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At least 307 records · Page 17Linked to original sources
USGS2026

On-demand global Landsat evapotranspiration product: Development, evaluation, and dissemination

Global actual evapotranspiration (ET) is one of the essential climate variables needed to understand and manage the relationships among food, energy, and water resources. The U.S. Geological Survey Earth Resources Observation and Science (EROS) Center launched a provisional ET product in 2020, offering on-demand, field-scale global coverage derived from Landsat data through the EROS Science Processing Architecture (ESPA) platform. The ESPA interface provides ET data for cloud-free Landsat overpasses starting in 1982 with Landsat 4 through the current Landsat 9. The ET data are delivered as a Provisional Level-3 Science product created using the Operational Simplified Surface Energy Balance (SSEBop) model. Landsat surface temperature and reference ET are the main model drivers along with vegetation index and net radiation for model parameterization. A large volume of Landsat-based ET orders (e.g., over 1,200,000 images from June 2020 through December 2025) around the world indicate increasing awareness and application of the ET data. The ESPA platform enables land and water resource managers and researchers to access a first-order ET product without requiring advanced knowledge of remote sensing technology or evapotranspiration modeling. We present the methodology and workflow of the on-demand Landsat ET product and its performance evaluations over diverse hydro-climatic settings. The product can help estimate field-scale consumptive water use and thus quickly and consistently assess historical water use, allocation, and budget to inform water management under changing environments. Future ET data aggregated to monthly and seasonal time scales are expected to enhance integration with decision-making tools and procedures.

Remote Sensing of Environment↗
USGS2017

Cloud detection algorithm comparison and validation for operational Landsat data products

Clouds are a pervasive and unavoidable issue in satellite-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. The Landsat project is uniquely suited for comparative validation of cloud assessment algorithms because the modular architecture of the Landsat ground system allows for quick evaluation of new code, and because Landsat has the most comprehensive manual truth masks of any current satellite data archive. Currently, the Landsat Level-1 Product Generation System (LPGS) uses separate algorithms for determining clouds, cirrus clouds, and snow and/or ice probability on a per-pixel basis. With more bands onboard the Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite, and a greater number of cloud masking algorithms, the U.S. Geological Survey (USGS) is replacing the current cloud masking workflow with a more robust algorithm that is capable of working across multiple Landsat sensors with minimal modification. Because of the inherent error from stray light and intermittent data availability of TIRS, these algorithms need to operate both with and without thermal data. In this study, we created a workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus (ETM +) and Landsat 8 OLI/TIRS data. We created a new validation dataset consisting of 96 Landsat 8 scenes, representing different biomes and proportions of cloud cover. We evaluated algorithm performance by overall accuracy, omission error, and commission error for both cloud and cloud shadow. We found that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using our validation data. The Artificial Thermal-Automated Cloud Cover Algorithm (AT-ACCA) is the most accurate nonthermal-based algorithm. We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.

Remote Sensing of Environment↗
USGS1972

ERTS and EROS

In June the National Aeronautics & Space Administration is to launch its first experimental satellite designed to view the Earth systematically with remote-sensing instruments that will provide new information about our resources and environment. The launching will culminate more than 8 years of planning and research by resource agencies of the Federal Government in cooperation with NASA, state and local governments, universities, and industry. The first Earth Resources Technology Satellite, ERTS-A, will be followed a year later by ERTS-B. Analyses of data from them, it is hoped, will lead to design of operational satellites for Earth resources investigations in the future. In the belief that satellite systems will be of significant assistance in meeting its responsibilities to map, monitor, and manage the vast resources and the public lands of the United States, the Department of the Interior assumed a major role in the ERTS-A Experiment.

Geotimes↗
USGS2026

Evaluating snow depth measurements from ground-penetrating radar and airborne lidar in boreal forest and tundra environments during the NASA SnowEx 2023 campaign

Snow is a vital component of high-latitude terrestrial systems, but environmental factors (e.g., permafrost) and complex vegetation challenge the accurate measurement of key snowpack properties. We evaluated local-scale ground-penetrating radar (GPR) and large-scale airborne lidar retrievals of snow depth collected during the NASA SnowEx 2023 campaign in tundra and boreal forest environments in Alaska along 44 short (3–12 m) transects. Compared to in situ observations, we identified modest biases for GPR snow depths (bias <0.03 m in tundra, +0.06 m in boreal forests) and larger biases for lidar snow depths in the boreal forests (–0.16 m). At the Upper Kuparuk-Toolik tundra site, lidar snow depths exhibited a small bias (–0.02 m), whereas the bias was much larger at the Arctic Coastal Plain tundra site (+0.19 m). For most sites, biases were primarily related to sub-snow vegetation, tussocks, and seasonally dynamic ground. However, we identified vertical alignment issues with the Arctic Coastal Plain lidar snow depth dataset that likely contributed to the higher bias. The complex ground surface and sub-snow vegetation in these environments present a challenge to established snow depth measurement methods, which needs to be considered when evaluating novel remote sensing approaches.

Alaska↗
USGS2019

Validating a time series of annual grass percent cover in the sagebrush ecosystem

We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of these fuels and can serve as a tool to assist land managers and scientists in understanding the ecosystem’s response to weather variations, disturbances, and management. Validating the time series of annual maps is important for determining the usefulness of the data. To validate these maps, we compare Bureau of Land Management Assessment Inventory and Monitoring (AIM) data to mapped estimates and use a leave-one-out spatial assessment technique that is effective for validating maps that cover broad geographical extents. We hypothesize that the time series of annual maps exhibits high spatiotemporal variability because precipitation is highly variable in arid and semiarid environments where sagebrush is native, and invasive annual grasses respond to precipitation. The remotely sensed data that help drive our regression-tree model effectively measures annual grasses’ response to precipitation. The mean absolute error (MAE) rate varied depending on the validation data and technique used for comparison. The AIM plot data and our maps had substantial spatial incongruence, but despite this, the MAE rate for the assessment equaled 12.62%. The leave-one-out accuracy assessment had an MAE of 8.43%. We quantified bias, and bias was more substantial at higher percent cover. These annual maps can help management identify actions that may alleviate the current cycle of invasive grasses because it enables the assessment of the variability of annual grass − percent cover distribution through space and time, as part of dynamic systems rather than static systems.

Rangeland Ecology and Management↗
USGS2020

A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery

Crop emergence is a critical stage for crop development and crop growth modeling. Mapping crop emergence using remote sensing data is challenging. Previous remote sensing phenology algorithms showed that crop stages could be detected around the V3-V4 (3 to 4 established leaves) vegetative stage. Traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for the within-season mapping in the early growing season. In the current paper, we developed a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2 day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENµS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote sensing green-up dates were about 4-5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5 day revisit) in BARC. The detected green-up dates from Sentinel-2 were found close to VENµS results. Some fields were not detected due to the lack of observations during emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENµS and the harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3-4 day revisit). The green-up dates were compared with crop progress reports of crop emergence dates from the National Agricultural Statistics Service (NASS) at the state-level. The WISE -detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data.

Maryland↗
USGS2013

Spectroscopic remote sensing of the distribution and persistence of oil from the Deepwater Horizon spill in Barataria Bay marshes

We applied a spectroscopic analysis to Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) data collected from low and medium altitudes during and after the Deepwater Horizon oil spill to delineate the distribution of oil-damaged canopies in the marshes of Barataria Bay, Louisiana. Spectral feature analysis compared the AVIRIS data to reference spectra of oiled marsh by using absorption features centered near 1.7 and 2.3 μm, which arise from CH bonds in oil. AVIRIS-derived maps of oiled shorelines from the individual dates of July 31, September 14, and October 4, 2010, were 89.3%, 89.8%, and 90.6% accurate, respectively. A composite map at 3.5 m grid spacing, accumulated from the three dates, was 93.4% accurate in detecting oiled shorelines. The composite map had 100% accuracy for detecting damaged plant canopy in oiled areas that extended more than 1.2 m into the marsh. Spatial resampling of the AVIRIS data to 30 m reduced the accuracy to 73.6% overall. However, detection accuracy remained high for oiled canopies that extended more than 4 m into the marsh (23 of 28 field reference points with oil were detected). Spectral resampling of the 3.5 m AVIRIS data to Landsat Enhanced Thematic Mapper (ETM) spectral response greatly reduced the detection of oil spectral signatures. With spatial resampling of simulated Landsat ETM data to 30 m, oil signatures were not detected. Overall, ~ 40 km of coastline, marsh comprised mainly of Spartina alterniflora and Juncus roemerianus, were found to be oiled in narrow zones at the shorelines. Zones of oiled canopies reached on average 11 m into the marsh, with a maximum reach of 21 m. The field and airborne data showed that, in many areas, weathered oil persisted in the marsh from the first field survey, July 10, to the latest airborne survey, October 4, 2010. The results demonstrate the applicability of high spatial resolution imaging spectrometer data to identifying contaminants in the environment for use in evaluating ecosystem disturbance and response.

Louisiana↗
USGS2006

Historical record of Landsat global coverage

The long-term, 34+ year record of global Landsat remote sensing data is a critical resource to study the Earth system and human impacts on this system. The National Satellite Land Remote Sensing Data Archive (NSLRSDA) is charged by public law to: “maintain a permanent, comprehensive Government archive of global Landsat and other land remote sensing data for long-term monitoring and study of the changing global environment” (U.S. Congress, 1992). The advisory committee for NSLRSDA requested a detailed analysis of observation coverage within the U.S. Landsat holdings, as well as that acquired and held by International Cooperator (IC) stations. Our analyses, to date, have found gaps of varying magnitude in U.S. holdings of Landsat global coverage data, which appear to reflect technical or administrative variations in mission operations. In many cases it may be possible to partially fill these gaps in U.S. holdings through observations that were acquired and are now being held at International Cooperator stations.

Photogrammetric Engineering and Remote Sensing↗
USGS2019

Assessment of the impacts of image signal-to-noise ratios in impervious surface mapping

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

Remote Sensing↗
USGS2003

Status and interconnections of selected environmental issues in the global coastal zones

This study focuses on assessing the state of population distribution, land cover distribution, biodiversity hotspots, and protected areas in global coastal zones. The coastal zone is defined as land within 100 km of the coastline. This study attempts to answer such questions as: how crowded are the coastal zones, what is the pattern of land cover distribution in these areas, how much of these areas are designated as protected areas, what is the state of the biodiversity hotspots, and what are the interconnections between people and coastal environment. This study uses globally consistent and comprehensive geospatial datasets based on remote sensing and other sources. The application of Geographic Information System (GIS) layering methods and consistent datasets has made it possible to identify and quantify selected coastal zones environmental issues and their interconnections. It is expected that such information provide a scientific basis for global coastal zones management and assist in policy formulations at the national and international levels.

Ambio↗
USGS1976

ERTS-1, a new window on our planet

The launch, on July 23, 1972, of the first Earth Resources Technology Satellite (ERTS-1) by the National Aeronautics and Space Administration was a major step forward in extending man 's ability to inventory the Earth 's resources and to evaluate objectively his impact upon the environment. ERTS spacecraft represent the first step in merging space and remote-sensing technologies into a system for inventorying and managing the Earth 's resources. Examples presented in this book demonstrate ERTS ' vast potential for inventorying resources, monitoring environmental conditions, and measuring changes. Such information is essential for the full evaluation of the Federal lands and determining their future use, as well as for improved planning of overall land use throughout the United States and the world. Ten bureaus of the U.S. Department of Interior have roles in the ERTS project. Nearly all of these participating bureaus are represented in almost 100 papers included in this book. Chapter 3 is entitled ' Applications to Water Resources ' and contains 23 separate sections. (Woodard-USGS)

Professional Paper↗
USGS2014

Experimental design and quality assurance: in situ fluorescence instrumentation

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

Book chapter↗
USGS2017

UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA

Forest vegetation classification and structure measurements are fundamental steps for planning, monitoring, and evaluating large-scale forest changes including restoration treatments. High spatial and spectral resolution remote sensing data are critically needed to classify vegetation and measure their 3-dimensional (3D) canopy structure at the level of individual species. Here we test high-resolution lidar, hyperspectral, and multispectral data collected from unmanned aerial vehicles (UAV) and demonstrate a lidar-hyperspectral image fusion method in treated and control forests with varying tree density and canopy cover as well as in an ecotone environment to represent a gradient of vegetation and topography in northern Arizona, U.S.A. The fusion performs better (88% overall accuracy) than either data type alone, particularly for species with similar spectral signatures, but different canopy sizes. The lidar data provides estimates of individual tree height ( R 2 = 0.90; RMSE = 2.3 m) and crown diameter ( R 2 = 0.72; RMSE = 0.71 m) as well as total tree canopy cover ( R 2 = 0.87; RMSE = 9.5%) and tree density ( R 2 = 0.77; RMSE = 0.69 trees/cell) in 10 m cells across thin only, burn only, thin-and-burn, and control treatments, where tree cover and density ranged between 22 and 50% and 1–3.5 trees/cell, respectively. The lidar data also produces highly accurate digital elevation model (DEM) ( R 2 = 0.92; RMSE = 0.75 m). In comparison, 3D data derived from the multispectral data via structure-from-motion produced lower correlations with field-measured variables, especially in dense and structurally complex forests. The lidar, hyperspectral, and multispectral sensors, and the methods demonstrated here can be widely applied across a gradient of vegetation and topography for monitoring landscapes undergoing large-scale changes such as the forests in the southwestern U.S.A.

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↗
USGS2023

Discovery of an active forearc fault in an urban region: Holocene rupture on the XEOLXELEK-Elk Lake fault, Victoria, British Columbia, Canada

Subduction forearcs are subject to seismic hazard from upper plate faults that are often invisible to instrumental monitoring networks. Identifying active faults in forearcs therefore requires integration of geomorphic, geologic, and paleoseismic data. We demonstrate the utility of a combined approach in a densely populated region of Vancouver Island, Canada, by combining remote sensing, historical imagery, field investigations, and shallow geophysical surveys to identify a previously unrecognized active fault, the XEOLXELEK-Elk Lake fault, in the northern Cascadia forearc, ∼10 km north of the city of Victoria. Lidar-derived digital terrain models and historical air photos show a ∼2.5-m-high scarp along the surface of a Quaternary drumlinoid ridge. Paleoseismic trenching and electrical resistivity tomography surveys across the scarp reveal a single reverse-slip earthquake produced a fault-propagation fold above a blind southwest-dipping fault. Five geologically plausible chronological models of radiocarbon dated charcoal constrain the likely earthquake age to between 4.7 and 2.3 ka. Fault-propagation fold modeling indicates ∼3.2 m of reverse slip on a blind, 50° southwest-dipping fault can reproduce the observed deformation. Fault scaling relations suggest a M 6.1–7.6 earthquake with a 13 to 73-km-long surface rupture and 2.3–3.2 m of dip slip may be responsible for the deformation observed in the paleoseismic trench. An earthquake near this magnitude in Greater Victoria could result in major damage, and our results highlight the importance of augmenting instrumental monitoring networks with remote sensing and field studies to identify and characterize active faults in similarily challenging environments.

Victoria↗
USGS2002

Remotely sensed geology from lander-based to orbital perspectives: Results of FIDO rover May 2000 field tests

Blind field tests of the Field Integration Design and Operations (FIDO) prototype Mars rover were carried out 7-16 May 2000. A Core Operations Team (COT), sequestered at the Jet Propulsion Laboratory without knowledge of test site location, prepared command sequences and interpreted data acquired by the rover. Instrument sensors included a stereo panoramic camera, navigational and hazard-avoidance cameras, a color microscopic imager, an infrared point spectrometer, and a rock coring drill. The COT designed command sequences, which were relayed by satellite uplink to the rover, and evaluated instrument data. Using aerial photos and Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data, and information from the rover sensors, the COT inferred the geology of the landing site during the 18 sol mission, including lithologic diversity, stratigraphic relationships, environments of deposition, and weathering characteristics. Prominent lithologic units were interpreted to be dolomite-bearing rocks, kaolinite-bearing altered felsic volcanic materials, and basalt. The color panoramic camera revealed sedimentary layering and rock textures, and geologic relationships seen in rock exposures. The infrared point spectrometer permitted identification of prominent carbonate and kaolinite spectral features and permitted correlations to outcrops that could not be reached by the rover. The color microscopic imager revealed fine-scale rock textures, soil components, and results of coring experiments. Test results show that close-up interrogation of rocks is essential to investigations of geologic environments and that observations must include scales ranging from individual boulders and outcrops (microscopic, macroscopic) to orbital remote sensing, with sufficient intermediate steps (descent images) to connect in situ and remote observations.

Journal of Geophysical Research E: Planets↗
USGS2008

Heat as a tracer to determine streambed water exchanges

This work reviews the use of heat as a tracer of shallow groundwater movement and describes current temperature-based approaches for estimating streambed water exchanges. Four common hydrologic conditions in stream channels are graphically depicted with the expected underlying streambed thermal responses, and techniques are discussed for installing and monitoring temperature and stage equipment for a range of hydrological environments. These techniques are divided into direct-measurement techniques in streams and streambeds, groundwater techniques relying on traditional observation wells, and remote sensing and other large-scale advanced temperature-acquisition techniques. A review of relevant literature suggests researchers often graphically visualize temperature data to enhance conceptual models of heat and water flow in the near-stream environment and to determine site-specific approaches of data analysis. Common visualizations of stream and streambed temperature patterns include thermographs, temperature envelopes, and one-, two-, and three-dimensional temperature contour plots. Heat and water transport governing equations are presented for the case of transport in streambeds, followed by methods of streambed data analysis, including simple heat-pulse arrival time and heat-loss procedures, analytical and time series solutions, and heat and water transport simulation models. A series of applications of these methods are presented for a variety of stream settings ranging from arid to continental climates. Progressive successes to quantify both streambed fluxes and the spatial extent of streambeds indicate heat-tracing tools help define the streambed as a spatially distinct field (analogous to soil science), rather than simply the lower boundary in stream research or an amorphous zone beneath the stream channel.

Water Resources Research↗
USGS2008

View_SPECPR: Software for plotting spectra (installation manual and user's guide, version 1.2)

This document describes procedures for installing and using the 'View_SPECPR' software system to plot spectra stored in SPECPR (SPECtrum Processing Routines) files. The View_SPECPR software is comprised of programs written in IDL (Interactive Data Language) that run within the ENVI (ENvironment for Visualizing Images) image processing system. SPECPR files are used by earth-remote-sensing scientists and planetary scientists for storing spectra collected by laboratory, field, and remote sensing instruments. A widely distributed SPECPR file is the U.S. Geological Survey (USGS) spectral library that contains thousands of spectra of minerals, vegetation, and man-made materials (Clark and others, 2007). SPECPR files contain reflectance data and associated wavelength and spectral resolution data, as well as meta-data on the time and date of collection and spectrometer settings. Furthermore, the SPECPR file automatically tracks changes to data records through its 'history' fields. For more details on the format and content of SPECPR files, see Clark (1993). For more details on ENVI, see ITT (2008). This program has been updated using an ENVI 4.5/IDL7.0 full license operating on a Windows XP operating system and requires the installation of the iTools components of IDL7.0; however, this program should work with full licenses on UNIX/LINUX systems. This software has not been tested with ENVI licenses on Windows Vista or Apple Operating Systems.

Open-File Report↗