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کد مقاله : 11626 بازدید : 140 صفحه: 81 - 93

10.22034/jest.2017.11626

نوع مقاله: پژوهشی

به‌کارگیری فتوگرامتری زمینی در برآورد زی‌توده درختان تک پایه بلوط ایرانی

محورهای موضوعی : سیستم اطلاعات جغرافیایی

زهرا عزیزی 1 , اصغر حسینی 2 , یعقوب ایرانمنش 3

1 - استادیار گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده محیط زیست و انرژی، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات تهران*(مسوول مکاتبات)
2 - کارشناس اداره کل منابع طبیعی استان چهارمحال و بختیاری، شهرکرد
3 - استادیار پژوهش، بخش تحقیقات منابع طبیعی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان چهارمحال و بختیاری، سازمان تحقیقات، آموزش و ترویج کشاورزی، شهرکرد

تاریخ دریافت : 1394/09/04 تاریخ پذیرش : 1395/02/12 تاریخ انتشار : 1396/10/01

کلید واژه: بلوط ایرانی, فتوگرامتری بردکوتاه, زی‌توده,

چکیده مقاله :

زمینه و هدف: برآورد دقیق زی توده جنگلی با هدف بررسی توان جنگل‌ها در ترسیب کربن اتمسفری از مسایل بسیار مهم در مدیریت جنگل‌هاست. پژوهش حاضر به‌منظور برآورد زی توده درختان تک پایه بلوط ایرانی با استفاده از روش غیرمخرب فتوگرامتری زمینی صورت گرفت. روش بررسی: در این تحقیق که در استان چهارمحال و بختیاری انجام شد، ابتدا 32 درخت تک پایه بلوط ایرانی در طبقه‌های قطری مختلف انتخاب و از هر درخت دو عکس در جهت عمود بر هم گرفته شد. سپس برای هر عکس مقیاس محاسبه گردید و حجم اجزای مختلف درخت اعم از تنه، شاخه ‌های اصلی، شاخه های فرعی و شاخ و برگ (تاج) تعیین گردید. با نمونه‌برداری از اجزاء مختلف درخت، چگالی هر جزء محاسبه و زی‌توده برای اجزای مختلف اندازه‌گیری شد. سپس زی‌توده برآورد شده از روش فتوگرامتری زمینی با زی‌توده به‌دست آمده از روش قطع و توزین مقایسه گردید. یافته‌ها: نتایج نشان داد که اختلاف آماری معنی داری بین زی‌توده برآورد شده از روش فتوگرامتری زمینی با شیوه قطع که دقیق ترین روش برآورد زی توده است وجود ندارد. بحث و نتیجه‌گیری: این تحقیق دقت و کارایی روش‌ فتوگرامتری زمینی را در برآورد زی‌توده روی زمینی در فرم‌ رویشی تک‌پایه بلوط ایرانی نشان داد.

چکیده انگلیسی:

Background and Objective: Accurate estimation of forest biomass for assessment of the potential of forests to sequester atmospheric carbon is an important aspect in forest management. The present study aimed to estimate the biomass of single-stem Quercus brantii trees by using terrestrial photogrammetry as a nondestructive method. Method: The study was conducted in Chaharmahal and Bakhtiari province. First, 32 individual trees from different diameter classes were selected and two photos were taken for each tree in perpendicular directions. Then, the scale of each photo was calculated and the volume of different components of trees was determined (trunk, main branches, branches and foliage (Crown)). Density of each component was measured using data collected from field and laboratory analysis; and biomass of each component was measured. Estimated biomass from terrestrial photogrammetry method was compared with the actual biomass obtained from the field method. Findings: Results showed that there is no significant difference between the terrestrial photogrammetry method and the field method, which is accurate method in order to evaluate biomass. Discussion and Conclusions: This study showed that terrestrial photogrammetry for estimation of above ground biomass for single-stem Quercus brantii trees is an accurate and efficient method.

منابع و مأخذ:


 

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21.  Soltani, S., Yaghmaee, L., Khodagholi, M., Sabohi, R., 2011. Bioclimatic Classification of Chahar-Mahal & Bakhtiari Province Using Multivariate Statistical Methods, Journal of Science and Technology of Agriculture and Natural Resources, Vol. 14(54): 53-68.

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    2.
  2. Iranmanesh, Y.,  Sagheb Talebi, K., Sohrabi, H., Jalali, S.G., Hosseini, S.M., 2014: Biomass and carbon Stocks of Brant's oak (Quercus brantii Lindl.) in two vegetation forms in Lordegan, Chaharmahal & Bakhtiari Forests, Iranian Journal of Forest and Poplar Research,Vol. 22(4): 749-762.
    3.
  3. Zianis, D., Muukkonen, P., Mäkipää, R. and Mencuccini, M., 2005.  Biomass and Stem Volume Equations for Tree Species in Europe. SILVA FENNICA. Monographs 4.52p.
    4.
  4. Husch, B., T.W. Beers & J.A. Kershaw, 2003. Forest mensuration. 4th Edition, John Wiley & Sons Inc., 443 pp.
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  5. West, P.W, 2009. Tree and Forest Measurement. Springer, 190 p.
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  6. Peper, P. and Mcpherson, G., 1998. Comparison of four foliar and woody biomass estimation methods applied to open-grown deciduous trees, Journal of arboriculture, 24: 191-200.
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  7. Good, N., M. Paterson., C. Brack and K. Mengersen., 2001. Estimating tree component biomass using variable probability sampling methods, Journal of agricultural, Biological and Environmental Statistics, 6: 258-267.
    8.
  8. Aguilar, R., Ghilardi, A., Vega, E., Skutsch, M. and Oyama, K., 2012. Sprouting productivity and allometric relationships of two oak species managed for traditional charcoal making in central Mexico. biomass and bioenergy, 36: 192-207.
    9.
  9. Bakhtiarvand Bakhtiari, S., Sohrabi, H., 2012: Preliminary results of estimating above- ground biomass using Randomized Branch Sampling method for planted Mulberry and Black Locust in Mobarakeh Steel region, Vol. 19(4): 562-571.
    10.
  10. Wang, X., Fang, J. and Zhu, B., 2008. Forest biomass and root–shoot allocation in northeast China. Forest Ecology and Management, 255: 4007–4020.
    11.
  11. Peichl, M., Arain, M.A., 2007. Allometry and partitioning of above- and belowground tree biomass in an age-sequence of white pine forests. Forest Ecology and Management, 253(1–3): 68–80.
    12.
  12. Gersonde, R.F. and O’Hara, K. L, 2005.Comparativetree growth efficiency in Sierra Nevada mixed-conifer forests. Forest Ecology and Management, 219, 95–108.
    13.

 

 

 

 

 

 

 




 




 

 

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  1. UNDP (United Nations Development Program), 1998. Human Development Report 1998. Oxford University Press, Oxford and New York.
    2.
  2. Zhang, X. Q. and Xu, D., 2003. Potential carbon sequestration in China’s forests. Environmental Science & Policy 6: 421–432.
    3.
  3. UNFCCC, 1997. Kyoto Protocol to the United Nations Framework Convention on Climate Change, Article 12.
    4.
  4. IPCC, 2003. Good practice guidance for land use, land-use change and forestry., Hayama, Japan: IPCC National Greenhouse Gas Inventories Programme, 295 pp.
    5.
  5. Kaonga, M.L., T.P. Bayliss-Smith, 2010. Allometric models for estimation of aboveground carbon stocks in improved fallows in eastern Zambia, Agroforestry Systems Journal, 78(3): 217-232.
    6.
  6. Ketterings, Q.M., R. Coe, M.V. Noordwijk, Y. Ambagau, and C.A.Palm, 2000. Reducing uncertainty in the use of allometric biomass equation for predicting above-ground tree biomass in mixed secondary forests, Forest Ecology and Management Journal, 146(2001): 199-209.
    7.
  7. Montes, N., T. Gauquelin, W. Badri, V. Bertaudiere, and El.H.Zaoui, 2000. A non-destructive method for estimating above-ground forest biomass in threatened woodlands, Forest Ecology and Management Journal, 130 (2000): 37-46.
    8.
  8. Saatchi, S.S., Houghton, A., Dos Santos Alvala, R.C., Soare, J.V. and Yu, Y., 2007. Distribution of above ground biomass in the Amazon. Global Change Biol., 13: 816-837.
    9.
  9. Zimble, D.A, D.L. Evans, G.C. Carlson, R.C. Parker, S.C. Grado and P.D. Gerard, 2003. Characterizing vertical forest structure using small-footprint airborne LiDAR, Remote Sensing of Environment Journal, 87(2003): 171-182.
    10.
  10. Sohrabi, H., Zobeiri, M., Hosseini, S.M., 2009. Preparation of Aerial Volume Table using Visual Interpretation of Digital Aerial Images, Journal of Forest and Wood Products (JFWP), Iranian Journal of Natural Resources, Vol. 62(3): 261-274.
    11.
  11. Collin, R. L, and N.W.T. Chisholm, 1991.The photogrammetric record, Geomorphological Photogrammetry Journal, 13(78):845-854.
    12.
  12. Welch, R. and T. Jordan, 1983. Analytical non-metric close-range photogrammetry for monitoring stream channel erosion, Photogrammetric Engineering and Remote Sensing Journal, 49(3): 367-374.
    13.
  13. Fryer, J.G., J.H. Chandler, and M.A.R. Cooper, 1994. On the accuracy of heighting from maps and aerial photographs: Implications for process modellers, Earth Surface Processes and Landforms, Vol.19: 577-583.
    14.
  14. Lane, S., K.S. Richards, and J.H. Chandler, 1993. Developments in photogrammetry; the geomorphological potential, Progress in Physical Geography Journal, 17(3): 306-328.
    15.
  15. Brunsden, D. and J.H. Chandler, 1996. The continuing evolution of the Black Ven mudslide, 1946-95, Advances in Hillslope Processes, Wiley, Chichester, pp. 869-898.
    16.
  16. Chandler, G., 1996. Business similarity as a moderator of the relationship between pre-ownership experience and venture performance, Entrepreneurship Theory and Practice Journal, 20(3): 51–65.
    17.
  17. Schenk, T.  and C.  Toth, 1992.  Conceptual Issues of Soft-copy Photogrammetric Workstations, Photogrammetric Engineering and Remote Sensing Journal, 58(1): 101-110.
    18.
  18. Stojic, M., J. Chandler, P. Ashrnore, and J. Luce, 1998. The assessment of sediment transport rates by automated digital photogrammetry, Photogrammetric Engineering and Remote Sensing Journal, 64(5): 387-395.
    19.
  19. Okuda, T., M. Suzuki, S. Numata, K. Yoshida, S. Nishimura, N. Adachib, K. Niiyama, N. Manokaran, and M. Hashim, 2004. Estimation of aboveground biomass in logged and primary lowland rainforests using 3-D photogrammetric analysis, Forest Ecology and Management Journal, 203(2004): 63-75.
    20.
  20. Seidel, D., F. Beyer, D. Hertel, S. Fleck, and C. Leuschner, 2011. 3D-laser   scanning:   A   non-destructive   method   for   studying   above-   ground biomass   and   growth   of   juvenile   trees, Agricultural and Forest Meteorology Journal, 151(2011): 1305–1311.
    21.

21.  Soltani, S., Yaghmaee, L., Khodagholi, M., Sabohi, R., 2011. Bioclimatic Classification of Chahar-Mahal & Bakhtiari Province Using Multivariate Statistical Methods, Journal of Science and Technology of Agriculture and Natural Resources, Vol. 14(54): 53-68.

  1. Hernandez, R.P, 2004. Assessing carbon stocks and modelling win–win scenarios of carbon sequestration through land-use changes, Food and agriculture organization of the United Nations, Rome, 156P.
    2.
  2. Iranmanesh, Y.,  Sagheb Talebi, K., Sohrabi, H., Jalali, S.G., Hosseini, S.M., 2014: Biomass and carbon Stocks of Brant's oak (Quercus brantii Lindl.) in two vegetation forms in Lordegan, Chaharmahal & Bakhtiari Forests, Iranian Journal of Forest and Poplar Research,Vol. 22(4): 749-762.
    3.
  3. Zianis, D., Muukkonen, P., Mäkipää, R. and Mencuccini, M., 2005.  Biomass and Stem Volume Equations for Tree Species in Europe. SILVA FENNICA. Monographs 4.52p.
    4.
  4. Husch, B., T.W. Beers & J.A. Kershaw, 2003. Forest mensuration. 4th Edition, John Wiley & Sons Inc., 443 pp.
    5.
  5. West, P.W, 2009. Tree and Forest Measurement. Springer, 190 p.
    6.
  6. Peper, P. and Mcpherson, G., 1998. Comparison of four foliar and woody biomass estimation methods applied to open-grown deciduous trees, Journal of arboriculture, 24: 191-200.
    7.
  7. Good, N., M. Paterson., C. Brack and K. Mengersen., 2001. Estimating tree component biomass using variable probability sampling methods, Journal of agricultural, Biological and Environmental Statistics, 6: 258-267.
    8.
  8. Aguilar, R., Ghilardi, A., Vega, E., Skutsch, M. and Oyama, K., 2012. Sprouting productivity and allometric relationships of two oak species managed for traditional charcoal making in central Mexico. biomass and bioenergy, 36: 192-207.
    9.
  9. Bakhtiarvand Bakhtiari, S., Sohrabi, H., 2012: Preliminary results of estimating above- ground biomass using Randomized Branch Sampling method for planted Mulberry and Black Locust in Mobarakeh Steel region, Vol. 19(4): 562-571.
    10.
  10. Wang, X., Fang, J. and Zhu, B., 2008. Forest biomass and root–shoot allocation in northeast China. Forest Ecology and Management, 255: 4007–4020.
    11.
  11. Peichl, M., Arain, M.A., 2007. Allometry and partitioning of above- and belowground tree biomass in an age-sequence of white pine forests. Forest Ecology and Management, 253(1–3): 68–80.
    12.
  12. Gersonde, R.F. and O’Hara, K. L, 2005.Comparativetree growth efficiency in Sierra Nevada mixed-conifer forests. Forest Ecology and Management, 219, 95–108.
    13.

 

 

 

 

 

 

 




 




 

 

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