scholarly journals Building and precision assessment of regression models for determining of cereals’ and legumes’ crop yield based on Earth remote sensing data and climatic characteristics

2020 ◽  
Vol 26 (3) ◽  
pp. 159-169
Author(s):  
Alexey Stepanov ◽  
Tatiana Aseeva ◽  
Konstantin Dubrovin
Keyword(s):  
Remote Sensing ◽  
Spring Wheat ◽  
Regression Models ◽  
Arable Land ◽  
Soil Layer ◽  
Remote Sensing Data ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
Climatic Characteristics ◽  
Sown Area

Crop yields are strictly dependent from natural and climatic conditions of the growing region, in addition specific weather conditions in the southern part of the Far East necessitates the analysis of a large number of factors when building a predictive regression model. The article presents regression models for assessing the average productivity of the main crops in Chernigovsky district of Primorsky region: soybean, spring wheat, barley and oat. Between 2012 and 2018 the sown area of these crops ranged from 78 to 86 % of the total sown area in the Chernigovsky district. We used the indicators obtained from Earth remote sensing data (the maximum weekly NDVI per year, calculated from the mask of arable land in the Chernigovsky district) and meteorological characteristics (from 2008 to 2018): hydrothermal Selyaninov coefficient, the duration of the growing season, temperature and humidity of the upper soil layer, photosynthetically active radiation and the Budyko radiation index. Climatic characteristics of arable land, representing reanalysis data and combining ground based and remote observations, were obtained using the Vega–Science web–service. Also, we used data about sown area and gross crop in the Chernigovsky region from 2008 to 2018. It was found that average annual oat yield has the biggest coefficient of variation (31.5 %). The corresponding indicator for the remaining crops is in range from 16 to 18 %. The accuracy analysis of the obtained models showed that the average error of the model in period from 2008 to 2017 was 4.1 % for barley, 5.1 % for oat and spring wheat, and 5.4 % for soybean.

scholarly journals Ecological assessment of the territorial complexes of Tatarbunars’kyi district of Odessa region

2020 ◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Negative Impact ◽  
Arable Land ◽  
Remote Sensing Data ◽  
Economic Structure ◽  
Anthropogenic Factors ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
The Impact

Formulation of the problem. The Tatarbunars’kyi District is located in the southwestern part of Odessa region and reflects the main features of the landscape-economic structure of the region: water, agricultural, resort and environmental areas. On the other hand, the form of land use is characterized by widespread plowing of land with degradation and erosion of soil cover. Land structure and use patterns have a complex negative impact on ecological and economic processes and cannot ensure the sustainable development of the region, in particular it is antagonistic to the unique transitional wetland ecosystems of international importance located within the area. To solve the issues of balanced environmental management and zoning of the landscape and economic structure of the region, Earth remote sensing (ERS) data can be used - spectrozonal satellite imagery and geographic information systems (GIS), which can simultaneously cover the research area as a whole, carry out regular monitoring and significantly reduce costs by expensive expeditionary work. Using space monitoring data allows you to get a large array of characteristics of the state of the territorial complexes of the region. Purpose of the work is: assessment of the ecological state of the landscape economic structure and development of recommendations for the protection of natural and territorial complexes of the Tatarbunar’skyi District of Odessa region based on the use of GIS and remote sensing data. Methods. Landsat8 satellite images with OLI and TIRS sensors, digital terrain models (SRTM) with a spatial resolution of 30 m were used as initial data. The spatial distribution of the population was carried out on the basis of OpenStreetMap data using automatic interpolation using the IDW method. Spatial analysis and data processing were carried out in the QGIS v3.4.6 software package. To quantify the vegetation cover, the Normalized Difference Vegetation Index - NDVI was calculated. Waterlog distribution was estimated using a modified normalized differential moisture index (NDMI). The analysis of the structure of land use and anthropogenic load was carried out on the basis of ranking of territorial objects into homogeneous groups to calculate geoecological coefficients. Results. The article discusses the possibilities of using Earth remote sensing data for a functional assessment of land changes as a result of anthropogenic activities, primarily arable land, analyzes the ecological and economic equilibrium of the region based on geoecological coefficients, identifies areas that are primarily exposed to environmental risks, exogenous processes and the impact anthropogenic factors. Measures are proposed to increase the environmental sustainability of agrolandscapes and the landscape-anthropogenic structure of the region’s lands. A detailed hydrological and morphometric analysis of the catchment basin was carried out. Karachaus within the boundaries of the District. For the catchment estuary, remediation and nature conservation measures based on GIS are proposed and designed.

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 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.

Software technologies for processing of earth remote sensing data

2013 ◽  
Vol 23 (4) ◽  
pp. 474-480 ◽  
Author(s):  
A. A. Buchnev ◽  
V. P. Pyatkin ◽  
E. V. Rusin
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Earth Remote Sensing ◽  
Sensing Data
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