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Earthquake early warning system uses high-speed computer network to transmit earthquake information to population center ahead of the arrival of destructive earthquake waves. This short (10 s of seconds) lead time will allow emergency responses such as turning off gas pipeline valves to be activated to mitigate potential disaster and casualties. However, the excessive false alarm rate of such a system imposes heavy cost in terms of loss of services, undue panics, and diminishing credibility of such a warning system. At the current, the decision algorithm to issue an early warning of the onset of an earthquake is often based on empirically chosen features and heuristically set thresholds and suffers from excessive false alarm rate. In this paper, we experimented with three advanced machine learning algorithms, namely, $K$ -nearest neighbor (KNN), classification tree, and support vector machine (SVM) and compared their performance against a traditional criterion-based method. Using the seismic data collected by an experimental strong motion detection network in Taiwan for these experiments, we observed that the machine learning algorithms exhibit higher detection accuracy with much reduced false alarm rate. [ABSTRACT FROM AUTHOR]

Variations of the ionosphere can significantly disrupt synthetic aperture radar (SAR) acquisitions and interferometric measurements of ground deformation. In this paper, we show how the ionosphere can also strongly modify C-band interferograms despite its smaller influence at higher frequencies. Thus, ionospheric phase screens should not be neglected: their compensation improves the estimation of ground deformation maps. The split-spectrum method is able to estimate the dispersive ionospheric component of the interferometric phase; we describe the implementation of this method for the burst modes TOPS and ScanSAR to estimate and remove ionospheric phase screens. We present Sentinel-1 interferograms of the 2016 Taiwan earthquake and ALOS-2 interferograms of the 2015 Nepal earthquake, which show strong ionospheric phase gradients, and their corrected versions. Finally, to validate the results and better understand the origin of these ionospheric variations, we compare the estimated differential ionosphere with global Total Electron Content maps and local Global Positioning System measurements. [ABSTRACT FROM PUBLISHER]

Rainfall-induced landslides are a major threat in Taiwan, particularly during the typhoon season. A precise survey of landslides after a super event is a critical task for disaster, watershed, and forestry land management. In this paper, we utilize high spatial resolution multispectral optical imagery and a digital elevation model (DEM) with an object-oriented analysis technique to develop a scheme for the recognition of landslides using multilevel segmentation and a hierarchical semantic network. Four case studies are presented to evaluate the feasibility of the proposed scheme. Three kinds of remote sensing imagery, namely pan-sharpened FORMOSAT-2 satellite images, aerial digital images from Z/I digital mapping camera, and images acquired by a digital single lens reflex camera mounted on a fixed-wing unmanned aerial vehicle are used. An accuracy assessment is accomplished by evaluating three test sites containing hundreds of landslides associated with the Typhoon Morakot. The input data include ortho-rectified image and DEM. Four spectral and one topographic object features are derived for semiautomatic landslide recognition. The threshold values are determined semiautomatically by statistical estimation from a few training samples. The experimental results show that the proposed approach can counteract the commission/omission errors and achieve missing/branching factors at less than 0.12 with a quality percentage of 81.7%. The results demonstrate the feasibility and accuracy of the proposed landslide recognition scheme even when different optical sensors are utilized. [ABSTRACT FROM AUTHOR]

The FORMOSAT-3 mission, also known as Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), is the third major project of the Formosa Satellite (FORMOSAT) series implemented by the National Space Organization of Taiwan. FORMOSAT-3/COSMIC is a joint Taiwan/U.S. mission consisting of six identical low Earth orbit satellites. All six cluster satellites were successfully launched by a single Minotaur launch vehicle on April 15, 2006. The retrieved global positioning system (GPS) radio occultation (RO) data have been freely available online to the science community since shortly after the completion of satellite bus in-orbit checkout. Having completed the verification and validation, the worldwide science communities are highly satisfied with the RO data. Scientists have hailed the RO sensors as offering the most accurate, precise, and stable thermometers in space. After one year in orbit, all six FORMOSAT-3/COSMIC satellites were in good condition (except FM2, which had power shortage issues) and were on their way toward the final constellation of six separate orbit planes with 30° separation. Four out of six satellites had already reached their final mission orbit of 800 km by mid-May 2007. Together, the six satellites have generated a total of more than 2500 RO data per day. However, only 50%-70% of the RO data as received one year after launch could be retrieved into useful atmosphere profiles. The retrieved RO data, about 1800 per day on average, have been assimilated into numerical weather prediction models by many major weather forecast centers and research institutes. This paper provides an overview of the constellation mission, the spacecraft system performance after one year in orbit, the technical challenges we have encountered, and the performance enhancements we have accomplished. [ABSTRACT FROM AUTHOR]

The FORMOSA Satellite Series No. 3/Constellation Observing System for Meteorology, Ionosphere and Climate (FORMOSAT-3/COSMIC) spacecraft constellation consisting of six low-earth-orbiting satellites is the world's first operational Global Positioning System (GPS) radio occultation mission. The mission has been jointly developed by the National Space Organization of Taiwan and the University Corporation for Atmospheric Research of the U.S. in collaboration with the Jet Propulsion Laboratory, NASA, and the Naval Research Laboratory for three onboard payloads, including a GPS Occultation Receiver, a triband beacon, and a tiny ionospheric photometer. The FORMOSAT-3/COSMIC mission was successfully launched from Vandenberg into the same orbit plane of the designated 516-km circular parking orbit altitude on April 15, 2006. After the six satellites completed the in-orbit checkout activities, the mission was started immediately at the parking orbit for in-orbit checkout, calibration, and experiment of three onboard payloads. Individual spacecraft thrust burns for orbit raising were performed to begin the constellation deployment of the satellites into six separate orbit planes. All six FORMOSAT-3/COSMIC satellites are maintained in a good state of health except spacecraft flight model no. 2, which has had power shortages. Five out of the six satellites had reached their final mission orbits of 800 km as of November 2007. This paper provides an overview of the constellation spacecraft design, constellation mission operations, constellation deployment time-line evolution, associated spacecraft mass property and moment of inertia results, orbit-raising challenges, and lessons learned during the orbit-raising operations. [ABSTRACT FROM AUTHOR]