Geology topics

Jeff T. Falgout

Publications and source records attributed to Jeff T. Falgout.

11 recordsLinked to original sources

USGS2025

Sparse genetic data limit biodiversity assessments in protected areas globally

Global conservation targets include protecting genetic diversity within species. Yet few studies have assessed whether protected areas (PAs) include genetically diverse populations across species globally. A first step is understanding the availability of population genetic data that could be used in these assessments. We surveyed georeferenced population-level nuclear (as opposed to mitochondrial or plastid-based) genetic data across continents and marine biomes (36,354 populations, 2809 species) and found substantial geographic and taxonomic gaps. Most data were concentrated in Europe and North America, with major gaps in Africa and Asia. For most taxonomic groups, data were available for <1% of described species. Globally, 52.08% of the total areal extent of PAs lacked genetically sampled populations. These gaps in data availability highlight the need for targeted genetic data collection, harmonization, and sharing to improve genetic diversity monitoring and conservation planning. Combined with proxy-based genetic indicators, such data are needed to inform PA assessments, bolster area-based conservation initiatives like 30 × 30, and support achievement of global genetic conservation targets.

Frontiers in Ecology and the Environment↗

USGS2025

A survey of mammal and fish genetic diversity across the global protected area network

Global conservation targets aim to expand protected areas and maintain species’ genetic diversity. Whether protected areas capture genetic diversity is unclear. We examined this question using a global sample of nuclear population-level microsatellite data comprising genotypes from 2513 sites, 134,183 individuals, and 176 mammal and marine fish species. The genetic diversity and differentiation of samples inside and outside protected areas were similar, with some evidence for higher diversity in protected areas for small-bodied mammals. Mammal populations, particularly large species, tended to be more genetically diverse when near multiple protected areas, regardless of whether samples were collected in or outside protected areas. Older marine protected areas tended to capture more genetically diverse fish populations. However, limited data availability in many regions hinders the systematic incorporation of genetic diversity into protected area design. Focusing on minimizing population decline and maintaining connectivity between protected areas remain essential proxies for maintaining genetic diversity.

Conservation Letters↗

USGS2025

Advanced Research Computing

Advanced Research Computing provides access to high-performance computing (HPC) resources as well as training and consultation for effective use of these powerful systems. This product outlines current HPC machines, capabilities, classes, and enabled science.

General Information Product↗

USGS2024

Real-time lava flow forecasting during the 2022 Mauna Loa eruption response

On November 27, 2022, Mauna Loa (Hawai‘i) erupted for the first time in 38 years, initially producing lava flows that covered the floor of its summit caldera, Moku‘āweoweo. Over the first 12 h following the summit eruption, four main fissures opened on Mauna Loa’s Northeast Rift Zone, with “fissure 3” quickly becoming the dominant source of lava flows. For the next 12 days, fissure 3 produced a 19-km-long lava flow to the north, crossing the Mauna Loa Weather Observatory access road and coming within 2.8 km of inundating the Daniel K. Inouye Highway (Saddle Road). Within 40 min of fissure 3 opening, inundation modeling efforts had begun. For the duration of the eruption, the computational fluid dynamics model Lava2d was run in real time, using flow front locations and other field observations to make sequential forecast improvements, eventually producing a set of models which accurately predicted the routing and arrival times of lava from fissure 3. These models were used to inform timing estimates of possible future inundation of the Saddle Road. As the eruption progressed, almost 4000 Lava2d models were made using high-performance computing resources, providing critical information on uncertainty in multi-week forecasts. To our knowledge, this was the first ever real-time physics-based ensemble lava flow modeling and forecasting effort. Here, we present a chronology of these real-time efforts, focusing on the successes and limitations of this approach.

Hawaii↗

USGS2020

The Landsat Burned Area algorithm and products for the conterminous United States

Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. In this study, we developed the Landsat Burned Area (BA) algorithm, an update from the Landsat Burned Area Essential Climate Variable (BAECV) algorithm. Here, we present the BA algorithm and products, changes relative to the BAECV algorithm and products, and updated validation metrics. We also present spatial and temporal patterns of burned area across the conterminous U.S., how burned area varies in relation to the number of operational Landsat sensors, and a comparison with other burned area datasets, including the BAECV, Monitoring Trends in Burn Severity (MTBS), GeoMAC, and Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1.006 data. The BA algorithm identifies burned areas in analysis ready data (ARD) time-series of Landsat imagery from 1984 through 2018 using machine learning, thresholding, and image segmentation. Validation with reference data from high-resolution commercial satellite imagery resulted in omission and commission error rates averaging 19% and 41%, respectively. In comparison, validation with Landsat reference data had omission and commission error rates averaging 40% and 28%, respectively when burned areas in cultivated crops and pasture/hay land-cover types were excluded. Both validation tests documented lower commission error rates relative to the BAECV products. The amount of burned area detected varies not only in response to climate but also with the number of operational sensors and scenes collected. The combined amount of burned area detected by multiple sensors was larger than from any individual sensor, but there was no significant difference between individual sensors. Therefore, we used BA products from individual sensors to assess trends over time and all available sensors to compare with other existing BA products. From 1984 through 2018, annual burned area averaged 30,000 km2, ranged between 14,000 km2 in 1991 and 46,500 km2 in 2012, and increased over time at a rate of 356 km2/year. Compared to existing burned area products, the new Landsat BA products identified 29% more burned area than the BAECV products (1984–2015), 183% more than the MTBS/GeoMAC products (1984–2018), and 56% more than the MCD64A1.006 products (2003–2018). The products had similar patterns of year-to-year variability; the R2 values of linear regressions between annual burned area were >0.70 with the BAECV products and the MTBS/GeoMAC products, but somewhat lower for the MCD64A1.006 product (R2 = 0.66). The BA products are routinely produced as new Landsat data are collected and provide a unique data source to monitor and assess the spatial and temporal patterns and the impacts of fire.

Remote Sensing of Environment↗

USGS2020

Progress toward a preliminary karst depression density map for the conterminous United States

Most methods for the assessment of sinkhole hazard susceptibility are predicated upon knowledge of pre-existing closed depressions in karst areas. In the United States (U.S.), inventories of existing karst depressions are piecemeal, and are often obtained through inconsistent methodologies applied at the state or county level and at various scales. Here, we present a first attempt at defining a karst closed depression inventory across the conterminous U.S. using a common methodology. Automated algorithms for extraction of closed depressions from 1/3 arc-second (approximately 10 m resolution) National Elevation Dataset (NED) were run on the U.S. Geological Survey (USGS) “Yeti” high-performance computing cluster. The full NED was first conditioned to reduce the creation of artificial closed depressions by breaching digital dams at road and stream crossings, using the flowlines and transportation route vectors from the USGS National Map. The resulting depressions were selected according to location within geologic units having the potential for karst, and screened for occurrence in areas of developed land, open water and wetlands, and areas of glacial and alluvial sediment cover. The results were used as the input to create a nationwide depression density map. Our results were compared with karst depression density maps for diverse karst regions within states that have existing closed depression inventories. The individual state-scale maps compared favorably to the results obtained from the method applied universally across the nation and illustrated regional sinkhole hotspots in known areas of well-developed karst. Limitations of the automated method includes false positive depressions resulting from artifacts generated during the computer processing of the elevation models, and inclusion of depressions resulting from non-karst geomorphic processes. More thorough examination of the screening criteria for depressions is required.

Conterminous United States↗

USGS2020

New operational national satellite burned area product

Introduction Lack of consistent spatial and temporal fire information with relevant spatial resolution hinders land management and broad-scale assessments of fire activity, especially in the eastern United States and the Great Plains where fi re is important ecologically and culturally. Remote sensing can be used to monitor fi re activity, augment existing fi re data, and fill information gaps. In particular, Landsat offers one of the most complete time series of remote sensing data sets as the Landsat satellites with spectral bands useful for mapping fi res and burn severity have been operational since 1984. Furthermore, Landsat satellite imagery collect data at a resolution useful for on-the-ground comparisons and management decisions. Methods A gradient-boosting regression model algorithm was used to predict burn probabilities (BP), indicating the likelihood that a pixel had burned in a fire. Then the algorithm translated the burn probability images to burn classification (BC) images using thresholding and region growing. Burned areas smaller than 5 acres were removed to reduce noise. The BP and BC products were generated for Landsat scenes collected from 1984 to present with <= 80% cloud cover and are available for download from USGSs EarthExplorer website (www.earthexplorer. usgs.gov). Annual summaries were also produced from the scene-level BC and BP products. These include: 1) the maximum burn probability value from all scenes in a year, (2) the burn classification count, or the number of times a pixel was classified as burned in a year, (3) the burn date, or the day of year of the first Landsat scene a pixel was classified as burned, and (4) a burn classification which removes burned areas still visible from previous years with a filter. Results The Landsat Burned Area Products identified 183% more burned area than the Monitoring Trends in Burn Severity and 56% more burned area than the MODIS MCD64A1.006 products2. From 1984 through 2018, annual burned area mapped by the Landsat Burned Area Products averaged 30,000 km2, ranged between 14,000 km2 in 1991 and 46,500 km2 in 2012, and increased over time at a rate of 356 km2/year. The Landsat Burned Area products consistently map burned areas 5 acres or larger over time while capturing unburned islands within fi res. Such characteristics are critical for understanding long-term impacts of fi res on human and natural systems. The products are being routinely produced as new Landsat data are collected and demonstrate the utility remote sensing offers for monitoring landscape change.

Report↗

USGS2017

Community for Data Integration 2016 annual report

The Community for Data Integration (CDI) represents a dynamic community of practice focused on advancing science data and information management and integration capabilities across the U.S. Geological Survey and the CDI community. This annual report describes the various presentations, activities, and outcomes of the CDI monthly forums, working groups, virtual training series, and other CDI-sponsored events in fiscal year 2016. The report also describes the objectives and accomplishments of the 13 CDI-funded projects in fiscal year 2016.

Open-File Report↗

USGS2017

Mapping burned areas using dense time-series of Landsat data

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables (ECVs), including information about burned area. In this paper, we present an algorithm that identifies burned areas in dense time-series of Landsat data to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm uses gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Burn classifications are generated from the burn probability surfaces using pixel-level thresholding in combination with a region growing process. The algorithm can be applied anywhere Landsat and training data are available. For this study, BAECV products were generated for the conterminous United States from 1984 through 2015. These products consist of pixel-level burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability and a burn classification. We compared the BAECV burn classification products to the existing Global Fire Emissions Database (GFED; 1997–2015) and Monitoring Trends in Burn Severity (MTBS; 1984–2013) data. We found that the BAECV products mapped 36% more burned area than the GFED and 116% more burned area than MTBS. Differences between the BAECV products and the GFED were especially high in the West and East where the BAECV products mapped 32% and 88% more burned area, respectively. However, the BAECV products found less burned area than the GFED in regions with frequent agricultural fires. Compared to the MTBS data, the BAECV products identified 31% more burned area in the West, 312% more in the Great Plains, and 233% more in the East. Most pixels in the MTBS data were detected by the BAECV, regardless of burn severity. The BAECV products document patterns of fire similar to those in the GFED but also showed patterns of fire that are not well characterized by the existing MTBS data. We anticipate the BAECV products will be useful to studies that seek to understand past patterns of fire occurrence, the drivers that created them, and the impacts fires have on natural and human systems.

continental United States↗

USGS2015

Automated extraction of natural drainage density patterns for the conterminous United States through high performance computing

Hydrographic networks form an important data foundation for cartographic base mapping and for hydrologic analysis. Drainage density patterns for these networks can be derived to characterize local landscape, bedrock and climate conditions, and further inform hydrologic and geomorphological analysis by indicating areas where too few headwater channels have been extracted. But natural drainage density patterns are not consistently available in existing hydrographic data for the United States because compilation and capture criteria historically varied, along with climate, during the period of data collection over the various terrain types throughout the country. This paper demonstrates an automated workflow that is being tested in a high-performance computing environment by the U.S. Geological Survey (USGS) to map natural drainage density patterns at the 1:24,000-scale (24K) for the conterminous United States. Hydrographic network drainage patterns may be extracted from elevation data to guide corrections for existing hydrographic network data. The paper describes three stages in this workflow including data pre-processing, natural channel extraction, and generation of drainage density patterns from extracted channels. The workflow is concurrently implemented by executing procedures on multiple subbasin watersheds within the U.S. National Hydrography Dataset (NHD). Pre-processing defines parameters that are needed for the extraction process. Extraction proceeds in standard fashion: filling sinks, developing flow direction and weighted flow accumulation rasters. Drainage channels with assigned Strahler stream order are extracted within a subbasin and simplified. Drainage density patterns are then estimated with 100-meter resolution and subsequently smoothed with a low-pass filter. The extraction process is found to be of better quality in higher slope terrains. Concurrent processing through the high performance computing environment is shown to facilitate and refine the choice of drainage density extraction parameters and more readily improve extraction procedures than conventional processing.

Conference Paper↗

USGS2015

Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry

Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of the features through cartographic generalization (Regnauld and McMaster 2008, Stanislawski et al. 2014). A variety of algorithms exist to simplify linear cartographic features, and all of the methods affect the positional accuracy of the features (Shahriari and Tao 2002, Regnauld and McMaster 2008, Stanislawski et al. 2012). In general, simplification operations are controlled by one or more tolerance parameters that limit the amount of positional change the operation can make to features. Using a single tolerance value can have varying levels of positional change on features; depending on local shape, texture, or geometric characteristics of the original features (McMaster and Shea 1992, Shahriari and Tao 2002, Buttenfield et al. 2010). Consequently, numerous researchers have advocated calibration of simplification parameters to control quantifiable properties of resulting changes to the features (Li and Openshaw 1990, Raposo 2013, Tobler 1988, Veregin 2000, and Buttenfield, 1986, 1989). This research identifies relations between local topographic conditions and geometric characteristics of linear features that are available in the National Hydrography Dataset (NHD). The NHD is a comprehensive vector dataset of surface 18 th ICA Workshop on Generalisation and Multiple Representation, Rio de Janiero, Brazil 2015 2 water features within the United States that is maintained by the U.S. Geological Survey (USGS). In this paper, geometric characteristics of cartographic representations for natural stream and river features are summarized for subbasin watersheds within entire regions of the conterminous United States and compared to topographic metrics. A concurrent processing workflow is implemented using a Linux high-performance computing cluster to simultaneously process multiple subbasins, and thereby complete the work in a fraction of the time required for a single-process environment. In addition, similar metrics are generated for several levels of simplification of the hydrographic features to quantify the effects of simplification over the various landscape conditions. Objectives of this exploratory investigation are to quantify geometric characteristics of linear hydrographic features over the various terrain conditions within the conterminous United States and thereby illuminate relations between stream geomorphological conditions and cartographic representation. The synoptic view of these characteristics over regional watersheds that is afforded through concurrent processing, in conjunction with terrain conditions, may reveal patterns for classifying cartographic stream features into stream geomorphological classes. Furthermore, the synoptic measurement of the amount of change in geometric characteristics caused by the several levels of simplification can enable estimation of tolerance values that appropriately control simplification-induced geometric change of the cartographic features within the various geomorphological classes in the country. Hence, these empirically derived rules or relations could help generate multiscale-representations of features through automated generalization that adequately maintain surface drainage variations and patterns reflective of the natural stream geomorphological conditions across the country.

Conference Paper↗