Monitoring Mangrove Cover Changes Using Satellite Images in Qeshm Island, Iran

Authors and Affiliations: 

Neda Bihamta (Ph.D student in Environmental Sciences, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran)
Ali Reza Soffianian (Associate Professor, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran)
Sima Fakheran(Assistant Professor, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran)
saeid Pourmanafi (Assistant Professor, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran)

Corresponding author: 
Sima Fakheran

The total mangrove area has reduced by over 50% during the last half century (Shi et al, 2016). The mangrove forests are highly productive ecosystems (Giri et al, 2011). In order to develop programs to apply guide conservation ecosystem management, it is needed to first monitoring and assessment the trends of mangrove dynamics. Remote sensing technique has a great potential for the mapping of mangrove extent and change (Fei et al, 2011). Various satellite- sensor data including Landsat, ASTER, SPOT and IKONOS have been used to map mangrove at different scales. Many methods based on visual interpretation, pixel- or object-based, support vector machine analysis has been applied to classify mangrove species (Son et al, 2015). Qeshm Island is an Iranian island, a Free Trade Zone near the entrance of the Persian Gulf in the Straits of Hormuz. This area was listed on the UNESCO’s national environmental heritage list in 2007 (Dilmaghani et al, 2011). In the last decades, land cover in coastal areas of the Qeshm Island has been vastly altered by humans. Changes in the detection of the mangrove area by satellite imaging over different time periods can be a suitable indicator for assessment of possible environmental damage caused by the extensive activities of the aforementioned industry (Pham and Yoshino 2015). The purpose of this study assesses spatial-temporal changes in the extent and width of mangroves and changes in adjacent land use. The Landsat data TM, ETM+ and OLI images acquired from the U.S. Geological survey (USGS) for 1989, 2003 & 2017 will be used to obtain comprehensive coverage and analysis of the current and historical mangrove conditions. We collect ground-truth points using GPS (Global Positioning Systems) to create training data for classification and for accuracy assessment of the classification results. The methodology of this study is composed of three main steps including (1) data pre-processing including geometric correction of Landsat images and atmospheric correction; (2) image classification and accuracy assessment, (3) change detection. In order to image classification, initially, a fusion is made among the panchromatic and the multispectral bands, resulting in an image with 15 m spatial resolution (Kanniah et al, 2015; Mountrakis et al, 2011). Five categories of land cover will be classified: mangrove, water bodies, rock, agriculture, and settlement. The accuracy of the classified images will be calculated using confusion matrices and kappa coefficients. The classification map of each period will be compared with investigating the mangrove deforestation and reforestation rates between each of these paired years. The major causes of mangrove destruction in this region are the development of the coastal region and limited mangrove cutting for animal feed. Systematic monitoring and control measures are is essential to guard the remaining mangrove cover from further loss (Viennois et al, 2016).


Dilmaghani, Y., Danehkar, A., Jozi, S.A. and Arjomandi, R., 2011. Codification of mangrove forests management strategies: Case study of Hara Protected Area, Iran. Journal of Food, Agriculture & Environment, 9(2), pp.508-513.
Fei, S.X., Shan, C.H. and Hua, G.Z., 2011. Remote sensing of mangrove wetlands identification. Procedia Environmental Sciences, 10, pp. 2287-2293.
Giri, C.; Ochieng, E.; Tieszen, L.L.; Zhu, Z.; Singh, A.; Loveland, T.; Masek, J.; Duke, N. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 2011, 20, 154–159.
Kanniah, K.D., Sheikhi, A., Cracknell, A.P., Goh, H.C., Tan, K.P., Ho, C.S. and Rasli, F.N., 2015. Satellite images for monitoring mangrove cover changes in a fast growing economic region in southern Peninsular Malaysia. Remote Sensing, 7(11), pp. 14360-14385.
Mountrakis, G.; Im, J.; Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. 2011, 66, 247–259.
Pham, T.D. and Yoshino, K., 2015. Mangrove Mapping and Change Detection Using Mult-itemporal Landsat imagery in Hai Phong city, Vietnam. In International Symposium on Cartography in Internet and Ubiquitous Environments.
Shi, T., Liu, J., Hu, Z., Liu, H., Wang, J. and Wu, G., 2016. New spectral metrics for mangrove forest identification. Remote Sensing Letters, 7(9), pp.885-894.
Son, N.T., Chen, C.F., Chang, N.B., Chen, C.R., Chang, L.Y. and Thanh, B.X., 2015. Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), pp.503-510.
Viennois, G., Proisy, C., Feret, J.B., Prosperi, J., Sidik, F., Rahmania, R., Longépé, N., Germain, O. and Gaspar, P., 2016. Multitemporal Analysis of High-Spatial-Resolution Optical Satellite Imagery for Mangrove Species Mapping in Bali, Indonesia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8), pp.3680-3686.

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