Geodesy and Cartography

Investigation of Hatay-Defne earthquake (20.02.2023) by using GNSS station

Atınç Pırtı — 2024-12-10

No studies have been conducted on the spatial changes related to the Hatay-Defne earthquake (20 February 2023) up to now. The reason for this situation is that the requested data cannot be accessed as a result of the power and internet interruptions caused by the earthquake. The southern Turkish city of Hatay had a 6.3 magnitude earthquake at 20:04 on February 20, 2023. Three minutes later, a 5.8 magnitude aftershock occurred, and 90 more aftershocks followed. These earthquakes, which were felt across the area, caused individuals who survived the horrific quakes on February 6, 2023, to experience new levels of dread. In the district of Hatay-Defne, the epicenter began. In its first assessment, the United States Geological Survey gave the earthquake a magnitude of 6.3 at a depth of 16 kilometers. In this study, firstly, GNSS data belonging to ARST station in 30 seconds recording interval were downloaded into CSRS-PPP. GNSS data was processed as static and then was processed as epoch to epoch by using kinematic method. The coordinate differences between the obtained coordinates by using the kinematic processing and the obtained coordinates by using the static processing were computed. According to the obtained results, a horizontal movement of approximately 8.30 cm in the south-west direction was observed at the ARST station, which is the closest to the earthquake center.

Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine

Anubhava Srivastava — 2024-12-16

By rapid growth in programming tools, accessibility to end consumer computing power, and the availability of free satellite data, the data science and remote sensing fields have begun to converge in recent years. Before this major processing time is wasted in collection of data. Google Earth Engine easily overcomes above problem; it contains data from different satellites and has power of processing and computation also. Well known data provider satellites are present in the library of GEE and users can easily process and track real time data from these satellites over GEE. “Sentinel”, a mission of the European Space Agency and “Landsat”, an American Earth observation satellite have been used in a variety of remote sensing applications. GEE makes these data sets available to the general public. These datasets are utilised for computing and analysis purposes. The objective of this study is to find change in study area by using above discussed two satellite data, over each season of year on different category of classification (Random Forest, CART, GTB and SVM). This work focuses on improving the classification accuracy of different classification algorithm by reviewing training samples and analyzing post-classification with image differencing in the algebraic technique. Because Landsat data have a medium spatial resolution, therefore point-wise computation was used. Lastly, we also detect which data sets are working better on an appropriate machine learning algorithm, so after final calculation we estimate accuracy of each algorithm by using confusion matrix and kappa.

Impact of data structure types and spatial resolution on landslide volumetric change measurements

Ján Šašak — 2024-12-18

Terrain is a dynamic component of the landscape, subject to rapid changes, particularly in scenarios such as landslides. This study investigates how the spatial resolution and data structure of digital terrain models (DTMs) influence the estimation of landslide volume changes. We selected a landslide formed by the undercutting action of the Belá River in Slovakia as our research site. Our findings indicate that raster data structures, across various spatial resolutions, generally yield more consistent volume estimates compared to 3D mesh data structures. Nonetheless, at higher spatial resolutions (0.1 m and 0.25 m), the 3D mesh data structure demonstrates superior capability in capturing detailed terrain features, resulting in more precise volume estimations of the landslide.