1. Light detection and ranging (LiDAR) technology provides ecologists with high-resolution data on three-dimensional vegetation structure. Large LiDAR datasets challenge predictive ecologists, who commonly simplify point clouds into structural attributes (namely LiDAR-based metrics such as canopy height), which are used as predictors in ecological models, potentially with loss of relevant information. 2. We illustrate an efficient alternative approach to reduce the dimensionality of LiDAR data that aims at minimal data filtering with no a priori assumptions on the ecology of the target species. We first fit the ecological model exploiting the full variability of the LiDAR point cloud, then we explain the results using post-modelling LiDAR-data classification for ecological interpretation only. This is the classical logic of explorative, hypothesis generating and predictive statistics, rather than testing specific vegetation-structural hypotheses. 3. First, we reduce the dimensionality of the LiDAR point cloud by Principal Component Analysis (PCA) to fewer predictors. Secondly, we show that LiDAR-PCs are capable to outperforming commonly used environmental predictors in ecological modelling, including LiDAR-based metrics. We exemplify this by modelling red deer (Cervus elaphus) and roe deer (Capreolus capreolus) resource selection in the Bavarian Forest National Park, Germany. After fitting the ecological model, we provide an interpretation of the information included in LiDAR-PCs, which allows users to draw conclusions whenever using them as predictors. We make use of the PCA rotation matrix and post-modelling data classification, and document deer selection for understory vegetation at unprecedented fine scale. 4. Our approach is the first attempt in animal ecology to avoid the use of LiDAR-based metrics as model predictors, but rather generate principal components able to capture most of the LiDAR point cloud variability. Our study demonstrates that LiDAR-PCs can boost ecological models. We envision a potential use of LiDAR-PCs in several applications, particularly species distribution and habitat suitability models. We demonstrate an application of our approach by building suitability maps for both deer species, which can be used by practitioners to visualize model spatial predictions and understand the type of forest structures selected by deer.

The dataset contains four subsets of original data (emulated HHDCs) presented in the study entitled "Generative Diffusion Models for Compressed Sensing of Satellite LiDAR Data: Evaluating Image Quality Metrics in Forest Landscape Reconstruction" submitted to the journal "Remote Sensing".

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES.

LITE_L1 data are LIDAR Vertical profile data along the orbital flight path of STS-64.Lidar In-Space Technology Experiment (LITE) used a three-wavelength (355 nm, 532 nm and 1064 nm) backscatter lidar which flew on the space shuttle Discovery as part of the STS-64 mission between September 9 and September 20, 1994. The LITE instrument was designed with the capability to make measurements of clouds, aerosols in the stratosphere and troposphere, the height of the planetary boundary layer, and atmospheric temperature and density in the stratosphere between 25 km and 40 km altitude. Additionally, limited measurements of the surface return strength over both land and ocean were collected to explore retrievals of surface properties.The LITE data were transmitted real time the by Ku-band system through TDRSS downlink to the LITE operations center at JSC. There was a gap in the high-rate coverage between 60 E and 85 E due to the zone of exclusion, where neither TDRSS satellite was in view. Additional random gaps in the data occurred due to telemetry dropouts during data transmission.The LITE L1 data product was formed by processing and reformatting the LITE high-rate telemetry data. The LITE L1 processing steps included:Correcting the profiles for instrument artifacts. Subtracting the DC offset from each lidar profile. Interpolating lidar profiles to a geolocated, common altitude grid, which extends from -4.985 to 40.0 km with a 15 m vertical resolution. Determining the LITE system calibration constants for the 355 nm and 532 nm wavelength profiles.Merged with the LITE L1 lidar profiles are: Identification Parameters, Time Parameters, Location Parameters, Operation Mode Parameters, Validity Flags, Measurement Location Descriptions, Temperature and Pressure Profiles Derived from NMC Data, Instrument Status Information.The archived files are concatenations of about 1000 (depending on data gaps) sets of headers and profiles. Read software programs written in C or IDL are available.

The dataset includes multimodal annotated data for remote sensing of Maya archaeology and is suitable for deep learning. The dataset covers the area around Chactún, one of the largest ancient Maya urban centres in the central Yucatán peninsula.It includes five types of data:high-resolution airborne laser scanning (ALS, lidar) data visualisations (sky view factor, positive openness, slope),high-resolution airborne laser scanning derived canopy height model,Sentinel-1 Short Aperture Radar (SAR) satellite data (yearly average Sigma0),Sentine-2 optical satellite data (12 bands + cloud mask, 17 dates), andmanual data annotations.The manual annotations (used as binary masks) represent three different types of ancient Maya structures (class labels: buildings, platforms, and aguadas – artificial reservoirs) within the study area, their exact locations, and boundaries.The dataset is ready for use with convolutional neural networks (CNNs) for object recognition, object localization (detection), and semantic segmentation. The dataset has already been used for the Discover the Mysteries of the Maya computer vision competition.We would like to provide this dataset to help more research teams develop their own computer vision models for investigations of Maya archaeology or improve existing ones.A detailed description of the datasets has been published by Kokalj, Ž., Džeroski, S., Šprajc, I. et al. Machine learning-ready remote sensing data for Maya archaeology. Scientific Data 10, 558 (2023). https://doi.org/10.1038/s41597-023-02455-xThe authors and institutions they are affiliated with exclude all liability for any reliance on the data.

Those data represent the Brazilian Amazon by 68,629,072 250-m pixels, and 141,032 pixels have LiDAR information and therefore had the AGB estimated, and converted to Mg ha-1 (file trainning_dataset.csv). To generate a wall-to-wall map of the Brazilian Amazon at 250-m resolution, we trained a Random Forest (RF) model 29 using the AGB estimated pixels and remote sensing layers formed by: MODIS vegetation indices, Shuttle Radar Topography Mission (SRTM) data, Tropical Rainfall Measuring Mission (TRMM), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) data, along with the central coordinates of each 250m pixel, stored in amazon_*.csv files.

Derived from MODIS, we used the Vegetation Indices 16-Day L3 Global 250m temporal series (MOD13Q1) products from 2016, including the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), from MODIS. From TRMM we used the 3B43 V6 precipitation data, with each pixel value representing the monthly accumulated precipitation from 1998 to 2016 at a resolution of 0.25 degrees. From PALSAR, we used the L-band image in the HH and HV polarizations, acquired in 2015. When necessary, the remote sensing products were resampled by means to a 250 m grid.

Random Forest models were tested using the H2O_Flow platform and produced the best model based on RMSE and R², containing NDVI q3, PALSAR HV, TRMM mean, X, SRTM, Y, PALSAR HH, EVI q1, EVI mean, NDVI mean, NDVI q1, EVI q3.

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.

Autonomous Satellite Docking LiDAR Market Outlook

According to our latest research, the global Autonomous Satellite Docking LiDAR market size reached USD 312 million in 2024 and is expected to grow at a robust CAGR of 14.2% during the forecast period, reaching USD 900 million by 2033. This remarkable growth is primarily driven by the increasing demand for precision navigation and autonomous operations in space missions, particularly for satellite servicing, in-orbit assembly, and space debris removal. The adoption of LiDAR technology for autonomous satellite docking is witnessing significant momentum due to its unparalleled accuracy, reliability, and adaptability in challenging space environments.

One of the most compelling growth factors for the Autonomous Satellite Docking LiDAR market is the surging investments in space infrastructure and the proliferation of satellite constellations by both commercial and governmental entities. As the number of satellites in orbit continues to rise, the need for advanced, reliable, and autonomous docking solutions becomes increasingly critical. LiDAR technology, with its high-resolution 3D mapping capabilities and robustness against harsh space conditions, is rapidly becoming the preferred choice for precise relative positioning and navigation during complex docking maneuvers. The rise of commercial space ventures and the growing emphasis on sustainability in space operations are further fueling the adoption of LiDAR-enabled autonomous docking systems.

Another significant driver for market expansion is the increasing focus on space debris mitigation and removal. With the accumulation of defunct satellites and debris posing substantial risks to active space assets, international space agencies and private companies are investing heavily in technologies that can autonomously identify, approach, and dock with non-cooperative targets. LiDAR systems, particularly those engineered for high sensitivity and rapid data processing, are proving indispensable in these missions. Their ability to generate real-time, high-fidelity spatial data enables precise maneuvering and collision avoidance, thereby enhancing mission safety and success rates. As regulatory frameworks around space debris management become more stringent, the demand for LiDAR-based autonomous docking solutions is expected to surge.

Technological advancements in LiDAR hardware and software are also playing a pivotal role in shaping the market landscape. Innovations such as solid-state and flash LiDAR are delivering enhanced performance, miniaturization, and lower power consumption, making them ideal for integration into small satellite platforms. Additionally, the integration of artificial intelligence and advanced data analytics with LiDAR systems is enabling smarter, more autonomous decision-making during docking operations. These technological breakthroughs are not only improving the operational efficiency of satellite docking but are also opening new avenues for in-orbit servicing, assembly, and even future space exploration missions.

From a regional perspective, North America currently dominates the Autonomous Satellite Docking LiDAR market, driven by the presence of leading aerospace companies, robust government funding, and a strong innovation ecosystem. Europe follows closely, propelled by collaborative space initiatives and significant investments in satellite servicing and debris removal projects. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by the rapid expansion of national space programs and increasing participation of private players. As these regions continue to invest in advanced space technologies, the global Autonomous Satellite Docking LiDAR market is poised for sustained growth throughout the forecast period.

Product Type Analysis

The Product Type segment of the Autonomous Satellite Docking LiDAR market is categorized into Solid-State LiDAR, Mechanical LiDAR, Flash LiDAR

This dataset is part of the larger data collection, “Aerial imagery object identification dataset for building and road detection, and building height estimation”, linked to in the references below and can be accessed here: https://dx.doi.org/10.6084/m9.figshare.c.3290519. For a full description of the data, please see the metadata: https://dx.doi.org/10.6084/m9.figshare.3504413.

Imagery data from the United States Geological Survey (USGS); building and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/); and the Lidar data are from U.S. National Oceanic and Atmospheric Administration (NOAA), the Texas Natural Resources Information System (TNRIS).

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.

Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France.

* AGB and Volume maps are available on request.

The methodology and validation of the maps are presented here: https://hal.science/hal-04249151

Please cite :

David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151

These test datasets are for use with the hbrc R package available here. The datasets correspond to a small region of the Hawke's Bay Region in Aotearoa New Zealand. They are used within the R package documentation to explain some of the capabilities.The test datasets were provided by Jan Schindler. The methods for preparing these data were developed with support from the MBIE Catalyst: Strategic Fund through the programme “Bridging the gap between remote sensing and tree modelling” Contract C09X1923.The work was supported by: the Strategic Science Investment Funding for Crown Research Institutes from the New Zealand Ministry of Business, Innovation and Employment’s Science and Innovation Group. This work was also supported by the Environmental Science section of Hawke’s Bay Regional Council via the LiDAR tools partnership project Contract Number HBRC-22-716.

ASIA-AQ_AircraftRemoteSensing_LaRC-G3_HSRL2_Data is the High Spectral Resolution Lidar (HSRL) data collected onboard the NASA LaRC G-III aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data collection for this product is complete.The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return. Knowing this time measurement scientists are able to create topography maps.LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. This LiDAR data was collected in 2007.This data shows the areas in Cork for which you can download LiDAR data and contains links to download the data. This is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The LiDAR coverage is shown as polygons. Each polygon is 2000m by 2000m in size and holds information on: the location, data provider, owner, licence, published date, capture date, surveyor, RMS error, resolution and a link to download the LiDAR raster data in 2000m by 2000m sections.

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) was a joint mission between NASA and the French space agency Centre National d'Etudes Spatiales. The main objective of the mission was to supply a unique data set of vertical cloud and aerosol profiles.

This dataset contains cloud and aerosol lidar level 2 vertical feature mask version 4-21 data product describes the horizontal and vertical distribution of the cloud and the aerosol layers observed by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). CAL_LID_L2_VFM-Standard-V4-21 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Vertical Feature Mask (VFM), Version 4-21 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The version of this product was changed from 4-20 to 4-21 to account for a change in the operating system of the CALIPSO production cluster. Data collection for this product is ongoing.

DEVOTE_ AircraftRemoteSensing_UC12_HSRL_Data are remotely sensed data collected by the High Spectral Resolution Lidar (HSRL) onboard the UC-12 aircraft as part of the Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) sub-orbital project. Data collection is complete.The Development and Evaluation of satellite Validation Tools by Experimenters (DEVOTE) project investigated aerosols and clouds with the specific goals of satellite validation and the improvement of satellite data retrieval algorithms. Conducted in September and October 2011, DEVOTE scientists collected measurements of aerosols and cloud optical and microphysical properties using airborne sensors over ground sites and along satellite overpasses to demonstrate the use of airborne platforms in future scientific measurement campaigns. These measurements were used to validate and improve satellite data retrieval algorithms from missions including the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission and the Aerosol, Cloud, Ecosystems (ACE) Decadal Survey mission.DEVOTE scientists conducted eleven science flights based at the NASA Langley Research Center throughout the campaign. The flight plans were specifically designed to coordinate with CALIPSO satellite overpasses and to fly over the Aerosol Robotic Network (AERONET) ground network sites. The DEVOTE sampling strategy required two aircraft dedicated to remote sensing and in-situ observations, which flew in coordinated flight patterns. This was implemented through use of the NASA UC-12 and the NASA B-200 airborne platforms. The UC-12 had the following remote sensing payload: the Research Scanning Polarimeter (RSP) and High Spectral Resolution Lidar (HSRL) instruments. The B-200 had an in-situ payload including the Polarized Imaging Nephelometer (PI-Neph), the Diode Laser Hygrometer (DLH), and Langley Aerosol Research Group Experiment (LARGE) instruments for aerosol microphysical and optical properties.DEVOTE was partly funded through the Hands-On Project Experience (HOPE) initiative. HOPE was a NASA development program designed to offer early career scientist opportunities to design, implement, and analyze small missions offering hands-on experience. Opportunities are increasingly limited for principal investigators, program managers, and system engineers to obtain mission life cycle training, and HOPE provides opportunities to those early on in their career or who are transitioning to a different field. Thus, DEVOTE had a focus on providing hands-on training in the mission life cycle to early career scientists in addition to its primary objective of using cloud and aerosol data collected from airborne sensors to validate and improve satellite data retrieval algorithms. Additionally, the information obtained from DEVOTE research was used to prepare for the implementation of ACE.

CAL_LID_L2_05kmCPro-Standard-V4-51 is the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Lidar Level 2 Cloud Profile, Version 4-51 data product. Data for this product was collected using the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. The CALIPSO satellite comprises three instruments: CALIOP, Imaging Infrared Radiometer (IIR), and Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency CNES (Centre National d'Etudes Spatiales).CALIPSO was launched on April 28, 2006, to study the impact of clouds and aerosols on the Earth's radiation budget and climate. From June 13, 2006, to September 13, 2018, CALIPSO was part of the A-Train constellation for coincident Earth Observations. After September 13, 2018, the satellite was lowered from 705 to 688 km to resume flying in formation with CloudSat, called the C-Train.

See readme file for full decription. This dataset consists of .csv files derived from airborne LiDAR, Landsat imagery, and Sentinel-2 imagery from 2016 and 2019.