This is a collection of 124 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to Lindersson et al. (accepted february 2020 in WIREs Water) for further information about review methodology.

The collection is a descriptive list, holding the following information for each dataset:

• Category - as structured in Lindersson et al. (in preparation).
• Sub-category- as structured in Lindersson et al. (in preparation).
• Abbreviation - official or as specified in Lindersson et al. (in preparation).
• Title - full title of dataset.
• Product(s) - type of product(s) offered by the dataset.
• Period - time period covered by the dataset, not defined for all datasets.
• Temporal resolution - not defined for static datasets.
• Angular spatial resolution - only defined for gridded datasets.
• Metric spatial resolution - only defined for gridded datasets.
• Map scale
• Extent - geographic coverage of dataset given in latitude limits.
• Description
• Creating institute(s)
• Data type - raster, vector or tabular.
• File format
• Primary EO type - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.
• Data sources - lists the data sources behind the dataset, to the extent this is feasible.
• Data sources also in this table - data sources that are also included as datasets in this collection.
• Intentionally compatible with - defines other datasets in this collection that the dataset is intentinoally compatible with.
• Citation - dataset reference or credit.
• Documentation - dataset documentation.
• Web address - dataset access link.

NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.

Description

This dataset consist of two vector files which show the change in the building stock of the City of DaNang retrieved from satellite image analysis. Buildings were first identified from a Pléiades satellite image from 24.10.2015 and classified into 9 categories in a semi-automatic workflow desribed by Warth et al. (2019) and Vetter-Gindele et al. (2019).

In a second step, these buildings were inspected for changes based on a second Pléiades satellite image acquired on 13.08.2017 based on visual interpretation. Changes were also classified into 5 categories and aggregated by administrative wards (first dataset: adm) and a hexagon grid of 250 meter length (second dataset: hex).

The full workflow of the generation of this dataset, including a detailled description of its contents and a discussion on its potential use is published by Braun et al. 2020: Changes in the building stock of DaNang between 2015 and 2017

Contents

Both datasets (adm and hex) are stored as ESRI shapefiles which can be used in common Geographic Information Systems (GIS) and consist of the following parts:

• shp: polygon geometries (geometries of the administrative boundaries and hexagons)
• dbf: attribute table (containing the number of buildings per class for 2015 and 2017 and the underlying changes (e.g. number of new buildings, number of demolished buildings, ect.)
• shx: index file combining the geometries with the attributes
• cpg: encoding of the attributes (UTF-8)
• prj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for ArcGIS
• qpj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for QGIS
• lyr: symbology suggestion for the polygons(predefined is the number of local type shophouses in 2017) for ArcGIS
• qml: symbology suggestion for the polygons (predefined is the number of new buildings between 2015 and 2017) for QGIS

Citation and documentation

To cite this dataset, please refer to the publication

• Braun, A.; Warth, G.; Bachofer, F.; Quynh Bui, T.T.; Tran, H.; Hochschild, V. (2020): Changes in the Building Stock of Da Nang between 2015 and 2017. Data, 5, 42. doi:10.3390/data5020042

This article contains a detailed description of the dataset, the defined building type classes and the types of changes which were analyzed. Furthermore, the article makes recommendations on the use of the datasets and discusses potential error sources.

New-ID: NBI44

Ocean mask for Africa.

Integrated Elevation and Bathymetry Dataset Documentation

File: AFELBA.IMG Code: 100048-001

Raster Member This IMG file is in IDRISI format

Integrated elevation and bathymetry data set is part of UNEP-GRID/FAO Africa data base incorporated into World Data Bank II by the World Data Center-A (WDC-A) for Solid Earth Geophysics, operated by the U.S. National Geophysical Data Center (NGDC). The dataset is provided on a diskette called The Global Change Data Base. The Data Bank II is part of larger project called Global Ecosystems Database Project. This is a cooperation between the National Oceanic and Atmospheric Administration (NOAA), NGDC and the U.S. Environmental Protection Agency (EPA). The National Center for Geographic Information and Analyses (NCGIA) in Santa Barbara, California joined the project to assist with training and evaluation. Sources used were the USSCS World Soil Map, UNESCO/FAO Soil Map of the World, DMA Topographic Maps of Africa, Raize Landform Map of North Africa, and Landsat mosaics. A scale was chosen that corresponds closely with the resolution of global AVHRR coverage was chosen to provide compatibility with other scales. All data are provided in geographic (longitude/latitude) projection. The dataset is accompanied by an ASCII documentation file which contains information necessary for use of the dataset in a GIS or other software. Contact : NGDC, 325 Broadway E/GC, Boulder, Colorado 80303, USA The AFELBA file shows integrated elevation and bathymetry (feet)

References:

Edwards, Margaret Helen. Digital Image Processing of Local and Global Bathymetric Data (1986). Master"'"s Thesis. Washington University, Dept. of Earch and Planetary Sciences, St. Louis, Missouri, p.106.

Haxby, W.F., et al. Digital Images of Combined Oceanic and Continental Data Sets and Their Use in Tectonic Studies (1983). EOS Transaction of the American Geophysical Union, vol.64, no.52, pp.995-1004.

NOAA. Global Change Data Base, Digital Data with Documentation (1992). National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, Colorado.

Hastings, David A., and Liping Di. Modeling of global change phenomena with GIS using the Global Change Data Base (1992). Remote sensing of environment, in review.

Clark, David M., Hastings, David A. and Kineman, John J. Global databases and their implications for GIS (1991). IN Maguire, David J., Goodchild, Michael F., and Rhind, David W., eds., Geographical Information Systems: Overview, Principles and Applications. Burnt Mill, Essex, United Kingdom, Longman. V.2, pp. 217-231.

Kineman, J.J., Clark, D.M., and Croze, H. Data integration and modelling for global change: An international experiment (1990). Proceeding of the International Conference and workshop on Global Natural Resource Monitoring and Assessments. Preparing for the 21st Century (Venice, Italy, 24-30 September 1989). Bethesda, Maryland, American Society of Photogrammetry and Remote Sensing, vol. 2, pp. 660-669. CERL. The Geographic Resources Analysis Support System (GRASS-GIS) version 4.0 (1991). U.S. Army Corps of Engineers, Construction Engineering Research Laboratory, Champaign, Illinois.

Source map : various sources Publication Date : Jun1985 Projection : Miller Oblated Stereographic resampled to lat/lon. Type : Raster Format : IDRISI

Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"

The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.

Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:

Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-222, in review, 2021.

https://github.com/jakobjassmann/ecodes-dk-lidar

Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example 7z on Windows, the archiving tool on macOS and bz2 on Linux).

A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 6 in the manuscript, can be found here.

Abstract (from manuscript)

Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~87 GB, compressed 16.4 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.

Acknowledgements (from manuscript)

We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set and Charles Davison for feedback regarding data use and handling. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).

Elevation data is the representation of the height of the ground at a location on earth. This data allows users to discover the extent of elevation products, and the metadata associated with it.

Ontario Elevation Data Index (Download: Shapefile | File Geodatabase)

Additional Documentation

Ontario Elevation Data Index - User Guide (PDF)

Status On going: data is being continually updated

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

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

GIS (Geographic Information System) data, which includes spatial data such as maps, satellite imagery, and other geospatial data, is typically created using various techniques and methods to ensure its accuracy, completeness, and reliability. The process of creating GIS data for use in metadata involves several key steps, which may include: Data Collection: The first step in creating GIS data for metadata is data collection. This may involve gathering data from various sources, such as field surveys, remote sensing, aerial photography, or existing datasets. Data can be collected using GPS (Global Positioning System) receivers, satellite imagery, LiDAR (Light Detection and Ranging) technology, or other data acquisition methods.Data Validation and Quality Control: Once data is collected, it goes through validation and quality control processes to ensure its accuracy and reliability. This may involve comparing data against known standards or specifications, checking for data errors or inconsistencies, and validating data attributes to ensure they meet the desired accuracy requirements.Data Processing and Analysis: After validation and quality control, data may be processed and analyzed to create meaningful information. This may involve data integration, data transformation, spatial analysis, and other geoprocessing techniques to derive new datasets or generate metadata.Metadata Creation: Metadata, which is descriptive information about the GIS data, is created based on established standards or guidelines. This may include information such as data source, data quality, data format, spatial extent, projection information, and other relevant details that provide context and documentation about the GIS data.Metadata Documentation: Once metadata is created, it needs to be documented in a standardized format. This may involve using metadata standards such as ISO 19115, FGDC (Federal Geographic Data Committee), or other industry-specific standards. Metadata documentation typically includes information about the data source, data lineage, data quality, spatial reference system, attributes, and other relevant information that describes the GIS data and its characteristics.Data Publishing: Finally, GIS data and its associated metadata may be published or made accessible to users through various means, such as online data portals, web services, or other data dissemination methods. Metadata is often used to facilitate data discovery, evaluation, and use, providing users with the necessary information to understand and utilize the GIS data effectively.Overall, the process of creating GIS data for use in metadata involves data collection, validation, processing, analysis, metadata creation, documentation, and data publishing, following established standards or guidelines to ensure accuracy, reliability, and interoperability of the GIS data.

!!! This is a teaser version of the data set (5MB) for the Husby Klit area !!!
For the full data set see: https://doi.org/10.5281/zenodo.4756556

------------------------------------------------------------------------------------------------------------------------------------------------

Seventy high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15"

The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency.

Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021)

Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are:

• New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented here.
• New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation here.
• Re-scaling of "solar_radiation" variable to MJ per 100 m² per year.

Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository:

Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-222, in review, 2021.

https://github.com/jakobjassmann/ecodes-dk-lidar

Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example 7z on Windows, the archiving tool on macOS and bz2 on Linux).

A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found here.

Abstract (from manuscript)

Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.

Acknowledgements (from manuscript)

We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).

Single photon light detection and ranging (SPL LiDAR) is an active remote sensing technology for:

mapping vegetation aspects including cover, density and height representing the earth's terrain and elevation contours

We acquired SPL data on an airborne acquisition platform under leaf-on conditions to support Forest Resources Inventory (FRI) development.

FRI provides:

information to support resource management planning and land use decisions within Ontario’s Managed Forest Zone information on tree species, density, heights, ages and distribution

The SPL data point density ranges from a min of 25pts/m. Each point represents heights of objects such as:

ground level terrain points heights of vegetation buildings

The LiDAR was classified according to the Ontario LiDAR classifications. Low, medium and tall vegetation are classed as 3, 4, 5 and 12 classes.

The FRI SPL products include the following digital elevation models:

digital terrain model canopy height model digital surface model intensity model (signal width to return ratio) forest inventory raster metrics forest inventory attributes predicted streams hydro break lines block control points

LiDAR fMVA data supports developing detailed 3D analysis of:

forest inventory terrain hydrology infrastructure transportation

We made significant investments in Single Photon LiDAR data, now available on the Open Data Catalogue Derivatives are available for streaming or through download.

The map reflects areas with LiDAR data available for download. Zoom in to see data tiles and download options. Select individual tiles to download the data.

You can download:

classified point cloud data can also be downloaded via .laz format derivatives in a compressed .tiff format Forest Resource Inventory leaf-on LiDAR Tile Index (Download: Shapefile | File Geodatabase | GeoPackage )

Web raster services

You can access the data through our web raster services. For more information and tutorials, read the Ontario Web Raster Services User Guide.

If you have questions about how to use the Web raster services, email Geospatial Ontario (GEO) at geospatial@ontario.ca.

Note: Internal Users replace “https://ws.” with “https://intra.ws.”

CHM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_CHM_SPL/ImageServer DSM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DSM_SPL/ImageServer DTM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DTM_SPL/ImageServer T1 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_Imagery_T1/ImageServer T2 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_T2_Imagery/ImageServer Land Cover - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Thematic/Ontario_Land_Cover_Compilation_v2/ImageServer

Service Endpoint

https://services1.arcgis.com/TJH5KDher0W13Kgo/arcgis/rest/services/FRI_Data_Access/FeatureServer

Additional Documentation

     Forest Resources Inventory | ontario.ca

Status

On going: data is being continually updated

Maintenance and Update Frequency

As needed: data is updated as deemed necessary

Contact

Natural Resources Information Unit, Forest Resources Inventory Program, FRI@ontario.ca

A map of the documented SAV habitat created from the aggregate extent of submerged aquatic vegetation in the Hudson River Estuary mapped in all six inventories (1997, 2002, 2007, 2014, 2016, 2018, 2022). In SAV communities Vallisneria americana and Myriophyllum spicatum are dominant in association with Ceratophyllum dermersum, Elodea muttallii, Najas sp., and Potamogeton perfoliatus. The designation "submerged aquatic vegetation" explicitly excludes plant communities in which Trapa natans is dominant in association with Spriodela polyrhiza, Myriophyllum spicatum, and Nuphar advena.SAV mapping in the Hudson River Estuary was conducted in six time periods with different sources of funding. The 1997 mapping was initiated with NOAA funds, Hudson River Foundation funds, and N.Y. State Environmental Protection Funds through the Hudson River Estuary Program (HREP). In 2002, 2007, 2014, 2016, 2018, and 2022 mapping was undertaken with N.Y. State Environmental Protection Funds through HREP.In 1994, a collaboration was initiated between the Institute of Ecosystem Studies (IES), now the Cary Institute of Ecosystem Studies, HRNERR/NYSDEC, and the Cornell Laboratory for Environmental Applications of Remote Sensing (CLEARS), now IRIS. In addition, New York Sea Grant, HREP, and the National Oceanic and Atmospheric Administration (NOAA) are identified as partners. These groups provided diverse expertise to enable the first broad delimitation of SAV in the Hudson.Major tributaries and coves were mapped in addition to the main river area.

Zoom in on the map above and click your area of interest to determine which package(s) you require for download.

A three-dimensional raster data set which represents a continuous elevation surface. This data set encompasses the Greater Toronto Area (GTA) and the surrounding area from Niagara to Lake Simcoe and the Kawartha Highlands to Port Hope. The Digital Elevation Model (DEM) data is organized into 20km x 20km tiles with a spatial resolution of 5m.

This data is intended to be used for pre-engineering survey and design as well as the production of planimetric mapping at differing accuracies.

This data is intended for GIS and remote sensing application that require a high resolution, high accuracy elevation model.

The source data for the GTA 2002 DEM can be found in the Ontario Mass Points and Breaklines.

Product Packages

GTA 2002 DEM - North East GTA 2002 DEM - North West GTA 2002 DEM - South West

Additional Documentation

GTA DEM 2002 - User Guide (Word)

GTA 2002 DEM Tile Index (.Zip)

Status

Completed: Production of the data has been completed

Maintenance and Update Frequency

Not planned: there are no plans to update the data

Contact

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

Abstract: Indigenous peoples of Canada’s North have long made use of boreal forestproducts, with wooden drift fences to direct caribou movement towards kill sites as uniqueexamples. Caribou fences are of archaeological and ecological significance, yet sparselydistributed and increasingly at risk to wildfire. Costly remote field logistics requiresefficient prior fence verification and rapid on-site documentation of structure and landscapecontext. Unmanned aerial vehicle (UAV) and very high-resolution (VHR) satelliteimagery were used for detailed site recording and detection of coarse woody debris (CWD)objects under challenging Subarctic alpine woodlands conditions. UAVs enabled discoveryof previously unknown wooden structures and revealed extensive use of CWD (n = 1745,total length = 2682 m, total volume = 16.7 m3). The methodology detected CWD objectsmuch smaller than previously reported in remote sensing literature (mean 1.5 m long,0.09 m wide), substantiating a high spatial resolution requirement for detection.Structurally, the fences were not uniformly left on the landscape. Permafrost patternedground combined with small CWD contributions at the pixel level complicated identificationthrough VHR data sets. UAV outputs significantly enriched field techniques andsupported a deeper understanding of caribou fences as a hunting technology, and they willaid ongoing archaeological interpretation and time-series comparisons of change agents.Key words: photogrammetry, heritage documentation, coarse woody debris, boreal forest,permafrost, archaeology, remote sensing, site sketch, caribou fence, game trail, alpine.