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

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

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

Juvenile Nasopharyngeal Angiofibroma – Characteristics, Diagnosis and Treatment

Ювенилният назофарингеален ангиофибром е рядък съдов тумор, срещащ се с честота под 0,5% от всички тумори на главата и шията. Въпреки че е хистологично бенигнен, той притежава локална инвазивност, подобно на злокачествените неоплазми. Лечението до ден-днешен е сериозно предизвикателство, криещо множество рискове от тежки, понякога животозастрашаващи усложнения.Juvenile nasopharyngeal angiofibroma is a rare high vascular tumor with a frequency of less than 0,5% of all head and neck tumors. Although histologicaly benign, it possess invasiveness similar to malignant neoplasms. The treatment is still a challenge with risk of sometimes lifethreatening complications