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This article provides an overview of the landslides that occurred in Wrangell, Alaska in November 2023. The landslides resulted in the destruction of homes and loss of life. Geologists conducted field visits and collected aerial lidar data to assess the extent and volume of the landslides. The article also discusses the weather conditions leading up to the landslides, including heavy rainfall and wind gusts. The text highlights the mapping of landslides in the Tongass National Forest and the use of LIDAR datasets for remote sensing. Additionally, the document describes the collection of lidar data for Wrangell Island and the identification of several landslides in the area. The investigation also found no recent mass movement near water reservoirs. [Extracted from the article]

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.

Even following the latter approach, the required attributes for most roles mean that, for countries other than the US and China, swarming munitions and UAVs/UCAVs will only be affordable in small quantities. If intended to operate alongside stealth aircraft without giving away the latter by their presence, such loyal wingman UCAVs will also need to carry advanced sensors and munitions internally within a low-observable airframe. Swarming Munitions, UAVs and the Myth of Cheap Mass Some projects such as the RAF's Alvina programme are examining significantly smaller and cheaper systems, but these will be very limited in range compared to larger UAVs and piloted combat aircraft, and/or sacrifice responsiveness by having a slow cruising speed.[18] With short-range systems, potentially vulnerable delivery platforms will have to get dangerously close to threats whilst carrying a large payload of still relatively expensive "attritable" UAVs. [Extracted from the article]

The USDA Forest Service uses many forms of technology to manage 193 million acres that comprise the National Forest System: handheld data-entry devices and satellites; modeling software and LiDAR (light detection and ranging) remote sensing; and desktop computers and servers Collectively, these technologies power the models that create the maps land managers use to monitor and understand landscape changes Adopting cloud-computing platforms, such as Google Earth Engine (GEE), offers potential to advance these monitoring efforts even further Researchers with the USDA Forest Service's Pacific Northwest and Rocky Mountain Research Stations and Google collaborated to customize GEE's cloud-computing capabilities Their goal: develop a way to store and process the vast amounts of the remote-sensing data needed for the Gradient Nearest Neighbor (GNN) model that produces forest attribute maps The research team used the new process to create forest attribute maps for the recently published report, North-west Forest Plan-The First 25 Years (1994-2018): Status and Trends of Late-Successional and Old-Growth Forests The researchers found that using GEE substantially decreased the processing time required to create the maps Additionally, two new online tools, the Landscape Change Monitoring System Data Explorer and the Gradient Nearest Neighbor Trend Tool, allow users to see the most recent model results within the Northwest Forest Plan area. [ABSTRACT FROM AUTHOR]

An optical radar for detecting targets in natural waters was built and tested in the Gulf of Mexico. The transmitter consists of a Q switched neodymium glass laser, with output amplified and doubled in KDP to 0.53 micrometer wavelength. The receiver incorporates a noval optical spatial filter to reduce the dynamic range required of the photodetector to a reasonable value. Detection of targets to a depth of 26 meters (84 feet) was achieved with a considerable sensitivity margin. The sensitivity of the radar is highly dependent on the optical attenuation coefficient. In general, measured returns fell between the values predicted on the basis of monopath and multipath attenuation. By means of simple physical arguments, a radar equation for the system was derived. To validate this theoretical model, measurements of optical attenuation and of water surface behavior were also instrumented, and some of these results are given.

The Jet Propulsion Laboratory has developed a set of computer programs known as the Solar System Data Processing System (SSDPS) which is employed in improving the ephemerides of the major planets and for improving the values of several associated astronomical constants. A group of solutions for the masses of the major planets, together with the AU and radii of Mercury, Venus, and Mars, is presented. These solutions based upon optical, radar, and spacecraft radio tracking data are preliminary. The relative power of radar and radio tracking data vis-a-vis purely optical data in a solution is shown. The problems which could arise by adopting solutions based upon a single data type are demonstrated.

Aerosol content of mesosphere with noctilucent clouds measured with optical radar in Norway

Upper atmospheric dust investigations using optical radar techniques

Atmospheric optical radar Mie scattering

Optical radar measurement of backscattering from mesopause region during July and August 1967

80 km atmospheric backscattering enhancement detected by optical radar

Optical radar investigations of atmosphere, and Mie scattering intensity functions for backscatter

Optical radar detection of backscatter from upper atmosphere

Optical radar system using corner reflector on lunar surface

Ruby laser optical radar detection of upper atmospheric backscattering

Optical radar experiments and meteoric fragmentation

Detection of the optical echoes from atmospheric constituents in the upper regions by optical radar

Detection of scattering layers in upper atmosphere by optical radar

The development of optical, radar, and candidate subsystems for Project Apollo is discussed. The design and development of the optical subsystems for both the Apollo command and lunar spacecraft are described. Design approaches, problems, and solutions are presented. The evolution of radar interfaces with the GN&C system is discussed; these interfaces involved both hardware and software in a relatively complex interrelationship. The design and development of three candidate subsystems are also described. The systems were considered for use in Apollo, but were not incorporated into the final GN&C system. The three subsystems discussed are the star tracker-horizon photometer, the map and data viewer and the lunar module optical rendezvous system.

Laser for radar device is retriggered when previous laser pulse is reflected from target. Target range R is computed from number of pulses triggered per time interval. Radar accurately measures distances up to 500 meters; it is useful for determining surface shape of relfectors in large, high-gain, highly directional antennas and for other short-range surveying.

The behavior of the photomultiplier is considered, as well as the method of derivation of the photomultiplier output pulse and its relation to the reflected light pulse width and amplitude, and the calibration of range precision and accuracy. Pulsed laser radars with light pulse widths of 30, 3, and 0.1 nanosec a considered, with the 0.1 nanosec system capable of highest precision in several modes of operation, including a high repetition rate, single photoelectron reception mode. An alternate calibration scheme using a fast, triggerable light pulser is described in detail.

Meteoritic dust accumulations at low latitudes in upper atmosphere evidenced by observation of noctilucent clouds, estimating scattering function

Lunar corner reflector landing proposal for laser beam precision measurements including Moon orbit, size, ephemeris time and libration

The system is composed of an optical cavity with a laser and a mode locking means to build up an optical pulse. An optical switch is also provided within the cavity to convert the polarization of the optical pulse generated within the cavity. The optical switch comprises an electro-optical crystal driven by a time delayed driver circuit which is triggered by a coincident signal made from an optical pulse signal and a gating pulse signal. The converted optical pulse strikes a polarization sensitive prism and is deflected out of the cavity toward the pending target in the form of a pulse containing most of the optical energy generated by the laser in the pulse build-up period. After striking the target, the reflected energy is picked up by a transceiver with the total travel time of the pulse being recorded.

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