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.

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.

This (zipped) shapefile is derived from the State Game Land (SGL) vector files provided by the state of Pennsylvania (http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=86). A 1 km buffer was added in QGIS.

For additional information, please see https://zenodo.org/record/4766351

This dataset is provided via WMS service (https://geomap.gsmma.gov.tw/mapguide/mapagent/mapagent.fcgi?version1.0.2&formatimage/png). Please add the URL shown in the downloaded file to GIS software (such as QGIS) to select this layer in the directory.

In the compressed folder, there is a 3-D view of the slope affected by rockfall and its defence nearby the town of Lauria (South Italy) To create a 3-D interactive view of the mitigation works (that can be used with any browser without installing GIS or other software), we used the Qgis2threejs plugin for QGIS. The LiDAR DTM was used as an elevation layer to create several high-resolution 3-D view models with different layers. Rockfall barriers Location of 2002 rockfall Area interested by 2017 wildfire building full Paper Merging Historical Archives with Remote Sensing Data: A Methodology to Improve Rockfall Mitigation Strategy for Small Communities {"references": ["Notti et al., 2021 Merging Historical Archives with Remote Sensing Data: A Methodology to Improve Rockfall Mitigation Strategy for Small Communities"]}

Download file containing two raster images in png format, accompanied by world file (georeferencing file) and style file for QGis. The first png format image is a squared representation with pink coloring of areas with slopes greater than 30°. The second image in PNG format is a representation of squares themed on four classes of areas with slopes greater than 30°. The download file also includes the transformation of areas with slopes greater than 30° from a raster image to polygons contained in a file in shape format.

These ZIP files contains lidar derivatives including a Digital Elevation Map, Slope Analysis, and Hillshade individually encompassing Pennsylvania State Game Lands 12-50 as well as a one kilometer buffer around each region. These files were derived from lidar data provided by the state of Pennsylvania and processed using LAStools and QGIS through Project Kappa (https://zenodo.org/record/4573004). When unzipped, this file is approximately 71.7 GB in size.

For additional information, please see https://zenodo.org/record/4766351.

The Science Case in the Caribbean region presents records on landslides, precipitation, maps used as inputs of hazard models and drone imagery over the region of interest.
For the Carribean study-case, an analysis of open and proprietary satellite based dataset was used to facilitate the setup and evaluation of physically-based multi-hazard models. These allow for qualification and quantification of spatio-temporal multi-hazard patterns. These form a crucial input into the general hazard and risk assessment workflow.

Presented here are the datasets employed for Case Study 4 in Deliverable D3.1 with a short description, produced and saved within the following folders:

Dominica_landslide: the landslides datasets mapped by ITC using high-resolution satellite imagery. It is intended to calibrate and validate the flood and landslide modelling. The folder contains four shapefiles:

· Landslide_Part.shp - Shapefile containing landslide extent, flash flood extents, and their attributes.

· Cloud.shp – Shapefile represents the cloud-filled areas in the satellite imagery where no mapping was possible.

· The other two shapefiles are self-explanatory.

GPM_Maria: NASA Global Precipitation Mission (GPM) precipitation maps processed for model input in LISEM. GPM is a hybrid fusion with satellite datasets for precipitation estimates. Mean as input data to represent precipitation in the landslide and flood modelling.

Maps_Models_Input : Soil and land use and channels, lots of custom work, SOILGRIDS, and SPOT image classification; all the datasets are ready for model input for OpenLISEM and LISEM Hazard or FastFlood. The dataset is meant to calibrate and validate the flood and landslide modelling.

The raster files are either in Geotiff format or PCraster map format. Both can be opened by GIS systems such as GDAL or QGIS. The projection of each file is in UTM20N.

Some key files are:

• dem.map -elevation model, the height of the landscape in meters above sea level.
• lai.map - leaf area index, estimated using empirical relationships based on NDVI (Normalized Difference Vegetation Index)). The source data to calculate NDVI is Sentinel-2.
• KSat.map - Saturated hydraulic conductivity of the soil, estimated based on a combination of SOILGRIDS soil texture, Saxton et al. (2006) Pedotransfer functions, and a national soil map for Dominica.
• clay.map - Clay texture fraction, SoilGrids resampling
• silt.map - Silt texture fraction, SoilGrids resampling
• sand.map - Sand texture fraction, SoilGrids resampling
• cover.map - Vegetation cover as a fraction, estimated using linear correlation with NDVI.
• lu_new.map - Spot satellite image classification at 10 meters resolution for predominant land use types.
• n.map - Mannings surface roughness coefficient, specific value based on the land use type.
• ndvi.map - Normalized Differential Vegetation Index, based on Sentinel-2 images in summer.
• ldd.map - Drainage network map for the island, which can be used for flow accumulation and streamflow detection
• catchments.map - Catchment ID's based on the ldd.map drainage network.
• Channelldd.map - Channel-only drainage network map, calibrated manually to have all channels on the island represented correctly.
• Soildepth - Soil depth in meters, based on a physically-based soil depth model in meters and observational data obtained from landslide-sites during fieldwork in 2018.
• Slope.map - Slope map in gradient of the elevation model (m/m) in the steepest direction

StakeholderQuestionnaire_Survey_ITC: The stakeholder questionnaires particularly relating to the tools developed partly by this project on rapid hazard modelling. Stakeholder Engagement survey and Stakeholder Survey Results prepared and implemented by Sruthie Rajendran as part of her MSc Thesis Twin Framework For Decision Support In Flood Risk Management supervised by Dr. M.N. Koeva (Mila) and Dr. B. van den Bout (Bastian) submitted in July 2024.

·Drone_Images_ 2024: Images captured using a DJI drone of part of the Study area in February 2024. The file comprises three different regions: Coulibistrie, Pichelin and Point Michel. The 3D models for Coulibistrie were generated from the nadir drone images using photogrammetric techniques employed by the software Pix4D. The image Coordinate System is WGS 84 (EGM 96 Geoid0), but the Output Coordinate System of the 3D model is WGS 84 / UTM zone 20N (EGM 96 Geoid). The other two folders contain only the drone images captured for that particular region's Pichelin and Point Michel. The dataset is used with other datasets to prepare and create the digital twin framework tailored for flood risk management in the study area.

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 ZIP file contains lidar derivatives including a Digital Elevation Map, Slope Analysis, and Hillshade individually encompassing Pennsylvania State Game Lands 261-280 as well as a one kilometer buffer around each region. These files were derived from lidar data provided by the state of Pennsylvania and processed using LAStools and QGIS through Project Kappa (https://zenodo.org/record/4573004). When unzipped, this file is approximately 82.8 GB in size.

For additional information, please see https://zenodo.org/record/4766351.

Forests of the world constitute one third of the total land area and are critical for e.g. carbon balance, biodiversity, water supply, and as source for bio-based products. Although the terrain within forest land has a great impact on accessibility, there is a lack of knowledge about the distribution of its variation in slope. The aim was to address that knowledge gap and create a globally consistent dataset of the distribution and area of forest land within different slope classes. A Geographic Information System (GIS) analysis was performed using the open source QGIS, GDAL, and R software. The core of the analysis was a digital elevation model and a forest cover mask, both with a final resolution of 90 metres. The total forest area according to the forest mask was 4.15 billion hectares whereof 82% was on slope less than 15°. The remaining 18% was distributed over the following slope classes, with 6% on a 15-20° slope, 8% on a 20-30° slope, and 4% on a slope >30°. Out of the major forestry countries, China had the largest proportion of forest steeper than 15° followed by Chile and India. A sensitivity analysis with 20 metres resolution resulted in increased steep areas by 1 percent point in flat Sweden and by 11 percent points in steep Austria. In addition to country-specific and aggregated results of slope distribution and forest area, a global raster dataset is also made freely available, to cover user-specific areas that are not necessarily demarcated by country borders. Apart from predicting the regional possibilities for different harvesting equipment, which was the original idea behind this study, the results can be used to relate geographical forest variables to slope. The results could also be used in strategic forest fire fighting and large scale planning of forest conservation and management.

Raster dataset in GeoTIFF format. The data is unprojected (EPSG: 4326) and the resolution is 90 m at most, however the map-unit is degrees.

Five files in total where the number in the filename indicates the proximity to the equator. File with number 1 covers the area from 0 to 49 degrees latitude, both north and south, number 2N covers latitude 50-59° north, number 2S covers latitude 50-59° south, number 3 covers latitude 60-69° north and number 4 covers latitude 70-79° north.

The GeoTIFF files are in high resolution and are intended to be used with GIS software. We also provide PNG versions of the raster datasets, with XML geographic metadata, generated using GDAL in low resolution, to enable quick preview with a standard picture viewer.

The CONtiguous United States (CONUS) “Flood Inundation Mapping Hydrofabric - ICESat-2 River Surface Slope” (FIM HF IRIS) dataset integrates river slopes from the global IRIS dataset for 117,357 spatially corresponding main-stream reaches within NOAA’s Office of Water Prediction operational FIM forecasting system, which utilizes the Height Above Nearest Drainage approach (OWP HAND-FIM) to help warn communities of floods. To achieve this, a spatial joining approach was developed to align FIM HF reaches with IRIS reaches, accounting for differences in reach flowline sources. When applied to OWP HAND-FIM, FIM HF IRIS improved flood map accuracy by an average of 31% (CSI) across eight flood events compared to the original FIM HF slopes. Using a common attribute, IRIS data were also transferred from FIM HF IRIS to the CONUS-scale Next Generation Water Resources Modeling Framework Hydrofabric (NextGen HF), creating the NextGen HF IRIS dataset. By referencing another common attribute, SWOT vector data (e.g., water surface elevation, slope, discharge) can be leveraged by OWP HAND-FIM and NextGen through the two resulting datasets. The spatial joining approach, which enables the integration of FIM HF with other hydrologic datasets via flowlines, is provided alongside the two resulting datasets.

The slope_iris_sword in FIM HF IRIS can be used with the Recalculate_Discharge_in_Hydrotable_useFIMHFIRIS.py script to regenerate the hydrotable for OWP HAND-FIM, where the discharge will be recalculated using slope_iris_sword. Consequently, the synthetic rating curves (SRCs) will be updated based on the new discharges (see more details in https://github.com/NOAA-OWP/inundation-mapping/wiki/3.-HAND-Methodology). The script can also be used to regenerate hydrotables using river slopes from other sources, such as NextGen HF, provided they are linked to the FIM HF flowlines.

The feature classes for FIMHF_IRIS and NextGenHF_IRIS are provided in formats of geopackage (.gpkg) and geodatabases (.gdb), which can be accessed using ArcGIS, QGIS, or relevant Python packages for inspection, visualization, or spatial analysis of slope_iris_sword.

More information can be found at: Chen, Y., Baruah, A., Devi, D., & Cohen, S. (2025). Improved River Slope Datasets for the United States Hydrofabrics [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15099149

This dataset contains the the EXPLORE Machine Learning Data Challenge 2022 QGIS project.

The project embed the following Archytas Dome layers:

Raster

• Narrow Angle Camera (NAC)
• DEM derived from NAC
• Slope computer on DEM

Vectorial

• POIs - Points Of Interest to be used in STEP 3

More information at: https://exploredatachallenges.space/

Images were processed from NASA PDS raw data using USGS ISIS and NASA ASP tools.

Almost a quarter of the Greenlandic freshwater input into the oceans stems from watersheds disconnected from the Greenland Ice Sheet. As the Ice Sheet shrinks, the relative importance of watersheds with comparatively little glacier cover will increase (Abermann et al., 2021). The watershed of the river Røde Elv on Disko Island (70° N, 54° W; 96 km²) is one of these. The area is characterised by a unique volcanic genesis with basalts, geothermal springs, and a typical mountainous periglacial landscape. A geomorphological mapping campaign was carried out to characterise the drainage basin and to explain the discharge patterns of the Røde Elv. The resulting map is presented in this contribution.The stream orders and the extent of the watershed were calculated using the ArcticDEM (Digital Elevation Model) with a native spatial resolution of 2 m. A hydrologically corrected DEM with filled surface depressions was used to derive the accumulated flow using the Deterministic 8 method (O'Callaghan & Mark, 1984). The drainage river system was calculated based on the flow, resulting in six logarithmically scaled Horton-Strahler stream orders. The extent of the watershed was determined by identifying the area contributing to the discharge at the delta.The geomorphological character of the study area is represented by landcover units, which were identified and schematised regarding their hydrological characteristics based on field observations, literature, and background knowledge (Richter, 2024: Table 1). This resulted in a classification of 19 landform classes as polygons, and the line classes debris flow and solifluction terraces. The map digitisation was conducted based on local knowledge and photographs taken during a field visit in August 2023, as well as several freely available base maps for remote areas (Dataforsyningen, Bing Maps, and Google Maps). In addition, an old geomorphological map of the southern part of the watershed was used as a reference (Andersen et al., 1976). A detailed description of the digitisation process and an analysis of the spatial patterns of the landforms, can be found in Richter (2024).The map aims to provide an overview of the hydro-morphological structure and characteristics of the watershed. Both the valley sides and the valley floor are distinct geomorphological systems, reflecting the landscape heritage of the last glacial period. This is evident in the depositional processes at their transition, as indicated by the large active Holocene alluvial fans and talus deposits. Water infiltration is comparatively limited and highly variable on a small scale in the classes of rocks, talus deposits, unvegetated upper talus deposits, moraines, block glaciers and paraglaciation. This results in rapid and increasingly channelled surface runoff, controlled by the slope, slowing down only in depressions forming small lakes or when reaching the vegetated lower talus slopes. The latter are characterised by high water storage and evapotranspiration rates due to the soils and the high friction of the vegetation. Here, the drainage pathways are altered by features like landslides, patterned ground, or the flatter tops of the solifluction terraces, leading to higher infiltration rates. In the well-drained alluvial deposits and gravel, the infiltration rate is even higher. Finally, the largest water buffers are associated with the braided river system, lakes, and swamps which are characterised by a connection to the groundwater.Geomorphology is an important explanatory factor for the highly variable discharge patterns of the Røde Elv on a diurnal to interannual scale, as it determines water availability and the capacity of water storage reservoirs. These water buffers are influenced not only by the seasonal variation in meteorology, but also by the interplay of aspect, elevation, slope, and geomorphological process activity, which collectively determine the distribution of landform classes shown on the geomorphological map (Richter, 2024).

This data contains values of bare sand area, modelled wind speed, aspect and slope at a 2.5 m spatial resolution for four UK coastal dune fields, Abberfraw (Wales), Ainsdale (England), Morfa Dyffryn (Wales), Penhale (England). Data is stored as a .csv file. Data is available for 620,756.25 m2 of dune at Abberfraw, 550,962.5 m2 of dune at Ainsdale, 1,797,756.25 m2 of dune at Morfa Dyffryn and 2,275,056.25 m2 of dune at Penhale. All values were calculated from aerial imagery and digital terrain models collected between 2014 and 2016. For each location, areas of bare sand were mapped in QGIS using the semi-automatic classification plugin (SCP) and the minimum distance algorithm on true-colour RGB images. The slope and aspect of the dune surface at each site was calculated in QGIS from digital terrain models. Wind speed at 0.4 m above the surface of the digital terrain model at each site was calculated using a steady state computational fluid dynamics (CFD). Data was collected to statistically test the relationship between bare sand and three abiotic physical factors on coastal dunes (wind speed, dune slope and dune slope aspect). Vertical aerial imagery was sourced from EDINA Aerial Digimap Service and digital terrain models from EDINA LIDAR Digimap Service. Wind speed data was generated and interpreted by Dr Thomas Smyth (University of Huddersfield). Full details about this dataset can be found at https://doi.org/10.5285/972599af-0cc3-4e0e-a4dc-2fab7a6dfc85

We here include the surface coseismic ruptures interpreted from the VDM in kmz, the DInSAR slope analysis performed in QGis as a GeoTiff (WGS84-UTM33N, EPSG:32633) and the ASCII file of the VDM 1 and HDM1.

THIS IS A DRAFT VERSION! All layers are visible in this linked webgis app. The layers available in this dataset are in a WGS84 geographic coordinate reference system (EPSG:4326) where latitude and longitude coordinates at 0.0009 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, slope and elevation represent Earth surface morphology Biomass - Biomass values at year 2020 Mg/ha Canopy Base Height - Height of canopy from the ground (m) Canopy Bulk Density - amount of canopy biomass per volume of canopy (kg/m3) Fuel Model FuelModelClasses - 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 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.

In order to obtain high-resolution data on the current status of glaciers in the Qilian Mountains, a glacier inventory of the Qilian Mountains was conducted from 2018 to 2019. In this cataloging work, 58 domestic high-resolution satellite remote sensing images were used, covering one set of digital elevation models in the Qilian Mountains and the eastern part of the Altai Mountains. The ground resolution of satellite remote sensing images is better than 2 meters, with only a few areas having a ground resolution of 5 meters. After geographical correction, satellite remote sensing images are manually vectorized to obtain glacier boundaries. Within the glacier range, information such as glacier elevation, slope, and aspect are statistically analyzed. This cataloging follows the glacier coding structure from China's first glacier cataloging. If there are omissions or glacier splits, the uncoded glaciers will only be updated to the fifth level watershed number and not the glacier number. The projection coordinate system for cataloging data adopts the Albers equal area cut cone projection suitable for east-west extension areas, and the ellipsoid is the WGS84 ellipsoid, which is consistent with the second Chinese glacier cataloging. The extraction and calculation of glacier attributes in vector layers were completed using QGIS software. This data can provide basic information for research on changes in the cryosphere.

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.

Here are the results in a paper entitled "Characterization of the 2008 phreatomagmatic eruption of Okmok from ArcticDEM and InSAR: deposition, erosion, and deformation" submitted to JGR Solid Earth in 2020.

The main revision compared to version 1: This revision does not use one DEM (acquired on 15 May 2016) that was partly contaminated by clouds in the north flank of Ahmanilix. This revision mostly improves the result of the elevation change rate (rate.tif), but it also slightly changes the elevation change data and its corresponding uncertainties.

It includes the 2-m resolution surface elevation change of the 2008 Okmok eruption (Fig. 3a in the paper) and the 2-m resolution post-eruptive elevation change rate map (Fig. 4), as well as the corresponding uncertainties (Fig. S3). It also includes the boundary of the proximal deposit field classified using a minimum elevation increase of 2 m, the boundary of large slope failure, and the shorelines of two lakes (Fig. 3a and S5) at different acquisition times.

The GeoTIFF files can be viewed in free and open-source software QGIS, in Google Earth, or by Matlab using code https://github.com/ihowat/setsm_postprocessing/blob/master/readGeotiff.m. The shapefiles can be viewed in QGIS. Google Earth may not show some of the shapefiles well.