scholarly journals Using Remote Sensing to Assess Alligator Gar Spawning Habitat Suitability in the Lower Mississippi River

2020 ◽  
Vol 40 (3) ◽  
pp. 580-594 ◽  
Author(s):  
Yvonne Allen ◽  
Kayla Kimmel ◽  
Glenn Constant
Keyword(s):  
Remote Sensing ◽  
Mississippi River ◽  
Habitat Suitability ◽  
Spawning Habitat ◽  
Alligator Gar ◽  
Lower Mississippi River

scholarly journals Long-Term Discharge Estimation for the Lower Mississippi River Using Satellite Altimetry and Remote Sensing Images

2020 ◽  
Vol 12 (17) ◽  
pp. 2693
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering ◽  
Florian Seitz
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Cross Sections ◽  
Mississippi River ◽  
Satellite Altimetry ◽  
Altimetry Data ◽  
Virtual Station ◽  
Lower Mississippi River ◽  
Satellite Altimetry Data

Despite increasing interest in monitoring the global water cycle, the availability of in situ gauging and discharge time series is decreasing. However, this lack of ground data can partly be compensated for by using remote sensing techniques to observe river stages and discharge. In this paper, a new approach for estimating discharge by combining water levels from multi-mission satellite altimetry and surface area extents from optical imagery with physical flow equations at a single cross-section is presented and tested at the Lower Mississippi River. The datasets are combined by fitting a hypsometric curve, 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 derived from the differences between virtual station elevations, which are computed in a least square adjustment from the height differences of all multi-mission satellite altimetry data that are close in time. Using the virtual station elevations, satellite altimetry data from multiple virtual stations and missions are combined to one long-term water level time series. All required parameters are estimated purely based on remote sensing data, without using any ground data or calibration. The validation at three gauging stations of the Lower Mississippi River shows large deviations primarily caused by the below average width of the predefined cross-sections. At 13 additional cross-sections situated in wide, uniform, and straight river sections nearby the gauges the Normalized Root Mean Square Error (NRMSE) varies between 10.95% and 28.43%. The Nash-Sutcliffe Efficiency (NSE) for these targets is in a range from 0.658 to 0.946.

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