The Rangeland Department in the Kamloops District from the Government of British Columbia has recently raised concerns regarding the observation on the reduction of the number and the surface area of the grassland ponds in the Lac du Bois Grasslands Protected Area. This study aims to distinguish between the ponds with stable groundwater inputs (i.e. connected ponds) and the ponds with unstable groundwater inputs (i.e. perched ponds) to assist the government in determining reliable water sources. This research started by categorizing ponds with different surface areas as either low resilience or threatened resilience. Different terrain models were created using Light Detection and Ranging (LiDAR) data in addition to the calculation of the topographic wetness index (TWI). The classifications were validated using Google Earth and drone imagery. An overall of 121 ponds was discovered with 86 of them considered as low resilience, while the remaining 27 ponds being threatened resilience. For the low resilience ponds, 19 of them were identified as perched ponds, 47 as connected ponds, and 20 as intermediate ponds with the risk of having unstable groundwater connection that requires further analysis in the field. For the threatened resilience ponds, 5 of them were found to be perched ponds, 17 as connected ponds, and 5 as intermediate ponds. The outcome of the pond distribution indicates that the perched ponds were more likely to be found in an area with a flat slope, surrounded by grass, and low canopy coverage. Additionally, the calculated TWI was unable to differentiate between the pond types as the median groundwater levels are spatially dependent on the local topographic features.

The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (https://doi.org/10.3390/su14148412).

Link to the Google Earth Engine (GEE) code (link: https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002)

You can analyze and visualize the following spatial layers by accessing the GEE link:

Daytime summer land surface temperature (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)

Surface thermal hot-spot (raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.

Surface albedo (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)

Impervious area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)

Tree cover (raster data, horizontal resolution 10 m, ISPRA data, year 2018)

Grassland area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)

Water bodies (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)

Sky View Factor (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)

Buildings' units of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%].

Here attached the .txt file of the GEE code.

E-mail

Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it

Marco Morabito, CNR-IBE, marco.morabito@cnr.it

Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it

Shallow nearshore coastal waters provide a wealth of societal, economic and ecosystem services, yet their structure is poorly mapped due to the use of expensive and time intensive methods. Bathymetric mapping from space has sought to alleviate this but has remained dependent upon in situ water-based measurements. Here we fuse ICESat-2 lidar data with Sentinel-2 optical imagery, within the Google Earth Engine geospatial cloud platform, to create wall-to-wall high-resolution bathymetric maps at regional-to-national scales in Florida, Crete and Bermuda. ICESat-2 bathymetric classified photons are used to train three common Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10-15%) when compared with in situ NOAA DEM data. We demonstrate a means of using ICESat-2 for both model calibration and validation, thus cementing a pathway for a fully space-borne approach to map nearshore bathymetry. Here we provide the Sentinel-2 mosaics, ICESat-2 bathymetric profiles and Satellite-Derived Bathymetry (SDB) models

This dataset consists of near-global, analysis-ready, multi-resolution gridded vegetation structure metrics derived from NASA Global Ecosystem Dynamics Investigation (GEDI) Level 2 and 4A products associated with 25-m diameter lidar footprints. This dataset provides a comprehensive representation of near-global vegetation structure that is inclusive of the entire vertical profile, based solely …

The AHN DEM is a 0.5m DEM covering the Netherlands. It was generated from LIDAR data taken in the spring between 2007 and 2012. This version contains both ground level samples and items above ground level (such as buildings, bridges, trees etc). The point cloud was converted to a 0.5m …

Web map displaying Wisconsin DNR-produced Digital Elevation Model (DEM) and Hillshade image services, along with their index layer, in formats that are clickable and can be symbolized and filtered. This map can also be used as a starting point to create a new map. To open the web map from DNR's GIS Open Data Portal, click the View Metadata: link to the right of the description, then click Open in Map Viewer.

The global navigation map market is experiencing robust growth, driven by increasing adoption of location-based services across various sectors. Our analysis projects a market size of $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The automotive industry's reliance on advanced driver-assistance systems (ADAS) and autonomous vehicles is a primary driver, demanding high-precision and regularly updated map data. Furthermore, the proliferation of mobile devices with integrated GPS and mapping applications continues to stimulate market growth. The burgeoning enterprise solutions segment, utilizing navigation maps for logistics, fleet management, and delivery optimization, contributes significantly to overall market value. Government and public sector initiatives promoting smart cities and infrastructure development further fuel demand. Technological advancements, such as the integration of LiDAR and improved GIS data, enhance map accuracy and functionality, attracting more users and driving market expansion. The market segmentation reveals substantial contributions from various application areas. The automotive segment is projected to maintain its dominance throughout the forecast period, followed closely by the mobile devices and enterprise solutions segments. Within the type segment, GIS data holds a significant market share due to its versatility and application across various sectors. However, LiDAR data is experiencing rapid growth, driven by its high precision and suitability for autonomous driving applications. Geographic regional analysis indicates strong market presence in North America and Europe, primarily driven by advanced technological infrastructure and high adoption rates. However, the Asia-Pacific region is poised for substantial growth, fueled by rapid urbanization, increasing smartphone penetration, and government investments in infrastructure development. Competitive landscape analysis reveals a blend of established players and emerging technology companies, signifying an increasingly dynamic and innovative market environment.

The AHN DEM is a 0.5m DEM covering the Netherlands. It was generated from LIDAR data taken in the spring between 2007 and 2012. It contains ground level samples with all other items above ground (such as buildings, bridges, trees etc.) removed. This version is interpolated; the areas where objects …

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.

Two 30-m resolution aboveground woody biomass (AGWB) model for the Brazilian Cerrado biome built based on optical satellite imagery (Landsat-5 and Landsat-8) and SAR imagery (ALOS and ALOS-2), using two machine learning algorithms (Random Forest and CART). Field data used to calibrate the models include and a set of plot-based and LiDAR-derived AGWB estimates (n=1,858) from a wide network of researchers in Brazil

“Automatically Extracted Buildings” is a raw digital product in vector format created by NRCan. It consists of a single topographical feature class that delineates polygonal building footprints automatically extracted from airborne Lidar data, high-resolution optical imagery or other sources.

Height of the top of canopy above bare earth (Canopy Height Model; CHM). The CHM is derived from the NEON LiDAR point cloud and is generated by creating a continuous surface of canopy height estimates across the entire spatial domain of the LiDAR survey. The point cloud is separated into …

The global forest canopy height map with a resolution of 30 m for 2020 (GlobeFCH_2020_30m_v1) was generated by integrating the new-generation space-borne LiDAR (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and Land Elevation Satellite-2, ICESat-2), Sentinel-1 SAR images, Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeFCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the forest canopy height value is centimeter. The GlobeFCH_2020_30m_v1 was divided into 305 files, and the range of each file is 10°×10°.

Two 30-m resolution aboveground woody biomass (AGWB) model for the Brazilian Cerrado biome built based on optical satellite imagery (Landsat-5 and Landsat-8) and SAR imagery (ALOS and ALOS-2), using two machine learning algorithms (Random Forest and CART). Field data used to calibrate the models include and a set of plot-based and LiDAR-derived AGWB estimates (n=1,858) from a wide network of researchers in Brazil

The global Mobile Mapping Systems market is experiencing robust growth, projected to reach $20,740 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.0% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of autonomous vehicles and the need for highly accurate and detailed maps for navigation and advanced driver-assistance systems (ADAS) are significantly fueling market demand. Furthermore, the growth of smart cities initiatives, requiring comprehensive infrastructure mapping for efficient urban planning and management, is a major contributor. Government and public sector investments in infrastructure projects, coupled with rising demand for location-based services across various sectors like transportation and logistics, real estate, and video entertainment, are also boosting market growth. The shift towards cloud-based solutions and the integration of advanced technologies like LiDAR and GPS are further enhancing the capabilities and efficiency of mobile mapping systems, attracting broader adoption. The market is segmented by system type (Direct Mobile Mapping System and Backpack Mobile Mapping System) and application (Automobile, Transportation & Logistics, Government & Public Sector, Video Entertainment, Real Estate, Travel & Hospitality, and Other). While the Automobile sector currently holds a significant market share, the Government & Public Sector and Transportation & Logistics segments are expected to witness substantial growth due to increasing infrastructure development and the need for efficient logistics management. Competition in the market is intense, with major players including Ericsson, Microsoft, Apple, Google, and TomTom continuously innovating and expanding their product offerings to cater to the evolving demands of various industries. The market's geographical distribution is diverse, with North America and Europe currently leading in adoption, followed by the Asia-Pacific region, which is expected to demonstrate significant growth potential in the coming years driven by economic development and increasing urbanization. This comprehensive report analyzes the burgeoning Mobile Mapping Systems (MMS) market, projected to reach $15 billion by 2030. It delves into key trends, competitive landscapes, and growth drivers, providing invaluable insights for businesses and investors alike. The report leverages extensive market research and data analysis to provide actionable intelligence on this rapidly evolving technology. Keywords: Mobile Mapping, LiDAR, 3D Mapping, GIS, Location-Based Services, Autonomous Vehicles, Mapping Technology, Geospatial Data.

This data set is derived from the original 2005 data collected over the southern San Andreas and San Jacinto fault zones in southern California, USA. These data have provided a fundamental resource for study of active faulting in southern California since they were released in 2005. However, these data were not classified in a manner that allowed for easy differentiation between bare ground surfaces and the objects and vegetation above that surface. This reprocessed (classified) dataset allows researchers easy and direct access to a "bare-earth" digital elevation data set as gridded half-meter resolution rasters (elevation and shaded relief) , "full-feature" digital elevation models as gridded one-meter resolution rasters (elevation and shaded relief) and as classified (according to ASPRS standards) point clouds in binary .laz format, and a spatial index in shapefile and Google Earth KML format.

Terrestrial LiDAR data collected by the team at University College London.

This is Version 1 containing raw data in RIEGL .rxp format for all scan positions as well as corresponding rotation matrices

UCL project name: 2017-02-08.001.riproject

Plot ID: RSQ

State or region: London

Date project started: 2/8/2017

Instrument: UCL RIEGL VZ-400

Scan pattern: Spiral (11 pos)

Angular resolution: 0.04

Images captured: Yes

Number of scans: 22.0

Google Maps URL: https://www.google.co.uk/maps/place/Russell+Square+Gardens/@51.5216334,-0.1261532,20.51z/data=!4m13!1m7!3m6!1s0x48761b2e1672c317:0x2eb39a3b3d33d9f5!2sMalet+St,+London!3b1!8m2!3d51.5214099!4d-0.1302396!3m4!1s0x48761b313fbfd0a1:0x494a624c4d12c02f!8m2!3d51.5216396!4d-0.1259804

Publications: https://doi.org/10.1186/s13021-018-0098-0

For more information on the methods used to capture TLS data please refer to Wilkes et al. 2017

Please acknowldege the producers of this data set if using this data for publication.

The LIDAR Composite DTM/DSM is a raster terrain model covering ~99% of England at 1m spatial resolution, produced by the UK Environment Agency in 2022. The model contains 3 bands of terrain data: a Digital Terrain Model (DTM), a first return Digital Surface Model (DSM), and a last return DSM. …

Lawrence Berkeley National Laboratory (LBNL) contracted the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) to observe watersheds of interest surrounding Crested Butte, CO with remotely sensed data, including LiDAR. The flight box design encompassed the watersheds, surveying a total area of 334 km2 across 72 lines. The instrument used was an Optech Gemini, which is capable of a pulse density of 2 pulses m-2 at the planned flight altitude. These LiDAR data are the primary data that were provided by NEON including LAZ files of a classified point cloud, and geotifs of a digital surface elevation model, digital terrain elevation model, and a canopy height model at 1 m resolution with a height threshold of > 2 m. Raster files can also be found on Google Earth Engine: https://code.earthengine.google.com/5c96bbc96ffd50e3c8b1433b34a0bb86.

This data set is derived from the original 2005 B4 lidar dataset collected over the southern San Andreas and San Jacinto fault zones in southern California, USA. These data have provided a fundamental resource for study of active faulting in southern California since they were released in 2005. However, these data were not classified in a manner that allowed for easy differentiation between bare ground surfaces and the objects and vegetation above that surface. This reprocessed (classified) dataset allows researchers easy and direct access to a \"bare-earth\" digital elevation data set as gridded half-meter resolution rasters (elevation and shaded relief), \"full-feature\" digital elevation models as gridded one-meter resolution rasters (elevation and shaded relief) and as classified (according to ASPRS standards) point clouds in binary .laz format, and a spatial index in shapefile and Google Earth KML format. The reprocessing of the 2005 B4 dataset was performed by Dr. Stephen B DeLong, USGS Earthquake Hazards Program, as a service to the community. The data available here were originally published on the USGS ScienceBase website as Classified point cloud and gridded elevation data from the 2005 B4 Lidar Project, southern California, USA.

Original B4 project description: The B4 Lidar Project collected lidar point cloud data of the southern San Andreas and San Jacinto Faults in southern California. Data acquisition and processing were performed by the National Center for Airborne Laser Mapping (NCALM) in partnership with the USGS and Ohio State University through funding from the EAR Geophysics program at the National Science Foundation (NSF). Optech International contributed the ALTM3100 laser scanner system. UNAVCO and SCIGN assisted in GPS ground control and continuous high rate GPS data acquisition. A group of volunteers from USGS, UCSD, UCLA, Caltech and private industry, as well as gracious landowners along the fault zones, also made the project possible. If you utilize the B4 data for talks, posters or publications, we ask that you acknowledge the B4 project. The B4 logo can be downloaded here. More information about the B4 Project.

Publications associated with this dataset can be found at NCALM's Data Tracking Center