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77 results for “Canopy cover”
CMS: LiDAR-derived Tree Canopy Cover for States in the Northeast USA
This data set provides high-resolution (1-m) tree canopy cover for states in the Northeast USA. State-level canopy cover data are currently available for Pennsylvania (data for nominal year 2008), Delaware (2014), and Maryland (2013). The data were derived with a rules-based expert system which facilitated integration of leaf-on LiDAR and imagery data into a single classification workflow, exploiting the spectral, height, and spatial information contained in the datasets. Additional states will be added as data processing is completed.
NAFD-ATT Forest Canopy Cover Loss from Landsat, CONUS, 1986-2010
Characterizing the cause of forest canopy changes through time is fundamental to understanding current and future forest functions. A better understanding of forest dynamics can help build linkages between patterns and processes. The North American Forest Dynamics (NAFD) products provided in this dataset predict characteristics related to the cause of forest canopy cover losses for the conterminous United States (CONUS) derived from Landsat images for the period 1986-2010. The characteristics are summarized in four separate data layers. The first layer labels the type of change event (stable-no change, removals, fire, stress, wind, conversion, other), the second labels the year of the event, the third and fourth layers measure dominance and diversity, measures of qualitative confidence metrics derived from the model predictions. For each pixel the maps depict the greatest magnitude event occurring between 1986-2010.
CMS: LiDAR-derived Aboveground Biomass, Canopy Height and Cover for Maryland, 2011
This data set provides 30-meter gridded estimates of aboveground biomass (AGB), canopy height, and canopy coverage for the state of Maryland in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery to select 848 field sampling sites for biomass measurements. The field-based estimates were related to LiDAR height and volume metrics through random forests regression models across three physiographic regions of Maryland.
LiDAR Derived Biomass, Canopy Height and Cover for Tri-State (MD, PA, DE) Region, V2
This dataset provides 30-meter gridded estimates of aboveground biomass (AGB), forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery in a model-based stratification that was used to select 848 sampling sites for AGB estimation. Field-based estimates were then related to LiDAR height and volume metrics through random forest regression models across three physiographic regions. Spatial errors were estimated at the pixel level using standard prediction intervals to assess the accuracy of the modeling approach. Estimates of biomass were further validated against the permanent network of FIA plots and compared with existing coarse resolution national biomass maps.
CMS: Tree Canopy Cover at 0.5-meter resolution, Vermont, 2016
This dataset contains estimates of tree canopy cover presence at high resolution (0.5m) across the state of Vermont for 2016 in Cloud-Optimized GeoTIFF (*.tif) format. Tree canopy was derived from 2016 high-resolution remotely sensed data as part of the Vermont High-Resolution Land Cover mapping project. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to ensure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:3000 and all observable errors were corrected. Tree canopy assessments have been conducted for numerous communities throughout the U.S. where the results have been instrumental in helping to establish tree canopy goals.
CMS: LiDAR-derived Biomass, Canopy Height and Cover, Sonoma County, California, 2013
This data set provides estimates of above-ground biomass (AGB), canopy height, and percent tree cover at 30-m spatial resolution for Sonoma County, California, USA, for the nominal year 2013. Biomass estimates, megagrams of biomass per hectare (Mg/ha) were generated using a combination of LiDAR data, field plot measurements, and random forest modeling approaches. Estimates of AGB uncertainty are also provided. Maximum canopy height and tree cover were derived from LiDAR data and high-resolution National Agriculture Imagery Program (NAIP) images.
AfriSAR: Canopy Cover and Vertical Profile Metrics Derived from LVIS, Gabon, 2016
This dataset includes footprint-level canopy structure products derived from data collected using NASA's Land, Vegetation, and Ice Sensor (LVIS) during flights over five forested sites in Gabon during February and March 2016. Three types of canopy structure information are included for each flight: 1) vertical profiles of canopy cover fraction in 1-meter bins, 2) vertical profiles of plant area index (PAI) in 1-meter bins, and 3) footprint summary data of total recorded energy, leaf area index, canopy cover fraction, and vertical foliage profiles in 10-meter bins. Canopy structure metrics are provided for each waveform (20-m footprint) collected by the LVIS instrument. These data were collected by NASA as part of the AfriSAR project. AfriSAR is a NASA collaboration with the European Space Agency (ESA), German Aerospace Center (DLR), and the Gabonese Space Agency (AGEOS) that is collecting data useful for deriving forest canopy structure and will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle.
GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V002
The Global Ecosystem Dynamics Investigation ([GEDI](https://gedi.umd.edu/)) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.The purpose of the GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B) is to extract biophysical metrics from each GEDI waveform. These metrics are based on the directional gap probability profile derived from the L1B waveform. Metrics provided include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). The GEDI02_B product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI02_B data product contains 96 layers for each of the eight-beam ground transects (or laser footprints located on the land surface). Datasets provided include precise latitude, longitude, elevation, height, canopy cover, and vertical profile metrics. Additional information for the layers can be found in the GEDI Level 2B Data Dictionary.Known Issues* Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).* Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this [document](https://lpdaac.usgs.gov/documents/2236/GEDI_CORRECTED_RGT_FILENAMES.pptx) for the correct RGT numbers.* Known Issues: Section 8 of the User Guide provides additional information on known issues.Improvements/Changes from Previous Versions* Metadata has been updated to include spatial coordinates.* Granule size has been reduced from one full ISS orbit (~1.19 GB) to four segments per orbit (~0.30 GB).* Filename has been updated to include segment number and version number.* Improved geolocation for an orbital segment.* Added elevation from the SRTM digital elevation model for comparison.* Modified the method to predict an optimum algorithm setting group per laser shot.* Added additional land cover datasets related to phenology, urban infrastructure, and water persistence.* Added selected_mode_flag dataset to root beam group using selected algorithm.* Removed shots when the laser is not firing.* Modified file name to include segment number and dataset version.
LiDAR Derived Biomass, Canopy Height, and Cover for New England Region, USA, 2015
This dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements. Canopy height and tree cover were derived directly from LiDAR data while AGBD was estimated by statistical models that link remote sensing data and FIA plots at the pixel level. Error in AGBD was calculated at the 90% confidence interval. This approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne LiDAR missions.
Deciduous Fractional Cover and Tree Canopy Cover for Boreal North America, 1992-2015
This dataset holds deciduous fraction and tree canopy cover at 30-m resolution over the North American boreal domain for 1992 to 2015. Deciduous fraction is the areal percentage of deciduous trees relative to all tree canopy cover within a pixel, and tree canopy cover is the areal percentage of a pixel that is covered by tree canopy. Deciduous fraction values are valid only for pixels with tree canopy cover >25 percent. Normalized difference vegetation index (NDVI)-based median-value image composites were derived from Landsat 5, 7, and 8 Collection 1 surface reflectance datasets for years 1987-1997, 1998-2002, 2003-2007, 2008-2012, and 2013-2018 to create composites for nominal years 1992, 2000, 2005, 2010, and 2015, respectively. These image composites were prepared for early spring, mid-summer, and mid-to-late fall seasons to identify key differences in deciduous and evergreen green-up amplitudes. Random Forest (RF) regression models were used to derive deciduous fraction and tree canopy cover from the image composites. These models were trained with data from in-situ samples across Alaska and Canada from a variety of studies. Seventy percent of the in-situ samples were used for training and 30% for validation. Per-pixel uncertainty for both deciduous fraction and tree canopy cover are included and were based on one standard deviation of output values across all decision trees in the RF regression. These datasets were developed as part of NASA's ABoVE project to capture forest composition changes over the North American boreal domain across the last several decades. The data are provided in GeoTIFF format.
ABoVE: Tree Canopy Cover and Stand Age from Landsat, Boreal Forest Biome, 1984-2020
This dataset contains Landsat-derived locally-calibrated estimates of tree canopy cover (TCC) and forest stand age across global boreal forests from 1984-2020 in Cloud-Optimized GeoTIFF (*.tif) format. These raster data span the circum-hemispheric boreal forest biome between 47 to 73 degrees north at 30 m resolution. Machine learning models calibrated with data from the World Reference System 2 were used to predict TCC from Landsat data at 30-m spatial resolution at annual temporal resolution. Through analysis of TCC time series, forest change estimates of stand age from 1984-2020 were developed. The broad spatial and temporal coverage of these data provide insight into forest and carbon dynamics of the global boreal forest system. Boreal forests store a large proportion of global soil and biomass carbon and have experienced disproportionately high levels of warming over the past century.
Tree Canopy Cover for the Circumpolar Taiga-Tundra Ecotone: 2000-2005
This data set provides a map of selected areas with defined tree canopy cover over the circumpolar taiga-tundra ecotone (TTE). Canopy cover was derived from the 500-meter MODIS Vegetation Continuous Fields (VCF) product as averaged over six years from 2000-2005 and processed as described in Ranson et al. (2011). This process identified patches of low tree canopy cover which are indicative of the transition from forest to tundra and differentiate the circumpolar taiga–tundra ecotone for the 2000–2005 period. The TTE is the Earth's longest vegetation transition zone and stretches for more than 13,400 km around Arctic North America, Scandinavia, and Eurasia. In Eurasia, the map extends from 60 degrees N to 70 degrees N, and in North America from 50 degrees N to 70 degrees N, excluding Baffin Island in northeastern Canada and the Aleutian Peninsula in southwestern Alaska. Note that for this product, taiga is being used one and the same as boreal forest.This circumpolar TTE area was classified according to VCF tree canopy cover.
Examining the distributional equity of urban tree canopy cover and ecosystem services across United States cities - PLOS ONE Data
<p>Shapefile data used in the analysis for each of the 9 cities examined (New York, Philadelphia, Washington, Cleveland, Pittsburgh, Chicago, Los Angeles, San Diego, Sacramento). Datasets provided have been trimmed following the procedures outlined in the Methods and represent the data used in the analysis.</p>
Tree Cover and Canopy Height median data of the Western Ghats, India
<p>The dataset encompasses median values for Tree cover (Hansen Global Forest Change v1.9 (2000-2021)) and Canopy height ( Global Forest Canopy Height, GEDI_V27), measured at a 30-meter resolution, across 25 protected areas in the Western Ghats of India. To provide a detailed analysis, the data is further segmented into eight distinct slope aspects characterized using Shuttle Radar Topography Mission (SRTM) data with 30 m resolution, allowing for a comprehensive understanding of how tree cover and canopy height vary across different topographical features. Additionally, the dataset includes median values computed for various slope and elevation ranges for different aspects, offering insights into how these factors influence vegetation characteristics in each protected area.</p>
Global tree-canopy cover over three decades
<p>Losses and gains in canopy cover of the world’s tree canopies affect carbon stocks, species habitats, water cycles, and human livelihoods. Consistent and multi-decadal global data on tree-canopy cover dynamics are needed for modelling climate scenarios, tracking progress towards restoration targets, and diverse other research, management and policy applications. However, most data only map binary ‘forest’/‘non forest’ distinctions that are regionally restricted or biassed by data gaps, and those mapping tree-canopy cover are limited to the 21st century. Here, we present an annual and global time-series of tree-canopy cover between 1992 and 2018. To develop these data, we integrated complementary products, using their respective strengths to compensate for weaknesses, and exploiting path dependencies in change processes to derive predictions into the data-sparse 1990s. Our model validation indicates we can accurately map tree-canopy cover (r2=0.95 [±0.01], RMSE=6.75% [±0.08], F1-score=0.97 [±0.0]) and our time-series agree well with national forest statistics (r2=0.94 [±0.0]).<br> <br> This repository contains the Global Tree-Canopy Cover Change dataset (GTCCC), which consists of a global time-series on per-pixel tree-canopy covers estimated at a 300-m resolution between 1992 and 2018. The repository contains the following:</p> <ul> <li><em>GTCCC_canopyDensity.tar.gz</em> - Annual GeoTiffs on per-piel tree-canopy cover estimates (EPSG:4326)</li> <li><em>GTCCC_uncertainty.tar.gz </em>- Annual GeotiFFs with per-pixel estimates of the 95% confidence interval of the the predictions of each RFReg decision tree.</li> <li><em>GTCCC_change.tar.gz</em> - Multiple outputs describing changes in tree-canopy cover between 1992 and 2018. The contents are described in a README.txt file found within.</li> <li><em>modelling_infrastructure.tar.gz </em>- Infrastructure to generate the GTCCC dataset, including code, and some intermediary outputs, such as reference samples and the predictive model. The contents are described in a README.txt file found within.</li> </ul> <p>The predictors used to generate the GTCCC are provided separately given large volume, and can be reached by <a href="https://doi.org/10.5281/zenodo.8217237">clicking here</a>.</p>
Predictor variables of the Global Tree-Canopy Cover Change dataset (GTCCC)
<p>Compilation of variables used to generate the Global Tree-Canopy Cover Change dataset (GTCCC). The contents are described in the file "variable_list.xlsx", and their use is documented in a complementary manuscript. The GTCC data and the code used to generate it can be found in a <a href="https://doi.org/10.5281/zenodo.7901290">separate Zenodo repository.</a></p>
GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V001
GEDI Version 1 data products were decommissioned on February 15, 2022. Users are advised to use the improved GEDI02_B Version 2 (https://doi.org/10.5067/GEDI/GEDI02_B.002) data product.The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station and collects data globally between 51.6 degrees N and 51.6 degrees S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date.The purpose of the GEDI Level 2B Canopy Cover and Vertical Profile Metrics product (GEDI02_B) is to extract biophysical metrics from each GEDI waveform. These metrics are based on the directional gap probability profile derived from the L1B waveform. Metrics provided include canopy cover, Plant Area Index (PAI), Plant Area Volume Density (PAVD), and Foliage Height Diversity (FHD). The GEDI02_B product is provided in HDF-5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI02_B data product contains 96 variables for each of the eight-beam ground transects (or laser footprints located on the land surface). Variables provided include precise latitude, longitude, elevation, height, canopy cover, and vertical profile metrics. Additional information for the variables can be found in the GEDI Level 2B Data Dictionary.Known Issues* Known Issues: Section 7 of the User Guide provides additional information on known issues.* Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
ScienceDex guides
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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)
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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.