Making objective forest stand maps of mixed managed forest with spatial interpolation and multi-criteria decision analysis

IForest

Volumn 6, Issue 5, 2013, Pages 268-277

  Destan, Sinan ^a   Yilmaz, Osman Yalçin ^a   Sahin, Abbas ^b  

^a ISTANBUL UNIVERSITY   (Turkey)

^b Istanbul Provincial Health Directorate   (Turkey)

Author keywords

Forest management; Forest map; Multi criteria decision analysis; Regularized spline with tension

Indexed keywords

EID: 84884877925     PISSN: None     EISSN: 19717458     Source Type: Journal    
DOI: 10.3832/ifor0099-006     Document Type: Article

References (66)

1. Akhavan R., Amiri GhZ, Zobeiri M (2010). Spatial variability of forest growing stock using geostatistics in the Caspian region of Iran. Caspian Journal Environmental Sciences 8: 43-53. [online] URL: http://research.guilan.ac.ir/cjes/.papers/ 1595.pdf
2. Aktas S., Yilmaz OY (2011). Producing of stand draft maps with spatial prediction methods. Journal of the Faculty of Forestry, Istanbul University, Turkey 62(2):131-146.
3. Anonymous (2011a). Forest Management Plan Forestry Asar 2010-2019. Regional Directorate of Forests in Çanakkale, General Directorate of Forests, Ministry of Environment and Forests, Ankara, Turkey.
4. Anonymous (2011b). Forest Management Plan Forestry Yenice 2010-2019. Regional Directorate of Forests in Çanakkale, General Directorate of Forests, Ministry of Environment and Forests, Ankara, Turkey.
5. Aurbi P., Debouzie D (2000). Geostatistical estimation variance for the spatial mean in two dimensional systematic sampling. Ecology 81: 543-553.-doi: 10.1890/0012-9658(2000)081[0543:GEVFTS]2.0.CO;2
6. Balteiro L.D., Romero C (2008). Making forestry decisions with multiple criteria: a review and an assessment. Forest Ecology and Management 255: 3222-3241.-doi: 10.1016/j.foreco.2008.01. 038
7. Burrough PA (2001). GIS and geostatistics: essential partners for spatial analysis. Environmental and Ecological Statistics 8: 361-377.-doi: 10. 1023/A:1012734519752
8. Dubayah R.O., Drake JB (2000). Lidar remote sensing for forestry applications. Journal of Forestry 98 (6): 44-52.
9. Dubayah R., Knox R, Hofton M, Blair JB, Drake J (2000). Land surface characterization using Lidar remote sensing. In: "Spatial information for land use management" (Hill MJ, Aspinall RJ eds). International Publishers Direct, Singapore, pp. 25-38.
10. D'Orazio M (2003). Estimating the variance of the sample mean in two-dimensional systematic sampling. Journal of Agricultural, Biological and Environmental Statistics 8: 280-295.-doi: 10.1198/1085711032174
11. Efron B., Gong G (1983). A leisurely look at the bootstrap, the jackknife, and cross-validation. The American Statistician 37: 36-48.-doi: 10.1080/00031305.1983.10483087
12. Estornell J., Ruiz LA, Velázquez-Martí B, Fernández-Sarría A (2011). Estimation of shrub biomass by airborne LiDAR data in small forest stands. Forest Ecology and Management 262: 1697-1703.-doi: 10.1016/j.foreco.2011.07.026
13. Feng Y., Tang S, Li Z (2006). Application of improved sequential indicator simulation to spatial distribution of forest types. Forest Ecology and Management 222: 391-398.-doi: 10.1016/j.-foreco.2005.10.045
14. Flores L.A., Martinez L I, Ferrer CM (2003). Systematic sample design for the estimation of spatial means. Environmetrics 14: 45-61.-doi: 10.1002/env.564
15. Forest Management Regulation Handbook (2008). General Directorate of Forests, Ministry of Environment and Forests in Turkey. Official Gazette of the Republic of Turkey No: 26778, pp. 18.
16. General Directorate of Forests (2006). Forests in Turkey. Ministry of Environment and Forests in Turkey, Ankara, pp. 160.
17. Graf R.F., Mathys L, Bollmann K (2009). Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps. Forest Ecology and Management 257: 160-167.-doi: 10.1016/j.foreco.2008.08.021
18. GRASS Development Team (2011). Geographic resources analysis support system (GRASS) Software. Open Source Geospatial Foundation Project. [online] URL: http://grass.osgeo.org
19. Guibal D (1973). Estimating oukoumés Gabon. Internal Memo #333, Center Morphology, Mathematics, Fontainbleau, France.
20. Gunnarsson F., Holm S, Holmgren P, Thuresson T (1998). On the potential of kriging for forest management planning. Scandinavian Journal of Forest Research 13: 237-245.-doi: 10.1080/028 27589809382981
21. Haywood A (2011). Semi-automating the stand delineation process in mapping natural eucalypt forests. Australian Forestry 74 (1): 13-22.-doi: 10.1080/00049158.2011.10676341
22. Heikkinen J (2006). Assement of uncertainty in spatially systematic sampling. Forest inventory, methodology and applications. Managing Forest Ecosystems 10 (1): 155-176.-doi: 10.1007/1-40 20-4381-3_10
23. Hengl T (2007). A practical guide to geostatistical mapping of environmental variables. EUR 22904 EN Scientific and Technical Research Series no. 143, Office for Official Publications of the European Communities, Luxemburg, Belgium.
24. Hofierka J., Parajka J, Mitasova H, Mitas L (2002). Multivariate interpolation of precipitation using regularized spline with tension. Transactions in GIS 6: 135-150.-doi: 10.1111/1467-9671.00101
25. Holmgren P., Thuresson T (1997). Applying objectively estimated and spatially continuous forest parameters in tactical planning to obtain dynamic treatment units. Forest Science 43: 317-326.
26. Huth A., Drechsler M, Köhler P (2005). Using multi-criteria decision analysis and a forest growth model to assess impacts of tree harvesting in Dipterocarp lowland rain forests. Forest Ecology and Management 207: 215-232.-doi: 10.1016/j.foreco.2004.10.028
27. Höck B.K., Payn TW, Shirley JW (1993). Using a geographic information system and geostatistics to estimate site index of Pinus radiata for Kangaroo Forest, New Zealand. N. Z. J. Forrest. Science 23: 264-277.
28. Jost A (1993). Geostatistical analysis of sampling error for systematic sampling. Ph.D. thesis, University of Freiburg, Breisgau, Germany, pp. 113. [In German]
29. Kangas J., Kangas A (2005). Multiple criteria decision support in forest management-the approach, methods applied, and experiences gained. Forest Ecology and Management 207: 133-143.-doi: 10.1016/j.foreco.2004.10.023
30. Kangas A., Kangas J, Pykäläinen J (2001). Outranking methods as tools in strategic natural resources planning. Silva Fennica 35 (2): 215-227. [online] URL: http://www.metla.fi/silvafennica/ full/sf35/sf352215.pdf
31. Khadka C., Vacik H (2012). Use of multi-criteria analysis (MCA) for supporting community forest management. iForest 5: 60-71.-doi: 10.3832/ ifor0608-009
32. Kirschbaum M.U., Watt MS (2011). Use of a process-based model to describe spatial variation in Pinus radiata productivity in New Zealand. Forest Ecology and Management 262: 1008-1019.-doi: 10.1016/j.foreco.2011.05.036
33. Kovacs J.M., Liu Y, Zhang C, Flores-Verdugo F, de-Santiago FF (2011). A field based statistical approach for validating a remotely sensed mangrove forest classification scheme. Wetlands Ecology and Management 19: 409-421.-doi: 10.1007/s11273-011-9225-3
34. Köhl M., Magnussen S, Marchetti M (2006). Sampling methods, remote sensing and GIS multiresource forest inventory. In: "Remote Sensing" (chapt. 4). Springer Verlag, Heidelberg, Germany.
35. Jayanath A., Gamini H (2009). A critical review of multi-criteria decision making methods with special reference to forest management and planning. Ecological Economics 68(10): 2535-2548.
36. Lappi J., Kangas A (2006). Use of additional information. In: "Forest inventory-Methodology and Application" (chapt. 7). Springer, The Hague, the Netherlands. pp. 107-117.
37. Li J., Heap AD (2008). A review of spatial interpolation methods for environmental ecientists. No. Record 2008/23, Geoscience Australia, Canberra, Australia.
38. Li J., Heap AD (2011). A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics 6: 228-241.-doi: 10.1016/j.ecoinf.2010.12.003
39. Lovell J.L., Jupp DJB, Newnham GJ, Coops NC, Culvenor DS (2005). Simulation study for finding optimal Lidar acquisition parameters for forest height retrieval. Forest Ecology and Management 214: 398-412.-doi: 10.1016/j.foreco. 2004.07.077
40. Magnussen S., Næsset E, Wulder MA (2009). Efficient multiresolution spatial predictions for large data arrays. Remote Sensing of Environment 109: 451-463.-doi: 10.1016/j.rse.2007.01.018
41. Mandallaz D (1991). A unified approach to sampling theory for forest inventory based on infinite population and super population models. Chair of forest management and planning, Swiss Federal Institute of Technology, Zurich, Switzerlands.-doi: 10.3929/ethz-a-000585900
42. Mendoza G.A., Martins H (2006). Multi-criteria decision analysis in natural resource management: a critical review of methods and new modeling paradigms. Ecology and Forest Management 230: 1-206.-doi: 10.1016/j.foreco.2006. 03.023
43. Mitas L., Mitasova H (1999). Spatial interpolation. In: "Geographical information systems: principles, techniques, management and applications" (Longley P, Goodchild MF, Maguire DJ, Rhind DW eds). GeoInformation International, Wiley, pp. 481-492.
44. Mitasova H., Mitas L, Brown WM, Gerdes DP, Kosinovski I., Baker T (1995). Modeling spatially and temporally distributed phenomena: new methods and tools for GRASS GIS. International Journal of Geographical Information Systems 9: 433.-doi: 10.1080/02693799508902 048
45. Mitasova H., Mitas L, Harmon RS (2005). Simultaneous spline approximation and topographic analysis for lidar elevation data in open-source GIS. Geoscience and Remote Sensing Letters IEEE 2: 375-379.-doi: 10.1109/LGRS.2005. 848533
46. Montes F., Alicia F, Ledo A (2010). Incorporating environmental and geographical information in forest data analysis: a new fitting approach for universal kriging. Canadian Journal of Forest Research 40 (9): 1852-1861.-doi: 10.1139/ X10-131
47. Nanos N., Calama R, Montero G, Gil L (2004). Geostatistical prediction of height/diameter models. Forest Ecology and Management 195: 221-235.-doi: 10.1016/j.foreco.2004.02.031
48. Neteler M., Mitasova H (2008). Open Source GIS: a GRASS GIS approach. Kluwer. Academic Publishers/ Springer, Boston, USA. pp.406.
49. Neteler M., Bowman MH, Landa M, Metz M (2012). GRASS GIS: a multi-purpose open source GIS. Environmental Modelling and Software 31: 124-130.-doi: 10.1016/j.envsoft.2011. 11.014
50. Palmer D.J., Höck BK, Kimberley MO, Watt MS, Lowe D.J., Payn TW (2009). Comparison of spatial prediction techniques for developing Pinus radiata productivity surfaces across New Zealand. Forest Ecology and Management 258: 2046-2055.-doi: 10.1016/j.foreco.2009.07.057
51. Perez-Soba M, Edwards D, Tabbush P (2006). Assessing the impacts of EU policies on sustainable forest management: the sensor approach. In: EFI Scientific Seminar "The role of Forestry in Integrated Environmental Assessment". Kerkade, 16/09/2006, The Netherlands.
52. Quantum GIS Development Team (2012). Quantum GIS geographic information system. Open Source Geospatial Foundation Project. [online] URL: http://qgis.osgeo.org
53. Schaffer C (1993). Selecting a classification method by cross-validation. Machine Learning 13 (1): 135-143.-doi: 10.1023/A:1022639714137
54. Shao J (1993). Linear model selection by crossvalidation. Journal of the American Statistical Association 88: 486-494.-doi: 10.1080/ 01621459.1993.10476299
55. Sherman M (1996). Variance estimation for statistics computed from spatial lattice data. Journal of the Royal Statistical Society 58: 509-523. [online] URL: http://www.jstor.org/stable/2345890
56. Smart L.S., Swenson JJ, Christensen NL, Sexton JO (2012). Three-dimensional characterization of pine forest type and red-cockaded woodpecker habitat by small-footprint, discrete-return Lidar. Forest Ecology and Management 281:100-110.-doi: 10.1016/j.foreco.2012.06.020
57. th Decision Deck Workshop. University of Mons (Belgium) 13 April 2011. [online] URL: http://mcda.univ-corse.fr/file/105 231/
58. Tang D., Bian F (2009). Forest site evaluation based on GIS and kriging. In: ICISE '09 Proceedings of the "2009 First IEE International Conference on Information Science and Engineering". Nanjing, Jiangsu China, December 26-December 28, IEEE Computer Society Washington, DC, USA, pp. 2063-2067.
59. Tattoni C., Ciolli M, Ferretti F, Cantiani MG (2010). Monitoring spatial and temporal pattern of Paneveggio forest (northern Italy) from 1859 to 2006. iForest 3 (1): 72-80.-doi: 10.3832/ ifor0530-003
60. Tattoni C., Ciolli M, Ferretti F (2011). The fate of priority areas for conservation in protected areas: a fine-scale Markov chain approach. Environmental Management 47: 263-278.-doi: 10.1007/s00267-010-9601-4
61. Tattoni C., Rizzolli F, Pedrini P (2012). Can LiDAR data improve bird habitat suitability models? Ecological Modelling 245: 103-110.-doi: 10.1016/j.ecolmodel.2012.03.020
62. Tuominen S., Fish S, Poso S (2003). Combining remote sensing, data from earlier inventories and geostatistical interpolation in multi-source forest inventory. Canadian Journal of Forest Research 33: 624-634.-doi: 10.1139/x02-199
63. Viana H., Aranha J, Lopes D, Cohen WB (2012). Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecological Modelling 226: 22-35.-doi: 10.1016/j.ecolmodel. 2011.11.027
64. Wallerman J., Joyce S, Vencatasawmy CP, Olsson H (2002). Prediction of forest stem volume using kriging adapted to detected edges. Canadian Journal of Forest Research 32: 509-518.-doi: 10.1139/x01-214
65. Wilsey C.B., Joshua J, Lawler JJ, David A, Cimprich DA (2012). Performance of habitat suitability models for the endangered black-capped vireo built with remotely-sensed data. Remote Sensing of Environment 119:35-42.-doi: 10. 1016/j.rse.2011.12.002
66. Zawadzki J., Cieszewski CJ, Zasada M, Lowe RC (2005). Applying geostatistics for investigations of forest ecosystems using remote sensing imagery. Silva Fennica 39 (4): 599-617. [online] URL: http://www.metsantutkimuslaitos.fi/silvafennica/ full/sf39/sf394599.pdf