The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 1 (January–March) of 2023. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was a Landsat 8 safehold anomaly. On January 26, 2023, the Global Positioning System (GPS) onboard Landsat 8 became invalid because the GPS fault tripped. Later that same day, the GPS was reinitialized, but a Field of View 1 fault trip occurred early the next morning, causing the observatory to go into Earth Point Safe mode, which put the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) into safehold. Once it was safe to reactivate the sensors, the OLI was transitioned to operational status late on January 27 and TIRS was reactivated early on January 28. Additional information about the Landsat 8 safehold anomaly is here: https://www.usgs.gov/landsat-missions/news/landsat-8-recovers-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231050","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., and Miller, J., 2023, ECCOE Landsat quarterly Calibration and Validation report—Quarter 1, 2023: U.S. Geological Survey Open-File Report 2023–1050, 39 p., https://doi.org/10.3133/ofr20231050.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-152817","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":419162,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":419161,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1050/images/"},{"id":419158,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1050/coverthb.jpg"},{"id":419181,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231050/full"},{"id":419160,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1050/ofr20231050.XML"},{"id":419159,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1050/ofr20231050.pdf","text":"Report","size":"4.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1050"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Transport of riverine and wetland-derived dissolved organic carbon (DOC) spanning tidal wetlands, estuaries, and continental shelf waters functionally connects terrestrial and aquatic carbon reservoirs, yet the magnitude and ecological significance of this variable and its spatiotemporal linkage remains uncertain for coastal deltaic regions, such as Mississippi River Delta Plain, which includes Mississippi (MR) and Atchafalaya (AR) rivers and estuaries with vast expanses of wetlands and coastal forests. We examined DOC dynamics and fluxes in this large river-dominated wetland-estuarine system for the period between 2019 and 2021 that included an extreme river flood event in 2019, two major hurricanes (Barry in 2019 and Ida in 2021), and cold front passage using an improved adaptive quasi-analytical algorithm (QAA-AD) applied to multi-satellite sensors (Sentinel 3A/B OLCI, Landsat-8/OLI and Sentinel-2A/B MSI) with varying spectral and spatial (10/30/300 m) resolutions. The DOC estimates from multi-satellite sensors in combination with water fluxes were used to assess DOC fluxes from two large rivers (MR and AR) and small channels across the delta plain. Overall, this system delivered a total of 6.7 Tg C yr-1 (1 Tg = 1012g) into the estuarine zone and the northern Gulf of Mexico (nGoM) during 2019. High DOC fluxes from the AR (1.3 Tg C yr-1) and MR (4.5 Tg C yr-1) were associated with the extreme flood event in 2019. Hurricanes that occurred in the study period also contributed to the wetland and estuarine DOC fluxes into continental shelf waters; for example, the passage of Hurricane Barry in July 2019, delivered over a 3-day period ~1.33 ×109 g DOC from Barataria Basin into the nGoM. Sentinel 2-MSI land and water classification revealed that Hurricane Ida eroded a total of 1.34×108 m2 of marshes in middle Barataria Basin, converting those habitats into open water with 3.0 m inundation depth and high DOC concentrations (16.4 mg L-1), a potentially large DOC source to the coastal waters. Overall, storms and flood events are major sources of DOC flux that facilitate transport of upstream carbon as well as transformation of carbon in the wetlands, through the conversion of vegetated wetland to open water.
","language":"English","publisher":"Frontiers Media S.A.","doi":"10.3389/fmars.2023.1159367","usgsCitation":"Liu, B., D’Sa, E.J., Messina, F., Baustian, M.M., Maiti, K., Rivera-Monroy, V.H., Huang, W., and Georgiou, I.Y., 2023, Dissolved organic carbon dynamics and fluxes in Mississippi-Atchafalaya deltaic system impacted by an extreme flood event and hurricanes: A multi-satellite approach using Sentinel-2/3 and Landsat-8/9 data: Frontiers in Marine Science, v. 10, 1159367, 24 p., https://doi.org/10.3389/fmars.2023.1159367.","productDescription":"1159367, 24 p.","ipdsId":"IP-148973","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":442812,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2023.1159367","text":"Publisher Index Page"},{"id":418810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Atchafalaya River, Mississippi River, Mississippi River Delta Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.31893133610087,\n 30.458926156651998\n ],\n [\n -92.31893133610087,\n 27.86368380104267\n ],\n [\n -89.10966256396617,\n 27.86368380104267\n ],\n [\n -89.10966256396617,\n 30.458926156651998\n ],\n [\n -92.31893133610087,\n 30.458926156651998\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2023-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Bingqing","contributorId":304014,"corporation":false,"usgs":false,"family":"Liu","given":"Bingqing","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":877207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"D’Sa, Eurico J.","contributorId":316255,"corporation":false,"usgs":false,"family":"D’Sa","given":"Eurico","email":"","middleInitial":"J.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":877208,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Messina, Francesca","contributorId":316256,"corporation":false,"usgs":false,"family":"Messina","given":"Francesca","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":877209,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":877210,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maiti, Kanchan","contributorId":316257,"corporation":false,"usgs":false,"family":"Maiti","given":"Kanchan","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":877211,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rivera-Monroy, Victor H. 0000-0003-2804-4139","orcid":"https://orcid.org/0000-0003-2804-4139","contributorId":200322,"corporation":false,"usgs":false,"family":"Rivera-Monroy","given":"Victor","email":"","middleInitial":"H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":877212,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huang, Wei","contributorId":316258,"corporation":false,"usgs":false,"family":"Huang","given":"Wei","email":"","affiliations":[{"id":40642,"text":"Oak Ridge National Lab","active":true,"usgs":false}],"preferred":false,"id":877213,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Georgiou, Ioannis Y.","contributorId":205361,"corporation":false,"usgs":false,"family":"Georgiou","given":"Ioannis","email":"","middleInitial":"Y.","affiliations":[{"id":37089,"text":"Pontchartrain Institute for Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":877214,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70247754,"text":"70247754 - 2023 - Riparian vegetation response amid variable climate conditions across the Upper Gila River watershed: Informing Tribal restoration priorities","interactions":[],"lastModifiedDate":"2023-08-16T12:13:14.531046","indexId":"70247754","displayToPublicDate":"2023-07-04T07:08:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Riparian vegetation response amid variable climate conditions across the Upper Gila River watershed: Informing Tribal restoration priorities","docAbstract":"Riparian systems across the Southwest United States are extremely valuable for the human and ecological communities that engage with them. However, they have experienced substantial changes and stresses over the past century, including non-native vegetation expansion, vegetation die-offs, and increased fire activity. Vegetation management approaches, such as ecological restoration, may address some of these stressors as well as reduce the risk of future impacts. We apply remote sensing to inform restoration priorities along the upper Gila River within the San Carlos Apache Reservation and Upper Gila River watershed. First, we develop a spatially and temporally explicit trend analysis across three observed climate periods (1985–1993, 1993–2014, 2014–2021) using the Landsat-derived indices to quantify changes in riparian vegetation conditions. These maps can be used to identify areas potentially more at risk for degradation. Second, we analyze changes in riparian vegetation within a climate framework to better understand trends and the potential effect of climate change. Vegetation greenness has largely increased throughout the watershed despite intensifying drought conditions across our study period, though areas within the lower watershed have shown increased stress and higher rates of wildfire and other disturbances over the past 5-years. Nevertheless, small-scale restoration activities appear to show improving vegetation conditions, suggesting efficacy of these restoration activities. Results from this study may be integrated with restoration objectives to develop a restoration plan that will help riparian vegetation communities adapt to change.
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 4 (October–December) of 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was the lowering of the Landsat 7 orbit. On April 6, 2022, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor was placed into standby mode, and a series of spacecraft burns was completed through the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at a lower orbit of 697 kilometers on May 5, 2022, extending the science mission. Additional information about the Landsat 7 orbit lowering is here: https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231044","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., and Miller, J., 2023, ECCOE Landsat quarterly Calibration and Validation report—Quarter 4, 2022: U.S. Geological Survey Open-File Report 2023–1044, 39 p., https://doi.org/10.3133/ofr20231044.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-149560","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":417400,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231044/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":417365,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":417362,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1044/ofr20231044.pdf","text":"Report","size":"4.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1044"},{"id":417361,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1044/coverthb.jpg"},{"id":417363,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1044/ofr20231044.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":417364,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1044/images"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Context: Changes in disturbance regimes, including reductions in flooding and geomorphic dynamism from dam construction and flow regulation, have facilitated invasion by eastern redcedar (Juniperus virginiana L.), an upland tree species, in the understory of floodplain forests along the Missouri National Recreational River (MNRR).
Objectives: Our aim was to determine the spatiotemporal patterns and environmental drivers of redcedar invasion along the MNRR.
Methods: We used the Normalized Difference Vegetation Index (NDVI) calculated from winter Landsat imagery to construct a time series of maps showing spatial changes in redcedar abundance and distribution from 1982 to 2017 in both riparian and upland habitats along the MNRR. We determined how environmental factors (e.g., soil drainage ability, flood recurrence interval, 1980s LULC, lateral distance to the river, and channel incision) have influenced current (2017) redcedar occurrence and abundance in riparian habitats using random forest models (RFM).
Results: Time-series maps indicated that detectable redcedar cover occurred over less than 5% of the study area before 1985, increased steadily from 1985 to 2000, and more than tripled from 2000 to 2010. After 2010, redcedar abundance continued to increase in upland areas but declined following the 2011 Missouri River flood in the floodplain. RFMs indicated that river incision, distance to the river, soil drainage, 1984 LULC, and flood recurrence interval were important features influencing redcedar occurrence and abundance in the floodplain.
Conclusion: Unless preventive measures are implemented, lack of floods and ongoing flow regulation will enable the continued spread of redcedar along the MNRR and other regulated rivers in the eastern Great Plains.
Landsat Collection 2 (C2) U.S. Analysis Ready Data (U.S. ARD) are bundles of tiled Landsat data that make the Landsat archive easier to analyze and reduce the amount of time users spend on data processing for time-series analysis. Landsat C2 was released in 2020 and includes improvements over Landsat Collection 1 data, including better geometric accuracy, which increases the number of available C2 U.S. ARD tiles. Landsat C2 U.S. ARD are processed to the highest scientific standards.
Landsat C2 U.S. ARD are available for the conterminous United States (1982–present), Alaska (1984–present), and Hawaii (1989–93 and 1999–present) using Landsat C2 Level-1 data processed into Albers Equal-Area Conic-projected Level-2 surface reflectance and surface temperature products.
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Addressing ecosystem degradation in the Anthropocene will require ecological restoration across large spatial extents. Identifying areas where natural regeneration will occur without direct resource investment will improve scalability of restoration actions.
An ecoregion in need of large scale restoration is the Great Basin of the Western US, where increasingly large and frequent wildfires threaten ecosystem integrity and its foundational shrub species. We develop a framework to forecast where post-wildfire regeneration of sagebrush cover (Artemisia spp.) is likely to occur within the burnt areas across the region (> 900,000 km2).
First, we parameterized population models using Landsat satellite-derived time series of sagebrush cover. Second, we evaluated the out-of-sample performance by predicting natural regeneration in wildfires not used for model training. This model assessment reproduces a management-oriented scenario: making restoration decisions shortly after wildfires with minimal local information. Third, we asked how accounting for increasingly fine-scale spatial heterogeneity could improve model forecasting accuracy.
Regional-level models revealed that sagebrush post-fire recovery is slow, estimating > 80-year time horizon to reach an average cover at equilibrium of 16.6% (CI95% 9–25). Accounting for wildfire and within-wildfire spatial heterogeneity improved out-of-sample forecasts, resulting in a mean absolute error of 3.5 ± 4.3% cover, compared to the regional model with an error of 7.2 ± 5.1% cover.
We demonstrate that combining population models and non-parametric spatial matching provides a flexible framework for forecasting plant population recovery. Models for population recovery applied to Landsat-derived time series will assist restoration decision-making, including identifying priority targets for restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-023-01621-1","usgsCitation":"Zaiats, A., Cattau, M.E., Pilliod, D., Rongsong, L., Requena-Mullor, J.M., and Caughlin, T., 2023, Forecasting natural regeneration of sagebrush after wildfires using population models and spatial matching: Landscape Ecology, v. 38, https://doi.org/10.1007/s10980-023-01621-1.","productDescription":"16 p.","startPage":"1306","ipdsId":"IP-144778","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":414693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.76040258225669,\n 42.96440805189778\n ],\n [\n -120.76040258225669,\n 35.34764677083406\n ],\n [\n -112.15082482848379,\n 35.34764677083406\n ],\n [\n -112.15082482848379,\n 42.96440805189778\n ],\n [\n -120.76040258225669,\n 42.96440805189778\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","edition":"1291","noUsgsAuthors":false,"publicationDate":"2023-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Zaiats, Andrii","contributorId":257073,"corporation":false,"usgs":false,"family":"Zaiats","given":"Andrii","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":867434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cattau, Megan E 0000-0003-2164-3809","orcid":"https://orcid.org/0000-0003-2164-3809","contributorId":295715,"corporation":false,"usgs":false,"family":"Cattau","given":"Megan","email":"","middleInitial":"E","affiliations":[{"id":63922,"text":"Department of Human-Environment Systems, Boise State University","active":true,"usgs":false}],"preferred":false,"id":867435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":867436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rongsong, Liu","contributorId":303384,"corporation":false,"usgs":false,"family":"Rongsong","given":"Liu","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":867437,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Requena-Mullor, Juan M.","contributorId":218132,"corporation":false,"usgs":false,"family":"Requena-Mullor","given":"Juan","email":"","middleInitial":"M.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":867438,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caughlin, Trevor 0000-0001-6752-2055","orcid":"https://orcid.org/0000-0001-6752-2055","contributorId":256964,"corporation":false,"usgs":false,"family":"Caughlin","given":"Trevor","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":867439,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241001,"text":"ofr20231013 - 2023 - ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2022","interactions":[],"lastModifiedDate":"2023-03-06T19:07:27.113555","indexId":"ofr20231013","displayToPublicDate":"2023-03-06T13:07:01","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1013","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 3, 2022","title":"ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2022","docAbstract":"The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 3 (July–September) of 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was the lowering of the Landsat 7 orbit. On April 6, 2022, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor was placed into standby mode, and a series of spacecraft burns was completed through the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at a lower orbit of 697 kilometers on May 5, 2022, extending the science mission to allow for essential data acquisition during the 2022 Northern Hemisphere fire and growing season. Additional information about the Landsat 7 orbit lowering is here: https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231013","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Tuli, F.T., Shaw, J.L., Denevan, A., Franks, S.,\nMicijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., and Miller, J., 2023, ECCOE Landsat\nquarterly Calibration and Validation report—Quarter 3, 2022: U.S. Geological Survey Open-File Report 2023–1013, 38 p., https://doi.org/10.3133/ofr20231013.","productDescription":"Report: vii, 38 p.; Dataset","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-146519","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":413661,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1013/images"},{"id":413659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1013/coverthb.jpg"},{"id":413663,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1013/ofr20231013.XML","text":"Report","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2022-1013"},{"id":413662,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":413660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1013/ofr20231013.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1013"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Ecological condition continues to decline in arid and semi-arid river basins globally due to hydrological over-abstraction combined with changing climatic conditions. Whilst provision of water for the environment has been a primary approach to alleviate ecological decline, how to accurately monitor changes in riverine trees at fine spatial and temporal scales, remains a substantial challenge. This is further complicated by constantly changing water availability across expansive river basins with varying climatic zones. Within, we combine rare, fine-scale, high frequency temporal in-situ field collected data with machine learning and remote sensing, to provide a robust model that enables broadscale monitoring of physiological tree water stress response to environmental changes via actual evapotranspiration (ET). Physiological variation of Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box) trees across 10 study locations in the southern Murray-Darling Basin, Australia, was captured instantaneously using sap flow sensors, substantially reducing tree response lags encountered by monitoring visual canopy changes. Actual ET measurement of both species was used to bias correct a national spatial ET product where a Random Forest model was trained using continuous timeseries of in-situ data of up to four years. Precise monthly AMLETT (Australia-wide Machine Learning ET for Trees) ET outputs in 30 m pixel resolution from 2012 to 2021, were derived by incorporating additional remote sensing layers such as soil moisture, land surface temperature, radiation and EVI and NDVI in the Random Forest model. Landsat and Sentinal-2 correlation results between in-situ ET and AMLETT ET returned R2 of 0.94 (RMSE 6.63 mm period−1) and 0.92 (RMSE 6.89 mm period−1), respectively. In comparison, correlation between in-situ ET and a national ET product returned R2 of 0.44 (RMSE 34.08 mm period−1) highlighting the need for bias correction to generate accurate absolute ET values. The AMLETT method presented here, enhances environmental management in river basins worldwide. Such robust broadscale monitoring can inform water accounting and importantly, assist decisions on where to prioritize water for the environment to restore and protect key ecological assets and preserve floodplain and riparian ecological function.
Remotely sensed surface temperature (ST) has been widely used to monitor and assess landscape thermal conditions, hydrologic modeling, and surface energy balance. Landsat thermal sensors have continuously measured the Earth surface thermal radiance since August 1982. The thermal radiance measurements are atmospherically compensated and converted to Landsat STs and delivered as part of the U.S. Geological Survey Landsat Collection 1 U.S. Analysis Ready Data; however, the low satellite revisit cycles combined with the presence of clouds and cloud shadows reduce the number of valid retrievals. This reduction can limit the ability to monitor annual or seasonal variations in the surface thermal budget. These factors reduce the ability to use the temperature data to fit time series for historical trend analysis to match background climate variations. In this study, we implemented an approach that uses linear harmonic least absolute shrinkage and selection operator regression models to fill gaps because of clouds, shadows, and coarse temporal resolution. The gap-filled data provide increased temporal density of Landsat ST records. The gap-filled Landsat ST, therefore, can allow for an improved monitoring of annual, seasonal, or even monthly landscape thermal conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231006","usgsCitation":"Xian, G., Shi, H., Arab, S., Mueller, C., Hussain, R., Sayler, K., and Howard, D., 2023, Improving temporal frequency of Landsat surface temperature products using the gap-filling algorithm: U.S. Geological Survey Open-File Report 2023–1006, 15 p., https://doi.org/10.3133/ofr20231006.","productDescription":"vi, 15 p.","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-144337","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412873,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1006/images"},{"id":412872,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1006/ofr20231006.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":412871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1006/ofr20231006.pdf","text":"Report","size":"41.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1006"},{"id":412880,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20231006/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1006/coverthb.jpg"},{"id":499732,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114340.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Georgia","city":"Atlanta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.9318883744094,\n 34.338976979151155\n ],\n [\n -84.9318883744094,\n 33.376859208686255\n ],\n [\n -83.70224614831253,\n 33.376859208686255\n ],\n [\n -83.70224614831253,\n 34.338976979151155\n ],\n [\n -84.9318883744094,\n 34.338976979151155\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
We proposed a new image compositing algorithm (MAX-RNB) based on the maximum ratio of Near Infrared (NIR) to Blue band (RNB), and evaluated it together with nine other compositing algorithms: MAX-NDVI (maximum Normalized Difference Vegetation Index), MED-NIR (median NIR band), WELD (conterminous United States Web-Enabled Landsat Data), BAP (Best Available Pixel), PAC (Phenology Adaptive Composite), WPS (Weighted Parametric Scoring), MEDOID (medoid measurement), COSSIM (cosine similarity), and NLCD (National Land Cover Database). Each algorithm was applied to time series of Landsat observations collected within two separate years at six locations around the world, to produce monthly (July 1 ± 15 days), seasonal (July 1 ± 45 days), and annual (July 1 ± 180 days) composite images free of cloud, cloud shadow, and snow/ice. By comparing the composite images to reference Landsat images acquired in the growing season (closest to July 1 within ±15 days) for each year, we evaluated the performance of the algorithms in preserving the spectral and spatial fidelity (hereafter referred to as spectral and spatial evaluation, respectively), as well as land cover classification and land change detection (hereafter referred to as application evaluation). The results demonstrated that no single algorithm outperformed all other algorithms in all the evaluations, but that performance depended on compositing intervals and cloud cover. For monthly composites, the MAX-RNB algorithm generally produced the best results in the spectral and application evaluations. For seasonal composites, the NLCD algorithm produced the best results in the spectral and application evaluations. For annual composites, the PAC algorithm produced the best results in the spectral evaluation and change detection, whereas BAP produced the best results in land cover classification. The BAP algorithm also produced the best results in the spatial evaluation for all the compositing periods. This study provides a comprehensive guidance for selecting the most appropriate image compositing algorithm for different Landsat-based applications.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113375","usgsCitation":"Qiu, S., Zhu, Z., Olofsson, P., Woodcock, C., and Jin, S., 2023, Evaluation of Landsat image compositing algorithms: Remote Sensing of Environment, v. 285, 113375, 23 p., https://doi.org/10.1016/j.rse.2022.113375.","productDescription":"113375, 23 p.","ipdsId":"IP-140731","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":444635,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113375","text":"Publisher Index Page"},{"id":413857,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"285","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Qiu, Shi","contributorId":302924,"corporation":false,"usgs":false,"family":"Qiu","given":"Shi","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":865843,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Zhe 0000-0001-8283-6407","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":190828,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[],"preferred":false,"id":865844,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olofsson, 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":865845,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woodcock, Curtis","contributorId":166666,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":865846,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","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":865847,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239954,"text":"70239954 - 2023 - Toward consistent change detection across irregular remote sensing time series observations","interactions":[],"lastModifiedDate":"2024-05-20T13:49:30.008054","indexId":"70239954","displayToPublicDate":"2023-02-01T07:04:01","publicationYear":"2023","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":"Toward consistent change detection across irregular remote sensing time series observations","docAbstract":"The use of remote sensing in time series analysis enables wall-to-wall monitoring of the land surface and is critical for assessing and understanding land cover and land use change and for understanding the Earth system as a whole. However, variability in remote sensing observation frequency through time and across space presents challenges for producing consistent change detection results throughout the available satellite record using approaches such as the Continuous Change Detection and Classification (CCDC) change detection methodology. Here we investigate new modifications to this methodology with the goal of improving accuracy and consistency in results and increasing flexibility for operational usage and future development. The modified method (Band-First Probability, or CCD-BFP) change detection procedure works by calculating a test for each band through time before summarizing between bands. We evaluate the CCD-BFP method compared to an existing implementation of CCDC using a variety of approaches, including a validation dataset of human-interpreted locations, comparison with data from fire events, use of simulated remote sensing data, and qualitative inspection of areas of interest. We find CCD-BFP improves consistency across time and space compared to the existing implementation of CCDC, with more similarity in rates of change across Landsat swath boundaries and before and after the launch of Landsat 7. Also, we find that CCD-BFP detects more of the change events in the validation dataset while reducing the overall number of change detections, indicating that it is able to more accurately capture the most notable land surface change events.
The National Land Cover Database (NLCD) 2016 products show that, between 2001 and 2016, nearly half of the land cover change in the conterminous United States (CONUS) involved forested areas. To ensure the quality of NLCD land cover and land cover change products, it is important to accurately detect the location and time of forest disturbance. We designed a comprehensive strategy to integrate a continuous time series forest change detection method and a discrete 2-date forest change detection method to produce the NLCD 1986–2019 forest disturbance product, which shows the most recent forest disturbance date between the years 1986 and 2019 for every 2- to 3-year interval. This method, the Time-Series method Using Normalized Spectral Distance (NSD) index (TSUN), uses NSD to detect multi-date forest land cover changes and was shown to be easily extended to a new date even when new images were processed in a different way than previous date images. The discrete 2-date method uses the Multi-Index Integrated Change Analysis (MIICA) method to detect changes between 2-date images. A method based on confidence and object grouping was designed to combine the multiple MIICA outputs to improve change detection accuracy. Finally, an aggregation scheme was implemented to combine the TSUN output, the integrated MIICA results, and ancillary data to produce the NLCD 2019 forest disturbance 1986–2019 product. The initial accuracy assessments from 1,600 samples over 4 Landsat path/rows show that the producer’s and user’s accuracies of the 2001–2019 forest disturbance map are 76% and 74%, respectively. The final CONUS-wide forest disturbance product is provided at https://www.mrlc.gov/nlcd-2019-science-research-products.
","language":"English","publisher":"AAAS","doi":"10.34133/remotesensing.0021","usgsCitation":"Jin, S., Dewitz, J., Li, C., Sorenson, D.G., Zhu, Z., Shogib, R., Danielson, P., Granneman, B., Costello, C., Case, A., and Gass, L., 2023, National Land Cover Database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance product: Journal of Remote Sensing, v. 3, 0021, 14 p., https://doi.org/10.34133/remotesensing.0021.","productDescription":"0021, 14 p.","ipdsId":"IP-147293","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":444676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.34133/remotesensing.0021","text":"Publisher Index Page"},{"id":413853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n 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Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865838,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gass, Leila 0000-0002-3436-262X lgass@usgs.gov","orcid":"https://orcid.org/0000-0002-3436-262X","contributorId":3770,"corporation":false,"usgs":true,"family":"Gass","given":"Leila","email":"lgass@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":865839,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70239931,"text":"fs20233004 - 2023 - Rangeland Condition Monitoring Assessment and Projection, 1985–2021","interactions":[],"lastModifiedDate":"2026-02-04T20:33:36.72143","indexId":"fs20233004","displayToPublicDate":"2023-01-26T09:48:28","publicationYear":"2023","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":"2023-3004","displayTitle":"Rangeland Condition Monitoring Assessment and Projection, 1985–2021","title":"Rangeland Condition Monitoring Assessment and Projection, 1985–2021","docAbstract":"The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project quantifies the percentage cover of rangeland components across the western United States using Landsat imagery from 1985 to 2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, nonsagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013 to 2018 and additional field data; for example, Bureau of Land Management Assessment, Inventory, and Monitoring instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. Neural network models have replaced Cubist models as our classifier. Continuous Change Detection and Classification synthetic Landsat images were obtained for six monthly periods for each region and were added as predictors. These data enhance the phenologic detail of imagery, improving discrimination among components. Postprocessing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience classes. Additionally, postprocessing has been enhanced through a revised noise detection model, based on third order polynomial models for each component and each pixel. These data can be used to answer critical questions regarding the effect of climate change and the suitability of management practices. Component products can be downloaded from the Multi-Resolution Land Characteristics Consortium website at https://www.mrlc.gov/data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/fs20233004","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Rigge, M.B., 2023, Rangeland Condition Monitoring Assessment and Projection, 1985–2021: U.S. Geological Survey Fact Sheet 2023–3004, 6 p., https://doi.org/10.3133/fs20233004.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-148071","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499564,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114284.htm","linkFileType":{"id":5,"text":"html"}},{"id":412363,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233004/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412361,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3004/images"},{"id":412360,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3004/fs20233004.XML"},{"id":412356,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3004/coverthb.jpg"},{"id":412359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3004/fs20233004.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023~3004"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102.89909633393279,\n 29.0682826215172\n ],\n [\n -102.29956963626972,\n 29.8090966774663\n ],\n [\n -100.87258967244983,\n 29.458733387985006\n ],\n [\n -100.13050111858448,\n 28.240769760178893\n ],\n [\n -100.11959574261397,\n 30.292475426842415\n ],\n [\n -101.92842251021852,\n 31.470932982781036\n ],\n [\n -103.36510762980765,\n 32.48047296916185\n ],\n [\n -100.01803379637153,\n 36.1490156792113\n ],\n [\n -99.27913819165613,\n 37.572777501564005\n ],\n [\n -103.20469410438153,\n 39.32750900585228\n ],\n [\n -104.0623305315396,\n 39.89802382204118\n ],\n [\n -101.43686566661609,\n 42.267519647308234\n ],\n [\n -100.61853110579801,\n 49.04539216233715\n ],\n [\n -123.36052981830295,\n 48.977345645871026\n ],\n [\n -123.7205891173356,\n 48.25142184919315\n ],\n [\n -124.9072340092169,\n 48.68713021246529\n ],\n [\n -124.91684175179148,\n 42.98584722268464\n ],\n [\n -124.79221124944036,\n 40.3836585578014\n ],\n [\n -122.5469831787114,\n 35.7301435211089\n ],\n [\n -120.64609875158641,\n 34.26400897631564\n ],\n [\n -117.38696400421276,\n 32.43463720185768\n ],\n [\n -114.95825417619707,\n 32.70325877979326\n ],\n [\n -110.57738226870978,\n 31.261610018918134\n ],\n [\n -108.12199560515279,\n 31.139851729510895\n ],\n [\n -108.13958017851652,\n 31.827219447629645\n ],\n [\n -106.284256297648,\n 31.694405166866275\n ],\n [\n -104.3199988686059,\n 29.50364197108891\n ],\n [\n -103.16152984843166,\n 28.840396342272896\n ],\n [\n -102.89909633393279,\n 29.0682826215172\n ],\n [\n -102.89909633393279,\n 29.0682826215172\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
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47914 252nd Street
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The primary purpose of this document is to convey product specifications of the OPERA (Observational Products for End-users from Remote-sensing Analysis) Level-3 Dynamic Surface Water Extent (DSWx) product that uses Harmonized Landsat-8 and Sentinel-2A/B (HLS) as the primary image-based inputs. This product, referred to by the short name DSWx-HLS, will be generated by the OPERA Data System (SDS). It will be openly distributed by NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC).
","language":"English","publisher":"NASA","usgsCitation":"Jones, J., and Shiroma, G., 2023, Product specification document for dynamic surface water extent from Harmonized Landsat and Sentinel-2, 28 p.","productDescription":"28 p.","ipdsId":"IP-141277","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":428344,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://podaac.jpl.nasa.gov/dataset/OPERA_L3_DSWX-HLS_PROVISIONAL_V0","linkFileType":{"id":5,"text":"html"}},{"id":428362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":900099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shiroma, G. 0000-0002-7753-1876","orcid":"https://orcid.org/0000-0002-7753-1876","contributorId":336189,"corporation":false,"usgs":false,"family":"Shiroma","given":"G.","affiliations":[{"id":27365,"text":"NASA Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900100,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70239747,"text":"sir20225107 - 2023 - Using Global Fiducials Library high-resolution imagery, commercial satellite imagery, Landsat and Sentinel satellite imagery, and aerial photography to monitor change at East Timbalier Island, Louisiana, 1953–2021","interactions":[],"lastModifiedDate":"2026-02-23T19:32:52.536466","indexId":"sir20225107","displayToPublicDate":"2023-01-20T10:30:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5107","displayTitle":"Using Global Fiducials Library High-Resolution Imagery, Commercial Satellite Imagery, Landsat and Sentinel Satellite Imagery, and Aerial Photography to Monitor Change at East Timbalier Island, Louisiana, 1953–2021","title":"Using Global Fiducials Library high-resolution imagery, commercial satellite imagery, Landsat and Sentinel satellite imagery, and aerial photography to monitor change at East Timbalier Island, Louisiana, 1953–2021","docAbstract":"This report documents morphological changes between 1953 and 2021 at East Timbalier Island, Louisiana, a Gulf of Mexico barrier island. East Timbalier Island, which was located west of the Mississippi River Delta at the front of Timbalier Bay, was one of the most rapidly changing barrier islands on Earth. Since aerial photographs were initially taken in 1953, the Island steadily lost length and area, finally eroding away by early summer 2021. After major storm events, sediment eroded from the Island and migrated hundreds of meters north. In August 1992, Hurricane Andrew breached the Island in several places, resulting in increased erosion and land loss. Until it completely eroded away, the Island underwent a cycle of washovers, vegetation removal, breaching, and erosion with sediment transport to the north. Satellite imagery shows that three such cycles occurred between 1992 and 2017, despite the partial restoration of the Island between 1998 and 2000. Each cycle increased the distance between the Island and the mainland to the east, reducing both the sediment supply from the east and the protection that Timbalier Bay and the adjacent coastal lands received from the barrier island.\n\nPreviously, the U.S. Geological Survey (USGS) National Civil Applications Center used 1-meter resolution imagery archived at the USGS Global Fiducials Library (GFL), collected between 2000 and 2010 by U.S. National Imaging Systems, to monitor the changes at the Island. New research expands this study retrospectively and prospectively using aerial photography collected from 1953 to 2012 and in 2020; declassified imagery collected in 1962, 1972, and 1975; DigitalGlobe satellite imagery collected since 2004; Landsat satellite imagery collected since 1972; Sentinel–2 satellite imagery collected since 2015; and GFL imagery collected from 1991 to 2020.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225107","isbn":"978-1-4113-4511-9","programNote":"Core Science Systems and the National Civil Applications Center","usgsCitation":"Fisher, G.B., Slonecker, E.T., Dilles, S.J., Molnia, B.F., and Angeli, K.M., 2023, Using Global Fiducials Library high-resolution imagery, commercial satellite imagery, Landsat and Sentinel satellite imagery, and aerial photography to monitor change at East Timbalier Island, Louisiana, 1953–2021 (ver. 1.1, May 2023): U.S. Geological Survey Scientific Investigations Report 2022–5107, 61 p., https://doi.org/10.3133/sir20225107.","productDescription":"Report: vii, 61 p.; Data Release","numberOfPages":"61","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123372","costCenters":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"links":[{"id":411976,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O71HYS","text":"USGS data release","linkHelpText":"Six decades of change at East Timbalier Island, Louisiana"},{"id":411971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5107/coverthb2.jpg"},{"id":416780,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5107/versionHist.txt","size":"4.31 KB","linkFileType":{"id":2,"text":"txt"}},{"id":411972,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5107/sir20225107.pdf","text":"Report","size":"150 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5107"},{"id":411975,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5107/images/"},{"id":500455,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114279.htm","linkFileType":{"id":5,"text":"html"}},{"id":411974,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5107/sir20225107.XML"}],"country":"United States","state":"Louisiana","otherGeospatial":"East Timbalier Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.4,\n 29.125\n ],\n [\n -90.4,\n 29.033\n ],\n [\n -90.233,\n 29.033\n ],\n [\n -90.233,\n 29.125\n ],\n [\n -90.4,\n 29.125\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: January 2023; Version 1.1: May 2023","contact":"Director, National Civil Applications Center
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In arid and Mediterranean regions, landscape-scale wetland conservation requires understanding how wildlife responds to dynamic freshwater availability and conservation actions to enhance wetland habitat. Taking advantage of Landsat satellite data and structured and community science bird survey data, we built species distribution models to describe how three duck species, the Northern Pintail (Anas acuta), Green-winged Teal (Anas crecca), and Northern Shoveler (Anas clypeata), respond to freshwater supply and food resources on different flooded land cover types in the Central Valley of California. Specifically, our models compared duck habitat suitability between the wettest and driest conditions in each month from September through April. Using abundance-weighted boosted regression trees, we created three sets of species occurrence models based on different covariates: (1) near real-time (hereafter “real-time”) covariates in which duck observations were matched to the water availability within the 16-day window of a Landsat observation, (2) a combination of real-time covariates and waterfowl food resource covariates describing annual corn and rice biomass and managed wetland moist soil seed yield estimates derived from Landsat data, and (3) long-term average covariates—the most common approach to species distribution modeling—in which long-term average surface water availability was used. We modeled the monthly occurrence of three duck species as a function of surface water availability, land cover type, road density, temperature, and bird data source. We found that dry conditions result in reduced habitat suitability, with the biggest reductions in November through January and in agricultural fields; in contrast, suitability of flooded wetland habitat was relatively robust to surface water availability. When models of habitat suitability based on long-term average climate conditions were compared to models based on real-time conditions, the highest long-term suitability values occurred in areas where suitability was high regardless of whether it was a wet or a dry year. While all models performed well, the inclusion of crop and wetland plant yield covariates resulted in slightly higher model performance. Overall, species distribution models created using data on the environmental conditions present at the time of bird observations can aid conservation efforts under extreme conditions over large spatial scales.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4367","usgsCitation":"Conlisk, E.E., Byrd, K.B., Matchett, E., Lorenz, A., Casazza, M.L., Golet, G.H., Reynolds, M.D., Sesser, K.A., and Reiter, M.E., 2023, Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California: Ecosphere, v. 14, e4367, 19 p., https://doi.org/10.1002/ecs2.4367.","productDescription":"e4367, 19 p.","ipdsId":"IP-144890","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":444827,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4367","text":"Publisher Index Page"},{"id":412024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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E.","contributorId":301022,"corporation":false,"usgs":false,"family":"Conlisk","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":861775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matchett, Elliott 0000-0001-5095-2884 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mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":861779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Golet, Gregory H.","contributorId":89844,"corporation":false,"usgs":false,"family":"Golet","given":"Gregory","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":861780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reynolds, Mark D.","contributorId":301023,"corporation":false,"usgs":false,"family":"Reynolds","given":"Mark","email":"","middleInitial":"D.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":861781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sesser, Kristin A.","contributorId":215294,"corporation":false,"usgs":false,"family":"Sesser","given":"Kristin","email":"","middleInitial":"A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":861782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reiter, Matthew E. 0000-0002-0587-786X","orcid":"https://orcid.org/0000-0002-0587-786X","contributorId":271031,"corporation":false,"usgs":false,"family":"Reiter","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":56258,"text":"Point Blue","active":true,"usgs":false}],"preferred":false,"id":861783,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241048,"text":"70241048 - 2023 - National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images","interactions":[],"lastModifiedDate":"2023-11-08T16:49:21.339686","indexId":"70241048","displayToPublicDate":"2023-01-11T06:41:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"National Land Cover Database 2019: A new strategy for creating clean leaf-on and leaf-off Landsat composite images","docAbstract":"Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. In the Delmarva Peninsula of the eastern United States, winter cover crops are essential for reducing nutrient and sediment losses from farmland. Cost-share programs have been created to incentivize cover crops to achieve conservation objectives. This program required that cover crops be planted and terminated within a specified time window. Usually, farmers report cover crop termination dates for each enrolled field (∼28,000 per year), and conservation district staff confirm the report with field visits within two weeks of termination. This verification process is labor-intensive and time-consuming and became restricted in 2020–2021 due to the COVID-19 pandemic. This study used Harmonized Landsat and Sentinel-2 (HLS, version 2.0) time-series data and the within-season termination (WIST) algorithm to detect cover crop termination dates over Maryland and the Delmarva Peninsula. The estimated remote sensing termination dates were compared to roadside surveys and to farmer-reported termination dates from the Maryland Department of Agriculture database for the 2020–2021 cover crop season. The results show that the WIST algorithm using HLS detected 94% of terminations (statuses) for the enrolled fields (n = 28,190). Among the detected terminations, about 49%, 72%, 84%, and 90% of remote sensing detected termination dates were within one, two, three, and four weeks of agreement to farmer-reported dates, respectively. A real-time simulation showed that the termination dates could be detected one week after termination operation using routinely available HLS data, and termination dates detected after mid-May are more reliable than those from early spring when the Normalized Difference Vegetation Index (NDVI) was low. We conclude that HLS imagery and the WIST algorithm provide a fast and consistent approach for generating near-real-time cover crop termination maps over large areas, which can be used to support cost-share program verification.
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