Thin layer placement for marsh enhancement: Planning, design, construction, and monitoring considerations

Thin layer placement (TLP) is the purposeful placement of thin layers of sediment in an environmentally acceptable manner to achieve a target elevation or thickness. TLP is used for a variety of purposes, such as sediment management, beneficial use of dredged material (DM), and ecological enhancement. The term “thin” is used to distinguish TLP from other methods of sediment placement in which sediments are applied in layers on the order of several meters thick. In this paper, DM disposal refers to the deposition of sediment in a location and manner where no beneficial use is attained; with DM placement the sediment is used to benefit society and the environment. The application of thin layers of sediment has advantages over more traditional, thicker sediment applications in environments where these thicker layers pose potential challenges to natural resources, infrastructure, navigation, or other assets. Although TLP projects are most often conducted in wetlands, there are open-water applications as well. But because TLP is relatively early in its development, there is a dearth of design and construction information and guidance available to practitioners. This paper provides a high-level summary of pending national TLP guidance being developed by the authors on behalf of the U.S. Army Corps of Engineers’ Engineer Research and Development Center (USACE ERDC).

Elevation Spatial Variation Error Compensation in Complex Scene and Elevation Inversion by Autofocus Method in GEO SAR

Due to the high altitude of geosynchronous synthetic aperture radar (GEO SAR), its synthetic aperture time can reach up to several hundred seconds, and its revisit cycle is very short, which makes it of great application worth in the remote sensing field, such as in disaster monitoring and vegetation measurements. However, because of the elevation of the target, elevation spatial variation error is caused in the GEO SAR imaging. In this paper, we focus on the compensation of the elevation space-variant error in the fast variant part with the autofocus method and utilize the error to carry out elevation inversing in complex scenes. For a complex scene, it can be broken down into a slow variant slope and the remaining fast variant part. First, the phase error caused by the elevation spatial variation is analyzed. Second, the spatial variant error caused by the slowly variant slope is compensated with the improved imaging algorithm. The error caused by the remaining fast variable part is the focus of this paper. We propose a block map-drift phase gradient autofocus (block-MD-PGA) algorithm to compensate for the random phase error part. By dividing sub-blocks reasonably, the elevation spatial variant error is compensated for by an autofocus algorithm in each sub-block. Because the errors of different elevations are diverse, the proposed algorithm is suitable for the scene where the target elevations are almost the same after the sub-blocks are divided. Third, the phase error obtained by the autofocus method is used to inverse the target elevation. Finally, simulations with dot-matrix targets and targets based on the high-resolution TerraSAR-X image verify the excellent effect of the proposed method and the accuracy of the elevation inversion.

Sensory gaze stabilization in echolocating bats

Sensing from a moving platform is challenging for both man-made machines and animals. Animals' heads jitter during movement, so if the sensors they carry are not stabilized, any spatial estimation might be biased. Flying animals, like bats, seriously suffer from this problem because flapping flight induces rapid changes in acceleration which moves the body up and down. For echolocating bats, the problem is crucial. Because they emit a sound to sense the world, an unstable head means sound energy pointed in the wrong direction. It is unknown how bats mitigate this problem. By tracking the head and body of flying fruit bats, we show that they stabilize their heads, accurately maintaining a fixed acoustic-gaze relative to a target. Bats can solve the stabilization task even in complete darkness using only echo-based information. Moreover, the bats point their echolocation beam below the target and not towards it, a strategy that should result in better estimations of target elevation.