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NASA NTRS2023

Commercial Smallsat Data Acquisition Program On-ramp #2 Airbus U.S. Synthetic Aperture Radar (SAR) Evaluation Report

In 2017, NASA’s Earth Science Division (ESD) launched the Private-Sector Small Constellation Satellite Data Product Pilot, now referred to as the Commercial Smallsat Data Acquisition (CSDA) program. The objective of CSDA is to identify, evaluate, and acquire commercial remote sensing data that support NASA’s Earth science research and application activities. The Pilot successfully concluded in early 2020, when CSDA transitioned into a sustained program with on-ramping opportunities for new vendors as the industry emerges with new candidates and capabilities. In October 2019, a Request for Information (RFI) seeking capability statements from parties interested in providing data from spaceborne platforms was released for the CSDA on-ramp #2 evaluations. To be responsive to the RFI, the commercial satellite constellations had to consist of three or more operating spacecraft actively collecting data in a non-geostationary orbit with full latitudinal coverage and be U.S. companies. Two vendors responded to the RFI and were evaluated by a committee composed of NASA ESD leadership, program managers, and scientists. Both vendors satisfied the RFI requirements and were asked to respond to a Request for Proposal (RFP). After review of the proposals, NASA entered into a Blanket Purchase Agreement (BPA) with Airbus Defense and Space GEO, Inc. (Airbus) U.S. in September 2021 and with BlackSky Geospatial Solutions, Inc. (BlackSky) in November 2021. In this report, CSDA provides an evaluation of the usefulness of data provided by the Airbus U.S. Synthetic Aperture Radar (SAR) satellite constellation, consisting of TerraSAR-X (launched in 2007), TanDEM-X (launched in 2010), and PAZ (launched in 2018), for advancing NASA’s Earth system science research and applications. The evaluation of the BlackSky commercial data will be provided in a separate report. To conduct the Airbus evaluation, NASA’s ESD augmented 13 existing research projects that could potentially benefit from, and had the expertise to evaluate, the commercial data being considered for longer-term purchase. Investigators from NASA’s Research and Analysis Program science focus areas and from NASA’s Applied Sciences Program elements participated in the evaluation. A summary of the research areas evaluated by the Principal Investigator (PI) teams is presented in Figure 3. CSDA also funded a dedicated activity to evaluate the satellite data quality (calibration and geolocation) independently by assessing the accuracy of data from Airbus. Evaluation activities were carried out by the selected PIs from December 7, 2022, to December 7, 2023. Delivery of datasets requested by the researchers began in January 2023. The vendors were evaluated on the accessibility of data, accuracy and completeness of metadata, and promptness and quality of user support services. Datasets purchased during the evaluation have been archived by NASA and will be made available to current and future government-funded researchers in accordance with the End User License Agreement (EULA). This synthesis report distills and integrates the findings of research reports commissioned by NASA for the Airbus evaluation. This report also includes recommendations that inform the way ahead for the program. The scientific results from the evaluations demonstrated that the commercial data from Airbus were able to advance NASA research and applications. However, the PIs encountered limitations that diminished the usefulness of the data due to the amount of effort that was required to access, preprocess, and analyze these data. One significant issue encountered was the limited spatial and temporal coverage of the data in the Airbus archive that could be used to conduct time series analyses or assessments over large spatial scales. Overall, however, the utility and the quality of the evaluated data outweighed the difficulties encountered, and NASA has concluded that the Airbus SAR data would complement NASA’s existing Earth observation capabilities and Airbus U.S. would qualify to participate in the sustained phase of the program.

Batuhan Osmanoglu↗

DOE OSTI2023

WHOLESCALE: Coordinates of wells at San Emidio, Nevada

This dataset includes position coordinates and elevation information for wells at the WHOLESCALE San Emidio project location. Well positions in the attached file are characterized by UTM coordinates (Easting, Northing) in meters, and WHOLESCALE coordinates (Easting, Northing) relative in meters from a chosen reference point. The elevation of the top and bottom of open intervals within each well (representing locations of perforated or open-hole sections) are measured in meters (positive) above WGS84 geoid (mean sea level). The WHOLESCALE acronym stands for Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses. The goal of the WHOLESCALE project is to simulate the spatial distribution and temporal evolution of stress in the geothermal system at San Emidio in Nevada, United States.

15 GEOTHERMAL ENERGY↗

NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗

DOE OSTI2024

Wind Turbine Oil and Gas Pipeline Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from oil and gas pipelines. A setback requirement is a minimum distance from a pipeline that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a median setback equivalent to 1.1 times the turbine tip-height, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where wind energy is prohibited or permitted across the contiguous United States. The turbine parameters used were a hub-height of 115 meters and a rotor diameter of 170 meters, as obtained from the Annual Technology Baseline (ATB) 2022. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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DOE OSTI2022

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R 2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m 3 /m 3 ). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

47 OTHER INSTRUMENTATION↗

NASA NTRS2004

Determining the Optimum Post Spacing of LIDAR-Derived Elevation Data in Varying Terrain for Flood Hazard Mapping Purposes in North Carolina and Texas

The major flood events in the United States in the past few years have made it apparent that many floodplain maps being used by State governments are outdated and inaccurate. In response, many Stated have begun to update their Federal Emergency Management Agency (FEMA) Digital Flood Insurance Rate Maps. Accurate topographic data is one of the most critical inputs for floodplain analysis and delineation. Light detection and ranging (LIDAR) altimetry is one of the primary remote sensing technologies that can be used to obtain high-resolution and high-accuracy digital elevation data suitable for hydrologic and hydraulic (H&H) modeling, in part because of its ability to "penetrate" various cover types and to record geospatial data from the Earth's surface. However, the posting density or spacing at which LIDAR collects the data will affect the resulting accuracies of the derived bare Earth surface, depending on terrain type and land cover type. For example, flat areas are thought to require higher or denser postings than hilly areas to capture subtle changes in the topography that could have a significant effect on flooding extent. Likewise, if an area has dense understory and overstory, it may be difficult to receive LIDAR returns from the Earth's surface, which would affect the accuracy of that bare Earth surface and thus would affect flood model results. For these reasons, NASA and FEMA have partnered with the State of North Carolina and with the U.S./Mexico Foundation in Texas to assess the effect of LIDAR point density on the characterization of topographic variation and on H&H modeling results for improved floodplain mapping. Research for this project is being conducted in two areas of North Carolina and in the City of Brownsville, Texas, each with a different type of terrain and varying land cover/land use. Because of various project constraints, LIDAR data were acquired once at a high posting density and then decimated to coarser postings or densities. Quality assurance/quality control analyses were performed on each dataset. Cross sections extracted form the high density and then the decimated datasets were individually input into an H&H model to determine the model's sensitivity to topographic variation and the effect of that variation on the resulting water profiles. Additional analysis was performed on the Brownsville, Texas, LIDAR data to determine the percentage of returns that "penetrated" various types of canopy or vegetative cover. It is hoped that the results of these studies will benefit state and local communities as they consider the post spacing at which to acquire LIDAR data (which affects cost) and will benefit FEMA as the Agency assesses the use of different technologies for updating National Flood Insurance Program and related products.

Berglund, Judith↗

DOE OSTI2022

UAS remote sensing (DJI Phantom 4 RTK platform): RGB orthomosaic, digital surface and canopy height models, plant functional type map, Seward Peninsula, Alaska, 2019

Airborne remote sensing data collected using a DJI Phantom 4 RTK unoccupied aerial system (UAS) ? operated by the University of Maine Wheatland Geospatial Lab (https://wheatlandlab.org/). This package includes data from 17 flights flown over the NGEE-Arctic Council, Kougarok, Kougarok Mile80, Teller, and Teller Mile32 sites in July 2019. The Phantom 4 RTK is a drone platform that collects very high spatial resolution optical red/green/blue (RGB) imagery. Derived image products include point cloud, ortho-mosaiced RGB, a digital surface model (DSM) using the structure from motion (SfM) technique, and a canopy height model (CHM). Unprocessed and processed data products (1,000+ files) are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *hdr,), ENVI image file (*.dat), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. 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 OSTI2024

Airport and Heliport Setbacks

This dataset represents a first-order quantification of airport and heliport setback requirements based on the Federal Aviation Administration (FAA) 14 CFR Part 77.9. It defines the following FAA notice criteria that we use to create runway buffers. Runways longer than 3,200 feet receive a 20,000 foot buffer, runways less than 3,200 feet long receive a buffer of 10,000 feet, and heliports receive a buffer of 5,000 feet. A setback requirement is a minimum distance from an airport or heliport that an energy project may be developed. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

airport↗

DOE OSTI2024

Solar PV Structure Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from structures based on county ordinances as of April 2022. A setback requirement is a minimum distance from a structure that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 61 meters was applied in counties without specific structure setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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DOE OSTI2024

Solar PV Road Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from roads based on county ordinances as of April 2022. A setback requirement is a minimum distance from a road that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 30 meters was applied in counties without specific road setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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DOE OSTI2024

Solar PV Water Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from bodies of water based on county ordinances as of April 2022. A setback requirement is a minimum distance from water that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a default setback of 30 meters was applied in counties without specific water setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where solar energy is prohibited or permitted across the contiguous United States. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗

DOE OSTI2025

Evaluating opportunity for distributed wind energy in rural and agricultural areas

Wind energy is among the most mature renewable energy technologies, accounting for 11% of the current US electricity generation in 2024, with the lowest average levelized cost. While it is known that substantial opportunity exists for further development, a key question has been where wind energy is best suited compared to other technologies. This study leverages an immense dataset of parcel-resolved technoeconomic potential for the contiguous United States, focusing on distributed wind (DW) energy—a configuration where one or more turbines, typically 30–60 m in height are used to satisfy nearby energy needs. The analysis is conducted at multiple spatial scales and considers land use, crop land, census, and incentive program data to determine the most opportune areas for market development. The results show that rural, agricultural and residential areas are most suited to DW. Connection type (in front of, or behind the meter) and regulations determine the best application, while siting constraints, economics, demand and the wind resource determines the optimal size of turbine.

17 WIND ENERGY↗

DOE OSTI2024

Offshore Wind Composite Siting Exclusions Scenarios

This dataset consolidates criteria that restrict the siting of offshore wind energy installations, an update from Zuckerman et al. (2023). All scenarios exclude area from wind energy development due to legal or administrative restrictions (e.g., Marine Protected Areas), existing infrastructure, and technical constraints (e.g., 1,300 m bathymetry limit). A TIF data file and a PNG cartographic map of the data are provided for three scenarios ("Open", "Reference", and "Limited"), showing areas where offshore wind energy is restricted across the contiguous United States. The "Open" scenario only excludes areas with known preclusions for offshore wind deployment. The "Reference" scenario excludes all areas from the "Open" scenario, as well as additional best management practices such as known proposed protected areas, and state waters. The "Limited" scenario excludes all area from the "Reference" scenario. Additionally, it has more stringent siting criteria such as larger setbacks from shore and infrastructure, as well as shallower depth limits on floating technology. For further details and citation, please refer to the publication linked below: Zuckerman, Gabriel R., Anthony Lopez, Travis Williams, Rebecca Green, and Grant Buster. 2023. Impacts of Siting Considerations on Offshore Wind Technical Potential in the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20- 85088.

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DOE OSTI2024

Wind Turbine Water Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from water based on existing county ordinances as of April 2022. A setback requirement is a minimum distance from water that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a median setback equivalent to 1.1 times the turbine tip-height was applied in counties that lacked specific water setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where wind energy is prohibited or permitted across the contiguous United States. The turbine parameters used were a hub-height of 115 meters and a rotor diameter of 170 meters, as obtained from the Annual Technology Baseline (ATB) 2022. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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DOE OSTI2024

Wind Turbine Transmission Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from transmission based on existing county ordinances as of April 2022. A setback requirement is a minimum distance from transmission infrastructure that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a median setback equivalent to 1.1 times the turbine tip-height was applied in counties that lacked specific transmission setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where wind energy is prohibited or permitted across the contiguous United States. The turbine parameters used were a hub-height of 115 meters and a rotor diameter of 170 meters, as obtained from the Annual Technology Baseline (ATB) 2022. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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DOE OSTI2024

Wind Turbine Structure Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from structures based on existing county ordinances as of April 2022. A setback requirement is a minimum distance from a structure that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a median setback equivalent to 2 times the turbine tip-height was applied in counties that lacked specific structure setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where wind energy is prohibited or permitted across the contiguous United States. The turbine parameters used were a hub-height of 115 meters and a rotor diameter of 170 meters, as obtained from the Annual Technology Baseline (ATB) 2022. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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DOE OSTI2024

Wind Turbine Road Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from roads based on existing county ordinances as of April 2022. A setback requirement is a minimum distance from a road that an energy project may be developed, and these varied widely across the counties in which they existed. Two versions are provided: one reflecting only the county ordinances and another incorporating extrapolated trends. In the extrapolated version, a median setback equivalent to 1.1 times the turbine tip-height was applied in counties that lacked specific railroad setback regulations. A TIF data file and a PNG map of the data are provided for both versions, showing areas where wind energy is prohibited or permitted across the contiguous United States. The turbine parameters used were a hub-height of 115 meters and a rotor diameter of 170 meters, as obtained from the Annual Technology Baseline (ATB) 2022. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

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