Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
73
datasets available to search
ShareScore release 0.9.0
Dataset results
73 results for “canopy height”
FCH and FS Datasets for the paper "Integrating Multi-Source Remote Sensing Data for Mapping Boreal Forest Canopy Height and Species in interior Alaska in Support of Radar Modeling"
<p>This dataset provides forest canopy height and forest species in Delta Junction, interior Alaska in 2017. This dataset was produced based on the multi-source remote sensing datasets (AirMOSS, UAVSAR, Sentinel-1, Sentinel-2, topography), using a XGBoost approach.</p>
The global 30-m forest canopy height map for 2020
<p>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°.</p>
Global canopy top height estimates from GEDI LIDAR waveforms for 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data from April-July 2020. The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data.</p> <p>See also the repository for the data from April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a>. This repository also contains the file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>with more information on how to parse and load the prediction orbit files.</p> <p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p> <p><strong>Citation: </strong></p> <p>Use of these data require citation of this dataset:</p> <p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2020 (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.7737869</p> <p>Original research article:</p> <p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <em>Remote Sensing of Environment</em>, <em>268</em>, 112760.</p>
Canopy top height models at 10m GSD from airborne LIDAR (derived from LVIS and small-footprint ALS)
<p>Rasterized canopy top height models (CTHM) at 10m ground sampling distance (GSD) derived from airborne LIDAR.</p><p>The CTHMs were created to be comparable to GEDI canopy top heights (within 25m footprints) using two sources:</p><p>1) NASA's LVIS airborne LIDAR campaigns (here we rasterized the RH98).<br>2) High-resolution canopy height models derived from small-footprint airborne laser scanning campaigns in Europe (max pooled with a circular 25m footprint corresponding to the GEDI footprint).</p><p>The original LVIS LIDAR data is available here: <a href="https://lvis.gsfc.nasa.gov">https://lvis.gsfc.nasa.gov</a></p><p>Links to the original ALS data are available here: <a href="https://publications.jrc.ec.europa.eu/repository/bitstream/JRC126223/jrc126223_jrc126223_lidaropensourcedata.pdf">https://publications.jrc.ec.europa.eu/repository/bitstream/JRC126223/jrc126223_jrc126223_lidaropensourcedata.pdf</a></p><p>Code to create GEDI-like canopy top heights from high-resolution ALS data is available here: https://github.com/langnico/global-canopy-height-model</p><p>More information is available in the Lang et al. (2022). Please cite our paper if you use these derived data in your own work.</p><p><strong>Reference:</strong></p><p>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, <a href="https://doi.org/10.1038/s41559-023-02206-6">https://doi.org/10.1038/s41559-023-02206-6</a></p>
Canopy height and epiphytic bryophytes shape fungal communities in a temperate rainforest
Open the record for dataset details and reuse information.
Data from: Canopy height explains species richness in the largest clade of Neotropical lianas
Aim: Tall and structurally complex forests can provide ample habitat and niche space for climbing plants, supporting high liana species richness. We test to what extent canopy height (as proxy of 3D habitat structure), climate and soil interact to determine species richness in the largest clade of Neotropical lianas. We expect that the effect of canopy height on species richness is higher for lianas from closed tropical rainforests compared to riparian and savanna habitats. Location: Neotropics Time period: PresentMajor taxa studied: Tribe Bignonieae (Bignoniaceae) Methods: We used structural equation models to evaluate direct and indirect effects of canopy height, climate (temperature, precipitation and precipitation seasonality), and soil (cation exchange capacity and soil types) on overall Bignonieae species richness (339 liana species), as well as on species richness of lianas from forest, riparian and savanna habitats, respectively. We further performed multiple regression models with Moran's eigenvector maps to account for spatial autocorrelation. Results: Canopy height was a key driver of liana species richness, in addition to climate and soil. Species richness of forest lianas showed a strong positive relationship with canopy height whereas the relationship was less pronounced for riparian species. Richness of savanna species decreased with increasing canopy height. Climate also explained a substantial proportion of variation in liana species richness whereas soil variables showed little explanatory power.Main conclusions: The relationship between canopy height and liana species richness differs among habitats. While forest and riparian lianas benefit from tall and complex habitats that provide physical support to reach the canopy to escape low light availability in the understory, a high light availability in open habitats and an increased risk of embolism of conductive vessels for lianas with long stems living in areas with high seasonality might explain the inverse relationship between species richness and canopy height in savannas.
Repeat LiDAR canopy height models for Borneo, eastern Amazon and Guiana Shield
<p>This repository contains LiDAR canopy height models (CHMS) for six tropical forest sites, derived from repeat LiDAR data. The data were used for analysis of canopy disturbance and recovery dynamics. The years of the data collection differ between sites, so the files are named 'a' for the first scan 'b' for the second scan and 'd' for the difference between scans. The 'dtm' files contain terrain models for these areas. Permanent plots and non-forest areas were masked out prior to analysis. </p>
Canopy Height Models of Giant Mountains National Park (2012 and 2022) at 10 m resolution
<p>This repository was created to provide datasets related with an article: "Harmonised airborne laser scanning products can address the limitations of large-scale spaceborne vegetation mapping". The original airborne laser scanning point clouds used for the generation of the canopy height models were kindly provided by Giant Mountains National Park.</p>
Three-dimensional stratification pattern in an old-growth lowland forest: how does height in canopy and season influence temperate bat activity?
<p class="PaperLauftext">The study of animal-habitat interactions is of primary importance for the formulation of conservation recommendations. Flying, gliding and climbing animals have the ability to exploit their habitat in a three-dimensional way and the vertical canopy structure in forests plays an essential role for habitat suitability. Forest bats as flying mammals may seasonally shift their microhabitat use due to differing energy demands or changing prey availability, but the patterns are not well understood.</p> <p>We investigated three-dimensional and seasonal habitat use by insectivorous bats in a temperate lowland old-growth forest, the <i>Belovezhskaya Pushcha</i> in Belarus. We acoustically sampled broadleaved and mixed coniferous plots in the forest interior and in gaps in three heights during two reproductive periods (pregnancy/lactation versus post-lactation). In canopy gaps, vertical stratification in bat activity was less pronounced than in the forest interior. Vertical activity patterns differed among species. The upper canopy levels were important foraging habitats for the open-space forager guild and for some edge-space foragers like the Barbastelle bat <i>Barbastella barbastellus</i> and the soprano pipistrelle <i>Pipistrellus pygmaeus</i>. <i>Myotis</i> species had highest activity levels near the ground in forest gaps. Moreover, we found species-dependent seasonal microhabitat shifts. Generally, all species and species groups considered except <i>Myotis</i> species showed higher activity levels during post-lactation. <i>Myotis</i> species tended towards higher activity in the forest interior during post-lactation. <i>P. pygmaeus</i> switched from high activity levels in the upper canopy during pregnancy and lactation to high activity levels near the ground during post-lactation. We conclude that a full comprehension of forest bat habitat use is only possible when height in canopy and seasonal patterns are considered.</p>
High-resolution Canopy Height Model of Hawaii Island 2018-2020
<p>Forest canopy height model for Hawaii Island using lairborne lidar data collected by NOAA in 2018, 2019 and 2020. The maps are produced by year at the resolution of 1 m. The raw point cloud data had am average point cloud density of 8 pulses per squre m. https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid12b/9635/index.html</p> <p>ALS 2018 data was reprocessed using Lastools software to reclassify ground class (2)</p> <p>ALS 2019_20 was also reprocessed using Lastools software to reclassify unclassified (1) points to vegetation (5)</p> <p>The CHM’s generation procedure is composed by four steps. It starts by the creation of 500m x 500m tiles using a 50m buffer, resorting to the lastile function, followed by the lasheight function that is used to compute the elevation of each point above the ground. Then, the lastile function is used again to remove the buffer from the normalised point clouds. These first three steps resort to the LASTools software. The fourth, and final step, consists in the generation of the CHM with a 1 m resolution resorting to the pit-free algorithm implemented in the rasterize_canopy function from the lidR package.</p> <p>The file is a GeoTIFF with LZW compression in ArcGIS pro 3.3 </p> <p>EPSG:6635</p> <p>Use of these data requires citation of this dataset </p>
ForestPaths: European canopy height map
<p>This repository contains a 10 m resolution canopy height map of the year 2020 over Europe derived from Sentinel-1 and Sentinel-2 data. The canopy height model was trained using both GEDI and ICESat-2 data.</p> <p>The map is available as COGs over a 100 km grid in the spatial reference system EPSG 3035 (ETRS89 / LAEA Europe). </p> <p> </p>
Canopy height, rather than neighborhood effects, shapes leaf herbivory in a tropical rainforest
<p>These files contain the datasets and R codes used in the data analyses in the paper " Canopy height, rather than neighborhood effects, shapes leaf herbivory in a tropical rainforest " that will be published in Ecology.</p>
Data from: Canopy height explains species richness in the largest clade of Neotropical lianas
Open the record for dataset details and reuse information.
Satellite and LiDAR imagery for canopy height and NDVI estimation for mangroves in Puerto Rico
Open the record for dataset details and reuse information.
Three-dimensional stratification pattern in an old-growth lowland forest: how does height in canopy and season influence temperate bat activity?
Open the record for dataset details and reuse information.
Tropical rain forest canopy height measurements 1999 - 2018
Open the record for dataset details and reuse information.
Height and canopy measurements of native shrubs in southern California
Open the record for dataset details and reuse information.
Canopy size, gap size, plant height, and scaled height in NEAT plots at Jornada Basin LTER, Summer 2017
This data package contains estimates of vegetation indicators derived from UAV overflights and comparable field observatons at the NEAT experiment in the Jornada Basin of southern New Mexico, USA. The purpose of this study is to develop a UAV-based remote sensing method that can estimate vegetation indicators in arid and semiarid rangelands. This method was used to characterize six rangeland indicators (canopy size, bare soil gap size, plant height, scaled height, vegetation cover, and bare soil cover) in a semiarid grass-shrub ecosystem at the NEAT site. The drone-based estimates were validated with field measurements by using the standard transect methods (gap intercept, drop disk, and line-point intercept methods) in the spring and summer of 2017. Further, we use these results to show possible applications of drone-based products on arid and semiarid rangelands: the spatially explicit input of an ecological model, to detect and characterize non-stationarity, and to detect landscape anisotropy.
Data from: Canopy height variation and environmental heterogeneity in the tropical dry forests of coastal Oaxaca, Mexico
Despite its importance for carbon storage and other ecosystem functions, the variation of vegetation canopy height is not yet well understood. We examined the relationship between this community attribute and environmental heterogeneity in a tropical dry forest of southern Mexico. We sampled vegetation in 15 sites along a 100-km coastal stretch of Oaxaca State, and measured the heights of all woody plants (excluding lianas). The majority of the ca. 4000 individuals recorded concentrated in the 4–8 m height range. We defined three plant sets to describe overall community canopy height at each site: a set including all plants, a set made up by the tallest plants representing 10 percent of all individuals, and a set comprising the 10 tallest plants. For each site we computed maximum height and the mean and median heights of the three sets. Significant collinearity was observed between the seven resulting height variables, but null distributions constructed through bootstrap revealed their different behaviors as functions of species richness and density of individuals. Through linear modeling and a model selection procedure, we identified 21 models that best described the variation of canopy height variables. These models pointed out to soil (measured as PC1 of a principal component analysis performed on 10 soil variables), water stress, and elevation as the main drivers of canopy height variation in the region. In the event of increasing water stress resulting from global climate change, the studied tropical dry forests could become shorter and thus decrease their carbon storage potential.
Global canopy top height estimates from GEDI LIDAR waveforms for 2019
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S. The map is based on the first four months of L1B Version 1 data (April-July 2019). The sparse footprint level predictions are averaged at 0.5 degree resolution (approx. 55 km raster cells at the equator) to obtain a dense map. We refer to the original research article below for further information, especially on how the predictions were filtered before the aggregation.</p> <p>The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data. The file <a href="https://zenodo.org/api/files/0a9300b5-2dea-4791-a019-319ed6209713/load_pred_RH98_files.py?versionId=6af41185-f13b-44aa-9042-a59efd4abb82">load_pred_RH98_files.py </a>contains more information on how to parse and load the prediction orbit files.</p> <p><strong>GEDI mission website</strong>: <a href="https://gedi.umd.edu/">https://gedi.umd.edu/</a>.</p> <p><strong>Citation: </strong>Use of these data require citation of this dataset and the original research article. These citations are as follows:</p> <p>Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. <em>Remote Sensing of Environment</em>, <em>268</em>, 112760.</p> <p>Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2019 (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5704852</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.