A new data layer provides Coastal and Marine Ecological Classification Standard (CMECS) labels for global coastal segments at 1 km or shorter resolution. These characteristics are summarized for six US Marine Biodiversity Observation Network (MBON) sites and one MBON Pole to Pole of the Americas site in Argentina. The global coastlines CMECS classifications were produced from a partitioning of a 30 m Landsat-derived shoreline vector that was segmented into 4 million 1 km or shorter segments. Each segment was attributed with values from 10 variables that represent the ecological settings in which the coastline occurs, including properties of the adjacent water, adjacent land, and coastline itself. The 4 million segments were classified into 81,000 coastal segment units (CSUs) as unique combinations of variable classes. We summarize the process to develop the CSUs and derive summary descriptions for the seven MBON case study sites. We discuss the intended application of the new CSU data for research and management in coastal areas.
","language":"English","publisher":"The Oceanography Society","doi":"10.5670/oceanog.2021.219","usgsCitation":"Sayre, R., Butler, K., Van Graafeiland, K., Breyer, S., Wright, D., Frye, C., Karagulle, D., Martin, M.T., Cress, J.J., Allen, T., Allee, R., Parsons, R., Nyberg, B., Costello, M., Harris, P., and Muller-Karger, F., 2021, A global ecological classification of coastal segment units to complement marine biodiversity observation network assessments: Oceanography, v. 34, no. 2, 10 p., https://doi.org/10.5670/oceanog.2021.219.","productDescription":"10 p.","ipdsId":"IP-129029","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"links":[{"id":450534,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5670/oceanog.2021.219","text":"Publisher Index Page"},{"id":436173,"rank":0,"type":{"id":30,"text":"Data 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Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Satellite-derived barrier response and recovery following natural and anthropogenic perturbations, northern Chandeleur Islands, Louisiana","docAbstract":"The magnitude and frequency of storm events, relative sea-level rise (RSLR), sediment supply, and anthropogenic alterations drive the morphologic evolution of barrier island systems, although the relative importance of any one driver will vary with the spatial and temporal scales considered. To explore the relative contributions of storms and human alterations to sediment supply on de-cadal changes in barrier landscapes, we applied Otsu’s thresholding method to multiple satel-lite-derived spectral indices for coastal land-cover classification and analyzed Landsat satellite imagery to quantify changes to the northern Chandeleur Islands barrier system since 1984. This high temporal-resolution dataset shows decadal-scale land-cover oscillations related to storm–recovery cycles, suggesting that shorter and (or) less resolved time series are biased toward storm impacts and may significantly overpredict land-loss rates and the timing of barrier mor-phologic state changes. We demonstrate that, historically, vegetation extent and persistence were the dominant controls on alongshore-variable landscape response and recovery following storms, and are even more important than human-mediated sediment input. As a result of exten-sive vegetation losses over the past few decades, however, the northern Chandeleur Islands are transitioning to a new morphologic state in which the landscape is dominated by intertidal envi-ronments, indicating reduced resilience to future storms and possibly rapid transitions in mor-phologic state with increasing rates of RSLR.","language":"English","publisher":"MDPI","doi":"10.3390/rs13183779","usgsCitation":"Bernier, J., Miselis, J.L., and Plant, N., 2021, Satellite-derived barrier response and recovery following natural and anthropogenic perturbations, northern Chandeleur Islands, Louisiana: Remote Sensing, v. v. 18, no. 18, 3779, 27 p., https://doi.org/10.3390/rs13183779.","productDescription":"3779, 27 p.","ipdsId":"IP-130774","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450663,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13183779","text":"Publisher Index Page"},{"id":436186,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HY3HOR","text":"USGS data release","linkHelpText":"Coastal Land-Cover and Feature Datasets Extracted from Landsat Satellite Imagery, Northern Chandeleur Islands, Louisiana"},{"id":395830,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.0277099609375,\n 29.58540020340835\n ],\n [\n -88.77777099609375,\n 29.58540020340835\n ],\n [\n -88.77777099609375,\n 30.063151406016434\n ],\n [\n -89.0277099609375,\n 30.063151406016434\n ],\n [\n -89.0277099609375,\n 29.58540020340835\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"v. 18","issue":"18","noUsgsAuthors":false,"publicationDate":"2021-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Bernier, Julie 0000-0002-9918-5353 jbernier@usgs.gov","orcid":"https://orcid.org/0000-0002-9918-5353","contributorId":3549,"corporation":false,"usgs":true,"family":"Bernier","given":"Julie","email":"jbernier@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834330,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plant, Nathaniel 0000-0002-5703-5672","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":81234,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":834332,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224369,"text":"fs20213052 - 2021 - Earth Resources Observation and Science Center—Keeping watch over Earth's resources","interactions":[],"lastModifiedDate":"2022-05-11T16:07:32.281348","indexId":"fs20213052","displayToPublicDate":"2021-09-23T11:43:55","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3052","displayTitle":"Earth Resources Observation and Science Center—Keeping Watch Over Earth’s Resources","title":"Earth Resources Observation and Science Center—Keeping watch over Earth's resources","docAbstract":"The Earth Resources Observation and Science (EROS) Center is the largest facility of its kind within the U.S. Geological Survey. As both a science and data center, EROS serves a unique and critical role in shaping our understanding of a changing planet.
EROS opened its doors in 1973 as a receiving station, data archive, and data distribution hub for the USGS Landsat series of Earth observing satellites. In the nearly five decades since, EROS has grown into a globally recognized leader in land change science.
The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.
From the shores of Jamestown and spreading north, south, and west, the lands that became the State of Virginia were some of the first in North America top experience rapid landscape change from European settlement. Imagery and data from the USGS Landsat series of satellites offer an unparalleled resource for the study, understanding, and preservation of Virginia’s land and water resources. From monitoring the health of water bodies to managing invasive species to planning for a range of climate change effects, the USGS National Land Imaging Program’s stewardship and public delivery of Landsat data have benefitted Virginians in myriad ways.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213050","usgsCitation":"U.S. Geological Survey, 2021, Virginia and Landsat (ver. 1.1, February 2023): U.S. Geological Survey Fact Sheet 2021–3050, 2 p., https://doi.org/10.3133/fs20213050.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-132606","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":413291,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20213050/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":413223,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3050/images"},{"id":413220,"rank":4,"type":{"id":31,"text":"Publication 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\"}}]}","edition":"Version 1.0: September 7, 2021; Version 1.1: February 22, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Water means a lot to Michigan, often called the Great Lakes State. The name “Michigan” comes from an Ojibwe word meaning large, or great, water or lake. As the only State touching four of the five Great Lakes—Michigan, Superior, Huron, and Erie—it claims the longest freshwater coastline in the United States.
Yet Michigan is not just about water—forests, agriculture, mines, cities, and even sand dunes stretch across the State’s landscape. Much of what happens on the land does connect in some way with Michigan’s inland and coastal waters. Michigan relies on a healthy environment to support its residents, abundant tourists, and diverse species of wildlife that call the State and its surrounding waters home. From hundreds of miles above, Landsat satellites provide a clearer picture of the connections among land, water, and the people and wildlife that inhabit the State.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Since 2017, the orbit of Landsat 7 has drifted outside its nominal mission requirement toward an earlier acquisition time because of limited onboard fuel resources. This makes quantitative analyses from Landsat 7 data potentially unreliable for many scientific studies. To comprehensively understand the effect of ongoing (2018–2020) orbit drift on Landsat 7 data, we compared surface reflectance and Top-Of-Atmosphere (TOA) reflectance of growing season observations (July 1 ± 30 days) from Landsat 7 with orbit drift and Landsat 8 with nominal orbit using a total of 10,000 randomly selected Northern Hemisphere (0–750 N) terrestrial pixels. To evaluate the future (2021–2023) effect of Landsat 7's orbit drift, we analyzed the historical Northern Hemisphere terrestrial growing season Earth Observing-1 (EO-1) TOA reflectance images, which shared a similar orbit drift as Landsat 7 but occurred much earlier. Results suggest that Landsat 7's orbit drift has already led to a general decrease in surface reflectance and TOA reflectance in 2019 and 2020, with a limited impact (overall reflectance changes less than 0.007). The influence of orbit drift is more substantial for the two shortwave infrared (SWIR) bands and the near infrared (NIR) band, but less for the three visible bands (i.e., Red, Green, and Blue). The Normalized Difference Vegetation Index (NDVI), derived from either surface reflectance or TOA reflectance, increased less than 0.003 in 2020. According to the historical EO-1 TOA reflectance data, we estimate that the effect of Landsat 7's orbit drift will be much more dramatic in the future (e.g., the NIR and SWIR bands will decrease more than 0.015 since July 1, 2021), and for different land cover types, the effects of orbit drift are also quite different. To reduce this influence, we examined the c-factor Bidirectional Reflectance Distribution Function (BRDF) normalization approach to correct the orbit drift impact for Landsat 7 surface reflectance data collected between 2019 and 2020. We found that the c-factor BRDF can reduce the data difference substantially, but how this approach works after Landsat 7's orbit drifts further still requires more investigation. Therefore, we determined that Landsat 7 can preserve its science capability until 2020, but will be less reliable for remote sensing applications that need accurate absolute radiometric values after 2020. Correction methods such as c-factor BRDF could be a potential viable approach to maintain its science capability going forward.
The U.S. Geological Survey (USGS) archive of Earth images acquired by Landsat 1-8 sensors is organized in collections of consistently calibrated, geolocated, and processed data products. Such an organization ensures consistent quality of the archived data within a collection over time and across all instruments within the Landsat mission. In December 2020, the USGS completed reprocessing of the archived Landsat data and released a new collection, Collection 2, which introduced surface reflectance and surface temperature Level-2 products, implemented improved ground control and elevation datasets, and brought several geometric and radiometric calibration enhancements. Radiometric enhancements include absolute and relative gain updates for both imaging sensors, Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), aboard Landsat 8 and a change in calculation of bias for OLI. In this paper we present the analysis of Collection 2 Landsat 8 products demonstrating stability over the mission lifetime, the improvement in OLI signal to noise ratio and along-track striping performance resulting from enhanced bias correction, as well as reduction of cross-track striping in TIRS data as a result of relative gain updates. In addition, we discuss effects of two safehold events that Landsat 8 experienced in November 2020 on the radiometric calibration of both sensors and product performance.
In late 2020 the U.S. Geological Survey (USGS) began the distribution of Landsat products associated with their collection 2 reprocessing of the archive. Several changes were implemented within the Landsat Product Generation System (LPGS) and the calibration parameters applied to the Landsat imagery for the collection 2 processing. When comparing between collection 1 and collection 2 products, radiometric and geometric differences will be present. One of the most substantial changes between the two collections was an adjustment to the ground control which made the control more accurate from an absolute and relative perspective. Some of the other changes associated with collection 2 processing were to the Digital Elevation Model (DEM) used in terrain correction, the Thermal Infrared Sensor (TIRS) relative gains, TIRS absolute calibration, Operational Land Imagery (OLI) absolute gain model, OLI relative gain, and OLI bias calculation to name a few. Although these changes also have an effect on the differences between the product associated with these two collections, the change in the ground control, although typically less than one 30-meter multispectral pixel in magnitude, will have the largest effect on the differences between the products. This change in ground control is also a spatially dynamic change that although is low in spatial frequency, is nonlinear and is not a change that can be modeled on a global or even on a local Worldwide Reference System-2 (WRS-2) path and row scale. The effects of these ground control changes will be discussed and demonstrated within this paper along with examples showing their effect on specific datasets. This paper demonstrates some of these geometric differences associated with the ground control through both the registration statistics created during product generation and through an example comparison of a set of collection 1 and collection 2 products.
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","largerWorkTitle":"SPIE proceedings volume 11829, earth observing systems XXVI","language":"English","publisher":"SPIE","doi":"10.1117/12.2596200","usgsCitation":"Lubke, M., Rengarajan, R., and Choate, M.J., 2021, Preliminary assessment of the geometric improvements to the Landsat Collection-2 archive, in SPIE proceedings volume 11829, earth observing systems XXVI, v. 11829, 1182901, 14 p., https://doi.org/10.1117/12.2596200.","productDescription":"1182901, 14 p.","ipdsId":"IP-131181","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":387782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11829","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lubke, Mark 0000-0002-7257-2337","orcid":"https://orcid.org/0000-0002-7257-2337","contributorId":261911,"corporation":false,"usgs":false,"family":"Lubke","given":"Mark","email":"","affiliations":[{"id":53079,"text":"KBR, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":820758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":242014,"corporation":false,"usgs":false,"family":"Rengarajan","given":"Rajagopalan","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":820759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":216866,"corporation":false,"usgs":true,"family":"Choate","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":820760,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222541,"text":"fs20213038 - 2021 - Vermont and Landsat","interactions":[],"lastModifiedDate":"2023-02-21T11:52:13.945986","indexId":"fs20213038","displayToPublicDate":"2021-08-03T16:00:13","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3038","displayTitle":"Vermont and Landsat","title":"Vermont and Landsat","docAbstract":"The Green Mountain State of Vermont is known for its vast swaths of deciduous forest, patches of evergreen, and the Green Mountains that run through its center.
Valuable insight into the forests and landscape features of Vermont can be gleaned from the 50-year historical record of Landsat satellite imagery. The archive is accessible at no cost to researchers, land managers, and the public thanks to the open data policy of the U.S. Geological Survey’s National Land Imaging Program.
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\"}}]}","edition":"Version 1.0: August 3, 2021; Version 1.1: February 17, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Landsat 9 is in its final preparations for launch from Vandenberg Space Force Base on 16 September 2021. It has completed its environmental testing at Northrop Grumman Space (NGSP) in Gilbert, Arizona and has been transported to its California launch site. It will be launched into a 705 km orbit replacing Landsat 7 to provide 8-day Earth land mass coverage in concert with Landsat 8. Landsat 8 carries the first Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS); Landsat 9 carries the second of each: OLI-2 and TIRS-2. Once launched it will undergo a 90-day activation, checkout, characterization and calibration, a.k.a. commissioning phase before transitioning to operations. For a several-day period during this commissioning phase, Landsat 9 will under-fly Landsat 8, allowing near simultaneous data collection by both sensors of common Earth targets. These data will be used to compare the radiometric calibrations of the instruments and allow for adjustments of processing parameters to provide more consistent data products.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SPIE: Earth observing systems XXVI","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SPIE Optics + Photonics 2021","conferenceDate":"Aug 1-5, 2021","conferenceLocation":"San Diego, CA","language":"English","publisher":"SPIE","doi":"10.1117/12.2595885","usgsCitation":"Markham, B., Anderson, C., Choate, M., Crawford, C., Jenstrom, D., Masek, J., Pedelty, J., Sauer, B., and Thome, K., 2021, Landsat 9: Ready for Launch, in Proceedings of SPIE: Earth observing systems XXVI, v. 11829, San Diego, CA, Aug 1-5, 2021, 118290J, https://doi.org/10.1117/12.2595885.","productDescription":"118290J","ipdsId":"IP-131273","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":391324,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11829","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Markham, Brian","contributorId":268247,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":826269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":826270,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":268248,"corporation":false,"usgs":true,"family":"Choate","given":"Michael J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":826271,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":826272,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jenstrom, Del","contributorId":268250,"corporation":false,"usgs":false,"family":"Jenstrom","given":"Del","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":826273,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Masek, Jeff","contributorId":268252,"corporation":false,"usgs":false,"family":"Masek","given":"Jeff","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":826274,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pedelty, Jeffery","contributorId":268253,"corporation":false,"usgs":false,"family":"Pedelty","given":"Jeffery","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":826275,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sauer, Brian 0000-0003-2205-1442 bsauer@usgs.gov","orcid":"https://orcid.org/0000-0003-2205-1442","contributorId":3534,"corporation":false,"usgs":true,"family":"Sauer","given":"Brian","email":"bsauer@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":826276,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thome, Kurtis","contributorId":268256,"corporation":false,"usgs":false,"family":"Thome","given":"Kurtis","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":826277,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70234099,"text":"70234099 - 2021 - Land surface temperature differences between natural and artificial turf sports fields as estimated from satellite: Examples from the United States and Europe","interactions":[],"lastModifiedDate":"2024-10-28T16:49:01.177992","indexId":"70234099","displayToPublicDate":"2021-07-31T06:48:06","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Land surface temperature differences between natural and artificial turf sports fields as estimated from satellite: Examples from the United States and Europe","docAbstract":"This report addresses system characterization of Planet’s Dove Classic satellites and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 4-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Dove Classic has an interior geometric performance in the range of −0.218 (−0.073 pixel) to −0.037 m (−0.012 pixel) in easting and −0.167 (−0.056 pixel) to −0.111 m (−0.037 pixel) in northing in band-to-band registration, an exterior geometric error of −6.841 (−2.280 pixels) in easting and −6.235 m (−2.078 pixels) in northing offset in comparison to Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.057 to −0.010 in offset and 0.963 to 1.298 in slope, and a spatial performance in the range of 2.77 to 3.35 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.003 to 0.010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030C","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2021, System characterization report on Planet’s Dove Classic, chap. C of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030C.","productDescription":"v, 28 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-126677","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":387443,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/c/ofr20211030c.pdf","text":"Report","size":"31.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030C"},{"id":387442,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/c/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Arizona is a land of massive grandeur, deep gorges, lofty mountains, immense plains, and elevated mesas—and, without question, its crown jewel is the Grand Canyon. The spectacular canyon, one of the seven natural wonders of the world, was created when the Colorado River carved a channel through northern Arizona, revealing nearly two billion years of the Earth's history.
Yet, for all its ancient beauty, Arizona and its landscapes are experiencing a transformation.
Arizonans face more extreme temperatures and drought because of climate change. Amid a drought in the western United States, Lake Mead, one of Arizona's main water resources, dropped to a record low level in June 2021. Climate change is making extreme weather events such as dust storms and heat waves more common, posing higher risks to human health, according to the Centers for Disease Control and Prevention.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213039","usgsCitation":"U.S. Geological Survey, 2021, Arizona and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2021–3039, 2 p., https://doi.org/10.3133/fs20213039.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-130596","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412226,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20213039/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412182,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3039/images"},{"id":412181,"rank":4,"type":{"id":31,"text":"Publication 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\"}}]}","edition":"Version 1.0: July 13, 2021; Version 1.1: January 23, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired across hundreds of wildland fires in the United States, primarily utilizing the Composite Burn Index (CBI) plot protocol. These observations have been coupled to spaceborne passive spectral reflectance indices (e.g. Landsat-derived variations of the Normalized Burn Ratio [NBR]) to produce regression models describing their relationship. Here we develop regression models by vegetation type for multiple vegetation classification systems representing a range of spatial scales, and a decision tree framework for evaluating these regression models. Our overall goals were to determine which scale of ecological classifications provided the best estimate of burn severity from Landsat data and how to choose the best regression model. We aggregated a total of 6280 CBI plots for 234 wildland fires that burned between 1994 and 2017 and produced Landsat-derived NBR and differenced NBR (dNBR) values for each plot. We then calculated best fit linear or higher order regression equations between CBI and NBR/dNBR for each landcover classification system from smallest to largest scale: LANDFIRE Biophysical Settings (BPS), National Vegetation Classification macrogroup (NVC) landcover classifications, Omernick III, II, and I ecoregions, LANDFIRE Fire Regime Groups (FRG), and the entire conterminous United States (CONUS) dataset. The CONUS regression model goodness of fit was moderate (R2 = 0.55, P < 0.001) for dNBR and poor (R2 = 0.30, P < 0.001) for NBR. Within landcover classifications, CBI was better fit by dNBR than NBR. Finer scale regional regression models including BPS (dNBR
Water temperature is a key element of freshwater ecological systems and a critical element within natural resource monitoring programs. In the absence of in situ measurements, remote sensing platforms can indirectly measure water temperature over time and space. The Earth Resources Observation and Science (EROS) Center has processed archived Landsat imagery into analysis ready data (ARD), including Level-2 Provisional Surface Temperature (pST) estimates derived from the Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Thermal Infrared Sensor (TIRS). We compared in situ measurements of water temperature within the Yukon River in Alaska with 52 instances of pST estimates between June 2014 and September 2020. Agreement was good with an RMSE of 2.25 °C and only a slight negative bias in the estimated mean daily water temperature of −0.47 °C. For the 52 dates compared, the average daily water temperature measured by the USGS streamgage was 11.3 °C with a standard deviation of 5.7 °C. The average daily pST estimate was 10.8 °C with a standard deviation of 6.1 °C. At least in the case of large unstratified rivers in Alaska, ARD pST can be used to infer water temperature in the absence of or in tandem with ground-based water temperature monitoring campaigns.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13122394","usgsCitation":"Baughman, C., and Conaway, J., 2021, Comparison of historical water temperature measurements with landsat analysis ready data provisional surface temperature estimates for the Yukon River in Alaska: Remote Sensing, v. 13, no. 12, 2394, 45 p., https://doi.org/10.3390/rs13122394.","productDescription":"2394, 45 p.","ipdsId":"IP-127623","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":451818,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13122394","text":"Publisher Index Page"},{"id":436300,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MCNPGK","text":"USGS data release","linkHelpText":"Historical Landsat-Derived Water Surface Temperature for Three Large Alaska Rivers 1984-2022"},{"id":386643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -164.53125,\n 61.48075950007598\n ],\n [\n -158.81835937499997,\n 61.48075950007598\n ],\n [\n -158.81835937499997,\n 63.35212928507874\n ],\n [\n -164.53125,\n 63.35212928507874\n ],\n [\n -164.53125,\n 61.48075950007598\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":818015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conaway, Jeff 0000-0002-3036-592X","orcid":"https://orcid.org/0000-0002-3036-592X","contributorId":214226,"corporation":false,"usgs":true,"family":"Conaway","given":"Jeff","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":818016,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221549,"text":"70221549 - 2021 - Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","interactions":[],"lastModifiedDate":"2021-06-23T12:28:09.472652","indexId":"70221549","displayToPublicDate":"2021-06-17T06:59:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","docAbstract":"In contrast to abrupt changes caused by land cover conversion, subtle changes driven by a shift in the condition, structure, or other biological attributes of land often lead to minimal and slower alterations of the terrestrial surface. Accurate mapping and monitoring of subtle change are crucial for an early warning of long-term gradual change that may eventually result in land cover conversion. Freely accessible moderate-resolution datasets such as the Landsat archive have great potential to characterize subtle change by capturing low-magnitude spectral changes in long-term observations. However, past studies have reported limited success in accurately extracting subtle changes from satellite-based time series analysis. In this study, we introduce a supervised framework named ‘PIDS’ to detect subtle forest disturbance from a comprehensive Landsat data archive by leveraging disturbance-based calibration sites. PIDS consists of four components: (1) Parameter optimization; (2) Index selection; (3) Dynamic stratified monitoring; and (4) Spatial consideration. PIDS was applied to map the early stage of bark beetle infestations (i.e., a lower per-pixel fraction of trees cover that show visual signs of infestation), which are a typical example of subtle change in conifer forests. Landsat Analysis Ready Data were used as the time series inputs for mapping mountain pine beetle and spruce beetle disturbance between 2001 and 2019 in Colorado, USA. PIDS-detection map assessment showed that the overall performance of PIDS (namely ‘F1 score’) was 0.86 for mountain pine beetle and 0.73 for spruce beetle, making a substantial improvement (> 0.3) compared to other approaches/products including COntinuous monitoring of Land Disturbance, LandTrendr, and the National Land Cover Database forest disturbance product. A sub-pixel analysis of tree canopy mortality percentage was performed by linking classified high-resolution (0.3- and 1-m) aerial imagery and 30-m PIDS-detection maps. Results show that PIDS typically detects mountain pine beetle infestation when ≥56% of a Landsat pixel is occupied by red-stage canopy mortality (one year after initial infestation), and spruce beetle infestation when ≥55% is occupied by gray-stage mortality (two years after initial infestation). This study addresses an important methodological goal pertinent to the utility of event-based reference samples for detecting subtle forest change, which could be potentially applied to other types of subtle land change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112560","usgsCitation":"Ye, S., Rogan, J., Zhu, Z., Hawbaker, T., Hart, S., Andrus, R.A., Meddens, A.J., Hicke, J.A., Eastman, J.R., and Kulakowski, D., 2021, Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance: Remote Sensing of Environment, v. 263, 112560, 16 p., https://doi.org/10.1016/j.rse.2021.112560.","productDescription":"112560, 16 p.","ipdsId":"IP-124774","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":488056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112560","text":"Publisher Index Page"},{"id":386642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.0283203125,\n 37.020098201368114\n ],\n [\n -106.14990234375,\n 37.020098201368114\n ],\n [\n -106.14990234375,\n 40.91351257612758\n ],\n [\n -109.0283203125,\n 40.91351257612758\n ],\n [\n -109.0283203125,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"263","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ye, Su","contributorId":260471,"corporation":false,"usgs":false,"family":"Ye","given":"Su","email":"","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogan, John","contributorId":260472,"corporation":false,"usgs":false,"family":"Rogan","given":"John","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Zhe","contributorId":260473,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":818019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":818020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Sarah J.","contributorId":260474,"corporation":false,"usgs":false,"family":"Hart","given":"Sarah J.","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":818021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andrus, Robert A.","contributorId":260475,"corporation":false,"usgs":false,"family":"Andrus","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":818022,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meddens, Arjan J.H.","contributorId":260476,"corporation":false,"usgs":false,"family":"Meddens","given":"Arjan","middleInitial":"J.H.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":818023,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hicke, Jeffery A.","contributorId":260477,"corporation":false,"usgs":false,"family":"Hicke","given":"Jeffery","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":818024,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eastman, J. Ronald","contributorId":260480,"corporation":false,"usgs":false,"family":"Eastman","given":"J.","email":"","middleInitial":"Ronald","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818025,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kulakowski, Dominik","contributorId":260482,"corporation":false,"usgs":false,"family":"Kulakowski","given":"Dominik","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818026,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70221437,"text":"fs20213035 - 2021 - South Dakota and Landsat","interactions":[],"lastModifiedDate":"2024-11-27T14:22:39.971064","indexId":"fs20213035","displayToPublicDate":"2021-06-16T11:24:39","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3035","displayTitle":"South Dakota and Landsat","title":"South Dakota and Landsat","docAbstract":"Few States derive as much of their social and economic well-being from the land as South Dakota. 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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213035","usgsCitation":"U.S. Geological Survey, 2021, South Dakota and Landsat (ver. 1.1, February 2023): U.S. Geological Survey Fact Sheet 2021–3035, 2 p., https://doi.org/10.3133/fs20213035.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-129411","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":413217,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3035/images"},{"id":413214,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3035/fs20213035.pdf","text":"Report","size":"1.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3035"},{"id":413216,"rank":4,"type":{"id":31,"text":"Publication 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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192