Welcome to International Network for Natural Sciences | INNSpub

Application of remote sensing techniques and environmental factors in separating and determining characteristics of vegetation (Case Study: Siahkooh Basin-Yazd)

Research Paper | January 1, 2015

| Download 13

M. hassanzadeh Nafooti, N. Baghestani Meybodi, Z. Ebrahimi Khusfi, M. chabok, M. Ebrahimi Khusfi

Key Words:

J. Bio. Env. Sci.6( 1), 333-343, January 2015


JBES 2015 [Generate Certificate]


Considering the capabilities of satellite imagery and Remote Sensing techniques, researchers employ these as a conventional method for exploring deserts and research carried out in arid regions. This study aims to evaluate the application of remote sensing techniques and climatic and geological factors in the separating and determining characteristics of vegetation in arid regions, especially in Siahkooh basin located in the province of Yazd (Iran). At first, in order to detect the vegetation fraction in the study area, 286 plots were sampled in the fieldwork. After applying the necessary preprocessing on the ASTER satellite imagery including the geometric and radiometric corrections, the soil line equation and 13 vegetation indices were calculated. To study the effect of environmental factors on the vegetation fraction, information layers such as geology formations, elevation, slope, aspect, temperature and precipitation were produced and standardized. In order to combine the mentioned layers and investigate the effect of each factor, the backward elimination method was used for training plots. Finally, the accuracy of models was assessed based on the correlation coefficient between measured and estimated values in the test plots. The results of this study showed that MSAVI1 is the most suitable index for estimating vegetation fraction in the case study. Furthermore, the results indicated that climatic and geological factors do not have any significant effect on increasing the accuracy of the models in Siahkooh basin.


Copyright © 2015
By Authors and International Network for
Natural Sciences (INNSPUB)
This article is published under the terms of the Creative
Commons Attribution Liscense 4.0

Application of remote sensing techniques and environmental factors in separating and determining characteristics of vegetation (Case Study: Siahkooh Basin-Yazd)

Abdullahi J, Baghestani N, Savaghebi MH, Rahimian MH. 2007. Determination of vegetation in arid regions using remote sensing and GIS (Case Study: Watershed Nodoushan). Journal of Science and Technology of Agriculture and Natural Resources 44, 313-301.

Arkhy S, Niazi Y, Adibnejad M. 2011. Monitoring vegetation cover changes using remote sensing techniques in Ilam dam basin. Geography and Development 24, 133-121.

Baret F, Guyot G. 1991. Potentials and limits of vegetation indices for LAI and APAR assesment. Remote Sensing Of Environment 35, 161-173.

Baugh WM, Groeneveld P. 2006. Broadband vegetation index performance evaluated for a low-cover environment.” International Journal of Remote Sensing 27,4715-4730.

Behbehani N, Fallah Shamsi SR, Frzadmehr J, Erfanfard SY, Ramezani M. 2010. Using vegetation index of ASTER-L1B images in a single estimate of canopy trees in arid rangelands. Case Study: Tak Ahmad Shahi – South Khorasan. Journal of Range Management 4, 103-93.

Cabacinha C, Castro S. 2009. Relationships between floristic and vegetation indices, forest structure and landscape metrics of fragments in Brazilia Cerrado. Forest and Ecology and Management 257, 2157-2165.

Casanova D, Epema GF, Goudriaan J. 1998. Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Research 55, 83-92

Clevers JGPW. 1989. The application of a weighted infrared-red vegetation index for estimating leaf area index by correcting soil moisture. Remote Sensing Of Environment 29, 25–37.

Crippen RE. 1990. Calculating the vegetation index faster. Remote Sensing Of Environment 34, 71–73.

Darvishzadehh R, Skidmore A, Atzberger C, Wieren S. 2008. Estimation of vegetation LAI from hyper spectral reflectance data: Effects of soil type and plant architecture. International Journal of Applied Earth Observation and Geoinformation, 10, 358-372.

Fox GA, Sabbagh GJ, Searcy SW, Yang C. 2004. An automated soil line identification routine for remotely sensed images. Soil Science Society American Journal 68, 1326-1331.

Ghaemi M, Sanaei Nejad SH, Astaraei AR, Mirhoseini P. 2009. Comparison of different vegetation index using ETM+ satellite images for vegetation studies of Nishapur Plain, Khorasan Razavi. Journal of Iranian Field Crop Research, Volume 8, Number 1, 137-128.

Griffiths GH. 1985. Mapping rangeland vegetation in northern Kenya from Landsat data. PhD. Thesis, University of Aston in Birmingham, 205pp.

Hayez R. 1997. Principals of Remote Sensing, Remote Sensing Center of Iran, Tehran University press, 645pp.

Huete H. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing Of Environment 25, 295–309.

Ishiyama T, Nakajima Y, Kajiwara K, Tsuchiya K. 1997. Extraction of vegetation covers in an arid area based on satellite data. Advances in Space Research , Calibration and Intercalibration of Satellite Sensors and Early Results of Radarsat 19, 1375-1378

Jabbari S, khajedin SJ, Soltani S, Jafari R. 2011. Determination of Range Vegetation Using Remote Sensing and GIS (Case Study: Semirom). National Conference on Geomatics, 10 p.

Ju C, Cai T, Yang X. 2008. Topography-based modeling to estimate percent vegetation cover in semi-arid Mu Us sandy land, China. Computers and Electronics in Agriculture 64, 133-139.

Jordan CF. 1969. Derivation of leaf area index from quality of light on the forest floor .Ecology 50, 663-666.

Kallel A, Sylvie L, Catherine O, Laurence H. 2007. Determination of vegetation cover fraction by inversion of a four-parameter model based on isoline parameterization. Remote Sensing Of Environment 111, 553-566.

Koide K, Koike K. 2012. Applying vegetation indices to detect high water table zones in humid warm-temperate regions using satellite remote sensing. International Journal of Applied Earth Observation and Geoinformation 19, 88-103.

Liang, S. 2003. A direct algorithm for estimating land surface broadband albedos from MODIS imagery. IEEE Trans Geosci Remote Sensing of Environment 41, 136-145.

Major DJ, Baret F, Guyot G. 1990. A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing 11, 727-740

Qi J, Chehbouni Al, Huete A, Kerr Y. 1994. A modified soil adjusted vegetation index (MSAVI). Remote Sensing Of Environment 48, 119- 126.

McVicar TR, Bierwirth PN. 2001.Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRR imagery. International Journal of Remote sensing 22, 2109−2128.

Rondeaux G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing Of Environment 55, 95−107.

Richardson AJ, Wiegand CL. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing 43, 1541–1552.

Rouse JW, Haas RH, Schell JA, Deering DW, Harlan JC. 1974. Monitoring the vernal advancement of retrogradation of natural vegetation. NASA/GSFC, Type III, final report, Greenbelt,MD.

Yang G, Ruiliag P, Zhang  J, Feng H, Wang  J. 2013. Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis cover mountain areas. ISPRS Journal of photogrammetry and Remote Sensing 77, 79-93.

Yoshioka H, Miura T, Dematte J, Batchily K, Huete R. 2009. Derivation of Soil Line Influence on Two-Band Vegetation Indices and Vegetation Isolines. Remote Sensing journal 1,842-857.