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USGS2010

Factors influencing the at-sea distribution of Cassin's Auklets (Ptychoramphus aleuticus) that breed in the Channel Islands, California

We used radiotelemetry to evaluate at-sea habitat use by Cassin's Auklets ( Ptychoramphus aleuticus ) that bred at Prince Island, off southern California, from 1999 through 2001. We used logistic regression to compare paired radiotelemetry (presence) with random (pseudo-absence) location-associated habitat variables derived from (1) satellite remote-sensing of sea surface temperature and chlorophyll- a concentration and (2) bathymetry. Compared with random locations within their foraging area and after controlling for distance to colony, odds ratios indicated that Cassin's Auklets with dependent young occurred in relatively shallower, warmer, and chlorophyll-rich water associated with chlorophyll fronts near the insular shelf break. These oceanographic features characterize habitats that support key euphausiid prey (e.g., Thysanoessa spinifera ) and also other krill predators. Radiotelemetry combined with satellite remote-sensing of the ocean provides an alternative to vessel-based surveys for evaluating seabird foraging habitats. In the absence of information on the actual distribution, abundance, and, hence, availability of Zooplankton prey for seabirds, environmental factors can serve as proxies to help elucidate distributional patterns of seabirds at sea.

California↗

USGS2008

Fire severity and ecosytem responses following crown fires in California shrublands

Chaparral shrublands burn in large high-intensity crown fires. Managers interested in how these wildfires affect ecosystem processes generally rely on surrogate measures of fire intensity known as fire severity metrics. In shrublands burned in the autumn of 2003, a study of 250 sites investigated factors determining fire severity and ecosystem responses. Using structural equation modeling we show that stand age, prefire shrub density, and the shortest interval of the prior fire history had significant direct effects on fire severity, explaining >50% of the variation in severity. Fire severity per se is of interest to resource managers primarily because it is presumed to be an indicator of important ecosystem processes such as vegetative regeneration, community recovery, and erosion. Fire severity contributed relatively little to explaining patterns of regeneration after fire. Two generalizations can be drawn: fire severity effects are mostly short-lived, i.e., by the second year they are greatly diminished, and fire severity may have opposite effects on different functional types. Species richness exhibited a negative relationship to fire severity in the first year, but fire severity impacts were substantially less in the second postfire year and varied by functional type. Much of this relationship was due to alien plants that are sensitive to high fire severity; at all scales from 1 to 1000 m 2 , the percentage of alien species in the postfire flora declined with increased fire severity. Other aspects of disturbance history are also important determinants of alien cover and richness as both increased with the number of times the site had burned and decreased with time since last fire. A substantial number of studies have shown that remote-sensing indices are correlated with field measurements of fire severity. Across our sites, absolute differenced normalized burn ratio (dNBR) was strongly correlated with field measures of fire severity and with fire history at a site but relative dNBR was not. Despite being correlated with fire severity, absolute dNBR showed little or no relationship with important ecosystem responses to wildfire such as shrub resprouting or total vegetative regeneration. These findings point to a critical need for further research on interpreting remote sensing indices as applied to postfire management of these shrublands.

Ecological Applications↗

USGS2009

Modeling fuel succession

Surface fuels data are of critical importance for supporting fire incident management, risk assessment, and fuel management planning, but the development of surface fuels data can be expensive and time consuming. The data development process is extensive, generally beginning with acquisition of remotely sensed spatial data such as aerial photography or satellite imagery (Keane and others 2001). The spatial vegetation data are then crosswalked to a set of fire behavior fuel models that describe the available fuels (the burnable portions of the vegetation) (Anderson 1982, Scott and Burgan 2005). Finally, spatial fuels data are used as input to tools such as FARSITE and FlamMap to model current and potential fire spread and behavior (Finney 1998, Finney 2006). The capture date of the remotely sensed data defines the period for which the vegetation, and, therefore, fuels, data are most accurate. The more time that passes after the capture date, the less accurate the data become due to vegetation growth and processes such as fire. Subsequently, the results of any fire simulation based on these data become less accurate as the data age. Because of the amount of labor and expense required to develop these data, keeping them updated may prove to be a challenge. In this article, we describe the Sierra Nevada Fuel Succession Model, a modeling tool that can quickly and easily update surface fuel models with a minimum of additional input data. Although it was developed for use by Yosemite, Sequoia, and Kings Canyon National Parks, it is applicable to much of the central and southern Sierra Nevada. Furthermore, the methods used to develop the model have national applicability.

Fire Management Today↗

USGS2009

Reflectance spectroscopy of organic compounds: 1. Alkanes

Reflectance spectra of the organic compounds comprising the alkane series are presented from the ultraviolet to midinfrared, 0.35 to 15.5 /??m. Alkanes are hydrocarbon molecules containing only single carbon-carbon bonds, and are found naturally on the Earth and in the atmospheres of the giant planets and Saturn's moon, Titan. This paper presents the spectral properties of the alkanes as the first in a series of papers to build a spectral database of organic compounds for use in remote sensing studies. Applications range from mapping the environment on the Earth, to the search for organic molecules and life in the solar system and throughout the. universe. We show that the spectral reflectance properties of organic compounds are rich, with major diagnostic spectral features throughout the spectral range studied. Little to no spectral change was observed as a function of temperature and only small shifts and changes in the width of absorption bands were observed between liquids and solids, making remote detection of spectral properties throughout the solar system simpler. Some high molecular weight organic compounds contain single-bonded carbon chains and have spectra similar to alkanes even ' when they fall into other families. Small spectral differences are often present allowing discrimination among some compounds, further illustrating the need to catalog spectral properties for accurate remote sensing identification with spectroscopy.

Journal of Geophysical Research E: Planets↗

USGS2012

Description and validation of an automated methodology for mapping mineralogy, vegetation, and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains, Colorado

The efficacy of airborne spectroscopic, or "hyperspectral," remote sensing for geoenvironmental watershed evaluations and deposit-scale mapping of exposed mineral deposits has been demonstrated. However, the acquisition, processing, and analysis of such airborne data at regional and national scales can be time and cost prohibitive. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor carried by the NASA Earth Observing System Terra satellite was designed for mineral mapping and the acquired data can be efficiently used to generate uniform mineral maps over very large areas. Multispectral remote sensing data acquired by the ASTER sensor were analyzed to identify and map minerals, mineral groups, hydrothermal alteration types, and vegetation groups in the western San Juan Mountains, Colorado, including the Silverton and Lake City calderas. This mapping was performed in support of multidisciplinary studies involving the predictive modeling of surface water geochemistry at watershed and regional scales. Detailed maps of minerals, vegetation groups, and water were produced from an ASTER scene using spectroscopic, expert system-based analysis techniques which have been previously described. New methodologies are presented for the modeling of hydrothermal alteration type based on the Boolean combination of the detailed mineral maps, and for the entirely automated mapping of alteration types, mineral groups, and green vegetation. Results of these methodologies are compared with the more detailed maps and with previously published mineral mapping results derived from analysis of high-resolution spectroscopic data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Such comparisons are also presented for other mineralized and (or) altered areas including the Goldfield and Cuprite mining districts, Nevada and the central Marysvale volcanic field, Wah Wah Mountains, and San Francisco Mountains, Utah. The automated mineral group mapping products described in this study are ideal for application to mineral resource and mineral-environmental assessments at regional and national scales.

Colorado↗

USGS2006

Scaling-up of CO2 fluxes to assess carbon sequestration in rangelands of Central Asia

Flux towers provide temporal quantification of local carbon dynamics at specific sites. The number and distribution of flux towers, however, are generally inadequate to quantify carbon fluxes across a landscape or ecoregion. Thus, scaling up of flux tower measurements through use of algorithms developed from remote sensing and GIS data is needed for spatial extrapolation of carbon fluxes and to identify regional sinks and sources of carbon. Spatial and temporal quantification of carbon dynamics are useful in understanding the biophysical factors that cause regions to be sinks or sources of carbon. We analyzed data sets from the Northern Great Plains and the Kazakh Steppe and found similarities in latitude, precipitation, and carbon fluxes between the two regions. These similarities allowed us to pool carbon flux data, remotely sensed data, and GIS data from these two regions to map gross primary productivity (Pg), total ecosystem respiration (Re), and net ecosystem exchange (NEE) for Kazakh Steppe for 2001 using regression tree techniques. We estimated 10-day Pg and Re with mean absolute errors of 3.2 and 2.7 g CO 2 /m 2 /day, respectively. The NEE for grasslands in the Kazakh Steppe during the growing season (April through October 2001) was 0.79 t C/ha. Localized carbon sinks and sources were positively correlated with growing season precipitation and Pg. The regression tree technique provided an effective method for the regional mapping of carbon dynamics as seasonally quantified by flux towers in the Northern Great Plains of North America and the Kazakh Steppe of Central Asia.

Conference Paper↗

USGS2025

Multiscale framework for assessing land cover change on barrier islands from extreme storms and restoration

Often found along the estuarine-marine interface, barrier islands and mainland coastal zones are shaped by tides, currents, extreme storms, and relative sea-level rise. These systems provide ecosystem services such as storm surge and wave attenuation, erosion protection to inland areas, habitat for fish and wildlife, recreation, and tourism. Given the importance of these ecosystems coupled with their dynamic nature, information on how these coastal systems are changing can help to inform natural resource management. Remote sensing advancements have led to an abundance of data for monitoring change in coastal settings. This study developed a multiscale framework that can provide trajectory information from screening-level analyses by using existing or custom moderate spatial resolution land cover maps. Using the north-central Gulf Coast as a case study, the trajectory of land cover area for barrier islands and mainland coastal zones was assessed using several geospatial data sets, including: (1) long-term moderate-resolution remote sensing products with an annual (or more frequent) temporal frequency; (2) a restoration database ( e.g. , beach/dune restoration, sediment placement, and dune enhancement); and (3) a tropical storm database. Due to the coarser spatial resolution of data sets used for screening-level analyses, detailed or application-specific analyses are often needed to reduce uncertainty in smaller changes that may not be captured. These may include land cover change analyses ( i.e. this study), periodic land cover maps with higher spatial resolution and more detailed land cover classes, or elevation-related analyses ( e.g. , dune change or inundation change). Using this framework, abrupt changes in land cover on Dauphin Island, Alabama, resulting from extreme storms were detected using moderate spatial resolution screening-level data, while restoration impact analyses may require higher resolution data. Further, land cover change analyses that incorporate change allocation provide robust information for understanding land cover change in dynamic coastal settings.

Alabama, Florida↗

USGS2022

Spatial scale selection for informing species conservation in a changing landscape

Identifying the relevant spatial scale at which species respond to features in a landscape (scale of effect) is a pressing research need as managers work to reduce biodiversity loss amid a variety of environmental challenges. Until recently, researchers often evaluated a subset of potential scales of effect inferred from previous studies in other locations, often based on different biological responses and environmental variables. These approaches, however, can create uncertainty as to whether relevant spatial scales were identified, and whether the effects of environmental variables at scale were accurately estimated. Identifying scales of effect is particularly relevant for the greater sage-grouse ( Centrocercus urophasianus ), a sagebrush-obligate species of conservation concern requiring large areas of intact sagebrush cover ( Artemisia spp.) for habitat. We demonstrate the application of a scale selection approach that jointly estimates the scale of effect and the effect of sagebrush cover on trends in population size using counts from 584 sage-grouse leks in southwestern Wyoming (2003–2019) and annual estimates of sagebrush cover from a remote sensing product. From this approach, we estimated a positive effect of mean sagebrush cover with a 95% probability that the scale of effect occurred within 5.02 km of leks. In an average year, we found that lower levels of sagebrush cover within these estimated scales could support increasing trends in sage-grouse population size when populations were small, but higher levels of sagebrush cover were needed to sustain growing populations when populations were larger. With standardized monitoring and annual estimates of vegetation from remote sensing, this scale selection approach can be applied to identify relevant scales for other populations, species, and biological responses such as demography and movement.

Wyoming↗

USGS2025

Climate change and infrastructure development drive ice-rich permafrost thaw in Point Lay (Kali), Alaska

Permafrost thaw and thermokarst development pose urgent challenges to Arctic communities, threatening infrastructure and essential services. This study examines the reciprocal impacts of permafrost degradation and infrastructure in Point Lay (Kali), Alaska, drawing on field data from ∼60 boreholes, measured and modeled ground temperature records, remote sensing analysis, and community interviews. Field campaigns from 2022–2024 reveal widespread thermokarst development and ground subsidence driven by the thaw of ice-rich permafrost. Borehole analysis confirms excess-ice contents averaging ∼40%, with syngenetic ice wedges extending over 12 m deep. Measured and modeled ground temperature data indicate a warming trend, with increasing mean annual ground temperatures and active layer thickness (ALT). Since 1949, modeled ALTs have generally deepened, with a marked shift toward consistently thicker ALTs in the 21st century. Remote sensing shows ice wedge thermokarst expanded from <5% in 1949 to >60% in developed areas by 2019, with thaw rates increasing tenfold between 1974 and 2019. In contrast, adjacent, undisturbed tundra exhibited more consistent thermokarst expansion (∼0.2% yr −1 ), underscoring the amplifying role of infrastructure, surface disturbance, and climate change. Community interviews reveal the lived consequences of permafrost degradation, including structural damage to homes, failing utilities, and growing dependence on alternative water and wastewater strategies. Engineering recommendations include deeper pile foundations, targeted ice wedge stabilization, aboveground utilities, enhanced snow management strategies, and improved drainage to mitigate ongoing infrastructure issues. As climate change accelerates permafrost thaw across the Arctic, this study highlights the need for integrated, community-driven adaptation strategies that blend geocryological research, engineering solutions, and local and Indigenous knowledge.

Alaska↗

USGS2024

2023-2024 Coastal sage scrub and chaparral community monitoring for western San Diego County

Western San Diego County is dominated by shrublands supporting biologically diverse native plant and animal communities. Widespread urbanization has led to regional habitat loss and fragmentation, and many species in these shrubland communities are rare, threatened, or endangered. Large-scale, multiple-species conservation planning has resulted in a regional preserve system that focuses on these shrubland communities. Several large-scale threats are leading to type conversion from shrub-dominated to non-native invasive annual grass-dominated vegetation. To understand the changes that are occurring to native shrublands, we have developed a vegetation monitoring program with several components at multiple spatial scales, focused on quantifying coastal sage scrub (CSS) and chaparral vegetation community characteristics. Several drivers of change associated with type conversion of native shrubland to non-native annual grassland have been identified by previous research including increasing fire frequency, nitrogen deposition from air pollution, and prolonged and intense drought associated with changing climate. Loss of ecological integrity indices have been proposed as useful measures of the threat of degradation and type conversion of shrublands in San Diego County. For this study, ecological integrity is defined as a system’s ability to maintain species’ relationships and functions comparable to natural habitat in the region. Previous studies have identified the percent cover of invasive non-native annual grasses as a proxy for overall ecological degradation (loss of integrity) that is consistent across native taxonomic groups. Increased cover of non-native grass is associated with lower integrity of the shrubland vegetation community as shrub-associated plant and animal species are replaced by species preferring grassy and disturbed habitats. The objectives of this CSS and chaparral vegetation community monitoring plan are to: 1) Determine the distribution, composition, structure, and integrity of CSS and chaparral vegetation communities on conserved lands in western San Diego County, 2) Identify whether these attributes of the vegetation communities are changing over time, and 3) Evaluate relationships of known drivers of change (threats) and environmental factors in association with changes in vegetation community attributes. The CSS and chaparral vegetation community monitoring program is divided into four components: 1) vegetation mapping, 2) GIS/remote sensing office analysis of landscape-scale data, 3) permanent field plots using Unmanned Aerial Systems (UAS) and field data collections, and 4) animal and target species surveys and rapid assessment protocols. The first component, which is not detailed in this vegetation monitoring plan, aims to map vegetation communities every 10-15 years based on a classification developed for western San Diego County. High resolution aerial imagery will be used to update the 2012 vegetation map and expand it across the entire study area. The second component uses remote sensing models to annually track ecological integrity of shrublands across the study area and will include a map of areas of change and areas of stability over several decades. These landscape-scale integrity classifications will be used to analyze ecological processes, threats, and abiotic factors relative to changes to shrubland ecological integrity over time and space. The third component includes field surveys of 100 permanent plots across areas historically mapped as shrublands. Surveys in the 1930s mapped vegetation types using plot data. By using this historical classification map, we included areas that have already type-converted from shrubland to non-native annual grassland. The plots were split between CSS (55 plots) and chaparral (45 plots) and stratified into four geographical eco-subregions to guarantee coverage over small patches of habitat along the heavily developed coast. Surveys will include the collection of UAS imagery at a very high resolution. Species-level identifications will be made from the imagery based on a plant list compiled of all species detected in the plot during a thorough field survey by botanists, combined with geo-referenced samples of plant species locations. In addition, herbaceous cover will be estimated in the field using nine 1-m diameter circles (one per subplot) to obtain ocular estimates of cover for each plant species within the circle frame. Soil samples will be collected and analyzed for important element compositions. These data will be analyzed to evaluate plot-level ecological integrity based on species composition and cover. Repeated monitoring will allow evaluation of changes in vegetation attributes over time with known drivers or threats and other environmental factors. In addition, analyses will focus on indicator species and various measures of biological diversity for the vegetation communities. Finally, animal species and rare plants will be assessed using either taxa-based rapid assessment protocols or specialized species-specific protocols for rarer species. The purpose of these assessments is to document the status, habitat, and threat covariates of specific species and confirm the species composition and diversity of animal taxonomic groups (e.g., pollinators). Diversity and abundance of animal species at vegetation plots will be used to refine measures and thresholds of ecological integrity. Rapid assessment protocols for animal taxonomic groups can include multiple detection methods such as, camera traps, cover boards, and bird point counts. Pollinators will be monitored at plots using a protocol currently being developed in conjunction with but separately from this plan. Target rare plant species will be monitored using the regional Inspect and Manage (IMG) protocol that measures the status of rare plant occurrences and habitat and threat covariates over time. Species-specific animal survey methods will be refined as these species are prioritized for future monitoring. The goal of this monitoring program is to classify CSS and chaparral vegetation community integrity, identify areas of degradation across western San Diego County, and characterize drivers, and environmental factors associated with loss of ecological integrity. A combination of vegetation mapping, landscape-scale remote sensing, and field plots will be used to address all the aspects of our research questions. Data compiled and collected will be available to conservation partners to help inform future management decisions.

California↗

USGS2019

Understanding tidal marsh trajectories: Evaluation of multiple indicators of marsh persistence

Robust assessments of ecosystem stability are critical for informing conservation and management decisions. Tidal marsh ecosystems provide vital services, yet are globally threatened by anthropogenic alterations to physical and biological processes. A variety of monitoring and modeling approaches have been undertaken to determine which tidal marshes are likely to persist into the future. Here, we conduct the most robust comparison of marsh metrics to date, building on two foundational studies that had previously and independently developed metrics for marsh condition. We characterized pairs of marshes with contrasting trajectories (of marsh cover ) across six regions of the United States, using a combination of remote-sensing and field-based metrics. We also quantified decadal trends in marsh conversion to mudflat/open water at these twelve marshes. Our results suggest that metrics quantifying the distribution of vegetation across an elevational gradient represent the best indicators of marsh trajectories. The unvegetated to vegetated ratio and flood-ebb sediment differential also served as valuable indicators. No single metric universally predicted marsh trajectories, and therefore a more robust approach includes a suite of spatially integrated, landscape-scale metrics that are mostly obtainable from remote sensing. Data from surface elevation tables and marker horizons revealed that degrading marshes can have higher rates of vertical accretion and elevation gain than more intact counterparts, likely due to longer inundation times potentially combined with internal recycling of material. A high rate of elevation gain relative to local sea-level rise has been considered critical to marsh persistence, but our results suggest that it also may serve as a signature of degradation in marshes that have already begun to deteriorate. This investigation, with rigorous comparison and integration of metrics initially developed independently, tested at a broad geographic scale, provides a model for collaborative science to develop management tools for improving conservation outcomes.

Environmental Research Letters↗

USGS2016

Evidence for nonuniform permafrost degradation after fire in boreal landscapes

Fire can be a significant driver of permafrost change in boreal landscapes, altering the availability of soil carbon and nutrients that have important implications for future climate and ecological succession. However, not all landscapes are equally susceptible to fire-induced change. As fire frequency is expected to increase in the high latitudes, methods to understand the vulnerability and resilience of different landscapes to permafrost degradation are needed. We present a combination of multiscale remote sensing, geophysical, and field observations that reveal details of both near-surface (<1 m) and deeper (>1 m) impacts of fire on permafrost. Along 11 transects that span burned-unburned boundaries in different landscape settings within interior Alaska, subsurface electrical resistivity and nuclear magnetic resonance data indicate locations where permafrost appears to be resilient to disturbance from fire, areas where warm permafrost conditions exist that may be most vulnerable to future change, and also areas where permafrost has thawed. High-resolution geophysical data corroborate remote sensing interpretations of near-surface permafrost and also add new high-fidelity details of spatial heterogeneity that extend from the shallow subsurface to depths of about 10 m. Results show that postfire impacts on permafrost can be variable and depend on multiple factors such as fire severity, soil texture, soil moisture, and time since fire.

Alaska↗

USGS2026

Improving estimates of spatially distributed agricultural land use using pesticide data

Accurate, spatially resolved agricultural land-use data are essential for estimating water demand, evaluating agricultural production, and understanding long-term changes in managed landscapes. However, existing land-use datasets are often temporally discontinuous, particularly for historical periods, and remotely sensed classifications face persistent challenges distinguishing spectrally similar crop types, capturing sub-annual rotations, and separating active from abandoned plantings. We developed the California Pesticide Use Reporting Land-Use Estimator (CalPUR-LUE), which converts grower-reported pesticide-application records into annual estimates of land use by crop class and cropped acreage at approximately 1-square-mile resolution across California. CalPUR-LUE performance was evaluated for 2018 using complementary spatial, categorical, and quantitative comparisons with California Department of Water Resources (CADWR) remotely sensed land-use data and county-level agricultural economic reports. At the county-meridian-township-range-section (COMTRS) scale, CalPUR-LUE and CADWR acreage estimates showed substantial proportional agreement (log-transformed Lin’s concordance correlation coefficient = 0.77) with a mean proportional bias of −15%. Crop-class agreement was high when CalPUR-LUE identified agricultural activity, with user’s accuracy exceeding 83% for all evaluated crop classes. Overall categorical agreement was 67% (Cohen’s kappa = 0.56). Grain crop underestimation contributed substantially to disagreement; however, there is evidence of CADWR systematic overestimation of grain during 2016–2019. County-level agricultural reports further corroborated CalPUR-LUE estimates and, in several cases, aligned more closely with CalPUR-LUE than with CADWR. These results demonstrate that pesticide-use reporting provides an independent and complementary source of agricultural land-use information that can corroborate contemporary estimates and reconstruct annual spatially distributed land use where historical data are limited.

California↗

USGS2021

OpenET: Filling a critical data gap in water management for the western United States

The lack of consistent, accurate information on evapotranspiration (ET) and consumptive use of water by irrigated agriculture is one of the most important data gaps for water managers in the western United States (U.S.) and other arid agricultural regions globally. The ability to easily access information on ET is central to improving water budgets across the West, advancing the use of data-driven irrigation management strategies, and expanding incentive-driven conservation programs. Recent advances in remote sensing of ET have led to the development of multiple approaches for field-scale ET mapping that have been used for local and regional water resource management applications by U.S. state and federal agencies. The OpenET project is a community-driven effort that is building upon these advances to develop an operational system for generating and distributing ET data at a field scale using an ensemble of six well-established satellite-based approaches for mapping ET. Key objectives of OpenET include: Increasing access to remotely sensed ET data through a web-based data explorer and data services; supporting the use of ET data for a range of water resource management applications; and development of use cases and training resources for agricultural producers and water resource managers. Here we describe the OpenET framework, including the models used in the ensemble, the satellite, meteorological, and ancillary data inputs to the system, and the OpenET data visualization and access tools. We also summarize an extensive intercomparison and accuracy assessment conducted using ground measurements of ET from 139 flux tower sites instrumented with open path eddy covariance systems. Results calculated for 24 cropland sites from Phase I of the intercomparison and accuracy assessment demonstrate strong agreement between the satellite-driven ET models and the flux tower ET data. For the six models that have been evaluated to date (ALEXI/DisALEXI, eeMETRIC, geeSEBAL, PT-JPL, SIMS, and SSEBop) and the ensemble mean, the weighted average mean absolute error (MAE) values across all sites range from 13.6 to 21.6 mm/month at a monthly timestep, and 0.74 to 1.07 mm/day at a daily timestep. At seasonal time scales, for all but one of the models the weighted mean total ET is within ±8% of both the ensemble mean and the weighted mean total ET calculated from the flux tower data. Overall, the ensemble mean performs as well as any individual model across nearly all accuracy statistics for croplands, though some individual models may perform better for specific sites and regions. We conclude with three brief use cases to illustrate current applications and benefits of increased access to ET data, and discuss key lessons learned from the development of OpenET.

western United States↗

USGS1999

Multiscale thermal refugia and stream habitat associations of chinook salmon in northwestern Oregon

We quantified distribution and behavior of adult spring chinook salmon ( Oncorhynchus tshawytscha ) related to patterns of stream temperature and physical habitat at channel-unit, reach-, and section-level spatial scales in a wilderness stream and a disturbed stream in the John Day River basin in northeastern Oregon. We investigated the effectiveness of thermal remote sensing for analyzing spatial patterns of stream temperature and assessed habitat selection by spring chinook salmon, evaluating whether thermal refugia might be responsible for the persistence of these stocks in rivers where water temperatures frequently exceed their upper tolerance levels (25°C) during spawning migration. By presenting stream temperature and the ecology of chinook salmon in a historical context, we could evaluate how changes in riverine habitat and thermal spatial structure, which can be caused by land-use practices, may influence distributional patterns of chinook salmon. Thermal remote sensing provided spatially continuous maps of stream temperature for reaches used by chinook salmon in the upper subbasins of the Middle Fork and North Fork John Day River. Electivity analysis and logistic regression were used to test for associations between the longitudinal distribution of salmon and cool-water areas and stream habitat characteristics. Chinook salmon were distributed nonuniformly in reaches throughout each stream. Salmon distribution and cool water temperature patterns were most strongly related at reach-level spatial scales in the warm stream, the Middle Fork (maximum likelihood ratio: P < 0.01), and most weakly related in the cold stream, the North Fork ( P > 0.30). Pools were preferred by adult chinook salmon in both subbasins (Bonferroni confidence interval: P ≤ 0.05); however, riffles were used proportionately more frequently in the North Fork than in the Middle Fork. Our observations of thermal refugia and their use by chinook salmon at multiple spatial scales reveal that, although heterogeneity in the longitudinal stream temperature profile may be viewed as an ecological warning sign, thermal patchiness in streams also should be recognized for its biological potential to provide habitat for species existing at the margin of their environmental tolerances.

Oregon↗

USGS2015

Mapping surficial minerals at high latitudes: The USGS 2014 imaging spectrometer data collection in Alaska

Passive optical remote sensing of high latitude regions faces many challenges including a short acquisition season and poor illumination due to low solar elevation. Additional complications are encountered in the identification of surface minerals for mineral resource characterization because minerals of interest commonly are exposed on steep terrain, further challenging reflectance retrieval and detection of mineral signatures. On shallow slopes and flat terrain, vegetation cover can interfere with or obscure the absorption features of minerals in rock and soil. The USGS is conducting a study to examine the viability of using remote sensing techniques for identification of large-tonnage, base metal-rich deposits in Alaska.

Alaska↗

USGS2018

Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes

Assessing climate-related ecological changes across spatiotemporal scales meaningful to resource managers is challenging because no one method reliably produces essential data at both fine and broad scales. We recently confronted such challenges while integrating data from ground- and satellite-based sensors for an assessment of four wetland-rich study areas in the U.S. Midwest. We examined relations between temperature and precipitation and a set of variables measured on the ground at individual wetlands and another set measured via satellite sensors within surrounding 4 km 2 landscape blocks. At the block scale, we used evapotranspiration and vegetation greenness as remotely sensed proxies for water availability and to estimate seasonal photosynthetic activity. We used sensors on the ground to coincidentally measure surface-water availability and amphibian calling activity at individual wetlands within blocks. Responses of landscape blocks generally paralleled changes in conditions measured on the ground, but the latter were more dynamic, and changes in ecological conditions on the ground that were critical for biota were not always apparent in measurements of related parameters in blocks. Here, we evaluate the effectiveness of decisions and assumptions we made in applying the remotely sensed data for the assessment and the value of integrating observations across scales, sensors, and disciplines.

Sensors↗

USGS2023

The sands of time: Predicting sea level rise impacts to barrier island habitats

Coastal beach ecosystems support critical habitat for numerous species and are vulnerable to sea level rise. Sand beaches are spatially and temporally dynamic, making it difficult to accurately predict future habitat loss – estimates that are crucial as species are being assessed for protection. We mapped sand beach habitat on 12 focal barrier islands and low-lying beaches off the Gulf Coast of Florida, USA using two methods for comparison - a remotely sensed land cover and hand-digitized aerial imagery that was collected concurrently with digital elevation data. We then compared estimates of beach habitat lost to sea level rise between the two methods and compared this sensitivity to control mangrove islands. Predictions suggest that most of the beach habitat in our study areas will be underwater within the next century. These beaches represent critical nesting habitat for vulnerable vertebrate species such as the snowy plover ( Charadrius nivosus ) and the loggerhead turtle ( Caretta caretta ). Importantly, we found that for some islands, using remotely sensed land cover data led us to under- or over-estimate the amount of beach habitat lost to sea level rise relative to the digitized data because of the temporal mismatch between land cover and elevation data. In contrast, we found relatively little difference between methods in the amount of mangrove forest lost to sea level rise on nearby control islands. We suggest that using land cover data collected at the same time as elevation data can produce more accurate predictions of beach habitat inundation from sea level rise.

Florida↗