نشریه سنجش از دور و GIS ایران

نشریه سنجش از دور و GIS ایران

ارزیابی اثر اشباع شاخص های گیاهی در محاسبه شاخص سطح برگ محصولات زراعی

نوع مقاله : مقاله پژوهشی

نویسندگان
مائده بهی فر 1
محسن آزادبخت 2
فرزانه حدادی 3
علی اکبر متکان 4
1 گروه سنجش از دور و GIS ، دانشکده جغرافیا، دانشگاه تهران
2 استادیار مرکز مطالعات سنجش از دور و GIS، دانشگاه شهید بهشتی
3 مرکز تحقیقات فضایی، پژوهشگاه فضایی ایران
4 استاد مرکز مطالعات سنجش از دور و GIS، دانشگاه شهید بهشتی
10.52547/gisj.11.1.33
چکیده
شاخص‌های پوشش گیاهی برای برآورد پارامترهای پوشش گیاهی با استفاده از تصاویر ماهواره ای بکارگرفته می‌شوند. با وجود قابلیت‌های فراوان، بسیاری از این شاخص ها در مقادیر بالای پوشش گیاهی دچار خطا می‌شوند و قادر به برآورد صحیح پارامتر مورد بررسی نمی‌باشند. گیاه یونجه به دلیل تراکم و مقدار کلروفیل بالا می‌تواند شاخص‌های سنجش از دوری پوشش گیاهی را اشباع کند و امکان مطالعه و بررسی تغییرات آن از طریق سنجش از دور محدود می‌شود. با وجود این، تمام شاخص‌ها بصورت یکسان عمل نمی‌کنند و از این نظر با یکدیگر اختلاف‌هایی دارند. در این تحقیق عملکرد شاخص‌های مختلف پوشش گیاهی در مقادیر مختلف شاخص سطح برگ گیاه یونجه بررسی شده است. نتایج تحقیق نشان داده است که شاخص های CIgreen، CIrededge و NGRDI بهترین عملکرد را در مقادیر LAI بالا داشته و حساسیت کمتری به اشباع از خود نشان داده‌اند. در مقابل شاخص‌های NDVI، NDREI و GNDI عملکرد مناسبی نداشته و در مقادیر متوسط و بالای سطح برگ اشباع می‌شوند.
کلیدواژه‌ها
اشباع شاخص
سنجش از دور
شاخص پوشش گیاهی
یونجه

عنوان مقاله English

Investigation of the saturation effect of vegetation indices in LAI estimation of crops

نویسندگان English

Maedeh Behifar 1
Mohsen Azadbakht 2
Farzaneh Hadadi 3
AliAkbar Matkan 4
1 Remote Sensing and GIS department, School of Geography, University of Tehran
2 Assistant Professor in Remote Sensing, Remote Sensing and GIS Research Center
3 Remote sensing expert, Iranian Space Research Center
4 Professor in Remote Sensing, Remote Sensing and GIS Research Center
چکیده English

Vegetation indices are used to estimate vegetation parameters from satellite images. Despite their capabilities, performance of some vegetation indices decreases in high vegetation densities, making them inappropriate for estimation of the desired parameters. Vegetation indices are saturated in alfalfa farms due to the high chlorophyll content and high vegetation density; therefore, monitoring the changes of this plant is hindered. However, all indices do not perform similarly. In this research, the performance of different vegetation indices at different LAI values were investigated. The results showed that the CIgreen, CIrededge and NGRDI indices gained the best performance at high LAI values and they were less saturated. In contrast, the NDVI, NDREI and GNDI indices did not perform well and they were saturated at medium and high levels of LAI.

کلیدواژه‌ها English

Alfalfa
Index saturation
remote sensing
Spectral Vegetation Index
  1. • Anderson, M. C., Neale, C. M. U., Li, F., Norman, J. M., Kustas, W. P., Jayanthi, H., & Chavez, J. (2004). Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote sensing of environment, 92(4), 447-464.
  2. • Asner, G. P. (1998). Biophysical and Biochemical Sources of Variability in Canopy Reflectance. Remote Sensing of Environment 64(3): 234-253.
  3. • Asrar, G. Q., Fuchs, M., Kanemasu, E. T., & Hatfield, J. L. (1984). Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat 1. Agronomy journal, 76(2), 300-306.
  4. • Blackburn, G. A., & Steele, C. M. (1999). Towards the remote sensing of matorral vegetation physiology: Relationships between spectral reflectance, pigment, and biophysical characteristics of semiarid bushland canopies. Remote sensing of Environment, 70(3), 278-292.
  5. • Carter, G. A., & Miller, R. L. (1994). Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote sensing of environment, 50(3), 295-302.
  6. • Cho, M. A., Skidmore, A. K., & Atzberger, C. (2008). Towards red‐edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAILH) simulated data. International Journal of Remote Sensing, 29(8), 2241-2255.
  7. • Curran, P. J., Kupiec, J. A., & Smith, G. M. (1997). Remote sensing the biochemical composition of a slash pine canopy. IEEE Transactions on Geoscience and Remote Sensing, 35(2), 415-420.
  8. • Dawson, T. P., Curran, P. J., North, P. R. J., & Plummer, S. E. (1999). The propagation of foliar biochemical absorption features in forest canopy reflectance: A theoretical analysis. Remote Sensing of Environment, 67(2), 147-159.
  9. • Gao, B.C., (1995). June. Normalized difference water index for remote sensing of vegetation liquid water from space. In Imaging Spectrometry (Vol. 2480, pp. 225-237). International Society for Optics and Photonics.
  10. • Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote sensing of Environment, 58(3), 289-298.
  11. • Gitelson, A. A., et al. (2003). "Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves." Journal of plant physiology 160(3): 271-282.
  12. • Gitelson, A. and Merzlyak, M.N., 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 22(3), pp.247-252.
  13. • Gitelson, A. A., & Kaufman, Y. J. (1998). MODIS NDVI optimization to fit the AVHRR data series—Spectral considerations. Remote Sensing of Environment, 66(3), 343-350.
  14. • Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., & Derry, D. (2002). Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction. International Journal of Remote Sensing, 23(13), 2537-2562.
  15. • Gitelson, A. A. (2004). Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of plant physiology, 161(2), 165-173.
  16. • Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18(12), 2691-2697.
  17. • Guyot, G., Baret, F., & Jacquemoud, S. (1992). Imaging spectroscopy for vegetation studies. Imaging spectroscopy: Fundamentals and prospective application, 2, 145-165.
  18. • Haboudane, D., Miller, J. R., Tremblay, N., Pattey, E., & Vigneault, P. (2004). Estimation of leaf area index using ground spectral measurements over agriculture crops: Prediction capability assessment of optical indices. In XXth ISPRS Congress:" Geo-Imagery Bridging Continents". Istanbul, Turkey (pp. 12-23).
  19. • Horler, D. N. H., DOCKRAY, M., & Barber, J. (1983). The red edge of plant leaf reflectance. International Journal of Remote Sensing, 4(2), 273-288.
  20. • Lucas, N. S., Curran, P. J., Plummer, S. E., & Danson, F. M. (2000). Estimating the stem carbon production of a coniferous forest using an ecosystem simulation model driven by the remotely sensed red edge. International Journal of Remote Sensing, 21(4), 619-631.
  21. • Ludeke, M., Janecek, A., & Kohlmaier, G. H. (1991). Modelling the seasonal CO2 uptake by land vegetation using the global vegetation index. Tellus B, 43(2), 188-196.
  22. • Major, D. J., Baret, F. and Guyot, G. (1990). A vegetation index adjusted for soil brightness, International Journal of Remote Sensing 11: 727–740.
  23. • Myneni, R. B., Ramakrishna, R., Nemani, R., & Running, S. W. (1997). Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Transactions on Geoscience and remote sensing, 35(6), 1380-1393.
  24. • Rivera Caicedo, J. P. (2014). Optimized and automated estimation of vegetation properties: Opportunities for Sentinel-2.
  25. • Rouse Jr, J., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS.
  26. • Rondeaux, G., et al. (1996). "Optimization of soil-adjusted vegetation indices." Remote Sensing of Environment 55(2): 95-107.
  27. • Shen, M., Chen, J., Zhu, X., Tang, Y., & Chen, X. (2010). Do flowers affect biomass estimate accuracy from NDVI and EVI?. International Journal of Remote Sensing, 31(8), 2139-2149.
  28. • Shen, M., Chen, J., Zhu, X., & Tang, Y. (2009). Yellow flowers can decrease NDVI and EVI values: evidence from a field experiment in an alpine meadow. Canadian Journal of Remote Sensing, 35(2), 99-106.
  29. • Small, C., & Lu, J. W. (2006). Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis. Remote Sensing of Environment, 100(4), 441-456.
  30. • Stenberg, P., Rautiainen, M., Manninen, T., Voipio, P., & Smolander, H. (2004). Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands.
  31. • Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote sensing of Environment, 71(2), 158-182.
  32. • Thomas, J. R., & Gausman, H. W. (1987). Leaf Reflectance vs. Leaf Chlorophyll and Carotenoid Concentrations for Eight Crops 1. Agronomy journal, 69(5), 799-802.
  33. • Todd, S. W., Hoffer, R. M., & Milchunas, D. G. (1998). Biomass estimation on grazed and ungrazed rangelands using spectral indices. International journal of remote sensing, 19(3), 427-438.
  34. • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150.
  35. • Wang, F. M., Huang, J. F., Tang, Y. L., & Wang, X. Z. (2007). New vegetation index and its application in estimating leaf area index of rice. Rice Science, 14(3), 195-203.
  36. • Weiss, M., & Baret, F. (1999). Evaluation of canopy biophysical variable retrieval performances from the accumulation of large swath satellite data. Remote sensing of environment, 70(3), 293-306.
  37. • Yoder, B. J., & Waring, R. H. (1994). The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentrations. Remote Sensing of Environment, 49(1), 81-91.
  38. • Zhao, J., Li, J., & Liu, Q. (2013). Analysis on inversion saturation of leaf area index based on muti-layer models. In Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International (pp. 3048-3051). IEEE.
  39. • Zhang, J., Xu, Y., Yao, F., Wang, P., Guo, W., Li, L., & Yang, L. (2010). Advances in estimation methods of vegetation water content based on optical remote sensing techniques. Science China Technological Sciences, 53(5), 1159-1167.