برآورد بیوماس روزمینی جنگل در جنگل های هیرکانی با استفاده از داده های ماهواره ای

محورهای موضوعی : منابع طبیعی

محدثه قنبری مطلق ¹ (دکتری جنگلداری، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران.)
ساسان بابایی کفاکی ² (دانشیار، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران. *(مسوول مکاتبات))
اسداله متاجی ³ (استاد، دانشکده منابع طبیعی و محیط زیست، دانشگاه آزاد اسلامی، واحد علوم و تحقیقات، تهران، ایران.)
رضا اخوان ⁴ (دانشیار پژوهش، مؤسسه تحقیقات جنگلها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران.)

تاریخ دریافت : 1397/03/02 تاریخ پذیرش : 1397/06/27 تاریخ انتشار : 1399/05/01

کلید واژه: ایران, جنگل, شاخص‌های پوشش‌گیاهی, بیوماس, تصاویر SPOT,

چکیده مقاله :

زمینه و هدف: اهمیت جنگل های شمال ایران به عنوان یکی از مهمترین و بزرگترین ذخایر کربن و نقش آن در ترسیب کربن اتمسفری در کشورمان برکسی پوشیده نیست. مطالعه بیوماس روزمینی این جنگل ها به عنوان یک ضرورت تلقی می گردد. در سال های اخیر مطالعات زیادی با استفاده از تکنولوژی سنجش از دور و شاخص های مختلف برای محاسبات بیوماس روزمینی جنگل ها انجام شده است. هدف پژوهش برآورد بیوماس جنگل های هیرکانی درشمال ایران با استفاده از اطلاعات ماهواره SPOT6 می باشد. روش بررسی: بیوماس روزمینی این جنگل ها با استفاده از تصاویر ماهواره SPOT6 و مدل های رگرسیونی در سه منطقه انتخابی در استانهای شمالی و در 2 دامنه ارتفاعی در تابستان 1395 بررسی شد. پس از آن که مقادیر متوسط بیوماس روزمینی بر حسب تن در هکتار با استفاده از برداشت های زمینی محاسبه گردید، از 3 شاخص پوشش گیاهی NDVI، RVI و TVI برای برآورد بیوماس بر اساس تصاویر ماهواره ای استفاده شد. یافته ها: نتایج نشان داد که رابطه بین مقادیر بیوماس روزمینی و شاخص های پوشش گیاهی یک رابطه خطی بوده و شاخص NDVI در سطح تمامی مناطق بیشترین سطح معنی داری و بالاترین ضریب همبستگی با بیوماس روزمینی را داشته است. بنابراین به منظور نقشه سازی بیوماس روزمینی از روابط رگرسیونی این شاخص استفاده شد. نتیجه گیری: بر اساس نتایج این تحقیق مقادیر بیوماس بین سه منطقه اصلی تحقیق و در طبقات ارتفاعی میان بند و بالابند دارای تفاوت های نسبتاً زیادی می باشند. بیشترین میزان بیوماس در منطقه اسالم و دامنه ارتفاعی بالابند دیده شده است.

چکیده انگلیسی:

Background and Objective: The importance of the northern forests of Iran as one of the most important and largest carbon reserves and its role in atmospheric carbon sequestration in our country is evident. The study of the above ground biomass of these forests is considered as a necessity. In recent years, many studies have been carried out using remote sensing technology and various indices for forest above ground biomass estimations. The purpose of this study is estimating Hyrcanian forests above ground biomass in northern Iran using satellite data (SPOT 6). Method: In this research, above ground biomass of these forests using SPOT satellite images and regression models in three selected regions in the Northern provinces (Asalem, Sardaraboud and Kordkuy) and in 2 altitudes were investigated. After calculating the average above ground biomass per hectare using field plots, three vegetation indices NDVI, RVI and TVI were used to estimate biomass based on satellite imagery. Findings: The results showed that the relationship between above ground biomass values and vegetation indices was linear and the NDVI has the highest level of significance in all parcels and has the highest correlation coefficient with above ground biomass. Therefore, regression relations with NDVI were used in order to map the above ground biomass. Discussion and Conclusion:  Based on the results of this study, the above ground biomass values between the three main study areas and in the elevation classes between the high lands and the middle land have a relatively large difference. The highest biomass in the Asalem region and the high lands has been observed.  

منابع و مأخذ:

1. Li, X.Y., and Tang, H.P., 2006. Carbon sequestration: manners suitable for carbon trade in China and function of terrestrial vegetation. J. Plant Ecology, 32:200-209.


2. Sedjo, R., 1993. The carbon cycle and global forest ecosystem. Water, air and soil pollution, 70:295-307.


3. Amini, J., and Sadeghi, Y., 2012. Satellite and radar images in modeling of forest biomass in northern Iran. J Remote Sensing and GIS Iran, 4(4): 69-82.(In Persian)


4. Dong, J., Kaufmann, R.K., Myneni, R.B., Compton, J.T., Kauppi, P.E.,Liski, J.,  Buermann, W., Alexeyev, V., Hughes, M.K., 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. J. Remote Sensing of Environment, 84: 393–410.


5. Lorenz, K., and Lal, R., 2010. Carbon sequestration in forest ecosystems. Springer Science & Business Media, Azar 4, 1388 AP - Science press; 277p.


6. Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., 2017. Estimation of aboveground biomass using optical and radar images (Case study: Nav-e Asalem forests, Gilan). Iranian Journal of Forest and Poplar Research, 25(2): 320-331. (In Persian)


7. Vashum, K.T., Jayakumar, S., 2012. Methods to estimate above-ground biomass and carbon stock in natural forests - A Review. J Ecosyst Ecogr, 2(4), P.7.


8. Devagiri, G.M., Money, S., Singh, S., Dadhawal, V.K., Patil, P., Khaple, A., Devakumar, A.S., Hubballi, S., 2013. Assessment of above ground biomass and carbon pool in different vegetation types of south western part of Karnataka, India using spectral modeling. Journal of Tropical Ecology, 54(2):149-165.


9. Sabbaghzadeh, S.,  Zare, M.,  Mokhtari, M.H.,2009. Biomass Estimation Using Landsat Satellite Images (Case Study: Merck Basin, Birjand). J. Pasture and Watershed Management, 69(4): 907-920. (In Persian)


10. Hosseini, S.Z., Abbasi, M., Bakhtiarvand, S., Salehi, M., 2015.  Proper models to estimate aboveground biomass using Quickbird satellite imagery in plantation areas of Isfahan’s Mobarakeh Steel Company. Iranian Journal of Forest and Poplar Research, 23(1):143-153. (In Persian)


11. Zhu, Y., Liu, K., Liu, L., Wang, S., Liu, H., 2015. Retrieval of mangrove aboveground biomass at the individual species level with WorldView-2 images. Journal of Remote Sensing, 7:12192-12214.


12. Kumar, K., Nagai, M., Witayangkurn, A., Kritiyutanant, K., Nakamura, S., 2016. Above ground biomass assessment from combined optical and SAR remote sensing data in Surat Thani province, Thailand. Journal of Geographic Information System, 8: 506-516.


13. Kalbi, S., Fallah, A., Shataei Joybari, Sh., 2014. Estimation of forest biophysical properties using SPOT HRG data (Case Study: Darabkola Experimental Forest). J. of Wood & Forest Science and Technology, 20(4): 117-133. (In Persian)


14. Yan, F., Wu, B., Wang, Y., 2013. Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years. Journal of Arid Land, 5(4): 521-530.


15. Dimitrov, P., and Roumenina, E.K., 2013. Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site. Central European Journal of Geosciences, 5(2): 208-222.


16. Günlü, A., Ercanli, I., Başkent, E.Z., Çakır, G., 2014. Estimating aboveground biomass using Landsat TM imagery: A case study of Anatolian Crimean pine forests in Turkey. Annals of Forest Research, 57(2): 289-298.


17. Clerici, N., Rubiano, K., Abd-Elrahman, A., Posada Hoestettler, J.M., Escobedo, F.J., 2016. Estimating aboveground biomass and carbon stocks in periurban Andean secondary forests using very high resolution imagery. Journal of Forests, 7(138), p.17.


18. Zhou, J.J., Zhao, Zh., Zhao, Q., Zhao, J., Wang, H., 2013. Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data. Journal of Applied Remote Sensing, 7(1), 17p.


19. Marvi-Mohajer M.R., 2005 , Silviculture. Tehran, university of Tehran press, 387p. (In Persian)


20. B M U F M F C B C H F L, 2016. Building a Multiple-Use Forest Management Framework to Conserve Biodiversity in the Caspian Hyrcanian Forest Landscape. Caspian Hyrcanian Forest Project Empowered Communities Sustainable Forest, Global Heritage. Faculty of Natural Resources, University of Tehran, IRAN associated with UNDP, 236pp.


21. Ponce-Hernandez, R., Koohafkan, P., Antoine, J., 2004. Assessing Carbon Stocks and Modelling Win-win Scenarios of Carbon Sequestration Through Land-use Changes. Rome. FAO.p.177.


22. Namiranian, M., 2010. Measurement of tree and forest biometry. Tehran, university of Tehran press. 593 p. (In Persian)


23. Vahedi, A.A., Bijani-Nejad, A.R., Djomo, A., 2016. Horizontal and vertical distribution of carbon stock in natural stands of Hyrcanian lowland forests: A case study, Nour Forest Park, Iran. Journal of Forest Science, 62(11): 501-510.


24. Henry, M., Besnard, A., Asante, W.A., Eshun, J., Adu-Bredu, S., Valentini R., Bernoux, M., Saint-André, L., 2010. Wood density, phytomass variations within and among trees, and allometric equations in a tropical rainforest of Africa. Forest Ecology and Management. 260:1375-1388.


25.  (www.astrium.geo.com), 2013. Spot6 & Spot7 imagery user guid. Astrium and EADS company, July 2013, SI/DC/13034-v1, 0.


26. Aricak, B., Bulut, A., Altunel, A.O., Sakici, O.E., 2015. Estimating above-ground carbon biomass using satellite image reflection values: A case study in camyazi forest directorate, Turkey. Journal of the Forestry Society of Croatia, 139(7-8): 369-376.


27. Zahriban, M., Fallah, A., Shataee, Sh., and Kalbi, S., 2015.  Estimating quantitative forest attributes using Pleiades satellite data and non-parametric algorithms in Darabkola forests, Mazandaran. Iranian Journal of Forest and Poplar Research, 23(3): 465-477. (In Persian)


28. Abdolahi H, and Shataee Joybari Sh,2012, Comparative evaluation of IRS-P6-LISS-III and LISS IV Images for canopy cover mapping of Zagros forests (Case Study: Javanroud Forests) J. of Wood & Forest Science and Technology, 19(1):43-60. (In Persian) 


29. Kalbi, S., Fallah, A., Shataee, SH., Oladi, D., 2013. Estimation of Forest Structural Attributes Using ASTER Data. Iranian Journal of Natural Resources, 65(4): 461-474. (In Persian)


30. Noorian, N., Shataee-Jouibary, SH., Mohammadi, J., 2016. Assessment of Different Remote Sensing Data for Forest Structural Attributes Estimation in the Hyrcanian Forests. Journal of Forest Systems, 25(3), p.19.


31. Wang, X., Shao, G., Chen, H., Lewis, B.J., Qi, G., Yu, D., Zhou, L., Dai, L., 2013. An Application Data in Mapping Landscape-Level Forest Biomass for Monitoring the Effectivness of forest policies in Northeastern China. Environmental Management, 52: 612–620.


32. Rouse, Jr.J., Haas, RH., Schell, J.A., Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1 Symposium. I: NASA, Washington, D.C., 1974, 309-317.p.


33. Pearson, R.L., Miller, L.D., 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grassland, Colorado. In: International Symposium on Remote Sensing of the Environment, 8., Ann Arbor. Proceedings. Ann Arbor, USA, 2-6 October 1972.1355-1379. P.


34. Perry, C.R., Lautenschlager, L.F., 1984. Functional equivalence of spectral vegetation indices. Journal of Remote Sensing of Environment, 14:169-182.


35. Mohammadi, J., Shataee, S., Namiranian, M., Næsset, E., 2017. Modeling biophysical properties of broad-leaved stands in the hyrcanian forests of Iran using fused airborne laser scanner data and ultraCam-D images. Int J Appl Earth Obs Geoinformation, 61:32–45.


36. Sefidi, K., Marvie Mohadjer, M.R., Mosandl, R., Copenheaver, C.A., 2011. Canopy gaps and regeneration in old-growth Oriental beech (Fagus orientalis Lipsky) stands, northern Iran. Forest Ecology and Management, 262: 1094–1099.


37. Girardin, C.A., Malhi, Y., Aragao, L.E., Mamani, M., Huaraca Huasco, W., Durand, L., Feeley, K.J., Rapp, J., SILVA‐ESPEJO, JE., Silman, M., Salinas, N., 2010. Net primary productivity allocation and cycling of carbon along a tropical forest elevational transect in the Peruvian Andes. J Global Change Biology, 16: 3176-3192.