Method and algorithms of forecasting the seasonal characteristics of anthropogenic impact areas using long-term remote sensing data

A method for predicting the characteristics of areas subject to the anthropogenic impact according to the Earth’s remote sensing data is proposed. The developed method is based on the identification of patterns using long-term periodic observations. These patterns are applied to the seasonal observa...

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Bibliographic Details
Published in:Journal of computer & systems sciences international Vol. 54; no. 3; pp. 406 - 414
Main Authors: Ignatiev, V. Yu, Murynin, A. B.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01.05.2015
Springer Nature B.V
Subjects:
Agricultural production
Algorithms
Analysis
Control
Engineering
Environmental impact
Forecasting
Information processing
Long term
Mechatronics
Methods
Pattern Recognition and Image Processing
Regions
Remote sensing
Robotics
Seasons
Studies
Russia
Yield Forecast
Vegetation State Index
Current Season
Term Observation
System Science International
ISSN:1064-2307, 1555-6530
Online Access:Get full text
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Summary:A method for predicting the characteristics of areas subject to the anthropogenic impact according to the Earth’s remote sensing data is proposed. The developed method is based on the identification of patterns using long-term periodic observations. These patterns are applied to the seasonal observations of the current year. The method is implemented in a set of algorithms and predictive models. An example of the use of the method of the agricultural yield forecasting is given. The training and validation of models for the prediction of crop yields based on the long-term spatial data on the state of vegetation are described. Different regions of the Russian Federation including the Arctic regions are considered.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:1064-2307
1555-6530
DOI:10.1134/S1064230715030119