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15 results for “canopy height model”
L2C - Canopy height models across the Brazilian Amazon
<p>Canopy height models derived from LiDAR data collected across the Brazilian Amazon. The files are provided in .tiff format in 7 zip folders. A full description of the data set is available here: https://zenodo.org/record/4968706#.YzB693ZKg5s</p> <p>We also provide the summary data used for statistical analysis in the associated publication: </p> <p>Reis and Jackson et al 2022. Forest disturbance and growth processes are reflected in the geographic distribution of large canopy gaps across the Brazilian Amazon. Journal of Ecology.</p> <p>Each transect covered 375 ha (12.5 km × 300 m) 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 square meters, the field of view was equal to 30°, the flying altitude was 600 m, and 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 set to be 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 under 0.5 m, respectively.</p> <p>The data collection was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvimento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7); Amazon Fund (grant 14.2.0929.1)</p> <p>The research project was funded by the UK Natural Environment Research Council project number <strong>NE/S010750/1</strong></p>
Comparison of high-resolution global canopy height maps and their applicability to biodiversity modelling - dataset
<p>This repository was created to provide datasets related with an article comparing high-resolution global canopy height maps and exploring their applicability to biodiversity modeling in temperate biomes.</p> <p>EBR stands for Entlebuch Biosphere Reserve, MRF stands for Mount Richmond Forest and TAW stands for Trinity Alps Wilderness.</p> <p>The original airborne laser scanning point clouds used for the generation of the canopy height models were sourced from the LINZ Data Service and OpenTopography, and licensed for reuse under the CC BY 4.0 licence (<a href="https://mcas-proxyweb.mcas.ms/certificate-checker?login=false&originalUrl=https%3A%2F%2Fdoi.org.mcas.ms%2F10.5069%2FG97D2SB0%3FMcasTsid%3D20893&McasCSRF=cf3ae9aed6f2016d3ceedda452d422646f4f8e5a5e6270380b370aad4964323a">https://doi.org/10.5069/G97D2SB0</a>); Federal Office of Topography swisstopo (<a href="https://mcas-proxyweb.mcas.ms/certificate-checker?login=false&originalUrl=https%3A%2F%2Fwww.swisstopo.admin.ch.mcas.ms%2Fen%2Fgeodata%2Fheight%2Fsurface3d.html%3FMcasTsid%3D20893&McasCSRF=cf3ae9aed6f2016d3ceedda452d422646f4f8e5a5e6270380b370aad4964323a">https://www.swisstopo.admin.ch/en/geodata/height/surface3d.html</a>); and U.S. Geological Survey (<a href="https://mcas-proxyweb.mcas.ms/certificate-checker?login=false&originalUrl=https%3A%2F%2Fapps.nationalmap.gov.mcas.ms%2Fdownloader%2F%3FMcasTsid%3D20893&McasCSRF=cf3ae9aed6f2016d3ceedda452d422646f4f8e5a5e6270380b370aad4964323a">https://apps.nationalmap.gov/downloader/</a>).</p> <p>The Global Forest Canopy Height Map - GFCH (Potapov et al. 2021; https://glad.umd.edu/dataset/gedi) and the high-resolution canopy height model of the Earth - HRCH (Lang et al. 2022, https://langnico.github.io/globalcanopyheight/) are provided free of charge, without restriction of use under Creative Commons Attribution 4.0 International License. Publications, models, and data products that make use of these datasets must include proper acknowledgement.</p> <p><em>P. Potapov, X. Li, A. Hernandez-Serna, A. Tyukavina, M.C. Hansen, A. Kommareddy, A. Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2021) Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 112165. <a href="https://doi.org/10.1016/j.rse.2020.112165">https://doi.org/10.1016/j.rse.2020.112165</a></em></p> <p><em>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2022). A high-resolution canopy height model of the Earth. arXiv preprint arXiv:2204.08322.</em></p> <p>R scripts related with this datasets are available at Github (https://github.com/lukasgabor/Comparison-of-high-resolution-global-canopy-height-maps-and-their-applicability; <a href="https://doi.org/10.5281/zenodo.7332716">DOI: 10.5281/zenodo.7332716</a>)</p> <p>In the previous version (1.0) the average was calculated for the canopy height. In this version (1.1), the maximum height is calculated for the canopy height.</p>
Urban Vegetation Data - Canopy Height Model (Brussels Capital Region, 2021)
<p>This GIS dataset was created for the following scientific publication, as part of the EU-funded <a href="https://coolschools.eu/">Cool Schools</a> research project (under Grant Agreement No. 101003758) : Gallez, E., Canters, F., Gadeyne, S., & Baró, F. (2024). <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000846?via%3Dihub">A multi-indicator distributive justice approach to assess school-related green infrastructure benefits in Brussels - ScienceDirect</a>. Ecosystem Services, 70, 101677. https://doi.org/10.1016/j.ecoser.2024.101677. </p> <p><em>Very-High Resolution Canopy Height Model (resolution : 25cm), distinguishing between 4 vegetation types (trees, high shrubs, low shrubs and grass) in the Brussels Capital Region.</em></p> <p><em>Coordinate system : Lambert_Belge_72.</em></p> <p><em>The CHM was built on </em><em>:</em></p> <ul> <li><em>VHR aerial orthophotos (visible RGB and NIR) (“UrbIS-Ortho N-S, 2021”) for the Brussels Capital Region, of 5x5cm resolution Source: Paradigm. (2021). UrbIS-Ortho N-S. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/fec72767-d6b6-41b9-a767-616df2779aae#access">https://datastore.brussels/web/urbisdownload</a>. and;</em></li> <li><em>digital terrain models (DSM and DTM) of 50x50cm, captured on 22/09/2021. Paradigm.Brussels. </em><em>Source: Paradigm. (2021). DSM / DTM. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/1d7bd49d-fe83-4388-af85-6f5dc8ec7909#access">https://datastore.brussels/web/urbisdownload.</a></em></li> </ul> <p><em>Both the orthophotos and digital terrain models were resampled to a 25x25cm resolution, using a bilinear interpolation method. </em></p> <p><em>The Canopy Height Model was then created by selecting NDVI values of 0.2 and higher, - a commonly used threshold value to distinguish vegetated land from built land (Hashim et al., 2019) -, </em><em>and vegetation height thresholds of < 0.5m (for grass), 0.5 - 2m (for low shrubs), 2 - 5m (for high shrubs), and > 5m (for trees) (Derkzen et al., 2015; Sankey et al., 2018). </em><em>Green roofs were excluded.The CHM raster was then converted to polygon features. </em></p> <p><em>Classification :</em></p> <ul> <li><em>From 0 to 0.5 m (nDSM value) : gridcode 1 = </em><em>grass</em></li> <li><em>From 0.5 to 2 m (nDSM value): gridcode 2 = </em><em>low shrubs</em></li> <li><em>From 2 to 5 m (nDSM value): gridcode 3 =</em><em> high shrubs</em></li> <li><em>From 5 to 113.96 m (nDSM value): gridcode 4 = </em><em>trees</em></li> </ul>
Canopy Height Model Dresden 2017
<p>The canopy height model (CHM) represents area-wide tree canopy heights within the City of Dresden (Germany). The CHM provides spatially explicit information on urban forest structure enabling the assessment of the small-scale impacts of urban trees and strategically managing the ecosystem services they provide. The high-resolution raster layer has a cell size of 0.5 meter and maps the height of the upper crown layer above the underlying ground.</p> <p>The CHM was derived from a classification of the urban forest in a LiDAR point cloud using a data fusion approach combining LiDAR with multispectral imagery and a 3D building model. LiDAR data were acquired in 2017. The classification is described in detail in <a href="https://doi.org/10.1016/j.ufug.2022.127637">this article</a>.</p> <p>The raster is available as a single-band GeoTIFF in the coordinate system ETRS89/UTM zone 33 (EPSG: 25833).</p> <p>The source data used was made freely available by the “Landesamt für Geobasisinformation Sachsen” (GeoSN) under the license "Data license Germany - attribution - Version 2.0" and can be downloaded under the following links:<br> LiDAR: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-hoehenmodelle-4851.html</a><br> 3D Building Model: <a href="https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html">https://www.geodaten.sachsen.de/downloadbereich-digitale-3d-stadtmodelle-4875.html</a><br> Aerial Imagery: <a href="https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html">https://www.geodaten.sachsen.de/downloadbereich-dop-4826.html</a></p>
Demo data for global-canopy-height-model
<p>Demo data for the example scripts provided in <a href="https://github.com/langnico/global-canopy-height-model">https://github.com/langnico/global-canopy-height-model</a>.</p><p>Please see the README in the github repository for further information and see Lang, et al. (2023) for more information.</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><p> </p>
Forest segmentation of multi-source national forest inventory biomass rasters and canopy height model from 2021
<p>The dataset is produced at Natural Resources Institute Finland (Luke) and the study is funded by the European Union's Horizon 2020 research and innovation programme (Holisoils, grant agreement No 101000289).</p> <p>Source data (multi-source National Forest Inventory, MS-NFI and peatland fertility map of Finland) of varying resolution (10m -16m) was reprojected to 10mx10m resolution from which stand polygons were formulated based on automatic segmentation and regional minimum size limit for a stand.</p> <p>The dataset is a file geodatabase with 5 regional layers, all including the polygons of stands with stand attributes based on MS-NFI 2021 information on the site type, fertility class, dominant height, basal area, diameter, age, volume as total and per tree species, total and aboveground biomass as total and per tree species.</p> <p>Coordinate system: ETRS-TM35FIN (EPSG:3067)</p>
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>
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>
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>
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>
Merged HLS2 and GEDI data for estimating canopy height with IBM's granite-geospatial-canopyheight model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and Global Ecosystem Dynamics Investigation (GEDI) L2A data following CRS:4326. It has been assembled for estimating canopy height with a fine-tuned granite geospatial foundation model developed by IBM Research. Please see https://huggingface.co/ibm-granite/granite-geospatial-canopyheight for more information on data preparation and model use.</p> <p><strong>HLS2—</strong>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <p><strong>GEDI L2A—</strong>Lee, J., S. Favrichon, S. Mauceri, Y. Yang, J. Armston, and S. Saatchi. 2023. Addressing underestimation in global forest structure mapping. <a href="https://doi.org/10.22541/essoar.167276451.10705079/v1">https://doi.org/10.22541/essoar.167276451.10705079/v1</a></p>
G-LiHT Canopy Height Model KML V001
Goddard’s LiDAR, Hyperspectral, and Thermal Imager ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.The purpose of G-LiHT’s Canopy Height Model Keyhole Markup Language (KML) data product (GLCHMK) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA’s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed. GLCHMK data are processed as a Google Earth overlay KML file at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format.
G-LiHT Canopy Height Model V001
Goddard’s LiDAR, Hyperspectral, and Thermal Imager ([G-LiHT](https://gliht.gsfc.nasa.gov/)) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.The purpose of G-LiHT’s Canopy Height Model data product (GLCHMT) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA’s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed.GLCHMT data are processed as a raster data product (GeoTIFF) at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format.
Canopy Height Models of Iberian Peninsula 2018-2021 at 10 m resolution
<p>Forest canopy height model for study areas distribuited along Spain (GALICIA/ EXTREMADURA/ANDALUCIA/LEON Regions, Rodeno, Guara (ARAGON), Cabañeros (CASTILLA Y LA MANCHA)), Portugal (7 study areas) using airborne Lidar data (ALS) collected in 2018, 2019, 2020 and 2021. The maps were produced by at the resolution of 10 m. The raw point cloud data information could be obtained:</p> <p>https://pnoa.ign.es/web/portal/pnoa-lidar/segunda-cobertura</p> <p>GAL,EXT,AND,CYL,ARA,CYM</p> <p>https://geocatalogo.icnf.pt/geovisualizador/agil/</p> <p>MAFRA,MONSANTO, OLEIROS, POMBAL, SINTRA, VILA POUÇA DE AGUIAR, SERRA DA LOUSA</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 10 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 Portugal EPSG: 3763, Spain EPSG:25830</p> <p>Use of these data requires citation of this dataset </p>
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