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USGS2009

Cacades: A reliable dissemination protocol for data collection sensor network

In this paper, we propose a fast and reliable data dissemination protocol Cascades to disseminate data from the sink(base station) to all or a subset of nodes in a data collection sensor network. Cascades makes use of the parentmonitor-children analogy to ensure reliable dissemination. Each node monitors whether or not its children have received the broadcast messages through snooping children's rebroadcasts or waiting for explicit ACKs. If a node detects a gap in its message sequences, it can fetch the missing messages from its neighbours reactively. Cascades also considers many practical issues for field deployment, such as dynamic topology, link/node failure, etc.. It therefore guarantees that a disseminated message from the sink will reach all intended receivers and the dissemination is terminated in a short time period. Notice that, all existing dissemination protocols either do not guarantee reliability or do not terminate [1, 2], which does not meet the requirement of real-time command control. We conducted experiment evaluations in both TOSSIM simulator and a sensor network testbed to compare Cascades with those existing dissemination protocols in TinyOS sensor networks, which show that Cascades achieves a higher degree of reliability, lower communication cost, and less delivery delay. ??2009 IEEE.

Conference Paper↗

USGS2004

Landsat sensor performance: history and current status

The current Thematic Mapper (TM) class of Landsat sensors began with Landsat-4, which was launched in 1982. This series continued with the nearly identical sensor on Landsat-5, launched in 1984. The final sensor in the series was the Landsat-7 Enhanced Thematic Mapper Plus (ETM+), which was carried into orbit in 1999. Varying degrees of effort have been devoted to the characterization of these instruments and data over the past 22 years. Extensive short-lived efforts early in the history, very limited efforts in the middle years, and now a systematic program for continuing characterization of all three systems are apparent. Currently, both the Landsat-5 TM and the Landsat-7 ETM+ are operational and providing data. Despite 20+ years of operation, the TM on Landsat-5 is fully functional, although downlinks for the data are limited. Landsat-7 ETM+ experienced a failure of its Scan Line Corrector mechanism in May 2003. Although there are gaps in the data coverage, the data remain of equivalent quality to prefailure data. Data products have been developed to fill these gaps using other ETM+ scenes.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2012

Dark and background response stability for the Landsat 8 Thermal Infrared Sensor

The Thermal Infrared Sensor (TIRS) is a pushbroom sensor that will be a part of the Landsat Data Continuity Mission (LDCM), which is a joint mission between NASA and the USGS. The TIRS instrument will continue to collect the thermal infrared data that are currently being collected by the Thematic Mapper and the Enhanced Thematic Mapper Plus on Landsats 5 and 7, respectively. One of the key requirements of the new sensor is that the dark and background response be stable to ensure proper data continuity from the legacy Landsat instruments. Pre launch testing of the instrument has recently been completed at the NASA Goddard Space Flight Center (GSFC), which included calibration collects that mimic those that will be performed on orbit. These collects include images of a cold plate meant to simulate the deep space calibration source as viewed by the instrument in flight. The data from these collects give insight into the stability of the instrument’s dark and background response, as well as factors that may cause these responses to vary. This paper quantifies the measured background and dark response of TIRS as well as its stability.

Conference Paper↗

USGS2014

Comparison of simulated HyspIRI with two multispectral sensors for invasive species mapping

This paper assesses the potential of a single HYSPIRI scene to estimate cover of the non-native invasive buffelgrass (Pennisetum ciliare) in a heterogeneous Sonoran Desert scrub ecosystem. We simulated HYSPIRI (60 m) along with two multispectral sensors, Thematic Mapper (TM; 30 m) and Advanced Space-borne Thermal Emission and Reflection Spectrometer (ASTER; 15 m), from high-resolution Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 3.2 m) imagery in an area infested by buffelgrass near Tucson, Arizona. We compared classification accuracies of all simulated sensors at spatial resolutions of 15 m, 30 m, and 60 m to evaluate tradeoffs of spectral and spatial resolution across the sensors. Although spectroscopically superior to Landsat TM and ASTER, ASTER easily outperformed HYSPIRI for small infestations (225 m 2 ) on account of its spatial resolution. Shortwave-infrared bands near 2.2 µm were key indicators for both HYSPIRI and ASTER, highlighting the benefit of narrow-wave SWIR for mapping invasive species in arid ecosystems.

Photogrammetric Engineering and Remote Sensing↗

USGS2010

Method for calculating self-noise spectra and operating ranges for seismographic inertial sensors and recorders

Understanding the performance of sensors and recorders is prerequisite to making appropriate use of them in seismology and earthquake engineering. This paper explores a critical aspect of instrument performance, the “self” noise level of the device and the amplitude range it can usefully record. Self noise limits the smallest signals, while instrument clipping level creates the upper limit (above which it either cannot produce signals or becomes unacceptably nonlinear). Where these levels fall, and the “operating range” between them, determines much of the instrument's viability and the applications for which it is appropriate. The representation of seismic-instrument self-noise levels and their effective operating ranges (cf., dynamic range) for seismological inertial sensors, recorders (data acquisition units, or DAUs), and integrated systems of sensors and recorders (data acquisition systems, or DASs) forces one to address an unnatural comparison between transient finite-bandwidth signals, such as earthquake records, and the instrument's self noise, an effectively stationary signal of infinite duration. In addition to being transient, earthquakes and other records of interest are characterized by a peak amplitude and generally a narrow, peaked spectral shape. Unfortunately, any power spectrum computed for such transient signals is ill defined, since the maximum of that spectrum depends strongly upon signal and record durations. In contrast, the noise floor of an instrument is approximately stationary and properly described by a power spectral density (PSD) or its root (rPSD). Put another way, earthquake records have units of amplitude ( e.g. , m/s 2 ) while PSDs have units of amplitude-squared per hertz ( e.g. , (m/s 2 ) 2 /Hz) and the rPSD has units of amplitude per root of hertz ( e.g. , (m/s 2 )/Hz 1/2 ). Thus, this incompatability is a conflict between earthquake (amplitude) and PSD (spectral density) units that requires one to make various assumptions before they can be compared. For purposes of instrument operational performance, we provide a means of evaluating signal and noise and the range between them in a manner representative of time-domain instrument performance. We call these “operating range diagrams” (ORDs), plots of instrument self noise and clipping level; the “operating range” is the range between these values. For frequency-domain performance we elect to show self noise as an rPSD that may be compared to another instrument's noise or to ambient Earth noise ( e.g. , Peterson 1993); however, to limit the number of arbitrary choices required to merge transient and stationary signals we do not compare the rPSD to transient signals in the frequency domain. Our solution for a time-domain comparison is not new but rather builds upon the consensus of the first and second Guidelines for Seismometer Testing workshops (Hutt et al. 2009) and long established practice in acoustics. We propose this method as a standard for characterizing seismic instruments, and it has been endorsed by the second workshop (Hutt et al. 2009, 2010) and the Advanced National Seismic System (ANSS) Working Group (2008) and recent ANSS procurement specifications.

Seismological Research Letters↗

USGS2021

System characterization report on Resourcesat-2 Advanced Wide Field Sensor

Executive Summary This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Advanced Wide Field Sensor (AWiFS) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management. Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the AWiFS, Linear Imaging Self Scanning-3, and Linear Imaging Self Scanning-4 sensors for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that AWiFS has an interior geometric performance in the range of −16.080 (−0.268 pixel) to 35.520 meters (m; 0.592 pixel) in easting and −25.680 (−0.428 pixel) to 23.400 m (0.390 pixel) in northing in band-to-band registration, an exterior geometric error of −64.262 (−1.071 pixels) to −19.059 m (−0.318 pixel) in easting and −29.028 (−0.484 pixel) to 41.249 m (0.687 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of 2.29–2.36 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.030–0.035.

Open-File Report↗

USGS2021

System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor

Executive Summary This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management. Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.036 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.

Open-File Report↗

USGS2024

Airborne lidar accuracy analysis for dual photogrammetric and lidar sensor pilot project in Colorado, 2019

This report presents accuracy assessment results of the light detection and ranging (lidar) data collected in Colorado during a pilot project in fall 2019. The purpose of the pilot project was to assess the accuracy of lidar and imagery data collected simultaneously for the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program and the U.S. Geological Survey National Geospatial Program 3D Elevation Program (3DEP). A multiagency group consisting of U.S. Department of the Interior agencies and USDA agencies participated in the effort. Department of the Interior agencies included Bureau of Land Management, National Park Service, and U.S. Geological Survey; USDA agencies included the Farm Services Agency, the Natural Resource Conservation Service, and U.S. Forest Service. This pilot project was designed to help determine if a lidar sensor system has the potential to meet future 3DEP topographic lidar collection requirements, ideally at the same altitudes and leaf-on times that National Agriculture Imagery Program is flown. The airborne sensor system from Leica Geosystems (part of Hexagon) (hereafter referred to as dual sensor system) was used in the pilot project and can collect imagery and three-dimensional point cloud data concurrently. This report examines the characteristics of lidar data from a geometric accuracy perspective. Field surveys were performed to evaluate the three-dimensional absolute and relative accuracy of the airborne lidar data and to determine if the data met 3DEP specifications.

Colorado↗

USGS2022

Field techniques for the determination of algal pigment fluorescence in environmental waters—Principles and guidelines for instrument and sensor selection, operation, quality assurance, and data reporting

The use of algal fluorometers by the U.S. Geological Survey (USGS) has become increasingly common. The basic principles of algal fluorescence, instrument calibration, interferences, data quantification, data interpretation, and quality control are given in Hambrook Berkman and Canova (2007). Much of the guidance given for instrument maintenance, data storage, and quality assurance in Wagner and others (2006) are also applicable to algal fluorometers, although they are not explicitly discussed. Algal fluorometers have advanced substantially since these guidance documents were published; so that while the basic principles remain unchanged, new guidance is needed. This techniques and methods report is intended to provide additional information on algal fluorescence-sensor calibration, maintenance, measurement, data storage, and quality assurance that meet stated objectives of USGS data-collection efforts. The operations described facilitate and standardize the collection and accurate communication of algal fluorescence data collected by the USGS across studies, sites, and instrument types. This report provides technical background information on algal fluorescence sensors; including specifications, operating principles, key features, and design elements. Maintenance and calibration protocols, quality-assurance techniques, and suggestions for data reporting are presented. Sensor performance issues, common interferences, and strategies for addressing them are also described.

Techniques and Methods↗

USGS2017

Collar temperature sensor data reveal long-term patterns in southern Beaufort Sea polar bear den distribution on pack ice and land

In response to a changing climate, many species alter habitat use. Polar bears Ursus maritimus in the southern Beaufort Sea have increasingly used land for maternal denning. To aid in detecting denning behavior, we developed an objective method to identify polar bear denning events using temperature sensor data collected by satellite-linked transmitters deployed on adult females between 1985 and 2013. We then applied this method to determine whether southern Beaufort Sea polar bears have continued to increase land denning with recent sea-ice loss and examined whether sea-ice conditions affect the distribution of dens between pack-ice and coastal substrates. Because land use in summer and autumn has also increased, we examined potential associations between summering substrate and denning substrate. Statistical process control methods applied to temperature-sensor data identified denning events with 94.5% accuracy in comparison to direct observations (n = 73) and 95.7% accuracy relative to subjective classifications based on temperature, location, and activity sensor data (n = 116). We found an increase in land-based denning during the study period. The frequency of land denning was directly related to the distance that sea ice retreated from the coast. Among females that denned, all 14 that summered on land subsequently denned there, whereas 29% of the 69 bears summering on ice denned on land. These results suggest that denning on land may continue to increase with further loss of sea ice. While the effects that den substrate have on nutrition, energetics, and reproduction are unclear, more polar bears denning onshore will likely increase human-bear interactions.

Alaska↗

USGS2021

Landsat 8 thermal infrared sensor scene select mechanism open loop operations

The Landsat 8 (L8) spacecraft and its two instruments, the operational land imager (OLI) and thermal infrared sensor (TIRS), have been consistently characterized and calibrated since its launch in February 2013. These performance metrics and calibration updates are determined through the United States Geological Survey (USGS) Landsat image assessment system (IAS), which has been performing this function since its launch. The TIRS on-orbit geometric calibration procedures in-clude TIRS-to-OLI alignment, TIRS sensor chip assembly (SCA) alignment, and TIRS band align-ment. In December 2014, the TIRS instrument experienced an anomalous condition related to the instrument’s ability to accurately measure the location of the scene select mechanism (SSM). The SSM is a rotating mirror that allows the instrument’s field of view to be pointed at the Earth, for normal imaging, or at either deep space or an onboard black body, for radiometric calibration purposes. This anomalous condition in the SSM’s position sensor made it necessary to implement a new mode of operation for this mirror, termed mode-0. Mode-0 involves operating the mirror in an open-loop control state during normal mission operations when acquiring Earth data. Closed-loop mode-4 is needed for directing the mirror towards the radiometric calibration targets and is used approximately once every two weeks to collect radiometric calibration data. Mode-0 is used for most operational imaging because it does not require SSM encoder data, thereby allowing the SSM en-coder electronics to remain unpowered most of the time, reducing its use throughout the lifetime of the TIRS instrument, thus helping to preserve its nominal behavior during it use. This paper dis-cusses the geometric calibration of the SSM mirror during its current normal mode-0 set of image operations, as its open-loop control allows the mirror to drift over time in its uncontrolled state and its impacts on products. The results shown in this paper demonstrate that the ability to have on-going updates to the modeling of the TIRS SSM mirror model, in both an automated fashion and with a set of more manual operations, allows accuracy that approaches mode-4 results within days from the start of a mode-0 event.

MDPI Remote Sensing↗

USGS2024

Current status of the community sensor model standard for the generation of planetary digital terrain models

The creation of accurate elevation models (topography) from stereo images are critical for a large variety of geospatial activities, including the production of digital orthomosaics, change detection, landing site analysis, geologic mapping, rover traverse planning, and spectral analysis. The United Stated Geological Survey, Astrogeology Science Center, continues to transition the supported planetary sensor models to the Community Sensor Model (CSM) standard. This paper describes the current state of use for this photogrammetric standard, supported sensor model types, and qualitatively compares derived topography between SOCET SET and SOCET GXP ( ® BAE Systems) using HiRISE stereo images of Mars. Our transition to the CSM standard will ensure an uninterrupted capability to make these valuable products for Mars and many other extraterrestrial planets and moons.

Remote Sensing↗

USGS2007

Online catalog of world-wide test sites for the post-launch characterization and calibration of optical sensors

In an era when the number of Earth-observing satellites is rapidly growing and measurements from these sensors are used to answer increasingly urgent global issues, it is imperative that scientists and decision-makers rely on the accuracy of Earth-observing data products. The characterization and calibration of these sensors are vital to achieve an integrated Global Earth Observation System of Systems (GEOSS) for coordinated and sustained observations of Earth. The U.S. Geological Survey (USGS), as a supporting member of Committee on Earth Observation Satellites (CEOS) and GEOSS, worked with partners around the world to establish an online catalogue of prime candidate world-wide test sites for the post-launch characterization and calibration of space-based optical imaging sensors. The online catalogue provides easy public web site access to this vital information for the global community. This paper further describes the catalogue, the test sites, and the methodologies to utilize the test sites. It also provides information regarding access to the online catalogue and plans for further development of the catalogue in cooperation with calibration specialists from agencies and organizations around the world. Through greater access to and understanding of these vital test sites and their use, the validity and utility of information gained from Earth remote sensing will continue to improve.

Conference Paper↗

USGS2015

Remote sensing systems – Platforms and sensors: Aerial, satellites, UAVs, optical, radar, and LiDAR

The American Society of Photogrammetry and Remote Sensing defined remote sensing as the measurement or acquisition of information of some property of an object or phenomenon, by a recording device that is not in physical or intimate contact with the object or phenomenon under study (Colwell et al., 1983). Environmental Systems Research Institute (ESRI) in its geographic information system (GIS) dictionary defines remote sensing as “collecting and interpreting information about the environment and the surface of the earth from a distance, primarily by sensing radiation that is naturally emitted or reflected by the earth’s surface or from the atmosphere, or by sending signals transmitted from a device and reflected back to it (ESRI, 2014).” The usual source of passive remote sensing data is the measurement of reflected or transmitted electromagnetic radiation (EMR) from the sun across the electromagnetic spectrum (EMS); this can also include acoustic or sound energy, gravity, or the magnetic field from or of the objects under consideration. In this context, the simple act of reading this text is considered remote sensing. In this case, the eye acts as a sensor and senses the light reflected from the object to obtain information about the object. It is the same technology used by a handheld camera to take a photograph of a person or a distant scenic view. Active remote sensing, however, involves sending a pulse of energy and then measuring the returned energy through a sensor (e.g., Radio Detection and Ranging [RADAR], Light Detection and Ranging [LiDAR]). Thermal sensors measure emitted energy by different objects. Thus, in general, passive remote sensing involves the measurement of solar energy reflected from the Earth’s surface, while active remote sensing involves synthetic (man-made) energy pulsed at the environment and the return signals are measured and recorded.

Book chapter↗

USGS2025

Spatial mapping of dissolved methane using an in situ sensor in Puget Sound

Release of methane, as gas bubbles or in the dissolved phase, from the seafloor has been observed in coastal waters (< 200 m) and deep ocean basins (> 1000 m). Methane dissolution within the water column affects the geochemistry of the surrounding water, leading to localized oxygen loss and potential escape to the atmosphere, particularly from shallower sites. Traditional methods for detecting and quantifying dissolved methane rely on collecting discrete water samples for ship- or land-based ex situ analysis and post processing. Here, we report on the use of a reduced response time, in situ methane sensor, the Sensor for Aqueous Gases in the Environment (SAGE), for detecting and quantifying dissolved methane concentrations in a wide range of seafloor environments. During a Fall 2022 research cruise on the R/V Thomas G. Thompson in Puget Sound, SAGE was integrated onto a towed conductivity/temperature/depth rosette and deep-sea camera system with live-stream 1 Hz telemetry and used to spatially map the concentration of methane approximately 1 m above the seafloor. The site had been previously identified as an active methane plume field characterized by gas bubbles, fluid venting, and a faulted seabed. The widespread background dissolved concentration of methane measured by SAGE was 83 nM, and a range of 78–670 nM was observed throughout the survey. The results highlight the capacity of SAGE to map the spatial and temporal variability of dissolved methane concentrations in situ and to identify and localize sites of variable methane emissions from the seafloor.

Washington↗

USGS2011

Taking the pulse of snowmelt: in situ sensors reveal seasonal, event and diurnal patterns of nitrate and dissolved organic matter variability in an upland forest stream

Highly resolved time series data are useful to accurately identify the timing, rate, and magnitude of solute transport in streams during hydrologically dynamic periods such as snowmelt. We used in situ optical sensors for nitrate (NO3 - ) and chromophoric dissolved organic matter fluorescence (FDOM) to measure surface water concentrations at 30 min intervals over the snowmelt period (March 21–May 13, 2009) at a 40.5 hectare forested watershed at Sleepers River, Vermont. We also collected discrete samples for laboratory absorbance and fluorescence as well as δ 18 O–NO3 - isotopes to help interpret the drivers of variable NO3 - and FDOM concentrations measured in situ. In situ data revealed seasonal, event and diurnal patterns associated with hydrological and biogeochemical processes regulating stream NO3 - and FDOM concentrations. An observed decrease in NO3 - concentrations after peak snowmelt runoff and muted response to spring rainfall was consistent with the flushing of a limited supply of NO3 - (mainly from nitrification) from source areas in surficial soils. Stream FDOM concentrations were coupled with flow throughout the study period, suggesting a strong hydrologic control on DOM concentrations in the stream. However, higher FDOM concentrations per unit streamflow after snowmelt likely reflected a greater hydraulic connectivity of the stream to leachable DOM sources in upland soils. We also observed diurnal NO3 - variability of 1–2 μmol l -1 after snowpack ablation, presumably due to in-stream uptake prior to leafout. A comparison of NO3 - and dissolved organic carbon yields (DOC, measured by FDOM proxy) calculated from weekly discrete samples and in situ data sub-sampled daily resulted in small to moderate differences over the entire study period (-4 to 1% for NO3 - and -3 to -14% for DOC), but resulted in much larger differences for daily yields (-66 to +27% for NO3 - and -88 to +47% for DOC, respectively). Despite challenges inherent in in situ sensor deployments in harsh seasonal conditions, these data provide important insights into processes controlling NO3 - and FDOM in streams, and will be critical for evaluating the effects of climate change on snowmelt delivery to downstream ecosystems.

Vermont↗

USGS2005

A simple and effective radiometric correction method to improve landscape change detection across sensors and across time

Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r 2 values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI "cancellation effect" where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a post fire chronosequence with normalized data. These findings indicate that the TIC method provides a simple, effective and repeatable method to create radiometrically comparable data sets for remote detection of landscape change. Compared to some previous relative radiometric normalization methods, this new method does not require high level programming and statistical skills, yet remains sensitive to landscape changes occurring over seasonal and inter-annual time scales. In addition, the TIC method maintains sensitivity to subtle changes in vegetation phenology and enables normalization even when invariant features are rare. While this normalization method allowed detection of a range of land use, land cover, and phenological/biophysical changes in the Siberian boreal forest region studied here, it is necessary to further examine images representing a wide variety of ecoregions to thoroughly evaluate the TIC method against other normalization schemes. ?? 2005 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗

USGS2014

Evaluation of sensor types and environmental controls on mapping biomass of coastal marsh emergent vegetation

There is a need to quantify large-scale plant productivity in coastal marshes to understand marsh resilience to sea level rise, to help define eligibility for carbon offset credits, and to monitor impacts from land use, eutrophication and contamination. Remote monitoring of aboveground biomass of emergent wetland vegetation will help address this need. Differences in sensor spatial resolution, bandwidth, temporal frequency and cost constrain the accuracy of biomass maps produced for management applications. In addition the use of vegetation indices to map biomass may not be effective in wetlands due to confounding effects of water inundation on spectral reflectance. To address these challenges, we used partial least squares regression to select optimal spectral features in situ and with satellite reflectance data to develop predictive models of aboveground biomass for common emergent freshwater marsh species, Typha spp. and Schoenoplectus acutus , at two restored marshes in the Sacramento–San Joaquin River Delta, California, USA. We used field spectrometer data to test model errors associated with hyperspectral narrowbands and multispectral broadbands, the influence of water inundation on prediction accuracy, and the ability to develop species specific models. We used Hyperion data, Digital Globe World View-2 (WV-2) data, and Landsat 7 data to scale up the best statistical models of biomass. Field spectrometer-based models of the full dataset showed that narrowband reflectance data predicted biomass somewhat, though not significantly better than broadband reflectance data [R 2 = 0.46 and percent normalized RMSE (%RMSE) = 16% for narrowband models]. However hyperspectral first derivative reflectance spectra best predicted biomass for plots where water levels were less than 15 cm (R 2 = 0.69, %RMSE = 12.6%). In species-specific models, error rates differed by species ( Typha spp.: %RMSE = 18.5%; S. acutus : %RMSE = 24.9%), likely due to the more vertical structure and deeper water habitat of S. acutus. The Landsat 7 dataset (7 images) predicted biomass slightly better than the WV-2 dataset (6 images) (R 2 = 0.56, %RMSE = 20.9%, compared to R 2 = 0.45, RMSE = 21.5%). The Hyperion dataset (one image) was least successful in predicting biomass (R 2 = 0.27, %RMSE = 33.5%). Shortwave infrared bands on 30 m-resolution Hyperion and Landsat 7 sensors aided biomass estimation; however managers need to weigh tradeoffs between cost, additional spectral information, and high spatial resolution that will identify variability in small, fragmented marshes common to the Sacramento–San Joaquin River Delta and elsewhere in the Western U.S.

California↗