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

CHARACTERIZATION OF RF AND DC MAGNETRON REACTIVE SPUTTERED TiO 2 THIN FILMS FOR GAS SENSORS

This study presents the technology for prep aring and characterization of titanium oxide thin films with proper ties suitable for gas sensors. For preparing the samples the reactive radio frequency (RF) and direct current (DC) magnetron sputtering methods were used. The composition and microstructure of the films were studied by X-ray photoelectron spectroscopy (XPS), X-ray diff raction (XRD) and Raman spectroscopy, the surface of the films was observed applying high-resolution scanning electron microscopy (SEM). For measuring the thickness and identifying the refractive indices of the films laser ellipsometry was used. The research was focuse d on the sensing behavior of the sputtered titania thin films applying quartz crystal microbalance (QCM) method, which allows detection of mass changes in the nanogram range. Prototype QCM sensors with TiO 2 thin films were made by our team and tested for sensitivity to NH 3 and NO 2 . These films even in as-deposited state and without heating th e substrates show good sensitivity. Additional thermal treatment is not necessary, making manufacturing of QCM gas sensor simple and cost-effective, as it is fully compatible with the technology for producing the initial resonator. The sorption is fully reversible and the studied TiO 2 films are stable, which makes them capable for meas urements for long terms

Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup

Training a model to provide natural language explanations (NLEs) for its predictions usually requires the acquisition of task-specific NLEs, which is time- and resource-consuming. A potential solution is the few-shot out-of-domain transfer of NLEs from a parent task with many NLEs to a child task. In this work, we examine the setup in which the child task has few NLEs but abundant labels. We establish four few-shot transfer learning methods that cover the possible fine-tuning combinations of the labels and NLEs for the parent and child tasks. We transfer explainability from a large natural language inference dataset (e-SNLI) separately to two child tasks: (1) hard cases of pronoun resolution, where we introduce the small-e-WinoGrande dataset of NLEs on top of the WinoGrande dataset, and (2) commonsense validation (ComVE). Our results demonstrate that the parent task helps with NLE generation and we establish the best methods for this setup

Characterization of thin MoO3 films formed by RF and DC-magnetron reactive sputtering for gas sensor applications

The present work discusses a technology f or deposition and characterization of thin molybdenum oxide (MoO x , MoO 3 ) films studied for gas sensor applications. T he samples were produced by reactive radio - frequency (RF) and direct c urrent (DC) magnetron sputtering. The composition and microstructure of the films were studied by XPS , XRD and Raman spectroscopy, the morphology , using high resolution SEM. T he research was focused on the sensing properties of the sputtered thin MoO 3 films. Highly sensitive gas sensor s were implemented by depositing films of various thicknesses on quartz resonators. Making use of the quartz crystal microbalance (QCM) method , the se sensors we re capable of detect ing chan ges in the molecular range. Prototype QCM structures with thin MoO 3 films were tested for sensitivity to NH 3 and NO 2 . E ven in as - deposited state and without heating the substrates, these films show ed good sensitivity . Moreover , no a dditional thermal treatment is necessary, which makes the production of such QCM gas sensor s simple and cost - effective, as it is fu lly compatible wit h the technology for producing t he initial resonator . Т he films are sensitive at room temperature and can reg ister concentrations as low as 50 ppm . The sorption is fully reversible , the films are stable and capable of long - term measuremen ts

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

STUDY OF COLCHICINE EFFECTS ON ELECTROCONVULSIVE EPILEPTIC EQUIVALENT IN RATS

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Optical measurements of electrophoretic suspension kinetics

Electrophoretic deposition (EPD) was originally used for formation of coatings, e. g. in the automotive industry. Recently EPD is successfully utili zed for thin film preparation with an app lication in the optics and electronics. This paper investigates the process of the suspension formation and aggregation by ultraviolet and visible spectroscopy (UV- VIS) spectroscopy and Dynamic Light Scattering (DLS) methods. The suspensions were formed by a precipitation of solution of poly[2-methoxy-5-(3 ′ ,7 ′ -dimethyloctyloxy)-1,4-phenylenevinylene] in toluene using acetonitrile as a precipitator. It could be concluded that the progressive suspension particle growth observed by DLS affects regularly the first derivative of the UV-VIS spectra. By a comparison of the results obtained by both methods it could be seen that UV-VIS spectroscopy combined with the spline method could be successfully used for an estimation of electrophoretic suspensions