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At least 379 records · Page 21Linked to original sources
USGS2018

Estimating post-fire debris-flow hazards prior to wildfire using a statistical analysis of historical distributions of fire severity from remote sensing data

Following wildfire, mountainous areas of the western United States are susceptible to debris flow during intense rainfall. Convective storms that can generate debris flows in recently burned areas may occur during or immediately after the wildfire, leaving insufficient time for development and implementation of risk mitigation strategies. We present a method for estimating post-fire debris-flow hazards prior to wildfire using historical data to define the range of potential fire severities for a given location based on the statistical distribution of severity metrics obtained from remote sensing. Estimates of debris-flow likelihood, magnitude, and triggering rainfall threshold based upon the statistically simulated fire severity data provide hazard predictions consistent with those calculated from fire severity data collected after wildfire. Simulated fire severity data also produce hazard estimates that replicate observed debris-flow occurrence, rainfall conditions, and magnitude at a monitored site in the San Gabriel Mountains of southern California. Future applications of this method should rely upon a range of potential fire severity scenarios for improved pre-fire estimates of debris-flow hazard. The method presented here is also applicable to modeling other post-fire hazards, such as flooding and erosion risk, and for quantifying trends in observed fire severity in a changing climate.

California, Colorado, Washington↗
USGS1974

Survey of remote sensing applications

Data from the first earth resources technology satellite (ERTS) as well as from NASA and other aircraft, contain much of the information indicative of the distribution of groundwater and the extent of its utilization. Thermal infrared imagery from aircraft is particularly valuable in studying groundwater discharge to the sea and other surface water bodies. Color infrared photography from aircraft and space is also used to locate areas of potential groundwater development. Anomalies in vegetation, soils, moisture, and their pattern of distribution may be indicative of underlying groundwater conditions. Remote sensing may be used directly or indirectly to identify stream reaches for test holes or production wells. Similarly, location of submarine springs increase effectiveness of groundwater exploration in the coastal zone.

Water Well Journal↗
USGS2008

Remote sensing and GIS approach for water-well site selection, southwest Iran

The Pabdeh-Lali Anticline of northern Khuzestan province is located in southwestern Iran and occupies 790 km2. This structure is situated in the Zagros folded belt. As a result of well-developed karst systems in the anticlinal axis, the water supply potential is high and is drained by many peripheral springs. However, there is a scarcity of water for agriculture and population centers on the anticlinal flanks, which imposes a severe problem in terms of area development. This study combines remotely sensed (RS) data and a geographical information system (GIS) into a RSGIS technique to delineate new areas for groundwater development and specific sites for drilling productive water wells. Toward these goals, RS data were used to develop GIS layers for lithology, structural geology, topographic slope, elevation, and drainage density. Field measurements were made to create spring-location and groundwater-quality GIS layers. Subsequently, expert choice and relational methods were used in a GIS environment to conjunctively analyze all layers to delineate preferable regions and 43 individual sites in which to drill water wells. Results indicate that the most preferred areas are, in preferential order, within recent alluvial deposits, the Bakhtiyari Conglomerates, and the Aghajari Sandstone. The Asmari Limestone and other units have much lower potential for groundwater supplies. Potential usefulness of the RSGIS method was indicated when six out of nine producing wells recently drilled by the Khozestan Water and Power Authority (which had no knowledge of this study) were located in areas preferentially selected by this technique.

Environmental & Engineering Geoscience↗
USGS2014

Remotely-sensed indicators of N-related biomass allocation in Schoenoplectus acutus

Coastal marshes depend on belowground biomass of roots and rhizomes to contribute to peat and soil organic carbon, accrete soil and alleviate flooding as sea level rises. For nutrient-limited plants, eutrophication has either reduced or stimulated belowground biomass depending on plant biomass allocation response to fertilization. Within a freshwater wetland impoundment receiving minimal sediments, we used experimental plots to explore growth models for a common freshwater macrophyte, Schoenoplectus acutus. We used N-addition and control plots (4 each) to test whether remotely sensed vegetation indices could predict leaf N concentration, root:shoot ratios and belowground biomass of S. acutus. Following 5 months of summer growth, we harvested whole plants, measured leaf N and total plant biomass of all above and belowground vegetation. Prior to harvest, we simulated measurement of plant spectral reflectance over 164 hyperspectral Hyperion satellite bands (350–2500 nm) with a portable spectroradiometer. N-addition did not alter whole plant, but reduced belowground biomass 36% and increased aboveground biomass 71%. We correlated leaf N concentration with known N-related spectral regions using all possible normalized difference (ND), simple band ratio (SR) and first order derivative ND (FDN) and SR (FDS) vegetation indices. FDN 1235, 549 was most strongly correlated with leaf N concentration and also was related to belowground biomass, the first demonstration of spectral indices and belowground biomass relationships. While S. acutus exhibited balanced growth (reduced root:shoot ratio with respect to nutrient addition), our methods also might relate N-enrichment to biomass point estimates for plants with isometric root growth. For isometric growth, foliar N indices will scale equivalently with above and belowground biomass. Leaf N vegetation indices should aid in scaling-up field estimates of biomass and assist regional monitoring.

California↗
USGS1998

Use of acoustic velocity methodology and remote sensing techniques to measure unsteady flow on the lower Yazoo River in Mississippi

Methodologies have been developed for computing continuous discharge during varied, non-uniform low and medium flows on the Yazoo River at the U.S. Geological Survey streamgage below Steele Bayou near Long Lake, Mississippi, using acoustic signal processing and conventional streamgaging techniques. Procedures were also developed to compute locations of discharges during future high flow events when the stream reach is subject to hi-directional and reverse flow caused by rising stages on the Mississippi River using a combination of acoustic equipment and remote sensing technology. A description of the study area is presented. Selected results of these methods are presented for the period from March through September 1997.

Conference Paper↗
USGS2023

Fusing geophysical and remotely sensed data for observing overwash occurrence, frequency, and impact

Overwash is an important process that enables a barrier island to migrate landward to adapt to rising sea levels but can also impact vegetated areas and create coastal hazards for populated barrier islands. Our overall objectives were to hindcast overwash events from September 2008 to November 2009 and assess whether overwash impacts could be detected using moderate-resolution imagery (30 m). Estimates of wave and still water levels can be benchmarked against morphological characteristics from elevation datasets to predict overwash events. These observations can be combined with optical remote sensing data used to monitor for changes in vegetation greenness over time to evaluate potential impacts from overwash. This study highlighted how physical-based overwash data can be paired with observations of greenness. The results from our study highlighted that a discernable drop in greenness can be detected for major hurricanes, such as Hurricane Gustav in 2008, with a weaker signal observed for smaller magnitude events in 2009 like Hurricane Ida. Tracking overwash impacts to vegetation can be helpful for observing impacts to vegetation associated with restoration efforts and advancing our understanding of general overwash impacts and recovery.

Conference Paper↗
USGS2014

Gully annealing by aeolian sediment: field and remote-sensing investigation of aeolian-hillslope-fluvial interactions, Colorado River corridor, Arizona, USA

Processes contributing to development of ephemeral gully channels are of great importance to landscapes worldwide, and particularly in dryland regions where soil loss and land degradation from gully erosion pose long-term land-management problems. Whereas gully formation has been relatively well studied, much less is known of the processes that anneal gullies and impede their growth. This study of gully annealing by aeolian sediment, spanning 95 km along the Colorado River corridor in Glen, Marble, and Grand Canyon, Arizona, USA, employed field and remote sensing observations, including digital topographic modelling. Results indicate that aeolian sediment activity can be locally effective at counteracting gully erosion. Gullies are less prevalent in areas where surficial sediment undergoes active aeolian transport, and have a greater tendency to terminate in active aeolian sand. Although not common, examples exist in the record of historical imagery of gullies that underwent infilling by aeolian sediment in past decades and evidently were effectively annealed. We thus provide new evidence for a potentially important interaction of aeolian–hillslope–fluvial processes, which could affect dryland regions substantially in ways not widely recognized. Moreover, because the biologic soil crust plays an important role in determining aeolian sand activity, and so in turn the extent of gully development, this study highlights a critical role of geomorphic–ecologic interactions in determining arid-landscape evolution.

Arizona↗
USGS2025

Confluence of time and space: An innovation for quantifying dynamics of hydrologic floodplain connectivity with remote sensing and GIS

Hydrologic connectivity is a crucial determinant of aquatic ecosystem services, governing the exchange of nutrients, sediments, chemicals, and biota. Various indices and metrics exist for quantifying hydrologic connectivity across diverse environments and scales. However, existing methodologies often fail to adequately capture lateral connectivity between floodplain lakes and streams across vast, low-relief, multi-lake floodplain systems. This study introduces a novel approach for quantifying lateral hydrologic connectivity specifically tailored for floodplain lakes connecting to streams within the expansive floodplain of the Lower Mississippi River. This approach centers on the spatial and temporal intersection of lakes and streams, leveraging remote sensing and GIS data to estimate nine distinct metrics of hydrologic connectivity. To assess the reliability of the method, the study estimated connectivity metrics for 92 randomly selected floodplain lakes, comprising 53 lakes connected to large streams (Strahler order >7), 13 lakes connected to medium (order 4-6) streams, and 26 lakes connected to small (order 1-3) streams. As expected, there was significant variability in hydrologic connectivity across different stream size classes. The outlined approach contributes valuable insights into the hydrologic connectivity of floodplain lakes and offers a generalizable framework applicable to other floodplains. Its versatility makes it a practical tool for understanding connectivity requirements for biota and facilitating applications in conservation and water resources management. Thus, this work represents a meaningful step toward advancing our understanding of lateral hydrologic connectivity dynamics in complex aquatic ecosystems.

Arkansas, Illinois, Kentucky, Louisiana, Mississip↗
USGS1980

A survey of image processing developments in support of remote sensing

New algorithm developments for image processing (IP) will occur throughout the 1980's, resulting from evolution in computer hardware and sensors as well as continuing research. This report will describe the areas of algorithm development that are occurring in applications, research, and operational environments. Included is an overview of image processing activities at institutions which are generally regarded as leaders in IP algorithm development and implementation. Finally, this report addresses directions in IP algorithm development that are being proposed for the EROS Data Center (EDC). The major applications of IP at EDC are developed for use in the processing, analysis, and extraction of remote sensing information from Landsat and aircraft data (platforms).

Report↗
USGS2002

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LM-NRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.

Environmental Management↗
USGS2024

Improving crop-specific groundwater use estimation in the Mississippi Alluvial Plain: Implications for integrated remote sensing and machine learning approaches in data-scarce regions

Study region The Mississippi Alluvial Plain (MAP) in the United States (US). Study focus Understanding local-scale groundwater use, a critical component of the water budget, is necessary for implementing sustainable water management practices. The MAP is one of the most productive agricultural regions in the US and extracts more than 11 km 3 /year for irrigation activities. Consequently, groundwater-level declines in the MAP region pose a substantial challenge to water sustainability, and hence, we need reliable groundwater pumping monitoring solutions to manage this resource appropriately. New hydrological insights for the region We incorporate remote sensing datasets and machine learning to improve an existing lookup table-based model of groundwater use previously developed by the U.S. Geological Survey (USGS). Here, we employ Distributed Random Forests, an ensemble machine learning algorithm to predict annual and monthly groundwater use (2014–2020) throughout this region at 1-km resolution, using pumping data from existing flowmeters in the Mississippi Delta. Our model compares favorably with the existing USGS model, with higher R 2 (0.51 compared to 0.42 in the previous model), and lower root mean square error (RMSE) and mean absolute error (MAE)— 0.14 m and 0.09 m, respectively in our model, compared to 0.15 m and 0.1 m in the previous model. Therefore, this work advances our ability to predict groundwater use in regions with scarce or limited in-situ groundwater withdrawal data availability.

Journal of Hydrology Regional Studies↗
USGS1990

Evaluation of line transect sampling based on remotely sensed data from underwater video

We used underwater video in conjunction with the line transect method and a Fourier series estimator to make 13 independent estimates of the density of known populations of bricks lying on the bottom in shallows of Lake Huron. The pooled estimate of density (95.5 bricks per hectare) was close to the true density (89.8 per hectare), and there was no evidence of bias. Confidence intervals for the individual estimates included the true density 85% of the time instead of the nominal 95%. Our results suggest that reliable estimates of the density of objects on a lake bed can be obtained by the use of remote sensing and line transect sampling theory.

Transactions of the American Fisheries Society↗
USGS2011

Applying remote sensing to invasive species science—A tamarisk example

The Invasive Species Science Branch of the Fort Collins Science Center provides research and technical assistance relating to management concerns for invasive species, including understanding how these species are introduced, identifying areas vulnerable to invasion, forecasting invasions, and developing control methods. This fact sheet considers the invasive plant species tamarisk (Tamarix spp), addressing three fundamental questions: *Where is it now? *What are the potential or realized ecological impacts of invasion? *Where can it survive and thrive if introduced? It provides peer-review examples of how the U.S. Geological Survey, working with other federal agencies and university partners, are applying remote-sensing technologies to address these key questions.

Fact Sheet↗
USGS2001

The roughness of natural terrain: A planetary and remote sensing perspective

We examine the various methods and parameters in common use for quantifying and reporting surface topographic "roughness." It is shown that scale-dependent roughness parameters are almost always required, though not widely used. We suggest a method of standardizing the parameters that are computed and reported so that topographic data gathered by different workers using different field techniques can be directly and easily intercompared. We illustrate the proposed method by analyzing topographic data from 60 different surfaces gathered by five different groups and examine the information for common features. We briefly discuss the implications of our analysis for studies of planetary surface roughness, lander safety, and radar remote sensing modeling and analysis.

Journal of Geophysical Research E: Planets↗
USGS2006

Shrinking ponds in subarctic Alaska based on 1950-2002 remotely sensed images

Over the past 50 years, Alaska has experienced a warming climate with longer growing seasons, increased potential evapotranspiration, and permafrost warming. Research from the Seward Peninsula and Kenai Peninsula has demonstrated a substantial landscape-level trend in the reduction of surface water and number of closed-basin ponds. We investigated whether this drying trend occurred at nine other regions throughout Alaska. One study region was from the Arctic Coastal Plain where depp permafrost occurs continuously across the landscape. The other eight study regions were from the boreal forest regions where discontinuous permafrost occurs. Mean annual precipitation across the study regions ranged from 100 to over 700 min yr-1. We used remotely sensed imagery from the 1950s to 2002 to inventory over 10,000 closed-basin ponds from at least three periods from this time span. We found a reduction in the area and number of shallow, closed-basin ponds for all boreal regions. In contrast, the Arctic Coastal Plain region had negligible change in the area of closed-basin ponds. Since the 1950s, surface water area of closed-basin ponds included in this analysis decreased by 31 to 4 percent, and the total number of closed-basin ponds surveyed within each study region decreased from 54 to 5 percent. There was a significant increasing trend in annual mean temperature and potential evapotranspiration since the 1950s for all study regions. There was no significant trend in annual precipitation during the same period. The regional trend of shrinking ponds may be due to increased drainage as permafrost warms, or increased evapotranspiration during a warmer and extended growing season. Copyright 2006 by the American Geophysical Union.

Journal of Geophysical Research G: Biogeosciences↗
USGS2003

The challenge of archiving and preserving remotely sensed data

Few would question the need to archive the scientific and technical (S&T) data generated by researchers. At a minimum, the data are needed for change analysis. Likewise, most people would value efforts to ensure the preservation of the archived S&T data. Future generations will use analysis techniques not even considered today. Until recently, archiving and preserving these data were usually accomplished within existing infrastructures and budgets. As the volume of archived data increases, however, organizations charged with archiving S&T data will be increasingly challenged (U.S. General Accounting Office, 2002). The U.S. Geological Survey has had experience in this area and has developed strategies to deal with the mountain of land remote sensing data currently being managed and the tidal wave of expected new data. The Agency has dealt with archiving issues, such as selection criteria, purging, advisory panels, and data access, and has met with preservation challenges involving photographic and digital media. That experience has allowed the USGS to develop management approaches, which this paper outlines.

Data Science Journal↗
USGS2011

The role of remote sensing observations and models in hydrology: The science of evapotranspiration

Over 15 years ago, Morton (1994) summarized the state of evapotranspiration (ET) research pessimistically: ‘There have been few significant advances in our knowledge of evaporation on an environmental scale over the past four decades, a state of affairs linked to the current sterility of hydrology and related environmental sciences. Furthermore, almost none of the advances have been used successfully in practice.’ He did not foresee the rapid progress in the ensuing years. These advances can be attributed largely to three convergent themes: 1) technical innovation; 2) synergy between disciplines; and 3) expressed need. The papers in this special issue address all of these three themes on remote sensing methods for ET estimation.

Hydrological Processes↗
USGS2022

Integrated strategies for enhanced rapid earthquake shaking, ground failure, and impact estimation employing remotely sensed and ground truth constraints

Estimating earthquake impacts using physical or empirical models is challenging because the three components of loss estimation-shaking, exposure, and vulnerabilities-entail inherent uncertainties. Loss modeling in near-real-time adds additional uncertainties, yet expectations for actionable information with a reasonable level of confidence in the results are real. The modeling approaches described herein augment inherently uncertain prior hazard and loss models with an integrated strategy for updating these priors with ground-truth observations, thereby greatly reducing their uncertainties. Two strategies are employed. Early reports of casualties are used in a Bayesian updating fashion to constrain the possible range of fatalities and to lower the prior models' uncertainties. Additionally, remotely sensed satellite radar data, in the form of a Damage Proxy Map (or DPM), are used in a Bayesian causal graph framework combined with machine learning to optimize the mapping among the physical processes that cause shaking-based building damage, landslides, and liquefaction to prior expectation models. The casual graph framework also affords the potential for removing anthropogenetic noise contained in the imagery. Ultimately, our two-fold model updating strategy will accommodate key ground-truth observations such as fatality reports, locations of building damage, and ground failure reports to converge on actual losses more rapidly.

Conference Paper↗