ارائه مدل‌‌های مناسب برای برآورد زی‌توده برخی درختان سوزنی‌برگ و پهن‌برگ با استفاده از تصاویر ماهواره‌ای Quickbird در جنگل‌کاری‌های مجتمع فولاد مبارکه اصفهان

نوع مقاله: علمی- پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد جنگلداری، گروه علوم جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد

2 استادیار، گروه علوم جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد

3 دانشجوی دکتری جنگلداری، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد

4 کارشناسی ارشد جنگلداری، گروه علوم جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد

چکیده

اندازه‌گیری مستقیم زی‌توده بسیار مشکل، پر‌هزینه، زمان‌بر و ازجمله روش‌های تخریبی به‌دلیل قطع درختان است. هدف پژوهش پیش‌رو برآورد زی‌توده درختان در چهار گونه شامل کاج تهران (Pinus eldarica)، سرو نقره‌ای (Cupressus arizonica)، اقاقیا (Robinia pseudoacacia) و توت (Morus alba) با استفاده از روش‌های مختلف سنجش از دوری براساس داده‌های ماهواره QuickBird در جنگل‌کاری منطقه صنعتی فولاد مبارکه اصفهان بوده است. با استفاده از سه روش شامل شاخص‌های گیاهی، روش تجزیه‌وتحلیل بافت به‌همراه شاخص‌های گیاهی و تجزیه مؤلفه اصلی (PCA)، اطلاعات مورد نیاز از تصویر ماهواره‌ای استخراج شد. سپس با استفاده از مقدار زی‌توده اندازه‌گیری‌شده زمینی و اطلاعات استخراج شده از روش‌های سنجش از دوری، محاسبات همبستگی در محل نمونه‌ها انجام شد و رابطه‌های برآورد زی‌توده برمبنای هر روش و برای هر گونه ارائه شد. روش‌های شاخص‌های گیاهی (DVI و NDVI) برای برآورد زی‌توده سوزنی‌برگان و روش‌های تجزیه‌وتحلیل بافت و تجزیه مؤلفه اصلی برای برآورد زی‌توده پهن‌برگان، نتایج معنی‌داری را نشان دادند. با توجه به مدل رگرسیونی ارائه‌شده برای هر گونه، مقدار زی‌توده برآورد و مقدار مجذور میانگین مربعات خطا به‌ترتیب برای کاج تهران، سرو نقره‌ای، اقاقیا و توت برابر با 53، 20، 30 و 50 بود. همچنین مقدار اریبی برای چهار گونه موردمطالعه به‌ترتیب برابر 30، 10، 30- و 44 به‌دست آمد. مقادیر مجذور میانگین مربعات خطا و اریبی به‌دست‌آمده کارایی روش‌های اجراشده در برآورد زی‌توده را نشان می‌دهد.

کلیدواژه‌ها


عنوان مقاله [English]

Proper models to estimate aboveground biomass using Quickbird satellite imagery in plantation areas of Isfahan’s Mobarakeh Steel Company

نویسندگان [English]

  • Seyyedeh Zahra Hosseini 1
  • Mojghan Abbasi 2
  • Siavash Bakhtiarvand 3
  • Mohammad Salehi 4
1 M.Sc. Student, Department of Forest Science, Faculty of Natural Resources and Earth Science, University of Shahrekord, Shahrekord, Iran
2 Assistant prof., Department of Forest Science, Faculty of Natural Resources and Earth Science, University of Shahrekord, Shahrekord, Iran
3 Ph.D. Student, Department of Forest Science, Faculty of Natural Resources and Earth Science, University of Shahrekord, Shahrekord, Iran
4 M.Sc. Forestry, Department of Forest Science, Faculty of Natural Resources and Earth Science, University of Shahrekord, Shahrekord, Iran
چکیده [English]

Direct measurement of aboveground biomass of trees is considered as one of the labor-intensive, expensive, time consuming and destructive tasks. The objective of this study was to estimate the biomass of four coniferous and deciduous trees species (Pinus eldarica, Cupressus arizonica, Robinia pseudoacacia and Morus alba) by means of high resolution Quickbird remotely-sensed data of over a plantation are established around the industrial domain of Isfahan’s Mobarakeh Steel Company. To this aim, three approaches based on vegetation indices, texture analysis and Principal Component Analysis (PCA) were applied to extract required information from satellite imagery. The correlation analysis between field-assessed biomass and the image-based information and regression models were built. The results using vegetation indices (DVI and NDVI) for coniferous species as well as athose from texture analysis and PCA for deciduous species showed significant corelations. As depicted by the species-specific regression of biomass revealed the amount of RMSE ​​ for P. eldarica, C. arizonica, R. pseudoacacia and M. alba to be 53, 20, 30 and 50, respectively. Moreover, species-specific biases for P. eldarica, C. arizonica, R. pseudoacacia and M. alba was shown to be 30, 10, -30 and 44 respectively. The results of this study supports the use of the applied Quickbird data for  model-based estimation of aboveground biomass across the study site.

کلیدواژه‌ها [English]

  • satellite image
  • plantation
  • biomass
  • Mobarakeh Steel Company
  • Quickbird
- Anonymous, 1992. Comprehensive and Detailed Plan for Landscape of Mobarake Steel Complex. Industrial Report, pp: 53-55.

- Anonymous., 2001. Review of the Potential for Soil Carbon Sequestration under Bioenergy Crops in the U.K. Scientific Report. Cranfield University Press, pp: 30-38.

- Bakhtiarvand Bakhtiari, S. and Sohrabi, H., 2012. Allometric equations for estimating above and below-ground carbon storage of four broadleaved and coniferous trees. Iranian Journal of Forest and Poplar Research 20(3): 481-492 (In Persian).

- Baret, F. and Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2-3): 161-173.

- Bordbar, S.K. and Mortazavi Jahromi, S.M., 2006. Carbon sequestration potential of Eucalyptus camaldulensis Dehnh and Acacia salicina Lindl. plantation in western areas of Fars province. Iranian Journal of Forest and Poplar Research, 21(3): 110-122 (In Persian).

- Cairns, M.A., Olmsted, I., Granados, J. and Argaez, J., 2003. Composition and aboveground tree biomass of a dry semi-evergreen forest on Mexico's Yucatan Peninsula. Forest Ecology and Management, 186(1-3): 125-132.

- Chen, J.M., Rich, P.M., Gower, S.T., Norman, J.M. and Plummer, S., 1996. Leaf area index of boreal forests: Theory, techniques, and measurements. Journal of Geophysical Research, 102(6): 429-443.

- Espinosa, M., Acuna, E., Cancino, J., Monoz, F. and Perry, A.D., 2005. Carbon sink potential of radiata pine plantations in Chile. Forestry, 78(1): 11-19.

- Firuzinezhad, M., Tarahi, A.A. and Abdolkhani, A., 2013. Comparison of classification algorithms for land-use mapping: A case study of woodlands Maroon-Behbahan. First National Conference on Strategies for Achieving Sustainable Development in the Agricultural, Natural Resources and the Environment, Tehran. 13 June, pp: 150-158 (In Persian). 

- Gao, X., Huete, A.R. and Miura, T., 2000. Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment, 74(12): 609-620.

- Ghasemi, N., Sahebi, M. and Mohammadzadeh, A., 2011. A review on biomass estimation methods using synthetic aperture radar data. International Journal of Geomatics and Geosciences, 1(4): 776-788.

- Jin, Y., Yang, X., Qiu, J., Li, J., Gao, T., Wu, Q., Zhao, F., Ma, H., Yu, H. and Xu, B., 2014. Remote Sensing-Based Biomass Estimation and its Spatio-Temporal Variations in Temperate Grassland, Northern China. Remote Sensing, 6(2): 1496-1513.

- Kabiri, K., 2009. Comparison of carbon sequestration and its spatial pattern in the above-Ground woody compartment of a pure and mixed Beech forest (A case study of Gorazbon forest, north of Iran). Ph.D. thesis, University of Tehran, 120p (In Persian).

- Karami, J., Shataee Joibary, Sh. and Hosseini, S.M., 2010. Capability assessment of IKONOS images for urban vegetation mapping. Journal of Wood and Forest Science and Technology, 17(2): 89-105 (In Persian).

- Laclau, P., 2003. Biomass and carbon sequestration and of ponderosa pine plantation and native cypress forest in northwest Patagona. Forest Ecology and Management, 181(28): 17-25.

- Main-Knorn, M., Moisen, G.G., Healey, S.P., Keeton, W.S., Freeman, E.A. and Hostert, P., 2011. Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians. Remote Sensing, 3(12): 1427-1446.

- Maudie, A., Bannari, A., Deguise, J.C., McNairn, H. and Staenz, K., 1999. Application of hyperspectral remote sensing for LAI estimation in Precision Farming. Proceeding of the 23rd Canadian Symposium on Remote Sensing – 10e Congres de l’Association Quebecoise de Teledetection, Quebec City, Canada, pp: 21-25.

- Momeni, A., 2013. Urban Forest Canopy Estimated Using Different Methods Field and Remote Sensing Data QuickBird and UltraCAMD. M.Sc. thesis, University of Tarbiat Modares, 89p (In Persian).

- Ouma, M. and Tateishi, R., 2006. Application of regression tree method for continental percent tree cover mapping. Proceedings of the Annual Conference of the Remote Sensing Society of Japan (RSSJ), Chiba University, Chiba, Japan, pp. 9-10.

- Panahi, P., Pourhashemi, M. and Hassani Nejad, M., 2011. Estimation of leaf biomass and leaf carbon sequestration of Pistacia atlantica in National Botanical Garden of Iran. Iranian Journal of Forest, 3(1): 1-12 (In Persian).

- Pourhashemi, M., Eskandari, S., Dehghani, M., Najafi, T., Asadi, A. and Panahi, P., 2012. Biomass and leaf area index of Caucasian Hackberry (Celtis caucasica Willd.) in Taileh urban forest, Sanandaj, Iran. Iranian Journal of Forest and Poplar Research, 19(4): 609-620 (In Persian).

- Porter, T.F., Chen, C., Long, J.A., Lawrence, R.L. and Sowell, B.F., 2014. Estimating biomass on CRP pastureland: A comparison of remote sensing techniques. Biomass and Bioenergy, 66: 268-274.

- Psomas, A., Kneubuhler, M., Huber, S., Itten, K. and Zimmermann, N.E., 2011. Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats. International Journal of Remote Sensing, 32(24): 9007-9031.

- Rafii, Y., Alavipanah, S.K., Malekmohammadi, B., Ramazani Mehrian, M. and Nasiri, H., 2012. Producing land cover maps using remote sensing and decision tree algorithm (Case study: Bakhtegan national park and wildlife refuge). Geography and Environmental Planning Journal, 47(3): 23-28 (In Persian).

- Sellers, P.J., 1998. Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6(12): 1335-1372.

- Steininger, M.K., 2000. Satellite estimation of tropical secondary forest aboveground biomass data from Brazil and Bolivia. International Journal of Remote Sensing, 21:1139-1157.

- Thenkabail, P.S., Stucky, N., Griscom, B.W., Ashton, M.S., Diels, J., Vander, A., Meer, B. and Enclonga, E., 2004. Biomass estimations and carbon stock calculations in the oil palm plantations of African derived savannas using IKONOS data. International Journal of Remote Sensing, 25(10): 5447-5472.

- Tian, D., Yongtao, H., Armistead, R. and Yuhang, W., 2004. Impacts of Biomass Burning Emissions on Ambient pm2.5 in the Southeastern United States Using cmaq. School of Civil and Environmental Engineering. Georgia Institute of Technology, Atlanta, GA, USA, 6p.

- Todd, S.W., Hoffer, R.M. and Milchunas, D.G., 1998. Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote sensing, 19(25): 427-438.

- Trakunpinut, J., Gajaseni, N. and Ruankawe, N., 2007. Carbon sequestration potential in aboveground biomass of Thong Pha Phum national forest, Thailand. Applied Ecology and Environmental Research, 5(9): 10-23.

- Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(10): 127-150.

- Varamesh, S. Hosseini, S.M., Abdi. N. and Akbarian, M., 2009. Increment of soil carbon sequestration due to forestation and its relation with some physical and chemical factors of soil. Iranian Journal of Forest, 2(4): 11-23 (In Persian).

- Zhao, K., Popescu, S. and Nelson, R., 2009. Lidar remote sensing of forest biomass: A scale-invariant estimation approach using. Remote Sensing of Environment, 113: 182-196.

- Zhou, J.J., Zhong, Z., Qingxia, Z., Jun, Z. and Haize, W., 2013. Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data. Remote Sensing, 7(1): 52-65.