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USGS2011

A promising tool for subsurface permafrost mapping-An application of airborne geophysics from the Yukon River Basin, Alaska

Permafrost is a predominant physical feature of the Earth's Arctic and Subarctic clines and a major consideration encompassing ecosystem structure to infrastructure engineering and placement. Perennially frozen ground is estimated to cover about 85 percent of the state of Alaska where northern reaches are underlain with continuous permafrost and parts of interior Alaska are underlain by areas of discontinuous and (or) sporadic permafrost (fig. 1). The region of Interior Alaska, where permafrost is scattered among unfrozen ground, is a complex mosaic of terrains and habitats. Such diversity creates arrays of lakes and surface-water and groundwater patterns that continental populations of migratory waterfowl and internationally significant fisheries have adapted to over time. A road or pipeline might pass over frozen and unfrozen ground, affecting the types of materials and engineering approaches needed to sustain the infrastructure. Effective mapping of discontinuous permafrost at scales meaningful ecologically and (or) from an engineering perspective has been a long-standing challenge. Using techniques such as borehole logging for site-specific assessments or botanical techniques that can suggest underlying permafrost distributions can be labor intensive and difficult to accomplish at the scale and remoteness of much of Alaska. The climate is changing in the Arctic and Subarctic regions. The warming observed throughout much of Alaska could create widespread changes in permafrost. How the warming of the permafrost affects near-surface processes, ecosystems, and community infrastructure and ecosystems is not clear. A better understanding of the dynamic distribution and physical properties of permafrost, from continuous to discontinuous, will provide knowledge of how the permafrost environment may change in the future and help inform engineering and natural resource response strategies. Here we discuss an application of an airborne remote sensing methodology for mapping and shade imaging permafrost characteristics at various scales. This work provides the first look into three-dimensional distribution of permafrost in the areas around Fort Yukon and is a demonstration of the application of airborne electromagnetic (AEM) to permafrost mapping. Such an approach is attractive, particularly in Arctic and Subarctic studies, where ground access is difficult and ecosystems are fragile. In June 2010, the U.S. Geological Survey (USGS) conducted an AEM survey near Fort Yukon, Alaska. The primary focus of this survey was to map the distribution of permafrost in selected areas in order to supply information for the development of groundwater models of the Yukon River Basin. However, the methodologies have more far-reaching ecological and engineering applications. Approximately 1,800 line kilometers were acquired in a combination of typical block style surveying in the immediate area of Fort Yukon and in long reconnaissance lines over a broader area. The widely spaced lines were flown to cross the modern Yukon River in "X" like patterns with intersections at features that have been previously studied (fig. 2). AEM is used to gather data on the electrical resistivity of materials in the subsurface below the flight path of the helicopter, which are then analyzed to interpret the subsurface lithology and the location and extent of permafrost. For this survey, the electrical resistivity was imaged to depths on the order of 50-100 meters. Images from the survey can be qualitatively compared with known permafrost features and suggest new permafrost features. Electrical properties of earth materials are affected by lithology as well as temperature and the presence of ice; frozen materials become substantially more resistive. This allows for the identification of permafrost from the resistivity image (Abraham and others, 2011). In the area of Fort Yukon, the AEM survey shows elevated resistivities extending to depth, likely indicative of thick permafrost. This depth corresponds well to observations from a borehole drilled in the area in the late 1990s, which detected permafrost to a depth of about 100 meters (Clark and others, 2009). In contrast to the area of Fort Yukon, the Yukon River and its floodplain are not associated with deep resistive sediments, suggesting a lack of deep permafrost, at least within the depth range of the AEM mapping (fig. 3).

Yukon River Basin↗

USGS2023

Editorial: Remote sensing of volcanic gas emissions from the ground, air, and space

When magma rises in volcanic systems, volatile species exsolve from the melt and are outgassed to the atmosphere. The melt composition and temperature, depth at which degassing occurs, extent of gas-water-rock interactions, and volume of ascending magma are all factors that determine the composition and rate of gas emissions at the surface. Interpreted in a petrological framework, gas measurements thus provide information on these fundamental parameters of volcanic systems. Volcanic gases have traditionally been sampled in the field and later analyzed with standard laboratory methods, but remote sensing measurements are playing an increasingly central role in characterizing emissions and the volcanoes from which they originate. The 17 contributions in this Research Topic summarize the state-of-the-art in volcanic gas remote sensing and identify key areas in which the field could further improve our understanding of global volcanism and its impact on Earth’s environment in the next decade.

Frontiers in Earth Science↗

USGS2003

Studies of Alaskan volcanoes using synthetic aperature radar and Landsat imagery

Approximately 10 percent of the world’s active volcanoes are located in the Alaskan Aleutian arc and produce about 3-4 explosive eruptions per year. Even with this high amount of volcanic activity, the remote locations and harsh environments of the Aleutian volcanoes conspire to keep them among some of the most poorly studied volcanoes in the world. Space-borne remote sensed imagery can play a significant role in improving our understanding of activity at these volcanoes. Synthetic aperture radar (SAR), Landsat imagery, and Digital Elevation Models (DEMs) derived from SRTM and the National Elevation Database (NED) are used to study several Alaskan volcanoes. Interferometric SAR (InSAR) techniques with ERS-1 and ERS-2 SAR imagery are used to measure ground-surface deformation, which enables the construction of detailed mechanical models that enhance the study of magmatic and tectonic processes. The 30-year historical archive of Landsat data is used to study land cover change, visualize the ash plumes of Aleutian volcanic eruptions, and to map the extent of lava flows. Differencing two DEMs that represent volcano topography before and after an eruption makes it possible to calculate the volume of extruded materials. This paper provides a progress report on how InSAR, Landsat imagery and digital elevation data can be used to better understand the volcanic processes at three Aleutian volcanoes.

Alaska↗

USGS2023

Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast

Arctic coastal environments are eroding and rapidly changing. A lack of pan-Arctic observations limits our ability to understand controls on coastal erosion rates across the entire Arctic region. Here, we capitalize on an abundance of geospatial and remotely sensed data, in addition to model output, from the North Slope of Alaska to identify relationships between historical erosion rates and landscape characteristics to guide future modeling and observational efforts across the Arctic. Using existing datasets from the Alaska Beaufort Sea coast and a hierarchical clustering algorithm, we developed a set of 16 coastal typologies that captures the defining characteristics of environments susceptible to coastal erosion. Relationships between landscape characteristics and historical erosion rates show that no single variable alone is a good predictor of erosion rates. Variability in erosion rate decreases with increasing coastal elevation, but erosion rate magnitudes are highest for intermediate elevations. Areas along the Alaskan Beaufort Sea coast (ABSC) protected by barrier islands showed a three times lower erosion rate on average, suggesting that barrier islands are critical to maintaining mainland shore position. Finally, typologies with the highest erosion rates are not broadly representative of the ABSC and are generally associated with low elevation, north- to northeast-facing shorelines, a peaty pebbly silty lithology, and glaciomarine deposits with high ice content. All else being equal, warmer permafrost is also associated with higher erosion rates, suggesting that warming permafrost temperatures may contribute to higher future erosion rates on permafrost coasts. The suite of typologies can be used to guide future modeling and observational efforts by quantifying the distribution of coastlines with specific landscape characteristics and erosion rates.

Alaska↗

USGS1977

Planimetric Martian triangulations

Narrow-angle photographs, which have severe drawbacks for stereophotogrammetry, have advantages for simple plane triangulations. Rectified narrow-angle pictures corrected for map projection effects can be combined in the map plane in relatively accurate planimetric triangulations. Provided the strict precepts of least squares are not followed, these triangulations can incorporate considerable overdetermination without increase in the labor of solving the equations. These plane triangulations have been used successfully in the cartography of Mars and are illustrated here by a triangulation of the environs of the prime Martian landing site.

Photogrammetric Engineering and Remote Sensing↗

USGS2008

Wide-area estimates of saltcedar (Tamarix spp.) evapotranspiration on the lower Colorado River measured by heat balance and remote sensing methods

In many places along the lower Colorado River, saltcedar ( Tamarix spp) has replaced the native shrubs and trees, including arrowweed, mesquite, cottonwood and willows. Some have advocated that by removing saltcedar, we could save water and create environments more favourable to these native species. To test these assumptions we compared sap flux measurements of water used by native species in contrast to saltcedar, and compared soil salinity, ground water depth and soil moisture across a gradient of 200–1500 m from the river's edge on a floodplain terrace at Cibola National Wildlife Refuge (CNWR). We found that the fraction of land covered ( f c ) with vegetation in 2005–2007 was similar to that occupied by native vegetation in 1938 using satellite-derived estimates and reprocessed aerial photographs scaled to comparable spatial resolutions (3–4 m). We converted f c to estimates of leaf area index (LAI) through point sampling and destructive analyses ( r 2 = 0·82). Saltcedar LAI averaged 2·54 with an f c of 0·80, and reached a maximum of 3·7 with an f c of 0·95. The ranges in f c and LAI are similar to those reported for native vegetation elsewhere and from the 1938 photographs over the study site. On-site measurements of water use and soil and aquifer properties confirmed that although saltcedar grows in areas where salinity has increased much better than native shrubs and trees, rates of transpiration are similar. Annual water use over CNWR was about 1·15 m year −1 .

Arizona, California↗

USGS2020

Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment

Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails ( Rallus obsoletus yumanensis ) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.

Arizona, California, Nevada↗

USGS2025

Hyperspectral imaging of river bathymetry using an ensemble of regression trees

Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and reflectance to select wavelength bands as predictors and then train a depth retrieval model; applying the model to the image yields a spatially continuous bathymetric map. We used data from five rivers with diverse morphologies and optical characteristics to assess whether HIRBERT can (1) provide more accurate depth estimates than a band ratio-based algorithm and (2) extend the range of depths detectable via remote sensing. Relative to single band combinations identified via optimal band ratio analysis (OBRA), regression tree ensembles improved depth retrieval performance, with observed versus predicted (OP) regression R 2 values increasing for all five sites. Similarly, HIRBERT provided more reliable depth estimates than OBRA over the full range of depths present along each river. These results suggest that by incorporating additional spectral information from multiple wavelength bands, ML could enhance bathymetric mapping across a range of river environments. In addition, we show how graphical tools can facilitate interpretation of ML-based depth retrieval models and yield insight regarding relationships between depth and reflectance. The HIRBERT workflow is packaged in free, standalone software developed to support applications in river research and management. Although ML can enhance remote sensing of river bathymetry, the limitations of this approach must also be acknowledged: Field measurements of water depth are required to train a depth retrieval model and the resulting model should only be applied to the image from which the training data were derived. The inherently image-specific nature of this approach implies that developing generalized regression tree ensembles that could be applied at larger scales would require additional research.

California, Idaho, Nebraska, Oregon, Wyoming↗

USGS2006

Mountain hydrology of the western United States

Climate change and climate variability, population growth, and land use change drive the need for new hydrologic knowledge and understanding. In the mountainous West and other similar areas worldwide, three pressing hydrologic needs stand out: first, to better understand the processes controlling the partitioning of energy and water fluxes within and out from these systems; second, to better understand feedbacks between hydrological fluxes and biogeochemical and ecological processes; and, third, to enhance our physical and empirical understanding with integrated measurement strategies and information systems. We envision an integrative approach to monitoring, modeling, and sensing the mountain environment that will improve understanding and prediction of hydrologic fluxes and processes. Here extensive monitoring of energy fluxes and hydrologic states are needed to supplement existing measurements, which are largely limited to streamflow and snow water equivalent. Ground‐based observing systems must be explicitly designed for integration with remotely sensed data and for scaling up to basins and whole ranges.

Water Resources Research↗

USGS2019

A preliminary assessment of hyperspectral remote sensing technology for mapping submerged aquatic vegetation in the Upper Delaware River National Parks

Hyperspectral remote sensing of submerged aquatic vegetation is a complex and difficult process that is affected by unique constraints on the energy flow profile near and below the water surface. In addition, shallow, winding, lotic systems, such as the Upper Delaware River, present additional remote sensing problems in the form of specular reflectance, variable depth and constituents in the water column and sometimes extremely weak signal strength due to absorption and scattering in the water column that can be statistically overwhelmed by the reflectance from upland vegetation in any individual image scene. Here we test hyperspectral imagery from the Civil Air Patrol’s (CAP), Airborne Real-time Cueing Hyperspectral Enhanced Recon (ARCHER) system in the scenic waters of two National Parks on the Upper Delaware River. A number of unique image processing problems were encountered, including specular reflectance from winding lotic systems, variable depth and flow dynamics of the riverine environment, and disproportionate signal strength from surface reflectance in this riverine environment. This were solved by applying a specular reflectance removal algorithm, applying field data collections to classification results and masking upland vegetation so as to not statistically overwhelm the weak reflectance signal from surface and near-surface water. Much was learned about conducting imaging spectroscopy in such difficult conditions. Significant results include successful mapping of SAV presenece/absence, advantages of upland masking of the reflectance signal, and a numkber of processing approaches that are unique to this environment. In this paper we summarize our results and identify unique issues that must be addressed in this environment

New Jersey, New York, Pennsylvania↗

USGS2025

Advancing broadscale spatial evapotranspiration modelling by incorporating sun-induced chlorophyll fluorescence measurements

Evapotranspiration (ET) describes the sum of water transfer from the ground surface through soil evaporation and water loss from leaf stomata into the atmosphere − critical factors linking the global water and carbon cycles. Myriad ET models based on remote sensing data provide spatially continuous estimates of ET; however, leaf photosynthetic information is critical to ensure accurate ET estimates, which are difficult to measure from space. Remotely sensed sun-induced chlorophyll fluorescence (SIF) provides a proxy of stomatal conductance activity with high performance in predicting plant transpiration, which can account for a large proportion of terrestrial and riverine ET. This study aims to improve estimates of tree water use in semi-arid to arid environments. In this study, a fixed stomatal conductance model and three SIF-driven canopy conductance (g sc ) models were applied to model potential ET (PET). The models estimated PET using the Penman-Monteith equation with: (1) a constant leaf stomatal conductance; (2) a transpiration-driven g sc model; (3) a g sc model based on electron-transfer rate and vapor pressure deficit, and a (4) Ball-Berry stomatal conductance model. A machine learning model was then applied to scale PET to actual ET (AET) using remote sensing and climate data. Accordingly, four AET models were cross-validated with in-situ measured AET at 52 sites, including 21 eddy covariance flux tower sites, and 31 sap-flow measurement sites (semi-arid and plantation area), for various plant functional types in Australia. This study demonstrated that SIF effectively captured seasonal variations of g sc , finding that AET models with SIF-driven g sc models correlated well with in-situ measured AET (R 2 = 0.64). Modelled AET with dynamic variations of g sc generated lower prediction error (0.85 mm day −1 ), while the AET model with fixed stomatal conductance tended to overestimate AET in floodplains and underestimate it in evergreen broadleaf forests, indicating using fixed stomatal conductance results in unstable performance when modelling AET. This study demonstrated that SIF-driven AET models improved broadscale estimation of ET. Our findings provide vital broadscale hydrological data to assist catchment and regional water management, particularly over unmonitored areas at risk of future climate-driven reductions in rainfall.

Journal of Hydrology↗

USGS2018

Status of tidal marsh mapping for blue carbon inventories

Remote-sensing-based maps of tidal marshes, both of their extents and carbon stocks, will play a key role in conducting greenhouse gas (GHG) inventories. The U.N. Environment Programme World Conservation Monitoring Centre has produced a new Global Distribution of Salt Marsh dataset that estimates global salt marsh area at 5.5 Mha. A Tier 1–2 GHG Inventory of U.S. Coastal Wetlands has been developed using the NOAA Coastal-Change Analysis Program Landsat-based land cover maps as a primary dataset. 180Successful mapping of tidal marsh biomass with optical satellite images provides opportunity to improve GHG Inventories. Further work is needed to map tidal marsh salinity gradients, the extent of tidal vs. non-tidal marshes, methane emissions, and high-resolution elevation.

Book chapter↗

USGS2002

Monitoring dust storms and mapping landscape vulnerability to wind erosion using satellite and ground-based digital images

Wind-induced dust emission in the southwestern United States is important regionally because of its impact on human health and safety and its influence on ecosystem dynamics. Factors that control dust emission include wind velocity, sediment availability, and surface conditions (e.g., vegetation type and degree of cover, surface crusts and armoring, and soil moisture - Gillette and Passi, 1988; Gillette and Hanson 1989; Marticorena and others, 1997). Emission of dust from the land surface is a process of degradation that depletes fine-grained minerals needed for optimum vegetation growth, creates potentially hazardous air quality for humans on a local and regional scale, and can affect climate on a regional and global scale. Future climatic change may lead to increased aridification of southwestern deserts, reducing protective vegetation and enhancing dust emissions, thereby increasing the impacts of dust in this region. It is currently not well understood how climate change in the Southwest will affect dust emission and, in turn, how dust emission will affect climate and human health. As part of a study of landscape vulnerability to wind erosion and the potential impacts of dust, we are investigating remotely sensed satellite, airborne, and ground-based image data to determine their ability to detect and monitor active dust storms, as well as to map areas vulnerable to wind erosion. A main objective has been to investigate the use of high temporal resolution digital images collected by satellite and a long-term, ground-based digital camera station, along with wind data collected at our field sites, to detect, monitor, and analyze the location, size, frequency, duration, and transport patterns of dust storms in the Mojave Desert of the southwestern United States. Generally, many current methods and instruments are not developed to the level required to routinely detect and monitor dust storms or to develop models that accurately predict total dust flux and emission rates. Improving these capabilities is critical to generating baseline datasets for assessments of landscape vulnerability to future climate change. On going modeling efforts are attempting to identify meteorological parameters and soil surface roughness parameters needed to predict the vulnerability of various geomorphic substrates to wind erosion. Datasets to calibrate model results are difficult to collect and typically not available. Results of our studies, incorporating digital image maps and new methods using remotely sensed images to identify and monitor dust sources, will be useful for the calibration of dust-emission models of the Mojave Desert and other arid environments. At the same time, we are exploring new methods to map the amount, sizes, and spatial distribution of particles at the surface to enable detailed wind-erosion vulnerability mapping at a regional scale.

California↗

USGS1979

Progress in remote sensing as it applies to missions of Committee for Coordination of Joint Prospecting for Mineral Resources in Asian Offshore Areas

The major thrusts of investigations of the use of space data for understanding our Earth continue to focus on the land and near-shore environments. This is expectable; people live on the land, draw most of their resources from the land or near-shore areas, and in these areas environmental degradation or improvement are most clearly observable. An exception to this focus of investigation was the launch of Seasat, a satellite dedicated to marine investigations. Seasat was short lived but the data produced strongly supported the use of imaging radar for land investigation, and, although studies are incomplete, suggest eventual benefits for the navigator, searchers for marine resources, and mariners--provided that the data and/or interpretations can be available to the marine users shortly after collection. In this regard, the positive steps now being taken in Southeast Asia to establish reception, processing, and interpretation centers are a major development.

Open-File Report↗

USGS2020

Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing

Landslides modify the natural landscape and cause fatalities and property damage worldwide. Quantifying landslide dynamics is challenging due to the stochastic nature of the environment. With its large area of ~1 km 2 and perennial motions at ~10–20 mm per day, the Slumgullion landslide in Colorado, USA, represents an ideal natural laboratory to better understand landslide behavior. Here, we use hybrid remote sensing data and methods to recover the four-dimensional surface motions during 2011–2018. We refine the boundaries of an area of ~0.35 km 2 below the crest of the prehistoric landslide. We construct a mechanical framework to quantify the rheology, subsurface channel geometry, mass flow rate, and spatiotemporally dependent pore-water pressure feedback through a joint analysis of displacement and hydrometeorological measurements from ground, air and space. Our study demonstrates the importance of remotely characterizing often inaccessible, dangerous slopes to better understand landslides and other quasi-static mass fluxes in natural and industrial environments, which will ultimately help reduce associated hazards.

Colorado↗

USGS2025

Scalable environmental DNA methods reveal strong associations between landscape-scale forest habitat and insect richness

While aquatic environmental DNA (eDNA) methods have reached relative maturity, terrestrial eDNA methods are nascent and have yet to reach widespread use. Field-ready applications require eDNA survey methods where samples are easy to collect by inexperienced practitioners, easy to transport between the field and lab, and easy to process thereafter. Here, we demonstrate methods that satisfy these requirements and show strong potential for characterizing diverse terrestrial eDNA samples collected from flower and leaf surfaces. We used novel methods to collect and process 236 flower eDNA samples and 21 leaf surface eDNA samples, obtaining 2,228 Arthropoda eDNA detections spanning 175 families using amplicon sequencing of two genetic markers. Detected taxa were diverse and included numerous groups of conservation concern, such as bees (Hymenoptera; Anthophila, 32 genera spanning 5 families) and Lepidoptera (209 genera from 21 families). Data reveal strong associations between insect community richness and remotely sensed measures of forest habitat, providing a quantitative perspective of relevance to insect conservation. It is increasingly clear that a variety of organisms readily disperse eDNA throughout the environment, supporting the notion that eDNA will be a powerful tool for characterizing species distributions and monitoring at-risk species. However, we conclude that researchers seeking to characterize fine-scale habitat associations or plant-pollinator interactions using eDNA will need to carefully design studies with appropriate field controls, such as the leaf surface eDNA samples collected here.

BioRxiv↗

USGS2007

The U.S. Geological Survey Land Remote Sensing Program

The fundamental goals of the U.S. Geological Survey's Land Remote Sens-ing (LRS) Program are to provide the Federal Government and the public with a primary source of remotely sensed data and applications and to be a leader in defining the future of land remote sensing, nationally and internationally. Remotely sensed data provide information that enhance the understand-ing of ecosystems and the capabilities for predicting ecosystem change. The data promote an understanding of the role of the environment and wildlife in human health issues, the requirements for disaster response, the effects of climate variability, and the availability of energy and mineral resources. Also, as land satellite systems acquire global coverage, the program coordinates a network of international receiving stations and users of the data. It is the responsibility of the program to assure that data from land imaging satellites, airborne photography, radar, and other technologies are available to the national and global science communities.

Fact Sheet↗

USGS2018

AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data

In this work we describe the AutoCNet library, written in Python, to support the application of Computer Vision techniques for n-image correspondence identication in remotely sensed planetary images and subsequent bundle adjustment. The library is designed to support exploratory data analysis, algorithm and processing pipeline development, and application at scale in High Performance Computing (HPC) environments for processing large data sets and generating foundational data products. We also present a brief case study illustrating high level usage for the Apollo 15 Metric camera.

SoftwareX↗