Forest density and orchard classification in Hyrcanian forests of Iran using Landsat 8 data K., Akhavan R. (2017): Forest density and orchard classification in Hyrcanian forests of Iran using Landsat 8 data. J. For. Sci., 63: 355-362.
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Satellite-based remote sensing is of crucial importance to provide timely and continuous thematic maps for practical forestry tasks. There is currently no existing remote sensing-based, large-scale inventory of canopy cover classes (and also adjacent orchards) on the full range of Hyrcanian forests. We used the freely available and large-scale coverage of Landsat 8 imagery acquired in 2014 to classify three forest density classes as well as non-forest and orchards. The supervised classification and support vector machine classifier were selected based on a pre-classification of three representative pilot regions. Classified final maps were validated by means of a two-stage sampling and 1,852 field samples. The total areas of the dense, semi-dense, sparse forests and orchards were 45, 36, 19 and 1.9% of the total studied area, respectively. The overall accuracy and Kappa coefficient of classified maps were 94.8 and 90%, respectively. The methodology introduced to map forest cover in Hyrcanian forests is concluded to enable providing a high quality forest database for further research, planning and management.
Akhavan R., Sagheb-Talebi Kh., Zenner E. K., Safavimanesh F. (2012): Spatial patterns in different forest development stages of an intact old-growth Oriental beech forest in the Caspian region of Iran. European Journal of Forest Research, 131, 1355-1366
Cochran W.G. (1977): Sampling Techniques. 3rd Ed. New York, John Wiley & Sons, Inc.: 428.
Dicks S.E., Lo T.H.C. (1990): Evaluation of thematic map accuracy in a land-use and land-cover mapping program. Pho-togrammetric Engineering and Remote Sensing, 56: 1247–1252.
Dixon B., Candade N. (2008): Multispectral landuse classification using neural networks and support vector machines: one or the other, or both?. International Journal of Remote Sensing, 29, 1185-1206
Dube Timothy, Mutanga Onisimo (2015): Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 36-46
Edwards Thomas C., Moisen Gretchen G., Cutler D.Richard (1998): Assessing Map Accuracy in a Remotely Sensed, Ecoregion-Scale Cover Map. Remote Sensing of Environment, 63, 73-83
FOODY G.M., McCULLOCH M. B., YATES W. B. (1995): The effect of training set size and composition on artificial neural network classification. International Journal of Remote Sensing, 16, 1707-1723
Gualtieri J.A., Cromp R.F. (1998): Support vector machines for hyperspectral remote sensing classification. In: Meric-sko R.J. (ed.): Proceedings of the 27th AIPR Workshop: Advances in Computer Assisted Recognition, Washington, D.C., Oct 14–16, 1998: 221–232.
Huang C., Davis L. S., Townshend J. R. G. (2002): An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23, 725-749
Intergraph (2013): ERDAS Field GuideTM. Huntsville, Intergraph Corporation Press: 792.
Jensen J.R. (2005): Introductory Digital Image Processing: A Remote Sensing Perspective. 3rd Ed. Upper Saddle River, Prentice Hall: 526.
Lohr S.L. (1999): Sampling: Design and Analysis. Pacific Grove, Brooks/Cole Publishing Company: 494.
Lu D., Weng Q. (2007): A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28, 823-870
Melgani F., Bruzzone L. (2004): Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, 1778-1790
Mirakhorlou K. (2003): Land use mapping of northern forests of Iran using Landsat 7 ETM+ data. Iranian Journal of Forest and Poplar Research, 11: 174–215. (in Persian with English abstract)
Mirakhorlou K., Akhavan R. (2008): Investigation on boundary changes of northern forests of Iran using remotely sensed data. Iranian Journal of Forest and Poplar Research, 16: 139–148. (in Persian with English abstract)
Mountrakis Giorgos, Im Jungho, Ogole Caesar (2011): Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247-259
Nusser S.M., Klaas E.E. (2003): Survey methods for assessing land cover map accuracy. Environmental and Ecological Statistics, 10: 309–331.
Pal M., Mather P. M. (2005): Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26, 1007-1011
PAOLA J. D., SCHOWENGERDT R. A. (1995): A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery. International Journal of Remote Sensing, 16, 3033-3058
Qin Yuanwei, Xiao Xiangming, Dong Jinwei, Zhang Geli, Shimada Masanobu, Liu Jiyuan, Li Chungan, Kou Weili, Moore Berrien (2015): Forest cover maps of China in 2010 from multiple approaches and data sources: PALSAR, Landsat, MODIS, FRA, and NFI. ISPRS Journal of Photogrammetry and Remote Sensing, 109, 1-16
Rezaee M.B., Rostamzadeh H., Feyzizade B. (2008): Evaluating of forest change detection using RS and GIS. Iranian Journal of Geographical Researches, 62: 143–159. (in Persian with English abstract)
Richards J.A. (2013): Remote Sensing Digital Image Analysis. 5th Ed. Berlin, Heidelberg, Springer-Verlag: 494.
Saadat Hossein, Adamowski Jan, Bonnell Robert, Sharifi Forood, Namdar Mohammad, Ale-Ebrahim Sasan (2011): Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 608-619
Salman Mahini A., Nadali A., Feghhi J., Riyazi B. (2012): Classification of Golestan province of forest areas by maximum likelihood algorithm using Landsat 7 ETM+ data. Iranian Journal of Environmental Science and Technology, 14: 57–72. (in Persian with English abstract)
Stehman S.V. (1992): Comparison of systematic and random sampling for estimating the accuracy of maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 58: 1343–1350.
Stehman Stephen V. (2009): Sampling designs for accuracy assessment of land cover. International Journal of Remote Sensing, 30, 5243-5272
Szuster Brian W., Chen Qi, Borger Michael (2011): A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, 31, 525-532
Tazeh M., Ghezelsaflou N., Sadeghi A.M. (2014): Evaluating capability of Landsat satellite images for forest mapping. In: Karkeabadi Z. (ed.): Proceedings of the 1st National Conference of Geography, Urban and Development, Tehran, Feb 27, 2014: 1518–1530. (in Persian with English abstract)
Shao Yang, Lunetta Ross S. (2012): Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 78-87
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