scholarly journals sUAS Remote Sensing to Evaluate Geothermal Seep Interactions with the Yellowstone River, Montana, USA

2021 ◽  
Vol 13 (2) ◽  
pp. 163
Author(s):  
Jesse Bunker ◽  
Raja M. Nagisetty ◽  
Jeremy Crowley
Keyword(s):  
Remote Sensing ◽  
Water Temperature ◽  
Structure From Motion ◽  
Water Surface ◽  
Small Scale ◽  
Average Width ◽  
Yellowstone River ◽  
Temperature Ranges ◽  
Thermal Optic

Small unmanned aerial systems (sUAS) are becoming increasingly popular due to their affordability and logistical ease for repeated surveys. While sUAS-based remote sensing has many applications in water resource management, their applicability and limitations in fluvial settings is not well defined. This study uses a combined thermal-optic sUAS to monitor the seasonal geothermal influence of a 1-km-long reach of the Yellowstone River, paired with in-situ streambed temperature profiles to evaluate geothermal seep interactions with Yellowstone River in Montana, USA. Accurate river water surface elevation along the shoreline was estimated using structure from motion (SfM) photogrammetry digital surface models (DSMs); however, water surface elevations were unreliable in the main river channel. Water temperature in thermal infrared (TIR) orthomosaics was accurate in temperature ranges of tens of degrees (>≈30 °C), but not as accurate in temperature ranges of several degrees (>≈15 °C) as compared to in-situ water temperature measurements. This allowed for identification of geothermal features but limited the ability to identify small-scale temperature changes due to river features, such as pools and riffles. The study concludes that rivers with an average width greater than or equal to 123% of the ground area covered by a TIR image will be difficult to study using structure from motion photogrammetry, given Federal Aviation Administration (FAA) altitude restrictions and sensor field of view. This study demonstrates the potential of combined thermal-optic sUAS systems to collect data over large river systems, and when combined with in-situ measurements, can further increase the sUAS utility in identifying river characteristics.

SVALBARD 2006 EXPERIMENTAL OIL SPILL UNDER ICE: REMOTE SENSING, OIL WEATHERING UNDER ARCTIC CONDITIONS AND ASSESSMENT OF OIL REMOVAL BY IN-SITUBURNING

2008 ◽  
Vol 2008 (1) ◽  
pp. 681-688 ◽  
Author(s):  
David Dickins ◽  
Per Johan Brandvik ◽  
John Bradford ◽  
Liv-Guri Faksness ◽  
Lee Liberty ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Field Experiments ◽  
New Technologies ◽  
Time Window ◽  
The Arctic ◽  
Small Scale ◽  
Oil Removal ◽  
Arctic Conditions

ABSTRACT This paper describes the findings from an experimental spill of 3,400 liters of Statfjord crude under first-year sea ice in Svalbard, Norway in March 2006. The objectives were to:1. Test commercially available radar and acoustics systems for detecting oil spilled under ice.2. Document the weathering processes governing crude oil behaviour in ice.3. Confirm the effectiveness of in-situ burning as an oil removal strategy. The results of this project will be used in planning new Arctic oil exploration and development programs. With the growing awareness of the Arctic basin as a potentially important province for new oil and gas discoveries, there is a critical need to: (1) develop new technologies to detect and map spills under ice; (2) increase the understanding of oil behaviour in ice and: (3) continue to demonstrate the capabilities of in-situ burning as an important and safe Arctic response tool. Tank tests conducted in 2004 (Dickins et al., 2005) showed that radar systems could detect and map oil pools as thin as 2 to 3 cm under controlled conditions under model sea ice up to 40 cm thick. This field experiment created a much larger-scale spill under thicker 65 cm natural sea ice to further evaluate potential remote sensing systems as practical operational spill response tools. The findings of the 2006 experiment: (1) demonstrated for the first time the ability of ground penetrating radar to detect and map oil under natural sea ice from the surface; (2) documented oil weathering with a relatively warm ice sheet under spring conditions; and (3) confirmed the effectiveness of in situ burning as a primary oil removal strategy under Arctic conditions. Oil weathering results are discussed and compared with small-scale field experiments performed on Svalbard during the period 2003–2006. Low temperatures and lack of waves in ice act to reduce oil spreading, evaporation, emulsification and dispersion. As a result, the operational time window for several spill response strategies such as dispersants and in-situ burning may be significantly extended compared to oil spills in open water.

scholarly journals Tropospheric CH<sub>4</sub> signals as observed by NDACC FTIR at globally distributed sites and comparison to GAW surface in situ measurements

2014 ◽  
Vol 7 (7) ◽  
pp. 2337-2360 ◽  
Author(s):  
E. Sepúlveda ◽  
M. Schneider ◽  
F. Hase ◽  
S. Barthlott ◽  
D. Dubravica ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Correction Method ◽  
Atmospheric Composition ◽  
Small Scale ◽  
Mean Values ◽  
Sensing Data ◽  
Globally Distributed

Abstract. We present lower/middle tropospheric column-averaged CH4 mole fraction time series measured by nine globally distributed ground-based FTIR (Fourier transform infrared) remote sensing experiments of the Network for the Detection of Atmospheric Composition Change (NDACC). We show that these data are well representative of the tropospheric regional-scale CH4 signal, largely independent of the local surface small-scale signals, and only weakly dependent on upper tropospheric/lower stratospheric (UTLS) CH4 variations. In order to achieve the weak dependency on the UTLS, we use an a posteriori correction method. We estimate a typical precision for daily mean values of about 0.5% and a systematic error of about 2.5%. The theoretical assessments are complemented by an extensive empirical study. For this purpose, we use surface in situ CH4 measurements made within the Global Atmosphere Watch (GAW) network and compare them to the remote sensing data. We briefly discuss different filter methods for removing the local small-scale signals from the surface in situ data sets in order to obtain the in situ regional-scale signals. We find good agreement between the filtered in situ and the remote sensing data. The agreement is consistent for a variety of timescales that are interesting for CH4 source/sink research: day-to-day, monthly, and inter-annual. The comparison study confirms our theoretical estimations and proves that the NDACC FTIR measurements can provide valuable data for investigating the cycle of CH4.

scholarly journals Urban Land Surface Temperature Monitoring and Surface Thermal Runoff Pollution Evaluation Using UAV Thermal Remote Sensing Technology

2021 ◽  
Vol 13 (20) ◽  
pp. 11203
Author(s):  
Shanshan Xu ◽  
Kun Yang ◽  
Yuanting Xu ◽  
Yanhui Zhu ◽  
Yi Luo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Surface Temperature ◽  
Water Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Surface ◽  
Surface Water Temperature ◽  
Before And After

With the continuous advancement of urbanization, the impervious surface expands. Urbanization has changed the structure of the natural land surface and led to the intensification of the urban heat island (UHI) effect. This will affect the surface runoff temperature, which, in turn, will affect the surface water temperature of urban lakes. This study will use UAS TIR (un-manned aerial system thermal infrared radiance) remote sensing and in situ observation technology to monitor the urban space surface temperature and thermal runoff in Kunming, Yunnan, in summer; explore the feasibility of UAS TIR remote sensing to continuously observe urban surface temperature during day and night; and analyze thermal runoff pollution. The results of the study show that the difference between UAS TIR LSTs and in situ LSTs (in situ air temperature 10 cm above the ground.) varies with the type of land covers. Urban surface thermal runoff has varying degrees of impact on water bodies. Based on the influence of physical factors such as vegetation and buildings and meteorological factors such as solar radiation, the RMSE between UAS LSTs and in situ LSTs varies from 1 to 5 °C. Land cover types such as pervious bricks, asphalt, and cement usually show higher RMSE values. Before and after rainfall, the in situ data of the lake surface water temperature (LSWT) showed a phenomenon of first falling and then rising. The linear regression analysis results show that the R2 of the daytime model is 0.92, which has high consistency; the average R2 at night is 0.38; the averages R2 before and after rainfall are 0.50 and 0.83, respectively; and the average RMSE is 1.94 °C. Observational data shows that thermal runoff quickly reaches thermal equilibrium with the land surface temperature about 30 min after rainfall. The thermal runoff around the lake has a certain warming effect on LSWT.

Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

2020 ◽  
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Water Level ◽  
River Discharge ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Remote Sensing Data ◽  
Sensing Data

&lt;p&gt;Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.&lt;/p&gt;&lt;p&gt;In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM&amp;#8217;s &amp;#8220;Database of Hydrological Time series of Inland Waters&amp;#8221; (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.&lt;/p&gt;&lt;p&gt;Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.&lt;/p&gt;

scholarly journals Remote Sensing for Optimal Estimation of Water Temperature Dynamics in Shallow Tidal Environments

2019 ◽  
Vol 12 (1) ◽  
pp. 51 ◽  
Author(s):  
Mattia Pivato ◽  
Luca Carniello ◽  
Daniele Pietro Viero ◽  
Chiara Soranzo ◽  
Andrea Defina ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Temperature ◽  
Large Scale ◽  
Transport Model ◽  
Proper Time ◽  
Model Development ◽  
Integrated Approach ◽  
High Temporal Resolution ◽  
Anthropogenic Pressures

Given the increasing anthropogenic pressures on lagoons, estuaries, and lakes and considering the highly dynamic behavior of these systems, methods for the continuous and spatially distributed retrieval of water quality are becoming vital for their correct monitoring and management. Water temperature is certainly one of the most important drivers that influence the overall state of coastal systems. Traditionally, lake, estuarine, and lagoon temperatures are observed through point measurements carried out during field campaigns or through a network of sensors. However, sporadic measuring campaigns or probe networks rarely attain a density sufficient for process understanding, model development/validation, or integrated assessment. Here, we develop and apply an integrated approach for water temperature monitoring in a shallow lagoon which incorporates satellite and in-situ data into a mathematical model. Specifically, we use remote sensing information to constrain large-scale patterns of water temperature and high-frequency in situ observations to provide proper time constraints. A coupled hydrodynamic circulation-heat transport model is then used to propagate the state of the system forward in time between subsequent remote sensing observations. Exploiting the satellite data high spatial resolution and the in situ measurements high temporal resolution, the model may act a physical interpolator filling the gap intrinsically characterizing the two monitoring techniques.

scholarly journals Tropospheric CH<sub>4</sub> signals as observed by NDACC FTIR at globally distributed sites and comparison to GAW surface in-situ measurements

2014 ◽  
Vol 7 (1) ◽  
pp. 633-701 ◽  
Author(s):  
E. Sepúlveda ◽  
M. Schneider ◽  
F. Hase ◽  
S. Barthlott ◽  
D. Dubravica ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Correction Method ◽  
Atmospheric Composition ◽  
Small Scale ◽  
Mean Values ◽  
Sensing Data ◽  
Globally Distributed

Abstract. We present lower/middle tropospheric column-averaged CH4 mole fraction time series measured by nine globally distributed ground-based FTIR (Fourier Transform InfraRed) remote sensing experiments of the Network for the Detection of Atmospheric Composition Change (NDACC). We show that these data are well representative of the tropospheric regional-scale CH4 signal, largely independent of the local small-scale signals of the boundary layer, and only weakly dependent on upper tropospheric/lower stratospheric (UTLS) CH4 variations. In order to achieve the weak dependency on the UTLS, we use an a posteriori correction method. We estimate a typical precision for daily mean values of about 0.5% and a systematic error of about 2.5%. The theoretical assessments are complemented by an extensive empirical study. For this purpose, we use surface in-situ CH4 measurements made within the Global Atmosphere Watch (GAW) network and compare them to the remote sensing data. We briefly discuss different filter methods for removing the local small-scale signals from the surface in-situ datasets in order to obtain the in-situ regional-scale signals. We find good agreement between the filtered in-situ and the remote sensing data. The agreement is consistent for a variety of time scales that are interesting for CH4 source/sink research: day-to-day, monthly, and inter-annual. The comparison study confirms our theoretical estimations and proves that the NDACC FTIR measurements can provide valuable data for investigating the cycle of CH4.

scholarly journals Matching Temporal Signatures of Solar Features to Their Corresponding Solar-Wind Outflows

Solar Physics ◽  
2021 ◽  
Vol 296 (4) ◽  
Author(s):  
D. de Pablos ◽  
D. M. Long ◽  
C. J. Owen ◽  
G. Valori ◽  
G. Nicolaou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Solar Wind ◽  
Time Windows ◽  
Synthetic Data ◽  
Solar Wind Plasma ◽  
Temporal Variations ◽  
Small Scale ◽  
Solar Dynamics Observatory ◽  
Mode Decomposition

AbstractThe role of small-scale coronal eruptive phenomena in the generation and heating of the solar wind remains an open question. Here, we investigate the role played by coronal jets in forming the solar wind by testing whether temporal variations associated with jetting in EUV intensity can be identified in the outflowing solar-wind plasma. This type of comparison is challenging due to inherent differences between remote-sensing observations of the source and in-situ observations of the outflowing plasma, as well as travel time and evolution of the solar wind throughout the heliosphere. To overcome these, we propose a novel algorithm combining signal filtering, two-step solar-wind ballistic back-mapping, window shifting, and Empirical Mode Decomposition. We first validate the method using synthetic data, before applying it to measurements from the Solar Dynamics Observatory and Wind spacecraft. The algorithm enables the direct comparison of remote-sensing observations of eruptive phenomena in the corona to in-situ measurements of solar-wind parameters, among other potential uses. After application to these datasets, we find several time windows where signatures of dynamics found in the corona are embedded in the solar-wind stream, at a time significantly earlier than expected from simple ballistic back-mapping, with the best-performing in-situ parameter being the solar-wind mass flux.

scholarly journals Evaluation of Global Surface Water Temperature Data Sets for Use in Passive Remote Sensing of Soil Moisture

2021 ◽  
Vol 13 (10) ◽  
pp. 1872
Author(s):  
Runze Zhang ◽  
Steven Chan ◽  
Rajat Bindlish ◽  
Venkataraman Lakshmi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Temperature ◽  
Temperature Data ◽  
Data Sets ◽  
Surface Water Temperature ◽  
Passive Remote Sensing ◽  
Water Temperature Data

Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.

Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences

1996 ◽  
pp. 51-54 ◽  
Author(s):  
N. V. M. Unni
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Human Life ◽  
Remote Sensing Data ◽  
Modern World ◽  
Small Scale ◽  
Vegetation Map ◽  
Gis Technologies ◽  
Forest Cover Mapping

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).

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