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Paper Information

Journal:   ENVIRONMENTAL EROSION RESEARCHES   SPRING 2011 , Volume 1 , Number 1; Page(s) 7 To 27.
 
Paper: 

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.
 
* RS & GIS DEPARTMENT, FACULTY OF EARTH SCIENCE, SHAHID BEHESHTI UNIVERSITY, EVIN, TEHRAN, IRAN
 
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%).

 
Keyword(s): VEGETATION INDICES, ALOS (A VNIR2) IMAGERY, VEGETATION FRACTION, ARID REGIONS
 
 
References: 
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Citations: 
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APA: Copy

MATKAN, 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. https://www.sid.ir/en/journal/ViewPaper.aspx?id=273015



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. 2011 [cited 2021July31];1(1):7-27. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=273015



IEEE: Copy

MATKAN, 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, [online] 1(1), pp.7-27. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=273015.



 
 
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