تحلیل و مدل‌سازی همبستگی بین LAI و شاخص‌های گیاهی حاصل از مشاهدات طیف‌سنجی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 استادیار دانشگاه صنعتی خواجه نصیرالدین طوسی، دانشکدة مهندسی نقشه‌برداری

2 دانشجوی دکتری سنجش از دور، دانشگاه صنعتی خواجه نصیرالدین طوسی

3 دانشجوی کارشناسی ارشد سنجش از دور، دانشگاه صنعتی خواجه نصیرالدین طوسی

چکیده

بررسی خصوصیات انواع پوشش‌های گیاهی به‌عنوان یکی از پارامترهای مؤثر در تبادل انرژی بین جو و سطح زمین در مطالعات زیست‌محیطی، منابع طبیعی و کشاورزی اهمیت بسیاری دارد. امروزه فناوری سنجش ‌از ‌دور با ارائة اطلاعات طیفی گسترده و متنوع موجب تسهیل در مطالعة پوشش‌های گیاهی در سطح زمین و به‌ویژه تخمین پارامترهای بیوفیزیکی آنها شده است. یکی از مهم‌ترین پارامترهای فیزیکی به‌کار گرفته‌شده در تحلیل‌های مختلف مربوط به مطالعة پوشش‌های گیاهی، شاخص سطح برگ  (LAI) است. در پژوهش حاضر ضمن تحلیل و مدل‌سازی ارتباط بین LAI و شاخص‌های گیاهی مختلف، با استفاده از مشاهدات طیف‌سنجی آزمایشگاهی، به بررسی محدودیت‌های مدل ریاضی موجود در برآورد LAI، ارائة راهکارهایی به‌منظور افزایش دقت و صحت نتایج این مدل و همچنین طراحی یک شاخص جدید پرداخته شده ‌است. نتایج نشان دادند که از میان شاخص‌های گیاهی متداول، دو شاخص Simple Ratio و SAVI-2 دارای کمترین RMSE (حدود 08/0 در واحد LAI) بوده و شدت اشباع‌شدگی مدلی که برازش‌ داده‌اند از شاخص‌های دیگر کمتر است. دو شاخص مذکور کارایی بالاتری در تخمین LAI به‌ویژه در مناطق با تراکم پوشش گیاهی آنها زیاد، دارند و می‌توان با اطمینان بالایی در مدل‌سازی خطی برآورد LAI از آنها استفاده کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Analyzing and Modeling of Correlation between LAI and Vegetation Indices using Spectroradiometric Observations

نویسنده [English]

  • A.A Abkar 1
1 Assistant Prof. of K.N. Toosi University of Technology
چکیده [English]

Investigation of various types of vegetation’s characteristics as an effective parameter in the energy exchange between the atmosphere and Earth's surface is very important in environmental, natural resources and agriculture studies. Nowadays, using remote sensing techniques with a wide range of valuable spectral information facilitate the study of vegetation, especially in estimation of the biophysical parameters. One of the most important biophysical parameters used in the various analyses related to the study of vegetation is Leaf Area Index (LAI). In this study, in addition to the analyzing and modeling of the relationship between LAI and vegetation indices (VIs)via spectrometry observations, the limitations of the mathematical model for estimation of LAI has been explored, some practical guidelines have been provided to improve the accuracy of the model as well as a new vegetation index has been designed. Finally, the results showed that through the conventional vegetation index, Simple Ratio (SR) and Second Soil Adjusted Vegetation Index (SAVI-2) have the minimum RMSE (about 0.08 in LAI unit) and the fitted models using their formulas in comparison with the other indices have the minimum rate of saturation. In other words, these indices are more efficient to estimation of the LAI; especially in high density vegetation area and can be used with high reliability in linear models for LAI estimation.

کلیدواژه‌ها [English]

  • Spectroradiometry
  • Leaf Area Index
  • Vegetation index
  • Sensitivity Analysis
  1. Atzberger, C., 1997, Estimates of Winter Wheat Production through Remote Sensing and Crop Growth Modeling, VWF Verlag, Berlin, Germany.
  2. Bacour, C., Jacquemoud, S., Tourbier, Y., Dechambre, M. & Frangi, P., 2002, Design and Analysis of Numerical Experiments to Compare Four Canopy Reflectance Models, Remote sensing of Enivronment, 79(1), PP. 72-83.
  3. Baret G. & Guyot F., 1991, Potentials and Limits of Vegetation Indices for LAI and APAR Assessment, Remote Sensing, Environment, Vol. 35, PP. 161–173.
  4. Broge, N.H. & Leblanc E., 2000, Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density, Remote Sensing of Environment 76,
  5. PP. 156-172.
  6. Broge, N.H. & Mortensen, J.V., 2002, Deriving Green Crop Area Index and Canopy Chlorophyll Density of Winter Wheat Fromspectral Reflectance Data, Remote Sensing of Environment, 81(1), PP. 45–57.
  7. Brown, L., Chen, J.M., Leblanc, S.G. & Cihlar, J. 2000. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: An image and model analysis. Remote Sensing of Environment, 71, PP. 16–25.
  8. Chen, J.M. & Cihlar, J., 1996, Retrieving Leaf Area Index of Boreal Conifer Forests using Landsat TM Images, Remote Sensing of Environment, 55 (2), PP. 153–162
  9. Chen, J.M., Pavlic, G., Brown, L., Cihlar, J., Leblanc, S.G., White, H.P., Hall, R.J., Peddle, D.R., King, D.J. & Trofymow, J.A., 2002, Derivation and Validation of Canada-wide Coarse-resolution Leaf Area Index Maps using High-resolution Satellite Imagery and Ground Measurements, Remote Sensing of Environment, 80 (1), PP. 165–184.
  10. Cohen, W.B., Maiersperger, T.K., Gower, S.T., Turner, D.P., 2003. An Improved Strategy for Regression of Biophysical Variables and Landsat ETM+ Data, Remote Sensing of Environment, 84 (4), PP. 561–571.
  11. Darvishzadeh, R., Skidmore, A.K., Atzberger, C. & Sip van Wieren, 2008, Estimation of Vegetation LAI from Hyperspectral Reflectance Data: Effects of soil type and plant architecture, International Journal of Applied Earth Observation and Geoinformation, 10, PP. 358–373.
  12. Darvishzadeh, R., Atzberger, C. & Skidmore, A.K., 2009, Leaf Area Index Derivation from Hyperspectral Vegetation Indices and the Red Edge Position, International Journal of Remote Sensing, Vol. 30, No. 23, PP. 6199-6218.
  13. Darvishzadeh, R. et al., 2008, LAI and Chlorophyll Estimation for a Heterogeneous Grassland using Hyperspectral Measurements, ISPRS Journal of Photogrammetry and Remote Sensing, 63(4), PP. 409-426.
  14. Deering D.W. & Rouse J.W., 1975, Measuring "Forage Production"of Grazing units from Landsat MSS Data, International Symposium on Remote Sensing of Environment, 10th, Ann Arbor, Mich,
  15. PP. 1169-1178.
  16. Dorigo, W. et al., 2007, A Review on Reflective Remote Sensing and Data Assimilation Techniques for Enhanced Agroecosystem Modeling, International journal of applied earth observation and geoinformation, 9(2), PP. 165-193.
  17. Houborg, R., Soegaard, H. & Boegh, E., 2007, Combining Vegetation Index and Model Inversion Methods for the Extraction of Key Vegetation Biophysical Parameters using Terra and Aqua MODIS Reflectance Data, Remote Sensing of Invironment, 106, PP. 39-58.
  18. Huete, A.R., 1988, A Soil-adjusted Vegetation index (SAVI), Remote Sensing of Environment, 25, PP. 295-309.
  19. Lee, K.S., Cohen, W.B., Kennedy, R.E., Maiersperger, T.K. & Gower, S.T., 2004, Hyperspectral versus Multispectral Data for Estimatingleaf Area Index in four Different Biomes, Remote Sensing ofEnvironment, 91 (3–4), PP. 508–520.
  20. le Maire, G. et al., 2011, Leaf Area Index Estimation with MODIS Reflectance Time Series and Model Inversion during Full Rotations of Eucalyptus Plantations, Remote Sensing of Environment, 115(2), PP. 586-599.
  21. Liang, L. et al., 2011, Wheat Leaf Area Index Inversion using Hyperspectral Remote Sensing Technology, Spectroscopy Spectral Analysis, 31(6), PP. 1658-1662.
  22. Lin, H. et al., 2013, Wheat Leaf Area Index Inversion with Hyperspectral Remote Sensing based on Support Vector Regression Algorithm, Transactions of the Chinese Society of Agricultural Engineering, 29(11), PP. 139-146.
  23. Major, D.J., Baret, F. & Guyot, G., 1990, A Ratio Vegetation Index Adjusted for Soil Brightness, International Journal of Remote Sensing, 11 (5), PP. 727–740.
  24. 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), PP. 1380–1393.
  25. Pearson, R.L. & Miller, L.D., 1972, Remote Mapping of Standing Crop Biomass for Estimation of the Productivity of the Short-grass Prairie, Pawnee National Grassland, Colorado, 8th International Symposium on Remote Sensing of Environment, ERIMA, Ann Arbor, MI,
  26. PP. 1357–1381.
  27. Peterson, D.L., Spanner, M.A., Running, S.W. & Teuber, K.B., 1987, Relationship of Thematic Mapper Simulator Data to Leaf Area Index of Temperate Coniferous Forests, Remote Sensing of Environment, 22 (3), PP. 323–341.
  28. Pierce, L.I. & Runnings, S.W., 1988, Rapid Estimation of Coniferous Forest Leaf Area Index using a Portable Integration Radiometer, Ecology, 69, PP. 1762-1767.
  29. Richardson, A.J. & Wiegand, C.L., 1977, Distinguishing Vegetation from Soil Background Information, Photogrammetric Engineering and Remote Sensing, 43,
  30. PP. 1541–1552.
  31. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. & Harlan, J.C., 1974, Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation, NASA/GSFC, Type III, final report, Greenbelt, MD.
  32. Schlerf, M., Atzberger, C. & Hill, J., 2005, Remote Sensing of Forest Biophysical Variables using HyMap Imaging Spectrometer Data, Remote Sensing of Environment, 95 (2),
  33. PP. 177–194.
  34. Tucker, C.J., 1979, Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of the Environment, 8, PP. 127-150.
  35. Turner, D.P., Cohen, W.B., Kennedy, R.E., Fassnacht, K.S. & Briggs, J.M., 1999, Relationships between Leaf Area Index and LandsatTM Spectral Vegetation Indices across three Temperate Zone Sites, Remote Sensing of Environment,
  36. (1), PP. 52–68.
  37. Van der Meer, F. et al., 2001, Spatial Scale Variations in Vegetation Indices and above-ground Biomass Estimates: Implications for MERIS, International Journal of Remote Sensing, 22(17),
  38. PP. 3381-3396.
  39. Wiegand, C.L., Maas, S.J., Aase, J.K., Hatfield, J.L., Pinter, P.J.Jr., Jackson, R.D., Kanemasu, E.T. & Lapitan, R.L., 1992, Multisite Analyses of Spectral - biophysical Data for Wheat, Remote Sensing of the Environment, 42, PP. 1 -21.