SUPER-RESOLUTION IMAGING OF REMOTE SENSED BRIGHTNESS TEMPERATURE USING A CONVOLUTIONAL NEURAL NETWORK

Steady improvements to the instruments used in remote sensing has led to much higher resolution data, often contemporaneous with lower resolution instruments that continue to collect data. There is a clear opportunity to reconcile recent high resolution satellite data with the lower resolution data of the past. Super-resolution (SR) imaging is a technique that increases the spatial resolution of image data by training statistical methods on simultaneously occurring lower and higher resolution data sets. The special sensor microwave/imager (SSMI) and advanced microwave scanning radiometer (AMSR2) brightness temperature data products are well suited to super-resolution imaging, and SR can be used to standardize the higher resolution across the entire record of observations. Of the methods used in super-resolution imaging, neural networks have led to major improvements in the realm of computer vision and have seen great success in the super-resolution of photographic images. We trained two neural networks, based on the design of the Resnet, to super-resolution the 25 kilometer resolution SSMI and AMSR2 brightness temperature data products up to a 10 kilometer resolution. The mean error over all frequencies and polarizations for the AMSR and SSMI models’ predictions is 0.84% and 2.4% respectively for the years 2013 and 2019

Temporal development of the displacement field of the Ponzano landslide in February 2017

The conducted research determined the temporal evolution of the displacement field for the Ponzano landslide case study. The offset-tracking method, so far used mainly for the relatively rapid but uniform displacement of glaciers, was tested for the 2017 study of the Ponzano landslide in Italy. The suitability of the method for high-resolution TerraSAR-X and medium-resolution Sentinel-1 imagery was investigated. The results proved the applicability of the OT method for studying processes with high and variable displacement dynamics. However, for such purposes, high-resolution radar data are crucial. With an uncertainty in the determination of residual displacements of about ±1 m, it was shown that the values of residual displacements occurring up to several days after the main phase of landslide movements are within the range of uncertainty but are determinable. The research conducted in the paper filled a gap in the analysis of the phenomenon just after the main movement phase. It allowed determination of the time and speed of extinction of landslide movements

Application of Resistivity and Seismic Refraction Tomography for Landslide Stability Assessment in Vallcebre, Spanish Pyrenees

Geophysical surveys are a noninvasive reliable tool to improve geological models without requiring extensive in situ borehole campaigns. The usage of seismic refraction tomography (SRT), electrical resistivity tomography (ERT) and borehole data for calibrating is very appropriate to define landslide body geometries; however, it is still only used occasionally. We present here the case of a Spanish Pyrenees slow-moving landslide, where ERT, SRT and lithological log data were integrated to obtain a geological three-dimensional model. The high contrasts of P-wave velocity and electrical resistivity values of the upper materials (colluvial debris and clayey siltstone) provided accurate information on the geometry of the materials involved in the landslide body, as well as the sliding surface. Geophysical prospecting allowed us to identify the critical sliding surface over a large area and at a reduced cost and, therefore, gives the geophysical method an advantage over borehole data. The three-dimensional model was used to carry out stability analyses of a landslide in 2D and 3D, which, coherently with previous studies, reveal that the lower part is more unstable than the upper units

MONITORING THE SLOWLY DEVELOPING LANDSLIDE WITH THE INSAR TECHNIQUE IN SAMSUN PROVINCE, NORTHERN TURKEY

Landslides are prominent natural events with high destructive power. Since they affect large areas, it is important to monitor the areas they cover and analyse their movement. Remote sensing data and image processing techniques have been used to monitor landslides in different areas. Synthetic aperture radar (SAR) data, particularly with the Interferometric SAR (InSAR) method, is used to determine the velocity vector of the surface motion. This study aims to detect the landslide movements in Samsun, located in the north of Turkey, using persistent scattering InSAR method. Archived Copernicus Sentinel-1 satellite images taken between 2017 and 2022 were used in both descending and ascending directions. The results revealed surface movements in the direction of the line of sight, ranging between −6 and 6 mm/year in the study area. Persistent Scatterer (PS) points were identified mainly in human structures such as roads, coasts, ports, and golf courses, especially in settlements. While some regions exhibited similar movements in both descending and ascending results, opposite movements were observed in some regions. The results produced in both descending and ascending directions were used together and decomposed into horizontal and vertical deformation components. It was observed that the western coastal part experienced approximately 4.5 cm/year vertical deformation, while the central part there is more significant horizontal deformation, reaching up to approximately 6 cm/year

A simple GIS-based tool for the detection of landslide-prone zones on a coastal slope in Scotland

Effective landslide detection is crucial to mitigate the negative impacts derived from the occurrence of these natural hazards. Research on landslide detection methods has been extensively undertaken. However, simplified methods for landslide detection requiring a minimum amount of data inputs are still lacking. Simple approaches for landslide detection should be particularly interesting for geographical areas with limited information or resources availability. The aim of this paper is to present a refined, simple, GIS-based tool for the detection of landslide-prone and slope restoration zones. The tool only requires a digital elevation model (DEM) dataset as input, it is interoperable at multiple spatial scales, and it can be implemented on any GIS platform. The tool was applied on a coastal slope prone to instability, located in Scotland, in order to verify the functionality of the tool. The results indicated that the proposed tool is able to detect both shallow and deeper landslides satisfactorily, suggesting that the spatial combination of steep and potentially wet soil zones is effective for detecting areas prone to slope failure

Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms

© 2019 SPIE. Landslides are type of natural geohazard interfering with many economical and social activities and causing serious damages on human life. It is ranked as a great disaster, threatening life, property and environment. Therefore, early prediction of landslide prone areas is vital. Variety of causative factors such as glaciers melting, excessive raining, mining, volcanic activities, active faults, earthquake, logging, erosion, urbanization, construction, and other human activities can trigger landslide occurrence. Then, identification of factors that directly influences the slide events is highly in demand. Some topographical, geological, and hydrological datasets (e.g., slope, aspect, geology, terrain roughness, vegetation index, distance to stream, distance to road, distance to fault, land use, precipitation, profile curvature, plan curvature) are considered to be effective conditioning factors. However, the importance of each factor differs from one study to another. This study investigates the effectiveness of four sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into four groups G1, G2, G3, and G4. Three machine learning algorithms namely, Random Forest (RF), Naive Bayes (NB), and Boosted Logistic Regression (LogitBoost) were constructed based on each dataset in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 227 landslide inventory datasets of the study area were used where 70% was used for training and 30% for testing. To this end, in the present research, the two main objectives were: 1) Investigation on effectiveness of 14 landslides conditioning factors (altitude, slope, aspect, total curvature, profile curvature, plan curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), distance to fault, distance to road, distance to stream, land use, and geology) by analyzing and determining the most important factors using variance-inflated factor (VIF), Pearson's correlation and Chi-square techniques. Consequently, 4 categories of datasets were defined; first dataset included all 14 conditioning factors, second dataset included Digital Elevation Models (DEM) derivatives (morphometrice factors), third dataset was only based on 5 factors namely lithology, land use, distance to stream, distance to road, and distance to fault, and last dataset was included 8 factors selected using factor analysis and optimization. 2) Evaluate the sensitivity of each modeling technique (NB, RF and LogitBoost) to different conditioning factors using the area under curve (AUC). Eventually, RF technique using optimized variables (G4) performed well with AUC of 0.940 followed by LogitBoost (0.898) and NB (0.864)

NOVI PRISTUP PRAĆENJA POMAKA KLIZIŠTA POMOĆU BESPILOTNIH FOTOGRAMETRIJSKIH SUSTAVA

Landslides represent great dangers that can cause fatalities and huge property damage. To prevent or reduce all possible consequences that landslides cause, it is necessary to know the kinematics of the surface and undersurface sliding masses. Geodetic surveying techniques can be used for landslide monitoring and creating a kinematic model of the landslide. One of the most used surveying techniques for landslide monitoring is the photogrammetric survey by Unmanned Aerial System. The results of the photogrammetric survey are dense point clouds, digital terrain models, and digital orthomosaic maps, where landslide displacements can be determined by comparing these results in two measurement epochs. This paper presents a new data processing method with a novel approach for calculating landslide displacements based on Unmanned Aerial System photogrammetric survey data. The main advantage of the new method is that it does not require the production of dense point clouds, digital terrain models, or digital orthomosaic maps to determine displacements. The applicability and accuracy of the new method were tested in a test field with simulated displacements of known values within the range of 20-40 cm in various directions. The new method successfully determined these displacements with a 3D accuracy of ±1.3 cm.Klizišta predstavljaju velike opasnosti koje mogu uzrokovati katastrofalne ljudske žrtve te nanijeti veliku materijalnu štetu. Da bi se spriječile ili umanjile sve moguće posljedice koje klizišta prouzročuju, važno je poznavati kinematiku kretanja površinskih i podzemnih kliznih masa klizišta. Geodetske tehnike izmjere mogu se koristiti za potrebe praćenja te za izradu kinematičkoga modela klizišta. U današnje vrijeme jedna od najčešće korištenih geodetskih tehnika za potrebe praćenja klizišta jest fotogrametrijsko snimanje pomoću bespilotnih zrakoplovnih sustava. Rezultati su takvih snimanja gusti oblaci točaka, digitalni modeli terena te digitalne ortomozaik karte, a na temelju usporedbe tih rezultata u dvjema mjernim epohama mogu se odrediti pomaci klizišta. Ovaj rad predstavlja novu metodu obrade podataka s novim pristupom za određivanje pomaka klizišta na temelju podataka fotogrametrijskoga snimanja bespilotnim zrakoplovnim sustavima. Glavna je prednost nove metode u tome što ne zahtijeva izradu gustih oblaka točaka, digitalnih modela terena ili digitalnih ortomozaik karata za određivanje pomaka. Primjenjivost i točnost nove metode ispitane su na testnome polju sa simuliranim pomacima poznatih vrijednosti čiji su se iznosi kretali u rasponu od 20 do 40 cm u različitim smjerovima. Nova metoda uspješno je odredila te pomake s 3D točnošću od ±1,3 cm

Identifikasi Karakteristik dan Faktor Pengaruh pada Berbagai Tipe Longsor

Landslide disaster mitigation is necessary in areas vulnerable to this disaster. Banjarnegara is one of the regencies in Central Java Province with high ground movement potential, hence, it is prone to landslides. The aim of this study was to determine the characteristics and factors that influence the type of landslides in Banjarnegara District. The observation of the research was based on the results of landslide vulnerability analysis. Identification of the landslides characteristics, both in types and factors that influence them, are carried out by a survey method with a purposive random sampling technique by considering the locations that have experienced landslides, and the level of vulnerability to landslides. The survey was conducted using a landslide control card (KKL) which was compiled based on the factors that cause landslides. Scoring was done to determine the determinants of the landslide type quantitatively, on a scale of 1 to 5. The results showed that there were three types of landslides found in the study location, namely rotational slide, creep slide and flows. Factors that influenced rotational slide in the study site were slope, soil depth, faults, and infrastructure, while for creep slide were faults, slope, length of slope, and infrastructure. In addition, flows were affected by faults and infrastructure. The highest KKL value was 77 in the rotation landslide type and the lowest was 51 in the creep landslide type. Rainfall is also a trigger factor for the three types of landslides. It is highly recommended to do technical mitigation by observing the principles of soil and water conservation and high adaptation to the people living in this area.