Recognition of abandoned agricultural lands using seasonal NDVI values
Terekhin E.A.

 

Belgorod State University, Federal and Regional Centre for Aerospace and Ground Monitoring of Objects and Natural Resources, Scientific and Technological Equipment Common Use Centre, Belgorod, Russia

Full text of article: Russian language.

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Abstract:
This paper explores the potentialities of discriminant analysis for the identification of abandoned agricultural lands using their reflectance spectrum properties. A method of automated detection of fallow lands is proposed. Using experimental data received from the agricultural lands of the Belgorod Region, we propose equations that allow an agrarian land to be classified as an arable or fallow land in an automated mode. The accuracy of fallow land recognition is 71%. It is found that seasonal normalized difference vegetation index (NDVI) values computed from MODIS data in the period of late September - early October are most informative in terms of abandoned agricultural land identification. It is shown that the use of the minimal NDVI values is much more efficient for the identification of fallow land when compared with the mean NDVI values.

Keywords:
abandoned agricultural lands, stepwise discriminant analysis, remote sensing, NDVI, MODIS, reflectance spectrum properties.

Citation:
Terekhin EA. Recognition of abandoned agricultural lands using seasonal NDVI values. Computer Optics 2017; 41(5): 719-725. DOI: 10.18287/2412-6179-2017-41-5-719-725.

References:

  1. Romanenko GA, ed. Agroecological state and prospects for the use of the lands of Russia, that left the active crop rotation [In Russian]. Moscow: "FGNU Rosinformagrotekh" Publisher; 2008.
  2. Cherkasov GN, Masyutenko NP, Kuznetsov AV. Evolution of fallow lands and perspectives of it use in Central chernozem region [In Russian]. Zemledeliye 2009; 7: 9-11.
  3. Prishchepov AV, Radeloff VC, Dubinin M, Alcantara C. The effect of Landsat ETM/ETM + image acquisition dates on the detection of agricultural land abandonment in Eastern Europe. Remote Sensing of Environment 2012; 126: 195-209. DOI: 10.1016/j.rse.2012.08.017.
  4. Baldina EA. Application of radar data to characterize the deposits in the Volga Delta [In Russian]. Geomatics 2012; 4: 28-33.
  5. Justice CO, Townshend JRG, Vermote EF, Masuoka E, Wolfe RE., Saleous N, Roy DP, Morisette JT. An overview of MODIS Land data processing and product status. Remote Sensing of Environment 2002; 83(1-2): 3-15. DOI: 10.1016/S0034-4257(02)00084-6.
  6. Zhou J, Jia L, Menenti M. Reconstruction of global MODIS NDVI time series: Performance of harmonic analysis of time series (hants). Remote Sensing of Environment 2015; 163: 217-228. DOI: 10.1016/j.rse.2015.03.018.
  7. Pringle MJ, Denham RJ, Devadas R. Identification of cropping activity in central and southern Queensland, Australia, with the aid of MODIS MOD13Q1 imagery. International Journal of Applied Earth Observation and Geoinformation 2012; 19: 276-285. DOI: 10.1016/j.jag.2012.05.015.
  8. le Maire G, Dupuy S, Nouvellon Y, Loos RA, Hakamada R. Mapping short-rotation plantations at regional scale using MODIS time series: Case of eucalypt plantations in Brazil. Remote Sensing of Environment 2014; 152: 136-149. DOI: 10.1016/j.rse.2014.05.015.
  9. Bartalev SA, Egorov VA, Loupian EA, Plotnikov DE, Uvarov IA. Recognition of arable lands using multi-annual satellite data from spectroradiometer MODIS and locally adaptive supervised classification [In Russian]. Computer Optics 2011; 35(1): 103-116.
  10. Kuznetsov KV, Lipilin DA. On the use of satellite imagery for the recognition of crops in the Krasnodar Territory [In Russian]. Newsleter of North-Caucasus State Technical University 2012; 3 (32): 88-92.
  11. Vorobiova NS, Denisova AY, Kuznetsov AV, Belov AM, Chernov AV, Myasnikov VV. How to use geoinformation technologies and space monitoring for controlling the agricultural sector in Samara Region. Pattern Recognition and Image Analysis 2015; 25(2): 347-353. DOI: 10.1134/S1054661815020261.
  12. Savin I, Tanov E, Kharzinov S. The use of NDVI profiles for estimating the quality of arable lands (exemplified by the Baksan region in Kabardino-Balkaria) [In Russian]. Bulletin of V.V. Dokuchaev Soil Science Institute 2015; 77: 51-65.
  13. Terekhin EA. Analysis of vegetation index long-term dynamics for crop areas [In Russian]. Current problems in remote sensing of the Earth from space 2015; 12(6): 48-58.
  14. Terekhin EA. Application of space survey materials to assess the area and condition of pure vapors of the Belgorod Region [In Russian]. Belgorod State University Scientific Bulletin. Natural Sciences 2015; 32(15): 178-183.
  15. Khalafyan AA. STATISTICA 6. Statistical analysis [In Russian]. Moscow: "Binom-Press" Publisher, 2007.
  16. Terekhin EA. Influence of crop areas vegetation cover fraction on their spectral reflectivity properties [In Russian]. Current problems in remote sensing of the Earth from space 2016; 13(3): 67-71. DOI: 10.21046/2070-7401-2016-13-3-61-71.

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