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1,559 records · Page 87Linked to original sources
USGS2010

Establishing a nationwide baseline of historical burn-severity data to support monitoring of trends in wildfire effects and national fire policies

There is a need to provide agency leaders, elected officials, and the general public with summary information regarding the effects of large wildfires. Recently, the Wildland Fire Leadership Council (WFLC), which implements and coordinates National Fire Plan (NFP) and Federal Wildland Fire Management Policies adopted a strategy to monitor the effectiveness and effects of the National Fire Plan and the Healthy Forests Restoration Act. One component of this strategy is to assess the environmental impacts of large wildland fires and identify the trends of burn severity on all lands across the United States. To that end, WFLC has sponsored a 6-year project, Monitoring Trends in Burn Severity (MTBS), which requires the U.S. Department of Agriculture, Forest Service (USDA-FS) and the U.S. Geological Survey (USGS) to map and assess the burn severity for all large current and historical fires. Using Landsat data and the differenced Normalized Burn Ratio (dNBR) algorithm, the USGS/EROS Data Center and USDA-FS/ Remote Sensing Applications Center will map burn severity of all fires occurring from 1984 to 2010. Only fires that are greater than 500 ac in the East, and 1,000 ac in the West will be included. We anticipate mapping a total of more than 9,000 historical fires and fires that occur during the course of the study. The MTBS project will generate burn-severity data, maps, and reports, which will be available for use at local, State, and national levels to evaluate trends in burn severity and help develop and assess the effectiveness of land management decisions. Additionally, the information developed will provide a baseline from which to monitor the recovery and health of fire-affected landscapes over time. Spatial and tabular data quantifying burn severity will augment existing information used to estimate risk associated with a range of current and future resource threats. For example, fire severity data along with associated biophysical characteristics provide an analytical basis for assessing risk from invasive species as well as native insects and pathogens. All data and results will be distributed to the public via a Web interface.

General Technical Report↗
USGS2024

Wave propagation in layered soil deposits

Recent advances in the general theory of viscoelastic waves and rays in layered media provide a rigorous mathematical framework for site-specific, soil-response models used for earthquake resistant design. The advances provide general closed-form anelastic solutions for the classic problems of the response of a stack of soil layers to S and P waves, ray theory for reflected and refracted waves, Rayleigh- and Love-Type surface waves, and head waves. These general solutions valid for anelastic media regardless of the amount of material damping yield new insights regarding the characteristics of seismic waves and their ray paths that are not provided by conventional models. They provide corresponding numerical ground-response models and ray-tracing computation algorithms that account for changes in velocity and attenuation of anelastic waves associated with changes in inhomogeneity of the waves induced by anelastic soil and soil-rock boundaries. These anelastic effects manifest themselves as variations in amplitude response, amplitude attenuation, raypath location, and travel time as observed at the Earth’s surface. Implications of these anelastic effects for soil-response models used for earthquake resistant design are provided herein.

Conference Paper↗
USGSDate not supplied

Comparative analysis of GPP products estimated from an empirical model and MODIS

Carbon-cycle models have uncertainties associated with data inputs, parameters, and model algorithms. The prerequisite for an applicable model is that it should perform at an acceptable level of accuracy and uncertainties should be documented. In this study, we validated the gross primary productivity (GPP) data from a piecewise regression (PWR) model and the MODIS GPP model at five grassland flux towers in the Northern Great Plains. The results showed a good agreement of GPP values (agreement coefficient d = 0.88–0.98) among PWR, MODIS, and tower measurements at Fort Peck, Mandan, and Cheyenne sites; but MODIS GPP did not agree well ( d = 0.62–0.79) with tower measurements at Miles City and Lethbridge sites. Additionally, we compared PWR GPP and MODIS GPP for grasslands in the entire study area. We found that the PWR GPP was lower than or similar to the MODIS GPP in the east and higher in the west and south. We explored possible factors that may cause the GPP difference in spatial distribution between the two models.

Conference Paper↗
USGS2005

Evaluating MODIS data to estimate irrigated crop production in Afghanistan using a thermal-based ET fraction approach

Accurate crop performance monitoring and production estimation is critical for timely assessment of the food balance of several countries in the world. Recently, the Famine Early Warning System Network (FEWS NET) has been monitoring crop performance and to some extent relative production using satellite derived data and simulation models in Africa, Central America and Afghanistan where ground based monitoring is limited due to the scarcity of weather stations. The commonly used crop monitoring models use a crop water balance algorithm with inputs from satellite-derived rainfall. While these models provide useful monitoring for rain-fed agriculture, they are ineffective for irrigated areas. Over 80% of the agricultural production in Afghanistan is from irrigated agriculture. In this study, we implemented a thermal-based ET fraction approach to monitor and assess the performance of irrigated agriculture in Afghanistan using the combination of 250-m NDVI and 1-km Land Surface Temperature (LST) data from MODIS. Six images per year were used to estimate seasonal evapotranspiration (ET) from irrigated lands in a given growing season between 2000 and 2004. Seasonal ET estimates from the different years were used as relative indicators of year-to-year production magnitude differences. The results were comparable to field reports and crop water balance based estimates for irrigated watersheds in that 2003 was a good year for crop production in Afghanistan. The advantage of this method over crop water balance method is that it helps identify irrigated areas directly and thus helps estimate total irrigated area and its spatial distribution in a given region.

Conference Paper↗
USGS2006

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

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

Conference Paper↗
USGS2003

A strategy for mapping mid-scale existing vegetation in support of national fire fuel assessment

Geospatial distribution of natural vegetation is among the very important environmental parameters required for applications ranging from global climate change to monitoring of natural hazards, monitoring of ecosystem vitality, and fire management practices. Increasingly sophisticated applications require vegetation datasets to cover large areas at a suitable scale and provide sufficiently detailed information. In this paper, we describe a research effort to develop a remote sensing methodology capable of producing 30-meter resolution, wall-to-wall coverage of existing vegetation types and structure variables in support of a multi-agency fire fuels and fire risks assessment project. Success of this remote sensing research effort is dependent on improved sensor and data qualities, a thorough understanding of regional and local vegetation ecology, successful integration of remote sensing with a large amount of field plot data, and flexible mapping algorithms. Preliminary results produced in the Wasatch Range and Uinta Mountains of central Utah include 28 vegetation types with an overall accuracy of 60% (average by life forms), percent canopy density (sub-pixel density) of forest, shrub, and herbaceous cover (correlation coefficient of 89, 60, and 55% respectively), and average top canopy height of forest, shrub, and herbaceous cover (correlation coefficient of 73, 50, 20% respectively). Techniques to improve the first-round results are discussed, including refinements of mapping models and use of relevant environmental gradients and potential vegetation classification associated with actual vegetation types.

Conference Paper↗
USGS2000

An effective filter for removal of production artifacts in U.S. Geological Survey 7.5-minute digital elevation models

Many digital elevation models (DEM) produced by the U.S. Geological Survey suffer from the presence of striping artifacts, which limit their utility. The most common approaches to filtering the affected DEMs rely on simple low-pass filters that blur fine details. A new filtering algorithm has been developed that isolates the stripes by applying a low-pass filter along the axis of the striping, followed by high-pass filtering orthogonal to the stripes. Once isolated, the artifacts are subtracted from the DEM to yield clean, detailed terrain data.

Conference Paper↗
USGS2024

Separating signals in elevation data improves supervised machine learning predictions for hydrothermal favorability

A recent study identified topography (land surface elevation above sea level) as an important input dataset (feature) for predicting the location of hydrothermal systems in the Great Basin in Nevada. Yet, topography is generally a result of more than one geological process and may consequently contain multiple distinct signals. For example, the geologic evolution of the Great Basin has produced both crustal thickening (i.e., regional-scale trends in elevation) and thinning via Basin and Range extensional faulting (i.e., valley-scale topographic relief). We postulate that these geologic processes may affect the occurrence of hydrothermal systems differently. Therefore, we separate the regional trend from the valley-scale signal in the Great Basin, and then use them separately to evaluate the importance of each as predictors for hydrothermal favorability. Our prior work applying supervised machine learning (ML) using the data from the Nevada Machine Learning Project demonstrated that employing a training strategy that randomly selects negative training sites produces better performing models for predicting hydrothermal favorability than a training strategy that uses expert-selected negatives. The models created using both training strategies exhibited a west-east geographic trend in the predictions for the favorability of hydrothermal resources. These models generally predicted higher favorability in western Nevada and lower favorability in eastern Nevada. This west-east trend in predicted favorability correlates with elevation across the Great Basin, which trends higher from west to east. By separating the original elevation feature into distinct features for elevation trend (i.e., regional-scale topography) and detrended elevation (i.e., valley-scale or local relative topography), we find that models using the separated topographic signals consistently outperform competing models that use the original elevation feature. Although western Nevada still exhibits higher favorability than eastern Nevada, using separated signals for regional elevation and local structure reduces the west-east prediction trend in the region and emphasizes structures associated with hydrothermal upflow. This work emphasizes how carefully engineering features to represent geological conditions relevant to hydrothermal systems allows ML algorithms to detect important patterns for predicting hydrothermal resource favorability and leads to better model performance.

Conference Paper↗
USGS2024

Global Food Security Support Analysis Data (GFSAD) using remote sensing in support of food and water security in the 21st century

The overarching goal of this chapter is to provide a comprehensive overview of the state-of-art of global cropland mapping procedures using remote sensing as characterized and envisioned by the “Global Food Security Support Analysis Data @ 30 m (GFSAD30)” project working group team. First, the chapter will provide an overview of existing cropland maps and their characteristics along with establishing the gaps in knowledge related to global cropland mapping. Second, definitions of cropland mapping along with key parameters involved in cropland mapping based on their importance in food security analysis, and cropland naming conventions for standardized cropland mapping using remote sensing will be presented. Third, existing methods and approaches for cropland mapping will be discussed. This will include the type of remote sensing data used in cropland mapping and their characteristics along with discussions on the secondary data, field-plot data, and cropland mapping algorithms. Fourth, currently existing global cropland products derived using remote sensing will be presented and discussed. Fifth, a synthesis of all existing products leading to a composite global cropland extent version 1.0 (GCE V1.0) is presented and discussed. Sixth, a way forward for advanced global cropland mapping is visualized.

Book chapter↗
USGS2024

Untangling the knots: A procedure for identifying discernibility conflicts on a cartographic line

Reducing detail on polyline features aids in legibility, allowing features to appear more distinct and preventing coalescence with other features. Current metrics for evaluating generalization outcomes emphasize geometric change rather than legibility. The present study reports on development and testing of a vector-based metric of the discernibility of a single polyline feature or group of features, defined as the absence of visual coalescence at a target map scale. This metric prioritizes legibility problems due to resolution and the physical/optical limits of discernibility. The metric identifies specific locations of coalescence, and is invariant to translation and rotation, providing a consistent measure across display contexts. A procedure for computing the above definition of discernibility and identifying the locations of discernibility conflicts will be presented. The algorithm is currently being tested in python code, and the goal is to include this tool in an open source python toolbox for cartographic generalization assessment.

Conference Paper↗
USGS2026

Advancing monitoring approaches to enhance tidal Chesapeake Bay habitat assessment for submerged aquatic vegetation, water clarity, chlorophyll a and dissolved oxygen

Water quality monitoring capacity has been declining for the Chesapeake Bay Program (CBP) at a time when information needs are growing, and data gaps exist to address critical decision-support for managers. The CBP Scientific Technical Assessment and Reporting Team is leading a Principal’s Staff Committee requested gap analyses toward understanding support needed to improve water quality monitoring and analysis programming. Advanced technologies and alternative monitoring approaches in the form of satellite-based measurements, Artificial Intelligence/Machine Learning (AI/ML) algorithms for data interpretation, continuous water quality in-situ sensor arrays, and community science efforts offer a growing portfolio of valuable opportunities for expanding data collections and analysis program capacities. However, since 1985, each of these options are examples of growing opportunities to enhance water quality assessments yet has seen limited adoption into elements of Chesapeake Bay water quality monitoring programs. Where new technologies have been adopted (e.g., shallow water continuous water quality monitoring), such temporally rich data streams have supported Bay health insights yet had limited use in regulatory water quality criteria assessment. This Scientific Technical Advisory Committee (STAC) supported workshop provided the ideal forum for engaging our CBP partnership regarding the maturity of new and evolving monitoring and analysis capacities to address program information needs while appreciating limitations with adopting new tools and approaches. Improving natural resources monitoring efficiency and effectiveness will expand the scientific and technical foundations for making robust, strategic choices on decisions for CBP Partnership community-based priorities, policies, and management actions. Workshop findings and recommendations reflect progress in science, technology, and analyses addressing long-standing programmatic limitations in data collection and analysis capacities. State-of-the-science updates highlighted in the workshop span the spectrum of efforts representing improvements, successes, remaining challenges toward operationalizing protocols, and guidance toward research, or adoption and implementation by monitoring programs.

Maryland, Pennsylvania, Virginia↗