Mapping of arid rangeland vegetation types using satellite data (study site: Ameri, Iran)

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Research Paper 01/03/2015
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Mapping of arid rangeland vegetation types using satellite data (study site: Ameri, Iran)

Shahram Yousefi Khanghah
J. Biodiv. & Environ. Sci. 6(3), 127-133, March 2015.
Copyright Statement: Copyright 2015; The Author(s).
License: CC BY-NC 4.0

Abstract

Remote sensing assessment is used along with field data to enhance sampling and site representation. The research was carried out in Ameri region located between 50° 05´ to 50° 16´ east longitude and 30° 03´ to 30° 13´ north latitude in south west of Iran, as a dry Climate and located in the coastal region with 15915 hectare area. The aim of the present research was to produce rangeland vegetation types using satellite data. Geometric corrections of images were applied using ground control points (GCP) and geo-referenced images with root mean square error (RMSE) less than one pixel, then images Co-registered together with RMSE less than 0.2 pixels. The atmospheric corrections of images were applied using Cost method. Image spatial resolution enhanced using fusion with a panchromatic band. Images classified using maximum likelihood (ML) algorithm of supervised classification with 100 training area, and produced five rangeland vegetation types, then accuracy of produced maps determined with ground truth samples. The results show that both sensors can produce suitable vegetation type’s map in study area, and ML classification method able to delineate rangeland vegetation type’s map with acceptable precision. As a result we imply that visual interpretation and manual mapping will be used to delineate vegetation type’s maps of arid rangelands.

Amiri F, Yeganeh H. 2012. Evaluation of vegetation indices for preparing vegetation cover percentage in semi-arid lands of central Iran (case study: Ghareh Aghaj watershed). Range and watershed management, 65 (2), 175-18.

Campbell JB, Wynne RH. 2011. Introduction to remote sensing, fifth Edition, Guilford Press, New York. 718 p.

Chavez PS. 1996. Image-Based atmospheric corrections revisited and improved, photogrammetric engineering and remote sensing, 62, 9, 1025-1036.

DeRose RC, Oguchi T, Morishima W, Collado M. 2011. Land cover change on Mt. Pinatubo, the Philippines, monitored using ASTER VNIR, remote sensing, 32 (24), 9279–9305.

Freeman EA, Moisen GG. 2008. A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecological modeling, 217 (1), 48–58.

Jensen R. 1986. Introductory digital image processing, Prentice-Hall, Englewood Cliffs, New Jersey, p. 379.

Lillesand TM, Kiefer RW, Chipman W. 2004. Remote sensing and image interpretation. 5th edition, New York, Jhon Willey and Sons, 763 p.

Mesdaghi M. 1999.  Range  management  in  Iran. University of Imam Reza press, Mashahd, Iran. 259p.

Richards JA. 1999. Remote sensing digital image analysis, Springer, Verlag, Germany, 240 p.

Shataee JS, Najjarlou S, Jabbary S, Moaiery H. 2008. Investigation on capability of multi spectral and fused LANDSAT7 and IRS1D data for forest extent mapping. Agriculture science and natural resource. 14 (5), 13-22.

Shirazi M, Matinfar HR, Nematolahi MJ, Zehtabian GR. 2011. Comparison of information content of Aster and LISS-III bands in arid areas (case study: Damghan playa). Applied RS & GIS techniques in natural resource science. 1 (1), 31-47.

Shoshany M, Karnibad L. 2011. Mapping shrubland biomass along Mediterranean climatic gradients: The synergy of rainfall-based and NDVI-based models. Remote sensing, 32 (24), 9497–9508.

Vescovo L, Tuohy M, Gianelle D. 2009. A preliminary study of mapping biomass and cover in NZ grasslands using multispectral narrow-band data. In: Jones S, Reinke K, eds. Innovations in remote sensing and photogrammetry. Springer, Heidelberg, Germany, pp. 281–90.

Vogelmann JE, Helderb D, Morfitta R, Choatea MJ, Merchantc JW, Bulley H. 2001. Effects of Landsat 5 thematic mapper and Landsat 7 enhanced thematic mapper plus radiometric and geometric calibrations and corrections on landscape characteri-zation. Remote sensing of environment, 78, 55-70.

Weeks ES, Gaelle A, Ausseil E, Shepherd JD, Dymond JR. 2013. Remote sensing methods to detect land-use/cover changes in New Zealand’s ‘indigenous’ grasslands, New Zealand geographer, 69 (1), 1-13.

Wu Q, Li HQ, Wang RS, Paulussen J, He Y, Wang M, Wang BH, Wang Z. 2006. Monitoring and predicting land use change in Beijing using remote sensing and GIS, Landscape and urban planning, 78, 322-333.

Xiaoling C, Xiaobin C, Hui L. 2006. Expert classification method based on patch-based neighborhood searching algorithm. Geo-spatial information science, 10 (1), 37-43.

Yüksel A, Akay A, Gundogan R. 2008. Using ASTER imagery in land use/cover classification of eastern mediterranean landscapes According to CORINE land cover project. Sensors, 8(2), 1237-1251.

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