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The cartographic presentation of objects and phenomena of the real world requires their digital transformation. Space and geoinformation technologies are successfully used for the digital presentation of information about the state of spatial objects. This article describes the technology of digital processing medium resolution images, which ensures updating the vector contours of the coastline of water bodies in the territory being mapped using satellite data. The technology involves the following stages of image processing: unpacking of “raw” space images, their geocoding and calibration, geometric and radiometric corrections, creating index images, the water surface detection, vectorization and smoothing of detected polygons. The vectorization of water body contours was done using the EXPOLRAS procedure, which allows converting the input raster image into a binary one with the help of the specified threshold values; to build polygons based on the created binary image; to filter them based on the area and compactness of values and to save the result into a vector layer. Smoothing of stepped lines obtained by converting a raster into a vector was done in two consecutive stages. To truncate rectangular corners of raster pixels the bit2poly procedure was applied; the further smoothing of vertices was done using the McMaster's averaging algorithm implemented in the SMMCMASTER procedure. The technology is shown on the example of water bodies of the Almaty region Thematic maps created using satellite images allowed to update vector contours of water bodies coastline in the territory being mapped. The refined contours of the surface water bodies have been used to update the cartographic base of the hydrogeological map of the Almaty region. [ABSTRACT FROM AUTHOR]

The study deals with a usage of spectral indices for the ecological control of the state of forestry with particular emphasis on the control of causes and effects of forest fires, their effective detection and prevention. The Karabai tract was selected as an object of study, which is a monoculturally planted pine forest with predominance of Rhinus sylvestris L. and P. pallasiana D. Don, located on the territory of Kherson region, Ukraine. This forestry is known for one of the largest forest fires in Ukraine, which took place in the summer of 2007, and by the results of which, directly at the site, more than 25 km² of forest was destroyed. Google Earth Engine and MapInfo Professional software were used as research tools. In the analysis of the object of study according to the results of the calculation of spectral indices, the following surfaces were constructed: NDVI (Normalized Difference Vegetation Index) – to determine the area and level of damage to the forest; NBRT (Normalized Burn Ratio Thermal) – for indicating the fire and the character of a fire spread; NDWI (Normalized Difference Water Index) – for the diagnosis of the level of aridity of forest areas, which is accordingly a sign of the risk of forest fires in the summer. It was investigated, that the fire occurred in the central part of the forestry war caused by excessive dryness (prolonged absence of rainfall and high air temperatures). It is proved that the fire ruined much more than the mentioned official area, which is caused by the extinction of adjacent burnt vegetated areas. Due to the results of the analysis, a number of forestry measures are proposed to prevent the occurrence of fires. In particular: placement of fire protection strips along the most arid areas of the forestry, placement of fire ponds, etc. [ABSTRACT FROM AUTHOR]

Recently, satellite monitoring has recognized a wide use in estimating ecological situations in vast areas, including ice cover layer; monitoring the negative effects of earthquakes, volcanic eruptions, landslides, mudflows and much more. Drought is one of the negative natural phenomena, which in its extreme forms can lead to life losses and substantial property damage. Satellite monitoring can be used to observe the change in lake coastline due to drought and predict the directions of further changes. The paper presents the analysis of the medium and high resolution satellite image potential to estimate drought impact on large freshwater lakes in California in 2010-2015. The method developed by the authors and used in the research was automated processing technology of the multispectral satellite images which provides higher reliability and efficiency of water area change detection. It was found that the Oroville and Folsom lakes' coastline suffered from the essential offset. The testing results confirmed the sufficient degree of qualitative and quantitative indicators: high accuracy of classification (more than 90%); good reproducibility; high sustainability of the proposed technology. The advantages of the multispectral image processing via Landsat-8 satellite are the high degree of automation and economic efficiency, as the remote sensing data used are available in the Internet for free. The proposed automated processing technology can be implemented into a geo-information web service, with the wide scope of potential uses from state regulating structures; state and private companies of water transport and tourist agencies to farmers and mass media companies as well as the population living in the areas potentially affected by droughts. The directions of further research include testing the proposed technology with other types of satellites and modernizing the existing version of the geo-information web service in terms of software hardware and data storage independence. [ABSTRACT FROM AUTHOR]

Worldwide, flooding causes more damage than any other natural hazard. The growing extensive flood risk has a large impact on the present and future economy, especially in developing countries, which have limited funds for prevention and mitigation measures. Moreover, the allocation of these funds is often done without sufficient and adequate information on high-risk and flood-prone areas, since the acquisition of flood data is very expensive as well. Therefore, this research focuses on a low-cost methodology to create the necessary, accurate flood maps in data-sparse regions. In this methodology, historic Sentinel-2A satellite imagery was used to detect water bodies, which in later research will be the basis for detecting flooded areas and creating flood maps. Multiple indexes that combine several bands of the satellite image to enhance the difference between water bodies and land use, were tested to determine which combination gives the best results. Two study areas, one in an urban and one in a rural environment, were chosen and compared since these environments have completely different characteristics, which result in different spectral properties and reflectance values in the satellite images. The indexes that were tested in both study areas are the NDWI (Normalized Difference Water Index), the MNDWI (Modified Normalized Difference Water Index) and the AWEI (Automated Water Extraction Index) for images with shade and without shade. The last index gives the most promising results, for urban areas as well as rural areas. Not only different indexes were tested, but also the threshold value of water per index was determined. The complete methodology was automated in a Python script, so the end user can easily create 2D flood maps without the need of extensive background knowledge. The first results of this tool are very promising in determining water bodies and flood areas. Future research will focus on combining multiple flood maps, generated with open source satellite images, into a flood prediction map that can help developing countries in understanding future flood risk. [ABSTRACT FROM AUTHOR]

In earlier studies, we conducted joint assessment of climate change in recent decades and change of vegetation cover in the taiga zone of Russia. We used Normalized Difference Vegetation Index (NDVI) to assess vegetation cover change. We found that shrub vegetation is important indicator of climate change in the Northern regions of Russia. Previously, we assessed change of climate parameters and vegetation cover within different growing seasons (periods of the calendar year when the surface air temperature is above +5°C and +10°C). These are the periods of active growth of the biomass. The differentiation of the growing seasons in our study we performed in manual mode according to the data collected on meteorological stations. For the purposes of extrapolation of the obtained dependencies on areas where meteorological stations are absent, we need a tool that allows to supply the differentiation of growing seasons in the automated mode, using remote sensing data. Normalized Difference Water Index (NDWI) proposed by B. Gao allows to determine beginning of the growing season and beginning of the leaves yellowing. Currently we have designed the technique for automation of differentiation of the growing seasons using NDWI and studied its application. The study was partially supported by the Russian Foundation for basic research (RFBR), research project No. 14-05-00858 A. [ABSTRACT FROM AUTHOR]

This research focuses on automatic extraction of river banks and other inland waters from remote sensing images. There are no up to date accessible databases of rivers and most of other waters objects for modelling purposes. The main reason for that is that some regions are hard to access with the traditional ground through techniques and thus the boundary of river banks are uncertain in many geographical positions. The other reason is the limitations of widely applied method for extraction of water bodies called normalized-difference water index (NDWI). There is a novel approach to extract water bodies, which is based on pixel level variability or entropy, however, the methods work somewhat satisfactory on high spatial resolution images, there is no verification of the method performance on moderate or low resolution images. Problems encounter identification of mixed water pixels and e.g. roads, which are built in attachment to river banks and thus can be classified as rivers. In this work we propose an automatic extraction of river banks using image entropy, combined with NDWI identification. In this study only moderate spatial resolution Landsat TM are tested. Areas of interest include both major river banks and inland lakes. Calculating entropy on such poor spatial resolution images will lead to misinterpretation of water bodies, which all exhibits the same small variation of pixel values as e.g. some open or urban areas. Image entropy thus is calculated with the modification that involves the incorporation of local normalization index or variability coefficient. NDWI will produce an image where clear water exhibits large difference comparing to other land features. We are presenting an algorithm that uses an NDWI prior to entropy processing, so that bands used to calculate it, are chosen in clear connection to water body features that are clearly discernible. As a result we visualize a clear segmentation of the water bodies from the remote sensing images and verify the coordinates with a given geographic reference. [ABSTRACT FROM AUTHOR]