سابِق|تال

Site-specific management zone of field scale based on remote sensing image in a black soil area

  • role： First author第一作者
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

  • Email：huanjunliu@yeah.net
  • Introduction：刘焕军(1981—　)，男，副教授，研究方向为地物高光谱遥感。E-mail：huanjunliu@yeah.net
LIU Huanjun,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

QIU Zhengchao,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

MENG Linghua,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

FU Qiang,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

  • Email：jbwneau@163.com
  • Introduction：姜佰文(1971—    )，男，教授，研究方向为植物营养与施肥技术。E-mail：jbwneau@163.com
JIANG Baiwen*,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

YAN Yan,  
  • Affiliation：

    College of Resources and Environmental Sciences, Northeast Agricultural University, Harbin 150030, China

XU Mengyuan

ملخص

Although the precision management-partition method, which is based on grid sampling and spatial interpolation, is highly precise, It has poor timeliness and high costs. Thus, this study proposes a precise management-partition method based on remote sensing images. Hongxing farm fields in the northeast agricultural reclamation area are used as the research objects. High-resolution remote sensing images of bare soil and grid sampling data in the field are utilized as data sources. This study focuses onsite-specific management zone in atypical black soil area. Management is based on the significant correlation between the spectral reflectance characteristics of bare soil and the main physical and chemical properties of black soil. In addition, the zoning result is evaluated using the soil’s physical and chemical properties and physical physiological parameters of crops. The object-oriented segmentation and spatial statistical analysis methods are utilized in this study. The following conclusions are drawn: (1) The spatial variation of soil nutrients is significantly distinct in the typical black soil field. (2) The site-specific management zone, which is based on a bare soil image and highly accurate object-oriented segmentation, enhances the differences of soil nutrients, the normalized difference vegetation indexes between zones, and the consistency in zones. (3) The two images obtained on May 20th and April 1stare processed into single image and double image partitions. The ratios of inter zone to in zone variable coefficients is 1.42, 1.39, and 7.63, which prove that the result of the site-specific management zone that is based on double images is better than those based on a single image. (4) The precision management-partition method that is based on the bare soil image and segmentation technique has strong timeliness, low cost, and good accuracy. Results of the study provide the basis for field variable fertilization, development of intelligent precision agriculture, and sustainable agricultural development.

مفهوم

bare soil;remote sensing images;precise-management zoning;object oriented;precision agriculture

References

  1. 1.
    Baatz M and Schäpe A. 2000. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation//Strobl J and Griesebner G, eds. Angewandte Geographische Informationsverarbeitung XII. Wichmann, Heidelberg: Beiträgezum AGIT-Symposium
  2. 2.
    Benz U C, Hofmann P, Willhauck G, Lingenfelder I and Heynen M. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information[J]. ISPRSJournal of Photogrammetry and Remote Sensing, 58(3/4): 239–258
  3. 3.
    Chen C L and Wu G. 2011.Evaluation of optimal segmentation scale with object-oriented method in remote sensing.Remote Sensing Technology and Application, 26(1): 96–102
  4. 4.
    Chen Y and Lü X. 2008.Definition of management zones of soil nutrients based on FCM algorithmin in oasis field. Scientia Agricultura Sinica, 41(7): 2016–2024
  5. 5.
    Fleming K L, Westfall D G, Wiens D W and Brodahl. 2000. Evaluating farmer defined management zone maps for variable rate fertilizer application. Precision Agriculture, 2(2): 201–215
  6. 6.
    Frogbrook Z L and Oliver M A. 2007.Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data.Soil Use and Management, 23(1): 40–51
  7. 7.
    Hasegawa K, Matsuyama H, Tsuzuki H and Sweda T. 2010. Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures.Remote Sensing Environment, 114(3): 514–519
  8. 8.
    Li F J, Song Z W, Liu S W, Wang J C and Lü X J. 2010. Study on monitoring system in facility agriculture. Tianjin Agricultural Sciences, 16(1): 127–129
  9. 9.
    Li M, Zhao G X, Cai M Q, Zhao Q Q and Tang J. 2012. Extraction of cotton information using remote sensing and precision management zoning at county scale. Journal of Natural Resources, 27(11): 1971–1980.
  10. 10.
    Li X, Pan Y C, Ma J Y, Zhao C J and Wang J H. 2007. Soil nutrients-based zoning for management of precision agriculture. ActaPedologicaSinica, 44(1): 14–20
  11. 11.
    Li X, Pan Y C, Zhao C J, Wang J H, Bao Y S and Wang J D. 2005a. Delineation and scale effect of precision agriculture management zones using yield monitor data over four years. Scientia Agricultura Sinica, 38(9): 1825–1833
  12. 12.
    Li X, Pan Y C, Zhao C J, Wang J H, Bao Y S,Liu L Y and Wang J D. 2005b. Delineating precision agriculture management zones based on spatial contiguous clustering algorithm. Transactions of the Chinese Society of Agricultural Engineering, 21(8): 78–82
  13. 13.
    Liu H J, Yu W T, Zhang X L, Ma Q, Zhou H and Jiang Z S. 2009. Study on the main influencing factors of black soil spectral characteristics. Spectroscopy and Spectral Analysis, 29(11): 3019–3022
  14. 14.
    Liu H J, Zhang B, Liu Z M, Song K S, Wang Z M and Duan H T. 2007. Spectral analysis of soils in Songnen plain, northeastern China. Journal of the Graduate School of the Chinese Academy of Sciences, 24(4): 439–445
  15. 15.
    Liu H J, Zhao C J, Wang J H, Huang W J and Zhang X L. 2011. Soil organic matter predicting with remote sensing image in typicalblacksoil area of Northeast China. Transactions of the CSAE, 27(8): 211–215
  16. 16.
    Liu J F. 2015. Research of spatial heterogeneity of soil organic matters and the influencing factors in Mangang section of Heshan farm.Daqing: Heilongjiang Bayi Agricultural University
  17. 17.
    Milne A E, Webster R, Ginsburg D and Kindred D. 2012. Spatial multivariate classification of an arable field into compact management zones based on past crop yields. Computers and Electronics in Agriculture, 80: 17–30
  18. 18.
    Mkhabela M S, Bullock P, Raj S, Wang S and Yang Y. 2011.Crop yield forecasting on the Canadian prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 151(3): 385–393
  19. 19.
    Ostergaard H S. 1999. Agronomic consequences of variable N fertilization//Stafford J V,ed. Proceedings of the 1st European Conference on Precision Agriculture.Oxford, UK: BIOS Scientific Publishers Ltd, 145-153
  20. 20.
    Peng J, Zhou Q, Zhang Y Z and Xiang H Y. 2013.Effect of soil organic matter on spectral characteristics of soil. Acta Pedologica Sinica,50(3): 517–524
  21. 21.
    Shi G B. 2011. Application of GPS and GIS technology in monitoring system of precision agriculture.Hubei Agricultural Science, 50(10): 1948–1950
  22. 22.
    Song X Y, Wang J H, Huang W J, Yan G J and Chang H. 2009. Monitoring spatial variance of winter wheat growth and grain qualityunder variable-rate fertilization conditions by remote sensing data. Transactions of the CSAE, 25(9): 155–162
  23. 23.
    Song X Y, Wang J H, Liu L Y, Huang W J, Shen T and Yu Z Z. 2007. Research of management zones generating based on Quickbird imagery. Scientia Agricultura Sinica, 40(9): 1996–2006
  24. 24.
    Song X Y, Wang J H, Xue X Z, Liu L Y, Chen L P and Zhao C J. 2004. Assessment of the influence of soil nitrogen supplies and variable fertilization on winter wheat growth condition using airborne hyperspectral image.Transactions of the CSAE, 20(4): 45–49
  25. 25.
    Wang S Q, Zhu S L and Zhou C H. 2001.Characteristics of spatial variability of soil thickness in China.Geographical Research, 20(2): 161–169
  26. 26.
    Zhao J, Ge C P, Shang L, Han X Z and Meng K. 2007. Analysis for spatial heterogeneity of soil organic content and totalnutrients in black soil crop area with different scales.System Sciences and Comprehensive Studies in Agriculture, 23(3): 280–284
  27. 27.
    Zhang N Q, Wang M H and Wang N. 2002. Precision agriculture—a worldwide overview.Computers and Electronics in Agriculture, 36(2/3): 113–132
  28. 28.
    Zhang Y J. 2001. Image Segmentation. Beijing: Science Press

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website