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At least 829 records · Page 46Linked to original sources

Remote sensing in the USGS Mineral Resource Surveys Program in the eastern United States

Mineral deposits commonly occur within special geologic units or structures, such as fault zones, which can be detected and mapped from aircraft and satellite images. Modern techniques analyze multispectral images that record the way solar energy is reflected or emitted by the materials exposed at the Earth's surface. In sparsely vegetated regions, including most of the Western United States, mineral composition is determined directly by analyzing the spectral properties of rock outcrops. In more densely vegetated terrain, such as the Eastern United States, rock and soil composition can be determined directly in manmade exposures, such as plowed fields and construction sites, or much more general determinations can be made indirectly by analyzing the distribution and apparent health of naturally occurring plants. The association of certain plants with particular rock or soil types has been known for decades. For example, coniferous trees grow preferentially on well-drained sandy soil, whereas deciduous trees dominate on shaly bedrock. These two forest types reflect solar radiation quite differently and, therefore, are distinguished readily in conventional aerial photographs. More subtle plant-bedrock associations require digital multispectral image analysis to infer compositional information from the spectral characteristics of the forest canopy.

Information Handout↗

A "Region-Specific Model Adaptation (RSMA)" based training data method in large-scale land cover mapping

An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the “Region-Specific Model Adaptation (RSMA)” method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE’s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data.

Remote Sensing↗

Mobile radar provides insights into hydrologic responses in burn areas

Background Wildfires often occur in mountainous terrain, regions that pose substantial challenges to operational meteorological and hydrologic observing networks. Aims A mobile, post-fire hydrometeorological observatory comprising remote-sensing and in situ instrumentation was developed and deployed in a burnt area to provide unique insights into rainfall-induced post-fire hazards. Methods Mobile radar-based rainfall estimates were produced throughout the burn area at 75-m resolution and compared with rain gauge accumulations and basin response variables. Key results The mobile radar was capable of resolving details in intra-basin rain fields as well as detecting storms approaching the burn area with accuracy equivalent to rain gauges. Runoff responses were complex and dependent on spatiotemporal patterns and magnitude of rainfall intensity over the burn area. Conclusions The complement of the mobile radar with the near-field, non-contact instruments measuring the hydrologic response provided valuable information in regions that are difficult to access and are not routinely monitored by conventional observing networks. Implications Post-fire observatories equipped with mobile radars deployed on burn areas provide real-time data, early alerting capabilities and visualizations to potentially guide impact-based decision support for local authorities.

Colorado↗

Estimating neighborhood variability with a binary comparison matrix.

A technique which utilizes a binary comparison matrix has been developed to implement a neighborhood function for a raster format data base. The technique assigns an index value to the center pixel of 3- by 3-pixel neighborhoods. The binary comparison matrix provides additional information not found in two other neighborhood variability statistics; the function is sensitive to both the number of classes within the neighborhood and the frequency of pixel occurrence in each of the classes. Application of the function to a spatial data base from the Kenai National Wildlife Refuge, Alaska, demonstrates 1) the numerical distribution of the index values, and 2) the spatial patterns exhibited by the numerical values. -Author

Photogrammetric Engineering and Remote Sensing↗

Specifications for updating USGS land use and land cover maps

To meet the increasing demands for up-to-date land use and land cover information, a primary goal of the U.S. Geological Survey's (USGS) national land use and land cover mapping program is to provide for periodic updating of maps and data in a timely and uniform manner. The technical specifications for updating existing USGS land use and land cover maps that are presented here cover both the interpretive aspects of detecting and identifying land use and land cover changes and the cartographic aspects of mapping and presenting the change data in conventional map format. They provide the map compiler with the procedures and techniques necessary to then use these change data to update existing land use and land cover maps in a manner that is both standardized and repeatable. Included are specifications for the acquisition of remotely sensed source materials, selection of compilation map bases, handling of data base corrections, editing and quality control operations, generation of map update products for USGS open file, and the reproduction and distribution of open file materials. These specifications are planned to become part of the National Mapping Division's Technical Instructions.

Open-File Report↗

Geographic Names of Iceland's Glaciers: Historic and Modern

Climatic changes and resulting glacier fluctuations alter landscapes. In the past, such changes were noted by local residents who often documented them in historic annals; eventually, glacier variations were recorded on maps and scientific reports. In Iceland, 10 glacier place-names are to be found in Icelandic sagas, and one of Iceland's ice caps, Snaefellsjokull, appeared on maps of Iceland published in the 16th century. In the late 17th century, the first description of eight of Iceland's glaciers was written. Therefore, Iceland distinguishes itself in having a more than 300-year history of observations by Icelanders on its glaciers. A long-term collaboration between Oddur Sigurdsson and Richard S. Williams, Jr., led to the authorship of three books on the glaciers of Iceland. Much effort has been devoted to documenting historical glacier research and related nomenclature and to physical descriptions of Icelandic glaciers by Icelanders and other scientists from as far back as the Saga Age to recent (2008) times. The first book, Icelandic Ice Mountains, was published by the Icelandic Literary Society in 2004 in cooperation with the Icelandic Glaciological Society and the International Glaciological Society. Icelandic Ice Mountains was a glacier treatise written by Sveinn Palsson in 1795 and is the first English translation of this important scientific document. Icelandic Ice Mountains includes a Preface, including a summary of the history and facsimiles of page(s) from the original manuscript, a handwritten copy, and an 1815 manuscript (without maps and drawings) by Sveinn Palsson on the same subject which he wrote for Rev. Ebenezer Henderson; an Editor's Introduction; 82 figures, including facsimiles of Sveinn Palsson's original maps and perspective drawings, maps, and photographs to illustrate the text; a comprehensive Index of Geographic Place-Names and Other Names in the treatise; References, and 415 Endnotes. Professional Paper 1746 (this book) is the second of the three books; it is being published in both English and Icelandic editions. This book provides information about all named glaciers in Iceland, historic and modern. Descriptions, with geographic coordinates, and bibliographic citations to all glacier place-names on published maps, books, and scientific articles are included. Maps, oblique aerial photographs, ground photographs, and satellite images document each of the 269 modern named glaciers of Iceland. The third book, Glaciers of Iceland, is Chapter D of the 11-chapter [volume] U.S. Geological Survey Professional Paper 1386-A-K. Chapter D includes a 1:500,000-scale Map of the Glaciers of Iceland; it is a comprehensive historical and modern review and assessment of what is currently known about glaciers in Iceland's eight Regional Glacier Groups from a review of the scientific literature and from analysis of maps and remotely sensed data (ground, airborne, and satellite); topics include geology and geography, climate and climate variability, types of glaciers, history of glacier variation (including the 21 surge-type glaciers), and frequency and magnitude of volcanic and lacustrine jokulhlaups.

Professional Paper↗

The benefits of improved national elevation data

This article describes how the National Enhanced Elevation Assessment (NEEA) has identified substantial benefits that could come about if improved elevation data were publicly available for current and emerging applications and business uses such as renewable energy, precision agriculture, and intelligent vehicle navigation and safety. In order to support these diverse needs, new national elevation data with higher resolution and accuracy are needed. The 3D Elevation Program (3DEP) initiative was developed to meet the majority of these needs and it is expected that 3DEP will result in new, unimagined information services that would result in job growth and the transformation of the geospatial community. Private-sector data collection companies are continuously evolving sensors and positioning technologies that are needed to collect improved elevation data. An initiative of this scope might also provide an opportunity for companies to improve their capabilities and produce even higher data quality and consistency at a pace that might not have otherwise occurred.

Photogrammetric Engineering and Remote Sensing↗

REMOTE-SENSING MINERAL DISCOVERIES IN THE MOJAVE DESERT OF CALIFORNIA.

As a result of remote sensing studies in the Mojave Desert of California three previously unknown stockwork molybdenum systems have been discovered. It is not known if economic deposits of molybdenum and associated minerals occur in these areas; there is, however, sufficient data to judge that these areas are worthy of further exploration. The purpose of this paper is to present case histories of two of these discoveries. These discoveries have been made from laboratory analyses of Landsat multispectral scanner images to map limonitic materials and from field reconnaissance to determine if the limonite is due to hydrothermal alteration. In those areas that seemed most promising, airborne spectrometer surveys were employed to mapped Al-OH minerals, and audio-magnetotelluric (AMT) and telluric-traversing surveys were performed to obtain information at depth.

Conference Paper↗

Detecting seasonal landslide movement within the Cascade landslide complex (Washington) using time-series SAR imagery

Detection of slow or limited landslide movement within broad areas of forested terrain has long been problematic, particularly for the Cascade landslide complex (Washington) located along the Columbia River Gorge. Although parts of the landslide complex have been found reactivated in recent years, the timing and magnitude of motion have not been systematically monitored or interpreted. Here we apply novel time-series strategies to study the spatial distribution and temporal behavior of the landslide movement between 2007 and 2011 using InSAR images from two overlapping L-band ALOS PALSAR-1 satellite tracks. Our results show that the reactivated part has moved approximately 700 mm downslope during the 4-year observation period, while other parts of the landslide complex have generally remained stable. However, we also detect about 300 mm of seasonal downslope creep in a terrain block upslope of the Cascade landslide complex—terrain previously thought to be stable. The temporal oscillation of the seasonal movement can be correlated with precipitation, implying that seasonal movement here is hydrology-driven. The seasonal movement also has a frequency similar to GPS-derived regional ground oscillations due to mass loading by stored rainfall and subsequent rebound but with much smaller magnitude, suggesting different hydrological loading effects. From the time-series amplitude information on terrain upslope of the headscarp, we also re-evaluate the incipient motion related to the 2008 Greenleaf Basin rock avalanche, not previously recognized by traditional SAR/InSAR methods. The approach used in this study can be used to identify active landslides in forested terrain, to track the seasonal movement of landslides, and to identify previously unknown landslide hazards.

Washington↗

Detecting the influence of best management practices on vegetation near ephemeral streams with Landsat data

Various best management practices (BMPs) have been implemented on rangelands with the goals of controlling nonpoint source pollution, reducing the impact of livestock in ecologically important riparian areas, and improving grazing distribution. Providing off-stream water sources to livestock in pastures, cross-fencing, and rotational grazing are common rangeland BMPs that have demonstrated success in drawing livestock grazing pressure away from streams. We evaluated the effects of rangeland BMP implementation with six commercial-scale pastures in the northern mixed-grass prairie. Four pastures received a BMP suite consisting of off-stream water, cross-fencing, and deferred-rotation grazing, and two pastures did not receive BMPs. We hypothesized that the BMPs increased the quantity of riparian vegetation cover relative to the conditions in these pastures during the pre-BMP period and to the two pastures that did not receive BMPs. We used a series of 30-m Landsat normalized difference vegetation index (NDVI) images to track the spatial and temporal changes (1984–2010, n = 24) in vegetation cover, to which NDVI has been well correlated. Validation indicated that the remotely sensed signal from in-channel vegetation was representative of ground conditions. The BMP suite was associated with a 15% increase in the in-channel NDVI (0–30 m from stream centerline) and 18% increase in the riparian NDVI (30–180 m from stream center line). Conversely, the in-channel and riparian NDVI of non-BMP pastures declined 30% and 18% over the study period. The majority of change occurred within 2 yr of BMP implementation. The patterns of in-channel NDVI among pastures suggested that BMP implementation likely altered grazing distribution by decreasing the preferential use of riparian and in-channel areas. We demonstrated that satellite imagery time series are useful in retrospectively evaluating the efficacy of conservation practices, providing critical information to guide adaptive management and decision makers.

Rangeland Ecology and Management↗

The spatially adaptable filter for error reduction (SAFER) process: Remote sensing-based LANDFIRE disturbance mapping updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Fire↗

Temperature variations in the northern Gulf of Alaska across synoptic to century-long time scales

Surface and subsurface moored buoy, ship-based, remotely sensed, and reanalysis datasets are used to investigate thermal variability of northern Gulf of Alaska (NGA) nearshore, coastal, and offshore waters over synoptic to century-long time scales. NGA sea surface temperature (SST) showed a larger positive trend of 0.22 ± 0.10 °C per decade over 1970–2021 compared to 0.10 ± 0.03 °C per decade over 1900–2021. Over synoptic time scales, SST covariance between two stations is small (<10%) when separation exceeds 100 km, while stations separated by 500 km retain 50% of their co-variability for seasonal and longer fluctuations. Relative to in situ sensor data, remotely sensed SST data has limited accuracy in some NGA settings, capturing 60–70% of the daily SST anomaly in coastal and offshore waters, but often <25% nearshore. North Pacific and NGA leading modes of SST variability leave 25–50% of monthly variance unresolved. Analysis of the 2014–2016 Pacific marine heatwave shows that NGA coastal surface temperatures warmed contemporaneously with offshore waters through 2013, but deep inner shelf waters (200–250 m) exhibited delayed warming. Offshore surface waters cooled from 2014 to 2016, while shelf waters continued to warm from the combined effects of local air-sea and advective heat fluxes. We find that annually averaged Sitka air temperature is a leading predictor (r 2 = 0.37, p < 0.05) for following-year NGA coastal water column temperature. Our results can inform future environmental monitoring designs, assist forward-looking projections of marine conditions, and show the importance of in situ measurements for nearshore studies that require knowledge of thermal conditions over time scales of days and weeks.

Alaska↗

ECCOE Landsat quarterly Calibration and Validation report — Quarter 2, 2021

Executive Summary The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) 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), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov . One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold .

Open-File Report↗

ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2021

Executive Summary The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) 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), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov . One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold .

Open-File Report↗

ECCOE Landsat Quarterly Calibration and Validation report— Quarter 4, 2021

Executive Summary The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) 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), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov . One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold .

Open-File Report↗

ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022

Executive Summary The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) 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), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov . One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold .

Open-File Report↗

Estimating the effective spatial resolution of an AVHRR time series

A method is proposed to estimate the spatial degradation of geometrically rectified AVHRR data resulting from misregistration and off-nadir viewing, and to infer the cumulative effect of these degradations over time. Misregistrations are measured using high resolution imagery as a geometric reference, and pixel sizes are computed directly from satellite zenith angles. The influence or neighbouring features on a nominal 1 km by 1 km pixel over a given site is estimated from the above information, and expressed as a spatial distribution whose spatial frequency response is used to define an effective field-of-view (EFOV) for a time series. In a demonstration of the technique applied to images from the Conterminous U.S. AVHRR data set, an EFOV of 3·1km in the east-west dimension and 19 km in the north-south dimension was estimated for a time series accumulated over a grasslands test site.

International Journal of Remote Sensing↗

Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets

The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.

Florida↗