A Cloud Removal Algorithm to Generate Cloud and Cloud Shadow Free Images Using Information Cloning

One of the main problems of optical remote sensing is clouds and cloud shadows caused by specific atmospheric conditions during data acquisition. These features limit the usage of acquired images and increase the difficulty in data analysis, such as normalized difference vegetation index values, misclassification, and atmospheric correction. Accurate detection and reliable cloning of cloud and cloud shadow features in satellite images are very useful processes for optical remote sensing applications. In this study, an automated cloud removal algorithm to generate cloud and cloud shadow free images from multitemporal Landsat-8 images is introduced. Cloud and cloud shadow areas are classified by using process-based rule set developed by using spectral and spatial features after applying simple linear iterative clustering superpixel segmentation algorithm to the image to find cloud pixel groups easily and correctly. Segmentation-based cloud detection method gives better results than pixel-based for detection of cloud and cloud shadow patches. After detection of clouds and cloud shadows, cloud-free images are created by cloning cloudless regions from multitemporal dataset. Spectral and structural consistency are preserved by considering spectral features and seasonal effects while cloning process. Statistical similarity tests are applied to find best cloud-free image to use for cloning process. Cloning results are tested with the structural similarity index metric to evaluate the performance of cloning algorithm.     

An open dataset for landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake,New Zealand

On November 14, 2016, the northeastern South Island of New Zealand was hit by the magnitude Mw 7.8 Kaikōura earthquake, which is characterized by the most complex rupturing mechanism ever recorded. The widespread landslides triggered by the earthquake make this event a great case study to revisit our current knowledge of earthquake-triggered landslides in terms of factors controlling the spatial distribution of landslides and the rapid assessment of geographic areas affected by widespread landsliding. Although the spatial and size distributions of landslides have already been investigated in the literature, a polygon-based co-seismic landslide inventory with landslide size information is still not available as of June 2021. To address this issue and leverage this large landslide event, we mapped 14,233 landslides over a total area of approximately 14,000 km². We also identified 101 landslide dams and shared them all via an open-access repository. We examined the spatial distribution of co-seismic landslides in relation to lithologic units and seismic and morphometric characteristics. We analyzed the size statistics of these landslides in a comparative manner, by using the five largest co-seismic landslide inventories ever mapped (i.e., Chi-Chi, Denali, Wenchuan, Haiti, and Gorkha). We compared our inventory with respect to these five ones to answer the question of whether the landslides triggered by the 2016 Kaikōura earthquake are less numerous and/or share size characteristics similar to those of other strong co-seismic landslide events. Our findings show that the spatial distribution of the Kaikōura landslide event is not significantly different from those belonging to other extreme landslide events, but the average landslide size generated by the Kaikōura earthquake is relatively larger compared to some other large earthquakes (i.e., Wenchuan and Gorkha).

     

Dynamic sub-surface characteristic and the active faults of the Genç District locating over the Bingöl Seismic Gap of the East Anatolian Fault Zone,Eastern Turkey

The Genç District is located on the Bingöl Seismic Gap (BSG) of the Eastern Anatolian Fault Zone (EAFZ) with its?~?34.000 residents. The Karl?ova Triple Junction, where the EAFZ, the North Anatolian Fault Zone, and the Varto Fault Zone meet, is only 80 km NE of the Genç District. To make an earthquake disaster damage prediction of the Genç District, carrying a high risk of disaster, we have (1) prepared a new geological map, and (2) conducted a single-station microtremor survey. We defined that three SW-NE trending active faults of the sinistral Genç Fault Zone are cutting through the District. We have obtained dominant period (T) as?<?0.2 s, the amplification factor (A) between 8 and 10, the average shear wave velocity for the first 30 m (Vs₃₀) as?<?300 m/s, and the seismic vulnerability index (Kg) as?>?20, in the central part of the Genç District. We have also prepared damage prediction maps for three bedrock acceleration values (0.25, 0.50, 0.75 g). Our earthquake damage prediction scenarios evidenced that as the bedrock acceleration values increase, the area of soil plastic behavior expands linearly. Here we report that if the average expected peak ground acceleration value (0.55–0.625 g) is exceeded during an earthquake, significant damage would be inevitable for the central part of the Genç District where most of the schools, mosques, public buildings, and hospitals are settled-down.

     

Evidence of NW extension of the North Anatolian Fault Zone in the Marmara Sea: a new interpretation of the Marmara Sea (İzmit) earthquake on 17 August 1999

Active faults aligning in NW–SE direction and forming flower structures of strike-slip faults were observed in shallow seismic data from the shelf offshore of Avc?lar in the northern Marmara Sea. By following the parallel drainage pattern and scarps, these faults were traced as NW–SE-directed lineaments in the morphology of the northern onshore sector of the Marmara Sea (eastern Thrace Peninsula). Right-lateral displacements in two watersheds of drainage and on the coast of the Marmara Sea and Black Sea are associated with these lineaments. This right-lateral displacement along the course of these faults suggests a new, active strike-slip fault zone located at the NW extension of the northern boundary fault of the Ç?narc?k Basin in the Marmara Sea. This new fault zone is interpreted as the NW extension of the northern branch of the North Anatolian Fault Zone (NAFZ), extending from the Ç?narc?k Basin of the Marmara Sea to the Black Sea coast of the Thrace Peninsula, and passing through B üy ük çekmece and K ü ç ük çekmece lagoons. These data suggest that the rupture of the 17 August 1999 earthquake in the NAFZ may have extended through Avc?lar. Indeed, Avc?lar and ?zmit, both located on the Marmara Sea coast along the rupture route, were strongly struck by the earthquake whereas the settlements between Avc?lar and ?zmit were much less affected. Therefore, this interpretation can explain the extraordinary damage in Avc?lar, based on the newly discovered rupture of the NAFZ in the Marmara Sea. However, this suggestion needs to be confirmed by further seismological studies.     

Seismic scattering and shallow structure of the moon in oceanus procellarum

Long, reverberating trains of seismic waves produced by impacts and moonquakes may be interpreted in terms of scattering in a surface layer overlying a non-scattering elastic medium. Model seismic experiments are used to qualitatively demonstrate the correctness of the interpretation. Three types of seismograms are found, near impact, far impact and moonquake. Only near impact and moonquake seismograms contain independent information. Details are given in the paper of the modelling of the scattering processes by the theory of diffusion.Interpretation of moonquake and artificial impact seismograms in two frequency bands from the Apollo 12 site indicates that the scattering layer is 25 km thick, with a Q of 5000. The mean distance between scatterers is approximately 5 km at 25 km depth and approximately 2 km at 14 km depth; the density of scatterers appears to be high near the surface, decreasing with depth. This may indicate that the scatterers are associated with cratering, or are cracks that anneal with depth. Most of the scattered energy is in the form of scattered surface waves.Communication presented at the Lunar Science Institute Conference on Geophysical and Geochemical Exploration of the Moon and Planets, January 10–12, 1973.     

Lunar velocity structure and compositional and thermal inferences

Seismic data from the Apollo Passive Seismic Network stations are analyzed to determine the velocity structure and to infer the composition and physical properties of the lunar interior. Data from artificial impacts (S-IVB booster and LM ascent stage) cover a distance range of 70–1100 km. Travel times and amplitudes, as well as theoretical seismograms, are used to derive a velocity model for the outer 150 km of the Moon. TheP wave velocity model confirms our earlier report of a lunar crust in the eastern part of Oceanus Procellarum.The crust is about 60 km thick and may consist of two layers in the mare regions. Possible values for theP-wave velocity in the uppermost mantle are between 7.7 km s^–1 and 9.0 km s^–1. The 9 km s^–1 velocity cannot extend below a depth of about 100 km and must decrease below this depth. The elastic properties of the deep interior as inferred from the seismograms of natural events (meteoroid impacts and moonquakes) occurring at great distance indicate that there is an increase in attenuation and a possible decrease of velocity at depths below about 1000 km. This verifies the high temperatures calculated for the deep lunar interior by thermal history models.Paper presented at the Lunar Science Institute Conference on Geophysical and Geochemical Exploration of the Moon and Planets, January 10–12, 1973.     

An anisotropic hardening model for soils and its application to cyclic loading

An anisotropic hardening model for soils is proposed by applying the concept of a field of hardening moduli developed previously for metals. Besides the yield surface, a set of nesting surfaces in the stress-space specifies the variation of hardening moduli during the deformation process. Both drained and undrained soil behaviour can be treated and distortional as well as volumetric strain cycles can be considered. The model can be applied in studying soil behaviour under cyclic loading and in particular to describe densification or liquefaction phenomena.     

Moonquakes and lunar tectonism

With the succesful installation of a geophysical station at Hadley Rille, on July 31, 1971, on the Apollo 15 mission, and the continued operation of stations 12 and 14 approximately 1100 km SW, the Apollo program for the first time achieved a network of seismic stations on the lunar surface. A network of at least three stations is essential for the location of natural events on the Moon. Thus, the establishment of this network was one of the most important milestones in the geophysical exploration of the Moon. The major discoveries that have resulted to date from the analysis of seismic data from this network can be summarized as follows:

1. Lunar seismic signals differ greatly from typical terrestrial seismic signals. It now appears that this can be explained almost entirely by the presence of a thin dry, heterogeneous layer which blankets the Moon to a probable depth of few km with a maximum possible depth of about 20 km. Seismic waves are highly scattered in this zone. Seismic wave propagation within the lunar interior, below the scattering zone, is highly efficient. As a result, it is probable that meteoroid impact signals are being received from the entire lunar surface.
2. The Moon possesses a crust and a mantle, at least in the region of the Apollo 12 and 14 stations. The thickness of the crust is between 55 and 70 km and may consist of two layers. The contrast in elastic properties of the rocks which comprise these major structural units is at least as great as that which exists between the crust and mantle of the earth. (See Toks?zet al., p. 490, for further discussion of seismic evidence of a lunar crust.)
3. Natural lunar events detected by the Apollo seismic network are moonquakes and meteoroid impacts. The average rate of release of seismic energy from moonquakes is far below that of the Earth. Although present data do not permit a completely unambiguous interpretation, the best solution obtainable places the most active moonquake focus at a depth of 800 km; slightly deeper than any known earthquake. These moonquakes occur in monthly cycles; triggered by lunar tides. There are at least 10 zones within which the repeating moonquakes originate.
4. In addition to the repeating moonquakes, moonquake ‘swarms’ have been discovered. During periods of swarm activity, events may occur as frequently as one event every two hours over intervals lasting several days. The source of these swarms is unknown at present. The occurrence of moonquake swarms also appears to be related to lunar tides; although, it is too soon to be certain of this point.

These findings have been discussed in eight previous papers (Lathamet al., 1969, 1970, 1971) The instrument has been described by Lathamet al. (1969) and Sutton and Latham (1964). The locations of the seismic stations are shown in Figure 1.     

Modelling and Remote Sensing of Land Surface Temperature in Turkey

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26?C45°E and 36?C42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eski?ehir, ?stanbul, ?zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST_ANN) and Becker and Li temperature(LST_B-L) method values have been found as 0.077?K and 0.091?K (training stations), 0.045?K and 0.003?K (testing stations), respectively.