DEFORESTATION MAPPING USING SENTINEL-1 AND OBJECT-BASED RANDOM FOREST CLASSIFICATION ON GOOGLE EARTH ENGINE

Abstract. Deforestation can be defined as the conversion of forest land cover to another type. It is a process that has massively accelerated its rate and extent in the last several decades. Mainly due to human activities related to socio-economic processes as population growth, expansion of agricultural land, wood extraction, etc. In the meantime, there are great efforts by governments and agencies to reduce these deforestation processes by implementing regulations, which cannot always be properly monitored whether are followed or not. In this work is proposed an approach that can provide forest loss estimations for a short period of time, by using Synthetic Aperture Radar imagery for an area in the Brazilian Amazon. SAR are providing data with almost no alteration due to weather conditions, however they may present other limitations. To mitigate the speckle effect, here was applied the dry coefficient, which is the mean of image values under the first quartile while preserving the spatial resolution. While for obtaining land cover maps containing only forest and non-forest areas an object-based machine learning classification on the Google Earth Engine platform was applied. The preliminary tests were carried out in a bitemporal manner between 2015 and 2019, followed by applying the approach monthly for the year of 2020. The outputs yielded very satisfactory and accurate results, allowing to estimate the forest dynamics for the area under consideration for each month

AN OVERVIEW OF GEOINFORMATICS STATE-OF-THE-ART TECHNIQUES FOR LANDSLIDE MONITORING AND MAPPING

Abstract. Natural hazards such as landslides, whether they are driven by meteorologic or seismic processes, are constantly shaping Earth's surface. In large percentage of the slope failures, they are also causing huge human and economic losses. As the problem is complex in its nature, proper mitigation and prevention strategies are not straightforward to implement. One important step in the correct direction is the integration of different fields; as such, in this work, we are providing a general overview of approaches and techniques which are adopted and integrated for landslide monitoring and mapping, as both activities are important in the risk prevention strategies. Detailed landslide inventory is important for providing the correct information of the phenomena suitable for further modelling, analysing and implementing suitable mitigation measures. On the other hand, timely monitoring of active landslides could provide priceless insights which can be sufficient for reducing damages. Therefore, in this work popular methods are discussed that use remotely-sensed datasets with a particular focus on the implementation of machine learning into landslide detection, susceptibility modelling and its implementation in early-warning systems. Moreover, it is reviewed how Citizen Science is adopted by scholars for providing valuable landslide-specific information, as well as couple of well-known platforms for Volunteered Geographic Information which have the potential to contribute and be used also in the landslide studies. In addition to proving an overview of the most popular techniques, this paper aims to highlight the importance of implementing interdisciplinary approaches

DISTANCE-TRAINING FOR IMAGE-BASED 3D MODELLING OF ARCHEOLOGICAL SITES IN REMOTE REGIONS

The impressive success of Structure-from-Motion Photogrammetry (SfM) has spread out the application of image-based 3D reconstruction to a larger community. In the field of Archeological Heritage documentation, this has opened the possibility of training local people to accomplish photogrammetric data acquisition in those remote regions where the organization of 3D surveying missions from outside may be difficult, costly or even impossible. On one side, SfM along with low-cost cameras makes this solution viable. On the other, the achievement of high-quality photogrammetric outputs requires a correct image acquisition stage, being this the only stage that necessarily has to be accomplished locally. This paper starts from the analysis of the well-know “3×3 Rules” proposed in 1994 when photogrammetry with amateur camera was the state-of-the art approach and revises those guidelines to adapt to SfM. Three aspects of data acquisition are considered: geometry (control information, photogrammetric network), imaging (camera/lens selection and setup, illumination), and organization. These guidelines are compared to a real case study focused on Ziggurat Chogha Zanbil (Iran), where four blocks from ground stations and drone were collected with the purpose of 3D modelling

Dipole-active optical phonons in YTiO_3: ellipsometry study and lattice-dynamics calculations

The anisotropic complex dielectric response was accurately extracted from spectroscopic ellipsometry measurements at phonon frequencies for the three principal crystallographic directions of an orthorhombic (Pbnm) YTiO_3 single crystal. We identify all twenty five infrared-active phonon modes allowed by symmetry, 7B_1u, 9B_2u, and 9B_3u, polarized along the c-, b-, and a-axis, respectively. From a classical dispersion analysis of the complex dielectric functions \tilde\epsilon(\omega) and their inverses -1/\tilde\epsilon(\omega) we define the resonant frequencies, widths, and oscillator strengths of the transverse (TO) and longitudinal (LO) phonon modes. We calculate eigenfrequencies and eigenvectors of B_1u, B_2u, and B_3u normal modes and suggest assignments of the TO phonon modes observed in our ellipsometry spectra by comparing their frequencies and oscillator strengths with those resulting from the present lattice-dynamics study. Based on these assignments, we estimate dynamical effective charges of the atoms in the YTiO_3 lattice. We find that, in general, the dynamical effective charges in YTiO_3 lattice are typical for a family of perovskite oxides. By contrast to a ferroelectric BaTiO_3, the dynamical effective charge of oxygen related to a displacement along the c-axis does not show the anomalously large value. At the same time, the dynamical effective charges of Y and ab-plane oxygen exhibit anisotropy, indicating strong hybridization along the a-axis.Comment: 8 pages, 7 figure

Ferromagnetism and Lattice Distortions in the Perovskite YTiO3_3

The thermodynamic properties of the ferromagnetic perovskite YTiO$_3$ are investigated by thermal expansion, magnetostriction, specific heat, and magnetization measurements. The low-temperature spin-wave contribution to the specific heat, as well as an Arrott plot of the magnetization in the vicinity of the Curie temperature $T_C\simeq27$ K, are consistent with a three-dimensional Heisenberg model of ferromagnetism. However, a magnetic contribution to the thermal expansion persists well above $T_C$, which contrasts with typical three-dimensional Heisenberg ferromagnets, as shown by a comparison with the corresponding model system EuS. The pressure dependences of $T_C$ and of the spontaneous moment $M_s$ are extracted using thermodynamic relationships. They indicate that ferromagnetism is strengthened by uniaxial pressures $\mathbf{p}\parallel \mathbf{a}$ and is weakened by uniaxial pressures $\mathbf{p}\parallel \mathbf{b},\mathbf{c}$ and hydrostatic pressure. Our results show that the distortion along the $a$- and $b$-axes is further increased by the magnetic transition, confirming that ferromagnetism is favored by a large GdFeO$_3$-type distortion. The c-axis results however do not fit into this simple picture, which may be explained by an additional magnetoelastic effect, possibly related to a Jahn-Teller distortion.Comment: 12 pages, 13 figure

Spectroscopic distinction between the normal state pseudogap and the superconducting gap of cuprate high T_{c} superconductors

We report on broad-band infrared ellipsometry measurements of the c-axis conductivity of underdoped RBa_{2}Cu_{3}O_{7-d} (R=Y, Nd, and La) single crystals. Our data provide a detailed account of the spectral weight (SW) redistributions due to the normal state pseudogap (PG) and the superconducting (SC) gap. They show that these phenomena involve different energy scales, exhibit distinct doping dependencies and thus are likely of different origin. In particular, the SW redistribution in the PG state closely resembles the one of a conventional charge- or spin density wave (CDW or SDW) system.Comment: 4 pages, 4 figure

STUDY OF COLCHICINE EFFECTS ON ELECTROCONVULSIVE EPILEPTIC EQUIVALENT IN RATS

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Genetic diversity of Agrobacterium vitis strains, isolated from grapevines and wild grapes in Bulgaria, assessed by Cleaved Amplified Polymorphic Sequences analysis of 16S-23S rDNA

Nineteen tumorigenic Agrobacterium vitis strains isolated from commercial vineyards and wild grapes at different locations in Bulgaria were studied in relation to the Ti plasmid type and chromosomal background. The PCR analysis showed that all but one of the strains harbor an octopine/cucumopine type of Ti plasmid and one carries a vitopine type. The genetic diversity among the studied strains and 20 more A. vitis strains originating from different geographic regions in Europe, Asia, USA and South Africa was assessed by Cleaved Amplified Polymorphic Sequences (CAPS) analysis of 16S-23S rDNA region. The comparison of the obtained CAPS profiles and performed cluster analysis showed a high level of polymorphism among the studied strains distributed in totally 15 different groups within two main clusters. All Bulgarian strains are located in only three groups within one of the clusters. A high level of correlation between the chromosomal background and type of the carried Ti plasmids was found. The performed CAPS analysis demonstrated that all A. vitis strains isolated from wild grapes (V. vinifera ssp. silvestris) showed CAPS profiles identical with a number of strains isolated from commercial vineyards from different vine-growing regions in Bulgaria. A possible origin of this group of strains from an ancestral A. vitis strain, which initially inhabits wild grapes (V. vinifera ssp. silvestris) and later has been disseminated to cultivated grapevines is proposed

APPLICATION OF LUCAS-KANADE DENSE FLOW FOR TERRAIN MOTION IN LANDSLIDE MONITORING APPLICATION

Landslides are natural hazards that can cause severe damage and loss of life. Optical cameras are a low-cost and high-resolution alternative among many monitoring systems, as their size and capabilities can vary, allowing for flexible implementation and location. Computer vision is a branch of artificial intelligence that can analyze and understand optical images, using techniques such as optical flow, image correlation and machine learning. The application of such techniques can estimate the motion vectors, displacement fields, providing valuable information for landslide detection, monitoring and prediction. However, computer vision also faces some challenges such as illumination changes, occlusions, image quality, and computational complexity. In this work, a computer vision approach based on Lucas-Kanade optical dense flow was applied to estimate the motion vectors between consecutive images obtained during landslide simulations in a laboratory environment. The approach is applied to two experiments that vary in their illumination and setup parameters to test its applicability. We also discuss the application of this methodology to images from Sentinel-2 satellite optical sensors for landslide monitoring in real-world scenarios