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At least 379 records · Page 21Linked to original sources
USGS2025

Harnessing geospatial artificial intelligence and deep learning for landslide inventory mapping: Advances, challenges, and emerging directions

Recent advancements in artificial intelligence (AI) and deep learning enable more accurate, scalable, and automated mapping. This paper provides a comprehensive review of the applications of AI, particularly deep learning, in landslide inventory mapping. In addition to examining commonly used data sources and model architectures, we explore innovative strategies such as feature enhancement and fusion, attention-boosted techniques, and advanced learning approaches, including active learning and transfer learning, to enhance model adaptability and predictability. We also highlight the remaining challenges and potential research directions, including the estimation of more diverse variables in landslide mapping, multimodal data alignment, modeling regional variability and replicability, as well as issues related to data misinterpretation and model explainability. This review aims to serve as a useful resource for researchers and practitioners, promoting the integration of deep learning into landslide research and disaster management.

Remote Sensing↗
USGS2024

Using citizen scientists to collect oxygen and hydrogen isotope data in southern Nevada

What is Citizen Science? Citizen science programs provide a means for Federal and non-Federal government agencies to make science more engaging, transparent, and accessible by partnering with the public for the purpose of problem solving, data collection, and monitoring. Public volunteers become directly involved in local research, thereby engaging in scientific projects. The public has already been included in existing citizen science programs that cover a broad range of disciplines, such as ecology, hydrology, and tectonics. Citizen science advances research while simultaneously fostering a sense of involvement and interest from the public. Beginning in 2017, the U.S. Geological Survey (USGS), U.S. Forest Service, Bureau of Land Management, U.S. Fish and Wildlife Service, and National Park Service collaborated with private, non-profit partners to inventory, survey, and rehabilitate springs in Clark County, Nevada. The USGS maintains the National Water Information System (NWIS), a publicly available online database of water-resources data for the Nation, and the agency is interested in using citizen science to add geochemical data from springs in southern Nevada. From 2021 to 2023, the USGS directly worked with citizen science partners, including the Springs Stewardship Institute and the Friends of Nevada Wilderness, to collect stable isotope and tritium samples from southern Nevada springs. The citizen science volunteers were provided the training and supplies for proper sample collection by USGS staff. As the citizen science partners traveled and hiked to the remote spring sites to complete spring surveys and perform restoration activities, they collected stable isotope and tritium samples for the USGS. Samples were shipped to national USGS laboratories for analysis, and the results were uploaded to the NWIS database (U.S. Geological Survey, 2024).

Nevada↗
USGS1988

Differences in visible and near-IR responses, and derived vegetation indices, for the NOAA-9 and NOAA-10 AVHRRs: a case study

This study evaluates the differences in the visible and near-IR responses of the Advanced Very High Resolution Radiometers (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA)-9 and -10 satellites for coincident sample locations. The study also evaluates the differences in vegetation indices computed from those data. Data were acquired of the southeast portion of the United States for the 6 December 1986 daylight orbits of NOAA-9 and NOAA-10 satellites. The results suggest that, with appropriate gain and offset, the vegetation indices of the two sensor systems may be interchangeable for assessment of land surfaces.

Photogrammetric Engineering and Remote Sensing↗
USGS2024

Disturbance amplifies sensitivity of dryland productivity to precipitation variability

Variability of the terrestrial global carbon sink is largely determined by the response of dryland productivity to annual precipitation. Despite extensive disturbance in drylands, how disturbance alters productivity-precipitation relationships remains poorly understood. Using remote-sensing to pair more than 5600 km of natural gas pipeline corridors with neighboring undisturbed areas in North American drylands, we found that disturbance reduced average annual production 6 to 29% and caused up to a fivefold increase in the sensitivity of net primary productivity (NPP) to interannual variation in precipitation. Disturbance impacts were larger and longer-lasting at locations with higher precipitation (>450 mm mean annual precipitation). Disturbance effects on NPP dynamics were mostly explained by shifts from woody to herbaceous vegetation. Severe disturbance will amplify effects of increasing precipitation variability on NPP in drylands.

Science Advances↗
USGS2022

Next-generation technologies unlock new possibilities to track rangeland productivity and quantify multi-scale conservation outcomes

Historically, relying on plot-level inventories impeded our ability to quantify large-scale change in plant biomass, a key indicator of conservation practice outcomes in rangeland systems. Recent technological advances enable assessment at scales appropriate to inform management by providing spatially comprehensive estimates of productivity that are partitioned by plant functional group across all contiguous US rangelands. We partnered with the Sage Grouse and Lesser Prairie-Chicken Initiatives and the Nebraska Natural Legacy Project to demonstrate the ability of these new datasets to quantify multi-scale changes and heterogeneity in plant biomass following mechanical tree removal, prescribed fire, and prescribed grazing. In Oregon's sagebrush steppe, for example, juniper tree removal resulted in a 21% increase in one pasture's productivity and an 18% decline in another. In Nebraska's Loess Canyons, perennial grass productivity initially declined 80% at sites invaded by trees that were prescriptively burned, but then fully recovered post-fire, representing a 492% increase from nadir. In Kansas' Shortgrass Prairie, plant biomass increased 4-fold (966,809 kg/ha) in pastures that were prescriptively grazed, with gains highly dependent upon precipitation as evidenced by sensitivity of remotely sensed estimates (SD ± 951,308 kg/ha). Our results emphasize that next-generation remote sensing datasets empower land managers to move beyond simplistic control versus treatment study designs to explore nuances in plant biomass in unprecedented ways. The products of new remote sensing technologies also accelerate adaptive management and help communicate wildlife and livestock forage benefits from management to diverse stakeholders.

Kansas, Nebraska, Oregon↗
USGS2019

Recognizing the Famine Early Warning Systems Network: Over 30 years of drought early warning science advances and partnerships promoting global food security

On a planet with more than 7 billion people, how do we identify the millions of drought-afflicted people who face a real threat of livelihood disruption or death without humanitarian assistance? Typically, these people are poor and heavily dependent on rainfed agriculture and livestock. Most live in Africa, Central America, or Southwest Asia. When the rains fail, incomes diminish while food prices increase, cutting off the poorest (most often women and children) from access to adequate nutrition. As seen in Ethiopia in 1984 and Somalia in 2011, food shortages can lead to famine. Yet these slow onset disasters also provide opportunities for effective intervention, as seen in Ethiopia in 2015 and Somalia in 2017. Since 1985, the US Agency for International Development's Famine Early Warning Systems Network (FEWS NET) has been providing evidence-based guidance for effective humanitarian relief efforts. FEWS NET depends on a Drought Early Warning System (DEWS) to help understand, monitor, model and predict food insecurity. Here we provide an overview of FEWS NET's DEWS using examples from recent climate extremes. While drought monitoring and prediction provides just one part of FEWS NET's monitoring system, it draws from many disciplines - remote sensing, climate prediction, agro-climatic monitoring, and hydrologic modeling. Here we describe FEWS NET's multi-agency multi-disciplinary DEWS and Food Security Outlooks. This DEWS uses diagnostic analyses to guide predictions. Mid-season droughts are monitored using multiple cutting-edge earth observing systems. Crop and hydrologic models can translate these observations into impacts. This information feeds into FEWS NET reports, helping to save lives by motivating and targeting timely humanitarian assistance.

Bulletin of the American Meteorological Society↗
USGS2014

Advances in a distributed approach for ocean model data interoperability

An infrastructure for earth science data is emerging across the globe based on common data models and web services. As we evolve from custom file formats and web sites to standards-based web services and tools, data is becoming easier to distribute, find and retrieve, leaving more time for science. We describe recent advances that make it easier for ocean model providers to share their data, and for users to search, access, analyze and visualize ocean data using MATLAB® and Python®. These include a technique for modelers to create aggregated, Climate and Forecast (CF) metadata convention datasets from collections of non-standard Network Common Data Form (NetCDF) output files, the capability to remotely access data from CF-1.6-compliant NetCDF files using the Open Geospatial Consortium (OGC) Sensor Observation Service (SOS), a metadata standard for unstructured grid model output (UGRID), and tools that utilize both CF and UGRID standards to allow interoperable data search, browse and access. We use examples from the U.S. Integrated Ocean Observing System (IOOS®) Coastal and Ocean Modeling Testbed, a project in which modelers using both structured and unstructured grid model output needed to share their results, to compare their results with other models, and to compare models with observed data. The same techniques used here for ocean modeling output can be applied to atmospheric and climate model output, remote sensing data, digital terrain and bathymetric data.

Journal of Marine Science and Engineering↗
USGS2016

Dynamic reusable workflows for ocean science

Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog search and data access make it now possible to create catalog-driven workflows that automate — end-to-end — data search, analysis and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS) which automates the skill-assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC) Catalog Service for the Web (CSW), then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS) for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enters the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased use of dynamic notebooks across the geoscience domains.

Journal of Marine Science and Engineering↗
USGS2022

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

Colorado↗
USGS1997

Multispectral thermal infrared mapping of sulfur dioxide plumes: A case study from the East Rift Zone of Kilauea Volcano, Hawaii

The synoptic perspective and rapid mode of data acquisition provided by remote sensing are well suited for the study of volcanic SO 2 plumes. In this paper we describe a plume-mapping procedure that is based on image data acquired with NASA's airborne thermal infrared multispectral scanner (TIMS) and apply the procedure to TIMS data collected over the East Rift Zone of Kilauea Volcano, Hawaii, on September 30, 1988. These image data covered the Pu‘u ‘O ‘o and Kupaianaha vents and a skylight in the lava tube that was draining the Kupaianaha lava pond. Our estimate of the SO 2 emission rate from Pu‘u ‘O ‘o (17–20 kg s −1 ) is roughly twice the average of estimates derived from correlation spectrometer (COSPEC) measurements collected 10 days prior to the TIMS overflight (10 kg s −1 ). The agreement between the TIMS and COSPEC results improves when we compare SO 2 burden estimates, which are relatively independent of wind speed. We demonstrate the feasibility of mapping Pu‘u ‘O ‘o - scale SO 2 plumes from space in anticipation of the 1998 launch of the advanced spaceborne thermal emission and reflectance radiometer (ASTER).

Journal of Geophysical Research B: Solid Earth↗
USGS2008

A Landscape Indicator Approach to the Identification and Articulation of the Ecological Consequences of Land Cover Change in the Chesapeake Bay Watershed, 1970-2000

The advancement of geographic science in the area of land surface status and trends and land cover change is at the core of the current geographic scientific research of the U.S. Geological Survey (USGS) (McMahon and others, 2005). Perhaps the least developed or articulated aspects of USGS land change science have been the identification and analysis of the ecological consequences of land cover change. Changes in land use and land cover significantly affect the ability of ecosystems to provide essential ecological goods and services, which, in turn, affect the economic, public health, and social benefits that these ecosystems provide. One of the great scientific challenges for geographic science is to understand and calibrate the effects of land use and land cover change and the complex interaction between human and biotic systems at a variety of natural, geographic, and political scales. Understanding the dynamics of land surface change requires an increased understanding of the complex nature of human-environmental systems and will require a suite of scientific tools that include traditional geographic data and analysis methods, such as remote sensing and geographic information systems (GIS), as well as innovative approaches to understanding the dynamics of complex systems. One such approach that has gained much recent scientific attention is the landscape indicator, or landscape assessment, approach, which has been developed with the emergence of the science of landscape ecology.

Fact Sheet↗
USGS2023

Sustainable aquifer management for food security

In aquifer-dependent regions, balancing aquifer protection, desalination, economic development, agricultural irrigation, and food security can be better managed through discovery and development of sources of sustainable groundwater pumping. Aquifer desalination for irrigation to protect food security can mitigate pressure on local freshwater aquifers. Despite its importance, little peer reviewed work to date has identified the economic capacity to pay for aquifer desalination for irrigation to mitigate freshwater aquifer drawdown. The novel contribution of this work is the development and application of an innovative method to assess the economic capacity to pay for aquifer desalination for irrigation for a recently discovered large saline aquifer. It develops an original framework to assess the capacity to pay for aquifer desalination, the results of which can help guide policymakers on efficient and sustainable pumping approaches across users, aquifers, and time periods. A mathematical programming model is developed to economically analyze the 200 billion cubic meter Lotikipi Aquifer, discovered in 2013 in northern Kenya using modern remote sensing methods. While initial pumping of the Lotikipi Aquifer was halted due to high groundwater salinity levels, interest remains strong in assessing the economic capacity to pay for groundwater desalination because of its potential role in protecting regional food security generated by aquifer pumping for irrigation. The model is formulated by calibrating optimized pumping patterns in two existing freshwater aquifers to replicate observed historical pumping levels. Based on that exercise, a second model is developed to identify a least cost set of pumping restrictions that return each of three regional aquifers to starting conditions over a seven-year time period. A third model extends the second by adding a constraint of a minimum required level of food grain security supported by irrigation pumping from the aquifer system. Results show that the economic capacity to pay for aquifer desalination for irrigated agriculture lies in the range of $0.08 - $0.18 USD per cubic meter under current economic conditions and desalination technologies available. While this economic capacity to pay is lower than its current cost in most places, the future could be more optimistic. Advances in desalination technology, higher crop prices, technical advance in agriculture, and development of drought-resistant crops can all contribute to a future capacity to economically justify the expense of desalination.

Agricultural Systems Journal↗
USGS2023

Large increases in methane emissions expected from North America’s largest wetland complex

Natural methane (CH 4 ) emissions from aquatic ecosystems may rise because of human-induced climate warming, although the magnitude of increase is highly uncertain. Using an exceptionally large CH 4 flux dataset (~19,000 chamber measurements) and remotely sensed information, we modeled plot- and landscape-scale wetland CH 4 emissions from the Prairie Pothole Region (PPR), North America’s largest wetland complex. Plot-scale CH 4 emissions were driven by hydrology, temperature, vegetation, and wetland size. Historically, landscape-scale PPR wetland CH 4 emissions were largely dependent on total wetland extent. However, regardless of future wetland extent, PPR CH 4 emissions are predicted to increase by two- or threefold by 2100 under moderate or severe warming scenarios, respectively. Our findings suggest that international efforts to decrease atmospheric CH 4 concentrations should jointly account for anthropogenic and natural emissions to maintain climate mitigation targets to the end of the century.

North Dakota↗
USGS2014

Experimental design and quality assurance: in situ fluorescence instrumentation

Both instrument design and capabilities of fluorescence spectroscopy have greatly advanced over the last several decades. Advancements include solid-state excitation sources, integration of fiber optic technology, highly sensitive multichannel detectors, rapid-scan monochromators, sensitive spectral correction techniques, and improve data manipulation software (Christian et al., 1981, Lochmuller and Saavedra, 1986; Cabniss and Shuman, 1987; Lakowicz, 2006; Hudson et al., 2007). The cumulative effect of these improvements have pushed the limits and expanded the application of fluorescence techniques to numerous scientific research fields. One of the more powerful advancements is the ability to obtain in situ fluorescence measurements of natural waters (Moore, 1994). The development of submersible fluorescence instruments has been made possible by component miniaturization and power reduction including advances in light sources technologies (light-emitting diodes, xenon lamps, ultraviolet [UV] lasers) and the compatible integration of new optical instruments with various sampling platforms (Twardowski et at., 2005 and references therein). The development of robust field sensors skirt the need for cumbersome and or time-consuming filtration techniques, the potential artifacts associated with sample storage, and coarse sampling designs by increasing spatiotemporal resolution (Chen, 1999; Robinson and Glenn, 1999). The ability to obtain rapid, high-quality, highly sensitive measurements over steep gradients has revolutionized investigations of dissolved organic matter (DOM) optical properties, thereby enabling researchers to address novel biogeochemical questions regarding colored or chromophoric DOM (CDOM). This chapter is dedicated to the origin, design, calibration, and use of in situ field fluorometers. It will serve as a review of considerations to be accounted for during the operation of fluorescence field sensors and call attention to areas of concern when making this type of measurement. Attention is also given to ways in which in-water fluorescence measurements have revolutionized biogeochemical studies of CDOM and how those measurements can be used in conjunction with remotely sense satellite data to understand better the biogeochemistry of DOM in aquatic environments.

Book chapter↗
USGS2008

Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions

By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several land surface parameters including impervious surfaces and land surface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Land surface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Land surface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban land surface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.

Advances in Space Research↗
USGS1992

The 1990 conterminous U. S. AVHRR data set

The U.S. Geological Survey, using NOAA-ll Advanced Very High Resolution Radiometer (AVHRR) 1-km data, has produced a time series of 19 biweekly maximum normalized difference vegetation index (NDV!) composites of the conterminous United States for the 1990 growing season. Each biweekly composite included data from approximately 20 calibrated and georegistered daily overpasses. The output is a data set which includes all five calibrated AVHRR channels, NOV! values, three satellite/solar viewing angles, and date of observation pointer for each biweekly composite. The data set is intended for assessing seasonal variations in vegetation condition and provides a foundation for studying long-term changes in vegetation resulting from human interactions or global climate alterations.

Photogrammetric Engineering and Remote Sensing↗
USGS2022

Fifty years of Landsat science and impacts

Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application, and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and programmatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.

Remote Sensing of Environment↗
USGS2014

Comparison of simulated HyspIRI with two multispectral sensors for invasive species mapping

This paper assesses the potential of a single HYSPIRI scene to estimate cover of the non-native invasive buffelgrass (Pennisetum ciliare) in a heterogeneous Sonoran Desert scrub ecosystem. We simulated HYSPIRI (60 m) along with two multispectral sensors, Thematic Mapper (TM; 30 m) and Advanced Space-borne Thermal Emission and Reflection Spectrometer (ASTER; 15 m), from high-resolution Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 3.2 m) imagery in an area infested by buffelgrass near Tucson, Arizona. We compared classification accuracies of all simulated sensors at spatial resolutions of 15 m, 30 m, and 60 m to evaluate tradeoffs of spectral and spatial resolution across the sensors. Although spectroscopically superior to Landsat TM and ASTER, ASTER easily outperformed HYSPIRI for small infestations (225 m 2 ) on account of its spatial resolution. Shortwave-infrared bands near 2.2 µm were key indicators for both HYSPIRI and ASTER, highlighting the benefit of narrow-wave SWIR for mapping invasive species in arid ecosystems.

Photogrammetric Engineering and Remote Sensing↗