The Landsat program has a long history of providing remotely sensed data to the user community. This history is being extended with the addition of the Landsat 9 satellite, which closely mimics the Landsat 8 satellite and its instruments. These satellites contain two instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI is a push-broom sensor that collects visible and near-infrared (VNIR) and short-wave infrared (SWIR) wavelengths at 30 m ground sample distance, along with a panchromatic 15 m band. The TIRS sensor contains two long-wave thermal spectral channels centered at 10.9 and 12 µm. The data from these two instruments, on both satellites, are combined into a single Landsat product. The Landsat 5–9 satellites follow a 16 day repeat cycle designated as the Worldwide Reference System (WRS-2), which provides a global notional gridded mapping for identifying individual Landsat scenes. The Landsat 8 and 9 satellites are flown such that their orbital tracks are separated by 8 days in this 16 day cycle. During the commissioning period of Landsat 9, and during its ascent to its operational WRS-2 orbit, the Landsat 9 satellite’s orbital track went under and crossed over the orbital track of the Landsat 8 satellite. This produced a unique situation where nearly time-coincident imagery could be obtained from the instruments of the two spacecrafts. From a radiometric standpoint, this allowed for near-time cross-calibration between the instruments to be performed. From a geometry perspective, calibration is achieved through high-resolution reference imagery over specific ground locations, thus ensuring calibration of the instruments and for the instruments to be well cross-calibrated geometrically. Although these underfly data do not provide calibration of the instruments between the platforms from a geometric perspective, they allow for the verification of the calibration steps involving the instruments and spacecraft. This paper discusses the co-registration of this unique set of data while also discussing other geometric aspects of these data by looking at and comparing the differences in sensor viewing and sun angles associated with the collections from the two platforms for imagery obtained over common geographic locations. The image-to-image comparisons between Landsat 8 and 9 coincident pairs, where both datasets are precision terrain products, are registered to within 2.2 m with respect to their root-mean-squared radial error (RMSEr). The 2.2 m represents less than 0.1 of a 30 m multispectral pixel in misregistration between the L9 and L8 underfly products that will be available to the user community. This unique dataset will provide well-registered, near-coincident image acquisitions between the two platforms that can be a key to any calibration or application comparisons. The paper also presents that, for images for which one of the image pairs failed precision corrections and became a terrain-corrected only product type, a range of 8–14 m RMSEr could be expected in co-registration, while, in cases where both image pairs failed the precision correction step and both images became a terrain-corrected only product type, a 14 m RMSEr could be expected for co-registration.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14153781","usgsCitation":"Choate, M.J., Rengarajan, R., Storey, J., and Lubke, M., 2022, Landsat 9 geometric characteristics using underfly data: Remote Sensing, v. 14, no. 15, 3781, 18 p., https://doi.org/10.3390/rs14153781.","productDescription":"3781, 18 p.","ipdsId":"IP-141262","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":446884,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14153781","text":"Publisher Index Page"},{"id":406670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"15","noUsgsAuthors":false,"publicationDate":"2022-08-06","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":851559,"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":851560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storey, James C. 0000-0002-6664-7232","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":242015,"corporation":false,"usgs":false,"family":"Storey","given":"James C.","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":851561,"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":851562,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233932,"text":"70233932 - 2022 - Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020)","interactions":[],"lastModifiedDate":"2024-01-19T15:18:40.15331","indexId":"70233932","displayToPublicDate":"2022-08-05T11:36:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8118,"text":"GIScience & Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020)","docAbstract":"Rangelands have a dynamic response to climate change, fire, and other anthropogenic disturbances. The Rangeland Condition, Monitoring, Assessment, and Projection (RCMAP) product aims to capture this response by quantifying the percent cover of eight rangeland components, associated error, and trends across the western United States using Landsat from 1985 to 2020. The current generation of RCMAP has been improved with more training data, regional-scale Landsat composites, and more robust change detection. We assess the temporal patterns in each component with a linear model and a structural change method that determines break points using an 8-year temporal moving window. The linear and structural change methods generally agreed on patterns of change, but the latter found breaks more often, with at least one break point in most pixels. The structural change model provides more robust statistics on the significant minority of pixels with non-monotonic trends, while detrending some interannual signal potentially superfluous from a long-term perspective. Although break point density within one year of fire and vegetation treatments was ~10× and ~4× that of unburned areas, respectively, break point detection in the correct year of fire was only moderately accurate. Climate responses in break points proved more robust, with strong spatiotemporal relation in break point density with both aridity index values and aridity index change. Break point density strongly responds to both increased and decreased aridity and is reflective of ecosystem resilience. Data provide spatiotemporal information on the occurrence of breaks, but even more importantly, attribute those change events to specific component(s).
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2022.2104786","usgsCitation":"Shi, H., Rigge, M.B., Postma, K., and Bunde, B., 2022, Trends analysis of Rangeland Condition Monitoring Assessment and Projection (RCMAP) fractional component time series (1985–2020): GIScience & Remote Sensing, v. 59, no. 1, p. 1243-1265, https://doi.org/10.1080/15481603.2022.2104786.","productDescription":"23 p.","startPage":"1243","endPage":"1265","ipdsId":"IP-135462","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":446897,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2022.2104786","text":"Publisher Index Page"},{"id":424623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SJXUI1","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Rangeland Condition Monitoring Assessment 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(Geography)","active":false,"usgs":true}],"preferred":true,"id":847707,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":293879,"corporation":false,"usgs":false,"family":"Postma","given":"Kory","affiliations":[{"id":63548,"text":"KBRwyle, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":847708,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunde, Brett 0000-0003-0228-779X","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":288364,"corporation":false,"usgs":false,"family":"Bunde","given":"Brett","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":847709,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237091,"text":"70237091 - 2022 - Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018","interactions":[],"lastModifiedDate":"2026-01-12T16:42:05.865776","indexId":"70237091","displayToPublicDate":"2022-08-01T09:21:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018","docAbstract":"This remote sensing section is based on Nagler et al. (in preparation for the journal Hydrological Processes) and is a summary of the USGS preliminary findings to date.
This report documents the changes in green foliage density (greenness) as measured by satellite vegetation index (VI) data and corresponding evapotranspiration (ET) in the riparian corridor of the Colorado River delta associated with the Minutes 319 and 323 environmental water deliveries using time-series data from 2013 through 2018. The report focuses on what happened only within the riparian corridor’s seven reaches since the 2014 flows, and despite being a continuation of measuring greenness and ET after the 2017 end of Minute 319, this study continued the tracking of these two variables, greenness and ET, in these original riparian corridor focal areas. Two spatial scales are used here: (1) Landsat satellite imagery at 30 m pixels and (2) the EOS-1 satellite sensor the Moderate Resolution Imaging Spectrometer (MODIS) with a resolution of 250 m pixels. The focal period includes 2013 (prepulse flow) and the years 2014-2018, with a focus on imagery collected from the Summer growing seasons 2014 through 2018 (one-year, pre-pulse and several post-pulse years, respectively).
This report re-creates the 2013-2017 Landsat-based results from Jarchow et al. (2017a, b) by using the same region of interest (ROI). The report now provides revised and re-created results using all new imagery acquisition and processing techniques, as well as extraction code, created by the Vegetation Index and Phenology (VIP) Lab of the Biosystems Engineering Department of the University of Arizona (UofA). In 2018, methods employed by the VIP lab (and not ArcGIS) were used. ArcGIS was only used in the newly processed data to display the final difference maps. The entire spatial tile data from NASA was downloaded and processed at the VIP Lab using satellite imagery at two resolutions: 250 m MODIS and 30 m Landsat using three sensors, Landsat 5, Landsat 7 ETM+ and Landsat 8 Operational Land Imager (OLI), with added scenes for each year based on new clear atmosphere requirements. The VIP lab clipped the river boundary and seven riparian reaches from the previously existing ROI used in Jarchow et al. (2017 a, b) for the analyses done under Minute 319. The NASA image datasets for this riparian corridor ROI in seven reaches were re-processed to produce additional vegetation index (VI) information for years 2013 to 2018 for this report. At the same time, the report acquired and processed imagery from 2000- 2018 (data outside the scope of this report and data not shown here). The additional VIs (NDVI, scaled NDVI, EVI, EVI2) were analyzed so that new assessments of greenness and ET could be produced from the imagery datasets following methods in Nagler et al. (2013). These VI choices were based on previous performance comparisons between biophysical ground-based data and radiometric satellite-based data collected from this riparian ecosystem (Nagler et al., 2001) as well as performance related to ET estimation (Nagler et al., 2005a, b) and current advancements in VIs such as EVI2.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Minute 323: Colorado River limitrophe and delta environmental flows monitoring interim report for 2018","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"International Boundary and Water Commission United States and Mexico","usgsCitation":"Nagler, P.L., Barreto-Munoz, A., Jarchow, C., and Didan, K., 2022, Section 5: Remote sensing of vegetation in the riparian corridor of the Colorado River’s delta 2013-2018, 10 p.","productDescription":"10 p.","startPage":"39","endPage":"48","ipdsId":"IP-114755","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":407594,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.17517089843749,\n 31.587894464070395\n ],\n [\n -114.3621826171875,\n 31.587894464070395\n ],\n [\n -114.3621826171875,\n 32.99484290420988\n ],\n [\n -115.17517089843749,\n 32.99484290420988\n ],\n [\n -115.17517089843749,\n 31.587894464070395\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barreto-Munoz, Armando","contributorId":131000,"corporation":false,"usgs":false,"family":"Barreto-Munoz","given":"Armando","email":"","affiliations":[{"id":7204,"text":"University of Arizona, Electrical and Computer Engineering","active":true,"usgs":false}],"preferred":false,"id":853314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":853315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":853316,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70235883,"text":"70235883 - 2022 - Special issue: Landsat update 50th anniversary","interactions":[],"lastModifiedDate":"2022-08-24T12:06:11.49232","indexId":"70235883","displayToPublicDate":"2022-07-31T07:05:51","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"title":"Special issue: Landsat update 50th anniversary","docAbstract":"Landsat Updates are prepared periodically and distributed electronically to provide information about Landsat activities and related topics of interest. This update highlights some of the articles and media that the USGS has developed over the last few months as well as upcoming events.","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Lloyd, A., 2022, Special issue: Landsat update 50th anniversary, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-143028","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":405518,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/landsat-missions/news/special-issue-landsat-update-50th-anniversary"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lloyd, Andrea 0000-0003-1396-7379","orcid":"https://orcid.org/0000-0003-1396-7379","contributorId":290504,"corporation":false,"usgs":false,"family":"Lloyd","given":"Andrea","email":"","affiliations":[{"id":62443,"text":"C2G, Inc., contractor under USGS","active":true,"usgs":false}],"preferred":false,"id":849611,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70234156,"text":"70234156 - 2022 - Fifty years of Landsat science and impacts","interactions":[],"lastModifiedDate":"2022-08-02T13:41:56.206949","indexId":"70234156","displayToPublicDate":"2022-07-28T08:08:38","publicationYear":"2022","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":"Fifty years of Landsat science and impacts","docAbstract":"Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application, and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and programmatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113195","usgsCitation":"Wulder, M., Roy, D., Radeloff, V., Loveland, T., Anderson, M.C., Johnson, D.M., Healey, S., Zhu, Z., Scambos, T.A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C., Masek, J.G., Hermosilla, T., White, J.C., Belward, A.S., Schaaf, C., Woodcock, C.E., Huntington, J., Lymburner, L., Hostert, P., Gao, F., Lyapustin, A., Pekel, J., Strobl, P., Eric Vermote, and Cook, B., 2022, Fifty years of Landsat science and impacts: Remote Sensing of Environment, v. 280, 113195, 21 p., https://doi.org/10.1016/j.rse.2022.113195.","productDescription":"113195, 21 p.","ipdsId":"IP-140037","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":446999,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113195","text":"Publisher Index 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For example, the Magnolia State was a hotbed for the Delta blues, an early 20th century musical genre with tremendous cultural effects in the United States and around the world. The “Delta” in Delta blues is the Mississippi River Delta, the largest delta in the United States. The Mississippi River is one of the longest in the Nation, having 1,800 navigable miles that serve as a natural transportation corridor that is foundational to America’s economic fortunes.
This important waterway forms Mississippi’s western border, and the fertile alluvial soils that fan out across its western third served to bolster its early economy and remain key drivers of its agriculture sector. These soils continue to support cotton, soybeans, corn, and agricultural products for which Mississippi is a national leader: rice and farmed catfish.
Such heavy reliance on the land necessitates a strong understanding of the health of—and threats to—the landscape. Spikes in extreme heat and associated wildfire dangers, the increasing frequency of powerful hurricanes on the Gulf Coast, and annual tornadoes all stand as challenges to the Magnolia State’s land resources.
The U.S. Geological Survey Landsat program serves a critical role in mapping, monitoring, and understanding changes across Mississippi. Here are a few examples of how Landsat benefits the State.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Alabama’s warm climate and rich soil bolster its agriculture and timber industries, but they also offer ideal conditions for natural playgrounds, enjoyed by humans and the wildlife that call the open green spaces home. Alabama has 21 State parks and 11 national parks, monuments, and trails across its diverse geography.
Cotton is no longer king in the Cotton State, but it remains a part of the fabric of its multibillion-dollar agriculture sector. The State also produces poultry, cattle, calves, corn, lumber, soybeans, and catfish.
The State is home to Dauphin Island, the first land mass seen by migratory birds and pollinators upon their return from South America. Dauphin Island acts as a landing zone for hundreds of species, including sandpipers, plovers, and herons, and protects Alabama’s coastline. Since 1961, Dauphin Island has been the home of the Audubon Bird Sanctuary.
The varied landscapes of the Cotton State face challenges from climate change, land change, and extreme weather. Satellite imagery from the U.S. Geological Survey Landsat Program can offer insight and understanding in these and other areas. Here are some ways Landsat benefits Alabama.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
From the flat, rich soil of western Tennessee to the Appalachian Mountains in the east, and rolling hills in between, “the Volunteer State” enjoys a wealth of natural resources.
The Tennessee, Cumberland, and Mississippi Rivers supply economically crucial navigation routes, along with recreation for residents and visitors. Additionally, 14 million acres of hardwood and softwood forests cover roughly one-half of the State, contributing an estimated $24 billion and nearly 100,000 jobs to Tennessee’s economy. Within a span of more than 400 miles, the State’s diverse agricultural products include cotton, corn, soybeans, poultry, horses, cattle, goats, hay, vegetables, nursery crops, and tobacco.
Energy production is important to Tennessee and the region, and power sources range from coal and nuclear to hydroelectric sources. Tourism also is a key industry, and music attractions and historical sites are balanced by natural features such as the Great Smoky Mountains National Park, which recorded 14.1 million visits and ranked second for most visited National Park Service site in the United States in 2021.
Landsat imagery’s broad geographic scale and rich historical archive have proven useful to land managers and State agencies for monitoring natural resources. Here are several ways Landsat has benefited Tennessee.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Rhode Island is an oasis of natural calm surrounded by heavily urbanized East Coast areas, which may explain why the smallest State in the United States is such a popular tourist destination for residents of New York, Pennsylvania, and New Jersey, or perhaps its popularity is a measure of the Ocean State’s abundant wildlife and picturesque views. Although small in land area, Rhode Island claims the largest estuary in New England in the 147-square-mile Narragansett Bay. Locals and visitors feast on clams caught in the bay, trek to glimpse shorebirds, or boat to 1 of 30 islands.
As with any coastal State, the natural wonders of Rhode Island face threats related to sea level rise and warming ocean temperatures. State agencies also work to fend off foes like the invasive Lymantria dispar (Linnaeus, 1758; spongy moth) and protect the forests that cover more than one-half of Rhode Island.
The U.S. Geological Survey Landsat Program, with 50 years of recurring Earth observations from space, offers a unique and freely available public data source for the study of land and coastal change across Rhode Island and the United States. Here are just a few of the ways Landsat imagery has been used to benefit the State.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Delaware’s status as the first State to ratify the U.S. Constitution is a well-known point of pride. “The First State” is among Delaware’s nicknames, alongside “the Blue Hen State,” “the Diamond State,” and “the Small Wonder,” the last of which relates to Delaware’s diminutive land area—larger only than Rhode Island.
Less well known, perhaps, is Delaware’s geographic distinction as the State with the lowest average elevation. Most of its land area rises no more than 80 feet above sea level. In fact, about 32,000 acres of Cypress Swamp, sometimes called the Great Cypress Swamp, stretch across its southern border.
These low elevations put Delaware at particular risk of sea level rise associated with climate change. Sea levels are rising more quickly than average for the Mid-Atlantic Region, which includes Delaware. The State has seen its coastal waters rise more than 1 foot over the past century.
The Landsat Program’s 50-year archive of repeat Earth observations offers an indispensable record of land change along the Nation’s coastlines. Imagery collected by Landsat satellites can inform studies of the coastline losses, flooding extents, and land cover conversions that affect climate resilience in Delaware. Landsat data also can support plans to mitigate those effects. Here are a few examples of the ways Delaware benefits from Landsat.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Louisiana holds a unique historical, economic, and cultural position in the national consciousness. Its off-shore oil operations help fuel the U.S. economy. The Port of South Louisiana is the busiest in the United States by cargo volume; the nearby Port of New Orleans is the sixth busiest. The former French and Spanish colony served as a key connection to the Caribbean long before U.S. independence, and Louisiana’s multinational effects soon melded into a Creole culture that had an outsized effect on America.
That heritage remains a powerful draw for the tourism industry in Louisiana. Gulf Coast breezes carry the aromas of tropical flowers, sweet beignets, and savory crawfish through the 13 colorful blocks of New Orleans’ French Quarter. Interwoven with the sounds of jazz, rock, country music, and zydeco, the city’s charms delight more than 18 million visitors each year.
The proximity of the Gulf Coast and the city’s elevation, however—just 6.5 feet above sea level—also offer an ominous warning of the ever-present threat of climate change and natural disaster. Hurricane Katrina battered New Orleans in 2005, an incident tied to more than 1,800 deaths that marks one of the most notorious U.S. weather-related tragedies in the 21st century. Environmental changes have amplified threats from tropical storms. Through more frequent and powerful storms, sea level rise threatens low-lying areas such as Lake Charles and creates unpredictable weather patterns that threaten the cities and agricultural operations to the north.
Landsat data offer rich information that can aid in early warning, disaster response, and the monitoring of recovery from natural disasters. Its historic, unparalleled 50-year archive of repeat Earth observations also serves to guide resiliency plans and feeds modeling that can help States like Louisiana prepare for coming coastal and inland change. Here are just a few examples of how Landsat has been used to study and understand Louisiana.
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"nation\":\"USA \"}}]}","edition":"Version 1.0: July 20, 2022; Version 1.1: March 19, 2025","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Iowa is famous for plenty of reasons—its State Fair butter sculptures, its first-in-the-Nation presidential caucuses, and the Iowa Hawkeyes football team, whose mascot doubles as the State nickname—but “corn” might be the first word to cross the mind of a non-Iowan.
Iowa consistently leads the United States in corn production and in the production of hogs, which in turn consume a sizable share of the corn grown there. Corn also drives Iowa’s ethanol industry, which put nearly 4.5 billion gallons of fuel into the supply chain in 2020—more than any other State.
Iowa owes its agricultural dominance largely to its fertile soils, making land management decisions critical to its future. Changes to land cover, more intensive land use, unusual precipitation patterns, and temperature changes, coupled with an influx of extreme weather events—some of which can be tied to or exacerbated by climate change—have placed pressure on the productive farm ground of Iowa’s 99 counties.
Landsat Program satellites can be especially useful in the monitoring and management of croplands across the United States. Backed by a 50-year record of Earth surface change, Landsat satellites can detect the details of vegetation health by peering into the infrared and near-infrared parts of the electromagnetic spectrum. Iowa has long served as a proving ground for Landsat-based agricultural research, and its residents and leaders have benefited from that work. Here are a few examples of how the Landsat Program benefits Iowa.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
At its widest point, a mere 80 miles separate the eastern and western borders of New Hampshire. Its northern and southern borders are just 175 miles apart. Even so, few States can boast as much rugged natural beauty per mile as the Nation’s fifth smallest.
Nestled within New Hampshire are 93 State parks teeming with moose, Ursus americanus (Pallas, 1780; black bears), coyotes, beavers, river otters, and foxes. The largest section of White Mountain National Forest cuts across north-central New Hampshire, drawing visitors to its lakes, streams, mountain peaks, and hardwood forests. New Hampshire also is home to Lake Winnipesaukee, the State’s largest lake, notable for its floating post offices, the annual “ice-out” contest that sees residents vying to guess the date its surface ice dissipates, and its supporting role in films such as “On Golden Pond” and “What About Bob?” However, the scenic forests of New Hampshire face challenges in the form of invasive species such as Lymantria dispar (Linnaeus, 1758; spongy moth), Adelges piceae (balsam woolly adelgid), and Agrilus planipennis (emerald ash borer). In recent years, New Hampshire’s lakes and streams have seen more cyanobacterial blooms as well.
The U.S. Geological Survey Landsat Program offers a consistent, reliable, and historically unmatched source of Earth observations that can aid in the mapping, monitoring, and management of New Hampshire’s land and water resources. Here are a few ways Landsat data have been used in the Granite State.
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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Kansas seems synonymous with agriculture, and rightly so—87 percent of Kansas land is devoted to it. As a key contributor to the State’s economy, agriculture makes Kansas one of the top producers of wheat, grain sorghum, and cattle in the country, but the State at the geographic center of the conterminous United States contains much more than fields and pastures.
Deciduous woodlands sprawl throughout the east. Tallgrass prairie—the only extensive stand remaining in the country—covers the east-central Flint Hills with more than 500 species of plants, many of them wildflowers, including the Sunflower State’s nickname inspiration. Near the center of Kansas, Cheyenne Bottoms—the largest marsh in the interior United States at 41,000 acres—welcomes migrating birds, including the endangered Grus americana (Linnaeus, 1758; whooping crane), by the thousands in the spring and fall. To the south, the inland saltwater marshes of Quivira National Wildlife Refuge attract many more.
Farther west, chalk outcroppings like Castle Rock and Monument Rocks rise above the landscape as fossil-bearing remnants of a sea floor from millions of years ago. Oil and natural gas fields exist throughout the State. After two University of Kansas professors discovered helium in a sample from one natural gas well in 1905, Kansas became a substantial supplier. The Landsat Program provides tools for monitoring and managing our conservation lands and their many resources. Here are several ways Landsat helps Kansas.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The rolling plains of Nebraska occupy a storied place in the American psyche. For those living outside the Midwest, the Cornhusker State may be seen as a symbol of the Nation’s heartland, cropped border to border, with country churches and barely standing barns to be found around every turn of its gravel roads.
Although the pioneer history and agricultural heritage of the 37th State lend credence to this idyllic view, Nebraska’s varied landscapes and modern economy make the reality of life in the State more complex than its rural image would suggest.
Agriculture remains Nebraska’s top industry, but manufacturing now represents 12 percent of the State’s gross domestic product. The financial services and insurance industries account for 8 percent of the gross domestic product in Nebraska, which is the headquarters of Berkshire Hathaway and Mutual of Omaha, the latter of which is named after Nebraska’s largest city, which grew nearly 12 percent between 2010 and 2020. Cropland is indeed a prominent feature in Nebraska, but the State also is home to 8 State parks, 5 national parks, 2 national forests, and 3 national grasslands. The grasses and dunes of the Nebraska Sand Hills that stretch across the north-central quarter of the State are a National Natural Landmark.
Data from the Landsat satellite program contribute to the study and management of Nebraska’s land in myriad ways, from monitoring crop productivity and aiding in rangeland management to tracking damage from floods, droughts, or hurricanes. Land cover maps produced using data pulled from the 50-year Landsat archive can offer important insights into urban growth, land use trends, and land change patterns. Here are a few examples of how Landsat has been used in Nebraska.
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12201 Sunrise Valley Drive
Reston, VA 20192
Wisconsin could be called a State of icons, and many of the icons can trace their roots to the abundant resources within its four borders. Big beer companies in Milwaukee that began in the 1800s made their beer from water from nearby lakes and rivers, kept it cool with ice from those same sources, and stored it in containers made of harvested wood from State forests. Dairy and cheese factories rely on milk from dairy farms with fields and pastures and generate billions in revenue for the State’s economy. The Wisconsin Dells, Door County, and the Northwoods draw tourists with their natural beauty and recreation opportunities.
Wisconsin contains more than 17 million acres of hardwood and coniferous forest, much of it on land reforested since the large-scale timber cutting of the 1800s and early 1900s. The Badger State boasts nearly 15,000 lakes within its borders and touches two Great Lakes—a bit of Lake Superior and a considerable length of western Lake Michigan. Wisconsin ranks second in the Nation for milk production, but it ranks first for cheese, cranberries, snap beans, and milk goats.
Data and imagery from Landsat Earth observation systems assist agencies and land managers in monitoring these resources and planning for future management. Here are several ways Landsat benefits Wisconsin.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
New Jersey ranks among the smallest of States but packs a lot within its borders. Of course, that includes the more than 9 million people who make it the most densely populated State, but it also includes diverse landscapes. Ranging from Atlantic Ocean barrier islands and beaches to the Appalachian Mountains, and Pine Barrens forests to swampland, the “Garden State” retains remnants of an agricultural past with produce, horse, and dairy farms and plant nurseries.
The third State to join the Union has had a strong geographic presence in U.S. history. More than 200 American Revolution battles and skirmishes were fought in New Jersey—more than in any other State. Manufacturing, tourism, and fishing have each had a significant effect on New Jersey’s industrial history. Today, many residents commute from this strategic location to work in New York City, just across the Hudson River to the northeast, or in Philadelphia, just across the Delaware River to the west.
A dense population and climate change can increase risks for residents and the natural resources around them. Landsat helps officials monitor and plan for resilient cities and landscapes. Here are a few specific ways Landsat benefits New Jersey.
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Jersey\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The State of North Dakota once did not figure prominently in the Nation’s economy. The sparsely populated State supported food production, and hunters and anglers were drawn to its lakes, rivers, and wide-open spaces, but its economy was overshadowed by that of other States. However, the State and its prairie expanses recently rocketed from an economic afterthought to a national energy leader with the soaring production of oil and natural gas in the Bakken oil patch.
The Bakken development has been transformative for North Dakota’s landscapes in myriad ways. It has boosted economic output, drawn thousands of new residents to cities like Williston and Watford City, and led to a proliferation of oil and gas pads.
In the past two decades, North Dakota experienced other major changes, such as the expansion of the depressional wetlands of the Prairie Pothole Region on the eastern side of the State. These critical breeding areas for waterfowl, which stretch across Minnesota, South Dakota, North Dakota, and Canada, are home to more than 50 percent of North America’s migratory birds.
Changes from oil and gas production, urban development, and wetland resurgence can all be tracked over time using the unparalleled Earth observation record of the U.S. Geological Survey Landsat data archive. Its 50-year record of repeat imagery also aids in the monitoring, cataloging, and management of cropland, invasive insect species, and natural or human-made disaster recovery. Here are just a few examples of the benefits offered to North Dakota by the Landsat Program.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Natural resources have always been a strength for Indiana. Once largely covered by forest, the State now includes a mix of forest, farmland, wetlands, and small lakes. In fact, farms and forested areas make up more than 80 percent of the land. The Ohio River forms the southern border, and the south shore of Lake Michigan contrasts with urban development in the northwestern corner. Indiana Dunes National and State Parks reside just east of the Chicago metropolitan area.
Agriculture adds more than $30 billion to the economy in Indiana, which placed eighth in the country for agricultural exports at $4.6 billion in 2017. Indiana ranks in the top five States nationally for the production of corn and soybeans. The “Hoosier State” also grows sizable crops of popcorn, spearmint, peppermint, pumpkins, tomatoes, and watermelon. Additionally, hogs, cattle, dairy, and poultry contribute to the agricultural economy. Other industries important to Indiana include manufacturing, medicine, energy, and mining. Mineral sources vary from coal, building stone, and gypsum to sand, gravel, and shale.
Landsat can help monitor the condition of natural resources and the effects of extreme weather events. Here are several ways Landsat has benefited Indiana.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2022.2088651","usgsCitation":"Gumma, M., Thenkabail, P., Panjala, P., Teluguntla, P., Yamano, T., and Mohammad, I., 2022, Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security: GIScience & Remote Sensing, v. 59, no. 1, p. 1048-1077, https://doi.org/10.1080/15481603.2022.2088651.","productDescription":"30 p.","startPage":"1048","endPage":"1077","ipdsId":"IP-135578","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":447129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2022.2088651","text":"Publisher Index 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(ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teluguntla, Pardhasaradhi","contributorId":294758,"corporation":false,"usgs":false,"family":"Teluguntla","given":"Pardhasaradhi","affiliations":[{"id":63639,"text":"Bay Area Environmental Research Institute (BAERI) @ USGS","active":true,"usgs":false}],"preferred":false,"id":848828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yamano, Takashi","contributorId":294759,"corporation":false,"usgs":false,"family":"Yamano","given":"Takashi","email":"","affiliations":[{"id":63641,"text":"Asian Development Bank (ADB)","active":true,"usgs":false}],"preferred":false,"id":848829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mohammad, Ismail","contributorId":294760,"corporation":false,"usgs":false,"family":"Mohammad","given":"Ismail","email":"","affiliations":[{"id":7069,"text":"International Crops Research Institute for the Semi Arid 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accomplished by added water and restoration efforts to offset declines measured since 2000 by Landsat. We use the two-band Enhanced Vegetation Index (EVI2; a proxy for greenness) and evapotranspiration (ET, mm/day) using EVI2 and potential ET(ETo) from Yuma Valley. We assess if restoration with only 7.5% landcover had an impact on the unrestored reach-level landcover by measuring these two metrics, EVI2 and ET(EVI2) by comparing restored and unrestored areas. A key finding is that over 21-years EVI2 in the unrestored corridor decreased by 23.6% and ET(EVI2) decreased by 32% (0.87 mm/day). Since 2011, the unrestored reaches lost EVI2 (11%) and −0.73 mm/day ET(EVI2) (28%), but restored sites increased in EVI2 (36%) and 0.58 mm/day ET(EVI2) (20%). Water delivered to restored sites increased EVI2 by 33.6% and 1.29 mm/day ET(EVI2) (58%). EVI2 and ET(EVI2) as a function of the Standardized Precipitation Evapotranspiration Index for drought periods highlight that restored and unrestored sites respond differently. Unrestored reaches are in decline; restored sites show increases in EVI2 and ET(EVI2). Restored sites do not have a significant impact on unrestored adjacent area, but smaller surface flows, a greater reliance on directed agricultural return flows, and deliveries of water to active restoration sites have revitalized habitat and increased ecosystem services in the delta.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.13036","usgsCitation":"Nagler, P.L., Sall, I., Barreto-Muñoz, A., Gómez-Sapiens, M., Nouri, H., Borujeni, S.C., and Didan, K., 2022, Effect of restoration on plant greenness and water use in relation to drought in the riparian corridor of the Colorado River delta: Journal of the American Water Resource Association (JAWRA), v. 58, no. 5, p. 746-784, https://doi.org/10.1111/1752-1688.13036.","productDescription":"39 p.","startPage":"746","endPage":"784","ipdsId":"IP-133058","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":489192,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13036","text":"Publisher Index Page"},{"id":403063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","otherGeospatial":"Colorado River delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.90600585937499,\n 31.1140915948987\n ],\n [\n -114.84832763671876,\n 31.015278981711266\n ],\n [\n -114.15069580078125,\n 31.50362930577303\n ],\n [\n -114.29351806640625,\n 31.580875273985466\n ],\n [\n -114.51873779296875,\n 31.69779270531287\n ],\n [\n -114.59564208984374,\n 31.823898240526955\n ],\n [\n -114.76043701171875,\n 31.98012335736804\n ],\n [\n -114.92248535156249,\n 32.115148622612445\n ],\n [\n -114.91424560546875,\n 32.13375715632646\n ],\n [\n -114.98565673828124,\n 32.19188435632485\n ],\n [\n -115.22735595703125,\n 31.982453066235763\n ],\n [\n -115.18615722656249,\n 31.847232251731132\n ],\n [\n -115.10650634765625,\n 31.80289258670676\n ],\n [\n -115.06256103515624,\n 31.704803074739214\n ],\n [\n -115.08453369140625,\n 31.68143311662596\n ],\n [\n -115.07354736328125,\n 31.48020882071693\n ],\n [\n -114.90600585937499,\n 31.1140915948987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":845784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sall, Ibrahima 0000-0002-7526-636X","orcid":"https://orcid.org/0000-0002-7526-636X","contributorId":251750,"corporation":false,"usgs":false,"family":"Sall","given":"Ibrahima","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":845785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barreto-Muñoz, Armando","contributorId":239891,"corporation":false,"usgs":false,"family":"Barreto-Muñoz","given":"Armando","affiliations":[{"id":48028,"text":"University of Arizona, Biosystems Engineering, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":845786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gómez-Sapiens, Martha","contributorId":292779,"corporation":false,"usgs":false,"family":"Gómez-Sapiens","given":"Martha","affiliations":[{"id":62998,"text":"Department of Geosciences, University of Arizona, Tucson, AZ 85721, USA","active":true,"usgs":false}],"preferred":false,"id":845789,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nouri, Hamideh","contributorId":178847,"corporation":false,"usgs":false,"family":"Nouri","given":"Hamideh","affiliations":[],"preferred":false,"id":845787,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Borujeni, Sattar Chavoshi","contributorId":240671,"corporation":false,"usgs":false,"family":"Borujeni","given":"Sattar","email":"","middleInitial":"Chavoshi","affiliations":[],"preferred":false,"id":845788,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Didan, Kamel","contributorId":292780,"corporation":false,"usgs":false,"family":"Didan","given":"Kamel","affiliations":[{"id":62999,"text":"Biosystems Engineering, University of Arizona, Tucson, AZ, 85721 USA","active":true,"usgs":false}],"preferred":false,"id":845790,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232502,"text":"fs20223045 - 2022 - Connecticut and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:04:51.64174","indexId":"fs20223045","displayToPublicDate":"2022-07-05T13:52:57","publicationYear":"2022","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":"2022-3045","displayTitle":"Connecticut and Landsat","title":"Connecticut and Landsat","docAbstract":"Connecticut, the third-smallest State by land area, is the fourth most densely populated in the United States. Connecticut’s tightly packed cities serve as international hubs for the finance and insurance industries. These same urban enclaves host highly regarded institutions of higher learning, such as Yale, the University of Connecticut, and Quinnipiac and Wesleyan Universities.
Yet Connecticut has surprisingly varied natural features. Its residents are never far from pristine environments that teem with wildlife and convey the sounds, scents, and scenery of the natural world.
The Connecticut River, the longest in New England at 407 miles, cuts through the State’s center and drains into Long Island Sound, as do the Thames and Housatonic Rivers. Together, these rivers form an estuary spawning ground for hundreds of freshwater and saltwater aquatic species. North and east of the coastal lowlands that hug the estuary are the highlands of the Southern Taconic Range and the Berkshires. North and west of the coastline, smaller rivers and valleys weave through densely forested landscapes dotted with State parks and scenic lakes.
Imagery from the U.S. Geological Survey Landsat archive, a trusted public resource with more than 50 years of worldwide Earth observations, provides a powerful tool for analyzing the interplay of human habitation and natural landscapes in Connecticut. Landsat data also offer land managers a critical information source for managing damage from invasive species and monitoring the local landscape effects of a changing global climate.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Many may recall “Take Me Home, Country Roads,” made famous by John Denver, leads with the lyric “almost heaven, West Virginia, Blue Ridge Mountains, Shenandoah River.” The descriptors are apt. Nicknamed the “Mountain State,” West Virginia inspires thoughts of coal mining or logging in the Appalachian Mountains and valleys, or the leaping trout in the winding waters of New River Gorge National Park & Reserve.
West Virginia is second only to Wyoming in coal production nationwide. Its mines produced more than 67 million tons of coal in 2020. Logging pumped about $3.4 billion into the State’s economy in 2019; tourist spending added another $4.6 billion.
Those industries are key for West Virginia, but agriculture and fisheries also play a role in the State's economic fortunes. Peaches and apples are major drivers of food production, as are beef and poultry. Trout, meanwhile, are caught and sold commercially, and are stocked throughout the State's rivers for local and visiting anglers.
Scientists, land managers, and others use imagery from the U.S. Geological Survey Landsat satellite program's deep historical archive to better understand and manage West Virginia’s storied forests, fields, mountains, and foothills.
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Virginia\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
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Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frsen.2022.894571","usgsCitation":"Friedl, M.A., Woodcock, C.E., Olofsson, P., Zhu, Z., Loveland, T., Stanimirova, R., Arevalo, P., Bullock, E.L., Hu, K., Zhang, Y., Turlej, K., Tarrio, K., Kristina, M., Gorelick, N., Wang, J.A., Barber, C., and Souza Jr., C., 2022, Medium spatial resolution mapping of global land cover and land cover change across multiple decades from Landsat: Frontiers in Remote Sensing, v. 3, 894571, 15 p., https://doi.org/10.3389/frsen.2022.894571.","productDescription":"894571, 15 p.","ipdsId":"IP-142442","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":447282,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frsen.2022.894571","text":"Publisher Index 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Pontus","contributorId":131007,"corporation":false,"usgs":false,"family":"Olofsson","given":"Pontus","email":"","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":901839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhe 0000-0003-4716-2309","orcid":"https://orcid.org/0000-0003-4716-2309","contributorId":272038,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":901840,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loveland, Thomas R. 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":337044,"corporation":false,"usgs":false,"family":"Loveland","given":"Thomas R.","affiliations":[{"id":7248,"text":"emeritus 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Center","active":true,"usgs":true}],"preferred":true,"id":901852,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Souza Jr., Carlos","contributorId":337053,"corporation":false,"usgs":false,"family":"Souza Jr.","given":"Carlos","affiliations":[{"id":80958,"text":"IMAZON","active":true,"usgs":false}],"preferred":false,"id":901853,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70254298,"text":"70254298 - 2022 - Satellite remote sensing of crop water use across the Missouri River Basin for 1986–2018 period","interactions":[],"lastModifiedDate":"2024-05-17T11:43:50.362857","indexId":"70254298","displayToPublicDate":"2022-06-24T06:42:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Satellite remote sensing of crop water use across the Missouri River Basin for 1986–2018 period","docAbstract":"Understanding historical crop water use (CWU) dynamics is important to improve land and water management. In this study, well-validated (coefficient of determination = 0.91, percent bias = 4%, and percent root mean square error = 11.8%) Landsat-based actual evapotranspiration (ETa) time-series estimations were used to (1) assess summer season CWU (CWU-Su) dynamics, (2) investigate CWU-Su trends over the study period (1986–2018; 33 years) at the regional- and pixel-scales, and (3) attribute CWU-Su driving factors across Missouri River Basin. Spatial variability of the ETa estimations along with the observed bimodal probability density distribution of ETa highlighted a strong relation between land cover and water uses across the basin. The bimodal distribution of ETa also indicated the presence of two major landcovers in the basin. The drier foothill regions in northwestern Missouri River Basin, dominated by grassland/shrubland, showed lower ETa (< 500 mm/year), whereas cropland dominated regions in lower semi-humid basin and forested subbasins exhibited higher ETa (> 600 mm/year). The CWU-Su anomalies revealed the vulnerability of the basin to year-to-year weather conditions. The CWU-Su trend analysis revealed a significant positive trend (p < 0.1) at the regional-scale affecting 30% of basin’s cropland pixels. The cropland pixels under positive CWU-Su trend were found to be clustered in the eastern and central Missouri River Basin as a result of the combined effect of increased crop production area, increased crop yields, crop practice shifts to higher biomass crops, and increased irrigated land. The effect of improved irrigation and water management practices on reducing CWU-Su was observed in western Missouri River Basin, which had a stable major crop throughout the study period. Overall, the study highlights the usefulness of Landsat imagery and remote sensing-based ETa modeling approaches in generating historical time-series ETa maps over a wide range of elevation, vegetation, and climate.