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19 results for “airborne laser scanning”

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zenodo48/100

Extracted trails from airborne laser scanning in the Oostvaardersplassen nature reserve

Ungulates and other mammalian herbivores can create trails in dense vegetation by trampling and browsing. This can affect vegetation structure and results in the fragmentation of closed, high vegetation, with subsequent impacts on biodiversity. Manually mapping trails in the field or from aerial photographs can be challenging and time consuming, especially in inaccessible or difficult to access habitats such as wetlands and if trails occur beneath the canopy. Airborne laser scanning provides an alternative method because it penetrates vegetation canopies and efficiently obtains highly accurate data in the form of dense 3D point clouds. This repository consists of the extracted trails in wetland area of the Oostvaardersplassen nature reserve in the Netherlands using 3D airborne point cloud data (AHN4) and the manually created 50 plots of ground truth in two regions, i.e. grazed only by red deer and grazed by both red deer and geese.

opencc-by-4.0Oct 2024View details →

zenodo44/100

Data repository of multi-temporal high-resolution data products of ecosystem structure derived from country-wide airborne laser scanning surveys of the Netherlands

This data repository contains a set of multi-temporal data products of ecosystem structure derived from four national ALS surveys of the Netherlands (AHN1–AHN4) (folders: 1_AHN1, 2_AHN2, 3_AHN3, and 4_AHN4). Four sets of 25 LiDAR-derived vegetation metrics representing ecosystem height, cover, and structural variability are provided at 10 m spatial resolution, providing valuable data sources for a wide range of ecological research and field beyond. A preview of all generated LiDAR metrics are also provided (folder: 5_Maps). All 25 LiDAR metrics were calculated using Laserfarm workflow  (<a href="https://laserfarm.readthedocs.io/en/latest/">https://laserfarm.readthedocs.io/en/latest/</a>) (building on the user-extendable features from the “Laserchicken” software: <a href="https://laserchicken.readthedocs.io/en/latest/#features">https://laserchicken.readthedocs.io/en/latest/#features</a>). All metrics are calculated with the normalized point cloud. More details on metric calculation are provided on GitHub (Laserchicken: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> and Laserfarm: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>), as well as on the “Laserchicken” documentation page (<a href="https://laserchicken.readthedocs.io/en/latest/">https://laserchicken.readthedocs.io/en/latest/</a>). We also provided masks to minimize the influence of water surfaces, buildings and roads, powerlines and NA values in the data products (folder: 6_Masks).  To supplement the generated data products, we also provided a set of raster layers that contains point/pulse density of each AHN survey and the DTM and DSM raster layers for each AHN dataset (folder: 7_Auxiliary_data). To test the robustness of the LiDAR metrics, we also compared the metrics generated from different pulse densities across different habitat types (folder: 8_Sensitivity_analysis). Two use cases demonstrated the utility of the presented data products: (use case 1) monitoring forest structural change across time using multi-temporal ALS data and (use case 2) comparison of vegetation structural difference within Natura 2000 sites. The used data are also provided (folder: 9_Use_case). Note that all the raster layers are provided at 10 m resolution under the local Dutch coordinate system “RD_new” (EPSG: 28992, NAP:5709). To gain more insights of the pre-classification accuracy of the AHN datasets, we also conducted a preliminary assessment of the effect of terrain filtering on vegetation change detection across AHN datasets (i.e. AHN2–AHN4). The data used in this analysis are made available (folder: 10_Ground_classification). An overview of all the folders in the repository: 1.     AHN1 2.     AHN2 3.     AHN3 4.     AHN4 5.     Maps Those folders contain four sets of 25 LiDAR metrics at 10 m resolution generated from each AHN dataset. The file names and their corresponding LiDAR metrics can be found in Table 1. An additional folder (5_Maps) contains the maps (.pdf format) of all 25 metrics for each AHN dataset. 6. Masks <ul> <li>ahn3_10m_mask_building_road_water.tif</li> <li>ahn4_10m_mask_building_road_water.tif</li> <li>ahn4_10m_mask_powerline.tif</li> <li>ahn1_10m_NA_mask.tif</li> <li>ahn2_10m_NA_mask.tif</li> <li>ahn3_10m_NA_mask.tif</li> <li>ahn4_10m_NA_mask.tif</li> </ul>   It contains two mask layers of water surfaces, buildings and roads for both AHN3 and AHN4 data products based on the Dutch cadaster data (TOP10NL) from 2018 (corresponding to AHN3) and 2021 (corresponding to AHN4) (<a href="https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl">https://www.kadaster.nl/zakelijk/producten/geo-informatie/topnl</a>). In the masks, water surfaces, buildings and roads were merged into one class with pixel value assigned to 1 and the rest has the pixel value of 0. There is also a powerline mask generated from the AHN4 dataset at 10 m resolution, where pixels containing powerlines were assigned a value of 1 and the rest as NoData. We provide those masks to minimize the inaccuracies of the data products caused by human infrastructures and water surfaces. We also provided a mask for each AHN dataset where NA value occurs — areas with no vegetation points (“unclassified” class in the AHN datasets). Pixels with NA value were assigned with a value of 1 and the rest as 0. 7. Auxiliary data (1) Point_density <ul> <li>ahn1_10m_point_density.tif</li> <li>ahn2_10m_point_density.tif</li> <li>ahn3_10m_point_density.tif</li> <li>ahn4_10m_point_density.tif</li> </ul> (2) Pulse_density <ul> <li>ahn3_10m_pulse_density.tif</li> <li>ahn4_10m_pulse_density.tif</li> </ul> (3) Flighttime <ul> <li>ahn3_10m_flighttime.tif</li> <li>ahn4_10m_flighttime.tif</li> </ul> (4) DTM_DSM <ul> <li>ahn2_10m_dtm.tif</li> <li>ahn2_10m_dsm.tif</li> <li>ahn3_10m_dtm.tif</li> <li>ahn3_10m_dsm.tif</li> <li>ahn4_10m_dtm.tif</li> <li>ahn4_10m_dsm.tif</li> </ul> It contains four raster layers representing the point density of each AHN dataset, two raster layers for pulse density of the AHN3 and AHN4, two raster layers for flight timestamp of the AHN3 and AHN4, and six DTM and DSM layers for AHN2–AHN4. All raster layers are provide at 10 m resolution. 8. Sensitivity analysis <ul> <li>Dunes</li> <li>Marsh</li> <li>Grassland</li> <li>Shrubland</li> <li>Woodland</li> <li>Code</li> <li>Figure</li> </ul> It contains the 25 metrics generated from point clouds with the original and down-sampled pulse densities (original pulse density of the AHN4, pulse density of the AHN3, ½ of the pulse density of the AHN3, and ¼ of the pulse density of AHN3) for each habitat type (i.e. dunes, marsh, grassland, shrubland, and woodland). We also provided the code and the figures generated from this analysis. 9. Use_case (1) Multi-temporal_AHN <ul> <li>Data</li> <li>Usecase_multi-temporal_AHN.R</li> </ul> It contains the input data for the use case data processing (i.e. Data folder), including the shapefile of the area (i.e. shp folder), and extracted pixel value from six selected LiDAR metrics from AHN1–AHN5 (i.e. Metrics folder), and the selected LiDAR metrics of the area (e.g. Hp95 folder), and the R code for data processing (i.e. Usecase_multi-temporal_AHN.R). (2) Natura2000 <ul> <li>Data</li> <li>Natura2000_end2021_HABITATCLASS.csv</li> <li>Natura2000_NL_habitat_grouped.csv</li> <li>Usecase_Natura2000.R</li> </ul> It contains a folder of the input data used for the use case (i.e. Data folder), including the shapefile (i.e. shp folder) of the Natura 2000 sites in the Netherlands (i.e. Nature2000_NL_RDnew.shp) and the 100 random sample plots from each habitat type (e.g. woodland_points.shp), and the LiDAR metrics from AHN4 used for demonstrating the vegetation  structure within each habitat type (i.e. AHN4_metrics folder). The table “Natura2000_end2021_HABITATCLASS.csv” is the original attribute table of Natura 2000 sites, including information related to the description of habitat classes (column “DESCRIPTION”), the code corresponding to the habitat class (column “HABITATCODE”), the code for the specific site (column “SITECODE”), and the percentage of the cover of a specific habitat class in one site (column “PERCENTAGECOVER”). The table “Natura2000_NL_habitat_grouped.csv” contains two subtabs, one (i.e. “Habitatclass”) is the copy of the original attribute table of Natura 2000 sites in the Netherlands, and the other one (i.e. “Habitat_class_summary”) is the grouped habitat type based on the dominant habitat class (i.e. class with the highest percentage cover) in each site. Different colors indicate different habitat types, corresponding to the colors in the first tab (“Habitatclass”) where the dominant habitat class was highlighted for each site. 10. Ground classification <ul> <li>Raw_point_cloud</li> <li>Computed_metrics </li> <li>Plottings_and_code</li> <li>ArcGIS_project</li> </ul> It contains four subfolders: (1) The original point cloud for each sample area (AHN2–AHN4) (subfolder: Raw_point_cloud); (2) The 25 LiDAR metrics computed from the original point clouds with pre-classification of AHN and from the new terrain filtering method across AHN2–AHN4 (subfolder: Computed_metrics); (3) Generated violin plots for the comparison of vegetation change detection and the python code employed (subfolder: Plottings_and_code); (4) an ArcGIS project which the shapefiles of the study area and sample plots are provided (subfolder: ArcGIS_project). Code availability Jupyter Notebooks for processing AHN datasets: <a href="https://github.com/ShiYifang/AHN">https://github.com/ShiYifang/AHN</a> Laserfarm workflow repository: <a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a> Laserchicken software repository: <a href="https://github.com/eEcoLiDAR/laserchicken">https://github.com/eEcoLiDAR/laserchicken</a> Code for downloading AHN dataset: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_downloading">https://github.com/ShiYifang/AHN/tree/main/AHN_downloading</a> Code for generating masks for AHN datasets: <a href="https://github.com/ShiYifang/AHN/tree/main/AHN_masks">https://github.com/ShiYifang/AHN/tree/main/AHN_masks</a> Code for demonstration of ecological use cases: <a href="https://github.com/ShiYifang/AHN/tree/main/Use_case">https://github.com/ShiYifang/AHN/tree/main/Use_case</a>

opencc-by-4.0Oct 2024View details →

zenodo40/100

Data from : Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning

This data repository contains the processed lidar metrics for characterizing the habitat structure for classifying main land cover and habitat types in the Lauwersmeer area in the northern part of the Netherlands in the province of Groningen (5754 ha). The lidar metrics were derived from Airborne Laser Scanning (ALS) data using the Actueel Hoogtebestand Nederland 2 (AHN2) openly available dataset from https://www.pdok.nl/.  The derived lidar metrics saved in *.grd file format and contain 32 bands. Each band represents a lidar metric and the water surface was masked out in the dataset. The *l1* in the file name indicates that the file was used for level 1 (wetland) classification and *l23* used for level 2 (land cover types within wetland) and level 3 (reedbed habitats) classification. The lidar metrics were calculated using lidR (<a href="https://github.com/Jean-Romain/lidR">https://github.com/Jean-Romain/lidR</a>) software package. Further details related to the lidar metrics extraction can be found at <a href="https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats">https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats</a> Github repository.

opencc-by-4.0May 2020View details →

zenodo40/100

EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set

Eighteen high-resolution ecological descriptors of vegetation and terrain for Denmark "EcoDes-DK15" The data are derived from the nationwide airborne laser scanning / LiDAR campaign of Denmark from 2014-2015 provided by the Danish Agency for Data Supply and Efficiency. Update: EcoDes-DK15 v1.1.0 (4 Dec. 2021) Following the recommendations and feedback during the first round of peer-review, we updated the EcoDes-DK processing pipeline and EcoDes-DK15 data set. The key changes are: <ul> <li>New version of the source data optimised to contain only point data collected before the end of 2015. The source data for EcoDes-DK15 v1.0.0 unintentionally contained data from 2018. The new source data is documented <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/source_data/readme.md">here</a>.</li> <li>New "date_stamp_*" auxiliary variables that illustrate the survey dates for the vegetation points in each cell. See updated descriptor documentation <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/documentation/descriptors.md">here</a>.</li> <li>Re-scaling of "solar_radiation" variable to MJ per 100 m2 per year.</li> </ul> Detailed documentation for the data set can be found in the accompanying manuscript and GitHub repository: Assmann, J. J., Moeslund, J. E., Treier, U. A., and Normand, S.: EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set, Earth Syst. Sci. Data Discuss. [preprint], <a href="https://doi.org/10.5194/essd-2021-222">https://doi.org/10.5194/essd-2021-222</a>, in review, 2021. <a href="https://github.com/jakobjassmann/ecodes-dk-lidar">https://github.com/jakobjassmann/ecodes-dk-lidar</a> Files are compressed using bzip2 and tar archiving. The compressed archives can be extracted using commonly available archiving tools (for example <a href="https://www.7-zip.org/">7z </a>on Windows, the archiving tool on macOS and bz2 on Linux).   A small example "teaser" subset (5 MB) of the data set, covering the Husby Klit area from Figure 7 in the manuscript, can be found <a href="https://github.com/jakobjassmann/ecodes-dk-lidar/blob/master/manuscript/figure_7/EcoDes-DK15_teaser.zip">here</a>. Abstract (from manuscript) Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark’s publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark’s terrestrial surface. The resulting data set is comparatively small (~94 GB, compressed 16.8 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond. Acknowledgements (from manuscript) We would like to thank Andràs Zlinszky for his contributions to earlier versions of the data set, Charles Davison for feedback regarding data use and handling, as well as Matthew Barbee and Zsófia Koma for sharing their insights on the source data merger and Zsófia’s script to generate summary statistics for the different versions of the DHM point clouds. Funding for this work was provided by the Carlsberg Foundation (Distinguished Associate Professor Fellowships) and Aarhus University Research Foundation (AUFF-E-2015-FLS-8-73) to Signe Normand (SN). This work is a contribution to SustainScapes – Center for Sustainable Landscapes under Global Change (grant NNF20OC0059595 to SN).

opencc-by-4.0Jun 2021View details →

zenodo40/100

LiDAR metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands

This data repository contains the LiDAR metrics generated from country-wide Airborne Laser Scanning (ALS) data from the Netherlands. The LiDAR metrics (10-meter resolution) are derived from AHN3 using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow. Raw point cloud data can be downloaded <a href="https://app.pdok.nl/ahn3-downloadpage/">here</a>.

opencc-by-4.0Mar 2022View details →

dryad40/100

Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery

1. In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape-level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large-scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~ 5 pulses/m2) in conjunction with unrectified color-infrared (CIR) aerial imagery. 2. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS-derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from 8 stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves, and dead trees by linear discriminant analysis. 3. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves, and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation data only ALS-derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation, respectively. 4. Our findings imply that wall-to-wall large-scale high density ALS data can be used to detect retention trees rather accurately – even larger dead trees – and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves, and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost-effective option for large area mapping of retention trees in countries with such data available.

opencc-zeroSep 2022View details →

dryad40/100

Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery

Open the record for dataset details and reuse information.

publicSep 2022View details →

zenodo36/100

Tiles of airborne laser scanning point clouds of Vienna, Austria (2016)

Airborne laser scanning of the city of Vienna, which was organized by the survey department of the City Administration of Vienna (MA41). The data acquisition was performed in eight flight missions in November 2015. After strip adjustment relative accuracy of the point cloud was in the order of 2cm. The absolute accuracy measured as RMSE is better than 5cm in planimetry and better than 4cm in elevation. The dataset was cleaned and does not involve obvious gross errors, e.g. points high up in the air or points far below ground level. The point density is measured as by last echoes per unit area and is more than 15 points/m2 for 97% of the area. The average point density is 33 points/m2. The LiDAR dataset of the city is organized in a number of 1270m×1020m tiles (including a 10m overlap of all neighboring tiles). Reference labels were generated semi-automatically: a rough filtering of main objects was firstly conducted by the software “Terrasolid”, and the final classification was refined by manual labelling. For the purpose of Vienna city administration, five classes are considered, namely ground, buildings, vegetation, others, water and bridges. All common street objects are categorized as others, such as (e.g.) streetlights, benches, shrubs, cars, construction sites and garbage bins. The different classes are defined by the following numeric integer codes: 2: Ground, 5: Vegetation, 6: Buildings, 8: Others, 9: Water and 17: Bridges. The quality of the labelling was manually checked. In 20 sites of 100m×100m the classification was manually verified, and the average accuracy of reference labels is 95%. Total 9 tiles are published, in which 4 tiles were used for the training and 5 tiles for the evaluation in the paper “A Comparison of Deep Learning Methods for Airborne LiDAR Point Clouds Classification”. Their locations in the city of Vienna can be found in the file of metadata, which also provides WGS84/GRS80 latitude and longitude coordinates of the extent corners of each tile (EPSG: 31256). This can be used to  access images of the tiles from Google Maps or other public map service.

opencc-by-4.0May 2021View details →

zenodo36/100

Individual tree dataset from airborne laser scanning data

<h1>General description</h1> Data about individual tree segments detected from the Finnish national 5 pt/m^2 airborne laser scanning data. The dataset contains 281304 individual trees from a test site of 3km x 3km located in Padasjoki, Finland. The ALS data has been collected in 2019. <h1>Data description</h1> The individual tree segment dataset has been published as a CSV file. The tree height, DBH, stem volume and above-ground biomass have been predicted using Random Forest Machine Learning models. The rest of the features have been calculated directly from the ALS point cloud. The CSV file contains the following columns: <ul> <li>LX and LY: tree location (location of max height in the canopy height model) in the ETRS-TM35FIN coordinate reference system (EPSG:3067)</li> <li>Hmax: tree height (based on highest returns)</li> <li>Gele: ground elevation at tree location (interpolated from ground points)</li> <li>lowBranch: height of lowest branch (limited accuracy)</li> <li>crownV: crown volume as 3D convex hull</li> <li>species: predicted species: 1 = pine, 2 = spruce, 3 = deciduous tree</li> <li>H: predicted tree height</li> <li>DBH: predicted diameter at breast height</li> <li>Volume: predicted stem volume</li> <li>Biomass: predicted above-ground biomass (dry mass)</li> </ul> <h1>Citation</h1> Any scientific publication using the data should cite the following paper: Hyyppä, M., Turppa, T., Hyyti, H., Yu, X., Handolin, H., Kukko, A., Hyyppä, J., & Virtanen, J. -P. (2024). Concepts Towards Nation-Wide Individual Tree Data and Virtual Forests. ISPRS International Journal of Geo-Information, 13(12), 424. https://doi.org/10.3390/ijgi13120424

opencc-by-nc-nd-4.0Nov 2024View details →

zenodo36/100

Supplementary material for: AIRBORNE LASER SCANNING CHANGE DETECTION FOR QUANTIFYING GEOMORPHOLOGICAL PROCESSES IN HIGH MOUNTAIN REGIONS

Supplementary material for: TCII/10-AIRBORNE LASER SCANNING CHANGE DETECTION FOR QUANTIFYING GEOMORPHOLOGICAL PROCESSES IN HIGH MOUNTAIN REGIONS For submission of the full paper to: International Society for Photogrammetry and Remote Sensing (ISPRS) / XXIV ISPRS Congress 2022 Nice, France, 6 - 11 June 2022. ADDITIONAL FIGURES AND MAPS Supplementary materials: Figure S1: Classification preparation and decision tree. Figure S2: 3D distance change and change offset overall map. Figure S3: Geomorphological map inventoried by manual mapping. Figure S4: Classified real changes of the valley area between 2006 and 2017. Figure S5: Classified real changes between 2006 and 2017.

opencc-by-4.0Mar 2022View details →

dryad36/100

Soundscapes and airborne laser scanning identify vegetation density and its interaction with elevation as main driver of bird diversity and community composition

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publicJul 2024View details →

dryad36/100

Data and code for: Identifying fine-scale habitat preferences of threatened butterflies using airborne laser scanning

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publicMar 2021View details →

dryad32/100

Data from: Ancient lowland Maya complexity as revealed by airborne laser scanning of northern Guatemala

Lowland Maya civilization flourished in the tropical region of the Yucatan peninsula and environs for more than 2500 years (~1000 BCE to 1500 CE). Known for its sophistication in writing, art, architecture, astronomy, and mathematics, Maya civilization still poses questions about the nature of its cities and surrounding populations because of its location in an inaccessible forest. In 2016, an aerial lidar survey across 2144 square kilometers of northern Guatemala mapped natural terrain and archaeological features over several distinct areas. We present results from these data, revealing interconnected urban settlement and landscapes with extensive infrastructural development. Studied through a joint international effort of interdisciplinary teams sharing protocols, this lidar survey compels a reevaluation of Maya demography, agriculture, and political economy and suggests future avenues of field research.

opencc-zeroDec 2017View details →

dryad32/100

Data from: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data

Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales. We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density. We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect. An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.

opencc-zeroDec 2015View details →

zenodo32/100

Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists

### Update 03/02/2025: the most up to date version of the processing pipeline presented here, also working on Linux, is available on github: https://github.com/fischer-fjd/GCA/tree/main, and a worked example with open data from the Dutch AHN surveys is available on Zenodo: https://zenodo.org/records/14722001 ### This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It accompanies the article "Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists" (accepted in Methods in Ecology and Evolution on 25/08/2024).  In the article, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to point cloud degradation (pulse density thinning, large scan angles, loss of higher-order returns) and how uncertainties and biases propagate to commonly used forest structure metrics. In addition, we provide a standardized processing pipeline in R to convert point clouds into CHMs.  The main data source for this study are ALS point clouds from nine Australian research sites belonging to the Terrestrial Ecosystem Research Network (TERN, 5 km x 5 km extent each). The underlying data can be found here: https://portal.tern.org.au/metadata/TERN/4ff0b4c9-cfa0-4d09-9520-b5402adc583f. For one site (Robson Creek), we also used field data to assess the sensitivity of aboveground biomass estimates to ALS point cloud characteristics. Data are available here: https://portal.tern.org.au/metadata/supersite.174.  To characterize climatic/environmental differences between sites, we used climatic data from the CHELSA/BIOCLIM+ climatology 1981-2010 (Brun et al. 2022: Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data, 14(12), 5573–5603. https://doi.org/10.5194/essd-14-5573-2022; Karger et al. 2017: Climatologies at high resolution for the earth's land surface areas. Scientific Data, 4(1), 170122. https://doi.org/10.1038/sdata.2017.122).  We note that the enormous size of the full set of manipulated point clouds (original + thinned + individual flightlines: ~400 GB) and the derived raster products (~200 GB) by far exceeds limits on data storage in Zenodo. However, all analyses can be recreated from scratch from the openly available data and the R code in this repository. In addition, we include derived products for the nine study sites that allow to replicate results in the main text without any point cloud processing (CHMs and other rasters across thinned point clouds + summary statistics).  The different data layers are: 01_rscripts.zip: <ul> <li>contains a sample script to test the processing pipeline (test.processing.R) as well as a collection of helper functions (ALS_processing_helperfunctions_v40.R); the script can be run directly after unzipping the folder, but an installation of LAStools (https://rapidlasso.de) is necessary (path_lastools = "PATH/TO/LASTOOLS/BIN"); we note that the script was developed on Windows PCs, its application with the recent Linux distribution of LAStools has not yet been tested</li> <li>contains the full set of scripts necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data (create.CHMs.R) as well as the overall robustness analysis (analyze.CHMs.R); to replicate the processing of the raw point clouds step by step, .laz files should be downloaded from the TERN repository (cf. citation above) and placed in a "data" folder, with subfolders for each site and with the same naming conventions as in this repository (e.g., "/data/Alice Mulga")</li> </ul> 02_reference.zip <ul> <li>contains reference digital surface models (DSMs), canopy height models (CHMs) and digital terrain models (DTMs) for all nine TERN sites, based on the original ALS point clouds</li> <li>note that these reference layers are produced with the "CHMhighest" algorithm, which provides an easily interpretable canopy description as long as pulse densities are high (>= 20 shots per squaremetre)</li> </ul> 03_climate.zip <ul> <li>contains site coordinates</li> <li>contains the climate layers from the CHELSA climatology (cf. citation above, only used to evaluate climatic ranges of sites)</li> </ul> 04_robson_additional.zip <ul> <li>contains biomass estimates for Robson Creek</li> <li>contains shapefiles for large trees at Robson Creek (only used for visualization purposes)</li> </ul> 05_downsampling_pulse_[Site name].zip <ul> <li>[Site name] is a stand-in for the nine TERN sites (e.g., "Alice Mulga.zip", "Credo.zip", etc.)</li> <li>contains the data necessary to reproduce results in the main text of the study, i.e. DSMs, CHMs, and DTMs for all nine TERN sites, and at different pulse density levels (from 16 down to 0.5 laser shots per squaremetre)</li> <li>also contains calculated summary statistics for each site</li> </ul> All zip files should be extracted into the same folder, except for 05_downsampling_pulse_[Site name].zip which should all be moved to a subfolder called "downsampling_pulse".

opencc-by-4.0Mar 2024View details →

dryad32/100

Data from: Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data

Open the record for dataset details and reuse information.

publicApr 2017View details →

dryad32/100

Data from: Ancient lowland Maya complexity as revealed by airborne laser scanning of northern Guatemala

Open the record for dataset details and reuse information.

publicSep 2019View details →

zenodo28/100

Country-wide data of ecosystem structure from the fourth Dutch airborne laser scanning survey (AHN4)

This data repository contains country-wide data products for the ecosystem structure metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands (AHN4). Twenty-five ecosystem structure metrics (at 10-meter resolution, GeoTIFF format) were derived from the AHN4 dataset (<a href="https://www.ahn.nl/ahn-viewer">https://www.ahn.nl/ahn-viewer</a>) using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow (<a href="https://zenodo.org/record/5636773">https://zenodo.org/record/5636773</a>). Laserfarm is a free and open-source workflow that enables efficient, scalable, and distributed processing of multi-terabyte LiDAR point clouds from national and regional ALS surveys into LiDAR metrics of ecosystem structure. All code of Laserfarm is hosted and freely available on GitHub (<a href="https://github.com/eEcoLiDAR/Laserfarm">https://github.com/eEcoLiDAR/Laserfarm</a>). Note that we also published the data products generated from AHN3 (2014-2019), which you can find in the repository here: <a href="https://doi.org/10.5281/zenodo.6421381">https://doi.org/10.5281/zenodo.6421381</a>. Laserfarm workflow was also employed to generate the data products from AHN4 (2020-2022).  The twenty-five LiDAR metrics are related to three key dimensions of ecosystem structure (ecosystem height, ecosystem cover, and ecosystem structural complexity). Each GeoTIFF layer represents one LiDAR metric at 10 m resolution covering the whole Netherlands (file name as "ahn4_10m_featrue_name.tiff"). A detailed description of all the metrics can be found in the README file (README.pdf).  Relevant publications: Kissling, W.D., Shi, Y., Koma, Z., Meijer, C., Ku, O., Nattino, F., Seijmonsbergen, A.C., & Grootes, M.W. (2022). Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds. Ecological Informatics, 72, 101836 (<a href="https://doi.org/10.1016/j.ecoinf.2022.101836">https://doi.org/10.1016/j.ecoinf.2022.101836</a>) Kissling, W.D., Shi, Y., Koma, Z., Meijer, C., Ku, O., Nattino, F., Seijmonsbergen, A.C., & Grootes, M.W. (2023). Country-wide data of ecosystem structure from the third Dutch airborne laser scanning survey. Data in Brief, 46, 108798 (<a href="https://doi.org/10.1016/j.dib.2022.108798">https://doi.org/10.1016/j.dib.2022.108798</a>)

opencc-by-4.0Oct 2023View details →

zenodo24/100

Supplementary Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists

This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It replicates the main analysis in the article Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists and accompanies the main research data set (<a href="https://doi.org/10.5281/zenodo.10878070" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10878070</a>). In this replication study, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to pulse density variation and how uncertainties and biases propagate to commonly used forest structure metrics. The main data source for this study are ALS point clouds from nine U.S. sites, acquired by the National Ecological Observatory Network (NEON, 6 sites, 3 km x 3 km) and by the United States Geological Survey's 3DEP program (3 sites, also 3 km x 3 km). The underlying data can be found here: https://data.neonscience.org/data-products/DP3.30024.001 (NEON) and here: https://apps.nationalmap.gov/lidar-explorer (3DEP) The different data layers are: replicate.US.R       is a single R script that contains all the code necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data as well as the overall robustness analysis. To replicate the processing of the raw point clouds step by step, this script should be located in a folder called "rscripts". pointclouds_original.zip    is the set of original point clouds (3 km x 3 km in extent) used for the replication test, separated into 9 subfolders/sites. Can be used to reproduce the original workflow by placing them in a folder called "data/original". The script will then automatically produce derived point clouds at pulse densities of 2 and 16 per squaremetre and process them into digital terrain models (DTMs), digital surface models (DSMs) and CHMs. Note that, for convienence, these derived products are also included in a separate .zip file (cf. below). processed_foranalysis.zip    is the set of derived products (DTMs, DSMs, CHMs), separated into 9 subfolders/sites, i.e. the result of processing the original point clouds. To use these layers directly with the provided script, they should be put into a folder called "processed_foranalysis". summaries.zip    is a set of summary statistics (as .csv files) that were used to generate the main analysis tables in the replication study. To use these summary statistics directly with the provided script, they should be put into a folder called "summaries". figures.zip    is a set of figures displayed in the Supplementary Material of the paper Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists.

opencc-by-4.0Jul 2024View details →

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Allen Brain Atlas

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dandi-nwb

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Last verified 2026-04-30Open record

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.

ibl

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Last verified 2026-04-29Open record

OpenNeuro

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neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record