Evaluation of K-tree Distance and Fixed-Sized Plot Sampling in Zagros Forests of Western Iran

Authors
H. Ramezani 1
A. Grafstrom 1
H. Naghavi 2
A. Fallah 3
Sh. Shataee 4
J. Soosani 3
1 Department of Forest Resource Management, Swedish University of Agricultural Sciences, SLU, SE-901 83 Ume&aring, Sweden.
2 Department of Forestry, Lorestan University, P. O. Box 465, Khorram Abad, Islamic Republic of Iran.
3 Sari University of Agricultural Sciences and Natural Resources, Department of Forestry, Sari, Islamic Republic of Iran.
4 Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Islamic Republic of Iran.
Abstract
Three k-tree distance and fixed-sized plot designs were used for estimating tree density in sparse Oak forests. These forests cover the main part of the Zagros mountain area in western Iran. They are non-timber-oriented forest but important for protection purposes. The main objective was to investigate the statistical performance of k-tree distance and fixed-sized plot designs in the estimation of tree density. In addition, the cost (time required) of data collection using both k-tree distance and fixed-sized plot designs was estimated. Monte-Carlo sampling simulation was used in order to compare the different strategies. The bias of the k-tree distance designs estimators decreased with increasing the value of k. The Moore’s estimator produced the smallest bias, followed by Kleinn and Vilcko andthen Prodan. In terms of cost-efficiency, Moore’s estimator was the best and Prodan’s estimator was superior to Kleinn and Vilcko’s estimator. Cost-efficiency of k-tree distance design is related to three factors: sample size, the value of k, and spatial distribution of trees in a forest stand. Moore’s estimator had the best statistical performance in terms of bias, in all four-study sites. Thus, it can be concluded that Moore’s estimator can have a better performance in forests with different tree distribution.

Keywords

1. Askari, Y., Kafash Saei, E. and Rezaei, D. 2013. Evaluation of Different Sampling Methods to Study Shrub Density in Zagros Forest. Euro. J. Exp. Bio., 3: 121-128
2. Askari, Y. and Tahmasebi, P. 2013. Evaluation of Accuracy of Nearest Neighbor Sampling Method in Zagross Forests. Intl. J. Adv. Biol. Biomed. Res., 9: 999-1008
3. Byth, K. and Ripley, B. D. 1980. On Sampling Spatial Patterns by Distance Methods. Biometrics, 36: 279-284.
4. Cottam, G. and Courtis, J. T. 1956. The Use of Distance Measures in Phytosocialogical Sampling. Ecology, 38: 610-622.
5. Clark, P. J. and Evans, F. C. 1954. Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations. Ecology, 35: 445–453.
6. Eberhardt, L. L. 1967. Some Developments in Distance Sampling. Biometrics, 23(2): 207-216.
7. Gregoire, T. G. and Valentine, H. T. 2008. Sampling Strategies for Natural Resources and the Environment Chapman and Hall/CRC, Boca Raton, Fla. London.
8. Haxtema, Z., Temesgen, H. and Marquardt, T. 2012. Evaluation of N-Tree Distance Sampling for Inventory of Headwater Riparian Forests of Western Oregon. West. J. Appl. For., 27:109-117.
9. Heidari, H. 2006. Studying Different Distance Methods in Inventory of Zagros Forest. PhD. Thesis, University of Tehran, Iran, 77P.
10. Haidari, M, Nabiollah, Y, Shabanian, N. 2013. Evaluation of Different Sampling Method to Study of Diameter at Breath Height in the Zagros Forest. International J. Adv. Biol. Biomed. Res., 1:96-103
11. Husch, B. 1963. Forest Mensuration and Statistics. Ronald, New York.
12. Jonsson, B., Holm, S. and H, K. 1992. A Forest Inventory Method Based on Density-Adapted Circular Plot Size. Scan. J. Forest Res., 7:405-421.
13. Keeley, J. E. and Fotheringham, C. J. 2005. Plot Shape Effects on Plant Species Diversity Measurements. J. Veg. Sci., 16:249-256.
14. Kleinn, C. and Vilcko, F. 2006a. Design-Unbiased Estimation for Point-to-Tree Distance Sampling. Can. J. For. Res.-Rev. Can. Rech. For., 36:1407-1414.
15. Kleinn, C. and Vilcko, F. 2006b. A New Empirical Approach for Estimation in K-Tree Sampling. For. Ecol. Manage., 237:522-533.
16. Köhl, M. 2003 New Approaches for Multi-Resource Forest Inventories. In: "Advance in Forest Inventory for Sustainable Forest Management and Biodiversity Monitoring" Corona, P., Köhl, M.andMarchetti, M. (Eds): Kluwer Academic, Netherlands.
17. Lessard, V. C., Reed, D. D. and Monkevich, N. 1994. Comparing N-Tree Distance Sampling with Point and Plot Sampling in Northern Michigan Forest Types. J. Appl. For., 11:12-16.
18. Lynch, T. B. 2012. A Mirage Boundary Correction Method for Distance Sampling. Can. J. Forest Res., 42:272-278.
19. Lynch, T. B. and Rusydi, R. 1999. Distance Sampling for Forest Inventory in Indonesian Teak Plantations. For. Ecol. Manage., 113:215-221.
20. Magnussen, S. 2012. Fixed-Count Density Estimation with Virtual Plots. Spatial Statistics, 2:33-46.
21. Magnussen, S., Kleinn, C. and Picard, N. 2008. Two New Density Estimators for Distance Sampling. European Journal of Forest Research, 127:213-224.
22. Moore, P. G. 1954. Spacing in Plant Populations. Ecology. 35(2):222–227.
23. Nothdurft, A., Saborowski, J., Nuske, R. S. and Stoyan, D. 2010. Density Estimation Based on K-Tree Sampling and Point Pattern Reconstruction. Canadian Journal of Forest Research, 40:953-967.
24. Paulo, M. J., Tome, M., Otten, A. and Stein, A. 2005. Comparison of Three Sampling Methods in the Characterization of Cork Oak Stands for Management Purposes. Can. J. For. Res.-Rev. Can. Rech. For., 35:2295-2303.
25. Payandeh, B. and Ek, R. A. 1986. Distance Methods and Density Estimators. Can. J. For. Res., 16:918-924.
26. Pielou, E. C. 1977. Mathematical Ecology. Wiley, New York.
27. Prodan, M. 1968. Punkstichprobe Fuè R Die Forsteinrichtung (in German). Forst. und Holzwirt, 23:225-226.
28. Riyahi, H. R. 2010. Evaluation of the Capability of Quickbrid Data for Automatic Delineation of Individual Tree Crowns in Sparse Deciduous Forests, Case-Study in the Zagros Region of Iran Georg-August University Göttingen, Location.
29. Sagheb Talebi, K., Toktam Sajedi, T, Pourhashemi, M. 2013. Forests of Iran: A Treasure from the Past, a Hope for the Future. Springer Netherlands, 10.
30. Salehi, A. 2009. Livelihood Dependensy and Management on Semiarid Oak Forests (Zagros Forsets). Faculty of forest sciences, Department of Forest Resource Management, Location,
31. Schreuder, H. T., Banyard, S. G. and Brink, G. E. 1987. Comparison of Three Sampling Methods in Estimating Stand Parameters for a Tropical Forest. For. Ecol. Manage., 21:119-127.
32. Shabanian, N, Soheili, S. and Haidari, M. 2013. Tree Spatial Patterns in the Zagros Forests (Case study: Kurdistan forests; western part of Iran). Euro. J. Exp. Bio., 3:121-125
33. Thompson, S. K. (2002). Sampling. New York: Wiley.
34. Tomppo, E., Gschwantner, T., Lawrence, M. and Mcroberts, R. E. 2009. National Forest Inventories.
 
Journal of Agricultural Science and Technology
Volume 18, Issue 1
January and February 2016
Pages 155-170