UTILIZING SATELLITE IMAGES AND ARTIFICIAL NEURAL NETWORKS IN ESTIMATION OF VEGETATION FRACTION IN ARID REGIONS

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MATKAN A.A.; DARVISHZADEH R.; HOSSEINIASL A.; EBRAHIMI KHUSFI M.

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Journal: Environmental Erosion Research Year:2011 | Volume:1 | Issue:1 Page(s): 7-27

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Title

UTILIZING SATELLITE IMAGES AND ARTIFICIAL NEURAL NETWORKS IN ESTIMATION OF VEGETATION FRACTION IN ARID REGIONS

Author(s)

MATKAN A.A. | DARVISHZADEH R. | HOSSEINIASL A. | EBRAHIMI KHUSFI M. | Issue Writer Certificate

Keywords

VEGETATION INDICESQ2

ALOS (A VNIR2) IMAGERYQ2

VEGETATION FRACTIONQ1

ARID REGIONSQ2

Abstract

Vegetation is an important component of global ecosystem and the knowledge about its cover and fraction are essential in understanding of land-atmosphere interactions and their effects on environmental issues. The main objective of this study was to estimate the VEGETATION FRACTION (Fv) of an arid area in central part of Iran (Sheitoor- Yazd) by using satellite images and artificial neural networks (ANN). To do so, the percentage of VEGETATION FRACTION for 52 randomly selected plots (50 meters by 50 meters), were measured on the field in July 2009. Next, an ALOS (AVNIR) image collected on 18 July 2009 and multilayer perceptron network were used to estimate the percentage of vegetation cover. Two types of transfer function, 12 training functions and six different combinations of spectral bands of satellite image as input were used to select the optimal network. Furthermore, the number of hidden neurons varied from one to six. Field measurements were used as target values to the network. To evaluate the effect of randomly selected training and test data, 30 and 35 out of 52 observed plots were considered as training data sets, and 22 and 17 plots as test data sets, respectively. Then, using linear regression models between the measured field data and estimated values, coefficients of determinations and RMSEs were calculated. Moreover in order to validate the results and remove possible errors due to random selection, cross validation algorithm was used. Results demonstrate that ANN can be used for accurate estimation of the percentage of vegetation cover in arid areas (R2>0.74 and RMSE <2%).

Cites

THE PREPARATION OF UPDATED VEGETATION MAPS BY PROCESSING SATELLITE IMAGES: A WAY IN SUSTAINABLE MANAGEMENT OF AGRICULTURE

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APA: Copy

MATKAN, A.A., DARVISHZADEH, R., HOSSEINIASL, A., & EBRAHIMI KHUSFI, M.. (2011). UTILIZING SATELLITE IMAGES AND ARTIFICIAL NEURAL NETWORKS IN ESTIMATION OF VEGETATION FRACTION IN ARID REGIONS. ENVIRONMENTAL EROSION RESEARCHES, 1(1), 7-27. SID. https://sid.ir/paper/223066/en

Vancouver: Copy

MATKAN A.A., DARVISHZADEH R., HOSSEINIASL A., EBRAHIMI KHUSFI M.. UTILIZING SATELLITE IMAGES AND ARTIFICIAL NEURAL NETWORKS IN ESTIMATION OF VEGETATION FRACTION IN ARID REGIONS. ENVIRONMENTAL EROSION RESEARCHES[Internet]. 2011;1(1):7-27. Available from: https://sid.ir/paper/223066/en

IEEE: Copy

A.A. MATKAN, R. DARVISHZADEH, A. HOSSEINIASL, and M. EBRAHIMI KHUSFI, “UTILIZING SATELLITE IMAGES AND ARTIFICIAL NEURAL NETWORKS IN ESTIMATION OF VEGETATION FRACTION IN ARID REGIONS,” ENVIRONMENTAL EROSION RESEARCHES, vol. 1, no. 1, pp. 7–27, 2011, [Online]. Available: https://sid.ir/paper/223066/en

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