برآورد تغییرات مکانی ضخامت لاشبرگ در سری یک و دو توده‌های جنگلی طبیعی و دست کاشت عرب داغ گلستان

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه جنگلداری، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران،

2 استاد گروه جنگلداری، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران،

3 دانشیار گروه جنگلداری، دانشکده علوم جنگل، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران،

4 استاد پژوهشی مؤسسه تحقیقات جنگل‌ها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران

چکیده

سابقه و هدف: با توجه به اهمیت ضخامت لاشبرگ در تنظیم میکروکلیما و خصوصیات فیزیکی، شیمیایی و زیستی خاک، تنظیم تبادلات ورودی و خروجی خاک، تحول و تداوم توده‌های جنگلی و بررسی وضعیت میزان آن در توده‌های دست کاشت و شرایط محیطی مختلف، این مطالعه با هدف برآورد ضخامت لاشبرگ و تهیه نقشه توزیع مکانی آن از طریق مقایسۀ روش‌های مختلف میان‌یابی قطعی و زمین‌آماری در منطقه جنگلی عرب‌داغ واقع در شمال شرق استان گلستان انجام گردید.
مواد و روش‌ها: اطلاعات ضخامت لاشبرگ در 422 قطعه‌نمونه دایره‌ای با مساحت 400 مترمربع در قالب شبکه خوشه‌ای منظم با ابعاد شبکه 600×400 (هر خوشه 5 قطعه نمونه با فواصل 100 متری) از توده‌های سوزنی‌برگان (کاج بروسیا، زربین، سرو نقره‌ای، کاج بادامی، کاج اروپایی)، پهن‌برگان (آزاد، ممرز، انجیلی، افراپلت) و مخلوط پهن‌برگان و پوشش درختچه‌ای و بوته‌ای (سیاه‌ال، سیاه‌تلو، انار وحشی) جمع‌آوری گردید و پس از آنالیز اولیه در نرم افزارهای آماری، بانک اطلاعاتی مکانی و توصیفی آنها در محیط GIS تهیه شد. سپس به‌منظور تولید نقشه موضوعی ضخامت لاشبرگ، کارایی روش‌های مختلف درون‌یابی EBK، OK، RBF، LPT، LDW و Co-kriging مورد مقایسه و آنالیز قرار گرفتند. اعتبارسنجی متقابل برای ارزیابی دقت تکنیک‌های مختلف درون‌یابی به‌وسیله ضریب تبیین (R2)، میانگین خطای نسبی (MRE)، میانگین خطای اریبی (MBE)، میانگین خطای مطلق (MAE) و میانگین مجذور مربعات خطا (RMSE) انجام شد.
نتایج: نتایج این پژوهش نشان داد بیشترین ضخامت لاشبرگ در توده‌های پهن‌برگ طبیعی و کمترین ضخامت در تیپ سوزنی برگ سرو نقره‌ای در منطقه بوده است. همچنین در گروه پهن‌برگ، تیپ خالص آزاد بیشترین تغییرات ضخامت لاشبرگ را داشته (09/1 سانتی‌متر) و تیپ خالص ممرز با کمترین مقدار انحراف معیار (27/0 سانتی‌متر)، تقریباً ضخامت ثابتی را نشان داد در حالی که در گروه سوزنی‌برگان تیپ‌های کاج بروسیا بیشترین تغییرات ضخامت لاشبرگ را به نمایش گذاشت. نتایج این مطالعه نشان داد که روش درون‌یابی زمین آماری کوکریجینگ معمولی به ترتیب با مدل‌های نمایی (783/0)، کروی (789/0) و گوسی (791/0) با داده کمکی سطح مقطع در هکتار با حداقل مجذور مربعات خطا و بیشترین ضریب تبیین، قابلیت بهتری در مقایسه با روش‌های درون‌یابی کریجینگ (8/0 تا 817/0) و قطعی (875/0 تا 05/1) در درون‌یابی توزیع مکانی ضخامت لاشبرگ اکوسیستم جنگلی منطقه عرب‌داغ دارد.
نتیجه‌گیری: با توجه به تأثیر ضخامت لاشبرگ بر روی برخی از فاکتورهای توده و رویشگاه مانند زادآوری، کیفیت و نفوذپذیری خاک و شدت برخی از آشفتگی‌های طبیعی رایج در منطقه مانند آتش‌سوزی و نتایج حاصل از این تحقیق، روش میان‌یابی کوکریجینگ معمولی با مدل‌های نمایی و گوسی در مقایسه با سایر روش‌‌ها، به دلیل دقت و صحت نتایج به دست آمده می‌تواند در تعیین ضخامت لاشبرگ توده-های جنگلی و همچنین مدیریت طرح‌های پرورشی و جنگلکاری مشابه کارایی بیشتری داشته باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Estimation of spatial variation of litter thickness in series one and two of natural and planted forest stands in in the Arabdagh area

نویسندگان [English]

  • ali mastouri 1
  • Shaban Shataee 2
  • mohamadhadi moayeri 3
  • khosro sagheb-talebi 4
1 Forestry Department, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources
2 Forestry department, Forest sciences faculty, Gorgan University of agricultural sciences and natural resources
3 Associate Professor of Department of Forestry
4 Professor, Research Institute of Forest and Rangelands. Tehran, I.R. Iran.
چکیده [English]

Background and purpose: the importance of litter thickness in the regulation of microclimate and the physical, chemical and biological characteristics of the soil, the regulation of soil input and output exchanges and evolution and continuity of forest stands and the study of its nature in natural endemic hardwood and planted forests and different environmental conditions is an essential issue. In this study, different techniques of interpolation were analyzed and compared to estimate the spatial variability of litter thickness in the Arabdagh area located in Northeast Golestan province.
Materials and Methods: Litter thickness data were collected in 422 sample plots with an area of 400m2 using a systematic cluster network (400×600m, each cluster includes 5 plots with a distance of 100 meters) in different hardwood, softwood tree stands and shrub&herb lands including Pinus brutia, Cupressus sempervirens var. horizontalis, Pinus pinea, Pinus sylvestries and broad-leaved species such as Zelkova carpinifolia, Carpinus betulus, Acer velutinum, Parrotia persica and mixed broadleave. Then, after an initial analysis of the data in statistical software, their spatial and descriptive database was prepared in GIS environment . In order to produce litter thickness thematic map, the efficiency of different interpolation methods of EBK, OK, RBF, LPT, LDW and Co-kriging were compared. Cross-validation was performed to evaluate the accuracy of different interpolation techniques by the coefficient of determination (R2), mean relative error (MRE), mean error (MBE), mean absolute error (MAE) and root mean square error (RMSE).
Results: The results of this study presented that the highest thickness of the litter was in the natural broad-leaved stands and the lowest thickness in the Cupressus arizonica type in the region. Also, in the broad-leaved group, the pure type of Zelkova carpinifolia had the most changes (1.09 cm). While, in the needle-leaved group, the Pinus brutia type showed the most variation in the thickness of the litter. The results of this study showed that the Co-kriging interpolation method with the exponential (0.783), spherical (0.789) and Gaussian (0.791) models using the auxiliary data of Basel area per hectare and with least-squares error and the highest coefficient of determination had high capability Compared to kriging (0.8 to 0.817) and deterministic models (0.875 to 1.05) in spatial distribution interpolation of litter thickness in Arabdagh region.
Conclusion: Due to the effect of litter thickness on some stand and habitat factors such as regeneration , soil quality and Permeability and The intensity of some natural disturbances in the area such as wild fire, it is recommended to take an effective step in managing plan and forestry projects by evaluating it.
Due to the effect of litter thickness on some factors of habitat and habitat such as regeneration, soil quality and permeability and the severity of some common natural disturbances in the region such as fire and the results of this study, the Co-kriging interpolation method with the exponential and Gaussian models compared to other methods, due to the accuracy of the results can be more effective in determining the litter thickness of forest stands as well as managing similar stands and afforestation plans.

کلیدواژه‌ها [English]

  • Litter thickness
  • Planted forest
  • Interpolation
  • Spatial variability
  • Arabdagh area

مراجع

1.Aghajani, H., Fallah, A., and Emadian, S.F. 2014. Modeling and analyzing the surface fire behavior in Hyrcanian forest of Iran.J. of Forest Science. 60: 9. 353-362.‏
2.Ali, S.M., and Malik, R.N. 2011. Spatial distribution of metals in top soils of Islamabad city, Pakistan. Environmental Monitoring and Assessment. 172: 1-16.
3.Andronikov, S.V., Davidson, D.A., and Spiers, R.B. 2000. Variability in contamination by heavy metals: sampling implications. Water, Air, Soil Pollution. 120: 29-45.
4.Basaran, M., Erpul, G., Ozcan, A.U., Saygin, D.S., Kibar, M., Bayramin, I.,and Yilman, F.E. 2011. Spatial information of soil hydraulic conductivity and performance of cokriging over kriging in a semi-arid basin scale. Environmental Earth Sciences J. 63: 827-838.
5.Behera, S.K., and Shukla, A.K. 2015. Spatial distribution of surface soil acidity, electrical conductivity, soil organic carbon content and exchangeable potassium, calcium and magnesium in some cropped acid soils of India. Land Degradation and Development. 26: 71-79.
6.Berg, B., and McClaugherty, C. 2003. Plant litter – decomposition, humus formation, carbon sequestration. Springer- Verlag,Berlin Heidelberg.
7.Bhunia, G.S., Shit, P.K., and Maiti, R. 2018. Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). J. of the Saudi Society of Agricultural Sciences. 17: 2. 114-126.‏
8.Burrough, P.A., and McDonnell, R.A. 1998. Principles of geographical information systems. Oxford University Press, Oxford.
9.Cheng, X.F., and Xie, Y. 2009. Spatial distribution of soil organic carbon density in Anhui province based on GIS. Scientia Geographica Sinica. 29: 4. 540-544.
10.Conti, G., Pérez-Harguindeguy, N., Quètier, F., Gorné, L.D., Jaureguiberry, P., Bertone, G.A., Enrico, L., Cuchietti, A., and Díaz, S. 2014. Large changes in carbon storage under different land-use regimes in subtropical seasonally dry forests of southern South America. Agriculture, Ecosystems and Environment. Agriculture, Ecosystems and Environment. 197: 68-76.
11.Cunha, G.D.M., Gama-Rodrigues, A.C.D., and Costa, G.S. 2005. Ciclagem de nutrientes em Eucalyptus grandis W. Hill ex Maiden no Norte Fluminense. Revista Árvore. 29: 353-363.
12.Forest Silvicultural plan of Arab Dagh area. 2016. General Department of natural resources and watershed management, 120p.
13.Fu, W.J., Jiang, P.K., Zhou, G.M., and Zhao, K.L. 2014. Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences. 11: 8. 2401-2409.‏
14.Goovaerts, P. 1999. Geostatistics in soil science. Geoderma. 89: 1-45.
15.Goovaerts, P. 1997. Geostatistics for natural resources evaluation. Oxford University Press, NewYork.
16.Goovaerts, P. 2009. AUTO-IK: a 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences. Computers and Geosciences. 35: 6. 1255-1270.
17.Hairiah, K., Sulistyani, H., Suprayogo, D., Purnomosidhi, P., Widodo, R.H., and Van Noordwijk, M. 2006. Litter layer residence time in forest and coffee agroforestry systems in Sumberjaya, West Lampung. Forest Ecology and Management. 224: 1-2. 45-57.
18.Hani, A., and Abari, S.A.H. 2011. Determination of Cd, Zn, K, pH, TNV, organic material and electrical conductivity (EC) distribution in agricultural soils using geostatistics and GIS (case study: South Western of Natanz, Iran). World Academy of Science, Engineering and Technology. 5: 12. 22-25.
19.Huang, C.C., Ge, Y., Zhu, J.R., Yuan, W.G., Qi, L.Z., Jiang, B., Shen, Q., and Chang, J. 2005. The litter of Pinus massoniana ecological public-welfare forest in Zhejiang province and its relationship with the community characters. Acta Ecologica Sinica.25: 2507-2513.
20.Isaaks, E.H., and Srivastava, R.M. 1989. An introduction to applied geostatistics. Oxford University, New York.
21.Johnston, K., Ver Hoef, J.M., Krivoruchko, K., and Lucas, N. 2001. Using ArcGIS geostatistical analyst. ESRI Press, Redlands, CA.
22.Jordan, C.F., and Escalante, G. 1980. Root productivity in an Amazonian rain forest. Ecology. 61: 14-18.
23.Jordan, C.F., and Herrera, R. 1981. Tropical rain forests: are nutrients really critical?. The American Naturalist.
117: 167-180.
24.Kedward, L., Allen, C.B., and Rendall, T.C. 2017. Efficient and exact mesh deformation using multiscale RBF interpolation. J. of Computational Physics. 345: 732-751.
25.Krivoruchko, K. 2012. Empirical bayesian kriging. esri, redlands,CA, USA. Available: online: <http:// www.esri.com/news/arcuser/1012/empirical-Bayesian-kriging.html> (accessed 17.10.15).
26.Krivoruchko, K., and Butler, K. 2013. Unequal probability-based spatial mapping. Esri, Redlands, CA, USA: Available online: <~/media/ Files/Pdfs/ news/arcuser/0313/unequal.pdf" xlink: type="simple"id="ir010">http://www.esri.com/esrinews/arcuser/spring2013/~/media/Files/Pdfs/news/arcuser/0313/unequal.pdf>.
27.Kunkel, M.L., Flores, A.N., Smith, T.J., McNamara, J.P., and Benner, S.G. 2011. A simplified approach for estimating soil carbon and nitrogen stocks in semi-arid complex terrain. Geoderma, 165: 1-11.
28.Lavelle, P., Barros, E., Blanchart, E., Brown, G., Desjardins, T., Mariani, L., and Rossi, J.P. 2001. Soil organic matter management in the tropics: why feeding the soil macro fauna? Nutrient Cycling in Agro Ecosystems. 61: 53-61.
29.Lavelle, P., and Spain, A.V. 2002. Soil ecology. Kluwer Academic Publication, Dordrecht, 654p.
30.Li, X.F., Chen, Z.B., Chen, H.B., and Chen, Z.Q. 2011. Spatial distribution of soil nutrients and their response to land use in eroded area of South China. Procedia Environmental Sciences. 10: 14-19.
31.Li, W., Zhang, C., and Wang, K. 2012. Comparison of geographically weighted regression and regression kriging for estimating the spatial distribution of soil organic matter. Geosciences and Remote Sensing. 49: 915-932.
32.Lin, Z., Chao, L., Wu, C., Hong, W., Hong, T., and Hu, X. 2017. Spatial analysis of carbon storage density of mid-subtropical forests using geostatistics: a case study in Jiangle County, southeast China. Acta Geochim. 37: 1. 90-101.
33.Ling, H., Chen, G.S., and Chen, Z.Q. 2009. The impact factors on forest litter of Chinese forest. J. of Subtropical Resources and Environment. 4: 66-71.
34.Liu, L., Wang, H., Dai, W., Lei, X., Yang, X., and Li, X. 2014. Spatial variability of soil organic carbon in the forestlands of northeast China. J. of Forestry Research. 25: 4. 867-876.
35.Liu, L., Jin, Y., Wang, J., Hong, Q., Pan, Z., and Zhao, J. 2019. Comparison of spatial interpolation methods on
slowly available potassium in soils. In IOP Conference Series: Earth and Environmental Science. 234: 1. 12-18. ‏
36.Liu, Z., Shao, M.A., and Wang, Y. 2011. Effect of environmental factors on regional soil organic carbon stocks across the Loess Plateau region,China. Agriculture, Ecosystems and Environment. 142: 184-194.
37.Luo, W., Taylor, M.C., and Parker, S.R. 2008. A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. International J. of Climatology. 28: 947-959.
38.Marimon Júnior, B.H. 2007. Relação entre diversidade arbórea e aspectos do ciclo biogeoquímico de uma floresta monodominante de Brosimum rubescens Taub. e uma floresta mista no leste mato-grossense.‏ Ph.D. Thesis. Departamento de Ecologia, Universidade de Brası ´lia, Brası ´lia, 252p. https://repositorio. unb.br/handle/10482/1145?locale=en.
39.Marimon-Junior, B.H., and John, D. 2008. A new instrument for measurement and collection of quantitative samples of the litter layer in forests. Forest Ecology and Management. 255: 7. 2244-2250.‏
40.Mishra, U., Torn, M.S., Masanet, E., and Ogle, S.M. 2012. Improving regional soil carbon inventories: combining
the IPCC carbon inventory method with regression kriging. Geoderma,189: 288-295.
41.Mueller, T.G., Pierce, F.J., Schabenberger, O., and Warncke, D.D. 2001. Map quality for site-specific fertility management. Soil Science Society of America J. 65: 5. 1547-1558.
42.Paucar-Munoz, H., Richer-Laflèche, M., and Bégin, Y. 2010. Field litter thickness assessed by gamma-ray spectrometry. Forest Ecology and Management. 260: 10. 1640-1645.‏
43.Pourbabai, H., and Namiranian, M. 1993. Investigating and determining the most appropriate dimensions of the sampling network and the plot size in planted forests (Tada pine). Master's thesis Forestry, Tarbiat Modares University. (In Persian)
44.Robertson, G.P. 2008. GS+: Geostatistics for the Environmental Sciences. Gamma Design, Plainwell, Mich.45.Qin, Q., Wang, H., Lei, X., Li, X.,Xie, Y., and Zheng, Y. 2019. Spatial variability in the amount of forest litter at the local scale in northeastern China: Kriging and cokriging approaches to interpolation. Ecology and Evolution. 10: 1. 778-790.
46.Robinson, T.P., and Metternicht, G. 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculture. 50: 97-108.
47.Saito, H., McKenna, S.A., Zimmerman, D.A., and Coburn, T.C. 2005. Geostatistical interpolation of object counts collected from multiple strip transects: ordinary kriging versus
finite domain kriging. Stochastic Environmental Research and Risk Assessment. 19: 1. 71-85.‏
 
48.Savelieva, E., Demyanov, V., Kanevski, M., Serre, M., and Christakos, G. 2005. BME-based uncertainty assessment
of the Chernobyl fallout. Geoderma. 128: 312-324.
49.Sayer, E.J. 2006. Using experimental manipulation to assess the roles of
leaf litter in the functioning of
forest ecosystems. Biological Reviews.
81: 1-31.
50.Scolforo, H.F., Scolforo, J.R.S., de Mello, J.M., de Mello, C.R., and Morais, V.A. 2016. Spatial interpolators for improving the mapping of carbon stock of the arboreal vegetation in Brazilian biomes of Atlantic forest and Savanna. Forest Ecology and Management.
376: 24-35.
51.Shen, Q., Wang, Y., Wang, X., Liu, X., Zhang, X., and Zhang, S. 2019. Comparing interpolation methods to predict soil total phosphorus in the Mollisol area of Northeast China. Catena. 174: 59-72.‏
52.Souza, J.A., and Davide, J.A. 2001. Deposic¸a ˜o de serapilheira e nutrientes em uma mata na ˜o minerada e em plantac¸o ˜es de bracatinga (Mimosa scabrella) e de eucalipto (Eucalyptus saligna) em a ´reas de minerac¸a ˜o de bauxita. CERNE, 7: 101-113: https:// www.redalyc.org/pdf/744/74470109.pdf.
53.Swift, M.J., and Bignell, D. 2000. Standard methods for assessment of soil biodiversity and land use practice. Alternatives to Slash and Burn Project Lecture Note 6B, ICRAF SE Asia, Bogor, 34p.
54.Vitousek, P.M., and Sanford, R.L. 1986. Nutrient cycling in moist tropical forests. Annual Review of Ecology, Evolution, and Systematics. 65: 285-298.
55.Yamamoto, J.K., and Landim, P.M.B. 2013. Geoestatística: Conceitos e aplicações, first ed. Oficina de Textos, São Paulo.
56.Yang, Y., Yang, J., Li, S., Zhang, X., and Zhu, D. 2009. Comparison of spatial interpolation methods for maize growth period. Transactions of the Chinese Society of Agricultural Engineering.
25: 9. 163-167.‏
57.Webster, R., and Oliver, M.A.2001. Geostatistics for environmental scientists. Wiley, New York.
58.Zare-mehrjardi, M., Taghizadeh-Mehrjardi, R., and Akbarzadeh, A. 2010. Evaluation of geostatistical techniques for mapping spatial distribution of soil PH, salinity and plant cover affected by environmental factors in Southern Iran. Notulae Scientia Biologicae. 2: 4. 92-103.
59.Zhang, C., and McGrath, D. 2004. Geostatistical and GIS analysis on soil organic carbon concentrations in grassland of southeastern Ireland from two different periods. Geoderma. 119: 261-275.
60.Zhang, F., Du, Q., Ge, H.L., Liu, A.X., Fu, W.J., and Ji, B.Y. 2012. Spatial distribution of forest carbon in Zhejiang Province with geostatistics based on CFI sample plots. Acta Ecologica Sinica.32: 5275- 5286.
61.Zhou, X., Wu, Z., Zheng, L., Cai, R., Luo, J., and Lin, H. 2008. Biomass and nutrient content of forest litter in natural forest of different intensity harvesting after ten years. Silva Science Teaching Resources. 44: 25-28.
پژوهش‌های علوم و فناوری چوب و جنگل
دوره 27، شماره 3
آذر 1399
صفحه 109-130

فایل ها

هم رسانی

ارجاع به این مقاله

آمار