عنوان مقاله [English]
As one of the most important understory evergreen species in Hyrcanian forests of Iran, information on the distribution of Box (Buxus Hyrcana Pojark.) are essential for both forest research and practice. Here, the capability of very high spatial resolution IKONOS satellite imagery acquired in leaf-off condition was tested for mapping Box distribution in a part of Khiboos-Anjili forest reserve in Mazandaran province. The IKONOS imagery was geometrically corrected with a georefrenced panchromatic Pleaides scene, which was orthorectified using 3D ground control points obtained using differential GPS (RMSE less than one pixel). Reference data samples from three classes of non-forested area, deciduous stands without Box understory and deciduous stands with Box understory were recorded using DGPS-supported field survey. By means of a number of vegetation indices, classes seperabilities were evaluated on main and synthetic image channels by partitioning 75% training area and transformed divergence. IKONOS image was classified using both main and best-selected bands and a number of nonparametric (Maximum Likelihood, Mahalonobis distance, Minimum distance to mean and Paralell piped) and parametric (Suport Vector Machine) classifiers. Then the classified images were assessed using 25 percent of unused sample points. Results of validation using the 25% left-out test data showed the highest performance by SVM algorithm compared to other algorithms, with overall accuracy and Kappa coefficient of 97.87% and 0.96, respectively. The results also showed the potential of IKONOS imagery from leaf-off season has to map Box trees in understory layer.
- Alimohammadi, A., Matkan, A. and Tabatabaee, H., 2009. Comparison of pixel based and object based classification method on mapping forest types using remote sensing data, Case Study: Astara forests. Journal of Applied Research in Geographical Sciences, 5: 7-26 (In Persian).
- Anonymous, 2014. Boxwood blight disease control and prevention guidelines. Published by Forests, Range and Watershed Management Organization, Tehran, 15p (In Persian).
- Arkhi, S. and Adibnejhad, M., 2011. Efficiency assessment of the support vector machines for land use classification using Landsat ETM+ data (Case study: Ilam dam catchment). Iranian Journal of Range and Desert Research, 18(3): 420-440 (In Persian).
- Asadi, H., Hosseini, M., Esmailzadeh, O. and Ahmadi, A., 2011. Flora, life form and chorological study of Box tree sites in Khybus protected forest, Mazandaran. Journal of Plant Biology, 8: 40-47 (In Persian).
- Baret, F. and Guyot, G., 1991. Potentials and limits of vegetation indices for LAI & APAR assessment. Remote Sensing of Environment, 35: 161-173.
- Broge, N.H. and Leblanc, E., 2000. Comparing predictive power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76: 156-172.
- Crippen, R., 1990. Calculating the vegetation index faster. Remote Sensing of Environment, 34: 71-73.
- Dixon, B. and Candade, N., 2008. Multispectral landuse classification using neural networks and support vector machines. International Journal of Remote Sensing, 29(4): 1185-1206.
- Esmaeilzadeh, O., Asadi, H. and Ahmadi, A., 2013. Phytosociology of Khybus Protected Area. Journal of Wood and Forest Science and Technology, 19(4): 1-20 (In Persian).
- Ghasemi, A., Fallah, A. and Shataee, Sh., 2016. Evaluation of four algorithms for estimation of canopy cover of mangrove forests by using aerial imagery. RS and GIS for Natural Resources, 7: 1-16 (In Persian).
- Gitelson, A., 2004. Remote estimation of leaf area index and green leaf biomass in maize canopies. Journal of Plant Physiology, 161: 165-173.
- Godarzi, S., Abbaspoor, R., Ahadnejhad, V. and Khakbaz, B., 2012. Comparison of maximum likelihood method with support vector methods and neural networks to separate the lithological units. Journal of Geology, 6: 45-56 (In Persian).
- Goel, N. and Qin, S., 1994. Influences of canopy architecture on relationships between various vegetation Indices and LAI and FPAR: a computer simulation. Remote Sensing Reviews, 10: 309-347.
- Guo, Y., De Jong, K., Liu, F., Wang, X. and Li, C., 2012. A Comparison of artificial neural networks and support vector machines on land cover classification. Computational Intelligence and Intelligent Systems, 25(3): 531-539.
- Guo, S., Gunn, R. and Nelson, J., 2008. Customizing kernel functions for SVM-based Hyperspectral image classification. IEEE Transactions on Image Processing, 17: 622-629.
- Hughes, G.F., 1968. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, 14(1): 55-63.
- Huang, C.S., Davis, M. and Townshend, J.R., 2002. An assessment of support vector machines for land cover classification, International Journal of Remote Sensing, 23(4): 725-749.
- Joshi, C., Leeuw, J., Van Andel, A., Skidmore, D. and Lekhak-Hari, I., 2006. Indirect remote sensing of a cryptic forest understory species. Forest Ecology and Management, 45(2): 245-256.
- Kaufman, Y. and Tanre, D., 1992. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote Sensing, 30: 261-270.
- Linderman, M., Liu, J., Qi, J., An, L., Ouyang, Z., Yang, J. and Tan, Y., 2004. Using artificial neural networks to map the spatial distribution of understory bamboo from remote sensing data. International Journal of Remote Sensing, 25(9): 1685-1700.
- Mathieu, R. and Aryal, J., 2005. Object-oriented classification and IKONOS multispectral imagery for mapping vegetation communities in urban areas. Abstracts of the 17th Annual Colloquium of the Spatial Information Research Centre, University of Otago, New Zealand, 24-25 Nov. 2005: 165-168.
- Morain, S.A., 1986. Surveying China’s agricultural resources: Patterns and progress from space. Geocarto International, 1: 15-24.
- Rafieeyan, O., 2003. Investigation of forest area changes in 1994 to 2001 using ETM+ images. M.Sc. thesis, Faculty of Natural Resources, University of Tehran, Karaj, 122p (In Persian).
- Richardson, A. and Wieg, C., 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43: 1541-1552.
- Roujean, J. and Breon, F., 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51: 375-384.
- Rouse, J. Haas, R., Schell, J., Deering, D. and Harlan, J., 1974. Monitoring the vernal advancement of retro gradation of natural vegetation. NASA Technical Report, United States, 8p.
- Shahsavari, A., 1994. Natural Forests and Woody Plants of Iran (translation). Published by Research Institute of Forests and Rangelands, Tehran, 125p (In Persian).
- Shojaeian, A., 2013. Application of Remote Sensing on Urban Planning (translation). Published by Negarehe-no, Tehran, 150p (In Persian).
- Tuanmu, M.N., Viña, A., Bearer, S., Xu, W., Ouyang, Z. and Zhang, H., 2010. Mapping understory vegetation using phonological characteristics derived from remotely sensed data. Remote Sensing of Environment, 114(8): 1833-1844.
- Tucker, C., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8: 127-150.
- Ward, K.T. and Johnson, G.R., 2007. Geospatial methods provide timely and comprehensive urban forest information. Urban Forestry and Urban Greening, 6: 15-25.
- Wilfong, D.L., Gorchov, M. and Henry, M., 2009. Detecting an invasive shrub in deciduous forest understories using remote sensing. Weed Science, 57(5): 512-520.
- Yousefi, S., Tazeh, S., Mirzaee, H., Moradi, H.R. and Tavangar, Sh., 2014. Comparison of different classification algorithms in satellite imagery to produce land use maps (Case study: Noor city). RS and GIS for Natural Resources, 5: 67-76 (In Persian).