A semi-distributed, physics-based hydrologic model using remotely sensed and Digital Terrain Elevation Data for semi-arid catchments

2004 ◽  
Vol 25 (20) ◽  
pp. 4351-4379 ◽  
Author(s):  
Getu Fana Biftu ◽  
Thian Yew Gan
Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 509
Author(s):  
Dipayan Mitra ◽  
Aranee Balachandran ◽  
Ratnasingham Tharmarasa

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.


2020 ◽  
Vol 10 (18) ◽  
pp. 6440
Author(s):  
Tea Heung Lim ◽  
Minho Go ◽  
Chulhun Seo ◽  
Hosung Choo

In this paper, we propose the analysis of the target detection performance of air-to-air airborne radars using long-range propagation simulations with a novel quad-linear refractivity model under abnormal atmospheric conditions. The radar propagation characteristics and the target detection performance are simulated using the Advanced Refractive Effects Prediction System (AREPS) software, where the refractivity along the altitude, array antenna pattern, and digital terrain elevation data are considered as inputs to obtain the path loss of the wave propagation. The quad-linear model is used to approximate the actual refractivity data, which are compared to the data derived using the conventional trilinear refractivity model. On the basis of the propagation simulations, we propose a detection performance metric in terms of the atmosphere (DPMA) for intuitively examining the long-range propagation characteristics of airborne radars in air-to-air situations. To confirm the feasibility of using the DPMA map in various duct scenarios, we employ two actual refractive indices to observe the DPMA results in relation to the height of the airborne radar.


2020 ◽  
Author(s):  
In-Young Yeo ◽  
Ali Binesh ◽  
Garry Willgoose ◽  
Greg Hancock ◽  
Omer Yeteman

<p>The water-limited region frequently experiences extreme climate variability.  This region, however, has relatively little hydrological information to characterize the catchment dynamics and its feedback to the climate system. This study assesses the relative benefits of using remotely sensed soil moisture, in addition to sparsely available in-situ soil moisture and stream flow observations, to improve the hydrologic understanding and prediction.  We propose a multi-variable approach to calibrate a hydrologic model, Soil and Water Assessment Tool (SWAT), a semi-distributed, continuous catchment model, with observed streamflow and in-situ soil moisture.  The satellite<span> soil moisture products (~ 5 cm top soil) from the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) are then used to evaluate the model estimates of soil moisture over the spatial scales through time.  The results show the model calibrated against streamflow only could provide misleading prediction for soil moisture.  Long term in-situ soil moisture observations, albeit limited availability, are crucial to constrain model parameters leading to improved soil moisture prediction at the given site.  </span><span>Satellite soil moisture products </span><span>provide useful information to assess simulated soil moisture results across the spatial domains, filling the gap on the soil moisture information at landscape scales.</span> <span>The preliminary results from this study suggest the potential to produce robust soil moisture and streamflow estimates across scales for a semi-arid region, using a distributed catchment model with in-situ soil network and remotely sensed observations and enhance the overall water budget estimations for multiple hydrologic variables across scales.  </span>This research is conducted on Merriwa catchment, a semi-arid region located in the Upper Hunter Region of NSW, Australia.</p>


Author(s):  
Youngjoo Kim ◽  
Hyochoong Bang

A vision-based navigation approach using digital terrain elevation data and a monocular camera is addressed for autonomous navigation of unmanned aircraft. Previous vision-based terrain-referenced navigation algorithms use visual measurements to update vehicle position. This study expands the use of the visual measurements and the terrain data by designing the navigation filter to update 3-axis attitude and velocity as well as position. An observation model for updating position and attitude compares height estimates of ground features, computed from the visual measurements, with terrain elevation data. Additionally, the eight-point algorithm is adopted to extract direction of camera movement to update velocity in the navigation filter. A simulation study is conducted to verify the feasibility of the proposed method and the effect of visual measurement errors.


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