There are expectations that increasing temperatures will lead to significant changes in structure and function of montane meadows, including greater water stress on vegetation and lowered vegetation production and productivity. We evaluated spatio-temporal dynamics in production and productivity in meadows within the Sierra Nevada mountain range of North America by: (1) compiling Landsat satellite data for the Normalized Difference Vegetation Index (NDVI) across a 37-year period (1985–2021) for 8,095 meadows >2,500 m elevation; then, (2) used state-space models, changepoint analysis, geographically-weighted regression (GWR), and distance-decay analysis (DDA) to: (a) identify meadows with decreasing, increasing or no trends for NDVI; (b) detect meadows with abrupt changes (changepoints) in NDVI; and (c) evaluate variation along gradients of latitude, longitude, and elevation for eight indices of temporal dynamics in annual production (mean growing season NDVI; MGS) and productivity (rate of spring greenup; RSP). Meadows with no long-term change or evidence of increasing NDVI were 2.6x more frequent as those with decreasing NDVI (72% vs. 28%). Abrupt changes in NDVI were detected in 48% of the meadows; they occurred in every year of the study and with no indication that their frequency had changed over time. The intermixing of meadows with different temporal dynamics was a consistent pattern for monthly NDVI and, especially, the eight annual indices of MGS and RSP. The DDA showed temporal dynamics in pairs of meadow within a few 100 m of each other were often as different as those hundreds of kilometers apart. Our findings point strongly toward a great diversity of temporal dynamics in meadow production and productivity in the SNV. The heterogeneity in spatial patterns indicated that production and productivity of meadow vegetation is being driven by interplay among climatic, physiographic and biotic factors at basin and meadow scales. Thus, when evaluating spatio-temporal dynamics in condition for many high elevation meadow systems, what might often be considered “noise” may provide greater insight than a “signal” embedded within a large amount of variability.
Landsat orthorectified products use Ground Control Points (GCPs) and Digital Elevation Models (DEM) to improve the geolocation accuracy and temporal consistency, and to account for the relief displacements due to the sensor-target geometry. In Collection-2, to improve the geometric harmonization between Landsat and Sentinel-2 (S2) orthorectified products, the Landsat GCP's absolute and relative accuracies were improved using the S2 Global Reference Image (GRI) dataset through a continent-level bundle adjustment method. The GRI is a highly accurate global image dataset that was developed by the European Space Agency (ESA) to improve the S2 multi-temporal geolocation accuracy. Since late August 2021, ESA has been using the GRI dataset in the geometric refinement process to generate S2 terrain-corrected (L1C) products. This paper presents the co-registration accuracy between the Landsat-8 (L8) Collection-2 terrain-corrected products and the S2 L1C products that were processed with and without the use of the GRI dataset. The image-to-image registration (I2I) analysis performed between the L8 and S2 data products over a set of globally distributed tiles shows a significant improvement in their co-registration accuracy when GRI is used in the S2 L1C product generation. The co-registration error is estimated to be <6 m circular error at 90% probability (CE90) when GRI is used, and >12 m CE90 when GRI is not used in the S2 product generation process. A similar I2I analysis was conducted between S2 L1C products, L8 L1TP products, and L8 and Landsat 9 (L9) L1TP products. The analysis shows that the S2 L1C products are co-registered with each other temporally to better than 5.1 m CE90 when GRI is used. The L8 L1TP products and L8 versus L9 L1TP products are both co-registered temporally to better than 3 m CE90.
Large-scale disturbances such as wildfire can have profound impacts on the composition, structure, and functioning of ecosystems. Bees are critical pollinators in natural settings and often respond positively to wildfires, particularly in forests where wildfire leads to more open conditions and increased floral resources. The use of Light Detection and Ranging (LiDAR) provides opportunities for quantifying habitat features across large spatial scales and is increasingly available to scientists and land managers for post-fire habitat assessment. We evaluated the extent to which LiDAR-derived forest structure measurements can predict forest bee communities after a large, mixed-severity fire. We hypothesized that LiDAR measurements linked to post-fire forest structure would improve our ability to predict bee abundance and species richness when compared to satellite-based maps of burn severity. To test this hypothesis, we sampled wild bee communities within the Douglas Fire Complex in southwestern Oregon, USA. We then used LiDAR and Landsat data to quantify forest structure and burn severity, respectively, across bee sampling locations. We found that the LiDAR forest structure model was the best predictor of abundance, whereas the Landsat burn severity model had better predictive ability for species richness. Furthermore, the Landsat burn severity model was better at predicting the presence and species richness of bumble bees (Bombus spp.), an ecologically distinct and economically important group within the Pacific Northwest. We posit that the divergent responses of the two modeling approaches are due to distinct responses by bee taxa to variation in forest structure as mediated by wildfire, with bumble bees in particular depending on closed-canopy forest for some portions of their life cycle. Our study demonstrates that LiDAR data can provide information regarding the drivers of bee abundance in post-wildfire conifer forest, and that both remote sensing approaches are useful for predicting components of wild bee diversity after large-scale wildfire.
","language":"English","doi":"10.1002/rse2.354","usgsCitation":"Galbraith, S.M., Valente, J., Dunn, C.J., and Rivers, J.W., 2024, Both Landsat- and LiDAR-derived measures predict forest bee response to large-scale wildfire: Remote Sensing in Ecology and Conservation, v. 10, no. 1, p. 24-38, https://doi.org/10.1002/rse2.354.","productDescription":"15 p.","startPage":"24","endPage":"38","ipdsId":"IP-144537","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":440572,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.354","text":"Publisher Index Page"},{"id":432888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Douglas Fire Complex, southwestern Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.576158064366,\n 43.53770092737449\n ],\n [\n -123.576158064366,\n 42.89081714418663\n ],\n [\n -123.04040779515724,\n 42.89081714418663\n ],\n [\n -123.04040779515724,\n 43.53770092737449\n ],\n [\n -123.576158064366,\n 43.53770092737449\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Galbraith, Sara M.","contributorId":340887,"corporation":false,"usgs":false,"family":"Galbraith","given":"Sara","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":907638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valente, Jonathon Joseph 0000-0002-6519-3523","orcid":"https://orcid.org/0000-0002-6519-3523","contributorId":340615,"corporation":false,"usgs":true,"family":"Valente","given":"Jonathon Joseph","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":910913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Christopher J.","contributorId":340888,"corporation":false,"usgs":false,"family":"Dunn","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":907640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rivers, James W.","contributorId":23072,"corporation":false,"usgs":false,"family":"Rivers","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":7005,"text":"Department of Forest Ecosystems and Society, Oregon State University","active":true,"usgs":false}],"preferred":false,"id":907641,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250972,"text":"70250972 - 2024 - National-scale remotely sensed lake trophic state from 1984 through 2020","interactions":[],"lastModifiedDate":"2024-05-16T15:36:46.875911","indexId":"70250972","displayToPublicDate":"2024-01-16T06:59:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"National-scale remotely sensed lake trophic state from 1984 through 2020","docAbstract":"Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.
Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly
Landsat 9 (L9) was launched on 27 September 2021. This spacecraft contained two instruments, the Operational Land Imager-2 (OLI-2) and Thermal Infrared Sensor-2 (TIRS-2), that allow for a continuation of the Landsat program and the mission to acquire multi-spectral observations of the globe on a moderate scale. Following a period of commissioning, during which time the spacecraft and instruments were initialized and set up for operations, with the initial calibration performed, the mission moved to an operational mode This operational mode involved the same cadence and methods that were performed for the Landsat 8 (L8) spacecraft and the two instruments onboard, the Operational Land Imager-1 (OLI-1) and Thermal Infrared Sensor-1 (TIRS-1), with respect to calibration, characterization, and validation. This paper discusses the geometric operational aspects of the L9 instruments during the first year of the mission and post-commissioning, and compares these same geometric activities performed for L8 during the same time frame. During this time, optical axes of the two sensors, OLI-1 and OLI-2, were adjusted to stay aligned with the spacecraft’s Attitude Control System (ACS), and the TIRS-1 and TIRS-2 instruments were adjusted to stay aligned with the OLI-1 and OLI-2 instruments, respectively. In this paper, the L9 operational adjustments are compared to the same operational aspects of L8 during this same time frame. The comparisons shown in this paper will demonstrate that both instruments aboard L8 and L9 performed very similar geometric qualities while fully meeting the expected requirements. This paper describes the geometric differences between the L9 imagery that was made available to the public prior to the reprocessing campaign that was performed using the new calibration updates to the sensor and to ACS and TIRS-to-OLI alignment parameters. This reprocessing campaign of L9 products involved data acquired from the launch of the spacecraft up to early 2023.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16010133","usgsCitation":"Choate, M., Rengarajan, R., Hasan, N., Denevan, A., and Ruslander, K., 2024, Operational aspects of Landsat 8 and 9 geometry: Remote Sensing, v. 16, no. 1, 133, 34 p., https://doi.org/10.3390/rs16010133.","productDescription":"133, 34 p.","ipdsId":"IP-157652","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":467043,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16010133","text":"Publisher Index Page"},{"id":462241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-12-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Choate, Michael J. 0000-0002-8101-4994","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":251780,"corporation":false,"usgs":true,"family":"Choate","given":"Michael J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":913917,"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":913918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hasan, Nahid 0000-0002-0463-601X","orcid":"https://orcid.org/0000-0002-0463-601X","contributorId":292342,"corporation":false,"usgs":false,"family":"Hasan","given":"Nahid","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":913919,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Denevan, Alex 0000-0002-1215-3261","orcid":"https://orcid.org/0000-0002-1215-3261","contributorId":270398,"corporation":false,"usgs":false,"family":"Denevan","given":"Alex","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":913920,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ruslander, Kathryn 0000-0003-3036-1731","orcid":"https://orcid.org/0000-0003-3036-1731","contributorId":330181,"corporation":false,"usgs":false,"family":"Ruslander","given":"Kathryn","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":913921,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250952,"text":"70250952 - 2024 - Crop water productivity from cloud-Based landsat helps assess California’s water savings","interactions":[],"lastModifiedDate":"2024-01-13T14:50:43.650126","indexId":"70250952","displayToPublicDate":"2023-07-07T08:46:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Crop water productivity from cloud-Based landsat helps assess California’s water savings","docAbstract":"Urban development and associated land-cover and land-use change alters the environment. The continued increase of developed land changes the Earth’s ecosystems and affects the resources provided to society. During the last 40 years, urban population in the United States has increased by more than 6.3 percent, and more than 80 percent of the U.S. population resides in urban areas. One of the changes associated with urbanization is the change of landscape features to structures such as buildings, roads, and other infrastructure that absorb and re-emit the heat of the sun more than natural landscapes such as forests and water bodies. This land-cover transition can result in an urban surface temperature that is higher than in a non-urban area, which is defined as a surface urban heat island (SUHI). A SUHI has a profound effect on the lives of urban residents and can exacerbate the risk of heat-related mortality associated with global climate change. The change of urban landscapes and climate conditions can affect the SUHI intensity. The U.S. Geological Survey (USGS) has developed a dataset of SUHI intensity and change from 1985 to 2020 over 50 cities in the United States using Landsat surface temperature (ST) and land-cover data. The data reveal SUHI spatial distributions and temporal trends in these cities. The 50-city mean SUHI intensity reaches 2.88 degrees Celsius (°C) (5.19 degrees Fahrenheit [°F]) and an average trend of 0.32 °C per decade (0.58 °F per decade). The data also provide spatial distributions of hotspots where annual mean ST is higher than in the surrounding areas that have the same urban land-cover type and high ST that repeated more than 50 percent of the time during 1985–2020 for 50 cities.
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U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Coastal morphological changes can be assessed using shoreline position observations from space. However, satellite-derived waterline (SDW) and shoreline (SDS; SDW corrected for hydrodynamic contributions and outliers) detection methods are subject to several sources of uncertainty and inaccuracy. We extracted high-spatiotemporal-resolution (~50 m-monthly) time series of mean high water shoreline position along the Columbia River Littoral Cell (CRLC), located on the US Pacific Northwest coast, from Landsat missions (1984–2021). We examined the accuracy of the SDS time series along the mesotidal, mildly sloping, high-energy wave climate and dissipative beaches of the CRLC by validating them against 20 years of quarterly in situ beach elevation profiles. We found that the accuracy of the SDS time series heavily depends on the capability to identify and remove outliers and correct the biases stemming from tides and wave runup. However, we show that only correcting the SDW data for outliers is sufficient to accurately measure shoreline change trends along the CRLC. Ultimately, the SDS change trends show strong agreement with in situ data, facilitating the spatiotemporal analysis of coastal change and highlighting an overall accretion signal along the CRLC during the past four decades.
Water clarity, defined in this study using measurements of the downwelling diffuse light attenuation coefficient (Kd) and turbidity, is an important indicator of lake trophic status and ecosystem health. We used in-situ measurements to evaluate existing semi-analytical models for Kd and turbidity, developed a regional turbidity model based on spectral shape, and evaluated the spatial and temporal trends in Lake Washington from 2013 to 2022 using Landsat-8/9 Operational Land Imager (OLI). We found no significant trends from 2013 to 2022 in Kd or turbidity when both the annual and full datasets were considered. In addition to the spring peak lasting from April through June, autumn Kd peaks were present at all sites, a pattern consistent with seasonal chlorophyll a and zooplankton concentrations. There existed no autumn peak in the monthly turbidity dataset, and the spring peak occurred two months before the Kd peak, nearly mirroring seasonal variability in the Cedar River discharge rates over the same period. The Kd and turbidity algorithms were thus each more sensitive to different sources of water clarity variability in Lake Washington.
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The DSWx-HLS product is the first of the DSWx suite, comprised of products each which map water from Earth Observation optical and SAR satellites. We detail the generation of high-resolution (3 m) validation datasets from a globally-stratified sample of dry, moderate, and wet sites. We provide the precise accounting of the classification metrics used to verify the Observational Products for End-users from Remote Sensing Analysis (OPERA) project requirements. We also report broader classification metrics across the validation datasets considered. OPERA performs validation in the public domain to ensure that the validation activities are transparent and reproducible. The resulting validation datasets and provisional OPERA products are publicly available; the software used for validation is also open-source.
","conferenceTitle":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"July 16-21, 2023","conferenceLocation":"Pasadena, CA","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS52108.2023.10283397","usgsCitation":"Arena, N., Bato, G., Bekaert, D., Bonnema, M., Chan, S., Chapman, B., Jones, J., Handwerger, A., Lewandowski, A., Marshak, C., Sangha, S., and Venkataramani, K., 2023, Opera Dynamic Surface Water extents for Harmonized Landsat Sentinel-2 (DSWX-HLS) validation activities, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, July 16-21, 2023, p. 2723-2726, https://doi.org/10.1109/IGARSS52108.2023.10283397.","productDescription":"4 p.","startPage":"2723","endPage":"2726","ipdsId":"IP-153954","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":428363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Arena, Nicholas","contributorId":336167,"corporation":false,"usgs":false,"family":"Arena","given":"Nicholas","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900087,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bato, Grace","contributorId":336168,"corporation":false,"usgs":false,"family":"Bato","given":"Grace","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bekaert, David","contributorId":336169,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900089,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnema, Matthew","contributorId":336170,"corporation":false,"usgs":false,"family":"Bonnema","given":"Matthew","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900090,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chan, Steven","contributorId":336171,"corporation":false,"usgs":false,"family":"Chan","given":"Steven","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900091,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Bruce","contributorId":336172,"corporation":false,"usgs":false,"family":"Chapman","given":"Bruce","email":"","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":900092,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":37786,"text":"WMA - 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2023 - Monitoring long-term changes of urban surface temperature using time-series land cover and remote sensing data across 50 major cities in the United States","interactions":[],"lastModifiedDate":"2024-05-28T14:42:03.934403","indexId":"70247132","displayToPublicDate":"2023-10-20T09:35:45","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Monitoring long-term changes of urban surface temperature using time-series land cover and remote sensing data across 50 major cities in the United States","docAbstract":"The increase of developed land changes the Earth’s ecosystems and, in doing so, impacts the natural environment and further affects the services it provides to humans. Urban growth and associated land cover transitions alter the thermal and physical properties of the land surface, resulting in surface temperature change in urban areas. In this study, we integrated both land cover and surface temperature information to characterize surface temperature spatiotemporal variations using the recently available time series of Landsat land surface temperature and annual land change products. We analyzed over thirty-year trends of land surface temperature (LST) in urban and surrounding non-urban lands. We found that the transitions of different land cover types to urban affected urban LST trends differently, and further impacted the temporal trend of urban heat island intensity.
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Product","interactions":[],"lastModifiedDate":"2024-05-28T14:05:25.727146","indexId":"70246672","displayToPublicDate":"2023-10-20T09:04:28","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mapping the Surface Urban Heat Island effect using the Landsat Surface Temperature Product","docAbstract":"Urban development and associated land cover and land use change alter the thermal, hydrological, and physical properties of the land surface. Urban areas usually exhibit relatively warmer air and surface temperatures than surrounding non-urban lands, a phenomenon recognized as Surface Urban Heat Island (SUHI). As urban areas continue to develop and the climate continues to warm, it has become increasingly important to quantify and map the SUHI effect and learn how to mitigate it. To help meet the expanding need of analysis ready data for SUHI based studies, a methodology was developed to evaluate Land Surface Temperature (LST) using the Landsat Collection 1 Provisional Surface Temperature Science Product. The Landsat derived LST products were processed for 50 major cities throughout the Conterminous U.S. The SUHI product package includes per-pixel annual surface temperature, annual intensity, annual hotspot, and hotspot probability bands from 1985 to 2020.
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0000-0002-9948-1304","orcid":"https://orcid.org/0000-0002-9948-1304","contributorId":302266,"corporation":false,"usgs":false,"family":"Mueller","given":"Chase","affiliations":[],"preferred":false,"id":877861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hussain, Reza 0000-0002-5445-3027","orcid":"https://orcid.org/0000-0002-5445-3027","contributorId":301245,"corporation":false,"usgs":false,"family":"Hussain","given":"Reza","affiliations":[{"id":65343,"text":"KBR, Contractor to U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":877862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":877863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shi, Hua 0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":302265,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":877864,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arab, Saeed 0000-0003-1602-8801","orcid":"https://orcid.org/0000-0003-1602-8801","contributorId":299964,"corporation":false,"usgs":false,"family":"Arab","given":"Saeed","email":"","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":877865,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251891,"text":"70251891 - 2023 - Benchmarking satellite-derived shoreline mapping algorithms","interactions":[],"lastModifiedDate":"2024-03-05T13:03:29.556435","indexId":"70251891","displayToPublicDate":"2023-09-29T06:59:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8956,"text":"Communications Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Benchmarking satellite-derived shoreline mapping algorithms","docAbstract":"Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications.
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 2 (April–June) 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 Thermal Infrared Sensor (TIRS) Scene Select Mechanism (SSM) excursion anomaly. On April 21, 2023, a TIRS SSM excursion error flag was indicated in telemetry during a calibration activity when the SSM encoder was powered on and the mirror was between the nadir position and the deep space position. An initial recovery plan indicated the SSM was moving erratically, so the instrument was put into a safe state for additional troubleshooting. A second recovery plan was developed and successfully executed on April 23, 2023. Additional information about the Landsat 8 TIRS SSM excursion anomaly is available at https://www.usgs.gov/landsat-missions/news/landsat-8-level-1-product-processing-resumes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231075","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Shrestha, A., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Ruslander, K., Micijevic, E., Choate, M.J., Anderson, C., Thome, K., Kaita, E., Barsi, J., Levy, R., Miller, J., and Ding, L., 2023, ECCOE Landsat quarterly Calibration and Validation report—Quarter 2, 2023: U.S. Geological Survey Open-File Report 2023–1075, 39 p., https://doi.org/10.3133/ofr20231075.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154779","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":421208,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":421207,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1075/images/"},{"id":421206,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1075/ofr20231075.XML","linkFileType":{"id":8,"text":"xml"}},{"id":421209,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231075/full","linkFileType":{"id":5,"text":"html"}},{"id":421204,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1075/coverthb.jpg"},{"id":421205,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1075/ofr20231075.pdf","text":"Report","size":"126 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1075"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.
No abstract available.
","language":"English","publisher":"NASA","usgsCitation":"Neigh, C., Crawford, C., and McGinty, E., 2023, Summary of the Final Activities of the 2018-2023 Landsat Science Team: The Earth Observer, v. 35, no. 5, p. 38-42.","productDescription":"5 p.","startPage":"38","endPage":"42","ipdsId":"IP-157514","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":424602,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://eospso.gsfc.nasa.gov/earthobserver/sep-oct-2023","linkFileType":{"id":5,"text":"html"}},{"id":424625,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Neigh, Christopher","contributorId":330146,"corporation":false,"usgs":false,"family":"Neigh","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":892862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":892863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGinty, Ellie","contributorId":333479,"corporation":false,"usgs":false,"family":"McGinty","given":"Ellie","email":"","affiliations":[{"id":79890,"text":"SSAI Inc","active":true,"usgs":false}],"preferred":false,"id":892864,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247785,"text":"70247785 - 2023 - Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud-based analysis","interactions":[],"lastModifiedDate":"2023-11-20T17:36:37.180823","indexId":"70247785","displayToPublicDate":"2023-08-06T10:59:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5456,"text":"Limnology and Oceanography Letters","active":true,"publicationSubtype":{"id":10}},"title":"Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud-based analysis","docAbstract":"Originally developed for terrestrial science and applications, the US Geological Survey Landsat surface reflectance (SR) archive spanning ~ 40 yr of observations has been increasingly utilized in large-scale water-quality studies. These products, however, have not been rigorously validated using in situ measured reflectance. This letter quantifies and demonstrates the quality of the SR products by harnessing a sizeable global dataset (N = 1100). We found that the Landsat 8/9 SR in the green and red bands marginally meet the targeted accuracy requirements (30%), whereas the uncertainties in the blue and coastal-aerosol bands ranged from 48% to 110%. We further observed > +25% biases in the visible bands of Landsat 5/7 SR, which can introduce an apparent downward trend when applied in time-series analyses combined with Landsat 8/9. Users must exercise caution when using this archive for trend analyses, and progress in atmospheric correction is required to foster advanced applications of the Landsat archive for aquatic science.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lol2.10344","usgsCitation":"Maciel, D.A., Pahlevan, N., Barbosa, C.C., de Moraes de Novo, E.M., Paulino, R.S., Martins, V.S., Vermote, E., and Crawford, C., 2023, Validity of the Landsat surface reflectance archive for aquatic science: Implications for cloud-based analysis: Limnology and Oceanography Letters, v. 8, no. 6, p. 820-858, https://doi.org/10.1002/lol2.10344.","productDescription":"9 p.","startPage":"820","endPage":"858","ipdsId":"IP-149014","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":442503,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10344","text":"Publisher Index Page"},{"id":419891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Maciel, Daniel Andrade","contributorId":328506,"corporation":false,"usgs":false,"family":"Maciel","given":"Daniel","email":"","middleInitial":"Andrade","affiliations":[{"id":78384,"text":"INPE","active":true,"usgs":false}],"preferred":false,"id":880451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pahlevan, Nima","contributorId":328507,"corporation":false,"usgs":false,"family":"Pahlevan","given":"Nima","affiliations":[{"id":78385,"text":"NASA GSFC/ SSAI","active":true,"usgs":false}],"preferred":false,"id":880452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barbosa, Claudio Clemente Faria","contributorId":328508,"corporation":false,"usgs":false,"family":"Barbosa","given":"Claudio","email":"","middleInitial":"Clemente Faria","affiliations":[{"id":78384,"text":"INPE","active":true,"usgs":false}],"preferred":false,"id":880453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Moraes de Novo, Evlyn Marcia Leao","contributorId":328509,"corporation":false,"usgs":false,"family":"de Moraes de Novo","given":"Evlyn","email":"","middleInitial":"Marcia Leao","affiliations":[{"id":78384,"text":"INPE","active":true,"usgs":false}],"preferred":false,"id":880454,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paulino, Rejane Souza","contributorId":328510,"corporation":false,"usgs":false,"family":"Paulino","given":"Rejane","email":"","middleInitial":"Souza","affiliations":[{"id":78384,"text":"INPE","active":true,"usgs":false}],"preferred":false,"id":880455,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martins, Vitor Souza","contributorId":328511,"corporation":false,"usgs":false,"family":"Martins","given":"Vitor","email":"","middleInitial":"Souza","affiliations":[{"id":78386,"text":"Missippssii State University","active":true,"usgs":false}],"preferred":false,"id":880456,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Vermote, Eric","contributorId":328512,"corporation":false,"usgs":false,"family":"Vermote","given":"Eric","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":880457,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":880458,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70247435,"text":"70247435 - 2023 - Biophysical factors control invasive annual grass hot spots in the Mojave Desert","interactions":[],"lastModifiedDate":"2023-10-23T15:50:31.324403","indexId":"70247435","displayToPublicDate":"2023-08-03T06:56:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Biophysical factors control invasive annual grass hot spots in the Mojave Desert","docAbstract":"Invasive annual grasses can promote ecosystem state changes and habitat loss in the American Southwest. Non-native annual grasses such as Bromus spp. and Schismus spp. have invaded the Mojave Desert and degraded habitat through increased fire occurrence, severity, and shifting plant community composition. Thus, it is important to identify and characterize the areas where persistent invasion has occurred, identifying where subsequent habitat degradation has increased. Previous plot and landscape-scale analyses have revealed anthropogenic and biophysical correlates with the establishment and dominance of invasive annual grasses in the Mojave Desert. However, these studies have been limited in spatial and temporal scales. Here we use Landsat imagery validated using an extensive network of plot data to map persistent and productive populations of invasive annual grass, called hot spots, across the entire Mojave Desert ecoregion over 12 years (2009–2020). We also identify important variables for predicting hot spot distribution using the Random Forest algorithm and identifying the most invaded subregions. We identified hot spots in over 5% of the Mojave Desert mostly on the western and eastern edges of the ecoregion, and invasive grasses were detected in over 90% of the Mojave Desert at least once in that time. Across the entire Mojave Desert, our results indicate that soil texture, aspect, winter precipitation, and elevation are the highest-ranking predictive variables of invasive grass hot spots, while anthropogenic variables contributed the least to the accuracy of the predictive model. The total area covered by hot spots varied significantly among subregions of the Mojave Desert. We found that anthropogenic variables became more important in explaining invasive annual establishment and persistence as spatial scale was reduced to the subregional level. Our findings have important implications for informing where land management actions can prioritize reducing invasive annual persistence and promoting restoration efforts.