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46 results for “Airborne Lidar”
SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey Raw V001
The data set described here provides raw lidar data 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. Digital terrain models (DTM), digital surface models (DSM) snow depth models, and canopy height models (CHM) derived from these point cloud data are available as <a href="https://nsidc.org/data/SNEX_MCS_Lidar">SnowEx Mores Creek Summit (MCS) Airborne LiDAR Survey, Version 1</a>.
SMAPVEX19-22 Massachusetts Airborne Lidar V001
These lidar measurements were collected in April and August 2022 in the vicinity of Petersham, MA 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.
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.
SMAPVEX19-22 Massachusetts Airborne Lidar V001
These lidar measurements were collected in April and August 2022 in the vicinity of Petersham, MA 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.
SnowEx23 Airborne Lidar-Derived 0.5M Snow Depth and Canopy Height V001
This data set provides digital terrain models, snow depth, and canopy height, acquired by a scanning lidar system and derived from Point Cloud Digital Terrain Models (PCDTMs) 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). The raw data from which these data are derived are available as <a href="https://nsidc.org/data/SNEX23_Lidar_Raw">SnowEx23 Airborne Lidar Scans Raw, Version 1</a>.
A reference airborne LiDAR dataset for forest research
<p>This repository contains the dataset presented in Parkan et al. (2018).</p> <p>Abstract:</p> <p>The benefits of Airborne Laser Scanning (ALS) to efficiently monitor and manage forests are widely accepted. Products derived from ALS have been successfully used in a range of different domains including ecosystem characterization, habitat modeling, timber volume estimation, forest fire management and territorial planning. Many of these applications are dependent on the estimation of biophysical parameters at the canopy and/or individual tree scale. These parameters are generally computed with area (stand) or object (tree) centric approaches. In particular, the development of processing chains directly or indirectly involving individual tree crown segmentation, tree species classification and allometric modeling constitute the bulk of scientific activity in the domain. However, the diversity of ALS data characteristics and non-standard error assessment procedures means that the results reported in different studies are often difficult to compare. In order to support standardization and benchmark studies, this article presents a reference ALS dataset, an error assessment framework and provides several example workflows to illustrate its potential use in forest research.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.