Demarcation and Estimation of Agricultural Lands Using ETM+ Imagery Data (Case study: Astan Ghods Razavi Great Farm)

Document Type : Research Article

Authors

F. Alipour

M. H. Aghkhani

M. H. Abbaspour-Fard

A. Sepehr

Ferdowsi University of Mashhad

https://doi.org/10.22067/jam.v4i2.34827

Abstract

Application of satellite imagery and remote sensing techniques in agriculture and other natural resources has been approved by many studies. In this study two ETM+ imagery data for May and September 2012 of Astan Ghods Razavi Great Farm were acquired to identify the boundaries of lands cultivated with different crops coverage and to create crops maps of that farm. . To classify the images, the supervised classification methods including Maximum Likelihood and artificial neural network were used. In order to compare the results of two applied classification methods, the same training and testing samples were used. To evaluate the accuracy of classification results, the produced map was compared with the ground control points extracted by GPS and local observations. Kappa coefficient and overall accuracy were estimated to be 82% and 85%, respectively by maximum likelihood method and these outputs were estimated to be 84% and 87%, respectively by neural network approach. The difference of cultivated area estimated by maximum likelihood and by neural network methods with actual measured area was 16.8% and 14.2%, respectively. The results of this study showed that satellite imagery has high capabilities to classify and estimate agricultural and cultivated areas. These data can be useful for strategic management to develop mechanization and cultivation plans.

Keywords

Maximum Likelihood

Remote sensing

ETM+

Neural Network Classification

Agricultural crops

20.1001.1.22286829.1393.4.2.12.8

Subjects

• Digital, Smart, and Precision Agriculture

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