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

Recieved April 7, 2020

Full text PDF
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.

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).

References

1. Alcantara C., Kuemmerle T., Prishchepov A.V., Radeloff V.C. Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sens. Environ., 2012, vol. 124, pp. 334–347. doi: 10.1016/j.rse.2012.05.019.
2. Bartalev S.A., Plotnikov D.E., Loupian E.A. Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique. Remote Sens. Lett., 2016, vol. 7, no. 3, pp. 269–278. doi: 10.1080/2150704X.2015.1130874.
3. Wardlow B.D., Egbert S.L., Kastens J.H. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sens. Environ., 2007, vol. 108, no. 3, pp. 290–310. doi: 10.1016/j.rse.2006.11.021.
4. Wardlow B.D., Egbert S.L. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains. Remote Sens. Environ., 2008, vol. 112, no. 3, pp. 1096–1116. doi: 10.1016/j.rse.2007.07.019.
5. Estel S., Kuemmerle T., Levers Chr., Baumann M., Hostert P. Mapping cropland-use intensity across Europe using MODIS NDVI time series. Environ. Res. Lett., 2016, vol. 11, no. 2, art. 024015, pp. 1–10. doi: 10.1088/1748-9326/11/2/024015.
6. Arvor D., Jonathan M., Meirelles M.S.P., Dubreuil V., Durieux L. Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil. Int. J. Remote Sens., 2011, vol. 32, no. 22, pp. 7847–7871. doi: 10.1080/01431161.2010.531783.
7. Bartalev S.A., Egorov V.A., Lupyan E.A., Plotnikov D.E., Uvarov I.A. Arable land mapping based on long-term satellite data from a MODIS spectroradiometer and locally adaptive classification. Komp’yut. Opt., 2011, vol. 35, no. 1, pp. 103–116. (In Russian)
8. Shao Y., Lunetta R.S. The use of MODIS-NDVI data for mapping cropland across the Great Lakes Basin, USA. Proc. IGARSS 2008 – 2008 IEEE Int. Geosci. Remote Sens. Symp. Boston, Mass., 2008, pp. 196–199. doi: 10.1109/IGARSS.2008.4780061.
9. Wardlow B.D., Egbert S.L. A comparison of MODIS 250-m EVI and NDVI data for crop mapping: A case study for southwest Kansas. Int. J. Remote Sens., 2010, vol. 31, no. 3, pp. 805–830. doi: 10.1080/01431160902897858.
10. Brown J.C., Kastens J.H., Coutinho A.C., Victoria D. de C., Bishop C.R. Classifying multiyear agricultural land use data from Mato Grosso using time-series MODIS vegetation index data. Remote Sens. Environ., 2013, vol. 130, pp. 39–50. doi: 10.1016/j.rse.2012.11.009.
11. Estel S., Kuemmerle T., Alcántara C., Levers Chr., Prishchepov A., Hostert P. Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series. Remote Sens. Environ., 2015, vol. 163, pp. 312–325. doi: 10.1016/j.rse.2015.03.028.
12. Hao P., Zhan Y., Wang L., Niu Z., Shakir M. Feature selection of time series MODIS data for early crop classification using random forest: A case study in Kansas, USA. Remote Sens., 2015, vol. 7, no. 5, pp. 5347–5369. doi: 10.3390/rs70505347.
13. Zhong L., Yu L., Li X., Hu L., Gong P. Rapid corn and soybean mapping in US Corn Belt and neighboring areas. Sci. Rep., 2016, vol. 6, art. 36240, pp. 1–14. doi: 10.1038/srep36240.
14. Alcantara C., Kuemmerle T., Baumann M., Bragina E.V., Griffiths P., Hostert P., Knorn J., Müller D., Prishchepov A.V., Schierhorn F., Sieber A., Radeloff V.C. Mapping the extent of abandoned farmland in Central and Eastern Europe using MODIS time series satellite data. Environ. Res. Lett., 2013, vol. 8, no. 3, art. 035035, pp. 1–9. doi: 10.1088/1748-9326/8/3/035035.
15. Lunetta R.S., Shao Y., Ediriwickrema J., Lyon J.G. Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data. Int. J. Appl. Earth Obs. Geoinf., 2010, vol. 12, no. 2, pp. 81–88. doi: 10.1016/j.jag.2009.11.005.
16. Chen Y., Lu D., Moran E., Batistella M., Dutra L.V., Sanches I.D., da Silva R.F.B., Huang J., Luiz A.J.B., de Oliveira M.A.F. Mapping croplands, cropping patterns, and crop types using MODIS time-series data. Int. J. Appl. Earth Obs. Geoinf., 2018, vol. 69, pp. 133–147. doi: 10.1016/j.jag.2018.03.005.
17. U.S. Geological Survey. Available at: https://www.usgs.gov/core-science-systems/nli/ landsat/data-tools.
18. Finley A., Banerjee S., Hjelle O., Bivand R. MBA: Multilevel B-spline approximation. R package version 0.0-9, 2017. Available at: https://cran.r-project.org/web/packages/ MBA/index.html.
19. Wood S.N. Generalized Additive Models: An Introduction with R. CRC Press, 2006. 410 р.
20. Bivand R., Keitt T., Rowlingson B., Pebesma E., Sumner M., Hijmans R., Rouault E., War-merdam F., Ooms J., Rundel C. rgdal: Bindings for the geospatial data abstraction library. R package version 1.2-18, 2018. Available at: https://cran.r-project.org/web/ packages/rgdal/index.html.
21. Hijmans R.J., van Etten J., Cheng J., Mattiuzzi M., Sumner M., Greenberg J.A., Lami-gueiro O.P., Bevan A., Racine E.B., Shortridge A., Ghosh A. Raster: Geographic Data Analysis and Modeling. R package version 2.6-7, 2017. Available at: https://cran.r-project.org/web/packages/raster/index.html.
22. Geograficheskii atlas dlya uchitelei srednei shkoly [Geographic Atlas for Secondary School Teachers]. Moscow, Gl. Upr. Geod. Kartogr. Sov. Minist. SSSR, 1982. 238 p. (In Russian)
23. Bulygina O.N., Razuvaev V.N., Aleksandrova T.M. Description of the dataset of daily air temperature and precipitation for weather stations of Russia and the former USSR (TTTR). Database State Registration Certificate no. 2014620942. 2014. (In Russian)
24. Lee S., Wolberg G., Shin S.Y. Scattered data interpolation with multilevel B-splines. IEEE Trans. Visualization Comput. Graphics, 1997, vol. 3, no. 3, pp. 228–244. doi: 10.1109/2945.620490.
25. Upton G., Cook I. Oxford Dictionary of Statistics. Oxford, Oxford Univ. Press, 2002. 420 p.
26. Ivanov M.A., Prishchepov A.V., Golosov V.N., Zalyaliev R.R., Efimov K.V., Kond­rat’eva A.A., Kinyashova A.D., Ionova Yu.K. Methods for mapping croplands dynamics in river basins of the European part of Russia for 1985–2015. Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Kosmosa, 2017, vol. 14, no. 5, pp. 161–171. doi: 10.21046/2070-7401-2017-14-5-161-171. (In Russian)
27. Bartalev S.A., Egorov V.A., Loupian E.A., Khvostikov S.A. A new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data. Remote Sens. Lett., 2014, vol. 5, no. 1, pp. 55–64. doi: 10.1080/2150704X.2013.870675.

The content is available under the license Creative Commons Attribution 4.0 License.