scholarly journals Developing Sensor Proxies for “Chemical Cocktails” of Trace Metals in Urban Streams

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2864
Author(s):  
Carol J. Morel ◽  
Sujay S. Kaushal ◽  
Maggie L. Tan ◽  
Kenneth T. Belt
Keyword(s):  
Trace Metals ◽  
Dissolved Oxygen ◽  
Best Management Practices ◽  
The United States ◽  
Specific Conductance ◽  
Urban Streams ◽  
Sensor Data ◽  
United States Geological Survey ◽  
Linear Relationships ◽  
Continuous Sensor

Understanding transport mechanisms and temporal patterns in the context of metal concentrations in urban streams is important for developing best management practices and restoration strategies to improve water quality. In some cases, in-situ sensors can be used to estimate unknown concentrations of trace metals or to interpolate between sampling events. Continuous sensor data from the United States Geological Survey were analyzed to determine statistically significant relationships between lead, copper, zinc, cadmium, and mercury with turbidity, specific conductance, dissolved oxygen, and discharge for the Hickey Run, Watts Branch, and Rock Creek watersheds in the Washington, D.C. region. We observed a significant negative linear relationship between concentrations of Cu and dissolved oxygen at Rock Creek (p < 0.05). Sometimes, turbidity had significant positive linear relationships with Pb and Hg concentrations. There were negative or positive linear relationships between Pb, Cd, Zn, and Hg and specific conductance. There also appeared to be relationships between watershed areal fluxes of Pb, Cu, Zn, and Cd in streams with turbidity. Watershed monitoring approaches using continuous sensor data have the potential to characterize the frequency, magnitude, and composition of pulses in concentrations and loads of trace metals, which could improve the management and restoration of urban streams.

scholarly journals Local Search for Optimal Global Map Generation Using Mid-Decadal Landsat Images

AI Magazine ◽  
2009 ◽  
Vol 30 (2) ◽  
pp. 84 ◽  
Author(s):  
Lina Khatib ◽  
Robert A. Morris ◽  
John Gasch
Keyword(s):  
Local Search ◽  
The United States ◽  
Thematic Mapper ◽  
Sensor Data ◽  
United States Geological Survey ◽  
Landsat Images ◽  
Remote Sensor ◽  
Map Generation ◽  
Landsat 7 ◽  
Generation Problem

NASA and the United States Geological Survey (USGS) are collaborating to produce a global map of the Earth using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) remote sensor data from the period of 2004 through 2007. The map is comprised of thousands of scene locations and, for each location, there are tens of different images of varying quality to chose from. Constraints and preferences on map quality make it desirable to develop an automated solution to the map generation problem. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. The paper also describes the integration of a GMG solver into a graphical user interface for visualizing and comparing solutions, thus allowing for solutions to be generated with human participation and guidance.

scholarly journals Comparison of Three Methods for Estimating Land Surface Temperature from Landsat 8-TIRS Sensor Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1450 ◽  
Author(s):  
Vicente García-Santos ◽  
Joan Cuxart ◽  
Daniel Martínez-Villagrasa ◽  
Maria Jiménez ◽  
Gemma Simó
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Validation Studies ◽  
Land Surface ◽  
Thermal Infrared ◽  
The United States ◽  
Stray Light ◽  
Sensor Data ◽  
United States Geological Survey ◽  
Landsat 8

After Landsat 8 was launched in 2013, it was observed that for Thermal Infrared sensor (TIRS) bands, radiance from outside of an instrument’s field-of-view produced a non-uniform ghost signal across the focal plane that varied depending on the out-of-scene content (i.e., the stray light effect). A new stray light correction algorithm (SLCA) is currently operational and has been implemented into the United States Geological Survey (USGS) ground system since February 2017. The SLCA has also been applied to reprocess historical Landsat 8 scenes. After approximately two years of SLCA implementation, more land surface temperature (LST) validation studies are required to check the effect of correction in the estimation of LST from different retrieval algorithms. For this purpose, three different LST estimation method algorithms (i.e., the radiative transfer equation (RTE), single-channel algorithm (SCA), and split-window algorithm (SWA)) have been assessed. The study site is located on the campus of the University of Balearic Islands on the island of Mallorca (Spain) in the western Mediterranean Sea. The site is considered a heterogeneous area that is composed of different types of surfaces, such as buildings, asphalt roads, farming areas, sloped terrains, orange fields, almond trees, lawns, and some natural vegetation regions. Data from 21 scenes, which were acquired by the Landsat 8-TIRS sensor and extracted from a 100 × 100 m2 pixel, were used to retrieve the LST with different algorithms; then, they were compared with in situ LST measurements from a broadband thermal infrared radiometer located on the same Landsat 8 pixel. The results show good performances of the three methods, with the SWA showing the lowest observed RMSE (within 1.6–2 K), whereas the SCA applied to the TIRS band 10 (10 µm) was also appropriate, with a RMSE ranging within 2.0–2.3 K. The LST estimates using the RTE algorithm display the highest observed RMSE values (within 2.0–3.6 K) of all of the compared methods, but with an almost unbiased value of −0.1 K for the case of techniques applied to band 10 data. The SWAs are the preferred method to estimate the LST in our study area. However, further validation studies around the world are required.

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