USGS Science⌕ Search

SEARCH · USGS Science

Results for “Sensors”

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 919 records · Page 51Linked to original sources
USGS2008

Eddy correlation measurements of submarine groundwater discharge

This paper presents a new, non-invasive means of quantifying groundwater discharge into marine waters using an eddy correlation approach. The method takes advantage of the fact that, in virtually all aquatic environments, the dominant mode of vertical transport near the sediment–water interface is turbulent mixing. The technique thus relies on measuring simultaneously the fluctuating vertical velocity using an acoustic Doppler velocimeter and the fluctuating salinity and/or temperature using rapid-response conductivity and/or temperature sensors. The measurements are typically done at a height of 5–15 cm above the sediment surface, at a frequency of 16 to 64 Hz, and for a period of 15 to 60 min. If the groundwater salinity and/or temperature differ from that of the water column, the groundwater specific discharge (cm d− 1) can be quantified from either a heat or salt balance. Groundwater discharge was estimated with this new approach in Salt Pond, a small estuary on Cape Cod (MA, USA). Estimates agreed well with previous estimates of discharge measured using seepage meters and 222Rn as a tracer. The eddy correlation technique has several desirable characteristics: 1) discharge is quantified under in-situ hydrodynamic conditions; 2) salinity and temperature can serve as two semi-independent tracers of discharge; 3) discharge can be quantified at high temporal resolution, and 4) long-term records of discharge may be possible, due to the low power requirements of the instrumentation.

Massachussetts↗
USGS2018

Deciphering the dynamics of inorganic carbon export from intertidal salt marshes using high-frequency measurements

The lateral export of carbon from coastal marshes via tidal exchange is a key component of the marsh carbon budget and coastal carbon cycles. However, the magnitude of this export has been difficult to accurately quantify due to complex tidal dynamics and seasonal cycling of carbon. In this study, we use in situ , high-frequency measurements of dissolved inorganic carbon (DIC) and water fluxes to estimate lateral DIC fluxes from a U.S. northeastern salt marsh. DIC was measured by a CHANnelized Optical Sensor (CHANOS) that provided an in situ concentration measurement at 15-min intervals, during periods in summer (July – August) and late fall (December). Seasonal changes in the marsh had strong effects on DIC concentrations, while tidally-driven water fluxes were the fundamental vehicle of marsh carbon export. Episodic events, such as groundwater discharge and mean sea water level changes, can impact DIC flux through altered DIC concentrations and water flow. Variability between individual tides within each season was comparable to mean variability between the two seasons. Estimated mean DIC fluxes based on a multiple linear regression (MLR) model of DIC concentrations and high-frequency water fluxes agreed reasonably well with those derived from CHANOS DIC measurements for both study periods, indicating that high-frequency, modeled DIC concentrations, coupled with continuous water flux measurements and a hydrodynamic model, provide a robust estimate of DIC flux. Additionally, an analysis of sampling strategies revealed that DIC fluxes calculated using conventional sampling frequencies (hourly to two-hourly) of a single tidal cycle are unlikely to capture a representative mean DIC flux compared to longer-term measurements across multiple tidal cycles with sampling frequency on the order of tens of minutes. This results from a disproportionately large amount of the net DIC flux occurring over a small number of tidal cycles, while most tides have a near-zero DIC export. Thus, high-frequency measurements (on the order of tens of minutes or better) over the time period of interest are necessary to accurately quantify tidal exports of carbon species from salt marshes.

Marine Chemistry↗
USGS2012

Gulf of Mexico Gas Hydrate Joint Industry Project Leg II logging-while-drilling data acquisition and analysis

One of the objectives of the Gulf of Mexico Gas Hydrate Joint Industry Project Leg II (GOM JIP Leg II) was the collection of a comprehensive suite of logging-while-drilling (LWD) data within gas-hydrate-bearing sand reservoirs in order to make accurate estimates of the concentration of gas hydrates under various geologic conditions and to understand the geologic controls on the occurrence of gas hydrate at each of the sites drilled during this expedition. The LWD sensors just above the drill bit provided important information on the nature of the sediments and the occurrence of gas hydrate. There has been significant advancements in the use of downhole well-logging tools to acquire detailed information on the occurrence of gas hydrate in nature: From using electrical resistivity and acoustic logs to identify gas hydrate occurrences in wells to where wireline and advanced logging-while-drilling tools are routinely used to examine the petrophysical nature of gas hydrate reservoirs and the distribution and concentration of gas hydrates within various complex reservoir systems. Recent integrated sediment coring and well-log studies have confirmed that electrical resistivity and acoustic velocity data can yield accurate gas hydrate saturations in sediment grain supported (isotropic) systems such as sand reservoirs, but more advanced log analysis models are required to characterize gas hydrate in fractured (anisotropic) reservoir systems. In support of the GOM JIP Leg II effort, well-log data montages have been compiled and presented in this report which includes downhole logs obtained from all seven wells drilled during this expedition with a focus on identifying and characterizing the potential gas-hydrate-bearing sedimentary section in each of the wells. Also presented and reviewed in this report are the gas-hydrate saturation and sediment porosity logs for each of the wells as calculated from available downhole well logs.

Gulf Of Mexico↗
USGS2014

Oil source-fingerprinting in support of polarimetric radar mapping of Macondo-252 oil in Gulf Coast marshes

Polarimetric synthetic aperture radar (PolSAR) data exhibited dramatic, spatially extensive changes from June 2009 to June 2010 in Barataria Bay, Louisiana. To determine whether these changes were associated with the Deepwater Horizon (DWH) oil spill, twenty-nine sediment samples were collected in 2011 from shoreline and nearshore–interior coastal marsh locations where oil was not observed visually or with optical sensors during the spill. Oil source-fingerprinting and polytopic vector analysis were used to link DWH oil to PolSAR changes. Our results prove that DWH oil extended beyond shorelines and confirm the association between presence of DWH oil and PolSAR change. These results show that the DWH oil spill probably affected much more of the southeastern Louisiana marshland than originally concluded from ground and aerial surveys and verify that PolSAR is a powerful tool for tracking oil intrusion into marshes with high probability even where contamination is not visible from above the canopy.

Louisiana↗
USGS2026

Topography-aware registration refinement of Landsat 8/9 L1TP products with Sentinel-2 L1C and NAIP orthoimages

Satellite orthoimage products are widely available, yet residual geometric misregistration across independently processed products remains common, limiting their direct interoperability for multi-source and multi-temporal analysis. This paper introduces a residual Multi-Layer Perceptron (MLP)–based, topography-aware framework to further increase the registration quality of existing orthoimages based on dense matching without requiring any sensor-specific metadata or physical imaging parameters. We evaluate our approach by precisely co-registering 26 Landsat 8/9 Collection 2 Level-1 Terrain Precision (L1TP) scenes across Indiana (USA) and Berlin (Germany) with Sentinel-2 L1C and National Agriculture Imagery Program (NAIP) orthoimages. The baseline Landsat misregistration to Sentinel-2 or NAIP typically ranges from 3 to 10 m, which is reduced to 2–4 m after applying our MLP-based refinement, outperforming conventional polynomial and Rational Polynomial Coefficient (RPC) models. Further empirical evaluation demonstrates that consistent registration precision can be achieved across various terrain datasets; specifically, 30 m global digital elevation models (DEMs) yield comparable performance to that of high resolution (1–10 m) national or regional DEMs. The developed MLP approach provides a precise and reliable framework that transcends Landsat-specific tasks. It is readily adaptable to other registration applications requiring high-fidelity geometric alignment and consistent integration of heterogeneous image sources.

Indiana↗
USGS2009

Design of smart sensing components for volcano monitoring

In a volcano monitoring application, various geophysical and geochemical sensors generate continuous high-fidelity data, and there is a compelling need for real-time raw data for volcano eruption prediction research. It requires the network to support network synchronized sampling, online configurable sensing and situation awareness, which pose significant challenges on sensing component design. Ideally, the resource usages shall be driven by the environment and node situations, and the data quality is optimized under resource constraints. In this paper, we present our smart sensing component design, including hybrid time synchronization, configurable sensing, and situation awareness. Both design details and evaluation results are presented to show their efficiency. Although the presented design is for a volcano monitoring application, its design philosophy and framework can also apply to other similar applications and platforms. ?? 2009 Elsevier B.V.

Pervasive and Mobile Computing↗
USGS2008

The imaging performance of the SRC on Mars Express

The Mars Express spacecraft carries the pushbroom scanner high-resolution stereo camera (HRSC) and its added imaging subsystem super resolution channel (SRC). The SRC is equipped with its own optical system and a 1024×1024 framing sensor. SRC produces snapshots with 2.3 m ground pixel size from the nominal spacecraft pericenter height of 250 km, which are typically embedded in the central part of the large HRSC scenes. The salient features of the SRC are its light-weight optics, a reliable CCD detector, and high-speed read-out electronics. The quality and effective visibility of details in the SRC images unfortunately falls short of what has been expected. In cases where thermal balance cannot be reached, artifacts, such as blurring and “ghost features” are observed in the images. In addition, images show large numbers of blemish pixels and are plagued by electronic noise. As a consequence, we have developed various image improving algorithms, which are discussed in this paper. While results are encouraging, further studies of image restoration by dedicated processing appear worthwhile. The SRC has obtained more than 6940 images at the time of writing (1 September 2007), which often show fascinating details in surface morphology. SRC images are highly useful for a variety of applications in planetary geology, for studies of the Mars atmosphere, and for astrometric observations of the Martian satellites. This paper will give a full account of the design philosophy, technical concept, calibration, operation, integration with HRSC, and performance, as well as science accomplishments of the SRC.

Planetary and Space Science↗
USGS2011

A working environment for digital planetary data processing and mapping using ISIS and GRASS GIS

Since the beginning of planetary exploration, mapping has been fundamental to summarize observations returned by scientific missions. Sensor-based mapping has been used to highlight specific features from the planetary surfaces by means of processing. Interpretative mapping makes use of instrumental observations to produce thematic maps that summarize observations of actual data into a specific theme. Geologic maps, for example, are thematic interpretative maps that focus on the representation of materials and processes and their relative timing. The advancements in technology of the last 30 years have allowed us to develop specialized systems where the mapping process can be made entirely in the digital domain. The spread of networked computers on a global scale allowed the rapid propagation of software and digital data such that every researcher can now access digital mapping facilities on his desktop. The efforts to maintain planetary missions data accessible to the scientific community have led to the creation of standardized digital archives that facilitate the access to different datasets by software capable of processing these data from the raw level to the map projected one. Geographic Information Systems (GIS) have been developed to optimize the storage, the analysis, and the retrieval of spatially referenced Earth based environmental geodata; since the last decade these computer programs have become popular among the planetary science community, and recent mission data start to be distributed in formats compatible with these systems. Among all the systems developed for the analysis of planetary and spatially referenced data, we have created a working environment combining two software suites that have similar characteristics in their modular design, their development history, their policy of distribution and their support system. The first, the Integrated Software for Imagers and Spectrometers (ISIS) developed by the United States Geological Survey, represents the state of the art for processing planetary remote sensing data, from the raw unprocessed state to the map projected product. The second, the Geographic Resources Analysis Support System (GRASS) is a Geographic Information System developed by an international team of developers, and one of the core projects promoted by the Open Source Geospatial Foundation (OSGeo). We have worked on enabling the combined use of these software systems throughout the set-up of a common user interface, the unification of the cartographic reference system nomenclature and the minimization of data conversion. Both software packages are distributed with free open source licenses, as well as the source code, scripts and configuration files hereafter presented. In this paper we describe our work done to merge these working environments into a common one, where the user benefits from functionalities of both systems without the need to switch or transfer data from one software suite to the other one. Thereafter we provide an example of its usage in the handling of planetary data and the crafting of a digital geologic map. ?? 2010 Elsevier Ltd. All rights reserved.

Conference Paper↗
USGS2022

Ten practical questions to improve data quality

High-quality rangeland data are critical to supporting adaptive management. However, concrete, cost-saving steps to ensure data quality are often poorly defined and understood. Data quality is more than data management. Ensuring data quality requires 1) clear communication among team members; 2) appropriate sample design; 3) training of data collectors, data managers, and data users; 4) observer and sensor calibration; and 5) active data management. Quality assurance and quality control are ongoing processes to help rangeland managers and scientists identify, prevent, and correct errors in past, current, and future monitoring data. We present 10 guiding data quality questions to help managers and scientists identify appropriate workflows to improve data quality by 1) describing the data ecosystem, 2) creating a data quality plan, 3) identifying roles and responsibilities, 4) building data collection and data management workflows, 5) training and calibrating data collectors, 6) detecting and correcting errors, and 7) describing sources of variability. Iteratively improving rangeland data quality is a key part of adaptive monitoring and rangeland data collection. All members of the rangeland community are invited to participate in ensuring rangeland data quality.

Rangelands↗
USGS2024

The influence of grazing on the spatiotemporal activity patterns of a primary sage-grouse nest predator

Perturbations in ecological processes can occur when wildlife alter their spatiotemporal activity patterns to avoid human activities that they perceive as a risk. Such perturbations can have cascading effects throughout wildlife communities. For greater sage-grouse ( Centrocercus urophasianus ; hereafter sage-grouse), nest predation plays an important role in population dynamics. Domestic cattle ( Bos taurus ) grazing has been hypothesized to increase nest predation by reducing grass height, and therefore reducing nest concealment, which may facilitate nest detection by predators. Grass height is lower on grazed pastures, but sage-grouse nest success appears similar on pastures grazed at varying intensities in several recent studies. Any reductions in nest concealment caused by grazing could potentially be offset by a localized response of one or more nest predators to the presence of cattle (i.e., the cattle avoidance hypothesis). A reduction in nest predator density or relative use within pastures could explain similar patterns of nest success on pastures grazed at varying intensities. Also, wildlife can potentially partition themselves temporally to avoid risks associated with human activities. For example, a shift in diel activity patterns by nest predators in response to cattle could result in predators being active during portions of the day when they are less efficient at locating sage-grouse nests. Thus, the effects of grazing could be offset by a temporal avoidance of cattle by predators. We deployed motion sensor cameras across six pastures to evaluate whether coyotes ( Canis latrans ; a primary sage-grouse nest predator) altered spatiotemporal activity patterns in response to cattle. We found that the probability of detecting coyotes had a positive relationship with cattle detections at camera sites (β = 0.22; 95% CI = 0.14,0.30). We also found that coyotes did not shift their diel activity patterns in response to cattle being in the pastures. Thus, in our system, similar sage-grouse nest success among pastures with different grazing intensities cannot be explained by the cattle avoidance hypothesis, at least for coyotes.

Idaho↗
USGS2019

Assessing beach and island habitat loss in the Chesapeake Bay and Delmarva coastal bay region, USA, through processing of Landsat TM and OLI imagery: A case study

Beaches and islands provide economic value to humans and critical habitat for breeding and foraging wildlife. These ecosystems, however, are being severely impacted by global climate change and sea level rise through increased erosion and frequency of inundation. The case study presented here aimed to document island loss in the Chesapeake Bay and Delmarva coastal bay region of the United States using image processing techniques within a GIS from 1986 to 2016. Satellite imagery from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) sensors were processed within ArcMap 10.5 to determine spatial and temporal trends in island and beach habitat. Calculation of unweighted Cohen’s Kappa showed that classified scenes were, on average, within the range of moderate agreement between the classified Landsat scenes and the validation imagery within Google Earth (0.539). Recommendations regarding existing beach habitat management and future supplementation were created based on these results. From 1986 to 2016, island area declined by over 1,200 hectares (ha) with agriculture/open field (all open vegetated spaces) declining by nearly 82% and beach, surprisingly, increasing nearly 2%. This study was the first to document Chesapeake Bay region-wide island loss beyond the mid-2000s. The accuracy of this study was limited slightly by the 30 m spatial resolution of the imagery used. This technique may be best suited for documenting trends on large islands and along the mainland coastline.

Maryland, Virginia↗
USGS2003

Analysis of potential debris flow source areas on Mount Shasta, California, by using airborne and satellite remote sensing data

Remote sensing data from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the first spaceborne imaging spectrometer, Hyperion, show hydrothermally altered rocks mainly composed of natroalunite, kaolinite, cristobalite, and gypsum on both the Mount Shasta and Shastina cones. Field observations indicate that much of the visible altered rock consists of talus material derived from fractured rock zones within and adjacent to dacitic domes and nearby lava flows. Digital elevation data were utilized to distinguish steeply sloping altered bedrock from more gently sloping talus materials. Volume modeling based on the imagery and digital elevation data indicate that Mount Shasta drainage systems contain moderate volumes of altered rock, a result that is consistent with Mount Shasta's Holocene record of mostly small to moderate debris flows. Similar modeling for selected areas at Mount Rainier and Mount Adams, Washington, indicates larger altered rock volumes consistent with the occurrence of much larger Holocene debris flows at those volcanoes. The availability of digital elevation and spectral data from spaceborne sensors, such as Hyperion and the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), greatly expands opportunities for studying potential debris flow source characteristics at stratovolcanoes around the world. ?? 2003 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2004

Land cover mapping of North and Central America—Global Land Cover 2000

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.

Remote Sensing of Environment↗
USGS2004

Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm

One of the major challenges in determining snow depth (SD) from passive microwave measurements is to take into account the spatiotemporal variations of the snow grain size. Static algorithms based on a constant snow grain size cannot provide accurate estimates of snow pack thickness, particularly over large regions where the snow pack is subjected to big spatial temperature variations. A recent dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from the Special Sensor Microwave/Imager (SSM/I) over the Northern Great Plains (NGP) in the US. In this paper, we develop a combined dynamic and static algorithm to estimate snow depth from 13 years of SSM/I observations over Central Siberia. This region is characterised by extremely cold surface air temperatures and by the presence of permafrost that significantly affects the ground temperature. The dynamic algorithm is implemented to take into account these effects and it yields accurate snow depths early in the winter, when thin snowpacks combine with cold air temperatures to generate rapid crystal growth. However, it is not applicable later in the winter when the grain size growth slows. Combining the dynamic algorithm to a static algorithm, with a temporally constant but spatially varying coefficient, we obtain reasonable snow depth estimates throughout the entire snow season. Validation is carried out by comparing the satellite snow depth monthly averages to monthly climatological data. We show that the location of the snow depth maxima and minima is improved when applying the combined algorithm, since its dynamic portion explicitly incorporate the thermal gradient through the snowpack. The results obtained are presented and evaluated for five different vegetation zones of Central Siberia. Comparison with in situ measurements is also shown and discussed. ?? 2004 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2005

Mineral mapping on the Chilean-Bolivian Altiplano using co-orbital ALI, ASTER and Hyperion imagery: Data dimensionality issues and solutions

Hyperspectral data coverage from the EO-1 Hyperion sensor was useful for calibrating Advanced Land Imager (ALI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images of a volcanic terrane area of the Chilean-Bolivian Altiplano. Following calibration, the ALI and ASTER datasets were co-registered and joined to produce a 13-channel reflectance cube spanning the Visible to Short Wave Infrared (0.4-2.4 ??m). Eigen analysis and comparison of the Hyperion data with the ALI + ASTER reflectance data, as well as mapping results using various ALI+ASTER data subsets, provided insights into the information dimensionality of all the data. In particular, high spectral resolution, low signal-to-noise Hyperion data were only marginally better for mineral mapping than the merged 13-channel, low spectral resolution, high signal-to-noise ALI + ASTER dataset. Neither the Hyperion nor the combined ALI + ASTER datasets had sufficient information dimensionality for mapping the diverse range of surface materials exposed on the Altiplano. However, it is possible to optimize the use of the multispectral data for mineral-mapping purposes by careful data subsetting, and by employing other appropriate image-processing strategies.

Remote Sensing of Environment↗
USGS2007

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub–shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.

Remote Sensing of Environment↗
USGS2007

Spectral reflectance and emissivity features of broad leaf plants: Prospects for remote sensing in the thermal infrared (8.0-14.0 μm)

Field emissivity measurements were made of leaves collected from nine deciduous tree and agricultural plant species. The data show, for the first time, that it is possible to discriminate subtle spectral emissivity features of leaves from the natural background emission. Under conditions of controlled measurement geometry (leaves arranged to cover a flat surface), the field emissivity spectra agreed fairly well with emissivity values calculated from laboratory directional hemispherical reflectance measurements. Spectral features associated with a variety of leaf chemical constituents, including cellulose, cutin, xylan, silica, and oleanolic acid could be identified in the field emissivity data. Structural aspects of leaf surfaces also influenced spectral behavior, notably the abundance of trichomes, as well as wax thickness and texture. Field spectral measurements made at increasing distances from natural plant canopies showed progressive attenuation of the spectral emissivity features. This attenuation is ascribed to increased multiple scattering that superimposes an opposite-in-sign reflected component on the emittance, and to the increasing number of canopy voids within the instrument field of view. Errors associated with the removal of atmospheric features and with the non-isotropic thermal characteristics of canopies also contribute to the loss of spectral information at greater measurement distances. In contrast to visible and short-wave infrared data, thermal infrared spectra of broad leaf plants show considerable spectral diversity, suggesting that such data eventually could be utilized to map vegetation composition. However, remotely measuring the subtle emissivity features of leaves still presents major challenges. To be successful, sensors operating in the 8–14 μm atmospheric window must have high signal-to-noise and a small enough instantaneous field of view to allow measurements of only a few leaf surfaces. Methods for atmospheric compensation, temperature–emissivity separation, and spectral feature analysis also will need to be refined to allow the recognition, and perhaps, exploitation of leaf thermal infrared spectral properties.

Virginia↗
USGS2008

Detection rates of the MODIS active fire product in the United States

MODIS active fire data offer new information about global fire patterns. However, uncertainties in detection rates can render satellite-derived fire statistics difficult to interpret. We evaluated the MODIS 1??km daily active fire product to quantify detection rates for both Terra and Aqua MODIS sensors, examined how cloud cover and fire size affected detection rates, and estimated how detection rates varied across the United States. MODIS active fire detections were compared to 361 reference fires (??? 18??ha) that had been delineated using pre- and post-fire Landsat imagery. Reference fires were considered detected if at least one MODIS active fire pixel occurred within 1??km of the edge of the fire. When active fire data from both Aqua and Terra were combined, 82% of all reference fires were found, but detection rates were less for Aqua and Terra individually (73% and 66% respectively). Fires not detected generally had more cloudy days, but not when the Aqua data were considered exclusively. MODIS detection rates decreased with fire size, and the size at which 50% of all fires were detected was 105??ha when combining Aqua and Terra (195??ha for Aqua and 334??ha for Terra alone). Across the United States, detection rates were greatest in the West, lower in the Great Plains, and lowest in the East. The MODIS active fire product captures large fires in the U.S. well, but may under-represent fires in areas with frequent cloud cover or rapidly burning, small, and low-intensity fires. We recommend that users of the MODIS active fire data perform individual validations to ensure that all relevant fires are included. ?? 2008 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗