Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Journal of Applied Remote Sensing”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources
USGS2012

Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: laying a foundation for monitoring

agebrush ecosystems in North America have experienced extensive degradation since European settlement. Further degradation continues from exotic invasive plants, altered fire frequency, intensive grazing practices, oil and gas development, and climate change – adding urgency to the need for ecosystem-wide understanding. Remote sensing is often identified as a key information source to facilitate ecosystem-wide characterization, monitoring, and analysis; however, approaches that characterize sagebrush with sufficient and accurate local detail across large enough areas to support this paradigm are unavailable. We describe the development of a new remote sensing sagebrush characterization approach for the state of Wyoming, U.S.A. This approach integrates 2.4 m QuickBird, 30 m Landsat TM, and 56 m AWiFS imagery into the characterization of four primary continuous field components including percent bare ground, percent herbaceous cover, percent litter, and percent shrub, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata Wyomingensis), and shrub height using a regression tree. According to an independent accuracy assessment, primary component root mean square error (RMSE) values ranged from 4.90 to 10.16 for 2.4 m QuickBird, 6.01 to 15.54 for 30 m Landsat, and 6.97 to 16.14 for 56 m AWiFS. Shrub and herbaceous components outperformed the current data standard called LANDFIRE, with a shrub RMSE value of 6.04 versus 12.64 and a herbaceous component RMSE value of 12.89 versus 14.63. This approach offers new advancements in sagebrush characterization from remote sensing and provides a foundation to quantitatively monitor these components into the future.

Wyoming↗
USGS2006

Uplift and subsidence associated with the great Aceh-Andaman earthquake of 2004

Rupture of the Sunda megathrust on 26 December 2004 produced broad regions of uplift and subsidence. We define the pivot line separating these regions as a first step in defining the lateral extent and the downdip limit of rupture during that great Mw ??? 9.2 earthquake. In the region of the Andaman and Nicobar islands we rely exclusively on the interpretation of satellite imagery and a tidal model. At the southern limit of the great rupture we rely principally on field measurements of emerged coral microatolls. Uplift extends from the middle of Simeulue Island, Sumatra, at ??? 2.5??N, to Preparis Island, Myanmar (Burma), at ??? 14.9??N. Thus the rupture is ??? 1600 km long. The distance from the pivot line to the trench varies appreciably. The northern and western Andaman Islands rose, whereas the southern and eastern portion of the islands subsided. The Nicobar Islands and the west coast of Aceh province, Sumatra, subsided. Tilt at the southern end of the rupture is steep; the distance from 1.5 m of uplift to the pivot line is just 60 km. Our method of using satellite imagery to recognize changes in elevation relative to sea surface height and of using a tidal model to place quantitative bounds on coseismic uplift or subsidence is a novel approach that can be adapted to other forms of remote sensing and can be applied to other subduction zones in tropical regions. Copyright 2006 by the American Geophysical Union.

Journal of Geophysical Research B: Solid Earth↗
USGS2009

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Journal of Exposure Science and Environmental Epid↗
USGS2024

A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale

Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km 2 ), about 461,000 km 2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes.

International Journal of Applied Earth Observation↗
USGS2007

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

1. Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. 2. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. 3. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. 4. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. 5. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. ?? 2007 The Authors.

Journal of Applied Ecology↗
USGS2007

Some guidelines for remote sensing in hydrology

Remote sensing in the field of hydrology is beginning to be applied to significant problems, such as thermal pollution, in many programs of the Federal and State Governments as well as in operation of many private organizations. The purpose of this paper is to guide the hydrologist to a better understanding of how he may collect, synthesize, and interpret remote sensing data.

Journal of the American Water Resources Associatio↗
USGS2008

Agreement evaluation of AVHRR and MODIS 16-day composite NDVI data sets

Satellite-derived normalized difference vegetation index (NDVI) data have been used extensively to detect and monitor vegetation conditions at regional and global levels. A combination of NDVI data sets derived from AVHRR and MODIS can be used to construct a long NDVI time series that may also be extended to VIIRS. Comparative analysis of NDVI data derived from AVHRR and MODIS is critical to understanding the data continuity through the time series. In this study, the AVHRR and MODIS 16-day composite NDVI products were compared using regression and agreement analysis methods. The analysis shows a high agreement between the AVHRR-NDVI and MODIS-NDVI observed from 2002 and 2003 for the conterminous United States, but the difference between the two data sets is appreciable. Twenty per cent of the total difference between the two data sets is due to systematic difference, with the remainder due to unsystematic difference. The systematic difference can be eliminated with a linear regression-based transformation between two data sets, and the unsystematic difference can be reduced partially by applying spatial filters to the data. We conclude that the continuity of NDVI time series from AVHRR to MODIS is satisfactory, but a linear transformation between the two sets is recommended.

International Journal of Remote Sensing↗
USGS2007

Estimating the effective spatial resolution of an AVHRR time series

A method is proposed to estimate the spatial degradation of geometrically rectified AVHRR data resulting from misregistration and off-nadir viewing, and to infer the cumulative effect of these degradations over time. Misregistrations are measured using high resolution imagery as a geometric reference, and pixel sizes are computed directly from satellite zenith angles. The influence or neighbouring features on a nominal 1 km by 1 km pixel over a given site is estimated from the above information, and expressed as a spatial distribution whose spatial frequency response is used to define an effective field-of-view (EFOV) for a time series. In a demonstration of the technique applied to images from the Conterminous U.S. AVHRR data set, an EFOV of 3·1km in the east-west dimension and 19 km in the north-south dimension was estimated for a time series accumulated over a grasslands test site.

International Journal of Remote Sensing↗
USGS2025

Too much and not enough data: Challenges and solutions for generating information in freshwater research and monitoring

Evaluating progress toward achieving freshwater conservation and sustainability goals requires transforming diverse types of data into useful information for scientists, managers, and other interest groups. Despite substantial increases in the volume of freshwater data collected worldwide, many regions and ecosystems still lack sufficient data collection and/or data access. We illustrate how these data challenges result from a diverse set of underlying mechanisms and propose solutions that can be applied by individuals or organizations. We discuss creative approaches to address data scarcity, including the use of community science, remote-sensing, environmental sensors, and legacy datasets. We highlight the importance of coordinated data collection efforts among groups and training programs to improve data access. At the institutional level, we emphasize the power of prioritizing data curation, incentivizing data publication, and promoting research that enhances data coverage and representativeness. Some of these strategies involve technological and analytical approaches, but many necessitate shifting the priorities and incentives of organizations such as academic and government research institutions, monitoring groups, journals, and funding agencies. Our overarching goal is to stimulate discussion to narrow the data disparities hindering the understanding of freshwater processes and their change across spatial scales.

Nahuel Huapi Lake, Lake Tahoe↗
USGS2019

Remote sensing of tracer dye concentrations to support dispersion studies in river channels

In river channels the flow field influences the dispersion of biota, contaminants, and other suspended or dissolved materials. Insight on patterns and rates of dispersion can be gained by injecting a pulse of visible dye and observing spatial and temporal variations in dye concentration as the pulse moves downstream. We evaluated the potential of passive optical remote sensing to enhance such tracer experiments by providing spatially distributed concentration information. During tests performed in both an experimental flume facility and a large natural channel, we made field measurements of Rhodamine WT dye concentration and above-water spectral reflectance. At Korea's River Experiment Center, a small unmanned aircraft system (sUAS) was used to acquire hyperspectral images of a sinuous outdoor flume. On the Kootenai River in northern Idaho, USA, field spectra were collected from a boat and hyperspectral image data and high resolution aerial photographs were obtained from manned aircraft. We modified an Optimal Band Ratio Analysis (OBRA) algorithm to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. For both the flume and field tests, we obtained very strong (R^2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths. On the Kootenai, we found that X vs. C relations derived from field spectra could be applied to airborne hyperspectral images and that dye concentrations could be estimated nearly as reliably from relatively simple three-band images as from hyperspectral data. These results imply that remote sensing could become a powerful tool for mapping dye patterns. Such a capability would advance our understanding of dispersion processes by enabling more rigorous testing of numerical flow models.

Journal of Ecohydraulics↗
USGS2012

Evaluation of NDVI to assess avian abundance and richness along the upper San Pedro River

Remote-sensing models have become increasingly popular for identifying, characterizing, monitoring, and predicting avian habitat but have largely focused on single bird species. The Normalized Difference Vegetation Index (NDVI) has been shown to positively correlate with avian abundance and richness and has been successfully applied to southwestern riparian systems which are uniquely composed of narrow bands of vegetation in an otherwise dry landscape. Desert riparian ecosystems are important breeding and stopover sites for many bird species but have been degraded due to altered hydrology and land management practices. Here we investigated the use of NDVI, coupled with vegetation, to model the avian community structure along the San Pedro River, Arizona. We also investigated how vegetation and physical features measured locally compared to those data that can be gathered through remote-sensing. We found that NDVI has statistically significant relationships with both avian abundance and species richness, although is better applied at the individual species level. However, the amount of variation explained by even our best models was quite low, suggesting that NDVI habitat models may not presently be an accurate tool for extensive modeling of avian communities. We suggest additional studies in other watersheds to increase our understanding of these bird/NDVI relationships.

Arizona↗
USGS2021

Ten years of volcanic activity at Mt Etna: High-resolution mapping and accurate quantification of the morphological changes by Pleiades and Lidar data

The topography of Mt. Etna, Italy, is subjected to continuous modifications depending on intensity and magnitude of eruptions that frequently occur at the volcano summit and flanks. In order to make high-resolution maps of morphological changes and accurately calculate the overall volume of the erupted products (e.g., lava flows, tephra fall out, scoriae cones) in ten years, we have compared the altimetry models of Mt. Etna derived from 2005 Airborne Laser Scanning data and 2015 Pleiades stereo satellite imagery. Both models cover a common area of 400 km 2 with spatial resolution of 2 m and comparable vertical accuracy (RMSE < 0.8 m). The results show that the area most affected by the erupted products is the mid-upper portion of the volcano with an altitude ranging from 1300 m to more than 3300 m a.s.l., value reached at the summit of the North East crater. In particular, this portion changes dramatically in the eastern sector due to the birth and growth of the New South-East Crater, the invasion of dozens of lava flows in the Valle del Bove, and the formation of the 2014 scoriae cones and lava field at the base of the North-East Crater. The total volume of products erupted in the investigated period results in 284.3±15.8 x 10 6 m 3 with a yearly average volume of 28.4 x 10 6 m 3 /y comparable with the previous decades. In addition, the products emitted by the 2014 sub-terminal eruption are mapped and quantified including, for the first time, the volume of the 2014 scoriae cones generated on the eastern flank of North-East Crater This study demonstrates how a rigorous comparison between digital elevation models derived from different remote sensing techniques produce high accurate mapping and quantifications of morphological changes applicable for worldwide active volcanoes. This allows to quantify volumes and areas of erupted products reducing the error estimations, a crucial point to provide precise data often used as key parameters for many volcanic hazard studies.

Mt. Etna, Sicily↗
USGS2013

Semi-automted analysis of high-resolution aerial images to quantify docks in Upper Midwest glacial lakes

Lake resources can be negatively affected by environmental stressors originating from multiple sources and different spatial scales. Shoreline development, in particular, can negatively affect lake resources through decline in habitat quality, physical disturbance, and impacts on fisheries. The development of remote sensing techniques that efficiently characterize shoreline development in a regional context could greatly improve management approaches for protecting and restoring lake resources. The goal of this study was to develop an approach using high-resolution aerial photographs to quantify and assess docks as indicators of shoreline development. First, we describe a dock analysis workflow that can be used to quantify the spatial extent of docks using aerial images. Our approach incorporates pixel-based classifiers with object-based techniques to effectively analyze high-resolution digital imagery. Second, we apply the analysis workflow to quantify docks for 4261 lakes managed by the Minnesota Department of Natural Resources. Overall accuracy of the analysis results was 98.4% (87.7% based on ) after manual post-processing. The analysis workflow was also 74% more efficient than the time required for manual digitization of docks. These analyses have immediate relevance for resource planning in Minnesota, whereas the dock analysis workflow could be used to quantify shoreline development in other regions with comparable imagery. These data can also be used to better understand the effects of shoreline development on aquatic resources and to evaluate the effects of shoreline development relative to other stressors.

Minnesota↗
USGS2024

Predicting characteristic length scales of barrier island segmentation in microtidal environments

Segmented barrier islands can be found in regions with small tidal ranges. In contrast to tidally dominated barriers, where inlet dynamics are thought to control island length scales, the controls on barrier island length scales in wave-dominated environments have not been quantified. These microtidal barriers typically have a curved shoreline, suggesting the influence of wave-driven alongshore sediment transport. Microtidal barriers are also typically hydrodynamically isolated from one another, as weak tidal flows limit interactions between adjoining barriers. To better understand the controls on and scales of barrier segmentation in the relative absence of tides, here we develop a theoretical framework to estimate the alongshore length scales at which a barrier will either breach or heal following a disturbance in the barrier morphology. The non-dimensional framework compares the timescales of overwash (advective) and alongshore sediment transport (diffusive) processes along barrier island chains. We then apply this framework to modern barrier islands in the microtidal Gulf of Mexico using wave hindcast data and the lengths, widths, heights, and lagoon depths measured from remotely sensed geospatial data and topobathymetric data. We find that most of these barriers are currently longer than their critical length scale, often as a result of coastal restoration efforts. Our critical length scale analysis suggests that most of the Gulf of Mexico barriers are vulnerable to segmentation despite coastal restoration efforts intended to protect fisheries and the mainland coasts.

Florida, Louisiana, Mississippi↗
USGS2025

Offsetting the noise: A framework for applying phenological offset corrections in remotely sensed burn severity assessments

Background Phenological correction of pre- and post-fire imagery is used to improve remotely sensed burn severity evaluations. Unburned offset values standardize greenness between image pairs; however, efficacy across diverse scenarios remains underexplored. Aims We evaluated the impact of phenological offset correction methods to support analyst decision-making across fire-prone environments. Methods We generated burn severity spectral index values for a dataset of Composite Burn Index (CBI) field plots across the conterminous US. The effectiveness of offset corrections was tested across image selection techniques, spectral indices, offset generation methods and burn perimeter sources. We assessed the influence of offset corrections on the modeled relationship with CBI, agreement between burn severity thresholds and potential bias. Key results Applying offset corrections consistently improved the modeled relationship with CBI by addressing extreme outlier severity values. However, automated offset corrections had the potential to introduce bias, systematically lowering severity values and reducing correspondence with observed burn severity categories. Conclusions Offset corrections offer benefits but also present trade-offs to accurately representing remotely sensed burn severity. Implications The utility of offset corrections depends on the environment, methods and scale of analysis. We propose a decision-tree framework for analysts to consider when employing offset corrections given their study scope.

conterminous United States↗
USGS2005

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2012

Enhanced visualization for the interpretation of Magellan radar data: Supplement to the Magellan special issue

The differences of radar data from more familiar photographic images, coupled with the unique geologic characteristics of Venus's surface, can make interpretation of the standard Magellan data products difficult for both planetary scientists and nonspecialists. We describe a set of digital processing techniques for transforming individual remote‐sensing datasets in order to make the information they contain more apparent, and for combining multiple datasets of diverse resolution and information content into synthetic images that allow the viewer to explore correlations among the data. The creation of derived data products by techniques like those we describe is becoming increasingly widespread and important, both for scientific research and for use in educational and media presentations. We apply our processing techniques to Magellan synthetic aperture radar images, altimetry, and microwave emissivity measurements of four areas that exemplify some of the most important styles of geologic activity on Venus.

Journal of Geophysical Research E: Planets↗
USGS2023

Crop water use dynamics over arid and semi-arid croplands in the lower Colorado River Basin

Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance methods (SEB). SEB models computationally require a large effort for application. VI-based methods are fast and easy to apply and could therefore potentially be applied at high resolution; however, the accuracy of VI-based methods in comparison to SEB-based models remains unclear. We tested the ETa computed with the modified 2-band Enhanced Vegetation Index (METEVI2) implemented in the Google Earth Engine – for mapping croplands’ water use dynamics in the Lower Colorado River Basin. We compared METEVI2 with the well-established RS-based products of OpenET (Ensemble, eeMETRIC, SSEBop, SIMS, PT_JPL, DisALEXI and geeSEBAL). METEVI2 was then evaluated with measured ETa from four wheat fields (2017–2018). Results indicated that the monthly ETa variations for METEVI2 and OpenET models were comparable, though of varying magnitudes. On average, METEVI2 had the lowest difference rate from the average observed ETa with 17 mm underestimation, while SIMS had the highest difference rate (82 mm). Findings show that METEVI2 is a cost-effective ETa mapping tool in drylands to track crop water use. Future studies should test METEVI2’s applicability to croplands in more humid regions.

Lower Colorado River basin↗