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.
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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":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic 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. 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12201 Sunrise Valley Drive
Reston, VA 20192
California has remarkable geographic diversity, from 1,100 miles of Pacific shoreline to 33 million acres of trees, including its famous redwood forests along the Pacific Coast Highway, U.S. Route 101. And although California’s nickname is “The Golden State,\" it may also deserve the title of “The Extreme State.” Mount Whitney, the highest peak in the conterminous United States, stands at 14,494 feet above sea level in the Sierra Nevada mountain range. Just 2.5 hours away by car, Death Valley’s Badwater Basin boasts the lowest point in the United States: 282 feet below sea level. Although hundreds of inches of snow fall on Mount Whitney each year, the rainfall in Death Valley, commonly called the hottest place on Earth, averages less than 2 inches.
California is the most populous U.S. State with 39 million residents, 26 million of whom live near the coast. It’s also the most agriculturally productive State, raising a variety of fruits, vegetables, nuts, grains, grasses, and flowers in addition to dairy and livestock. Although climate change affects many of these people, production, and places, Landsat can help agencies and residents monitor their landscapes and plan for a resilient future.
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\"}}]}","edition":"Version 1.0: June 16, 2021; Version 1.1: January 30, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Landsat surface temperature and land cover products have been used to estimate surface temperatures in urban and surrounding nonurban areas and to quantify urban heat island intensity. Understanding the intensity and long-term temporal trends of urban heat islands enables the heat-related health challenges associated with heat waves to be monitored and the effects for human health and ecosystems to be better understood.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213031","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Xian, G.Z., 2021, Monitoring and assessing urban heat island variations and effects in the United States: U.S. Geological Survey Fact Sheet 2021–3031, 2 p., https://doi.org/10.3133/fs20213031.","productDescription":"Report: 2 p.; Data Release","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-129124","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":386098,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H6E1FZ","text":"USGS data release","linkHelpText":"Land surface thermal feature change monitoring in urban and urban wild land interface (ver. 3.0, June 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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
This report addresses system characterization of Gaofen-1 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 in 2020. These reports present the detail 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.
Gaofen represents a series of Chinese high-resolution Earth observation satellites. More than 12 satellites have been launched in the Gaofen series, beginning with Gaofen-1 in 2013. Satellites within the series have varying infrared, radar, and optical imaging capabilities. The primary goal for the satellite is to provide near real-time observations for climate change monitoring, geographical mapping, precision agriculture support, environmental and resource surveying, and disaster prevention. More information on Chinese satellites and sensors is available within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at http://www.cnsageo.com/#/detailIndex?secondIndex=2&id=3&code=8.
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 Gaofen-1 has an interior geometric performance of −0.48 meter (m) (−0.03 pixel) northing and 0.42 m (0.03 pixel) easting offset for band 1, −0.99 m (−0.06 pixel) northing and −0.38 m (−0.02 pixel) easting offset for band 2, −0.45 m (−0.03) northing and 0.83 m (0.05 pixel) easting offset for band 3, −3.20 m (−0.20 pixel) northing and 1.44 m (0.09 pixel) easting offset for band 4 in band-to-band registration. Similarly, Gaofen-1 has an exterior geometric performance of 7.50 m (0.48 pixel) easting and 109.50 m (7.30 pixels) northing offset in comparison to the Landsat 8 Operational Land Imager; a radiometric performance in the range of −0.014 to 0.149 (absolute reflective difference); and a spatial performance in the range of 1.1 to 2.0 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.040 to 0.250.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030B","usgsCitation":"Shrestha, M., Sampath, A., Ramaseri Chandra, S.N., Christopherson, J.B., Shaw, J., Stensaas, G.L., and Anderson, C., 2021, System characterization report on the Gaofen-1, chap. B of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 11 p., https://doi.org/10.3133/ofr20211030B.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-126808","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":386255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/b/coverthb.jpg"},{"id":386256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/b/ofr20211030b.pdf","text":"Report","size":"3.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030B"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
This report addresses system characterization of the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) 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 the methodology and procedures for characterization and the technical and operational information about the specific sensing system being evaluated. These reports also provide a description of data measurements, data retention practices, and data analysis results and provide system characterization conclusions.
In partnership with Teledyne Brown Engineering, DLR built the DESIS hyperspectral instrument, which Teledyne Brown Engineering then integrated onto its International Space Station-based imaging platform, the Multi-User System for Earth Sensing. DLR developed the processing software and, together with Innovative Imaging and Research, completes the validation and calibration of the data products. DESIS was launched in 2018, and the data are used for scientific research in atmospheric physics and Earth sciences. The DESIS sensor contributes to the scientific and commercial utilization of the International Space Station and helps to further hyperspectral remote sensing technologies for future satellites. More information on DLR satellites and sensors is included within the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.dlr.de/DE/Home/home_node.html.
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 DESIS has an interior geometric performance of less than a 3.30-meter (less than 0.11 pixel) root mean square error in band-to-band registration, an exterior geometric performance in the range of a 2.40- (0.08 pixel) to 17.40-meter (0.58 pixel) offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.013 to 1.011 (offset and slope), and a spatial performance for band 130 of 1.5 pixels at full width at half maximum, with a modulation transfer function at a Nyquist frequency of 0.167.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030A","usgsCitation":"Shrestha, M., Sampath, A., Ramaseri Chandra, S.N., Christopherson, J.B., Shaw, J., and Anderson, C., 2021, System characterization report on the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS), chap. A of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 9 p., https://doi.org/10.3133/ofr20211030A.","productDescription":"iv, 9 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-126586","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":386252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/a/coverthb.jpg"},{"id":386253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/a/ofr20211030a.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030A"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Riparian ecosystems provide critical habitat for many species, yet assessment of vegetation condition at local scales is difficult to measure when considering large areas over long time periods. We present a framework to map and monitor two deciduous cover types, upland and riparian, occupying a small fraction of an expansive, mountainous landscape in north-central Wyoming. Initially, we developed broad-scale predictions of predominant woody vegetation types by integrating Landsat data into species distribution models and combining subsequent outputs into a synthesis map. Then, we evaluated a 35-year Landsat time series (1985–2019) using the Mann-Kendall test to identify significant trends in the condition of upland and riparian deciduous vegetation and assessed the rate and direction of change using the Theil-Sen estimator. Finally, we used plot level data to assess the utility of the framework to detect bottom-up controls (ungulate browse pressure and management actions) on vegetation condition. The synthesis map had an overall correct classification rate of 87% and field data indicated deciduous vegetation within 45 m of coniferous forest faces increased pressure of conifer expansion. The trend assessment identified consistent patterns operating at the landscape scale across both upland and riparian deciduous vegetation; a predominant greening trend was observed for 12 years followed by a 9-year browning trend, before switching back to a greening trend for the last 13 years of the study. Our results indicate trends are driven by the climate of the measurement period at the landscape scale. Although we did not find conclusive evidence to establish a strong link between browse pressure and satellite data, we highlight examples where prevailing trends can be overridden by local disturbance or management intervention. This framework is transferable to other understudied riparian environments throughout western North America to provide insight on ecohydrological processes and assess global and local stressors across broad spatiotemporal scales.
Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.
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The Chesapeake Bay faces threats from erosion, pollution, rising sea levels, and natural disasters. Because the Chesapeake Bay is prominent in Maryland’s history, economy, natural diversity, and way of life, protecting its waters and ecosystems is a priority for the State. Landsat imagery helps with a number of these efforts. Maryland also has a special relation with Landsat satellites; the USGS manages their flight operations out of NASA’s Goddard Space Flight Center in Greenbelt.
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\"}}]}","edition":"Version 1.0: April 26, 2021; Version 1.1: January 18, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
From the iconic skyline of New York City to the forested landscapes of the Adirondack Mountains and the countryside of the Allegheny Plateau, the State of New York is overflowing with diversity and life. Bordered by the Atlantic Ocean on the east and two of the Great Lakes to the north and west, New York has more than 7,600 lakes, ponds, and reservoirs and more than 70,000 miles of rivers and streams. New York’s stewardship of its freshwater resources is fundamental to the health and well-being of all who work at, reside in, and visit the State’s landmarks and places.
Harmful algal blooms in the State’s waterbodies are a growing concern and threaten the health of the region and its inhabitants. Images and data from Landsat satellites continue to provide critical information to scientists, public health officials, and resource managers who are studying the effects and risks of the problem.
Here is a closer look at just a few examples of the value of Landsat to New York.
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York\",\"nation\":\"USA \"}}]}","edition":"Version 1.0: April 26, 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
The Landsat 7 (L7) spacecraft and its instrument, the enhanced thematic mapper plus (ETM+), have been consistently characterized and calibrated since its launch in April of 1999. These performance metrics and calibration updates are determined through the U. S. Geological Survey (USGS) Landsat image assessment system (IAS), which has been performing this function since launch. Starting in 2016, the USGS adopted a tiered collection management structure for its Landsat data products that ensures a consistent method of processing for the Landsat archive within a given collection while allowing a set of calibration updates to be performed between any two given collections. The time frame between 2016 and the end of 2020 was part of the Landsat data Collection 1, in the middle of 2020 was the start of the Landsat Collection 2 data products. The start of a given collection initiates the reprocessing of the Landsat archive, which may involve one or more of a set of updated calibration parameters, improvements in the support data needed for product generation, and improved algorithms used in both the processing flow of products along with the characterization and calibration of the Landsat instruments and spacecraft. This paper discusses only the ETM+ geometric spacecraft and instrument calibration improvements for Collection 2. Three ETM+ calibration updates were made for the ETM+; updates to the thermal band odd-to-even detector alignment, sensor to attitude control system (ACS) alignment, and a cold-to-warm focal plane alignment adjustment. The sensor alignment updates impact only the accuracy of the systematic terrain products (L1GT), which are the products generated before applying any corrections based on the ground control used in registration. The band alignment changes impacted only bands 5, 6, and 7 within the focal plane. Other geometric calibration updates, such as scan mirror alignment, are done on a routine basis and are not part of the Collection 2 updates due to their more dynamic characteristics.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13091638","usgsCitation":"Choate, M., Rengarajan, R., Storey, J.C., and Lubke, M., 2021, Geometric calibration updates to Landsat 7 ETM+ instrument for Landsat Collection 2 products: Remote Sensing, v. 13, no. 9, 1638, 20 p., https://doi.org/10.3390/rs13091638.","productDescription":"1638, 20 p.","ipdsId":"IP-128282","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13091638","text":"Publisher Index Page"},{"id":399585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-04-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Choate, Mike 0000-0002-8101-4994 choate@usgs.gov","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":4618,"corporation":false,"usgs":true,"family":"Choate","given":"Mike","email":"choate@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":841295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110 rrengarajan@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":192376,"corporation":false,"usgs":true,"family":"Rengarajan","given":"Rajagopalan","email":"rrengarajan@contractor.usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":841296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":841297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":841298,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221215,"text":"70221215 - 2021 - Investigating vegetation responses to underground nuclear explosions through integrated analyses","interactions":[],"lastModifiedDate":"2021-06-07T13:08:15.404703","indexId":"70221215","displayToPublicDate":"2021-04-15T08:04:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Investigating vegetation responses to underground nuclear explosions through integrated analyses","docAbstract":"Vegetation has the potential to respond to underground nuclear explosions, yet these links have not been fully explored. Given the lack of previously described signatures, the changes in vegetation are possibly subtle. The integration of multiple different data streams is potentially a useful approach to improve signal detection. Here, we investigate whether semi-arid vegetation growth patterns responded to eight legacy underground nuclear tests at the Nevada National Security Site in southern Nevada, USA. We tested for spatial and temporal changes in vegetation cover, tree growth patterns, and tree leaf spectral properties using ground-based measurements, including those from tree-rings and hyperspectral surface vegetation reflectance, as well as space-based measurements of Normalized Difference Vegetation Index (NDVI) from Landsat. Multiple data streams suggest a localized (<1.2 km) spatial pattern whereby tree growth is enhanced closer to the source of the underground test relative to sites further away. We also observed a more regional (>1.2–9 km) pattern whereby tree growth is suppressed coincident with a drought beginning 1 year before the 1989 tests, but continuing in the 5 years following the tests, which is anomalous relative to what is expected based on the response of tree growth to previous droughts. Quantification of the relative effects of the tests on vegetation remains a challenge due to the coincident drought and the potential for other disturbances to have impacted tree growth at this time, but the integration of these data reveals a more nuanced growth response than any other one data set indicates alone.
Oregon’s landscape is as complex and diverse as it is beautiful. Mountain peaks in the Cascade Range soar higher than 10,000 feet. Crater Lake sinks to a depth of 1,943 feet, making it the deepest lake in the United States. Oregon’s lands feature forests, farm fields, grasslands, ocean coastline, rivers, a semidesert, and mountain ranges that stretch across the State. A wide range of birds, animals, and fish—including 16 federally endangered species—share this space with more than 4 million people.
With Oregon’s economy tied to these natural resources, industries like agriculture, timber, and fishing interlace with the well-being of wildlife and residents. Landsat data and imagery are one of many U.S. Geological Survey tools used by State resource managers and scientists to help achieve a natural balance and provide information about forests, habitats, and much more to Oregon decision makers. This will become even more important as research indicates climate change will make extreme weather more likely, leading to the likely increase in droughts, infestation, wildfires, and other natural hazards.
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\"}}]}","edition":"Version 1.0: April 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
Assessments of surface urban heat island (UHI) have focused on using remote sensing and land cover data to quantify UHI intensity and spatial distribution within a certain time period by including land cover information. In this study, we implemented a prototype approach to characterize the spatiotemporal variations of UHI using time series of Landsat land surface temperature products and annual land change information. We analyzed UHI distribution and change in Sioux Falls, South Dakota, in the north-central United States and found that the mean UHI intensity in the region was as large as 2.2°C during the period 1986–2017 with an increasing trend of 0.02°C per year within the area with a 5-km non-urban extent. The UHI intensity associated with high intensity urban land cover usually is stronger than with low intensity urban land cover. We evaluated the impact of different non-urban reference extents on UHI variation using different non-urban buffers. The result also suggests that the overall temporal trends of UHI intensity are almost the same when using a 5-km or 10-km non-urban buffer surrounding the urban core. The prototype approach provides a framework to consistently quantify UHI and monitor its change to a large geographic extent.
The State of Texas has the largest land area of any in the contiguous United States, and its sprawling landscapes show rich geographic diversity. The Lone Star State has cactus flats in the high plains of its far western panhandle, rolling hills in its western Trans-Pecos region, farms and ranchlands stretching across central Texas, thick forests and swamplands spread through the east, and 3,359 miles of Gulf of America coastline. The consistent, reliable, and historically unique Landsat data archive provides an important tool for Texans to track landscape changes and enhance their economy and environment.
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\"}}]}","edition":"Version 1.0: March 30, 2021; Version 1.1: October 17, 2022; Version 1.2: March 19, 2025","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Multispectral remote sensing data acquired by the Landsat 8 Operational Land Imager (OLI) sensor were analyzed using a new, automated technique to generate a map of exposed mineral and vegetation groups in the western San Juan Mountains, Colo., and the Four Corners Region of the United States. Band ratio results were combined into displayed mineral and vegetation groups using Boolean algebra. New analysis logic has been implemented to exploit the coastal aerosol band in Landsat 8 OLI data and identify concentrations of iron sulfate minerals. These may indicate the presence of near-surface pyrite, which can be a potential nonpoint source of acid rock drainage. Zoned occurrences of iron sulfate minerals in areas surrounding and down gradient of known sources of pyrite have been mapped using this technique. They show high correlation with occurrences of jarosite-bearing mineral assemblages, as mapped using airborne imaging spectrometer data and supporting field verification surveys. Mapping the occurrence of iron sulfate minerals produced by the weathering and oxidation of pyrite in exposed hydrothermally altered rocks can also provide an important indicator of the genetic environment of alteration and the associated mineral deposit type. The automated analysis methodology is being employed to rapidly and cost-effectively generate maps of large regions of the United States in support of U.S. Geological Survey mineral resource and mineral-environmental assessments. This map, which includes the ERDAS IMAGINE thematic raster format in the data release, has been attributed by pixel value with mineral and vegetation group classification data, which can be queried in most image processing and GIS software packages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3466","usgsCitation":"Rockwell, B.W., Gnesda, W.R., and Hofstra, A.H., 2021, Improved automated identification and mapping of iron sulfate minerals, other mineral groups, and vegetation using Landsat 8 Operational Land Imager data, San Juan Mountains, Colorado, and Four Corners Region: U.S. Geological Survey Scientific Investigations Map 3466, 1 sheet, scale 1:325,000, 37-p. pamphlet, https://doi.org/10.3133/sim3466.","productDescription":"Pamphlet: v, 37 p.; 1 Sheet: 38.72 x 37.71 inches; 2 Data Releases: Read Me","onlineOnly":"Y","ipdsId":"IP-079681","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":382731,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ONOKKX","text":"USGS data release","linkHelpText":"Digital map of iron sulfate minerals, other mineral groups, and vegetation of the San Juan Mountains, Colorado, and Four Corners Region derived from automated analysis of Landsat 8 satellite data"},{"id":383537,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BYV5H4","text":"USGS data release","linkHelpText":"Digital map of iron sulfate minerals, other mineral groups, and vegetation of the western United States derived from automated analysis of Landsat 8 satellite data"},{"id":382727,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3466/coverthb2.jpg"},{"id":382729,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3466/sim3466_sheet1.pdf","text":"Sheet 1. Improved Automated Identification and Mapping of Iron Sulfate Minerals, Other Mineral Groups, and Vegetation using Landsat 8 Operational Land Imager Data, San Juan Mountains, Colorado, and Four Corners Region","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3466 map sheet","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."},{"id":382728,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3466/sim3466_pamphlet.pdf","text":"Report","size":"2.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3466 pamphlet"},{"id":382730,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3466/ReadMe.txt","text":"Read Me","size":"12.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3466 map read me file"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Four Corners region, San Juan Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.4737548828125,\n 36.70365959719456\n ],\n [\n -106.2872314453125,\n 38.50948995925553\n ],\n [\n -109.039306640625,\n 39.10022600175347\n ],\n [\n -110.31372070312499,\n 36.87522650673951\n ],\n [\n -107.4737548828125,\n 36.70365959719456\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
Monitoring temporal dynamics of rangelands to detect and understand change in vegetation cover and composition provides a wealth of information to improve management and sustainability. Remote sensing allows the evaluation of both abrupt and gradual rangeland change at unprecedented spatial and temporal extents. Here, we describe the production of the National Land Cover Database (NLCD) Back in Time (BIT) dataset which quantified the percent cover of rangeland components (bare ground, herbaceous, annual herbaceous, litter, shrub, and sagebrush (Artemisia spp. Nutt.) across the western United States using Landsat imagery from 1985 to 2018. We evaluate the relationships of component trends with climate drivers at an ecoregion scale, describe the nature of landscape change, and demonstrate several case studies related to changes in grazing management, prescribed burns, and vegetation treatments. Our results showed the net cover of shrub, sagebrush, and litter significantly (p < 0.01) decreased, bare ground and herbaceous cover had no significant change, and annual herbaceous cover significantly (p < 0.05) increased. Change was ubiquitous, with a mean of 92% of pixels with some change and 38% of pixels with significant change (p < 0.10). However, most change was gradual, well over half of pixels have a range of less than 10%, and most change occurred outside of known disturbances. The BIT data facilitate a comprehensive assessment of rangeland condition, evaluation of past management actions, understanding of system variability, and opportunities for future planning.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13040813","usgsCitation":"Rigge, M.B., Homer, C., Shi, H., Meyer, D., Bunde, B., Granneman, B.J., Postma, K., Danielson, P., Case, A., and Xian, G.Z., 2021, Rangeland fractional components across the western United States from 1985 to 2018: Remote Sensing, v. 13, no. 4, 813, 24 p., https://doi.org/10.3390/rs13040813.","productDescription":"813, 24 p.","ipdsId":"IP-119778","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453335,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13040813","text":"Publisher Index Page"},{"id":436491,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ODAZHC","text":"USGS data release","linkHelpText":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2021"},{"id":384677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395378,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95IQ4BT","text":"USGS data release","description":"USGS data release","linkHelpText":"Rangeland Condition Monitoring Assessment and Projection (RCMAP) Fractional Component Time-Series Across the Western U.S. 1985-2020"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Kansas, Montana, Nebraska, Nevada, New Mexico, North Dakota, Oklahoma, Oregon, South Dakota, Texas, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.4697265625,\n 29.53522956294847\n ],\n [\n -102.6123046875,\n 32.58384932565662\n ],\n [\n -104.1064453125,\n 34.488447837809304\n ],\n [\n -100.0634765625,\n 37.020098201368114\n ],\n [\n -103.095703125,\n 39.50404070558415\n ],\n [\n -104.23828125,\n 40.38002840251183\n ],\n [\n -104.2822265625,\n 41.672911819602085\n ],\n [\n -101.162109375,\n 43.58039085560784\n ],\n [\n -102.3486328125,\n 48.60385760823255\n ],\n [\n -104.0185546875,\n 48.951366470947725\n ],\n [\n -121.025390625,\n 48.980216985374994\n ],\n [\n -122.25585937500001,\n 43.58039085560784\n ],\n [\n -123.04687499999999,\n 39.232253141714885\n ],\n [\n -121.81640624999999,\n 36.27970720524017\n ],\n [\n -120.58593749999999,\n 34.27083595165\n ],\n [\n -116.98242187499999,\n 32.54681317351514\n ],\n [\n -114.697265625,\n 32.69486597787505\n ],\n [\n -114.78515624999999,\n 32.39851580247402\n ],\n [\n -111.97265625,\n 31.690781806136822\n ],\n [\n -111.1376953125,\n 31.316101383495624\n ],\n [\n -108.19335937499999,\n 31.39115752282472\n ],\n [\n -108.19335937499999,\n 31.728167146023935\n ],\n [\n -106.2158203125,\n 31.765537409484374\n ],\n [\n -104.67773437499999,\n 30.29701788337205\n ],\n [\n -104.58984375,\n 29.649868677972304\n ],\n [\n -103.22753906249999,\n 28.9600886880068\n ],\n [\n -102.39257812499999,\n 29.916852233070173\n ],\n [\n -101.4697265625,\n 29.53522956294847\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin 0000-0003-4755-8135","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":252931,"corporation":false,"usgs":false,"family":"Homer","given":"Collin","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":811153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meyer, Debbie 0000-0002-8841-697X debbie.meyer.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":192361,"corporation":false,"usgs":true,"family":"Meyer","given":"Debbie","email":"debbie.meyer.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bunde, Brett 0000-0003-0228-779X brett.bunde.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":198821,"corporation":false,"usgs":true,"family":"Bunde","given":"Brett","email":"brett.bunde.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":811156,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Granneman, Brian J. 0000-0002-1910-0955 grann@usgs.gov","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":4209,"corporation":false,"usgs":true,"family":"Granneman","given":"Brian","email":"grann@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811157,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":252852,"corporation":false,"usgs":true,"family":"Postma","given":"Kory","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":811158,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Danielson, Patrick 0000-0002-2990-2783 pdanielson@usgs.gov","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":3551,"corporation":false,"usgs":true,"family":"Danielson","given":"Patrick","email":"pdanielson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":811159,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Case, Adam 0000-0002-6342-5853","orcid":"https://orcid.org/0000-0002-6342-5853","contributorId":252932,"corporation":false,"usgs":true,"family":"Case","given":"Adam","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":811160,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":811161,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70218279,"text":"70218279 - 2021 - Detecting shrub recovery in sagebrush steppe: Comparing Landsat-derived maps with field data on historical wildfires","interactions":[],"lastModifiedDate":"2021-02-24T13:02:35.006174","indexId":"70218279","displayToPublicDate":"2021-02-19T07:02:40","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Detecting shrub recovery in sagebrush steppe: Comparing Landsat-derived maps with field data on historical wildfires","docAbstract":"The need for basic information on spatial distribution and abundance of plant species for research and management in semiarid ecosystems is frequently unmet. This need is particularly acute in the large areas impacted by megafires in sagebrush steppe ecosystems, which require frequently updated information about increases in exotic annual invaders or recovery of desirable perennials. Remote sensing provides one avenue for obtaining this information. We considered how a vegetation model based on Landsat satellite imagery (30 m pixel resolution; annual images from 1985 to 2018) known as the National Land Cover Database (NLCD) “Back-in-Time” fractional component time-series, compared with field-based vegetation measurements. The comparisons focused on detection thresholds of post-fire emergence of fire-intolerant Artemisia L. species, primarily A. tridentata Nutt. (big sagebrush). Sagebrushes are scarce after fire and their paucity over vast burn areas creates challenges for detection by remote sensing. Measurements were made extensively across the Great Basin, USA, on eight burn scars encompassing ~500 000 ha with 80 plots sampled, and intensively on a single 113 000 ha burned area where we sampled 1454 plots.
Estimates of sagebrush cover from the NLCD were, as a mean, 6.5% greater than field-based estimates, and variance around this mean was high. The contrast between sagebrush cover measurements in field data and NLCD data in burned landscapes was considerable given that maximum cover values of sagebrush were ~35% in the field. It took approximately four to six years after the fire for NLCD to detect consistent, reliable signs of sagebrush recovery, and sagebrush cover estimated by NLCD ranged from 3 to 13% (equating to 0 to 7% in field estimates) at these times. The stabilization of cover and presence four to six years after fire contrasted with previous field-based studies that observed fluctuations over longer time periods.
While results of this study indicated that further improvement of remote sensing applications would be necessary to assess initial sagebrush recovery patterns, they also showed that Landsat satellite imagery detects the influence of burns and that the NLCD data tend to show faster rates of recovery relative to field observations.
","language":"English","publisher":"Springer","doi":"10.1186/s42408-021-00091-7","usgsCitation":"Applestein, C., and Germino, M., 2021, Detecting shrub recovery in sagebrush steppe: Comparing Landsat-derived maps with field data on historical wildfires: Fire Ecology, v. 17, no. 5, 11 p., https://doi.org/10.1186/s42408-021-00091-7.","productDescription":"11 p.","ipdsId":"IP-121781","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":453394,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-021-00091-7","text":"Publisher Index Page"},{"id":383586,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Idaho, Nevada, Utah","otherGeospatial":"Sagebrush steppe of the Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.35546875000001,\n 40.84706035607122\n ],\n [\n -111.357421875,\n 40.84706035607122\n ],\n [\n -111.357421875,\n 44.15068115978094\n ],\n [\n -119.35546875000001,\n 44.15068115978094\n ],\n [\n -119.35546875000001,\n 40.84706035607122\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-02-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":218003,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":810810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":810811,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70220098,"text":"70220098 - 2021 - Landsat 8 thermal infrared sensor scene select mechanism open loop operations","interactions":[],"lastModifiedDate":"2021-04-19T12:55:23.904501","indexId":"70220098","displayToPublicDate":"2021-02-09T07:52:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7131,"text":"MDPI Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Landsat 8 thermal infrared sensor scene select mechanism open loop operations","docAbstract":"The Landsat 8 (L8) spacecraft and its two instruments, the operational land imager (OLI) and thermal infrared sensor (TIRS), have been consistently characterized and calibrated since its launch in February 2013. These performance metrics and calibration updates are determined through the United States Geological Survey (USGS) Landsat image assessment system (IAS), which has been performing this function since its launch. The TIRS on-orbit geometric calibration procedures in-clude TIRS-to-OLI alignment, TIRS sensor chip assembly (SCA) alignment, and TIRS band align-ment. In December 2014, the TIRS instrument experienced an anomalous condition related to the instrument’s ability to accurately measure the location of the scene select mechanism (SSM). The SSM is a rotating mirror that allows the instrument’s field of view to be pointed at the Earth, for normal imaging, or at either deep space or an onboard black body, for radiometric calibration purposes. This anomalous condition in the SSM’s position sensor made it necessary to implement a new mode of operation for this mirror, termed mode-0. Mode-0 involves operating the mirror in an open-loop control state during normal mission operations when acquiring Earth data. Closed-loop mode-4 is needed for directing the mirror towards the radiometric calibration targets and is used approximately once every two weeks to collect radiometric calibration data. Mode-0 is used for most operational imaging because it does not require SSM encoder data, thereby allowing the SSM en-coder electronics to remain unpowered most of the time, reducing its use throughout the lifetime of the TIRS instrument, thus helping to preserve its nominal behavior during it use. This paper dis-cusses the geometric calibration of the SSM mirror during its current normal mode-0 set of image operations, as its open-loop control allows the mirror to drift over time in its uncontrolled state and its impacts on products. The results shown in this paper demonstrate that the ability to have on-going updates to the modeling of the TIRS SSM mirror model, in both an automated fashion and with a set of more manual operations, allows accuracy that approaches mode-4 results within days from the start of a mode-0 event.","language":"English","publisher":"MDPI","doi":"10.3390/rs13040617","usgsCitation":"Choate, M.J., Rengarajan, R., Storey, J.C., and Beckmann, T., 2021, Landsat 8 thermal infrared sensor scene select mechanism open loop operations: MDPI Remote Sensing, v. 13, no. 4, 617, 15 p., https://doi.org/10.3390/rs13040617.","productDescription":"617, 15 p.","ipdsId":"IP-124617","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":453535,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13040617","text":"Publisher Index Page"},{"id":385187,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-09","publicationStatus":"PW","contributors":{"authors":[{"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":814471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, R. 0000-0003-1860-7110","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":56036,"corporation":false,"usgs":true,"family":"Rengarajan","given":"R.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":814473,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beckmann, Tim 0000-0002-2557-0638","orcid":"https://orcid.org/0000-0002-2557-0638","contributorId":87995,"corporation":false,"usgs":true,"family":"Beckmann","given":"Tim","affiliations":[],"preferred":false,"id":814474,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218770,"text":"70218770 - 2021 - Ground‐penetrating radar, electromagnetic induction, terrain, and vegetation observations coupled with machine learning to map permafrost distribution at Twelvemile Lake, Alaska","interactions":[],"lastModifiedDate":"2021-08-17T16:09:40.432278","indexId":"70218770","displayToPublicDate":"2021-02-05T08:03:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3032,"text":"Permafrost and Periglacial Processes","active":true,"publicationSubtype":{"id":10}},"title":"Ground‐penetrating radar, electromagnetic induction, terrain, and vegetation observations coupled with machine learning to map permafrost distribution at Twelvemile Lake, Alaska","docAbstract":"We collected ground‐penetrating radar (GPR) and frequency‐domain electromagnetic induction (FDEM) profiles in 2011 and 2012 to identify the extent of permafrost relative to surface biomass and solar insolation around Twelvemile Lake near Fort Yukon, Alaska. We compared a Landsat‐derived biomass estimate and modeled solar insolation from a digital elevation model to the geophysical measurements. We show correspondence between vegetation type and biomass relative to permafrost extent and seasonal freeze–thaw. Thicker permafrost (≥25 m) was covered by greater biomass, and seasonal thaw depths in these regions were minimal (1 m). Shallow (1–3 m depth) and thin (20–50 cm) newly forming permafrost or frozen layers from the previous winter occurred below northward oriented slopes with thin biomass cover. South‐facing slopes exhibited permafrost when there was enough biomass to shield incoming solar energy. We developed an artificial neural network to predict permafrost extent across the broader region by mapping GPR‐observed instances of permafrost to FDEM, biomass, and terrain observations with 90.2% accuracy. We identified a strong linear correlation (r = −0.77) between permafrost probability and seasonal thaw depth, indicating that our models may also be used to explore thaw patterns and variability in active layer thickness. This study highlights the combined influence of biomass and terrain on the presence of permafrost and the value of evaluating such parameters via remote sensing to predict permafrost spatial or temporal variability. Incorporating diverse geophysical datasets with in‐situ validation into machine learning models demonstrates a useful approach to upscale estimated permafrost extent across large Arctic expanses.
Landsat Collections ensure that all Landsat Level-1 data are consistently calibrated and processed and retain traceability of data quality provenance. Landsat Collection 2 introduces improvements that harness recent advancements in data processing, algorithm development, data access, and distribution capabilities. Collection 2 includes Landsat Level-1 data for all sensors since 1972 and global Level-2 surface reflectance and surface temperature scene-based products for data acquired since 1982 starting with the Landsat Thematic Mapper sensor era.
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