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The purpose of the Workshop on Luminescence Techniques was threefold: (1) to review the state-of-the-art capabilities of luminescence methods, (2) to consider a variety of concepts for the development of future luminescence sensor systems, and (3) to discuss appropriate research and development strategies for advancing the current state-of-the-art.
The purpose of the Workshop on Luminescence Techniques was threefold: (1) to review the state-of-the-art capabilities of luminescence methods, (2) to consider a variety of concepts for the development of future luminescence sensor systems, and (3) to discuss appropriate research and development strategies for advancing the current state-of-the-art.
As part of CRMS, Digital Orthophoto Quarter Quadrangles (DOQQs) for the coastal region of Louisiana are created for years when coastwide land-water classifications are required. A DOQQ is a raster image in which displacement in the image caused by sensor orientation and terrain relief has been corrected. These images combine the image characteristics of a photo with the geometric qualities of a map. The DOQQs generated for this project consist of four components or spectral bands of information: blue, green, red and very-near infrared (VNIR). These images are referred to as Color Infrared (CIR) digital imagery. CRMS site-level assessments of land-water coverage will be based off of color-infrared photography acquired for coastal Louisiana and clipped to each 1-km2 CRMS-Wetlands site. Unless otherwise noted as a specific preliminary condition, all vegetation such as scrub-shrub, emergent vegetation, and forested areas will fall under the land classification, while open water, non-vegetated, regularly flooded mud flats, and aquatic vegetation beds will be characterized as water. CRMS imagery contracts are managed and issued by the United States Geological Survey (USGS) Wetland and Aquatic Research Center.
In the summer of 2022, the U.S. Fish & Wildlife Service and partners successfully completed the first single-year, aerial photo survey of sea otters across the entire Southeast stock of northern sea otters. This document presents the latest findings from the 2022 Southeast Alaska Sea Otter Survey, providing up-to-date information regarding occupancy, abundance and carrying capacity. We collected two independent data streams of sea otter observations during the survey using novel image sensor technologies for marine wildlife surveys and recently developed statistical methods. We calculated an abundance estimate of 22,359 (95% Bayesian credible interval: 19,595, 25,290, CV = 0.064) sea otters. Based on the these results, the population size of the Southeast stock of northern sea otters is still increasing and below the estimated carrying capacity for the region of 48,083 (95% Bayesian credible interval: 40,575, 58,570) sea otters. Results from this report include the first updated abundance estimates for the Southeast stock of sea otters in 10 years, which will help inform stakeholders from across the region and addresses the top priority identified by the 2019 Southeast Sea Otter Stakeholder Working Group.
Many coastal estuaries have experienced declines in pH over the past few decades due to coastal acidification. However, mean monthly water column pH values (collected during daylight hours) have increased in Tampa Bay, Florida over recent decades concurrent with seagrass recovery. We measured changes in carbonate system and water quality variables in Tampa Bay and the near-coastal Gulf of Mexico environment to quantify diurnal to seasonal trends, drivers, and controls of carbonate chemistry; identify exposure periods to low pH conditions; and to examine the potential for seagrasses to buffer acidification in Tampa Bay. Autonomous sensor packages deployed in Tampa Bay and the Gulf of Mexico from December 2017 to June 2020 recorded hourly measurements of seawater temperature, salinity, pressure, pH T (total scale), carbon dioxide (pCO 2 ), dissolved oxygen (DO), and photosynthetically active radiation. Results indicated strong temperature and biological influence on DO, pH T , and pCO 2 in Tampa Bay during the dry season, and only weak to moderate correlation of these variables with temperature and salinity during the wet season. Strong influence from biological processes during the wet season was coincident with spring-to-summer periods of maximum seagrass growth rates. Gulf of Mexico results indicated higher pH T and DO, and lower pCO 2 than in Tampa Bay, with similar but attenuated seasonal variation. Results suggest potential benefits from seagrass photosynthesis increasing pH T , DO, and decreasing pCO 2 in Tampa Bay, and delivery of high pH T , low pCO 2 Gulf of Mexico water to Tampa Bay during flood tides. Approximately 30% of pH T and pCO 2 data records collected in Tampa Bay were below pH T 7.900 and above pCO 2 of 600 μ atm, primarily during the wet season, indicating potential for dissolution of carbonate sediments that may also help buffer acidification conditions in Tampa.
The April bottom trawl survey and Alewife Alosa pseudoharengus population assessment provides science to inform Lake Ontario fisheries management. The 2023 survey included 215 trawls in the main lake and embayments, and sampled depths from 6.5 to 252 m (21-833 ft). The survey captured 1,012,178 fish from 32 species with a total weight of 12,136 kg (26,700 lbs.). Alewife were 92% of the catch by number while Rainbow Smelt, Osmerus mordax , Deepwater Sculpin, Myoxocephalus thompsonii , and Round Goby, Neogobius melanostomus , comprised 3%, 3%, and 1% of the catch, respectively. To improve the accuracy of prey fish biomass and density estimates we reanalyzed trawl sensor data from each of three participating survey vessels and created vessel-specific relationships predicting how bottom trawl bottom contact time, wing width, and area-swept varies with depth. Total Alewife biomass increased in 2023 due to growth and survival of the abundant 2020 year class (now age-3) and an abundant 2022 year class (age-1). The 2023 mean Alewife biomass (81.1 kg·ha -1 ) was the largest since whole lake sampling began in 2016 and was the ninth largest value observed in the modern time series (1997-2023, maximum value in 2000 = 91.8 kg·ha -1 ). The 2023 Alewife density (6795 n·ha -1 ) was the greatest density observed in the modern time series. These high biomass and density values are due to above average Alewife reproductive success in 2020 and 2022. Simulation modeling suggests the 2024 and 2025 Alewife biomass index may be substantially higher than the 2023 observations. In 2023, the Rainbow Smelt biomass index increased relative to the 2022 index, as did the biomass index for Cisco, Coregonus artedi . In contrast, Emerald Shiner Notropis atherinoides and Threespine Stickleback Gasterosteus aculeatus , biomass values continue to be low (< 0.01 kg·ha -1 ). Three Bloater Coregonus hoyi , were captured during the 2023 survey. Hydroacoustic sampling conducted during the bottom trawl survey estimated prey fish densities in pelagic habitats not sampled by the bottom trawl (3 m below the surface to 3 m above the lake bottom) and these densities were hundreds to thousands of times lower than bottom trawl-based densities. These results support the idea that, in April, when the warmest water is on the lake bottom, Alewife and most other pelagic prey fish are near the lake bottom and can be effectively sampled with bottom trawling.
Unoccupied Aerial Systems (UAS) can accommodate a variety of tools for mapping and monitoring hydrothermal systems (e.g., magnetic, gas, photogrammetry, and thermal infrared [TIR]). These platforms offer increased speed, coverage area, and uniformity compared to ground-based measurements, as well as lower flight height – and therefore higher resolution – than occupied aircraft. We adapted a suite of tools for use with UAS and implemented these methods in a study focused on the area around Shady Rest Park, Mammoth Lakes, California, within the Long Valley Caldera. This location, which contains tree kills, gas vents, soil gas emissions, heated ground, and hydrothermal alteration, is the site of ongoing efforts to monitor changes in the surface expression of the local hydrothermal system. The methods applied in this study include: (1) airborne visible imagery for surficial mapping and the creation of high-resolution digital elevation models; (2) airborne magnetic measurements; (3) airborne TIR imagery; (4) airborne gas emission measurements; and (5) ground-based gravity measurements. We conducted these surveys in May and October of 2021, in part to establish baseline TIR and gas data against which future changes to the hydrothermal system may be assessed. UAS-based magnetic and ground-based gravity data were collected to map subsurface geology and to characterize potential subsurface controls on thermal anomalies and gas emissions. Results of these efforts at mapping and monitoring the hydrothermal system at Mammoth Lakes demonstrate how an integrated UAS- and ground-based approach may be applied more broadly to study other known or potential hydrothermal and volcanic systems. We consider the benefits and limitations of each method, particularly the TIR and gas sensors, which have less well-developed processing techniques in place for UAS applications. By integrating results from several of these different methods, however, the limitations facing each individual approach may be mitigated, and a better understanding of the hydrothermal system may be reached.
The Group on Earth Observation (GEO) required for their Forest Carbon Tracking (FCT) task the assistance of the Committee on Earth Observation Satellites (CEOS) space agencies to implement coordinated data acquisition strategies from Earth Observation (EO) over key areas of interest to demonstrate the value of linking coordinated acquisition of satellite data with standardised processing methods, forest inventory and ecosystem models. Both optical and radar of midresolution have been acquired within the coordinated acquisition campaigns developed in 2009 and 2010. Homogeneous and coherent EO databases have already started by means of full key areas coverage according with suitable sensor operating modes. CEOS effort seeks now continuing with an updated strategy over expanded area coverage.
Large-magnitude avalanches often alter vegetation composition, avalanche path dimensions, and subsequent avalanche return periods. Understanding temporal changes in individual avalanche path trimlines, runout zones, and geomorphic characteristics helps forecasters, planners, and engineers estimate potential avalanche destructive size and impact on infrastructure or settlements in the runout zone. Understanding these changes on a large scale also provides information on post-cycle avalanche distribution. Here, we use remote sensing platforms and change detection techniques to examine vegetation change in avalanche paths in Montana and Colorado. In northwest Montana, we implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. In Colorado, we investigated changes in avalanche path vegetation characteristics prior to and after a widespread large-magnitude avalanche cycle. The highest frequency of avalanche return periods was broadly characterized by grassland and shrubland, but topography greatly influences vegetation classes and return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. We used optical sensors from satellite imagery to analyze changes in Normalized Difference Vegetation Index (NDVI) to calculate ground cover change over time. NDVI, a measure of near-infrared and red bands within the imagery, allowed us to distinguish between green vegetation (e.g., trees and shrubs) and non-vegetated ground cover (e.g., dead and downed trees, rocks, and dirt) within avalanche paths. For this study, we calculated changes in NDVI values by comparing imagery from 2018 to imagery from 2019 after a widespread large magnitude avalanche cycle occurred in March 2019 in Colorado, United States. We applied a filtering process to reduce error, classified NDVI change based on the value distribution, and then calculated area change of all areas within each avalanche path. We completed this process for 1633 avalanche paths throughout Colorado. We found that using NDVI difference values pre- and post-avalanche cycle allowed us to identify ground cover change in avalanche paths throughout Colorado. These changes span from a slight expansion of existing avalanche paths to substantial landscape disturbance. For example, a size D5 avalanche caused severe ground cover change in 18% of one single path near Aspen, Colorado. This suggests that large magnitude avalanches can redefine avalanche path dimensions and could impact subsequent avalanche size and frequency. Using NDVI from satellite imagery is a simple way to detect ground cover changes in avalanche paths on a large scale or in remote areas. In general, remote sensing products to detect and examine vegetation and ground cover change in avalanche paths can help inform avalanche distribution and benefit planning efforts.
The USGS ShakeAlert® earthquake early warning (EEW) system is operational and providing public alerting in three West Coast states: California, Washington, and Oregon. Since 2006 the USGS has pursued a strategy of incrementally developing and rolling out EEW for increasingly larger areas and uses. As funding from federal and state budgets grew the system became more capable, detection methods were developed and improved, core network sensor stations were built or upgraded, and partners were enlisted to deliver alerts and implement protective actions. In the fall of 2018, the system became sufficiently functional to publicly declare it “open for business” in all three states for use by licensed partners to alert personnel in limited settings and take automated machine-to-machine actions. State-wide public alerting began in California in October of 2019, expanded to Oregon in March of 2021, and to Washington in May of 2021. Today millions of people can receive ShakeAlert-powered EEW through a variety of delivery methods and dozens of machine-to-machine protective systems are in place in transportation systems, utilities, fire stations, schools, hospitals, and public and private buildings. The ShakeAlert System implementation plan calls for a supporting network of 1,675 seismic stations. 1,129 (73%) have been completed and the rest should be done by 2025.
Geospatial distribution of natural vegetation is among the very important environmental parameters required for applications ranging from global climate change to monitoring of natural hazards, monitoring of ecosystem vitality, and fire management practices. Increasingly sophisticated applications require vegetation datasets to cover large areas at a suitable scale and provide sufficiently detailed information. In this paper, we describe a research effort to develop a remote sensing methodology capable of producing 30-meter resolution, wall-to-wall coverage of existing vegetation types and structure variables in support of a multi-agency fire fuels and fire risks assessment project. Success of this remote sensing research effort is dependent on improved sensor and data qualities, a thorough understanding of regional and local vegetation ecology, successful integration of remote sensing with a large amount of field plot data, and flexible mapping algorithms. Preliminary results produced in the Wasatch Range and Uinta Mountains of central Utah include 28 vegetation types with an overall accuracy of 60% (average by life forms), percent canopy density (sub-pixel density) of forest, shrub, and herbaceous cover (correlation coefficient of 89, 60, and 55% respectively), and average top canopy height of forest, shrub, and herbaceous cover (correlation coefficient of 73, 50, 20% respectively). Techniques to improve the first-round results are discussed, including refinements of mapping models and use of relevant environmental gradients and potential vegetation classification associated with actual vegetation types.
Understanding temporal variations in a geothermal field can support operators in decision making that pertains to optimizing production and mitigating hazards. Between 2021 and 2023, The Geysers geothermal field in northern California was monitored with an array of continuous passive seismic sensors and annual repeat magnetotelluric (MT) measurements. Each of these data sets were analyzed and modelled separately to understand the data, sensitivity, and any observable changes. Then, the data were inverted jointly using a crossgradient method to further constrain temporal changes in geophysical properties within the geothermal field. Multiple permutations of annual datasets were used as inputs to the joint inversion. Results demonstrate seismic data constrain smooth inversion of the MT data, and the MT data provide supplementary information about the location of temporal fluid changes. Estimating relative changes in steam saturation for various time intervals of the joint models shows compartmentalized changes in the field, and good spatial correlation with the location of injection wells. These results demonstrate that collecting both passive seismic and MT measurements then modeling them jointly provide complementary information and a relatively inexpensive method for monitoring temporal changes in an active geothermal field that provides results to support operators.
Water quality monitoring capacity has been declining for the Chesapeake Bay Program (CBP) at a time when information needs are growing, and data gaps exist to address critical decision-support for managers. The CBP Scientific Technical Assessment and Reporting Team is leading a Principal’s Staff Committee requested gap analyses toward understanding support needed to improve water quality monitoring and analysis programming. Advanced technologies and alternative monitoring approaches in the form of satellite-based measurements, Artificial Intelligence/Machine Learning (AI/ML) algorithms for data interpretation, continuous water quality in-situ sensor arrays, and community science efforts offer a growing portfolio of valuable opportunities for expanding data collections and analysis program capacities. However, since 1985, each of these options are examples of growing opportunities to enhance water quality assessments yet has seen limited adoption into elements of Chesapeake Bay water quality monitoring programs. Where new technologies have been adopted (e.g., shallow water continuous water quality monitoring), such temporally rich data streams have supported Bay health insights yet had limited use in regulatory water quality criteria assessment. This Scientific Technical Advisory Committee (STAC) supported workshop provided the ideal forum for engaging our CBP partnership regarding the maturity of new and evolving monitoring and analysis capacities to address program information needs while appreciating limitations with adopting new tools and approaches. Improving natural resources monitoring efficiency and effectiveness will expand the scientific and technical foundations for making robust, strategic choices on decisions for CBP Partnership community-based priorities, policies, and management actions. Workshop findings and recommendations reflect progress in science, technology, and analyses addressing long-standing programmatic limitations in data collection and analysis capacities. State-of-the-science updates highlighted in the workshop span the spectrum of efforts representing improvements, successes, remaining challenges toward operationalizing protocols, and guidance toward research, or adoption and implementation by monitoring programs.
Remotely sensed data and derived information contribute substantially to mission-critical work across the Department of the Interior (DOI). 1 This DOI section highlights a sample of DOI remote sensing applications and illustrates a range of technology, platforms, and specialized sensors employed.
This paper describes a study that deployed underwater acoustic sensors (hydrophones) to detect streambed mobilization in the Wild and Scenic reach of the Rio Chama, New Mexico. Fine sediment accumulation in the gravel‑cobble bed affects brown trout spawning habitat, and understanding incipient motion thresholds is essential for planning effective environmental flushing flows released from El Vado Dam. Two hydrophone stations were operated during 2021–2022, capturing audio data during a high‑flow pulse in late 2021. Acoustic processing techniques, including spectral analysis and threshold‑based impact detection, were used to identify sediment‑generated noise associated with coarse sediment transport. Results indicate detectable sediment mobilization during the high‑flow pulse, demonstrating hydrophones’ potential as a passive monitoring tool for environmental flow planning and sediment‑management decision support.
Active long-path differential optical absorption spectroscopy (LP-DOAS) has been an effective tool for measuring atmospheric trace gases for several decades. However, instruments were large, heavy and power-inefficient, making their application to remote environments extremely challenging. Recent developments in fibre-coupling telescope technology and the availability of ultraviolet light emitting diodes (UV-LEDS) have now allowed us to design and construct a lightweight, portable, low-power LP-DOAS instrument for use at remote locations and specifically for measuring degassing from active volcanic systems. The LP-DOAS was used to measure sulfur dioxide (SO 2 ) emissions from La Fossa crater, Vulcano, Italy, where column densities of up to 1.2 × 10 18 molec cm −2 (~ 500 ppmm) were detected along open paths of up to 400 m in total length. The instrument's SO 2 detection limit was determined to be 2 × 10 16 molec cm −2 (~ 8 ppmm), thereby making quantitative detection of even trace amounts of SO 2 possible. The instrument is capable of measuring other volcanic volatile species as well. Though the spectral evaluation of the recorded data showed that chlorine monoxide (ClO) and carbon disulfide (CS 2 ) were both below the instrument's detection limits during the experiment, the upper limits for the X / SO 2 ratio (X = ClO, CS 2 ) could be derived, and yielded 2 × 10 −3 and 0.1, respectively. The robust design and versatility of the instrument make it a promising tool for monitoring of volcanic degassing and understanding processes in a range of volcanic systems.
Current, accurate, and reliable information on the areal extent and spatial distribution of mangrove forests in the Philippines is limited. Previous estimates of mangrove extent do not illustrate the spatial distribution for the entire country. This study, part of a global assessment of mangrove dynamics, mapped the spatial distribution and areal extent of the Philippines’ mangroves circa 2000. We used publicly available Landsat data acquired primarily from the Global Land Survey to map the total extent and spatial distribution. ISODATA clustering, an unsupervised classification technique, was applied to 61 Landsat images. Statistical analysis indicates the total area of mangrove forest cover was approximately 256,185 hectares circa 2000 with overall classification accuracy of 96.6% and a kappa coefficient of 0.926. These results differ substantially from most recent estimates of mangrove area in the Philippines. The results of this study may assist the decision making processes for rehabilitation and conservation efforts that are currently needed to protect and restore the Philippines’ degraded mangrove forests.
Changes in the distribution and abundance of mangrove species within and outside of their historic geographic range can have profound consequences in the provision of ecosystem goods and services they provide. Mangroves in the conterminous United States (CONUS) are believed to be expanding poleward (north) due to decreases in the frequency and severity of extreme cold events, while sea level rise is a factor often implicated in the landward expansion of mangroves locally. We used ~35 years of satellite imagery and in situ observations for CONUS and report that: (i) poleward expansion of mangrove forest is inconclusive, and may have stalled for now, and (ii) landward expansion is actively occurring within the historical northernmost limit. We revealed that the northernmost latitudinal limit of mangrove forests along the east and west coasts of Florida, in addition to Louisiana and Texas has not systematically expanded toward the pole. Mangrove area, however, expanded by 4.3% from 1980 to 2015 within the historic northernmost boundary, with the highest percentage of change in Texas and southern Florida. Several confounding factors such as sea level rise, absence or presence of sub-freezing temperatures, land use change, impoundment/dredging, changing hydrology, fire, storm, sedimentation and erosion, and mangrove planting are responsible for the change. Besides, sea level rise, relatively milder winters and the absence of sub-freezing temperatures in recent decades may be enabling the expansion locally. The results highlight the complex set of forcings acting on the northerly extent of mangroves and emphasize the need for long-term monitoring as this system increases in importance as a means to adapt to rising oceans and mitigate the effects of increased atmospheric CO 2 .