Estimation of forest development stage and crown closure using different classification methods and satellite images: A case study from Turkey S., Günlü A., Keleş S. (2019): Estimation of forest development stage and crown closure using different classification methods and satellite images: A case study from Turkey. J. For. Sci., 65: 18-26.
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The objective of this study is to estimate stand development stages (SDS) and stand crown closures (SCC) of forest using different classification methods (maximum likelihood, support vector machine: linear, polynomial, radial and sigmoid kernel functions and artificial neural network) based on satellite imagery of different resolution (Landsat 7 ETM+ and IKONOS). The results showed that SDS and SCC were estimated with Landsat 7 ETM+ image using the artificial neural network with a 0.83 and 0.78 kappa statistic value, and 92.57 and 89.77% overall accuracy assessments, respectively. On the other hand, SDS and SCC were predicted with IKONOS image using support vector machine (polynomial) method with a 0.94 and 0.88 kappa statistic value, and 95.95 and 91.17% overall accuracy assessments, respectively. Our results demonstrated that IKONOS satellite image and support vector machine (polynomial) method produced a better estimation of SDS and SCC as compared to Landsat 7 ETM+ and other supervised classification methods used in this study.

Anonymous (2007): Management Plan of Uğurlu Forest Planning Unit between 2007–2026. Ardahan, Erzurum Forest Regional Directorate, Göle Forest Enterprise: 343.
Anonymous (2008): Forest Management Guidelines. Ankara, Republic of Turkey, General Directorate of Forestry: 215.
Bakırman T. (2014): Comparison of satellite images classified by different methods. In: Maktav D. (ed.): 5th Uzaktan Algılama-CBS Sempozyumu, Istanbul, Oct 14–17, 2014: 1–7.
Başkent Emin Zeki, Kadioğullari Ali Ihsan (2007): Spatial and temporal dynamics of land use pattern in Turkey: A case study in İnegöl. Landscape and Urban Planning, 81, 316-327
Bulut S., Günlü A. (2016): Comparison of different supervised classification algorithms for land use classes. Kastamonu University Journal of Forest Faculty, 16: 528–535.
Bulut S., Günlü A., Keleş S. (2017): Evaluation of different supervised classification algorithms for crown closure classes: A case study of Yapraklı Forest Planning Unit, Çankırı. In: Fakir H. (ed.): International Symposium on New Horizons in Forestry, Isparta, Oct 18–20, 2017: 133–137.
Chang Chih-Chung, Lin Chih-Jen (2011): LIBSVM. ACM Transactions on Intelligent Systems and Technology, 2, 1-27
ERDAS LLC (2002): ERDAS Field Guide. 6th Ed. Atlanta, ERDAS LLC: 658.
Gebreslasie M.T., Ahmed F.B., van Aardt Jan A.N. (2010): Predicting forest structural attributes using ancillary data and ASTER satellite data. International Journal of Applied Earth Observation and Geoinformation, 12, S23-S26
Günlü A. (2012): Estimation of certain stand type parameters (growth stage and crown closure) and land cover using Landsat TM satellite image. Kastamonu University Journal of Forest Faculty, 12: 71–79.
Günlü Alkan, Sivrikaya Fatih, Baskent Emin, Keles Sedat, Çakir Günay, Kadiogullari Ali (2008): Estimation of Stand Type Parameters and Land Cover Using Landsat-7 ETM Image: A Case Study from Turkey. Sensors, 8, 2509-2525
Hall R.J., Skakun R.S., Arsenault E.J., Case B.S. (2006): Modeling forest stand structure attributes using Landsat ETM+ data: Application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, 225, 378-390
Hazini Sharifeh, Hashim Mazlan (2015): Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping. Arabian Journal of Geosciences, 8, 9763-9773
Hsu C.W., Chang C.C., Lin C.J. (2016): A Practical Guide to Support Vector Classification. Taipei, National Taiwan University: 16.
Hyyppä Juha, Hyyppä Hannu, Inkinen Mikko, Engdahl Marcus, Linko Susan, Zhu Yi-Hong (2000): Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128, 109-120
Kadioğullari Ali Ihsan, Başkent Emin Zeki (2008): Spatial and temporal dynamics of land use pattern in Eastern Turkey: a case study in Gümüşhane. Environmental Monitoring and Assessment, 138, 289-303
Kavzoglu T., Colkesen I. (2009): A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11, 352-359
Kayitakire F., Hamel C., Defourny P. (2006): Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sensing of Environment, 102, 390-401
Kulkarni A.D., Lowe B. (2016): Random forest algorithm for land cover classification. International Journal on Recent and Innovation Trends in Computing and Communication, 4: 58–63.
Lu Dengsheng, Mausel Paul, Brondı́zio Eduardo, Moran Emilio (2004): Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, 198, 149-167
Mohammadi J., Shataee Joibary Shaban, Yaghmaee F., Mahiny A. S. (2010): Modelling forest stand volume and tree density using Landsat ETM+ data. International Journal of Remote Sensing, 31, 2959-2975
Peuhkurinen J., Maltamo M., Vesa L., Packalén P. (2008): Estimation of forest stand characteristics using spectral histograms derived from an IKONOS satellite image. Photogrammetric Engineering & Remote Sensing, 74: 1335–1341.
Pilger N., Peddle D.R., Luther J.E. (2002): Estimation of forest cover type and structure from Landsat TM imagery using a canopy reflectance model for biomass mapping in Western Newfoundland. IEEE International Geoscience and Remote Sensing Symposium, 3: 1324–1326.
Sivrikaya (2011): The importance of spatial accuracy in characterizing stand types using remotely sensed data. AFRICAN JOURNAL OF BIOTECHNOLOGY, 10, -
Sivrikaya F., Keleş S., Çakır G., Başkent E.Z., Köse S. (2006): Comparing accuracy of classified Landsat data with land use maps reclassified from the stand type maps. In: Caetano M., Painho M. (eds): 7th International Symposium on Spatial Accuracy, Lisbon, July 5–7, 2006: 643–652.
Srivastava Prashant K., Han Dawei, Rico-Ramirez Miguel A., Bray Michaela, Islam Tanvir (2012): Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50, 1250-1265
Taati A., Sarmadian F., Mousavi A., Pour C.T.H., Shahir A.H.E. (2015): Land use classification using support vector machine and maximum likelihood algorithms by Landsat-5 ETM images. Walailak Journal Science & Technology, 12: 681–687.
Tolessa Terefe, Senbeta Feyera, Abebe Tariku (2016): Land use/land cover analysis and ecosystem services valuation in the central highlands of Ethiopia. Forests, Trees and Livelihoods, 26, 111-123
Topaloğlu Raziye Hale, Sertel Elif, Musaoğlu Nebiye (2016): ASSESSMENT OF CLASSIFICATION ACCURACIES OF SENTINEL-2 AND LANDSAT-8 DATA FOR LAND COVER / USE MAPPING. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8, 1055-1059
Ustuner Mustafa, Sanli Fusun Balik, Dixon Barnali (2017): Application of Support Vector Machines for Landuse Classification Using High-Resolution RapidEye Images: A Sensitivity Analysis. European Journal of Remote Sensing, 48, 403-422
Were Kennedy, Bui Dieu Tien, Dick Øystein B., Singh Bal Ram (2015): A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52, 394-403
Wu T.F., Lin C.J., Weng R.C. (2004): Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, 5: 975–1005.
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