Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 2, pp. 112-127

Automated annual cropland mapping from reconstructed time series of Landsat data

D.E. Plotnikov 1 , P.A. Kolbudaev 1 , S.A. Bartalev 1 , E.A. Loupian 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 05.04.2018
DOI: 10.21046/2070-7401-2018-15-2-112-127
The paper is devoted to the method for annual cropland mapping over two distant regions of Russia with reconstructed seasonal time series of Landsat data. The method makes use of time-series-based spectro-temporal metrics extracted from time series of surface reflectance data in red, near infrared and short-wave infrared, as well as from vegetation indices. The new method aimed at time series reconstruction, which relies on seasonal phenology similarities within group of related objects, is proposed to meet requirements imposed on temporal density for cropland mapping. LAGMA mapping method and Random Forest classifier were used to account for diversity of cropping techniques and environmental conditions within Moscow region and Primorksy krai. Cropland map validation was based on in situ data and satellite imagery of very high spatial resolution and was performed for Kashirsky District (Moscow Region) and Oktyabrsky District (Primorsky Krai), where overall accuracy reached 88.7 % and 84.4 %, respectively.
Keywords: remote sensing, mcropland mapping, Landsat, time series reconstruction, spatio-temoral analysis
Full text

References:

  1. Bartalev S. A., Egorov V. A., Loupian E. A., Plotnikov D. E., Uvarov I. A., Raspoznavanie pakhotnykh zemel’ na osnove mnogoletnikh sputnikovykh dannykh spektroradiometra MODIS i lokal’no-adaptivnoi klassifikatsii (Recognition of arable lands using multi-annual satellite data from spectroradiometer modis and locally adaptive supervised classification), Komp’yuternaya optika, Samara: ISOI RAN, 2011, Vol. 35, No. 1, pp. 103–116.
  2. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Sostoyanie i perspektivy razvitiya metodov sputnikovogo kartografirovaniya rastitel’nogo pokrova Rossii (Current state and development prospects of satellite mapping methods of Russia’s vegetation cover), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 203–221.
  3. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shaba­nov N. V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data), Moscow: IKI RAN, 2017, 208 p.
  4. 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., Metodika kartografirovaniya dinamiki pakhotnykh ugodii v basseinakh rek Evropeiskoi territorii Rossii za period 1985–2015 gg. (Method of croplands dynamics mapping in river basins of the European part of Russia for the period of 1985–2015), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 5, pp. 161–171.
  5. Kolbudaev P. A., Bartalev S. A., Plotnikov D. E., Matveev A. M., Tekhnologiya obrabotki sputnikovykh dannykh Landsat-TM/ETM+, XIV Vserossiiskaya otkrytaya konferentsiya “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (XIV All-Russia Open Conference “Current Problems in Remote Sensing of the Earth from Space”), Book of Abstracts, Moscow, IKI RAN, 2016, p. 37.
  6. Loupian E. A., Proshin A. A., Burtsev M. A., Balashov I. V., Bartalev S. A., Efremov V. Yu., Kashnits­kii A. V., Mazurov A. A., Matveev A. M., Sudneva O. A., Sychugov I. G., Tolpin V. A., Uvarov I. A., Tsentr kollektivnogo pol’zovaniya sistemami arkhivatsii, obrabotki i analiza sputnikovykh dannykh IKI RAN dlya resheniya zadach izucheniya i monitoringa okruzhayushchei sredy (IKI center for collective use of satellite data archiving, processing and analysis systems aimed at solving the problems of environmental study and monitoring), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 5, pp. 247–267.
  7. Lyuri D. I., Goryachkin S. V., Karavaeva N. A., Denisenko E. A., Nefedova T. T., Dinamika sel’skokho­zyaistvennykh zemel’ Rossii v XX veke i postagrogennoe vosstanovlenie rastitel’nosti i pochv, Moscow: GEOS, 2010, 416 p.
  8. Plotnikov D. E., Metod segmentatsii vremennoi serii sputnikovykh izobrazhenii, XII Vserossiiskaya otkrytaya konferentsiya “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (XIV All-Russia Open Conference “Current Problems in Remote Sensing of the Earth from Space”), Book of Abstracts, Moscow, IKI RAN, 2014, p. 375.
  9. Plotnikov D. E., Bartalev S. A., Loupian E. A., Priznaki raspoznavaniya pakhotnykh zemel’ na osnove mnogoletnikh ryadov dannykh sputnikovogo spektroradiometra MODIS (The recognition features to map arable lands based on multi-annual MODIS Earth observation data), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2010, Vol. 7, No. 1, pp. 330–341.
  10. Plotnikov D. E., Miklashevich T. S., Bartalev S. A., Vosstanovlenie vremennykh ryadov dannykh distantsionnykh izmerenii metodom polinomial’noi approksimatsii v skol’zyashchem okne peremennogo razmera (Using local polynomial approximation within moving window for remote sensing data time-series smoothing and data gaps recovery), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, Vol. 11, No. 2, pp. 103–110.
  11. Plotnikov D. E., Kolbudaev P. A., Bartalev S. A., Avtomaticheskoe raspoznavanie ispol’zuemykh pakhotnykh zemel’ na osnove vremennykh serii sintezirovannykh ezhednevnykh izobrazhenii Landsat na territoriyu Primorskogo kraya, XIV Vserossiiskaya otkrytaya konferentsiya “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (XIV All-Russia Open Conference “Current Problems in Remote Sensing of the Earth from Space”), Book of Abstracts, Moscow, IKI RAN, 2016, p. 387.
  12. Plotnikov D. E., Kolbudaev P. A., Bartalev S. A., Loupian E. A., Raspoznavanie ispol’zuemykh pakhotnykh zemel’ na osnove sezonnykh vremennykh serii vosstanovlennykh izobrazhenii Landsat na primere Moskovskoi oblasti, XV Vserossiiskaya otkrytaya konferentsiya “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (XV All-Russia Open Conference “Current Problems in Remote Sensing of the Earth from Space”), Book of Abstracts, Moscow, IKI RAN, 2017, p. 382.
  13. 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 Sensing Letters, 2014, Vol. 5, No. 1, pp. 55–64, DOI: 10.1080/2150704X.2013.870675.
  14. Bartalev S. A., Plotnikov D. E., Loupian E. A., Mapping of arable land in Russia using multiyear time series of MODIS data and the LAGMA classification technique, Remote Sensing Letters, 2016, Vol. 7, No. 3, pp. 269–278, DOI: 10.1080/2150704X.2015.1130874.
  15. Breiman L., Random Forests, Machine Learning, 2001, Vol. 45, Issue 1, DOI: 10.1023/A:1010933404324.
  16. Chen J., Chen J., Liao A., Cao X., Chen L., Chen X., He C., Han G., Peng S., Lu M., Zhang W., Tong X., Mills J., Global land cover mapping at 30 m resolution: A POK-based operational approach, ISPRS J. Photogrammetry and Remote Sensing, 2015, Vol. 103, pp. 7–27, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2014.09.002.
  17. Förster S., Kaden K., Förster M., Itzerott S., Crop type mapping using spectral-temporal profiles and phonological information, Computers and Electronics in Agriculture, 2012, Vol. 89, pp. 30–40, URL: http://doi.org/10.1016/j.compag.2012.07.015.
  18. Hua L., Zhang X., Chen X., Yin K., Tang L., A feature-based approach of decision tree classification to map time series urban land use and land cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China. October 2017, International J. Geo-Information, 2017, Vol. 6, No. 11, p. 331, DOI: 10.3390/ijgi6110331.
  19. Maxwell S. K., Craig M. E., Use of Landsat ETM+ SLC-off segment-based gap-filled imagery for crop type mapping, 2008, Geocarto International, Vol. 23, No. 3, pp. 169–179, DOI: 10.1080/10106040701207399.
  20. Richardson A. J., Weigand C. L., Distinguishing vegetation from soil background information, Photogram­metric Engineering & Remote Sensing, 1977, Vol. 43, pp. 1541–1552.
  21. Stehman S. V., Selecting and Interpreting Measures of Thematic Classification Accuracy, Remote Sensing of Environment, 1997, Vol. 62, pp. 77–89, DOI: 10.1016/S0034-4257(97)00083-7.
  22. Waldner F., Canto G. S., Defourny P., Automated annual cropland mapping using knowledge-based temporal features, ISPRS J. Photogrammetry and Remote Sensing, 2015, Vol. 110, pp. 1–13, DOI: 10.1016/j.isprsjprs.2015.09.013.
  23. Xu Y., Yu L., Zhao Y., Feng D., Cheng Y., Cai X., Gong P., Monitoring cropland changes along the Nile River in Egypt over past three decades (1984–2015) using remote sensing. Intern. J. Remote Sensing, 2017, Vol. 38, pp. 4459–4480, DOI: 10.1080/01431161.2017.1323285.
  24. Yan L., Roy D. P., Conterminous United States crop field size quantification from multi-temporal Landsat data, Remote Sensing of Environment, 2016, Vol. 172, pp. 67–86, ISSN 0034-4257, DOI: 10.1016/j.rse.2015.10.034.
  25. Zhao X., Wang X., Cao G., Chen K., Tang W., Zhang Z., Crop identification by using seasonal parameters extracted from time series Landsat images in a mountainous agricultural county of Eastern Qinghai Province, China, J. Agricultural Science, 2017, Vol. 9, No. 4, pp. 116–127, ISSN 1916-9752, DOI: 10.5539/jas.v9n4p116.