - 50.5 GBChapleau 2024 - 127 GBDryden 2024 - 187 GBIgnace 2024 - 10.7 GBNortheastern Ontario 2024 - 82.3 GBSioux Lookout 2024 - 112 GBAtikokan 2024 - 64 GBGTA 2023 - 985 GB StatusOn going: Data is continually being updated Maintenance and Update FrequencyAs needed: Data is updated as deemed necessary ContactOntario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

This collection of files are part of a larger dataset uploaded in support of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB, DOE Project DE-EE0006726). Phase 1 of the GPFA-AB project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This was accomplished through analysis of 4 key criteria: thermal quality, natural reservoir productivity, risk of seismicity, and heat utilization. Each of these analyses represent a distinct project task, with the fifth task encompassing combination of the 4 risks factors. Supporting data for all five tasks has been uploaded into the Geothermal Data Repository node of the National Geothermal Data System (NGDS).

This submission comprises the data for Thermal Quality Analysis (project task 1) and includes all of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the GPFA-AB project. The identified Geothermal Play Fairways are also provided with the larger dataset. Figures (.png) are provided as examples of the shapefiles and rasters. The regional standardized 1 square km grid used in the project is also provided as points (cell centers), polygons, and as a raster. Two ArcGIS toolboxes are available: 1) RegionalGridModels.tbx for creating resource and risk factor maps on the standardized grid, and 2) ThermalRiskFactorModels.tbx for use in making the thermal resource maps and cross sections. These toolboxes contain item description documentation for each model within the toolbox, and for the toolbox itself. This submission also contains three R scripts: 1) AddNewSeisFields.R to add seismic risk data to attribute tables of seismic risk, 2) StratifiedKrigingInterpolation.R for the interpolations used in the thermal resource analysis, and 3) LeaveOneOutCrossValidation.R for the cross validations used in the thermal interpolations.

Some file descriptions make reference to various 'memos'. These are contained within the final report submitted October 16, 2015.

Each zipped file in the submission contains an 'about' document describing the full Thermal Quality Analysis content available, along with key sources, authors, citation, use guidelines, and assumptions, with the specific file(s) contained within the .zip file highlighted.

UPDATE: Newer version of the Thermal Quality Analysis has been added here: https://gdr.openei.org/submissions/879 (Also linked below) Newer version of the Combined Risk Factor Analysis has been added here: https://gdr.openei.org/submissions/880 (Also linked below) Regional Grid Shapefiles and Raster used in Thermal Quality Analysis task of Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. Polygon (Fishnet2.shp and associated files), Point (Fishnet2_label.shp and associated files) and Raster grid (GridNAD.tif) are included, made using ArcGIS Create Fishnet tool.

There is an associated file containing the ArcGIS Toolbox with the Regional Grid Models, (ArcGISToolbox_RegionalGridModels.zip) .

The shapefiles, ArcGIS toolbox, and R script contained within these two .zip files were used to convert vector and raster files to the standardized 1 square km grid used in this project. The code is general enough to be used in other studies that may need to work on a standard grid. ArcGIS 10.1 or later is needed to use the models in the toolbox.

Details regarding methods for seismic risk factor conversion (within the toolbox) may be found in the memo contained within the project final report entitled 14_GPFA-AB_SeismicRiskMapCreationMethods.pdf (Smith and Horowitz, 2015).

The R script AddNewSeisFieldsFunctions.R implements some of the methods described in the memo.

Details about all of the ArcGIS toolbox models may be found in the memo entitled 16_GPFA-AB_RiskAnalysisAndRiskFactorDescriptions.pdf (Whealton, et al., 2015). Some models have been given different names since the memo was written. These models have the former names listed next to the current model name in the list above.

Aerial surveys were undertaken throughout the Kimberley for a 12-month period from November 2012 – October 2013. The three main survey areas incorporated into the sampling design were Camden Sound, Dampier Peninsula and 80 Mile Beach. Also Eastern Kimberley for one survey. In total, 41 aerial surveys were completed throughout the Kimberley from November 2012 – October 2013. During this time period, several thousand photographic images were taken and examined and nearly 4,000 images showed evidence of human use. These images were used to classify and catalogue shore and boat-based recreational activity. Data outputs are separated into a) processed data (Geodatabase of point data - Kimberley_Aerial_ASA and raster layers of density for the 2 survey areas - 80M_DensityOutputs and DP_DensityOutputs) b) raw data (folders containing .csv, shapefiles and .jpegs from each survey) and c) survey information (information about each survey e.g. date, time, pilot, observers, start, finish location etc.)

Forest biomass study in the regional area of Andalusia to determine wood and biomass stocks per unit area using complex stock and production models, based on fundamentals of forest ecology, and supported by territorial analysis of a multitude of variables, managed and treated through geographic information systems. The content referred to in this metadata includes the following exploitation information of the project "Forest biomass in Andalusia. Model of stocks, growth and production. Conifers. 2011?: Raster information layers of environmental variables (Orographic, Climate and Edaphic), Station Quality and Site Index for the current and potential distribution of species of the genus Pinus, Raster information layers of Dasometric Parameters of species of genus Pinus (totals) updated to 2011, Raster information layers of Dendrometric Parameters of species of genus Pinus (totals) updated to 2011, Geodatabase with point data of plots of inventory of collected management projects and IFN3, as well as polygon coverage related to the dasocratic division of collected Management Projects

The Delmarva Peninsula is a 220-kilometer-long headland, spit, and barrier island complex that was significantly affected by Hurricane Sandy. A U.S. Geological Survey cruise was conducted in the summer of 2014 to map the inner continental shelf of the Delmarva Peninsula using geophysical and sampling techniques to define the geologic framework that governs coastal system evolution at storm-event and longer timescales. Data collected during the 2014 cruise include swath bathymetry, sidescan sonar, chirp and boomer seismic-reflection profiles, acoustic Doppler current profiler, and sample and bottom photograph data. Processed data in raster and vector format are released here for the bottom photographs and sediment samples. More information about the USGS survey conducted as part of the Hurricane Sandy Response-- Geologic Framework and Coastal Vulnerability Study can be found at the project website or on the WHCMSC Field Activity Web pages: https://woodshole.er.usgs.gov/project-pages/delmarva/ and https://cmgds.marine.usgs.gov/fan_info.php?fan=2014-002-FA

This is a seamless bare earth digital elevation model (DEM) created from lidar terrain elevation data for the Commonwealth of Massachusetts. It represents the elevation of the surface with vegetation and structures removed. The spatial resolution of the map is 1 meter. The elevation of each 1-meter square cell was linearly interpolated from classified lidar-derived point data.This version of the DEM stores the elevation values as integers. The native VALUE field represents the elevation above/below sea level in meters. MassGIS added a FEET field to the VAT (value attribute table) to store the elevation in feet as calculated by multiplying VALUE x 3.28084.Dates of lidar data used in this DEM range from 2010-2015. The overlapping lidar projects were adjusted to the same projection and datum and then mosaicked, with the most recent data replacing any older data. Several very small gaps between the project areas were patched with older lidar data where necessary or with models from recent aerial photo acquisitions. See https://www.mass.gov/doc/lidar-project-areas-original/download for an index map.This DEM is referenced to the WGS_1984_Web_Mercator_Auxiliary_Sphere spatial reference.See the MassGIS datalayer page to download the data as a file geodatabase raster dataset.View this service in the Massachusetts Elevation Finder.

The downloadable ZIP file contains model documentation and contact information for the model creator. For more information, or a copy of the project report which provides greater model detail, please contact Ryan Urie - traigo12@gmail.com.This model was created from February through April 2010 as a central component of the developer's master's project in Bioregional Planning and Community Design at the University of Idaho to provide a tool for identifying appropriate locations for various land uses based on a variety of user-defined social, economic, ecological, and other criteria. It was developed using the Land-Use Conflict Identification Strategy developed by Carr and Zwick (2007). The purpose of this model is to allow users to identify suitable locations within a user-defined extent for any land use based on any number of social, economic, ecological, or other criteria the user chooses. The model as it is currently composed was designed to identify highly suitable locations for new residential, commercial, and industrial development in Kootenai County, Idaho using criteria, evaluations, and weightings chosen by the model's developer. After criteria were chosen, one or more data layers were gathered for each criterion from public sources. These layers were processed to result in a 60m-resolution raster showing the suitability of each criterion across the county. These criteria were ultimately combined with a weighting sum to result in an overall development suitability raster. The model is intended to serve only as an example of how a GIS-based land-use suitability analysis can be conceptualized and implemented using ArcGIS ModelBuilder, and under no circumstances should the model's outputs be applied to real-world decisions or activities. The model was designed to be extremely flexible so that later users may determine their own land-use suitability, suitability criteria, evaluation rationale, and criteria weights. As this was the first project of its kind completed by the model developer, no guarantees are made as to the quality of the model or the absence of errorsThis model has a hierarchical structure in which some forty individual land-use suitability criteria are combined by weighted summation into several land-use goals which are again combined by weighted summation to yield a final land-use suitability layer. As such, any inconsistencies or errors anywhere in the model tend to reveal themselves in the final output and the model is in a sense self-testing. For example, each individual criterion is presented as a raster with values from 1-9 in a defined spatial extent. Inconsistencies at any point in the model will reveal themselves in the final output in the form of an extent different from that desired, missing values, or values outside the 1-9 range.This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

NGA 133 US Cities Disclaimer and Explanation ReadmeAs part of the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program,high resolution lidar data was acquired over 133 US Cities from 2003 to 2015. In 2017, this data wasunclassified and made available to the public by NGA. Most of the data prior to 2008 only contain rasterdigital elevation models (DEMs) while many of the projects produced after 2008 include lidar point clouddata as well as vector shapefiles depicting forest areas, tree points, and building footprints.The United States Geological Survey (USGS) is providing public access to the data through our FTP site..However, USGS makes no claims, no representations, and no warranties, express or implied, concerningthe validity, the reliability, or the accuracy of the GIS data and GIS data products furnished by NGA,including the implied validity of any uses of such data. The burden for determining accuracy,completeness, timeliness, merchantability, and fitness for or the appropriateness for use rests solely onthe user accessing this information. The user acknowledges and accepts all inherent limitations of thedata. USGS has not provided any quality control/quality assurance of this data and is posting it in itsoriginal form except where noted.The GIS data included in each project come in various geographic projections. The typical horizontaldatum is World Geodetic System 1984 or North American Datum 1983 (no explicit realization). The datais projected in Universal Transverse Mercator meters. The vertical datum is in North American VerticalDatum 1988 (various Geoid models) in meters. USGS has created and included footprint shapefiles (inthe matching spatial reference system) for projects that include lidar point cloud data. Many of thesepoint clouds do not have a projection expressly defined, so you will have to define the projection in a GISsoftware program that has this capability. Where provided, the lidar point cloud data are typically in LAS1.2 or LAZ format and classified to include Class 1 (Default), Class 2 (Ground), Class 5 (Vegetation), andClass 7 (Low Noise). The products generated from the lidar point cloud are at 1.0 meter resolution inIMG format, and include bare earth DEMs, first return rasters, and last return rasters. These rasterDEMs are not hydro-flattened. Intensity images derived from the point cloud are also included.

2017 Cook County 1 ft. elevation contours for PLSS Township Area 27. Data is derived from 2017 lidar. If you plan on downloading this dataset it is recommended to use the File Geodatabase option. The shapefile format may not work for larger datasets.Details about creating the one foot contours:The contours were processed by the Cook County GIS Department in order to add contour classifications as Index Contours (every 5 feet), Intermediate Contours (every 1 foot), Index Depression Contours, and Intermediate Depression Contours. To create the classification Cook County GIS used the Identify Contour tool in ArcPro. The input was the contour feature and the 2017 DEM that was delivered along with the LiDAR data. Details about the LiDAR Acquisition:IL 4 County QL1 Lidar project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a derived nominal pulse spacing (NPS) of 1 point every 0.35 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, U.S Survey Feet and vertical datum of NAVD88 (GEOID12B), U.S. Survey Feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 15,414 individual 2500 ft x 2500 ft tiles, as tiled Reflectance Imagery, and as tiled bare earth DEMs; all tiled to the same 2500 ft x 2500 ft schema.Ground Conditions: Lidar was collected April-May 2017, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Ayers established a total of 66 ground control points that were used to calibrate the lidar to known ground locations established throughout the WI Kenosha-Racine Counties and IL 4 County QL1 project area. An additional 195 independent accuracy checkpoints, 116 in Bare Earth and Urban landcovers (116 NVA points), 79 in Tall Grass and Brushland/Low Trees categories (79 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the dataDetails about the DEM:To acquire detailed surface elevation data for use in conservation planning, design, research, floodplain mapping, dam safety assessments and elevation modeling, etc. Classified LAS files are used to show the manually reviewed bare earth surface. This allows the user to create Reflectance Images, Breaklines and Raster DEM. The purpose of these lidar data was to produce high accuracy 3D hydro-flattened Digital Elevation Model (DEM) with a 2 foot cell size. These raw lidar point cloud data were used to create classified lidar LAS files, Reflectance Images, 3D breaklines, 1 foot contours, and hydro-flattened DEMs as necessary.

Photogrammetry is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From drone, aeroplane or helicopter, photographs are taken. Multiple overlapping photographs of the ground are taken. Precise measurements from the photographs can be taken to create topography maps.This data was collected using a drone carrying a digital camera in 2020 and 2021.A software package was used extract points (X,Y,Z (x & y coordinates) and z (height)) from the photographs. The data is then converted into gridded (GeoTIFF) data to create a Digital Surface Model of the earth.An ordnance datum (OD) is a vertical datum used as the basis for deriving heights on maps. This data is referenced to the Malin Head Vertical Datum which is the mean sea level of the tide gauge at Malin Head, County Donegal. It was adopted as the national datum in 1970 from readings taken between 1960 and 1969 and all heights on national grid maps are measured above this datum. Digital Terrain Models (DTM) are bare earth models (no trees or buildings) of the Earth’s surface.Digital Surface Models (DSM) are earth models in its current state. For example, a DSM includes elevations from buildings, tree canopy, electrical power lines and other features.Hillshading is a method which gives a 3D appearance to the terrain. It shows the shape of hills and mountains using shading (levels of grey) on a map, by the use of graded shadows that would be cast by high ground if light was shining from a chosen direction.This data shows the hillshade of the DSM.The Kilmichael Point and Dalkey Island data was collected by the Geological Survey Ireland. The Bremore Head, Bunmahon, Dunbeg, Ferriters and Illauntannig data was collected by the CHERISH Project. The CHERISH project looks at coastal sites that are important to human history. These sites have important structures (for example buildings or burial sites) that may be impacted by changes to our coast. All data formats are provided as GeoTIFF rasters. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. This data has a grid cell size of 0.25 meter by 0.25 meter. This means that each cell (pixel) represents an area of 0.25meters squared.

This submission revises the analysis and products for Thermal Quality Analysis for the northern half of the Appalachian Basin (https://gdr.openei.org/submissions/638) This submission is one of five major parts of a Low Temperature Geothermal Play Fairway Analysis. Phase 1 of the project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This submission includes a subset of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the project. This subset is those contents that were improved upon during calendar year 2016. Figures are provided as examples of some shapefiles and rasters. See also: Final Report: Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (https://gdr.openei.org/submissions/899). The 2015 data submission should be visited to obtain: 1) the regional standardized 1 square km grid used in the project as points (cell centers), polygons, and as a raster, 2) the raw well data for the state well temperature databases, 3) the COSUNA section shapefile and formation thermal conductivities by state as .xlsx files, 4) the sediment thickness map and 30 m Digital Elevation Model for the Appalachian Basin as GeoTIFF raster files, 5) the BHT correction sections shapefile and drilling fluid databases as .csv files, 6) the unbuffered interpolation regions as shapefiles, 7) several 50 km buffered interpolation regions as shapefiles, 8) several gridded interpolation regions as raster files, 9) an R script for organizing the thermal data and running the local spatial outlier analysis, 10) shapefiles and rasters for the prediction, uncertainty, and cross validation of the temperature at 1.5 km, 2.5 km, and 3.5 km depth, 11) shapefiles and rasters for the prediction, uncertainty, and cross validation depth to 100 degrees C, 12) an ArcGIS toolbox for thermal risk factor models, 13) an ArcGIS model for extracting results specific to each county of interest, 14) thermal resource cross section plots, 15) the geothermal Play Fairways.

The Dauphin County, PA 2016 QL2 LiDAR project called for the planning, acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meters. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of NAD83 (2011) State Plane Pennsylvania South Zone, US survey feet; NAVD1988 (Geoid 12B), US survey feet. LiDAR data was delivered in RAW flight line swath format, processed to create Classified LAS 1.4 Files formatted to 711 individual 5,000-foot x 5,000-foot tiles. Tile names use the following naming schema: "YYYYXXXXPAd" where YYYY is the first 3 characters of the tile's upper left corner Y-coordinate, XXXX - the first 4 characters of the tile's upper left corner X-coordinate, PA = Pennsylvania, and d = 'N' for North or 'S' for South. Corresponding 2.5-foot gridded hydro-flattened bare earth raster tiled DEM files and intensity image files were created using the same 5,000-foot x 5,000-foot schema. Hydro-flattened breaklines were produced in Esri file geodatabase format. Continuous 2-foot contours were produced in Esri file geodatabase format. Ground Conditions: LiDAR collection began in Spring 2016, while no snow was on the ground and rivers were at or below normal levels. In order to post process the LiDAR data to meet task order specifications, Quantum Spatial established a total of 84 control points (24 calibration control points and 60 QC checkpoints). These were used to calibrate the LIDAR to known ground locations established throughout the project area.