Recent activities of the Priroda State Remote Sensing Center
No abstract availalbe.
Mapping Sciences and Remote Sensing↗
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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.
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Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.
ACM Transactions on Intelligent Systems and Techno↗
Benchmarking satellite-derived shoreline mapping algorithms
Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications.
Communications Earth & Environment↗
Final project peport for “Mapping riparian vegetation response to climate change on the San Carlos Apache Reservation and Upper Gila River watershed to inform restoration priorities: 1935 to present – Phase 2: Focus on tamarisk vegetation”
Riparian ecosystems play a critical role in supporting wildlife habitats, maintaining water quality, and sustaining ecological resilience in arid landscapes. In the Upper Gila River level-4 Hydrologic Unit Code (HUC-4; Identification Number – 1504) watershed of Arizona and New Mexico, and across areas of the San Carlos Apache Tribe of the San Carlos Apache Reservation (hereafter, Tribe/Tribal – entity; or Reservation - place), riparian ecosystems have been substantially altered by the widespread expansion of tamarisk ( Tamarix spp.), an invasive and non-native riparian species. Tamarisk has high water use and increased flammability, making it a growing concern as droughts intensify across the southwestern United States (U.S.). Recent research indicates that tamarisk is increasingly stressed under prolonged drought conditions, which can elevate wildfire risk and further degrade riparian habitat. In response, the U.S. Geological Survey (USGS) Western Geographic Science Center, in collaboration with the Tribe, developed remote sensing–based tools to map riparian vegetation composition and monitor vegetation condition over time. These tools enable accurate identification of tamarisk extent, detection of vegetation stress, and comparison with native species such as cottonwood and willow. This information directly supports restoration and management actions, including targeted tamarisk removal, protection of endangered species habitat, and prioritization of areas for native vegetation recovery. The tools also provide insight into how riparian vegetation responds to changing climate and hydrologic conditions, strengthening long-term planning for riparian forest management. Findings from this work demonstrate that riparian vegetation responses vary across river systems, indicating differences in plant composition and hydro-climatic conditions. Tamarisk on both the Gila and San Carlos Rivers generally exhibit greater declines in greenness in response to higher temperature as well as lower precipitation and river flow, while cottonwood ( Populus fremontii ) and willow (Salix spp.) are particularly sensitive to changes in discharge along the San Carlos River and respond more directly to both high and low flows, with more moderate responses to precipitation and temperature. By providing actionable, science-based information, this research supports climate adaptation planning, wildfire risk reduction, and improved riparian ecosystem health, with benefits that extend beyond the Reservation to regional water and wildlife conservation efforts.
Arizona, New Mexico↗
Yellowstone grizzly bear investigations: Annual report of the Interagency Grizzly Bear Study Team, 2004
The contents of this Annual Report summarize results of monitoring and research from the 2004 field season. The report also contains a summary of nuisance grizzly bear ( Ursus arctos horribilis ) management actions. The study team continues to work on issues associated with counts of unduplicated females with cubs-of-the-year (COY). These counts are used to establish a minimum population size, which is then used to establish mortality thresholds for the Recovery Plan (U.S. Fish and Wildlife Service [USFWS] 1993). A computer program that defines the rule set used by Knight et al. (1995) to differentiate unique family groups was completed in spring 2005. We will use an improved version of this model to verify the accuracy of the rules using known bears and their telemetry locations in test runs. We hope to have this work complete by the end of 2005. The grizzly bear recovery plan (USFWS 1993) established mortality quotas at 4% of the minimum population estimate derived from female with COY data and no more than 30% of the 4% (1.2%) could be female bears. Simulation modeling (Harris 1984) established sustainable mortality at around 6% of the population. We used the latest information on reproduction and survival to estimate population trajectory in the same simulation model originally used by Harris. A Wildlife Monograph has been accepted for publication and should be available by summer 2005. Our project addressing the potential application of stable isotopes and trace elements to quantify consumption rates of whitebark pine ( Pinus albicaulis ) and cutthroat trout ( Oncorhynchus clarki ) by grizzly bears was completed. Our manuscript on consumption rates of whitebark pine has been published (Canadian Journal of Zoology 81:763-770). The manuscript on fish consumption rates was also accepted and is published in the Canadian Journal of Zoology 82:493-501. Both can be found on the Interagency Grizzly Bear Study Team (IGBST) website http://www.nrmsc.usgs.gov/research/igbst-home.htm. We began a new study in Grand Teton National Park evaluating habitat use both temporally and spatially between grizzly and black ( Ursus americanus ) bears. We will employ a new form of Global Positioning System (GPS) technology that incorporates a spread spectrum communication system. Spread spectrum allows for transfer of stored GPS locations from the collar to a remote receiving station. Results of our first yea r’s field season are summarized in this report. Whitebark pine (WBP) has been identified as one of the import ant fall foods of the Yellowstone grizzly bear. Previous efforts to map the distribution of WBP were for the Cumulative Effects Model. Consequently the only coverage of WBP distribution was for the grizzly bear Recovery Zone. We were successful in getting financial support through the U.S. Geological Survey Land Remote Sensing Program and Interdisciplinary Science Support Activities Project to create an ecosystem-wide map of the distribution of WBP. The results of that project are reported in Appendix A. The study team annually estimates WBP cone production on a series of transects. That information is reported annually in our reports. Concern over the long-term health of WBP prompted us to investigate the usefulness of cone counts as an indirect index of WBP health. Results of this analysis (Appendix B) indicated that cone production is too variable to serve this purpose. Consequently, we partnered with several 2 other agencies and embarked on a program to develop a long-term monitoring program directed specifically at WBP health in the Greater Yellowstone Ecosystem (GYE). Our team (Greater Yellowstone Whitebark Pine Monitoring Working Group) was successful in obtaining funds to develop and implement a WBP health monitoring program. Results of our first years work are presented in Appendix C. We also successfully competed for funds in 2005 and will continue to collect information on WBP health. Army cutworm moths ( Euxoa auxiliaris ) are also a very important food for a segment of the GYE grizzly bear population. Hillary Robison, graduate student at University of Nevada, Reno, is nearing completion of her program. In this report, we post her annual work summary, and abstracts of her most recently submitted publications. These include one on the levels of pesticides in cutworm moths and their potential affect on grizzly bears (Appendix D), a spatial analysis to identify army cutworm moth habitat (Appendix E), and the results of a preliminary analysis of pollen grains on the mouth parts of moths (Appendix F) to help identify which plant species are commonly fed upon. Other study team members have also been working on various aspects of grizzly bear science. Study team member Kerry Gunther hosted a workshop on habituated grizzly bears in North America. A copy of the abstract of that report can be found in Appendix G. Additionally, Kerry Gunther and Doug Smith, wolf researcher in Yellowstone National Park (YNP), reported on the interactions between gray wolves ( Canis lupus ) and female grizzly bears with young. They report that of 15 interactions between these 2 carnivores, 8 involve d females with COY. They observed 2 incidents where cubs were killed by wolves at ungulate carcasses (Appendix H). The annual reports of the IGBST summarize annual data collection. Because additional information can be obtained after publication, data summaries are subject to change. For that reason, data analyses and summaries presented in this report supersede all previously published data. The study area and sampling techniques are reported by Blanchard (1985), Mattson et al. (1991 a ), and Haroldson et al. (1998).
Idaho, Montana, Wyoming↗
Ground-truthing coral-reef maps produced from remote-sensing data
No abstract available.
Sound Waves: Coastal science and research news fro↗
Satellite images of the September 2013 flood event in Lyons, Colorado
The U.S. Geological Survey (USGS) Special Applications Science Center (SASC) produced an image base map showing high-resolution remotely sensed data over Lyons, Colorado—a city that was severely affected by the flood event that occurred throughout much of the Colorado Front Range in September of 2013. The 0.5-meter WorldView-2 data products were created from imagery collected by DigitalGlobe on September 13 and September 24, 2013, during and following the flood event. The images shown on this map were created to support flood response efforts, specifically for use in determining damage assessment and mitigation decisions. The raw, unprocessed imagery were orthorectified and pan-sharpened to enhance mapping accuracy and spatial resolution, and reproduced onto a cartographic base map. These maps are intended to provide a snapshot representation of post-flood ground conditions, which may be useful to decisionmakers and the general public. The SASC also provided data processing and analysis support for other Colorado flood-affected areas by creating cartographic products, geo-corrected electro-optical and radar image mosaics, and GIS water cover files for use by the Colorado National Guard, the National Park Service, the U.S. Forest Service, and the flood response community. All products for this International Charter event were uploaded to the USGS Hazards Data Distribution System (HDDS) website (http://hdds.usgs.gov/hdds2/) for distribution.
Colorado↗
Promoting synergy in the innovative use of environmental data—Workshop summary
From December 2 to 4, 2015, NatureServe and the U.S. Geological Survey organized and hosted a biodiversity and ecological informatics workshop at the U.S. Department of the Interior in Washington, D.C. The workshop objective was to identify user-driven future directions and areas of collaboration in advanced applications of environmental data applied to forecasting and decision making for the sustainability of biodiversity and ecosystem services. Substantial effort to recruit attendees from diverse Federal, State, and private sector organizations successfully attracted participants from 20 Federal agencies and 48 different institutions in the academic, nonprofit, State government, and commercial sectors; the total number of attendees ranged from 100 to 144 during the 3-day workshop. The first one-half of the workshop was divided into 7 plenary sessions and 3 sets of lightning talk sessions organized by sector, providing 48 oral and visual plenary presentations that shared diverse perspectives on biodiversity and ecological informatics, including original biospatial analyses from 6 graduate student map contest winners. The second one-half of the workshop focused on 10 breakout sessions with participant-driven themes from the environmental data sphere and concluded with an address by the Director of the U.S. Fish and Wildlife Service. The workshop was structured to encourage interactivity. About 80–90 percent of attendees provided direct feedback using clicker devices for specific questions related to biodiversity and ecological data uses and needs, and 10 breakout session leaders shared the highlights of their group discussions during the final workshop plenary sessions. Participants were encouraged to use the Twitter hashtag #ShareUrData. Over lunch on day 2 there were 20 simultaneous presentations of tools and apps during a special “Tools Café” session. The 10 participant-defined breakout session topics are listed below: Ecosystem services and ecological indicators Inventory and monitoring Biogeographic map of the Nation Pollinators Invasive species Remote sensing Drivers of agricultural change Citizen science Climate Hydrology and watersheds Numerous common themes that emerged from the workshop include the following: The vital importance of completing foundational environmental datasets that are nationally consistent and are essential to multiple sectors, such as the Soil Survey Geographic database high-resolution soils data, a minimum 5-meter resolution digital elevation model, national hydrographic data, high-resolution land cover data, time series high-resolution spatial climate data from historical to future time steps, and a national wetland inventory. Improved, nationally consistent environmental datasets (integrated with targeted observations) will dramatically advance forecasting capacity and support early warning systems (that is, drought, forest disease); however, multiagency coordination should focus on decision support tools that convey appropriate actions and responses to adapt to, and mitigate, potential negative consequences. Digitizing and providing access to the vast stores of underused historical data that can be leveraged for this purpose is of national importance. Modern computational techniques and the ever-increasing flow of environmental data from ground and remote observations can support improved understanding of environmental change. Success of understanding patterns of change for decision making requires establishing baselines from which change can be measured. The value of digitized historical data is greater than ever before. There is a need to recognize the multifaceted potential of citizen science to engage the public in resource stewardship, to create the next generation of science, technology, engineering, math, and environmental leaders, and to have sufficient field personnel to monitor environmental trends, including early detection of alien invasive species, phenological shifts, shifting distribution and abundance of indicator species, and species inventories. The Federal government has an essential role in creating the infrastructure to dramatically improve mobilization of citizen science (and other) data by fostering the following: creation of data standards, creation of nationally consistent framework datasets, vertical integration of observation data, visualization and dissemination of aggregated datasets, and calculation and communication of derived trends. Current and near future trends in the availability of remotely sensed data (rapid expansion of satellite fleets and drones) is revolutionizing access to near-real-time ecological data. Targeted integration with ground-based observations and instrumentation has an extremely valuable role in validating remotely sensed data, filling data gaps, improving data quality, and fully realizing the potential of the near-real-time monitoring of environmental indicator trends. Integrated management of environmental data at the landscape scale is required even as specific actions on the ground are largely local in nature. The workshop highlighted numerous success stories; however, almost every breakout group pointed out the still-too-fragmented nature of the current data landscape. Management and delivery of the necessary data, tools, and analyses to sustain our Nation’s environmental capital must be a collaborative effort between Federal, State, and local governments, academia, nonprofits, and the commercial sector, even though the responsibilities of each sector are different.
Open-File Report↗
The spatially adaptable filter for error reduction (SAFER) process: Remote sensing-based LANDFIRE disturbance mapping updates
LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.
Fire↗
Changes to Monitoring Trends in Burn Severity Program’s production procedures and data products
The Monitoring Trends in Burn Severity (MTBS) program has been providing the fire science community with large fire perimeter and burn severity data for the past 14 years. As of October 2019, 22 969 fires have been mapped by the MTBS program and are available on the MTBS website ( https://www.mtbs.gov ). These data have been widely used by researchers to examine a variety of fire and climate science topics. However, MTBS has undergone significant changes to its fire mapping methodology, the remotely sensed imagery used to map fires, and the subsequent fire occurrence, burned boundary, and severity databases. To gather a better understanding of these changes and the potential impacts that they may have on the user community, we examined the changes to the MTBS burn mapping protocols and whether remapped burned area boundary and severity products differ significantly from the original MTBS products.
Fire Ecology↗
Remote sensing of global croplands for food security: Way forward
This book opens a new pathway for global mapping that is focused on a specific land use theme, such as irrigated or rain-fed croplands and classes within these themes. Since croplands use most of the water consumed by humans, specific knowledge of irrigated and rain-fed croplands will be critical for precise estimates of water use. At present and in the coming decades, irrigated and rain-fed cropland area mapping is crucial for food security studies. Throughout this book, various subjects pertaining to global croplands are discussed comprehensively.
Book chapter↗
Burn severity mapping in Australia 2009
In 2009, the Victoria Department of Sustainability and Environment estimated approximately 430,000 hectares of Victoria Australia were burned by numerous bushfires. Burned Area Emergency Response (BAER) teams from the United States were deployed to Victoria to assist local fire managers. The U.S. Geological Survey Earth Resources Observation and Science Center (USGS/EROS) and U.S. Forest Service Remote Sensing Applications Center (USFS/RSAC) aided the support effort by providing satellite-derived "soil burn severity " maps for over 280,000 burned hectares. In the United States, BAER teams are assembled to make rapid assessments of burned lands to identify potential hazards to public health and property. An early step in the assessment process is the creation of a soil burn severity map used to identify hazard areas and prioritize treatment locations. These maps are developed primarily using Landsat satellite imagery and the differenced Normalized Burn Ratio (dNBR) algorithm.
Conference Paper↗
Mapping predicted ecological states at landscape scales using remote sensing data and machine learning
Dryland ecosystems, covering 45% of the Earth's land and supporting over one-third of the global population, face significant threats from land degradation and ecological state change. Managing these ecosystems is complex, and science-based frameworks like Ecological Site Descriptions and state-and-transition models are essential tools for guiding decisions to support ecological health while maintaining stakeholder values such as grazing, wildlife, and recreation. However, alignment of these frameworks with smaller scale soil survey maps limits their applicability to broader ecological processes. Here, we extend these frameworks to larger landscapes with a machine learning approach that integrates large-scale, high-resolution vegetation data with identified ecological states from a data-driven state-and-transition model developed for a landscape-scale Ecological Site Group. A “global” model, which used combined inputs from multiple remotely sensed datasets, outperformed individual dataset models based on evaluation with independent data. Ecological state maps generated through this approach broaden the utility of state-and-transition models across Ecological Site Groups, providing a more spatially robust tool for land management at watershed and larger landscape scales. These methods, and the associated ecological state maps, can help meet critical needs for improved land condition assessments that support development of resource management plans and help identify priority areas for restoration and conservation.
Arizona, Colorado, New Mexico, Utah, Wyoming↗
Sand resources, regional geology, and coastal processes of the Chandeleur Islands Coastal System: An evaluation of the Breton National Wildlife Refuge
Breton National Wildlife Refuge, the Chandeleur Islands chain in Louisiana, provides habitat and nesting areas for wildlife and is an initial barrier protecting New Orleans from storms. The U.S. Geological Survey (USGS) in partnership with the University of New Orleans Pontchartrain Institute for Environmental Sciences undertook an intensive study that included (1) an analysis of island change based on historical maps and remotely sensed shoreline and topographic data; (2) a series of lidar surveys at 3- to 4-month intervals after Hurricane Katrina to determine barrier island recovery potential; (3) a discussion of sea level rise and effects on the islands; (4) an analysis of sea floor evolution and sediment dynamics in the refuge over the past 150 years; (5) an assessment of the local sediment transport and sediment resource availability based on the bathymetric and subbottom data; (6) a carefully selected core collection effort to groundtruth the geophysical data and more fully characterize the sediments composing the islands and surrounds; (7) an additional survey of the St. Bernard Shoals to assess their potential as a sand resource; and (8) a modeling study to numerically simulate the potential response of the islands to the low-intensity, intermediate, and extreme events likely to affect the refuge over the next 50 years. Results indicate that the islands have become fragmented and greatly diminished in subaerial extent over time: the southern islands retreating landward as they reorganize into subaerial features, the northern islands remaining in place. Breton Island, because maintenance of the Mississippi River-Gulf Outlet (MRGO) outer bar channel requires dredging, is deprived of sand sufficient to sustain itself. Regional sediment transport trends indicate that large storms are extremely effective in transporting sand and controlling the shoreline development and barrier island geometry. Sand is transported north and south from a divergent zone near Monkey Bayou at the southern end of the Chandeleur Islands. Numerical simulation of waves and sediment transport supports the geophysical results and indicates that vast areas of the lower shoreface are affected and are undergoing erosion during storm events, that there is little or no fair weather mechanism to rework material into the littoral system, and that as a result, there is a net loss of sediment from the system. Lidar surveys revealed that the island chain immediately after Hurricane Katrina lost about 84 percent of its area and about 92 percent of its prestorm volume. Marsh platforms that supported the islands’ sand prior to the storm were reduced in width by more than one-half. Repeated lidar surveys document that in places the shoreline has retreated about 100 m under the relatively low-energy waves since Hurricanes Katrina and Rita; however, this retreat is nonuniform. Recent high-resolution geophysical surveys of the sea floor and subsurface within 5–6 km of the Chandeleur Islands during 2006 and 2007 show that, in addition to the sand that is rebuilding portions of the island chain, a large volume of sand is contained in Hewes Point, in an extensive subtidal spit platform that has formed at the northern end of the Chandeleur Islands. Hewes Point appears to be the depositional terminus of the alongshore transport system. In the southern Chandeleurs, sand is being deposited in a broad tabular deposit near Breton Island called the southern offshore sand sheet. These two depocenters account for approximately 70 percent of the estimated sediment volume located in potential borrow sites. An additional large potential source of sand for restoration lies in the St. Bernard Shoals, which are estimated to contain approximately 200 × 10 6 m 3 of sand. Successful restoration planning for the Breton National Wildlife Refuge should mimic the natural processes of early stages of barrier island evolution including lateral transport to the flanks of the island chain from a centralized sand source that will ultimately enhance the ability of the islands to naturally build backbarrier marsh, dunes, and a continuous sandy shoreline. Barrier island sediment nourishment should be executed with the understanding that gulf shoreline erosion is inevitable but that island area can be maintained and enhanced during retreat (thus significantly prolonging the life of the island chain) with strategic sand placement.
Louisiana↗
The Martian surface
The past four years have been particularly fruitful for Martian research as the enormous volumes of data collected during the Viking mission became readily available to the general science community, and as reformatting of the remote sensing data into cartographic products made the data more useable. The 1:5,000,000‐scale map series is complete, and 1:2,000,000‐scale controlled mosaics of the entire planet are nearing completion. In addition 1:500,000‐scale mosaics have been prepared of about thirty local areas. A uniform scale, digital imaging data base is in preparation (Batson, 1986), and the infrared data have been reformatted to make it more accessible to the general user (Martin and Kieffer, 1985).
Reviews of Geophysics↗
Integrating airborne remote sensing and field campaigns for ecology and Earth system science
In recent years, the availability of airborne imaging spectroscopy (hyperspectral) data has expanded dramatically. The high spatial and spectral resolution of these data uniquely enable spatially explicit ecological studies including species mapping, assessment of drought mortality and foliar trait distributions. However, we have barely begun to unlock the potential of these data to use direct mapping of vegetation characteristics to infer subsurface properties of the critical zone. To assess their utility for Earth systems research, imaging spectroscopy data acquisitions require integration with large, coincident ground‐based datasets collected by experts in ecology and environmental and Earth science. Without coordinated, well‐planned field campaigns, potential knowledge leveraged from advanced airborne data collections could be lost. Despite the growing importance of this field, documented methods to couple such a wide variety of disciplines remain sparse. We coordinated the first National Ecological Observatory Network Airborne Observation Platform (AOP) survey performed outside of their core sites, which took place in the Upper East River watershed, Colorado. Extensive planning for sample tracking and organization allowed field and flight teams to update the ground‐based sampling strategy daily. This enabled collection of an extensive set of physical samples to support a wide range of ecological, microbiological, biogeochemical and hydrological studies. We present a framework for integrating airborne and field campaigns to obtain high‐quality data for foliar trait prediction and document an archive of coincident physical samples collected to support a systems approach to ecological research in the critical zone. This detailed methodological account provides an example of how a multi‐disciplinary and multi‐institutional team can coordinate to maximize knowledge gained from an airborne survey, an approach that could be extended to other studies. The coordination of imaging spectroscopy surveys with appropriately timed and extensive field surveys, along with high‐quality processing of these data, presents a unique opportunity to reveal new insights into the structure and dynamics of the critical zone. To our knowledge, this level of co‐aligned sampling has never been undertaken in tandem with AOP surveys and subsequent studies utilizing this archive will shed considerable light on the breadth of applications for which imaging spectroscopy data can be leveraged.
Colorado↗
Spatial spectroscopic models for remote exploration
Ancient hydrothermal systems are a high-priority target for a future Mars sample return mission because they contain energy sources for microbes and can preserve organic materials (Farmer, 2000 ; MEPAG Next Decade Science Analysis Group, 2008 ; McLennan et al., 2012 ; Michalski et al., 2017 ). Characterizing these large, heterogeneous systems with a remote explorer is difficult due to communications bandwidth and latency; such a mission will require significant advances in spacecraft autonomy. Science autonomy uses intelligent sensor platforms that analyze data in real-time, setting measurement and downlink priorities to provide the best information toward investigation goals. Such automation must relate abstract science hypotheses to the measurable quantities available to the robot. This study captures these relationships by formalizing traditional “science traceability matrices” into probabilistic models. This permits experimental design techniques to optimize future measurements and maximize information value toward the investigation objectives, directing remote explorers that respond appropriately to new data. Such models are a rich new language for commanding informed robotic decision making in physically grounded terms. We apply these models to quantify the information content of different rover traverses providing profiling spectroscopy of Cuprite Hills, Nevada. We also develop two methods of representing spatial correlations using human-defined maps and remote sensing data. Model unit classifications are broadly consistent with prior maps of the site's alteration mineralogy, indicating that the model has successfully represented critical spatial and mineralogical relationships at Cuprite. Key Words: Autonomous science—Imaging spectroscopy—Alteration mineralogy—Field geology—Cuprite—AVIRIS-NG—Robotic exploration. Astrobiology 18, 934–954.
Astrobiology↗
The optical river bathymetry toolkit
Spatially distributed information on water depth is essential for many applications in river research and management and, under certain circumstances, can be inferred from remotely sensed data. Although fluvial remote sensing has emerged as a rapidly developing subdiscipline of the riverine sciences, more widespread adoption of these techniques has been hindered by a lack of accessible software. The Optical River Bathymetry Toolkit (ORByT) fills this void by providing a standalone package for mapping water depth from passive optical image data. The ORByT interface enables end users to import images and field‐based depth measurements, create and refine water masks, and perform spectrally based depth retrieval via an Optimal Band Ratio Analysis algorithm. The resulting bathymetric map can be exported as an image file, point cloud, and/or cross section; a thorough accuracy assessment also is incorporated into the workflow. In addition, image‐derived depth estimates can be subtracted from water surface elevations to obtain bed elevations suitable for input to a hydrodynamic model. Potential users of ORByT must bear in mind the inherent limitations of passive optical remote sensing: reliable bathymetry can only be inferred in clear‐flowing, shallow streams; this approach is not appropriate for more turbid, deeper rivers.
River Research and Applications↗