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46 results for “Airborne Lidar”
Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar
<p>Dataset consists of a single folder that includes all data used for the publication entitled: Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar. Within the folder is a README file that describes the data structure. </p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Maranhão e Tocantins)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Maranhão (1 zip file) and Tocantins (1 zip file).</p>
Supplementary Material for "Automated detection of an insect-infested keystone vegetation phenotype using airborne LiDAR"
<p>Ecologists, foresters and conservation practitioners need "biodiversity scanners" to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using airborne LiDAR remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these "extended phenotypes" could serve as environmental landmarks of insect presence in LiDAR data. To test the feasibility of this approach, we studied the symbiotic ants that alter canopy shapes of whistling thorn acacia, a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol of LiDAR data collection, training data preparation (including a customizable tree-segmentation algorithm) and convolutional neural network-based classification for the detection of ant-infested, within-species acacia tree phenotypic variations. Surveying ant occupancy of 402 hectares of 9,680 acacia trees took 1,000 work hours, while surveyed patterns of ant distribution was replicated by trained classifier based on an hour-long airborne LiDAR collection. We suggest that large scale surveys of insect occupancy (or insect-vectored disease) can be automated through a combination of airborne LiDAR and machine learning.</p>
Data from: From lidar waveforms to vegetation products: 7380 km2 of high-resolution airborne and simulated GEDI data over Sierra Nevada, California
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Penzance Harbour, from Airborne LiDAR data
Model of Penzance Harbour for creating the Re-gen Video Source: Objaverse 1.0 / Sketchfab
Karst Sinkhole Detecting and Mapping Using Airborne LiDAR
<p>Corresponding data set for Tran-SET Project No. 18GTUNM01. Abstract of the final report is stated below for reference:</p> <p>"The focus of this study is to detect sinkhole hazards using airborne light detection and ranging (LiDAR) data. The premise is sinkholes, particularly those close to transportation infrastructure assets, could cause substantial damages to infrastructure assets, and therefore, being able to accurately and rapidly detect them is essential. However, it is expensive, time-consuming, labor-intensive, and unsafe to survey sinkholes using conventional ground observation methods. This research project was focused on developing accurate and rapid airborne LiDAR-based sinkhole detection and mapping methods, and transfer the technologies to transportation engineers for implementation and workforce development. The project team also identified best practices for implementation of a state-level sinkhole hazard management system (SHMS). In addition, a guidebook was developed for airborne LiDAR-based sinkhole detection and mapping for professional education and training.</p> <p>The effectiveness of LiDAR to detect existing sinkholes has received very limited attention. Most of the research on LiDAR-based sinkhole detection postulates that morphological-based surface feature extraction methods can effectively detect sinkholes because of their geometric properties – sinkholes are oval-shaped concave depressions in the Earth’s surface. However, sinkholes have varying sizes, shapes, and appearance given various landforms, which adds even greater challenges to further improving the detection accuracy of methods that are based solely on morphology; for example, a dry stock pond may be incorrectly detected as a sinkhole. The proposed research used airborne LiDAR data in combination with auxiliary context such as site and association to improve the accuracy of the morphological-based sinkhole detection methods, and implement these by developing tools that can be used in standard geographic information systems (GIS). This methodology allows for the development of a robust LiDAR-based sinkhole detection toolset that provides an adequate degree of accuracy while maximizing the ability to assist inspectors with varying expertise."</p>
Data from: Characterizing forest structure variations across an intact tropical peat dome using field samplings and airborne LiDAR
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CLPX-Airborne: Infrared Orthophotography and Lidar Topographic Mapping, Version 1
The data set consists of color infrared orthophotography (TerrainVision® - High resolution Topographic Mapping & Aerial Photography, with 6-inch pixel resolution), lidar elevation returns (raw/combined, filtered to bare ground/snow, and filtered to top of vegetation), elevation contours (0.5 meter) and snow depth contours (0.1 meter).
ACT-America: L2 Remotely Sensed Column-avg CO2 by Airborne Lidar, Lite, Eastern USA
This dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1-second column CO2 reporting frequency, is included, but not limited to, latitude, longitude, altitude, and attitude.
ACT-America: L1 DAOD Measurements by Airborne CO2 Lidar, Eastern USA
This dataset provides Level 1 (L1) remotely sensed differential absorption optical depth (DAOD) measurements made through the Multi-Functional Fiber Laser Lidar (MFLL; Harris Corporation) during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. DAOD were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with MFLL. The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Data users should note that a Level 2 (L2) MFLL data product is available (related dataset) that contains all data variables (plus the column-average CO2) included in this L1 MFLL data product but has undergone additional processing and calibrations and is recommended for most use cases.
ACT-America: L2 Remotely Sensed Column-average CO2 by Airborne Lidar, Eastern USA
This dataset provides Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Processing for this Level 2 (L2) product included additional processing and calibration procedures described in this document as applied to retrieval of column CO2 from L1 MFLL data. Data users should use this L2 data unless different CO2 retrieval criteria are preferred.
Amazon Forest Structure from Airborne Lidar, ED2 Initial Condition Files, 2016
This dataset provides initial condition files for initializing the Ecosystem Demography Model (ED2). This dataset holds regional forest structure characteristics across the Brazilian Amazon that were derived from 545 airborne lidar transects (300 x 12500 m each) acquired during the Amazon Biomass Estimation Project (EBA2016) campaign in 2016. These data contain vertical distributions of stem density, carbon storage, and other vegetation traits for over 1,300,000 columns (50 x 50 m each) that were aggregated into 288 grid cells (1 x 1 degree). This dataset also contains soil edaphic characteristics obtained from existing datasets and carbon stored in litter and soil layers estimated from the land use history and limited measurements in different land use types. Three types of files are provided: Site files (*.sss) hold soil and terrain characteristics. Patch files (*.sss) hold patch location, area, disturbance type, stem density, stem basal area, leaf area index (LAI), aboveground biomass (AGB), along with carbon and nitrogen density in several categories for patches within sites. Cohort files (*.css) hold diameter at breast height, plant height, stem density, mass of living and dead biomass, LAI, AGB), and plant functional type for cohorts of stems within patches and sites. The data are provided in text format compatible with the ED2 model.
ACT-America: L2 Weighting Functions for Airborne Lidar Column-avg CO2, Eastern USA
This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction.
Data from: Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR
There is a public perception that large high severity wildfires decrease biodiversity and increase fire hazard by homogenising vegetation composition and increasing the cover of mid-story vegetation. But a growing literature suggests that vegetation responses are nuanced. LiDAR technology provides a promising remote sensing tool to test hypotheses about post-fire vegetation regrowth because vegetation cover can be quantified within different height strata at fine-scales over large areas. We assess the usefulness of airborne LiDAR data for measuring post-fire mid-story vegetation regrowth over a range of spatial resolutions (10x10m, 30x30m, 50x50m, 100x100m cell size) and investigate the effect of fire severity on regrowth amount and spatial pattern following a mixed severity wildfire in Warrumbungle National Park, Australia. We predicted that recovery would be more vigorous in areas of high fire severity, because park managers observed dense post-fire regrowth in these areas. Moderate to strong positive associations were observed between LiDAR and field surveys of mid-story vegetation cover between 0.5–3m. Thus our LiDAR survey was an apt representation of on-ground vegetation cover. LiDAR-derived mid-story vegetation cover was 22–40% higher in areas of low and moderate than high fire severity. Linear mixed-effects models showed that fire severity was among the strongest biophysical predictors of mid-story vegetation cover irrespective of spatial resolution. However much of the variance associated with these models was unexplained, presumably because soil seedbanks varied at finer-scales than our LiDAR maps. Dense patches of mid-story vegetation regrowth were small (median size 0.01ha) and evenly distributed between areas of low, moderate and high fire severity, demonstrating that high severity fires do not homogenise vegetation cover. Our results are relevant for ecosystem conservation and fire management because they: indicate that native vegetation are responsive and resilient to high severity fire, and show the usefulness of remote sensing tools such as LiDAR to monitor post-fire vegetation recovery over large area in situ.
Data from: Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR
Open the record for dataset details and reuse information.
Data from NASA Langley Airborne Lidar flights.
Data from the 1982 NASA Langley Airborne Lidar flights following the eruption of El Chichon beginning in July 1982 and continuing to January 1984. Data in ASCII format.
AMSRIce06 Airborne Topographic Mapper (ATM) Lidar Data, Version 1
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains Lidar measurements of sea ice in the Chukchi and Beaufort Seas of the Arctic Ocean, and of snow cover off the northern coast of Alaska, USA. The Lidar data were obtained by the Airborne Topographic Mapper (ATM) instrument mounted on a P3 aircraft.
SnowEx23 Airborne Lidar Scans Raw V001
This data set provides raw lidar data from two regions of Alaska, USA collected as part of the NASA SnowEx 2023 field campaign. The study sites include a boreal forest environment in the Fairbanks region of central Alaska (the Bonanza Creek Experimental Forest, Caribou Poker Creek watershed, and Farmer’s Loop/Creamer’s Field) and a coastal tundra environment in the North Slope region of the northern Alaska coastal plain (Arctic coastal plain and Upper Kuparuk Toolik). Processed data, including digital terrain models, snow depth, and canopy height derived from Point Cloud Digital Terrain Models (PCDTMs) are available as <a href="https://nsidc.org/data/SNEX23_Lidar">SnowEx23 Airborne Lidar-Derived 0.25M Snow Depth and Canopy Height, Version 1</a>.
SMAPVEX19-22 Millbrook Airborne Lidar V001
These lidar measurements were collected in April and August 2022 in the vicinity of Millbrook, NY during the SMAPVEX19-22 campaign. This location was chosen due to its forested land cover, as SMAPVEX19-22 aims to validate satellite derived soil moisture estimates in forested areas. The two acquisition periods were selected to characterize differences during "leaf-off" and "leaf-on" conditions.
SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey V001
The data set provides digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM), derived from point cloud data (available as <a href="https://nsidc.org/data/SNEX_MCS_Lidar_Raw">SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw, Version 1</a>) acquired by airborne lidar scanning. Data were collected as part of a multi-year effort to monitor monthly snow distribution over a 35 km² region of the Mores Creek Headwaters in the Boise Mountains of central Idaho between 2021 and 2024. Data acquisition in 2021 overlapped temporally with the NASA SnowEx 2021 field campaign.
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