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Remote Ground-Based and Satellite Monitoring of Vegetation / A. P. Shevyrnogov [et al.] // Her. Russ. Acad. Sci. - 2018. - Vol. 88, Is. 6. - P469-474, DOI 10.1134/S1019331618060138. - Cited References:20. - This study was performed within the RAS Siberian Branch Integrated Basic Research Program "Interdisciplinary Integrative Studies" for 2018-2020 (project no. 74) and a state assignment (state registration AAAA-A17-117013050027-1). . - ISSN 1019-3316. - ISSN 1555-6492

РУБ History & Philosophy Of Science + Multidisciplinary Sciences
Рубрики:
GROWTH
Кл.слова (ненормированные):
agrocenoses -- grass vegetation -- halophytes -- ground spectrometry -- satellite sensing -- chlorophyll photosynthetic potential -- mathematical -- modeling
Аннотация: Prospects for remote ground and satellite sensing to monitor agricultural (agrocenoses) and grass (meadows and steppes) vegetation are considered. This helps assess chlorophyll contents, crop yields, impurities of territories and identify agrocenoses. Investigation of vegetation on salinized soils identified the necessity to consider the succession of limiting factors (temperature and the degree of soil salinization). The results of studies on grassland plant communities in Khakassia based on geobotanical descriptions and ground spectral measurements are presented, allowing the refinement of methods that improve the accuracy of deciphering satellite images of medium and low resolutions.

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Scopus
Держатели документа:
Russian Acad Sci, Siberian Branch, Krasnoyarsk Res Ctr, Inst Biophys, Krasnoyarsk, Russia.

Доп.точки доступа:
Shevyrnogov, A. P.; Botvich, I. Yu.; Kononova, N. A.; Pis'man, T. I.; RAS Siberian Branch Integrated Basic Research Program "Interdisciplinary Integrative Studies" for 2018-2020 [74]

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Seasonal Dynamics of Vegetation on Fallow Lands in Krasnoyarsk Forest Steppe According to Terrain and Satellite Data / A. P. Shevyrnogov, T. I. Pisman, N. A. Kononova [et al.] // Izv. Atmos. Ocean. Phys. - 2019. - Vol. 55, Is. 9. - P1353-1361, DOI 10.1134/S0001433819090470. - Cited References:29. - This study was performed according to the Complex Program of Fundamental Research of the Siberian Branch of the Russian Academy of Sciences Interdisciplinary Integration Researches for 2018-2020 (project no. 74) and State Task registration no. AAAA-A17-117013050027-1. . - ISSN 0001-4338. - ISSN 1555-628X

РУБ Meteorology & Atmospheric Sciences + Oceanography
Рубрики:
MODIS
Кл.слова (ненормированные):
fallow lands -- vegetation indices -- terrain spectophotometry -- MODIS -- geobotanical researches -- Krasnoyarsk krai
Аннотация: This article presents investigation data on the seasonal dynamics of productivity, status, and species composition of vegetation on fallow lands in the Krasnoyarsk forest steppe (Middle Siberia) obtained from terrain and satellite materials from 2017. The results of the study of grass plant communities on the basis of geobotanical descriptions and terrain spectrometry were have been used for a more accurate interpretation of cosmic photographs of moderate and low resolution. For studying vegetation on fallow lands, we analyze the seasonal dynamics of various vegetative indices (NDVI, EVI, LSWI, and LAI) and parameters (NPP, FPAR, and LST (land surface temperature)) obtained from MODIS satellite images. Our analysis of satellite data shows the absence of evidences of plowing and mowing in the studied area. A positive correlation is revealed between vegetation indices of biomass (NDVI, EVI, LAI, and NPP) and parameters of hydrothermal conditions (LSWI, FPAR, and LST).

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Держатели документа:
Russian Acad Sci, Siberian Branch, Inst Biophys, Krasnoyarsk, Russia.

Доп.точки доступа:
Shevyrnogov, A. P.; Pisman, T. I.; Kononova, N. A.; Botvich, I. Yu.; Larko, A. A.; Vysotskaya, G. S.; Kononova, Natalia; Complex Program of Fundamental Research of the Siberian Branch of the Russian Academy of Sciences [74, AAAA-A17-117013050027-1]

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Information Content of Spectral Vegetation Indices for Assessing the Weed Infestation of Crops Using Ground-Based and Satellite Data / T. I. Pisman, M. G. Erunova, I. Y. Botvich [et al.] // Izv. Atmos. Ocean. Phys. - 2021. - Vol. 57, Is. 9. - P1188-1197, DOI 10.1134/S0001433821090577. - Cited References:32 . - ISSN 0001-4338. - ISSN 1555-628X

РУБ Meteorology & Atmospheric Sciences + Oceanography
Рубрики:
DIFFERENTIATION
REFLECTANCE
Кл.слова (ненормированные):
vegetation indices -- PlanetScope -- ground-based spectrometry -- geobotanical -- studies -- wheat crops -- Krasnoyarsk krai
Аннотация: This paper presents the results of a study assessing the degree of weed infestation of wheat crops. They are obtained using optical ground-based and satellite spectral data with a 3-m spatial resolution from PlanetScope Dove satellites for 2019. The vegetation indices, including the normalized difference vegetation index (NDVI), the relative chlorophyll index (Chlorophyll Index Green-ClGreen or GCI), the modified soil-adjusted vegetation index (MSAVI2), and the visible atmospherically resistant index (VARI) are used in the interpretation of ground-based spectrometric and space images. This paper indicates the possibility of assessing the degree of weed infestation of agricultural fields. The higher the weed infestation, the lower the index values. The dynamics of VARI is found to be different from the dynamics of NDVI, ClGreen, and MSAVI2 during the growing season. The strong correlation between NDVI, ClGreen, and MSAVI2 and the weak correlation between VARI and other indices are observed. The possibility of identifying weedy sites in the agricultural fields is shown using the spatial distribution map of ClGreen dated August 2, 2019.

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Держатели документа:
Russian Acad Sci, Inst Biophys, Siberian Branch, Krasnoyarsk, Russia.
Russian Acad Sci, Fed Res Ctr, Siberian Branch, Krasnoyarsk Sci Ctr, Krasnoyarsk, Russia.
Russian Acad Sci, Fed Res Ctr, Krasnoyarsk Sci Res Inst Agr, Siberian Branch,Krasnoyarsk Sci Ctr, Krasnoyarsk, Russia.

Доп.точки доступа:
Pisman, T., I; Erunova, M. G.; Botvich, I. Yu; Emelyanov, D., V; Kononova, N. A.; Bobrovsky, A., V; Kryuchkov, A. A.; Shpedt, A. A.; Shevyrnogov, A. P.

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