Document Type : Original Article


1 Student of forestry in PhD in Ilam University

2 The Associate Professor and Faculty Member of Forest Sciences Department in University of Ilam


Land cover maps are regarded one of the main inputs for land use planning and environmental modeling. One of the main reasons of unsuitable spatial array of the urban areas can be related to the rural communities' emigration, which in turn cause complete degradation of the farmlands and rural structures. Such event can be regards as a factor which is responsible for demolishing of the Haft Barm area an important recreational and touristic areas in  the vicinity of Shiraz metropolis. Therefore, recognizing the natural conditions of the area, preparing resource maps like land use and land cover, and monitoring their changes during the time is critical issue in the  environmental planning and management. To this aim, WorldView 2 images with eight bands were used and  mentioned maps were produced. The mapping analysis way was relied on an object-based classification methodology and using a decision tree which was applied in the WorldView 2 images categorization. The process shall be as the following: a) segmentation, b) terrain selecting, c) creating a decision tree for images' classification, and d) ultimate classification and evaluation of the accuracy. The area was divided into 10 user classes. The results indicated successful classes categorization with overall accuracy of 87.45%. The highest accuracy of classification was obtained for water, forest, product, building classes respectively. Planted forests patches as well as natural forests were identified and classified using OBC approach (object-based method) while additional coastal bands were used to distinguishing among barren and covered lands. Distance to tree and shadow play an important role in identifying buildings.


Abd-Elrahman A., Pearlstine L., Percival F. 2005. Development of pattern recognition algorithm for automatic bird detection from unmanned aerial vehicle imagery. Surveying and Land Information Science, 65, 37.

Benz  Ursula C., Hofmann  Peter., Willhauck  Gregor., Lingenfelder  Iris., Heynen  Markus. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry & Remote Sensing. 58, 239– 258

Baatz M., Schape A. 2000. Multiresolution segmentation- An optimization approach for high quality multiscale image segmentation. In Angewandte Geographische Informationsverarbeitung XII. (Eds: Strobl, J. and Blaschke, T.), Beitrage zum AGIT Symposium Salzburg 2000, Karhlsruhe, Herbert Wichmann Verlag, pp.12–23.

Belluco E., Camuffo M., Ferrari S., Modenese L., Silvestri S., Marani A., Marani M. 2006. Mapping saltmarsh vegetation by multispectral and hyperspectral remote sensing. Remote Sensing of Environment, 105, 54–67.

Chubey M. S., Franklin S. E., Wulder M. A. 2006. Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing, 72, 38.

Conchedda G., Durieux L., Mayaux P., Iee e. 2007. Object-based monitoring of land cover changes in mangrove ecosystems of Senegal., In 4th International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 44-49. Louvain, BELGIUM.

Desclee B., P. Bogaert., Defourny P. 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment, 102, 1-11.

Draguţ L., Eisank C. 2012. Automated object- based classification of topography from SRTM data, Geomorphology 141-142, 21–33.

Ge S., Carruthers R., Gong P., Herrera A. 2006. Texture analysis for mapping Tamarix parviflora using aerial photographs along the Cache Creek, California. Environmental Monitoring and Assessment, 114, 65–83.

Gao Y., Marpu P., Morales Manilla L. M. 2016. Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico. Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V. Vol. 9263.

Gao, Yan., Mas J. F., Maathuis B. H. P., Zhang X., Van Dijk P. M. 2006. Comparison of pixel-based and objectoriented image classification approaches – A case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27, 4039–4055.

Hay G. J., Castilla G., Wulder M., Ruiz J. R. 2005. An automated object-based
approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation, 7, 339-359.

Huang C., Song K., Kim S., Townshend  J. R. G., Davis P., Masek J., et al. 2008. Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sensing of Environment, 112, 970−985.

Laliberte A.S., Rango A. 2009. Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery. IEEE Transactions on Geoscience and Remote Sensing, 47, 761–770.

Lucieer V.L. 2008. Object-oriented classification of sidescan sonar data for mapping benthic marine habitats. International Journal of Remote Sensing, 2008, 29(3): 905–921.

Maheu-Giroux M., de Blois S. 2005. Mapping the invasive species Phragmites australis in linear wetland corridors. Aquatic Botany, 83, 310–320. doi:10.1016/j. aquabot.2005.07.002

Marshall V., Lewis M., Ostendorf B. 2012. Do Additional Bands In Worldview-2 Multispectral Imagery Improve Discrimination of an Invasive Tussock, Buffel Grass (Cenchrus Ciliaris)? International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

Mathieu R., Aryal J. 2007. Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery. Landscape and Urban Planning, 81(3): 179-192.

Myint S. W., Giri C. P., Le W., Zhu Z. L., Gillette S. C. 2008. Identifying mangrove species and their surrounding land use and land cover classes using an object-oriented approach with a lacunarity spatial measure. Giscience & Remote Sensing, 45, 188-208.

Quirós E., Felicísimo Á.M., Cuartero A. 2009. Testing multivariate adaptive regression splines (MARS) as a method of land cover classification of TERRA-ASTER satellite images. Sensors, 9: 9011-9028.

Rango A., Laliberte A., Steele C., Herrick J.E., Bestelmeyer B., Schmugge T., Jenkins V. 2006. Research article: Using unmanned aerial vehicles for rangelands: Current applications and future potentials. Environmental Practice, 8, 159–168.

Asmaryan S., Warner T.A., Muradyan V., Nersisyan G. 2013. Mapping tree stress associated with urban pollution using the WorldView-2 Red Edge band. Remote Sensing Letters. Vol. 4, No. 2, February 2013, 200–209.

Sims D.A., Gamon J.A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81(2-3): 337-354.

Trimble. 2012. eCognition Developer 8.8 Reference Book. Trimble, Munich, Germany.436 pp.

Van Coillie F.M.B., Verbeke, L.P.C., De Wulf R. R. 2007. Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium. Remote Sensing of Environment, (110): 476–487.

Wang L., Sousa W.P., Gong P., Biging G.S. 2004. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sensing of Environment, 91, 432–440. doi:10.1016/j.rse.2004.04.005

Waser L.T., Küchler M., Jütte K., Stampfer T. 2014. Evaluating the potential of WorldView-2 data to classify tree species and different levels of Ash mortality. Remote Sensing, 6: 4515-4545.

Watts A.C., Bowman W.S., Abd-Elrahman A.H., Mohamed A., Wilkinson B.E., Perry J., Kaddoura Y.O., Lee, K. 2008. Unmanned aircraft systems (UASs) for ecological research and natural-resource monitoring (Florida). Ecol. Restor. 26(1): 13–14. doi: 10.3368/er.26.1.13.

Willhauck G. 2000. Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos.Amsterdam, The Netherlands, XIX Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS).

Wulder M. A., White J. C., Hay G. J., Castilla G. 2008. Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery. Forestry Chronicle, 84, 221-230.

Zhao H.M., Chen X.L. 2005. Use of Normalized Difference Bareness Index in Quickly Mapping Bare Areas from TM/ETM+. In Proceedings of 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, 25–29 July 2005; Volume 3, pp. 1666−1668.