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781 results for “canopies”
Vegetation profile and canopy height of Tabonuco, Colorado and Cloud Forests
The overall height and the presence of vegetation in different height intervals above ground is recorded above points on a 5 x 5 m grid in three hectare-sized plots in the Luquillo Experimental Forest, Puerto Rico. One plots each is in tabonuco forest (350 m elevation), colorado forest (650 m) and cloud forest (1000 m). The first records were made shortly before Hurricane Hugo, in 1989. Post hurricane measurements have been made five time to six time in each plot, the most recent been in 1998 for all plots. The results show the damage to forest structure wreaked by Hurricane Hugo, steady recovery of forest structure after Hugo, minor damage by Hurricane Hortense in 1996, and resumption of recovery after Hortense. Maximum canopy height and the vertical distribution of vegetation in the forests are returning to pre-hurricane Hugo values. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Luquillo canopy trimming experiment - meteorological data, soil sensor, and cannopy measurements
The data archive is here: https://doi.org/10.2737/RDS-2019-0051 please use this DOI when citing this dataset. This data publication contains average daily means for field and satellite data from 3 treated and 3 control plots from the Canopy Trimming Experiment (CTE) located near El Verde Field Station (419 meters; 18°20’ N, 65°49’ W) in the Luquillo Experimental Forest (El Yunque National Forest), Puerto Rico collected from 2003 through 2019. In spring of 2005 (CTE1) and December of 2014 (CTE2), in 0.09 hectare (ha) square plots near the El Verde Field Station the forest canopy was trimmed and the canopy debris was littered to the forest floor. The plot size and trim amounts were based on the patch disturbance after the two most recent hurricanes before 2017, both category 3 hurricanes at the location of El Verde: Hugo in September 1989, and Georges in September 1998 (Zimmerman et al. 2014). Details of the trimming and littering treatment, as well as the biotic response to the 2005 experiment have been extensively documented (Richardson et al. 2010; Shiels et al. 2014, 2015; Shiels and González 2014). The data were collected in the inner 0.04 ha quadrants of the 0.09 ha trimmed plots to minimize edge effects. There were 3 sets of control and treated plots, with each set near El Verde field station. Field data include: solar radiation, throughfall, air temperature, soil temperature, relative humidity shallow soil, volumetric water content, soil profile volumetric water content, canopy leaf saturation, litter leaf saturation, and above canopy air temperature. Satellite data include: Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index converted into solar radiation, MODIS land surface temperature, and Advanced Microwave Scanning Radiometer (AMSR) soil volumetric water content. \<para\> Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952
Canopy damage and recovery following Hurricane Maria using multitemporal lidar data, Mar-2017 - Mar-2020, Puerto Rico
The data archive is here: http://dx.doi.org/10.15486/ngt/1797399 please use this DOI when citing this dataset. Hurricane Maria (Category 4) snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100 to 800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥1 m in canopy height (mean = -7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13% of the study area had canopy heights ≤2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss (<-1 m, 2017-2018) followed by rapid height gain (>1 m, 2018-2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no structural changes >|1| m in either period. Canopy layers <10 m accounted for most increases in canopy height and fractional cover between 2018-2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage. Included in the attached zip file are four TIF and four KML files. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National
Northeastern Puerto Rico open-canopy and under-canopy temperature and moisture data on an elevational gradient
The data archive is here:https://doi.org/10.2737/RDS-2022-0051 please use this DOI when citing this dataset. This data publication contains monthly means of temperature and moisture data collected from August 2006 through September 2021 from 22 locations along an elevational gradient, from 0 to 1045 meters, in Northeastern Puerto Rico. The higher elevational data are in the Luquillo Experimental Forest (El Yunque National Forest). Five kinds of data are included: air temperature and precipitation measured in the open (not under canopy) at 20 locations, air temperature and soil temperature both measured under the canopy at all 22 locations, and soil moisture under the canopy at 4 locations. Two locations have 3 sites each, measured in different canopy types at the same location. The other 20 locations have one site each, making a total of 26 measurement sites. Data are provided as monthly averages at each site. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Landslide Revegetation Canopy Measurements & Cover Estimates
The purpose of this data set is to document recovery of vegetation in new landslides in the Luquillo Experimental Forest (LEF) Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Canopy height profile starting 1992, 1994 and 1996 of the Luquillo Forest Dynamics Plot (LFDP), Puerto Rico
File LFDP_canopy contain the canopy heights for the Luquillo forest Dynamics plot. The first measurement started in 1992, and it was planned to measure the canopy height profile every 2 years. So far censuses starting in 1992, 1994, and 1996 have been completed. In the 1992 census the canopy height profile at points along the East and North limits of the plot were not included. In 1994 and 1996 these extra points were assessed. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Principal Investigators request that researchers intending to use this data comply with the requests below. Through complying with these requests we can ensure that the data are interpreted correctly, analyses are not repeated unnecessarily, beneficial collaboration between users is promoted and the Principle Investigators investment in this project is protected. Submit to the LFDP PIs a short (1 page) description of how you intend to use the data; · Invite LFDP PIs to be co-authors on any publication that uses the data in a substantial way (some PIs may decline and other LFDP scientists may need to be included); If the LFDP PIs are not co-authors, send the PIs a draft of any paper using LFDP data, so that the PIs may comment upon it; In the methods section of any publication using LFDP data, describe that data as coming from the "Luquillo Forest Dynamics Plot, part of the Luquillo Experimental Forest Long-Term Ecological Research Program"; Acknowledge in any publication using LFDP data the "The Luquillo Experim
Canopy invertebrate near El Verde and the Long Term Elevation Plots
We study long-term responses of insects and other arthropods to various disturbances and environmental changes, as well as important roles in ecosystem processes. Five tree species in 1991, six during 1992-1995, and seven since 1997 were selected to represent early and late successional forests at El Verde. These trees were sampled in both gap and non-gap plots resulting from Hurricane Hugo. During the past 27 years, these plots have been subjected to multiple disturbances, including Hurricane Georges in 1998 and major droughts in 1994, 1997 and 2002, in addition to several minor hurricanes and droughts. Each of these disturbances has influenced the arthropod community. This project is ongoing. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
SBC LTER: Reef: California kelp canopy and environmental variable dynamics
This dataset contains time series of kelp canopy area for giant kelp, Macrocystis pyrifera, and bull kelp, Nereocystis luetkeana, and canopy biomass of giant kelp derived from Landsat satellite imagery along with time series of environmental variables known to be associated with kelp forest dynamics. Data are organized into a single NetCDF file and kelp canopy data are co-located with the nearest environmental data point.
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
Snow Spotter Canopy-Snow Interception Dataset #1
<p>This dataset was used in a manuscript titled "<em>Evaluating multiple canopy-snow unloading parameterizations in SUMMA with time-lapse photography characterized by citizen scientists</em>" submitted to AGU WRR by the corresponding first-author, Cassie A. Lumbrazo (lumbraca@uw.edu). <br> An excerpt from the manuscript is provided below. Please see the published manuscript for the full dataset summary and the dataset ReadMe file for information on the dataset structure. </p> <p>Time-lapse images from high-resolution digital cameras were collected from the PhenoCam Network (Milliman et al., 2018), the 2017 NASA SnowEx Field Campaign (Currier et al., 2019; Kim et al., 2017), and the Olympic Mountain Experiment (OLYMPEX) ground validation campaign (Currier et al., 2017; Houze et al., 2017; Lundquist et al., 2018). These images were then uploaded to a citizen science platform called Zooniverse (<a href="https://www.zooniverse.org/"><em>Zooniverse.org</em></a>) and classified by thousands of volunteers as part of the <em>Snow Spotter</em> project. </p> <p>The goal of Snow Spotter was to mobilize citizen scientists to collect information about canopy interception. A total of 6,700 volunteer citizen scientists responded to questions about 13,600 images from sites across the United States to create this first canopy-snow interception Snow Spotter dataset. </p>
Global Airborne Observatory: Plot-level Forest Canopy Properties in Sabah, Malaysia
<p>Plot-level mapping data derived from the Global Airborne Observatory mission in Sabah (Borneo), Malaysia in 2016. Use of these data requires citation of this publication as well as this dataset as follows:</p> <p>Ordway E.M., G.P. Asner, D. Burslem, S. Lewis, R. Martin, R. Nilus, M.J. O’Brien, O. Phillips, L. Qie, N.R. Vaughn, and P.R. Moorcroft. 2022. Mapping tropical forest functional variation at satellite remote sensing resolutions depends on key traits. <em>Communications Earth & Environment.</em></p> <p>Asner, G.P., E. Ordway, J. Heckler, and N.R. Vaughn. 2022. Global Airborne Observatory: Plot-level Forest Canopy Properties in Sabah, Malaysia (1.0) [Data set]. <em>Zenodo</em>. https://doi.org/10.5281/zenodo.7051897</p> <p>Data details:</p> <p>(1) Data cover the Danum and Sepilok sites as described in Ordway et al. (2022) <em>Communication Earth and Environment. </em>All data layers are presented in GeoTIFF format.</p> <p>(2) ACD = Aboveground carbon density in units of Mg C per hectare at 30 meter spatial resolution, as described in <em>https://www.sciencedirect.com/science/article/pii/S0006320717310790</em></p> <p>(3) LAD = Leaf area density in units of m2 per m3 at 50 meter spatial resolution.</p> <p>(4) chems = Leaf chemical traits at 4 meter spatial resolution, as described in <em>https://www.mdpi.com/2072-4292/10/2/199</em></p> <p>(5) TCH = top-of-canopy height in units of meters, as described in <em>https://www.sciencedirect.com/science/article/pii/S0006320717310790</em></p> <p> </p>
Canopy gap sizes across the Brazilian Amazon
<p>This data frame contains the raw gap data used to study the gap size-frequency distributions across the Brazilian Amazon in:</p> <p>Reis, C., Jackson, T., 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>They gaps were extracted from the EBA LiDAR data set https://zenodo.org/record/4968706#.YygSGHZKg5s</p>
Canopy solar-induces chlorophyll fluorescence measurements at seven sites
<p>This dataset will accompany the paper "Direct validation of TROPOMI solar-induced chlorophyll fluorescence products using tower-based measurements reveals shortcomings with the current satellite SIF products" in the Remote Sensing of Environment. This dataset includes the canopy SIF measurements at noon timescale for six sites and a public hourly canopy SIF dataset that accompanies the paper "Mechanistic evidence for tracking the seasonality of photosynthesis with solar induced fluorescence" in the Proceedings of the National Academy of Sciences. All relevant methodological information can be found in the paper: Shanshan, D., Xinjie, L., Jidai, C., Weina, D., and Liangyun, L. in press.</p>
Data for "Vertical characterization of highly oxygenated molecules (HOMs) below and above a boreal forest canopy"
<p>This excel file consists of the data been analyzed in the manuscript "Vertical characterization of highly oxygenated molecules (HOMs) below and above a boreal forest canopy". For more details, please contact the author (qiaozhi.zha@helsinki.fi). </p>
Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ
<p>This repository contains data reported in the below study:</p> <p>Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ. <em>Remote Sensing of Environment</em>.</p> <p>Each text file contains the data-set used to produce the relevant figure (see file name). You can find the data to produce A.4. online at https://avaa.tdata.fi/web/smart/smear/ </p> <p>Please pay attention to the following before using this data.</p> <ol> <li><strong>Figure2_lampRadPanel_Wm2srnm.txt</strong>: Note that the shapes are of interest here. The magnitude is not the same as the incident light at top of canopy, as these spectra were measured in a laboratory. See paper section A.1. for more details. </li> <li><strong>Figure3_LEDIFspectra_Wm2srnm.txt</strong>: This data contains the whole observed spectrum including the non-fluorescence regions, which were saturated (warped) in the visible. The fluorescence region is approximately > 650 nm. </li> <li><strong>Figure4_AQYspectra_nm.txt</strong>: As with Figure3 the whole spectrum is included here.</li> <li><strong>FigureA3_repLEDIFspectra_[pmay/psep/usep]._nm.txt</strong>: Data from which the mean spectra (Figure3) were calculated, including the uncorrected red spectra. I have split these by canopy type to avoid name conflicts.</li> </ol> <p> </p>
R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics
<p>This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.</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 top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines
<p>Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines. The provided land cover maps follow the high carbon stock approach (HCSA) stratifying vegetation based on the estimated carbon density (aboveground biomass). A deep convolutional neural network was trained to estimate canopy top height from Sentinel-2 optical satellite images using reference data derived from GEDI lidar waveforms. Carbon density and high carbon stock classes were derived from these dense canopy height maps using calibration data from an airborne lidar campaign in Sabah, Borneo. The resulting maps have a ground sampling distance (GSD) of 10 m and are based on images between 1st of September 2020 and 1st of March 2021.</p> <p>The style files (color_style_HCS.qml, color_style_canopy_top_height.qml) contain the color coding and can be loaded for visualization (e.g. in QGIS).</p> <p>The indicative HCS maps contain 9 land cover categories noted as "Label: name [colorcode]":</p> <p> 0: Open land (OL) [#440154]<br> 1: Scrub (S) [#404387]<br> 2: Young regenerating forest (YRF) [#29788e]<br> 3: Low density forest (LDF) [#22a884]<br> 4: Medium density forest (MDF) [#7ad251]<br> 5: High density forest (HDF) [#fde725]<br> 10: Oil palm [#fcffa4]<br> 11: Coconut [#a4feff]<br> 50: Urban [#fa0000]<br> 255: No data</p> <p><strong>Citation: </strong>Use of these data require citation of this dataset and the original research articles. These citations are as follows:</p> <p>Lang, N., Schindler, K., & Wegner, J. D. (2021). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR. arXiv preprint arXiv:2107.07431.</p> <p>Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D. (2021). Mapping oil palm density at country scale: An active learning approach. <em>Remote Sensing of Environment</em>, <em>261</em>, 112479.</p> <p>Lang, N., Rodríguez, A. C., Schindler, K., & Wegner, J. D. (2021). Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5012448</p> <p> </p>
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>
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>
ScienceDex guides
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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.