5 Data sets used to validate

SimpleForest - a comprehensive tool for 3d reconstruction of trees from forest plot point clouds

Subsets:

• Data containing demol in the name is originated from:

https://zenodo.org/record/4557401

and should be cited:

Demol, Miro, Gielen, Bert, & Verbeeck, Hans. (2021). QSMs, point cloud and harvest data from a destructive forest biomass experiment in Belgium using terrestrial laser scanning (Version Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4557401

• Data containing deTanago in the name is originated from:

http://lucid.wur.nl/datasets/terrestrial-lidar-of-tropical-forests

and should be cited:

Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P. Martius, C., Goodman, R. C., Disney, M., Manuri, S., Burt, A., Calders, K. (2017). Estimation of above-ground biomass of large tropical trees with Terrestrial LiDAR. Methods in Ecology and Evolution. DOI:10.1111/2041-210X.12904

• Data containing hackenberg should be cited:

Hackenberg J, Wassenberg M, Spiecker H, Sun D. Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density. Forests. 2015; 6(4):1274-1300. https://doi.org/10.3390/f6041274

• Data containing wytham should be cited:

Calders K, Origo N, Burt A, Disney M, Nightingale J, Raumonen P Åkerblom M, Malhi Y and Lewis P. Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling. Remote Sensing; 2018; 10(6): 10.3390/rs10060933.

• Data containing leibzig gives credit to:

The clouds's origin: Leipzig Canopy Crane facility financed by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig.

The clouds's owner: Systematic Botany and Functional Biodiversity, Institute for Biology, Leipzig University.

This dataset was used to producee the figures and statistics of the publication "Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions".

This dataset contains 391 entries. Each entry is a tree that was terrestrial laser scanned and consecutively harvested to assess its aboveground biomass (AGB). AGB was also obtained from allometric scaling equations. Several ancillary tree properties such as stem diameter, foliage conditions,... and scan metadata (type of scanner, pattern) are included. We refer to the tab 'headers' for an explanation and units of the respective columns. Elaborate method descriptions can be found in the publication or in the following original publications:

• Burt, A., Boni Vicari, M., da Costa, A. C. L., Coughlin, I., Meir, P., Rowland, L., et al. (2021). New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. Royal Society Open Science 8, 201458. doi:10.1098/rsos.201458
• Calders, K., Newnham, G., Burt, A., Murphy, S., Raumonen, P., Herold, M., et al. (2015). Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods in Ecology and Evolution 6, 198–208. doi:10.1111/2041-210X.12301
• Demol, M., Calders, K., Krishna Moorthy, S. M., Van den Bulcke, J., Verbeeck, H., and Gielen, B. (2021). Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning. Trees 35, 671–684. doi:10.1007/s00468-020-02067-7
• Gonzalez de Tanago, J., Lau, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., et al. (2018). Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods in Ecology and Evolution 9, 223–234. doi:10.1111/2041-210X.12904
• Hackenberg, J., Wassenberg, M., Spiecker, H., and Sun, D. (2015). Non destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests 6, 1274–1300. doi:10.3390/ f6041274
• Kükenbrink, D., Gardi, O., Morsdorf, F., Thürig, E., Schellenberger, A., and Mathys, L. (2021). Aboveground biomass references for urban trees from terrestrial laser scanning data. Annals of Botany, 1–16doi:10.1093/aob/mcab002
• Lau, A., Calders, K., Bartholomeus, H., Martius, C., Raumonen, P., Herold, M., et al. (2019). Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana. Forests 10, 527. doi:10.3390/f10060527
• Momo Takoudjou, S., Ploton, P., Sonke, B., Hackenberg, J., Griffon, S., Coligny, F., et al. (2018). Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods in Ecology and Evolution 9, 905–916.
  doi:10.1111/2041-210X.12933
• Stovall, A. E., Vorster, A. G., Anderson, R. S., Evangelista, P. H., and Shugart, H. H. (2017). Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sensing of Environment 200, 31–42. doi:10.1016/j.rse.2017.08.013
  

The data set comprises an inter- and intra-annual timeseries of ten high-resolution (0.5 x 0.5 m) binary snow covered area (SCA) maps derived from TLS scans at the Weisssee Snow Research Site in Austria between March 2017 and November 2019. TLS based digital elevation models and difference (snow depth) grids can be downloaded as a separate dataset (Fey et al., 2018; https://doi.org/10.1594/PANGAEA.896843). The binary classification of snow-covered and snow-free areas is based on intensity and snow depth. An intensity threshold of 3000 was defined based on histogram analysis in patchy snowpack conditions. Snow-covered areas were delineated according to TLS based snow depth information. Snow-depth related classifications were based on a threshold value representing the precision of the TLS acquisition represented by the standard deviation of snow-free surfaces (see Fey et al., 2019). The resulting classification was validated with fully snow covered scenes. For the scene of 2017-05-07 two available TLS scans, one with a Riegl VZ-4000 and another with a Riegl VZ-6000 scanner, were combined into one snow covered area map. This was done due to the fact that the VZ-4000 data is better suited for snow cover discrimination based on intensity data, while not providing data on wet snow surfaces in larger distance where the VZ-6000 scanner still provides snow depth observations. The overall coverage of the scan area is identical to the one of the DGM dataset. The SCA dataset comprises three classes: snow-free (0), snow-covered (1) and NoData (-99999). No data areas are caused by obstacles in the field-of-view of the laserscanner. The SCA data can be used for validating remote sensing products including fractional snow coverage from e.g. Landsat and Sentinel-2 as done in the related literature.

Introduction
The data generated in this study provide a valuable resource for developing and testing algorithms focused on circumference fitting and diameter estimation of tree stems. By combining field-measured diameters and high-resolution terrestrial laser scanning (TLS) data, the dataset includes precise point clouds representing individual trees, with diameter at breast height (DBH) measured at 1.30 meters above ground level. This standardized height ensures consistency in diameter measurements, a crucial metric in forestry and ecological studies. The dataset supports advancements in remote sensing applications, offering researchers an opportunity to refine methods for accurately determining DBH and enhancing the reliability of biomass and carbon estimation models.

Study Area
The research was conducted in Mexico within a permanent forest research plot, established based on the methodology outlined by Corral-Rivas et al. The plot covers a quadrangular area of 625 m² and is situated in the Sierra Madre Occidental region, specifically within the "La Victoria" management unit in the municipality of Pueblo Nuevo, Durango, Mexico. This area experiences a temperate climate, with average annual temperatures between 20 and 22 °C and rainfall ranging from 800 to 1200 mm per year. The dominant vegetation type is a coniferous forest, primarily consisting of Pinus cooperi, with an estimated tree density of 960 trees per hectare.

Field Data
In this study, we focused on 50 trees with diameters exceeding 10 cm, a commonly used threshold in forest inventory protocols for estimating biomass and carbon, chosen to enhance accuracy and consistency. According to Hoover and Smith, excluding smaller trees generally has a negligible effect on biomass estimates across most forest types, which supports the practicality of this 10 cm cutoff. This threshold also helps eliminate saplings, which can add variability due to their inconsistent growth patterns, and aligns with standard inventory practices. We conducted a conventional inventory to measure DBH for each tree, using a Häglof caliper and taking two measurements at perpendicular angles to achieve a reliable average (data_field.xlsx).

Data Capture and Acquisition via Terrestrial Laser Scanning
The terrestrial laser scanning data were collected using a FARO Focus M70 scanner, capable of measuring distances up to 70 meters with an accuracy of ±3 mm. The scanner was set to an "exterior" profile with a resolution of 1/4 and quality level of 4x, achieving an average point density of 234,679 points per square meter and a resolution of 10,310 x 4,268 points. Before starting the scans, ten targets were strategically positioned on-site to assist with alignment during post-processing. Four scans were then performed, with a 180-degree vertical angle and a 360-degree horizontal angle, capturing a complete view of the surrounding environment. The scans were merged using FARO Scene software to ensure accurate alignment. All details regarding the scanner and tree positions can be found in Appendix A.

Extraction of 2D Planes from Individual Tree Stem Point Clouds
The point cloud was initially loaded into CloudCompare, where selected trees were manually segmented, and ground points were removed to minimize slope effects. Each tree was represented as a series of n points in three-dimensional space:

(x_1, y_1, z_1), (x_2, y_2, z_2), ..., (x_i, y_i, z_i), ..., (x_n, y_n, z_n)

For each tree stem, a specific cylindrical section was isolated by selecting points with z-coordinates between 1.25 m and 1.35 m, filtering out points outside this range. This method enabled the extraction of a segment corresponding to the diameter at breast height (DBH).

Following this step, the z-coordinates within the selected cylindrical sections were excluded, and duplicate points were removed, resulting in a two-dimensional dataset. This reduction in dimensionality produced a new collection of n points:

(x_1,y_1),(x_2,y_2),…,(x_i,y_i),…,(x_n,y_n).

The information about the planes and their representation can be found in tls_tree_discs.zip and tls_tree_imgs.zip.

The data set comprises the inter- and intra-annual snow depth distribution recorded by TLS scans at Weisssee snow research site in Austria between November 2014 and May 2018. The data set comprises 23 snow-on digital elevation models (DEM), one snow-off DEM, and the difference raster calculated between a snow-off and snow-on scans. The relative accuracy of the TLS scans was determined by measuring the distance between snow-free planes from the snow-on and snow-off scans and shows mean values smaller than 0.03 m and standard deviations ranging between 0.02 and 0.1 m. The reliability of the snow depths derived from TLS was further assessed by comparing snow depths from snow probing, GNSS measurements, and continuous snow depth measurements from the weather station. Comparison of the different measurement methods shows average deviations of less than 0.1 m. The data can be used for analysing snow distributions, or for assessing the representativeness of conventional snow depth sensors. Other use cases include assessing other in-situ sensors like Cosmic-Ray-Neutron Sensors, or space-borne snow-covered area products. More details are described in an article submitted to the Water Resources Research Special Issue: Advances in remote sensing, measurement, and simulation of seasonal snow.

This dataset contains LiDAR scanning derived products (raw scanner data, geo-located point clouds, individual 3D tree models) collected over the north-eastern part (200 m x 200 m) of FBRMS-01: Paracou, French Guiana plot 6. The campaign took place from the 10th of October to the 15th of November 2019. Terrestrial LiDAR Scanning (TLS) was conducted on a regular grid with spacing of 10 m with a RIEGL VZ-400 scanner and retro-reflective targets for scan registration. Unpiloted Aerial Vehicle Laser Scanning (UAV-LS) was conducted with a RIEGL Ricopter with VUX-SYS VUX-1UAV system with varying flight heights and flight directions.

The TLS point clouds were collected to produce explicit 3D models of individual trees and subsequently estimate their above-ground biomass (AGB). The UAV-LS point clouds were collected to test scanner settings and inspect point clouds properties, in particular with regard to their suitability to model individual trees and their AGB.

The campaign was conducted by researchers Benjamin Brede, Harm Bartholomeus and Alvaro Lau of the Laboratory of Geo-Information Science and Remote Sensing of Wageningen University & Research (The Netherlands) with support from Nicolas Barbier of AMAP Lab (Botany and Modeling of Plant Architecture and Vegetation).

The model and observational datasets analyzed in the 2025 PNAS Santer et al. paper entitled "Human Influence on Climate Detectable in the Late 19th Century" are provided here. The codes used to produce the five main text figures are also provided, along with the "fingerprint" code.

Here is some brief information about the file naming conventions:

"sm6" - Denotes netCDF files with zonal-mean monthly-mean atmospheric temperature for six different atmospheric layers. These layers are:

• Stratospheric Sounding Unit channel 3 (SSU3)
• Stratospheric Sounding Unit channel 2 (SSU2)
• Stratospheric Sounding Unit channel 1 (SSU1)
• Microwave Sounding Unit/Advanced Microwave Sounding Unit temperature of the lower stratosphere (MSU/AMSU TLS)
• Microwave Sounding Unit/Advanced Microwave Sounding Unit temperature of the troposphere (MSU/AMSU TTT)
• Microwave Sounding Unit/Advanced Microwave Sounding Unit temperature of the lower troposphere (MSU/AMSU TLT)

"tf2" - Denotes TTT data. tf2 indicates that tropospheric temperatures have been adjusted for the effects of lower stratospheric cooling.

"RSS" - Denotes netCDF files containing observational MSU/AMSU TLS, TTT, or TLT data from Remote Sensing Systems in Santa Rosa, California.

"UAH" - Denotes netCDF files containing observational MSU/AMSU TLS, TTT, or TLT data from the University of Alabama at Huntsville.

"STR" - Denotes netCDF files containing observational MSU/AMSU TLS, TTT, or TLT data from the NOAA Center for Satellite Applications and Research (STAR).

"ss3" - Denotes SSU3 data from the STAR research group.

"ss2" - Denotes SSU2 data from the STAR research group.

"ss1" - Denotes SSU1 data from the STAR research group.

"piControl" - Denotes synthetic SSU and MSU/AMSU data from CMIP6 pre-industrial control runs.

"hst_ssp585" - Denotes synthetic SSU and MSU/AMSU data from CMIP6 extended historical simulations.

"r1", "r2", "r3", etc. - Identifies realization number for CMIP6 extended historical simulations.

"Fingerprint_Code1.F" - Fingerprint detection code.

"Main_Text_Figure_01.py" - Matplotlib Python code for plotting Figure 1 (there is a separate code for each of the five figures).

Further details of all model and observational temperature data sets are provided in the Materials and Methods and Supporting Information of the 2025 PNAS Santer et al. paper entitled "Human Influence on Climate Detectable in the Late 19th Century".

Ground lidar, also known as Terrestrial Laser Scanning (TLS), is a ranging instrument that provides detailed 3D measurements directly related to the quantity and distribution of plant materials in the canopy. Measurements can be used for applications requiring quantification of vegetation structure parameters, tree and stand reconstruction, and terrain analysis. Scans have been collected in Australia using two Riegl VZ400 waveform recording TLS instruments. Single scan raw point and waveform data in the sensors own coordinate system [RXP]. \r Additional resources \r - Data Access Details

Data are available for download at: https://arcticdata.io/data/10.18739/A2QJ78024/ These Terrestrial Laser Scanning (TLS) data were collected on April 17, 22, and 30, 2020 at the Multidisciplinary Drifting Observatory for Arctic Climate (MOSAiC) Expedition. The MOSAiC Expedition continuously made in-situ measurements on a collection of drifting sea ice floes In the Arctic Ocean from October 2019 to May 2020. TLS, also known as Terrestrial Light Detection and Ranging (LiDAR), is an active remote sensing modality in which a tripod-mounted, rotating laser emitter and detector (the TLS sensor) scans the surroundings emitting pulses of light and tracking the time it takes for a pulse to return. From the time of flight and the orientation of each pulse, the sensor creates a point cloud of the surroundings. The TLS sensor used here was a Riegl VZ1000, and the data were acquired using Riegl proprietary RiSCAN software. The data have been converted into entirely open-source formats (see below). This dataset contains point clouds of the same unit of sea ice acquired on different days. In addition to the raw data, we provide the rigid transformations needed to align all data into a common, ice-fixed reference frame (see below). Within this data, we can quantify snow deposition and erosion within the different footprints of the remote sensing instruments and elsewhere. These data are provided in a directory structure that contains meaningful information about each file. To download these data in this directory structure, please press 'Download All'. Beneath the top level directory, is a directory for the TLS scan collected on each day (e.g. 'mosaic_rs_170420.RiSCAN' is the data collected on April 17, 2020). Within each scan directory are the files 'tiepoints.csv' and 'ScanPosXXX.DAT' where XXX is the scan number (e.g. '008'). Additionally, there are subdirectories: 'lasfiles', 'npyfiles', and 'transforms'. 'lasfiles' contains the raw data collected by the scanner and written out in the LAS 1.4 format, a standard open file format for LiDAR data: https://www.asprs.org/committee-general/laser-las-file-format-exchange-activities.html. The raw data are collected in the orientation of the TLS sensor on the tripod, which is not exactly level due to human error (most operators can level the tripod to within 2 degrees). The scanner contains built-in level sensors, and from these the RiSCAN software computes the 4x4 rigid transformation matrix (https://en.wikipedia.org/wiki/Transformation_matrix) that levels the raw data. These 4x4 matrices are written out in ascii format in the 'ScanPosXXX.DAT' files. The raw data do not distinguish between TLS points from wind-blown snow particles (a major source of noise in TLS scans from snow in windy environments) and otherwise. These data have been processed and analyzed by FlakeOut (Clemens-Sewall 2021: https://github.com/davidclemenssewall/flake_out/tree/v1.0.0 or https://zenodo.org/record/5657286#.YZRRcLtOlH4) to label wind-blown snowflakes with the Classification flag '65'. The processed data are provided in the 'npyfiles' subdirectory. The processed data have been saved in the open NumPy format (.npy: https://numpy.org/devdocs/reference/generated/numpy.lib.format.html). This format has several advantages over LAS 1.4 in terms of the speed of reading and writing data and is more widely used in the earth science community than LAS 1.4. Finally, although the scans collected on different days were examining the same area of the ice, the tripod cannot be located in precisely the same location each time, and so transformations are need to align the scans into a common, ice-fixed reference frame. This reference frame is anchored by a set of tiepoints that were manually identified to be the same object in each scan (and whose locations are available in the 'tiepoints.csv' files). The rigid transformations are provided in .npy format in the 'transforms' subdirectory.

This data set contains 15-min snow depth observations for two study sites on Grand Mesa, CO, USA, acquired as part of NASA's 2017 SnowEx campaign. The data were recorded using two arrays of Judd Communications Ultrasonic Depth Sensors, configured as a TLS K footprint on the west side of the mesa and a TLS N footprint in the east. The sensors were positioned to represent three primary vegetation conditions: open-canopy; canopy-edge; and closed-canopy. A total of 10 and 7 sensors recorded usable data at the west and east sites, respectively, from the beginning of the snow season in November 2016 through the end in June 2017.These data can be used for a variety of purposes, including: model forcing, calibration, and validation; evaluation of airborne and satellite remote sensing data; to analyze how vegetation affects snow accumulation and ablation.

These Terrestrial Laser Scanning (TLS) data were collected on February 22, 2020 at the Multidisciplinary Drifting Observatory for Arctic Climate (MOSAiC) Expedition. The MOSAiC Expedition continuously made in-situ measurements on a collection of drifting sea ice floes In the Arctic Ocean from October 2019 to May 2020. TLS, also known as Terrestrial Light Detection and Ranging (LiDAR), is an active remote sensing modality in which a tripod-mounted, rotating laser emitter and detector (the TLS sensor) scans the surroundings emitting pulses of light and tracking the time it takes for a pulse to return. From the time of flight and the orientation of each pulse, the sensor creates a point cloud of the surroundings. These point clouds have been written out as LAS 1.4 files. This dataset consists of nine point clouds collected from scan positions in the vicinity of the Remote Operated Vehicle (ROV) tent on February 22, 2020. This dataset contains 9 Terrestrial Laser Scans (TLS) of snow and sea ice on the MOSAiC Expedition. There were moderate blowing snow conditions on the day these data were collected. These data are used by Clemens-Sewall et al. 2021 to demonstrate the efficacy of FlakeOut, a filter designed to remove wind-blown snowflakes from TLS data. They were collected by Ian Raphael and processed by David Clemens-Sewall. These data are provided in a directory structure such that they can be processed and analyzed by FlakeOut (Clemens-Sewall 2021: https://github.com/davidclemenssewall/flake_out/tree/v1.0.0 or https://zenodo.org/record/5657286#.YZRRcLtOlH4). To download these data in this directory structure, please press 'Download All'.

Abstract High resolution topography (HRT) surveys is an important tool to model landscapes, especially in zones subjected to strong environmental changes, such as Antarctica, where landform is highly influenced by cryoclasty and permafrost melting. The aim of this work was to obtain a high accurate DTM for Keller Peninsula, Maritime Antarctica. The survey study was assessed in the 2014/2015 and 2015/2016 during the austral summer, by using Terrestrial Laser Scanner (TLS). In order to cover 8 km² of the Peninsula, the TLS equipment was installed in 81 different points. Results of the DTM generated by TLS (hereafter, HRT-DTM), and the terrain variables Aspect, Slope and Hillshade obtained were compared with previous models generated by aerophotographic survey (hereafter, APG-DTM). RMSE for the HRT and APG-DTM were 0.726 and 2.397 m, respectively. Spatial resolution of the DTMs was 0.20 m. Morphometric variables obtained from the two methods presented visual differences on the thematic maps, especially related to the Aspect. Generalization was the main process, whereas interpolation occurred for the HRT survey, being the process of choice for the APG method. A large number of points are obtained by the TLS, providing a dense cloud of points, spatially well-distributed, enabling the generalization process to obtain surface models with high performance.

Close Range Remote Sensing Benchmark for different LiDAR and photogrammetric Sensors in a mixed temperate forest. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices.

Delineations of Retrogressive Thaw Slump (RTS) expansion and light detection and ranging (LiDAR) datasets (LAS files) of RTS sites were used to model how rates of RTS change are influenced by topographic and climatic characteristics across northern Alaska. LiDAR data were collected at ten sites, where five were collected from an uncrewed aerial system (UAS) and five were collected from a terrestrial LiDAR systems (TLS). LiDAR datasets were used to bias correct the open-source ArcticDEM (2 meter-resolution) for calculating annual rates of RTS volumetric losses across all sites. RTS Delineations were used to calculate annual rates of RTS areal expansion and summarize topographic characteristics calculated from the corrected ArcticDEM. Two shapefiles were created from historic satellite and aerial imagery (1949-2021) to summarize RTS areal change across 44 slumps: AK_RTS_ExansionDelineations.shp summarizes the area of RTS expansion between two time periods and AK_RTS_Delineations.shp summarizes the total RTS outline in each year where RTS expansion occurs. LiDAR UAS and TLS data are provided as LAS files from 12 slumps (five sites) near Toolik Lake and 9 slumps (5 sites) within the Noatak National Preserve.

Vegetation biomass estimates across drylands at regional scales are critical for ecological modeling, yet the low-lying and sparse plant communities characterizing these ecosystems are challenging to accurately quantify and measure their variability using spectral-based aerial and satellite remote sensing. To overcome these challenges, multi-scale data including field-measured biomass, terrestrial laser scanning (TLS) and airborne laser scanning (ALS) data, were combined in a hierarchical modeling framework. Data derived at each scale were used to validate an increasingly broader index of sagebrush (Artemisia tridentata) aboveground biomass. First, two automatic crown delineation methods were used to delineate individual shrubs across the TLS plots. Second, three models to derive shrub volumes were utilized with TLS data and regressed against destructively-sampled individual shrub biomass measurements. Third, TLS-derived biomass estimates at 5 m were used to calibrate a biomass prediction model with a linear regression of ALS-derived percent vegetation cover (adjusted R2 = 0.87, p < 0.001, RMSE = 3.59 kg). The ALS prediction model was applied to the study watershed and evaluated with independent TLS plots (adjusted R2 = 0.55, RMSE = 4.01 kg, normalized RMSE = 35%). The biomass estimates at the scale of 5 m is sufficient for capturing the variability of biomass needed to initialize models to estimate ecosystem fluxes, and the contiguous estimates across the watershed support analyzing patterns and connectivity of these dynamics. Our model is currently optimized for the sagebrush-steppe environment at the watershed scale and may be readily applied to other shrub-dominated drylands, and especially the Great Basin, U.S., which extends across five western states. Improved derived metrics from ALS data and collection of additional TLS data to refine the relationship between TLS-derived biomass estimates and ALS-derived models of vegetation structure, will strengthen the predictive power of our model and extend its range to similar shrubland ecosystems.

The dataset is saved in TXT and image file. The results of measurements by the three scanners are stored in separate folders, respectively. Measurements were made for nine different distances. The following lunar soil simulants were used in the studies: LHS-1, AGK-2010, CHENOBI, LMS-1, JSC-1A, OPRL2N. The filename of data contains the abbreviation name of lunar soil simulant, the name of used TLS, and the measurement distance.

Tiff files: Maps of Above Ground Biomass change (2019-2020) over the study region near Iñapari, Peru, derived from the texture of the NIR band for SPOT-7 (SPOT_DeltaAGB_Map), PlanetScope (PlanetScope_DeltaAGB_Map.tif) and Sentinel-2 (Sentinel2_DeltaAGB_Map.tif) data for a 1-ha resolution.QML file contains the style for the biomass change maps. Shapefile contains location of four selectively logged plots.CSV file contains data on observed changes in these four plots, obtained by TLS and manual inventory.

Abstract

Assessing habitat quality is a primary goal of ecologists. However, evaluating habitat features that relate strongly to habitat quality at fine-scale resolutions across broad-scale extents is challenging. Unmanned aerial systems (UAS) provide an avenue for bridging the gap between relatively high spatial resolution, low spatial extent field-based habitat quality measurements and lower spatial resolution, higher spatial extent satellite-based remote sensing. Our goal in this study was to evaluate the potential for UAS structure from motion (SfM) to estimate several dimensions of habitat quality that provide potential security from predators and forage for pygmy rabbits (Brachylagus idahoensis) in a sagebrush-steppe environment. 2.At the plant and patch scales, we compared UAS-derived estimates of vegetation height, volume (estimate of food availability), and canopy cover to estimates from ground-based terrestrial laser scanning (TLS), and field-based measurements. Then, we mapped habitat features across two sagebrush landscapes in Idaho, USA, using point clouds derived from UAS SfM. 3.At the individual plant scale, the UAS-derived estimates matched those from TLS for height (r2 = 0.85), volume (r2 = 0.94), and canopy cover (r2 = 0.68). However, there was less agreement with field-based measurements of height (r2 = 0.67), volume (r2 = 0.31), and canopy cover (r2 = 0.29). At the patch scale, UAS-derived estimates provided a better fit to field-based measurements (r2 = 0.51-0.78) than at the plant scale. Landscape-scale maps created from UAS were able to distinguish structural heterogeneity between key patch types. 4.Our work demonstrates that UAS was able to accurately estimate habitat heterogeneity for a key terrestrial vertebrate at multiple spatial scales. Given that many of the vegetation metrics we focus on are important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring fine-resolution habitat quality across broad landscapes for use in studies of animal ecology, conservation, and land management.

Usage Notes
Landscape-scale maps of structural quality derived from UAS SfM at the Camas study site, Idaho, USA
Unmanned aerial system (UAS) structural quality maps derived from structure from motion (SfM) photogrammetry at the Camas study site in Idaho, USA. The dense point cloud was produced in Agisoft PhotoScan, and then height filtered with the BCAL LiDAR Tools to create a canopy height model (5-cm pixel resolution). Separate maps of maximum vegetation height, volume, and canopy cover were then produced in ArcGIS at 1-m pixel resolution.
Camas_landscape_maps.zip

Landscape-scale maps of structural quality derived from UAS SfM at the Cedar Gulch study site, Idaho, USA
Unmanned aerial system (UAS) structural quality maps derived from structure from motion (SfM) photogrammetry at the Cedar Gulch study site in Idaho, USA. The dense point cloud was produced in Pix4D, and then height filtered with the BCAL LiDAR Tools to create a canopy height model (5-cm pixel resolution). Separate maps of maximum vegetation height, volume, and canopy cover were then produced in ArcGIS at 1-m pixel resolution.
Cedar_landscape_maps.zip

UAS-TLS plant-scale structural metrics
Plant-scale comparison of unmanned aerial system (UAS) structure from motion (SfM) and terrestrial laser scanning (TLS) structural metrics (shrub height, shrub volume, and canopy cover) at two study sites in Idaho, USA.
uas_tls_plant.csv

UAS-Field plant-scale structural metrics
Plant-scale comparison of unmanned aerial system (UAS) structure from motion (SfM) structural metrics and field-based measurements (shrub height, shrub volume, and canopy cover) at two study sites in Idaho, USA.
uas_field_plant.csv

UAS-Field patch-scale structural metrics
Patch-scale comparison of unmanned aerial system (UAS) structure from motion (SfM) structural metrics and field-based measurements (shrub height, shrub volume, and canopy cover) at two study sites in Idaho, USA.
uas_field_patch.csv

Data Use
License
CC0-1.0
Recommended Citation
Olsoy PJ, Shipley LA, Rachlow JL, Forbey JS, Glenn NF, Burgess MA,Thornton DH. 2018. Data from: Unmanned aerial systems measure structural habitat features for wildlife across multiple scales [Dataset]. Dryad. https://doi.org/10.5061/dryad.631q1

Funding
US National Science Foundation: DEB-1146368

Ground lidar, also known as Terrestrial Laser Scanning (TLS), is a ranging instrument that provides detailed 3D measurements directly related to the quantity and distribution of plant materials in the canopy. This dataset contains raw instrument data and ancillary data for numerous sites across northern and eastern Australia from 2012 onwards. Scans have been collected using two Riegl VZ400 waveform recording TLS instruments. One is co-owned and operated by the Remote Sensing Centre, Queensland Department of Environment and Science (DES) and the TERN Auscover Brisbane Node, University of Queensland. The second is owned and operated by Wageningen University, Netherlands. Data can be accessed from https://field.jrsrp.com/ by selecting the combinations Field, Ground Lidar. Raw data are accessible by selecting individual locations on the map and then clicking on the TLS scan directory link on the right hand site of the screen.

This data publication includes data collected to evaluate the use of a suite of remote sensing approaches and field measurements to quantify plot-scale ladder fuels in oak woodlands and mixed forests in the same region and relate measurements of ladder fuels to wildfire burn severity. Remote sensing techniques included a terrestrial laser scanner (TLS), a handheld-mobile laser scanner (HMLS), an unoccupied aerial system (UAS) with multispectral camera and Structure from Motion (SfM) processing (UAS-SfM), and an airborne laser scanner (ALS). Field measurements include canopy base height (CBH) and the use of a photo banner and wildfire burn severity estimated via the Relativized delta Normalized Burn Ratio (RdNBR). Additionally, the coordinates for plot center locations and a map of the study are included.While fire is an important ecological process in the western United States, wildfire size and severity have increased over recent decades as a result of climate change, historical fire suppression, and lack of adequate fuels management. Due to the urgency to build ecosystem resilience and reduce risk to life and property in light of future wildfire events, land managers are implementing fuel management programs. Technology used to quantify forest ladder fuels, which bridge the gap between the surface and the canopy and lead to more severe canopy fires, can help inform management treatments to reduce future wildfire risk.For more information about this study and these data see Forbes et al. (2022).

These data were published on 12/09/2021. On 04/11/2022 the metadata was updated to include reference to newly published article.