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Results for “IEEE Sensors Journal”

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7 recordsLinked to original sources
USGS2021

Invasive sea lamprey detection and characterization using interdigitated electrode (IDE) contact sensor

The ability to monitor invasive sea lamprey (Petromyzon marinus) populations in the Laurentian Great Lakes is critical to protecting the region’s $ 7 billion USD fishing industry and preserving its biodiversity. Monitoring these invaders requires considerable fieldwork and human power, making remote lamprey detection systems attractive for their continuous monitoring capabilities and potential for workload reduction. However, a lack of available methods for detecting sea lamprey hampers development of such systems. Here we present a sensor composed of two exposed planar interdigitated electrodes (IDE) along with a DC measurement system for the detection of lamprey attachment underwater. Measuring voltage instead of impedance, reduces cost and signal processing complexity, making the device more attractive for field deployment. The system is calibrated to a baseline output voltage and deviations from this baseline occur when objects touch the IDE. Validation was done through testing on live adult sea lampreys using video recordings to correlate lamprey attachments to the sensor response. Three response types were identified corresponding to different attachments: sustained, short and sliding-sustained. Sensor response to sustained and sliding-sustained attachments showed a characteristic exponential decay whereas the response due to short attachments was indistinguishable from measurement noise. Lamprey size was found to have a weak linear correlation with both response parameters, positive for the voltage drop and negative for the time constant of voltage drop. A representative circuit for the lamprey-sensor interaction is proposed and simulated using element values calculated from the response parameters. The response of the model shows agreement with experimental data.

Great Lakes↗
USGS2022

Soft pressure sensor for underwater sea lamprey detection

In this paper, an economical and effective soft pressure sensor for underwater sea lamprey detection is proposed, which consists of an array of piezoresistive elements between two layers of perpendicular copper tape electrodes, forming a passive resistor network. With multiplexers, the apparent resistance corresponding to each pixel of the sensing matrix can be measured directly, where the pixel is identified with the row and the column of the respective electrodes. However, this measured two-point resistance is not equal to the actual cell resistance for that pixel due to the crosstalk effect in the resistor network. Since the cell resistance reflects directly the pressure applied on each pixel, the relationship between the cell resistance and the measured two-point resistance is analyzed for a passive matrix of any size. More importantly, several regularized least-squares algorithms are proposed to reconstruct the cell resistance profile from the two-point resistance measurements, with enhanced robustness of the reconstruction in the presence of measurement noises and modeling errors. The proposed pressure sensor is applied to detect the suction attachment of sea lampreys, a devastating invasive species in the Great Lakes region. Experimental results demonstrate that the pressure sensor can successfully capture the rim profile of the lamprey’s sucking mouth. Moreover, the performance and computational complexity of the reconstruction algorithms with different regularization functions are compared.

IEEE Sensors Journal↗
USGS2023

Automated soft pressure sensor array-based sea lamprey detection using machine learning

Sea lamprey, a destructive invasive species in the Great Lakes in North America, is among very few fishes that rely on oral suction during migration and spawning. Recently, soft pressure sensors have been proposed to detect the attachment of sea lamprey as part of the monitoring and control effort. However, human decision is still required for the recognition of patterns in the measured signals. In this article, a novel automated soft pressure sensor array-based sea lamprey detection framework is proposed using object detection convolutional neural networks. First, the resistance measurements of the pressure sensor array are converted to mappings of relative change in resistance. These mappings typically show two different types of patterns under lamprey attachment: a high-pressure circular pattern corresponding to the mouth rim compressed against the sensor (“compression” pattern), and a low-pressure blob corresponding to the partial vacuum region of the sucking mouth (“suction” pattern). Three types of object detection algorithms, single-shot detector (SSD), RetinaNet, and YOLOv5s, are applied to the dataset of measurements collected in the presence of sea lamprey attachment, and the comparison of their performance shows that YOLOv5s model achieves the highest mean average precision (mAP) and the fastest inference speed. Furthermore, to improve the accuracy of the prediction model and reduce the false positive (FP) rate due to the sensor’s memory effect, a filter branch with different detection thresholds for the compression and suction patterns, respectively, is added to the original machine-learning algorithm. The trained model is validated and used to automatically detect sea lamprey attachments and locate the suction area on the sensor in real time.

IEEE Sensors Journal↗
USGS2013

EO-1 Hyperion reflectance time series at calibration and validation sites: stability and sensitivity to seasonal dynamics

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends and their stability consistently is within 2.5-5 percent throughout most of the spectral range spanning the 12+ year data record. Using three vegetated sites instrumented with eddy covariance towers, the Hyperion reflectance time series were evaluated for their ability to determine important variables of ecosystem function. A number of narrowband and derivative vegetation indices (VI) closely described the seasonal profiles in vegetation function and ecosystem carbon exchange (e.g., net and gross ecosystem productivity) in three very different ecosystems, including a hardwood forest and tallgrass prairie in North America, and a Miombo woodland in Africa. Our results demonstrate the potential for scaling the carbon flux tower measurements to local and regional landscape levels. The VIs with stronger relationships to the CO 2 parameters were derived using continuous reflectance spectra and included wavelengths associated with chlorophyll content and/or chlorophyll fluorescence. Since these indices cannot be calculated from broadband multispectral instrument data, the opportunity to exploit these spectrometer-based VIs in the future will depend on the launch of satellites such as EnMAP and HyspIRI. This study highlights the practical utility of space-borne spectrometers for characterization of the spectral stability and uniformity of the calibration sites in support of sensor cross-comparisons, and demonstrates the potential of narrowband VIs to track and spatially extend ecosystem functional status as well as carbon processes measured at flux towers.

Nevada↗
USGS2013

Radiometric cross-calibration of EO-1 ALI with L7 ETM+ and Terra MODIS sensors using near-simultaneous desert observations

The Earth Observing-1 (EO-1) satellite was launched on November 21, 2000, as part of a one-year technology demonstration mission. The mission was extended because of the value it continued to add to the scientific community. EO-1 has now been operational for more than a decade, providing both multispectral and hyperspectral measurements. As part of the EO-1 mission, the Advanced Land Imager (ALI) sensor demonstrates a potential technological direction for the next generation of Landsat sensors. To evaluate the ALI sensor capabilities as a precursor to the Operational Land Imager (OLI) onboard the Landsat Data Continuity Mission (LDCM, or Landsat 8 after launch), its measured top-of-atmosphere (TOA) reflectances were compared to the well-calibrated Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors in the reflective solar bands (RSB). These three satellites operate in a near-polar, sun-synchronous orbit 705 km above the Earth's surface. EO-1 was designed to fly one minute behind L7 and approximately 30 minutes in front of Terra. In this configuration, all the three sensors can view near-identical ground targets with similar atmospheric, solar, and viewing conditions. However, because of the differences in the relative spectral response (RSR), the measured physical quantities can be significantly different while observing the same target. The cross-calibration of ALI with ETM+ and MODIS was performed using near-simultaneous surface observations based on image statistics from areas observed by these sensors over four desert sites (Libya 4, Mauritania 2, Arabia 1, and Sudan 1). The differences in the measured TOA reflectances due to RSR mismatches were compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of each sensor. For this study, the spectral profile of the target comes from the near-simultaneous EO-1 Hyperion data over these sites. The results indicate that the TOA reflectance measurements for ALI agree with those of ETM+ and MODIS to within 5% after the application of SBAF.

Arabia↗
USGS2022

New generation hyperspectral data From DESIS compared to high spatial resolution PlanetScope data for crop type classification

Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luftund Raumfahrt- (DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in California's Central Valley during the 2020 growing season. The DESIS sensor onboard the International Space Station collects data at 235 hyperspectral narrowbands (HNB) each with 2.55 nm bandwidth from 400–1000 nm and 30 m spatial resolution. In contrast, PlanetScope Dove-R data have four multispectral broadbands (MBB) with 3–4 m spatial resolution. We obtained best classification accuracies using 14 DESIS HNB from the August 2020 image, with an overall accuracy of 85% and producer's and user's accuracies of 72–100% and 75–100%, respectively, for the eight crops. The best classification accuracies using PlanetScope data were obtained using an image mosaic pair from June and August 2020; this resulted in an overall accuracy of 79% and producer's and user's accuracies of 56–100% and 61–100%, respectively. Combining the best 14 DESIS HNB from August 2020 with the 4 PlanetScope MBB from August 2020 yielded an overall accuracy of 82% and producer's and user's accuracies of 65–100% and 60–94%, respectively. On one-to-one single date comparisons of DESIS versus PlanetScope data, the hyperspectral data always outperformed high spatial resolution data in crop type classification. Nevertheless, high spatial resolution data will remain invaluable in assessing within-field variability and crop biophysical/biochemical modeling in precision agriculture.

IEEE Journal of Selected Topics in Applied Earth O↗
USGS2007

In situ time-series measurements of subseafloor sediment properties

The capabilities and diversity of subsurface sediment sensors lags significantly from what is available for the water column, thereby limiting progress in understanding time-dependent seabed exchange and high-frequency acoustics. To help redress this imbalance, a new instrument, the autonomous sediment profiler (ASP), is described herein. ASP consists of a four-electrode, Wenner-type resistivity probe and a thermistor that log data at 0.1-cm vertical intervals over a 58-cm vertical profile. To avoid resampling the same spot on the seafloor, the probes are moved horizontally within a 20 times 100-cm -2 area in one of three preselected patterns. Memory and power capacities permit sampling at hourly intervals for up to 3-mo duration. The system was tested in a laboratory tank and shown to be able to resolve high-frequency sediment consolidation, as well as changes in sediment roughness. In a field test off the southern coast of France, the system collected resistivity and temperature data at hourly intervals for 16 d. Coupled with environmental data collected on waves, currents, and suspended sediment, the ASP is shown to be useful for understanding temporal evolution of subsurface sediment porosity, although no large depositional or erosional events occurred during the deployment. Following a rapid decrease in bottom-water temperature, the evolution of the subsurface temperature field was consistent with the 1-D thermal diffusion equation coupled with advection in the upper 3-4 cm. Collectively, the laboratory and field tests yielded promising results on time-dependent seabed change.

IEEE Journal of Oceanic Engineering↗