scholarly journals Digital approaches in agriculture crop monitoring

2021 ◽  
Vol 937 (3) ◽  
pp. 032098
Author(s):  
E Barbotkina ◽  
Ie Dunaieva ◽  
V Popovych ◽  
V Pashtetsky ◽  
V Terleev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Technologies ◽  
Spatial Information ◽  
Morphological Characteristics ◽  
Remote Sensing Data ◽  
Digital Information ◽  
Agricultural Crops ◽  
Potential Yield ◽  
Earth Remote Sensing ◽  
Wide Range

Abstract Implementation of modern technologies for collecting and processing spatial information, primarily Earth remote sensing data, has made it possible to solve a wide range of tasks for specialists in the agricultural industry. The work aim is to assess the state of agricultural crops on the territory of Krymskorozovskoe rural settlement of the Belogorsky district of the Republic of Crimea using materials of Earth remote sensing and modern information technologies. The article reviews the literature on the research topic, studies the most significant works on this theme. The article presents the possibilities of digital information technologies in the framework of solving agricultural problems including creation of maps of fields and database formation, study of the territory relief and the features of its morphological characteristics, prompt identification of changes in agricultural fields, based on the calculation of vegetation indices, with the use of remote sencing; classification and identification of objects by satellite images; forecasting the potential yield of agricultural crops.

scholarly journals Efficient land use based on remote sensing data

2021 ◽  
Vol 3 ◽  
pp. 180-185
Author(s):  
Y. M. Kenzhegaliyev ◽  
◽  
◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Remote Sensing Data ◽  
Crop Productivity ◽  
The State ◽  
Agricultural Crops ◽  
Earth Remote Sensing ◽  
Spectral Reflectivity ◽  
Sensing Data ◽  
Current Location

The goal -is to explore ways of using Earth remote sensing data for efficient land use. Methods - detailed information on current location of certain types of agricultural crops in the study areas has been summarized, which opens up opportunities for the effective use of cultivated areas. It was revealed that the basis of the principle of the method under consideration is the relationship between the state and structure of vegetation types with its reflective ability. It has been determined that information on the spectral reflective property of the vegetation cover in the future can help replace more laborious methods of laboratory analysis. For classification of farmland, satellite images of medium spatial resolution with a combination of channels in natural colors were selected. Results - a method for identifying agricultural plants by classification according to the maximum likelihood algorithm was considered. The commonly used complexes of geoinformation software products with modules for special image processing allow displaying indicators in the form of raster images. It is shown that the use of Earth remote sensing data is the most relevant solution in the field of crop recognition and makes it possible to simplify the implementation of such types of work as the analysis of the intensity of land use, the assessment of the degree of pollution with weeds and determination of crop productivity. Conclusions - the research results given in the article indicate that timely information on the current location of certain types of agricultural crops in the studied territories significantly simplifies the implementation of the tasks and increases the resource potential of agricultural lands. In turn, the timing of the survey and the state of environment affect the spectral reflectivity of vegetation.

scholarly journals Assessment of possibility for using remote sensing data and Markov chains for prediction of vegetation cover development

2020 ◽  
Vol 58 (2) ◽  
pp. 176-184
Author(s):  
T. N. Myslyva ◽  
V. I. Bushueva ◽  
V. A. Volyntseva
Keyword(s):  
Remote Sensing ◽  
Markov Chains ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Global Climate ◽  
Remote Sensing Data ◽  
Agricultural Crops ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
The Earth

In conditions of global climate change, it is important to develop reliable models allowing to reliably predict plant development based on combination of the Earth remote sensing data and statistical modeling. Modeling by means of Markov chains is an efficient and at the same time simple way to predict random events, which include prediction of performance of phytomass of agricultural crops. The Earth remote sensing data obtained from the Sentinel-2 satellite with spatial resolution of 10 m were used to calculate the value of vegetation index NDVI and obtain different time rasters (2017-2019) with different degrees of vegetation cover development. To construct the matrix of probability of transition from one state to another for different levels of vegetation cover development, functionality of geoinformation systems (GIS) were used allowing to classify raster images, transform them into vector layers, and establish intersection areas. The probability matrix was later used to predict vegetation cover development using the Markov model as a predictor. The developed prediction model was tested for feasibility of the χ2 test. The results obtained showed that both the modeled values and the actual area of vegetation distribution with different degrees of development, determined from the available raster image of 2019, correlated well with each other. The research results can be useful both in developing forecasting methods and in directly predicting the crop yield of primarily dense-cover agricultural crops, as well as for estimating performance of pastures and creating efficient pasture rotations.

scholarly journals Defining Boundaries and Dynamics of Urbanized Areas According to Remote Data Sensing

2020 ◽  
pp. 46-52
Author(s):  
Andrii Marushchynets
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Spatial Development ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
Wide Range ◽  
The Usa ◽  
Urbanized Areas

The article is devoted to the original approach of defining continuously built-up urban areas as well as the dynamics of this process by means of Earth remote sensing data analysis. Basing on this approach, the spreading borders of continuously built-up urban areas of Kyiv city and its suburbs during 1976-2018 have been defined. Earth remote sensing data, as a valuable source of information on land surface in general and built-up areas in particular, provides wide range of opportunities for researching the process of spatial development of urbanized areas. Analysis of built-up territories during significant period of time allows defining spatial development vectors of urbanized regions, modern continuously built-up areas and their borders. The review of similar researches has revealed that the most convenient sources of Earth remote sensing data for defining the area of built-up territories are represented by multispectral space footages of Landsat space program of the USA. The deciphering of space footages and defining of built-up areas has been conducted involving spectral indexes, which is the most precise method of deciphering the Earth remote sensing data. Thus, we managed to define built-up and non-built-up areas as well as water objects of Kyiv city and its suburbs for 1976, 1985, 2002 and 2008. A set of illustrating schematic maps has been created, depicting borders of built-up area. A continuously built-up urban area has amalgamated Kyiv city and a number of surrounding settlements into a highly-urbanized core. During 1976-2018, the area of continuously built-up urban territory of Kyiv expanded 1,5 times and mostly southwestwards.

Conversion and Display of the Calculated Data of Spectral Remote Sensing Data on the Basis of GeoServer Extensions and Distributed Storage Technologies

2021 ◽  
Vol 12 (2) ◽  
pp. 107-112
Author(s):  
I. E. Kharlampenkov ◽  
◽  
A. U. Oshchepkov ◽  
Keyword(s):  
Remote Sensing ◽  
Information Technologies ◽  
File System ◽  
Distributed Storage ◽  
Calculated Data ◽  
Remote Sensing Data ◽  
Distributed Computing Systems ◽  
Apache Hadoop ◽  
Distributed Information ◽  
Sensing Data

The article presents methods for caching and displaying data from spectral satellite images using libraries of distributed computing systems that are part of the Apache Hadoop ecosystem, and GeoServer extensions. The authors gave a brief overview of existing tools that provide the ability to present remote sensing data using distributed information technologies. A distinctive feature is the way to convert remote sensing data inside Apache Parquet files for further display. This approach allows you to interact with the distributed file system via the Kite SDK libraries and switch on additional data processors based on Apache Hadoop technology as external services. A comparative analysis of existing tools, such as: GeoMesa, GeoWawe, etc is performed. The following steps are described: extracting data from Apache Parquet via the Kite SDK, converting this data to GDAL Dataset, iterating the received data, and saving it inside the file system in BIL format. In this article, the BIL format is used for the GeoServer cache. The extension was implemented and published under the Apache License on the GitHub resource. In conclusion, you will find instructions for installing and using the created extension.

Synthetic and Comparative Hydrology of Earth, Mars, and Titan

2021 ◽  
Author(s):  
Vojtěch Cuřín ◽  
Johanna Blöcher ◽  
Petr Brož ◽  
Yannis Markonis ◽  
Jan Masner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Precipitation ◽  
Morphological Characteristics ◽  
Remote Sensing Data ◽  
Machine Learning Techniques ◽  
Carbon Sphere ◽  
Spatial Homogeneity ◽  
Planetary Bodies ◽  
Network Properties ◽  
Visual Properties

<p>Earth, Mars, and Titan are the only known planetary bodies in our solar system where flowing liquids have shaped surface topography and formed extensive river networks. Fed by atmospheric precipitation and carved by fluvial erosion, these channels are observable in remote sensing data. They carry information about the interactions between the atmosphere, the hydro(carbon)sphere, and the lithosphere and allow for investigation of the conditions that had prevailed during their formation. Comparison of drainage basins, which developed in these profoundly different environments, could yield insights into the past and ongoing hydrological processes in addition to climatic, chemical, and topographic conditions of the planetary bodies. Increased computing capacities allow for building and utilization of a vast database of hydrological, climatological, and geological data as well as algorithmic evaluation of remote sensing products. Here, we propose a classification of basins from Earth, Mars, and Titan using several machine learning techniques based on their morphological characteristics, network properties, spatial homogeneity, cross-scale self-similarity, and visual properties. Constraints on climatic and geologic properties of the terrestrial basin classes will be identified, and the results of their morphology-climatic relationship extrapolated to Mars and Titan. To find out more, visit our project’s website https://www.schemata-project.com/.</p>

scholarly journals Self-Organizing B2B Enterprise of Distributed Photogrammetric Processing of Terrain Images in ETRIS

2020 ◽  
pp. 155-179
Author(s):  
Oleg Karsaev ◽  
Igor Shuklin ◽  
Sergey Yushchenko
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Remote Sensing Data ◽  
Earth Remote Sensing ◽  
Distributed Information System ◽  
Distributed Information ◽  
Sensing Data ◽  
Geographically Distributed ◽  
Software Information ◽  
Self Organizing

An approach to the dynamic formation (adjustment) of schedules for distributed photogrammetric image processing in a network of ground centers included in the United geographically distributed information system for receiving and processing Earth remote sensing data from space is considered. Having the fullest satisfaction of requirements of consumers to the satellite images of necessary areas, the approach provides the formation of self-organizing B2B enterprises in the specified network providing information, software and hardware resources of the ground-based facilities of various departmental and other accessories for photogrammetric processing of any received images of the area from the the United geographically distributed information system. It is shown, that a search in B2B enterprise nodes and borrowing the required resources will allow ground centers to flexibly scale physical and virtual means of photogrammetric processing of Earth remote sensing data, quickly form their local structural and functional organizations depending on the current properties of the consumer requests flow for receiving Earth remote sensing data in the United geographically distributed information system, characteristics of the flow of terrain survey materials from orbital monitoring tools, and also take into account the visual and measuring properties of images of the area subject to photogrammetric processing. A method for truncating the set of potential performers of the application in accordance with the existing semantic and other restrictions on the composition of the desired set of performers is proposed. Also mechanisms to encourage ground centers to provide idle resources to B2B enterprise nodes are proposed. They are based on the possibility of receiving monetary or other remuneration from a ground center for participating in distributed application servicing. The development of a well-known model of a self-organizing B2B enterprise creates conditions for a more efficient organization of servicing the flow of applications in the United geographically distributed information system by attracting unused software, information and hardware resources of ground centers of various departmental affiliations.

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