بررسی امکان برآورد مقاومت تک محوری خاک جاده‌های جنگلی با استفاده تکنیک حذف پیوستار

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد گروه جنگلداری و اقتصاد جنگل، دانشکدة منابع طبیعی، دانشگاه تهران، کرج

2 استادیار گروه جنگلداری و اقتصاد جنگل، دانشکدة منابع طبیعی، دانشگاه تهران، کرج

3 دانش آموختة دکتری گروه جنگلداری و اقتصاد جنگل، دانشکدة منابع طبیعی، دانشگاه تهران، کرج

10.22059/jfwp.2022.336653.1202

چکیده

خت مقاومت خاک برای احداث و نگهداری جاده‌های جنگلی بسیار ضروری است. تعیین مقاومت خاک‌های جنگلی که بیشر از نوع ریزدانه و خمیری‌اند با استفاده از روش آزمایش مقاومت فشاری تک‌محوری انجام می‌گیرد. طیف‌ سنجی روشی سریع و غیرمخرب است که به شناخت خصوصیات خاک کمک می‌کند. پژوهش حاضر با هدف بررسی توانایی داده‌ های طیف ‌سنجی برای برآورد مقاومت تک ‌محوری خاک انجام گرفته ‌است. بدین منظور از روش حذف پیوستار، شاخص‌های ابرطیفی و ترکیب هر دو روش استفاده شد. در طیف خاک سه محدودۀ جذبی با مرکزیت طول‌موج ‌های 1400، 1900 و 2200 نانومتر وجود دارد که در برآورد مقاومت تک‌ محوری خاک مؤثرند. پس از جدا کردن محدوده ‌های مورد نظر از طیف کامل خاک، روش حذف پیوستار بر روی این محدوده‌ها اعمال و شاخص‌های جذب پیوستار برای 45 نمونه خاک محاسبه شد. مدلسازی برآورد مقاومت تک‌محوری خاک با استفاده از رگرسیون گام ‌به ‌گام انجام گرفت. روش ارزیابی متقابل و آماره‌های ضریب تعیین (R2)، درصد جذر میانگین مربعات خطا (rRMSE) برای انتخاب بهترین مدل به‌کار برده شد. نتایج مدلسازی نشان داد که شاخص‌های NINSON و NSMI با 75/0R2 = و 29/11 = rRMSE% و شاخص NBDI حذف پیوستار با 78/0R2 = و 16/10 = rRMSE% نتایج بهتری را ارائه دادند. خطای به‌نسبت کم مدل حاصل از روش حذف پیوستار نشان داد که از داده‌های طیف‌سنجی و روش حذف‌ پیوستار می‌توان برای برآورد مقاومت خاک استفاده کرد.

کلیدواژه‌ها


عنوان مقاله [English]

Estimating unconfined compressive soil strength of forest roads using continuum removal technique

نویسندگان [English]

  • Sotoudeh Babai 1
  • Parviz Fatehi 2
  • Fatemeh Mousavi 3
1 M.Sc student, Deportment of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran
2 Assistant Prof. Deportment of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
3 PhD. Graduated, Deportment of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran.
چکیده [English]

Understanding the soil resistance and soil strength are essential for the construction and maintenance of forest roads. Unconfined compressive strength (UCS) test approach is used to determine the strength of fine-grained and cohesive forest soils. Spectroscopy is a fast and non-destructive method that can be used to understand, analysis, and assessment of the soil properties. The present study was aimed to investigate the capability of spectroscopy data to estimate UCS. To do so, a continuum removal technique, narrow band (i.e., hyperspectral) indices, and a combination of both methods were used. Three absorption ranges with wavelengths of 1400, 1900, and 2200 nm are effective in estimating the unconfined strength of the soil. The continuum removal technique was applied on the selected absorption regions and its indices were calculated for 45 soil sample plots. In addition, NINSON and NSMI hyperspectral indices were calculated. The capability of these data to estimate unconfined soil strength was evaluated using multiple stepwise regression analysis. The results of this study showed that NINSON and NSMI indices had an R2 = 0.75 and an rRMSE% of 11.29%. Continuum removal index (i.e. NBDI) gained an R2 = 0.78 and an rRMSE% = 10.16 which shows a better result compared to the individual index. The results of the present study (i.e., a reasonable rRMSE%) showed that the spectroscopy data and continuum removal techniques can be used to estimate soil strength.

کلیدواژه‌ها [English]

  • continuum removal
  • field spectroscopy
  • hyperspectral indices
  • Stepwise regression
  • UCS
[1]. Abdi, E., Salimizand, M., and Mousavi, F. (2019). The impact of different methods of Atterberg limits determination on the resulted Unified soil classification. Iranian Journal of Forest, 10, 403-413.
[2]. Milton, E. J., Schaepman, M. E., Anderson, K., Kneubühler, M., and Fox, N. (2009). Progress in field spectroscopy. Remote Sensing of Environment, 113, S92-S109.
[3]. Iranian Space Agency. (2020). Spectroscopy Applications: https://rs.isa.ir//index.php?module=cdk&func=loadmodule&system=cdk&sismodule=user/content_view.php&sisOp=view&ctp_id=602&cnt_id=52517&id=3761
[4]. Daughtry, C. S., Walthall, C. L., Kim, M. S., De Colstoun, E. B., and McMurtrey Iii, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229-239.
[5]. Malek, M. H., Berger, D. E., and Coburn, J. W. (2007). On the inappropriateness of stepwise regression analysis for model building and testing. European Journal of Applied Physiology, 101(2), 263-264.‌
[6]. Huang, Z., Turner, B. J., Dury, S. J., Wallis, I. R., and Foley, W. J. (2004). Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sensing of Environment, 93(1-2), 18-29.
[7]. Mousavi, F., Abdi, E., Ghalandarzadeh, A., Bahrami, H., and Majnounian, B. (2019). Laboratory evaluation of the PLSR method to estimate Atterberg limits of soil by field spectroscopy, Iranian Journal of Forest. 11, 151-164.
[8]. Abbasi, M., Darvishsefat, A.A., and Schaepman, M. (2010). Comparison of adaxial and abaxial spectral reflectance of Fagus orientalis Lipsky and Carpinus betulu using field spectroradiometer and spectral indices. Iranian Forest Journal, Iranian Forestry Association, 3, 263-272.
[9]. Kokaly, R. F. (2010). Spectroscopic analysis for material identification and mapping using PRISM, an ENVI/IDL based software package, Proceedings of IGARSS 2010.
[10]. Mousavi, F., Abdi, E., Fatehi, P., Ghalandarzadeh, A., Bahrami, H. A., Majnounian, B., and Ziadi, N. (2021). Rapid determination of soil unconfined compressive strength using reflectance spectroscopy. Bulletin of Engineering Geology and the Environment, 80(5), 3923-3938.
[11]. Thompson, B. (1995). Stepwise regression and stepwise discriminant analysis need not apply here: A guidelines editorial. Educational and Psychological Measurement, 55(4), 525-534.
[12]. Diek, S., Chabrillat, S., Nocita, M., Schaepman, M. E., and de Jong, R. (2019). Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping. Geoderma, 337, 607-621.
[13]. Akan, R., Keskin, S. N., and Uzundurukan, S. (2015). Multiple regression model for the prediction of unconfined compressive strength of jet grout columns. Procedia Earth and Planetary Science, 15, 299-303.
[14]. Mousavi, F., Abdi, E., Ghalandarayeshi, S., and Page-Dumroese, D. S. (2021). Modeling unconfined compressive strength of fine-grained soils: Application of pocket penetrometer for predicting soil strength. Catena, 196, 104890.
[15]. Etemad, V., Moridi., M., and Sefidi, K. (2017). Quantification of the horizontal structure of mixed Fagus stands in the evolutionary phase of rootstock reduction. Forest and Wood Products, 4, 647-656.
[16]. Yitagesu, F. A., van der Meer, F., van der Werff, H., and Zigterman, W. (2009). Quantifying engineering parameters of expansive soils from their reflectance spectra. Engineering Geology, 105(3-4), 151-160.
[17]. Gomez, C., Lagacherie, P., and Coulouma, G. (2008). Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurements. Geoderma, 148(2), 141-148.
[18]. ASTM, (2020). www.astm.org/Standards/D2166.htm.
[19]. Fatehi, P., Damm, A., Schaepman, M. E., and Kneubühler, M. (2015). Estimation of alpine forest structural variables from imaging spectrometer data. Remote Sensing, 7(12), 16315-16338.
[20]. Mobasheri, M. R., Amani, M., Beikpour, M., and Mahdavi, S. (2019). Soil moisture content estimation using water absorption bands. Geomatica, 73(3), 63-73.
[21]. Main, R., Cho, M. A., Mathieu, R., O’Kennedy, M. M., Ramoelo, A., & Koch, S. (2011). An investigation into robust spectral indices for leaf chlorophyll estimation ISPRS Journal of Photogrammetry and Remote Sensing, 66(6), 751-761.
[22]. Townshend, J. R., and Justice, C. O. (1986). Analysis of the dynamics of African vegetation using the normalized difference vegetation index. International Journal of Remote Sensing, 7(11), 1435-1445.
[23]. Wu, C., Niu, Z., Tang, Q., and Huang, W. (2008). Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology, 148(8-9), 1230-1241.
[24]. Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., and Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119-126.
[25]. Rondeaux, G., Steven, M., and Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107.
[26]. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., and Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2-3), 416-426.
[27]. Haubrock, S. N., Chabrillat, S., Lemmnitz, C., and Kaufmann, H. (2008). Surface soil moisture quantification models from reflectance data under field conditions. International Journal of Remote Sensing, 29(1), 3-29.
[28]. Fabre, S., Briottet, X., and Lesaignoux, A. (2015). Estimation of soil moisture content from the spectral reflectance of bare soils in the 0.4–2.5 µm domain. Sensors, 15(2), 3262-3281.
[29]. Middleton, E. M., Huemmrich, K. F., Landis, D. R., Black, T. A., Barr, A. G., and McCaughey, J. H. (2016). Photosynthetic efficiency of northern forest ecosystems using a MODIS-derived Photochemical Reflectance Index (PRI). Remote Sensing of Environment,187, 345-366.
[30]. Ma, S., Zhou, Y., Gowda, P. H., Dong, J., Zhang, G., Kakani, V. G., and Jiang, W. (2019). Application of the water-related spectral reflectance indices: A review. Ecological Indicators, 98, 68-79.
[31]. Kokaly, R. F., and Clark, R. N. (1999). Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sensing of Environment, 67(3), 267-287.
[32]. Curran, P. J., Dungan, J. L., and Peterson, D. L. (2001). Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies. Remote Sensing of Environment, 76(3), 349-359.
[33]. Sanches, I. D. A., Souza Filho, C. R., and Kokaly, R. F. (2014) Spectroscopic remote sensing of plant stress at leaf and canopy levels using the chlorophyll 680nm absorption feature with continuum removal. ISPRS Journal of Photogrammetry and Remote Sensing, 97, 111–122.