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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

ORION Code Infrastructure Upgrades

Prior to this update, ORION was capable of processing cases and data that spanned a single UTM zone and assumed that all spatial inputs were given in units of meters. During the 2024 SMART bridge funding period, we completed upgrades to the code that permit general spatial projections, basin-scale datasets, and units.

58 GEOSCIENCES↗
DOE OSTI2019

Sub-Basin Delineation for the Upper East River, Colorado, United States

Upper East sub-basins constructed 10/7/2016 using 10m digital elevation model obtained from the United States Geological Survey (USGS) National Elevation Data set (NED) and ArcGIS Hydrology tool box (ESRI v 10.3.1). Sub-watersheds are delineated as area contributing flow to SFA 2016 stream gage locations. Shapefile attributes include sub-basin ID, name, area (m2 and km2) and codes for spatial contributions to EBC and PH used by Carroll et al. (2018). Geospatial reference is UTM 1983 zone 13. JPEG image provided to show sub-basin delineation with respect to surface geology (Gothic Quadrangle: Gaskill et al., 1991; Maroon Bells Quadrangle: Bryant, 1969).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Failure Strain and Related Triaxiality of Aluminum 6061-T6, A36 Carbon Steel, 304 Stainless Steel, and Nitronic 60 Metals, Part I: Experimental Investigation

The objective of this study is to develop failure-limit material models for Aluminum 6061-T6, A36 Carbon Steel, 304 Stainless Steel, and Nitronic 60 metals, based on parameters of plastic equivalent strain (failure strain) and stress triaxiality. The research is conducted in two parts. This paper presents Part One of the study. In Part One, custom-designed test specimens undergo controlled uniaxial tension and compression testing at ambient temperature. These tests are performed at quasi-static speeds using Universal Testing Machines (UTMs) in accordance with ASTM E8 and ASTM E9 standards. Experimental data, specifically engineering stress–strain and force–displacement curves, are recorded from the onset of loading until specimen fracture, or in the case of compression tests, until the capacity of the testing machine is reached. In Part Two, the emphasis shifts to the calibration of Finite Element Analysis (FEA) models of the custom-designed test specimens. Plastic equivalent strain and the corresponding stress triaxiality values at failure are extracted from each test specimen for the given metal. These values are then systematically plotted onto a single graph to construct the failure-limit curve, which delineates the boundary conditions for material failure. This approach will facilitate the development of a comprehensive material property definition that correlates plastic equivalent strain with stress triaxiality at failure for Aluminum 6061-T6, A36 Carbon Steel, 304 Stainless Steel, and Nitronic 60 metals.

Harwell, Ron↗
DOE OSTI2016

Airborne Imagery Collections, Utqiagvik (Barrow), Alaska, 2013

The data here are orthomosaics, digital surface models (DSMs), and individual frames captured during low altitude airborne flights in 2013 at the Barrow Environmental Observatory. The orthomosaics, thermal IR mosaics, and DSMs were generated from the individual frames using Structure from Motion techniques. Data were collected to characterize the optical and thermal properties of the landscape and how they differ between two points in time with different vegetative and hydrologic conditions. The orthomosaic tiles, thermal IR mosaics, and DSMs are geotiffs in NAD 83 UTM Zone 4 named by their lower-left coordinate. Metadata for the raw imagery are contained in the .exif headers in the files for the optical imagery only. XYZ files contain the IR temperatures at various longitudes and latitudes. Supported by the U.S. DOE Arctic NGEE program and the Alaska Climate Science Center. The LiDAR dataset used for accuracy assessment was provided by Cathy Wilson(cjw@lanl.gov) and Chandana Gangodagamage(chandana@lanl.gov). File types included: *.tif; *.png; *.kmz; *.bmp; *.pgw, *xyz, *.jpg. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Low-altitude remote sensing dataset of DEM and RGB mosaic for AB corridor on July 13 2013 and L2 corridor on July 21 2013, Utqiagvik (Barrow), Alaska

Photogrammetry and topography data were acquired in July 2023 along a 475 m long, 20 m wide corridor (named L2 or Z0) and a 500 m long, 40 m wide corridor (AB), both covering various polygon types within the Barrow Environmental Observatory (BEO) on the Alaskan Arctic Coastal Plain, approximately 4 miles from the Beaufort Sea near Utqiaġvik, Alaska. This dataset was collected to improve the understanding of landscape organization and obtain a high-resolution digital surface model to investigate co-variability between above- and below-ground structure. The reference list provided in this archive indicates studies using this dataset. The photogrammetry data were acquired using an unoccupied aerial vehicle (UAV) (specifically, a kite) and were used to generate a digital elevation model and an RGB mosaic. A real-time kinematic (RTK) GPS was used to survey ground control points for the aerial imagery. The processes datasets are provided in geotiff format with a UTM coordinate system. The dataset contains 1 *.csv metadata files, and 6 *.tif files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Digital Elevation Model, 0.25 m, Barrow Environmental Observatory, Utqiagvik (Barrow), Alaska, 2013

This 0.25m horizontal resolution digital elevation model (DEM) was developed from Airborne Laser Altimetry flown by Aerometric Inc, now known as Quantum Spatial, Inc. on July 12, 2013. One Mission was flown and the data jointly processed with LANL personnel to produce a 0.25m DEM covering a region approximately 2.8km wide and 12.4km long extending from the coast above North Salt Lagoon to south of Gas Well Road. This DEM encompasses a diverse range of hydrologic, geomorphic, geophysical and biological features typical of the Barrow Peninsula. Vertical accuracy at the 95% confidence interval was computed as 0.143m. The coordinate system, datum, and geoid for this DEM are UTM Zone 4N, NAD83 (2011), NAVD88 (GEOID09). This dataset includes a variety of file types.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Geophysical and Environmental Monitoring Data, Lower Watershed, Teller Road Mile Marker 27, Seward Peninsula, Alaska, 2017-2019

This data set contains geophysical and environmental monitoring data acquired between September 2017 and 2019 at the lower Teller watershed, Seward Peninsula, Alaska. Geophysical data comprises processed resistivity data, acquired daily between Spring and Fall of 2018 and 2019, and a baseline measurement of Fall 2017. The environmental monitoring data comprises depth resolved, distributed soil moisture and soil temperature data. These measurements were obtained used Decagon 5TE sensors placed at 0.1 m, 0.2 m , 0.3 m, and 0.4 m depth. In addition, temperature data at 0.5 m, 1.0 m, and 1.5 m were acquired using Hobo temperature sensors. Data were collected to improve our understanding of the hydrological response of discontinuous permafrost systems, in particular focusing on multi-annual dynamics and short term disturbances, such as snowmelt or precipitation events. The electrical resistivity tomography (ERT) monitoring data are included as processed resistivity models, with model cells x dimension equal to the distance along the profile, and model z dimension being elevation. The start and end point of the transect are (UTM Zone 3N): E 454881.66 m, N 7178949.45 m, and E 454772.60, N 7178885.81. Locations of the soil moisture and temperature sensors are provided in the data package. This dataset is discussed in detail in the Uhlemann, S. et al 2021 paper listed in the references. This dataset includes one *.pdf user guide and 315 *.csv data files included within four zipped files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data

Geophysical surveys were conducted at the NGEE Arctic Teller mile marker 27 site, Teller mile marker 47 site, and Kougarok mile marker 64 site during the summers of 2018, 2019, and 2021. Additional data was collected at Teller mile marker 47 during September 2021 and August 2022. These surveys were used to identify locations of near-surface permafrost during the period of maximum seasonal thaw depth for ground truth data used in machine learning predictions of near-surface permafrost extent at each site. This dataset contains CSV files of ground truth observations of near-permafrost presence or absence for each site, where PF = 1 indicates permafrost presence and PF = 0 indicates permafrost absence. The dataset also includes 2 sets of binary rasters (WGS84 UTM zone 3) of permafrost extent for each site using 1) all of the training data and 2) the transferred model. For both sets of rasters, 0 = non-permafrost and 1 = permafrost. Included are 6 *.tif files and 5 *.csv files that include a data dictionary (dd.csv) and file-level metadata (flmd.csv). This dataset is in support of the paper "Machine learning-derived high-resolution maps of near-surface permafrost for three watersheds on the Seward Peninsula, Alaska" that is in review (May 2023). The Next-Generation Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

BSEC ecohydrological and water quality fluxes from RHESSys Simulations in USGS gauged watersheds

Baltimore Environmental Social Collaborative (BSEC) Water and Water Quality Simulations from RHESSys Model The repository contains RHESSys (Tague & Band, 2004; source code) simulated ecohydrological and nutrient (nitrogen only) fluxes at daily, basin-average (RHESSys_basin_output) and monthly, grid (RHESSys_patch_output) levels. We currently simulated the following 8 watersheds in Baltimore: Dead Run Baisman Run Scotts Level Branch Moores Run Powder Mill Run Maidens Choice Run Stony Run The watershed boundaries of all studied watersheds are stored in Watershed_Boundary folder. Variables and their units are listed in the metadata. Spatial projection, NAD83 / UTM zone 18N (EPSG:26918) is used for patch-level, netCDF-format files. For more information, please contact Ruoyu Zhang (rz3jr@virginia.edu).

Baltimore MD↗
NASA NTRS1972

Inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Seventy-two ground sensors were interfaced with three DCP'S at the Black Hills test site. Unfortunately, the transmitters had to be returned for modification and forestry sensed data is not available. The DCP's did operate properly from the Berkeley laboratory and data were recovered from the Goldstone and Alaska stations via Goddard. Replicated training sets and test sets have been selected from all three test site areas in preparation for the receipt of ERTS imagery and digital tapes. From 600 and 800 points have been selected at each site location and UTM coordinates determined. Templates are being made of these sets. As of September 1, ERTS-generated data had not been received and no statements can be made regarding quality or suitability for forest and range experiments. Aerial photography (scale 1:32,000) of the Manitou (226 C) and Black Hills (226 A) sites was taken with CIR in June. Various scales (1:2,000; 1:10,000; 1:20,000; and 1:40,000) of 70 mm photographs were obtained at Manitou with normal color, CIR, and panchromatic in August.

Heller, R. C.↗
NASA NTRS1972

The precision-processing subsystem for the Earth Resources Technology Satellite.

Description of the precision processor, a subsystem in the image-processing system for the Earth Resources Technology Satellite (ERTS). This processor is a special-purpose image-measurement and printing system, designed to process user-selected bulk images to produce 1:1,000,000-scale film outputs and digital image data, presented in a Universal-Transverse-Mercator (UTM) projection. The system will remove geometric and radiometric errors introduced by the ERTS multispectral sensors and by the bulk-processor electron-beam recorder. The geometric transformations required for each input scene are determined by resection computations based on reseau measurements and image comparisons with a special ground-control base contained within the system; the images are then printed and digitized by electronic image-transfer techniques.

Chapelle, W. E.↗
NASA NTRS1973

All digital precision processing of ERTS images

The author has identified the following significant results. Experimentation was conducted to evaluate the performance of the Sequential Similarity Detection Algorithm (SSDA) to detect and locate ground central points (GCP) automatically using MSS data. Recent experiments with ERTS data having a temporal separation of from 17 to 72 days between the search area and the GCP have shown that the algorithm can find the GCP's and with an overall probability of 88%. Band 5 appears to give the best results. A modified reseau detection algorithm has been applied to 2 RBV scenes separated by a 12 day period. The algorithm correctly located all 486 reseaus. No false reseaus were located in a companion experiment. Changes in apparent reseau position, due to camera characteristics, were never greater than 3 picture elements in either axis. The positional error of a geometrically corrected image has been predicted by the use of an APL program. The maximum deviation of the GCP's from true UTM coordinate position was computed to be 190 meters. The RMS positional error of all GCP's was 106 meters. Further refinement of the algorithm is expected to reduce the errors.

Bernstein, R.↗
NASA NTRS1973

Overall evaluation of ERTS imagery for cartographic application

The author has identified the following significant results. The apparently successful fitting of a plane coordinate (UTM) grid to an ERTS-1 bulk image represents a breakthrough of potential economic importance. If such results continue to be attained it means that ERTS-1 imagery even in bulk form can be reproduced as planimetric image maps that meet National Map Accuracy Standards up to the 1:250,000 scale. Such maps permit positions of image points to be geodetically defined to within 75 meters (rms). Previous efforts to map with ERTS-1 images resulted in errors approaching 300 meters (rms).

Colvocoresses, A. P.↗
NASA NTRS1973

Land resources survey for the state of Michigan

The usefulness of ERTS-1 satellite imagery as an information source for a statewide inventory of Michigan's land resources is documented. The project is currently relying on photointerpretation of MSS photography and computed processed results. The first task completed was a 1:500,000 scale land use map of the state in four classes: urban, forest, water, and agriculture and other. This map was constructed from existing (pre-ERTS-1) information sources including federal, state and county maps, and aerial photography. An ERTS color IR photomosaic for the entire state will also be constructed at a scale of 1:250,000. The Institute is currently working on tape processed data that will include both recognition data as well as a UTM coordinate addressing capability so that the final tape can be inputed directly into computerized land use and transportation corridor analysis models.

Sellman, B.↗
NASA NTRS1973

Overall evaluation of ERTS-1 imagery for cartographic application

The author has identified the following significant results. Scientific results may be summarized as follows: (1) A geodetic (UTM) grid can be fitted to MSS bulk images with a positional accuracy in the order of 50 meters (rms). (2) The MSS imagery as corrected and printed in bulk form is in effect or a defined map projection. This projection, if optimized for cartographic presentation, will have distortions in the order of only 1 part in 10,000. (3) The orbit of ERTS-1 and image delineation of MSS imagery can be held by NASA so that predesignated scenes may be identified and used as a basis for a map series. This concept could reduce the time and cost of ERTS-1 image map production many fold as compared to standard quadrangle format production.

Colvocoresses, A. P.↗
NASA NTRS1973

Evaluation of digitally corrected ERTS imagery

Utilizing all digital processing techniques, precision rectified ERTS multispectral imagery have been produced. Precision geometric correction is accomplished by: (1) utilizing a low order piecewise approximation to the image distortions; (2) incorporating spacecraft attitude refinement derived from ground control points utilized in a Kalman filter; (3) utilizing interpolation techniques which preserve value and slope continuity in the image data. Imagery produced is represented in a UTM projection, and has been evaluated for precision by statistical and analytical methods. Extrapolations to modest size computers indicate good throughput with no sacrifice in precision.

Rifman, S. S.↗
NASA NTRS1979

A system for processing Landsat and other georeferenced data for resource management applications

The NASA Earth Resources Laboratory has developed a transferrable system for processing Landsat and disparate data with capabilities for digital data classification, georeferencing, overlaying, and data base management. This system is known as the Earth Resources Data Analysis System. The versatility of the system has been demonstrated with applications in several disciplines. A description is given of a low-cost data system concept that is suitable for transfer to one's available in-house minicomputer or to a low-cost computer purchased for this purpose. Software packages are described that process Landsat data to produce surface cover classifications and that geographically reference the data to the UTM projection. Programs are also described that incorporate several sets of Landsat derived information, topographic information, soils information, rainfall information, etc., into a data base. Selected application algorithms are discussed and sample products are presented. The types of computers on which the low-cost data system concept has been implemented are identified, typical implementation costs are given, and the source where the software may be obtained is identified.

Whitley, S. L.↗