scholarly journals Spectral characteristics of some agricultural crops in different phenological phases of vegetation

2020 ◽  
pp. 56-70
Author(s):  
V. A. Tabunschik ◽  
Т. M. Chekmareva ◽  
R. V. Gorbunov
Keyword(s):  
Software Package ◽  
Satellite Images ◽  
Spectral Characteristics ◽  
Spectral Brightness ◽  
Space Images ◽  
Brightness Coefficient ◽  
Spectral Reflectivity ◽  
Spectral Brightness Coefficient ◽  
Sentinel 2 ◽  
Phenological Phases

For deciphering crops from satellite images at different time periods, it is necessary to have information about the spectral reflectivity of plants during their passage through the phenological phases of vegetation. An attempt was made to evaluate the spectral reflectivity of the main fruit crops and grapes in different phenological phases of the growing season using Sentinel-2 satellite images and the ENVI software package. Field research methods, plots were selected on which peach, grapes, cherries, apple trees, plums, and apricots grow are used. It was established that planting crops was carried out by mixing cultivars in order to reduce the risk of additional costs as a result of possible adverse natural processes and phenomena. For each section, the maximum, minimum, and average values of the spectral brightness coefficient were obtained and analyzed within 13 bands of Sentinel-2 satellite images. Space images were selected for 04/07/2019, 04/27/2019 and 05/12/2019, as the most suitable for the periods of the beginning of flowering (04/07/2019), the end of flowering (04/27/2019) and the beginning of fruit ripening (12/05/2019), with minimal cloud overlap values. To eliminate the external influence of the soil within each pixel of the image, the linear spectral separation module of the ENVI software package was used, a reference soil fragment was selected and its spectral characteristics were obtained, which made it possible to depict graphs of the spectral curves of the crops under study within each section. It was not possible to obtain a distinction of the spectral brightness coefficient for all sections, which is associated with the presence of additional external elements.

scholarly journals Analysis of the spectral properties of wheat growth in different vegetation periods

2021 ◽  
Vol 6 (2 (114)) ◽  
pp. 96-102
Author(s):  
Akbota Yerzhanova ◽  
Akmaral Kassymova ◽  
Gulzira Abdikerimova ◽  
Manshuk Abdimomynova ◽  
Zhuldyz Tashenova ◽  
...  
Keyword(s):  
Plant Species ◽  
Satellite Images ◽  
Action Plan ◽  
Main Tool ◽  
Normal Growth ◽  
Spectral Brightness ◽  
Space Images ◽  
Brightness Coefficient ◽  
Spectral Brightness Coefficient ◽  
Spectral Ranges

The article presents a technique for studying space images based on the analysis of the spectral brightness coefficient (SBC) of space images of the earth's surface. Recognition of plant species, soils, and territories using satellite images is an applied task that allows to implement many processes in agriculture and automate the activities of farmers and large farms. The main tool for analyzing satellite imagery data is the clustering of data that uniquely identifies the desired objects and changes associated with various reasons. Based on the data obtained in the course of experiments on obtaining numerical SBC values, the patterns of behavior of the processes of reflection of vegetation, factors that impede the normal growth of plants, and the proposed clustering of the spectral ranges of wave propagation, which can be used to determine the type of objects under consideration, are revealed. Recognition of these causes through the analysis of SBC satellite images will create an information system for monitoring the state of plants and events to eliminate negative causes. SBC data is divided into non-overlapping ranges, i.e. they form clusters reflecting the normal development of plant species and deviations associated with negative causes. If there are deviations, then there is an algorithm that determines the cause of the deviation and proposes an action plan to eliminate the defect. It should be noted that the distribution of the brightness spectra depends on the climatic and geographical conditions of the plant species and is unique for each region. This study refers to the Akmola region, where grain crops are grown

scholarly journals Spectral properties of soils and agricultural crops for analysis of satellite images of the Akmola region

2021 ◽  
Vol 79 (1) ◽  
pp. 315-324
Author(s):  
A.E. Yerzhanova ◽  
◽  
S.E. Kerimkulov ◽  
Keyword(s):  
Data Analysis ◽  
Wavelength Range ◽  
Spectral Properties ◽  
Satellite Images ◽  
Recognition Algorithm ◽  
Spectral Brightness ◽  
Agricultural Crops ◽  
Brightness Coefficient ◽  
Spectral Brightness Coefficient ◽  
Different Types

This paper considers the spectral properties of soils and vegetation and their analysis for further application of the results of the article for processing satellite images. Basically, the soils and soils of the Akmola region and agricultural crops inherent in this region are considered. When analyzing the spectral brightness coefficient (SCR), there are differences in the SCR of soils of different types and vegetation. Based on the results of data analysis, the following conclusions were obtained: soil recognition is informative in the wavelength range from 700 nm to 1300 nm; crop recognition is informative in the wavelength range from 850 nm to 1100 nm. When developing an object recognition algorithm, two fixed points of 0.55 microns and a point of 0.66-0.68 microns will be considered for the presence of extremes to determine the type of object.

Sensitivity of the spectral brightness coefficient to variation of the components of seawater

1987 ◽  
Vol 47 (3) ◽  
pp. 951-955
Author(s):  
A. A. Kovalev ◽  
S. B. Kostyukevich ◽  
E. K. Naumenko ◽  
V. E. Plyuta
Keyword(s):  
Spectral Brightness ◽  
Brightness Coefficient ◽  
Spectral Brightness Coefficient

scholarly journals Designing a methodological concept for the diagnosis of early development of the main wheat diseases pathogens

2020 ◽  
Vol 21 ◽  
pp. 00002
Author(s):  
Oksana Kremneva ◽  
Roman Danilov ◽  
Olga Tutubalina ◽  
Igor Sereda ◽  
Kurilov Artem
Keyword(s):  
Early Development ◽  
Near Infrared ◽  
Puccinia Striiformis ◽  
Plant Protection ◽  
Spectral Characteristics ◽  
Infrared Range ◽  
Spectral Brightness ◽  
Wheat Varieties ◽  
Brightness Coefficient ◽  
Experimental Plots

The studies presented in the article were carried out in 2018-2019 on the experimental field of the All-Russian Research Institute of Biological Plant Protection. The aim of the research was to assess the feasibility of diagnosing the early development of major diseases pathogens based on the results of ground-based spectrometry and the use of phytomonitoring technology, taking into account the genotypes of different winter wheat varieties. There were three options of the experimental plots for the research: the 1st – protected against diseases by fungicides, the 2nd – with an artificial infectious background, the 3rd – with the natural development of diseases. According to the results of data analysis, the most significant changes in the spectral characteristics of the studied plant backgrounds were noted at the time of the first signs of disease in the form of a decrease in the spectral brightness coefficient in the near infrared range. Using special tools in the experimental plots, the following pathogens were identified before the appearing of disease symptoms: Blumeria graminis (DC.) Speer f. sp. tritici Marchal , Puccinia striiformis West., Pyrenophora tritici-repentis Died., Puccinia triticina Erikss. Data on the diseases development, plant infestation by pathogens are compared with spectrometric measurements.

scholarly journals POSSIBILITIES OF USING SATELLITE IMAGES AND GIS TECHNOLOGIES FOR MONITORING INUNDATION OF PIPELINE SYSTEMS

2020 ◽  
Vol 4 (1) ◽  
pp. 21-28
Author(s):  
Vyacheslav A. Melkiy ◽  
Daniil V. Dolgopolov ◽  
Alexey A. Verkhoturov
Keyword(s):  
Satellite Images ◽  
Spectral Characteristics ◽  
Remote Sensing Data ◽  
Space Systems ◽  
Water Index ◽  
Geotechnical Monitoring ◽  
Pipeline Systems ◽  
Normalized Difference Water Index ◽  
Automated Interpretation ◽  
Sentinel 2

The purpose of this research is the study of possibilities of practical use of multi-zone satellite images for implementation of geotechnical monitoring of pipeline transport facilities during floodings. Modern methods and approaches are required for monitoring extended objects and analyzing large amount of remote sensing data. Such methods can be applied for studying of spectral characteristics of the Earth's surface obtained using space systems, collected in databases using geoinformation technologies (GIS). Use of special indexes and technologies for automated interpretation of multi-zone satellite images allows obtaining and analyzing information about state of pipeline systems at time of flooding. Research showed that Sentinel-2 satellite data makes it possible for fairly correctly determine of flood situation by image indexed with using of Normalized Difference Water Index (NDWI) and highlight areas and objects flooded of water.

scholarly journals A Novel Approach for Cloud Detection in Scenes with Snow/Ice Using High Resolution Sentinel-2 Images

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 44 ◽  
Author(s):  
Ling Han ◽  
Tingting Wu ◽  
Qing Liu ◽  
Zhiheng Liu
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Matched Filter ◽  
Spectral Characteristics ◽  
Cloud Detection ◽  
Novel Approach ◽  
Visible Wavelength Range ◽  
Number Frequency ◽  
Detection Effect ◽  
Sentinel 2

The recognition of snow versus clouds causes difficulties in cloud detection because of the similarity between cloud and snow spectral characteristics in the visible wavelength range. This paper presents a novel approach to distinguish clouds from snow to improve the accuracy of cloud detection and allow an efficient use of satellite images. Firstly, we selected thick and thin clouds from high resolution Sentinel-2 images and applied a matched filter. Secondly, the fractal digital number-frequency (DN-N) algorithm was applied to detect clouds associated with anomalies. Thirdly, spatial analyses, particularly spatial overlaying and hotspot analyses, were conducted to eliminate false anomalies. The results indicate that the method is effective for detecting clouds with various cloud covers over different areas. The resulting cloud detection effect possesses specific advantages compared to classic methods, especially for satellite images of snow and brightly colored ground objects with spectral characteristics similar to those of clouds.

Monitoring Acid Mine Drainage’s Effects on Surface Water in the Kizel Coal Basin with Sentinel-2 Satellite Images

2021 ◽  
Author(s):  
Sergey V. Pyankov ◽  
Nikolay G. Maximovich ◽  
Elena A. Khayrulina ◽  
Olga A. Berezina ◽  
Andrey N. Shikhov ◽  
...  
Keyword(s):  
Surface Water ◽  
Satellite Images ◽  
Coal Basin ◽  
Acid Mine ◽  
Sentinel 2

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

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