From electronic navigational chart data to sea-bottom models: Kriging approaches for the Bay of Pozzuoli

<p class="Abstract">Electronic Navigational Charts (ENCs), official databases created by a national hydrographic office and included in Electronic Chart Display and Information System (ECDIS), supply, among essential indications for safe navigation, data about sea-bottom morphology in terms of depth points and isolines. Those data are very useful to build bathymetric 3D models: applying interpolation methods, it is possible to produce a continuous representation of the seafloor for supporting studies concerning different aspects of a marine area, such as directions and intensity of currents, sensitivity of habitats and species, etc. Many interpolation methods are available in literature for bathymetric data modelling: among them kriging ones are extremely performing, but require deep analysis to define input parameters, i.e. semi-variogram models. This paper aims to analyze kriging approaches for depth data concerning the Bay of Pozzuoli. The attention is focused on the role of semi-variogram models for Ordinary and Universal kriging. Depth data included in two ENCs, namely IT400129 and IT400130, are processed using Geostatistical Analyst, an extension of ArcGIS 10.3.1 (ESRI). The results testify the relevance of the choice of the mathematical functions of the semi-variogram: Stable Model supplies, for this case study, the best performance in terms of depth accuracy for both Ordinary and Universal kriging.</p>

Development of cancerogenic risk index maps for the territory of Vladikavkaz

В последние годы наблюдается значительный рост численности населения, которому соответствуют ускоренные темпы индустриализации. Это сопровождается всё более значительным загрязнением окружающей среды тяжелыми металлами. Наибольший вклад в загрязнение окружающей среды Республики Северная Осетия – Алания долгие годы вносила горнодобывающая промышленность, в последнее же время к ней, в качестве не менее весомого источника загрязнения присоединились нестационарные источники. Загрязнение тяжелыми металлами создает серьезную опасность для здоровья населения и окружающей среды из-за их токсичной природы. Цель исследования. Разработка карт пространственного распределения загрязняющих веществ является основой в оценке уровня загрязнения окружающей среды. Цель настоящего исследования – разработка карт индекса канцерогенного риска и установление его взаимосвязи с онкозаболеваемостью населения. Методы исследования. В процессах создания карт используются методы интерполяции, так как количество проб и измерений содержания вредных веществ в той или иной среде, как правило, весьма ограничено. Согласно зарубежным исследованиям, для большинства наиболее часто используемых методов интерполяции обеспечивается одинаково высокая точность. В настоящей работе при расчёте индексов канцерогенного риска использовался метод обратного взвешивания расстояний. Результаты работы Отобран материал по заболеваемости населения злокачественными образованиями по различным районам г. Владикавказа. Основной удельный вес вновь зарегистрированных злокачественных новообразований приходится на возрастные группы свыше 60 лет. Анализ онкозаболеваемости показал, что в тех районах г. Владикавказа, где уровень загрязнения окружающей среды выше, выше и показатели онкозаболеваемости. Проведен расчёт индекса риска для здоровья населения при воздействии химических веществ загрязняющих окружающую среду. По всем исследуемым показателям наблюдается значительное превышение предельно допустимых значений, за исключением ртути. Полученные данные о высоком уровне канцерогенного риска согласуются с высоким уровнем онкологической заболеваемости на территории города, что говорит о тесной взаимосвязи заболеваемости и индекса канцерогенного риска. In recent years, there has been a significant increase in the population, which is matched by the accelerated rate of industrialization. This is accompanied by increasingly significant pollution of the environment with heavy metals. For many years, the mining industry has made the greatest contribution to the environmental pollution of the Republic of North Ossetia – Alania, non-stationary sources have recently joined as an equally significant source of pollution. Heavy metal pollution poses a serious threat to public health and the environment due to its toxic nature. Aim. The development of maps of the spatial distribution of pollutants is the basis for assessing the level of environmental pollution. The aim of this study is to develop maps of the carcinogenic risk index and to establish its relationship with the incidence of cancer in the population. Methods. In the process of creating maps, interpolation methods are used, since the number of samples and measurements of the content of harmful substances in a particular environment is usually very limited. According to foreign studies, for most of the commonly used interpolation methods, equally high accuracy is provided. In the present work, we used the method of the inverse weighting of distances for calculating the indices of carcinogenic risk. Results. The material on the incidence of malignant neoplasms in the population in various districts of Vladikavkaz was selected. The main share of newly registered malignant neoplasms falls on the age groups over 60 years old. The analysis of cancer morbidity showed that in those areas of Vladikavkaz where the level of environmental pollution is higher, the indicators of cancer morbidity are also higher. The calculation of the risk index for public health under the influence of chemical substances polluting the environment has been carried out. For all the studied indicators, there is a significant excess of the maximum permissible values, with the exception of mercury. The obtained data on the high level of carcinogenic risk is consistent with the high level of cancer incidence in the city; it indicates a close relationship between the incidence and the carcinogenic risk index.

Geological Feature Modeling and Reserve Estimation of Uranium Deposits Based on Multiple Interpolation Methods

Uranium resource distribution and accurate reserve evaluation are important references for mineral investment and production. Eight kinds of interpolation methods in the Groundwater Modeling System (GMS), including ordinary kriging (OK), are used in this study to predict the spatial distribution of reserve-related parameters, such as uranium grade, ore thickness and uranium content per square meter. The present study draws the following conclusions: (1) Cross-validation found that the uranium grade value using the spherical method is the closest to the actual value. The spherical method has the best interpolation effect. (2) The relative error, which is +3.62%, between the uranium reserves that is calculated by the spherical interpolation method and that by the traditional calculation value is the smallest. (3) The setting of the number of interpolation grids is related to the actual number of boreholes. The ratio between the two will affect the accuracy of reserve estimation, and different interpolation methods have different degrees of influence on reserve estimation. This method is applicable to all in-situ leaching sandstone uranium mines. Further study needs to be carried out toward heterogeneity of three-dimensional space, which will make the estimation more accurate.

Interpolation-Based Smart Video Stabilization

Video stabilization is the process of eliminating unwanted camera movements and shaking in a recorded video. Recently, learning-based video stabilization methods have become very popular. Supervised learning-based approaches need labeled data. For the video stabilization problem, recording both stable and unstable versions of the same video is quite troublesome and requires special hardware. In order to overcome this situation, learning-based interpolation methods that do not need such data have been proposed. In this paper, we review recent learning-based interpolation methods for video stabilization and discuss the shortcomings and potential improvements of them.

COMPARATIVE ANALYSIS FOR METHODS OF BUILDING DIGITAL ELEVATION MODELS FROM TOPOGRAPHIC MAPS USING GEOINFORMATION TECHNOLOGIES

The article considers the question of estimating the accuracy of interpolation methods for building digital elevation models using Soviet topographic maps. The territory of the Kursk region of the Russian Federation was used as the study area, because it is located on the Central Russian Upland and characterized by the complex structure of the vertical and horizontal dissection of the relief. Contour lines automatically obtained using a Python algorithm were used as the initial elevation data to build a digital elevation model. Digital elevation models obtained by thirteen different interpolation methods in ArcGIS and Surfer software were built and analyzed. Special attention is paid to the ANUDEM method, which allows to obtain hydrologically correct digital elevation models. Recommendations for the use of one or another method of interpolation are given. The results can be useful for professionals who use topographic maps in their work and deals with the design using digital elevation models.

Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation

The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes.