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16 results for “Lidar canopy height”
Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020
<p>Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S from L1B Version 1 data for April-July 2019 and 2020. We refer to the original research article below for further information. The footprint data were filtered with respect to predictive uncertainty and MODIS non-vegetated probability.</p><p>The unfiltered data organized in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data is available here:</p><p>April-July 2019: <a href="https://doi.org/10.5281/zenodo.5704852">https://doi.org/10.5281/zenodo.5704852</a></p><p>April-July 2020: <a href="https://doi.org/10.5281/zenodo.7737869">https://doi.org/10.5281/zenodo.7737869</a></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). Filtered canopy top height estimates from GEDI LIDAR waveforms for 2019 and 2020 (1.0) [Dataset]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7737946">https://doi.org/10.5281/zenodo.7737946</a></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. <i>Remote Sensing of Environment</i>, <i>268</i>, 112760.</p><p>This filtered dataset (2019 and 2020) was used to develop the global canopy height model fusing Sentinel-2 and GEDI that is presented in:</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>
Forest metrics derived from the 2008 Lidar point clouds, includes canopy closure, percentile height, and stem mapping for the Andrews Experimental Forest.
There are three types of forest metrics within this database. They all are derived from the raw Lidar point clouds using the FUSION software. The three types are canopy closure, height metric, and stem mapping. The canopy closure and height metric grids cover a variety of canopy heights and grid cell sizes. 1. Canopy closure: This metric measures the canopy closure of a given horizontal cell above a given vertical threshold (height break). Canopy closure can inform many landscape models and provide insight on how much light will reach the forest floor. 2. Height Metric: This metric measures the height at which a given percent of the first return points are below. This analysis is done in a given grid cell size. Height metrics give various statistics of the elevation above ground for a given set of Lidar points. In forested landscapes, first return height metrics describe the forest canopy. 3.This stem map locates the approximate center of all trees in the HJ Andrews Research Forest greater than 10 meters. In addition to the stem location, a canopy radius is also provided. FUSION and TreeVaWA software programs were used to develop this data. Watershed Sciences, Inc. (WS) collected Light Detection and Ranging (LiDAR) data from HJ Andrews and the Willamette National Forest (NF) on August 10th and 11th 2008. Total area for this AOI is 17,705 acres. The total area of delivered LiDAR including 100 m buffer is 19,493 acres.
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>
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>
Satellite and LiDAR imagery for canopy height and NDVI estimation for mangroves in Puerto Rico
Open the record for dataset details and reuse information.
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>
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.
AfriSAR: Rainforest Canopy Height Derived from PolInSAR and Lidar Data, Gabon
This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency.
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.
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.
Canopy Height and Biomass from LiDAR Surveys at La Selva, Costa Rica, 1998 and 2005
This data set contains land-use, canopy height, and aboveground carbon estimates derived from LiDAR data collected at La Selva Biological Station in Costa Rica in March 1998 and March 2005. The data are provided as GeoTIFFs (*.tif) of 100-m (1-ha) resolution. A look-up table is provided that relates modeled changes in height to changes in stand characteristics (including age and carbon content). The data were used to test the accuracy and scale-dependency of high-resolution predictions of vegetation dynamics and carbon flux by the Ecosystem Demography (ED). The ED model is an individual-based terrestrial ecosystem model that predicts both ecosystem structure and corresponding ecosystem fluxes from climate, soil, and land-use inputs.
CMS: GLAS LiDAR-derived Global Estimates of Forest Canopy Height, 2004-2008
This data set provides estimates of forest canopy height derived from the Geoscience Laser Altimeter System (GLAS) LiDAR instrument that was aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite. A global GLAS waveform data set (n=12,336,553) from collection periods between October 2004 and March 2008 was processed to obtain canopy height estimates.Estimates of GLAS maximum canopy height and crown-area-weighted Lorey's height are provided for 18,578 statistically-selected globally distributed forested sites in a point shapefile. Country is included as a site attribute.Also provided is the average canopy height for the forested area of each country, plus the number of GLAS data footprints (shots), number of selected sample sites, and estimates of the variance for each country.
CMS: LiDAR-derived Canopy Height, Elevation for Sites in Kalimantan, Indonesia, 2014
This dataset provides canopy height and elevation data products derived from airborne LiDAR data collected over 90 sites on the island of Borneo in late 2014. The sites cover approximately 100,000 hectares of forested land in Kalimantan, Indonesia. The data were produced as part of an effort to improve a national forest monitoring system for Indonesia that uses a combination of remote sensing and ground-based forest carbon inventory approaches.
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>.
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