Mapping croplands with a long history of crop cultivation using time series of MODIS vegetation indices

M.A. Ivanov, S.S. Mukharamova, O.P. Yermolaev, B. Essuman-Quainoo

Kazan Federal University, Kazan, 420008 Russia

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DOI: 10.26907/2542-064X.2020.2.302-313

For citation: Ivanov M.A., Mukharamova S.S., Yermolaev O.P., Essuman-Quainoo B. Mapping croplands with a long history of crop cultivation using time series of MODIS vegetation indices. Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki, 2020, vol. 162, no. 2, pp. 302–313. doi: 10.26907/2542-064X.2020.2.302-313.

Для цитирования: Ivanov M.A., Mukharamova S.S., Yermolaev O.P., Essuman-Quainoo B. Mapping croplands with a long history of crop cultivation using time series of MODIS vegetation indices // Учен. зап. Казан. ун-та. Сер. Естеств. науки. – 2020. – Т. 162, кн. 2. – С. 302–313. – doi: 10.26907/2542-064X.2020.2.302-313.

Abstract

In the study, a method of automated detection of croplands and natural grasslands using multi-year time series of the vegetation indices was developed and implemented. The metho­dology is based on a new recognition feature enabling identification of all lands cultivated over a long period. The new recognition feature was calculated from time series of the MODIS vegetation indices. It takes into account both intraseasonal and interannual characteristics of the vegetation period for arable lands with spring and winter crops, as well as for natural grasslands. The methodology determines: the procedure of obtaining time series of the NDVI and EVI vegetation indices for the period from March to November in the given years; gene­ralization of the time series by land cover categories and agro-climatic zones; dates for calculating the recognition feature in each zone; calculation of the recognition feature; adaptation of the decision rules to the regional environmental conditions (threshold values for the agro-climatic zones); procedure of classification into croplands and grasslands. Croplands within the territory of the European part of Russia were mapped for the period from 2000 to 2016. The assessment of recognition accuracy and comparison of the results with the existing products confirm that the developed recognition feature can be successfully used for cropland detection and can improve the accuracy of data interpretation when applied along with other features.

Keywords: MODIS, NDVI, EVI, cropland, European part of Russia

Acknowledgments. The work was supported by the Russian Science Foundation (project no. 19-17-00064).

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Recieved

April 7, 2020

 

Ivanov Maxim Andreevich, PhD in Geography, Associate Professor of Landscape Ecology Department

Kazan Federal University

ul. Kremlyovskaya 18, Kazan, 420008 Russia

E-mail: maximko-87@mail.ru

Mukharamova Svetlana Sayasovna, PhD in Biology, Associate Professor of Department of Ecological Systems Modeling

Kazan Federal University

ul. Kremlyovskaya 18, Kazan, 420008 Russia

E-mail: smukhara@gmail.com

Yermolaev Oleg Petrovich, Doctor of Geography, Professor of Landscape Ecology Department

Kazan Federal University

ul. Kremlyovskaya 18, Kazan, 420008 Russia

E-mail: oyermol@gmail.com

Essuman-Quainoo Benedict, Graduate Student of Landscape Ecology Department

Kazan Federal University

ul. Kremlyovskaya 18, Kazan, 420008 Russia

E-mail: benessquainoo@gmail.com

 

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