The Effectiveness of N-Tree Sampling Methods in Estimating Some Quantitative Characteristics of Zagros Forests

Document Type : Complete scientific research article

Authors

1 Forestry, Natural Resources, Agriculture Sciences and Natural Resources Sari, Sari, Iran

2 University of Natural Resources and Agriculture Sciences of Sari

3 Sweden, Umeau, Swedish University of Agriculture and Natural Resources

4 Assistant Professor, Department of Forestry, Faculty of Agricultural Sciences and Natural Resources, Lorestan University.

5 Associate Professor, Department of Forestry, Natural Resources Faculty, University of Agricultural Sciences and Natural Resources, Sari Address: Faculty of Natural Resources, Sari University of Agricultural Sciences and Natural Resources

Abstract

Background and objectives:Quantitative and qualitative characteristics of forests can provide valuable information on the impact of forests on the environment. Quantitative and qualitative characteristics of forests can provide valuable information on the impact of forests on the environment. Detailed information from forest areas can be obtained by sampling or sampling. Although it is desirable to have complete censuses, sampling methods are preferred in many cases because of the cost and time savings involved. One of the sampling methods is the multi-tree method. In the present study, the performance of 3 to 10 tree sampling method was evaluated in estimating the number of trees per hectare, chest area and canopy cover and its results were compared with full inventory.
Materials and methods: In the present study, the performance of 3 to 10 tree sampling method was evaluated in estimating the number of hectares per hectare, chest area and canopy cover .For this purpose, 30 hectares of one hectare of forests of watershed Oladghobad in west of Lorestan province were selected and data on all trees within these plots were recorded. In the next step, using tree simulation in Geographic Information System (GIS) environment, sampling was done using multi-tree method within one hectare plots.
Results: Results of comparing the number of trees per hectareand canopy characteristics showed no significant difference between different polycrystalline methods with full inventory. Also, the results of comparing the mean rank of three characteristics Number of trees per hectare, basal area and canopy cover of each tree in different multi-tree methods with full inventory showed the nearest estimation in nine-tree, four-tree and five-tree methods, respectively. The results showed that the spatial pattern of the trees was due to the clustering of the species and the higher density of the branching species than the clustering species. The most precision for these characteristics was obtained for the three (9.27), four (3.65) and seven (2.67) tree methods, respectively. Finally, three characteristics were tested using E%2×T index. The results showed that three (14139.45), four (2393.16) and six (1678.87) tree methods, respectively, are suitable for sampling in the region's forests. O-ring function was also used to study the spatial pattern of Iranian oak trees.
Conclusion: Also according to the characteristics of the bream cross section, the four tree method, given the smallest distance with the real mean of the community, also has the lowest value (2393.16) according to the E%2×T index, as an appropriate sampling method for watershed forests Oladghobad is recommended.

Keywords

References

1.Burch, B.D., and Sánchez Meador,A.J. 2018. Comparison of forest age estimators using k-tree, fixed-radius, and variable-radius plot sampling. Canadian J. of Forest Research. 48: 8. 942-951.
2.Fereidoni, S., Soleimani, N., and Derikvand, B. 2005. National report on providing vegetation map of lorestan province, natural resources office of lorestan province. 57p. (In Persian)
3.Foshat, M., Hosseini, S.M., Fallah, A., and Fakhari, M.A. 2011. Determination of suitable N-tree sampling method in Populus delteoides plantations. Forest Science and Engineering. 1: 3. 65-76.(In Persian)
4.Heidari, R.H., Zobeiri, M., Namiranian, M., and Sobhani, H. 2009. Comparison of circular plot and transect sampling methods in the Zagros Oak forest (Case study: educational and research forest Razi university, Kermanshah province). Iranian J. of Forest and Poplar Research. 17: 3. 359-368. (In Persian)
5.Husch, B.C., Miller, I., and beers, T.W. 1983. Forest menstruation. Reprint edition. Wiley, New York, 402p.
6.Indu Indirabai, M.V., Harindranathan, N., Jaishankar, R., and Nidamanuri, N.R.R. 2019. Optical remote sensing for biophysical characterisation in forests: a review. International J. of Applied Engineering Research. 14: 2. 344-354.
7.Kiani, B., Fallah, A., Tabari, M., Hosseini, S.M., and Parizi, M.H. 2013. A comparison of distance sampling methods in Saxaul (Halloxylon ammodendron (C.A. Mey Bunge) shrub-lands. J. of Ecology. 61: 2. 207-219. (In Persian)
8.Kleinn, C., and Vilčko, F. 2006. A new empirical approach for estimation in k-tree sampling. Forest Ecology and Management. 237: 1-3. 522-533.
9.Krebs, C.J. 1989. Ecological Methodology. Harper Collins, New York, 653p.
10.Krebs, C.J. 1999. Ecological Methodology. 2nd ed., Addison-Wesley Educational and P. Pub. Inc: California. 620p.
11.Lessard, V.C., Drummer, T.D., and Reed, D.D. 2002. Precision of density estimates from fixed radius plots compared to n-tree distance sampling. Forest Science. 48: 1. 1-6.
12.Lynch, T.B., and Rusydi, R. 1999. Distance sampling for forest inventory in indonesian teak plantation. Forest Ecology and Management.113: 2-3. 215-221.
13.Mahdavi, A. 2012. Physical design of north forest inventories database based on Entity – relationship data model. J. of Conservation and Utilization of Natural Resources. 1: 2. 85-104. (In Persian)
14.Mohagheghi, M., Azarnosh, M.R., and Sheykholeslami, A. 2014. Comparison of multi-tree sampling methods and regular intervals estimation of canopy cover. Second National Student Conference on Forest Sciences, University of Tehran. May 7 and 8, 2018. 8p.
15.Nazariani, N., Fallah, A., Lotfalian, M., and Imani Rastabi, M. 2017. Forest dwellers livelihood dependence on forest resources (Case study: Namjoo watershed of Kouhdasht County). Iranian J. of Forest and Poplar Research. 25: 1. 95-105. (In Persian)
16.Nouraldini, A., and Pourshakouri, F.2011. Classification of forest canopy on aerial photos using histological analysis (Case study: Lorestan Tawforest Forest). Remote Sensing and GIS Iran. 3: 4. 46-33. (In Persian)
17.Prodan, M. 1968. Punktstichprobe furdie forsteinrichtung. Forst. Und Holzwirt. 23: 11. 225-226.
18.Ramezani, H., Grafström, A., Naghavi, H., Fallah, A., Shataee, Sh., and Soosani, J. 2016. Evaluation of K-tree distance and fixed-sized plot sampling in Zagros forests of western Iran. J. of Agricultural Science and Technology. 18: 155-170. (In Persian)
19.Salarvand Shamsi, H. 2015. A study of accuracy of sampling methods of the nearest tree in Hey west of Iran (Case study: Dorood of Lorestan), Master's thesis, University of Guilan, Faculty of Natural Resources. 95p. (In Persian)
20.Salarvand Shamsi, H., Bonyad, A.A., and Pourbabaee, H. 2017. The effect of forest stratification on precision estimation of quantitative features of trees by using N-tree sampling method in the forests of West Iran (Case Study: Dorood  Lorestan province). Forest and Wood Products. 70: 3. 469-478.(In Persian)
21.Sohrabi, H. 2018. Adaptive k-tree sample plot for the estimation of stem density: An empirical approach. J. of Forest Science. 64: 1. 17-24.
22.Zobeiri, M. 2007. Forest Biometrics. Tehran Univ. Press. 405p. (In Persian)
Journal of Wood and Forest Science and Technology
Volume 27, Issue 2
September 2020
Pages 47-61

Files

Share

How to cite

Statistics