COMPARE THE MAXIMUM LIKELIHOOD AND ARTIFICIAL NEURAL NETWORKS EVALUATE THE CHANGES USING LANDSAT SATELLITE IMAGES IN MANGROVE FORESTS IN THE GANDO PROTECTED AREA, SISTAN-BALUCHISTAN PROVINCE

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TAGHAVI MOGHADAM E.; BAHRAMI SH.; AKBARI E.

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Paper Information

Journal: Journal of Wood and Forest Science and Technology Year:2016 | Volume:23 | Issue:SUPPLEMENT 1 Page(s): 23-48

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Title

COMPARE THE MAXIMUM LIKELIHOOD AND ARTIFICIAL NEURAL NETWORKS EVALUATE THE CHANGES USING LANDSAT SATELLITE IMAGES IN MANGROVE FORESTS IN THE GANDO PROTECTED AREA, SISTAN-BALUCHISTAN PROVINCE

Author(s)

TAGHAVI MOGHADAM E. | BAHRAMI SH. | AKBARI E. | Issue Writer Certificate

Keywords

CHANGES IN LAND COVERQ1

SATELLITE IMAGESQ3

MANGROVESQ1

MAXIMUM LIKELIHOODQ1

NEURAL NETWORKSQ1

GANDOQ1

Abstract

Background and objectives: Awareness of forest area changes has always been one of the world's most important environmental actions. Mangrove forests are the rarest and most beautiful coastal landscapes is Iran. Logging of mangrove, coastal forest use change on infrastructure, oil pollution and changes in hydrodynamics of the sea-coast is the most important factor threatening these plant resources. Satellite imagery and image processing techniques are the highly accurate tools for navigation and Evaluate changes in forest areas. The purpose of this study is to evaluate the efficiency of MAXIMUM LIKELIHOOD and artificial NEURAL NETWORKS to detect changes in forest of cover in GANDO protected area, Sistan and Baluchistan province.Materials and methods: GANDO is a protected area in the far south-eastern Iran, in the Persian part of goiter Gulf is very important. Because there a tremendous ecological value so mangrove forest cover and habitat for crocodiles Iranian short nozzle. This research is used of information and topographic maps, images sensors, tm, mss, ETM + Landsat, the years 1972-1995 and 2015, and algorithms (classifiers), MAXIMUM LIKELIHOOD and artificial neural network. To evaluate the accuracy of classifications used GPS devices and set ground control points and randomly and the accuracy of error is calculated with statistical parameters such as kappa coefficient and total accuracy.Results: The results show a map of the neural network algorithm is an overall accuracy of 98.32% and kappa coefficient MAXIMUM LIKELIHOOD algorithm is applied 0.9781 and maps of the overall accuracy of 92.45% and kappa coefficient 0.901. The result is more accurate results than using artificial neural network algorithm compared to MAXIMUM LIKELIHOOD algorithm results in land cover mapping.Conclusion: According to the results, although the use of neural network algorithm, higher accuracy compared with MAXIMUM LIKELIHOOD classification method offers classified Nevertheless, the method of MAXIMUM LIKELIHOOD classification accuracy of 45.92% is a good method to evaluate changes.According to calculations, dense forests of MANGROVES scattered in the region in 1972 at a rate of 4.2 square kilometers has found that the amount of change in 1995 and in 2015 to 1.4 square kilometers to 2.4 square kilometers. In general, the gains in forest area affected by global-scale climate changes. But the area has an impact on many factors such as rising sea levels and the progress of the water to the shore of the Sea of Oman and consequently increases the muddy surface which has created favorable conditions for the growth and expansion of mangrove forests. Therefore, any planning of coastal zone management and the management of wildlife and vegetation of the GANDO protected natural area is should be based on and environmental and natural considerations. So that to preserve the natural heritage of the region to exploit the maximum possibilities of the region to improve the livelihood of the inhabitants of this remote region of the country.

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APA: Copy

TAGHAVI MOGHADAM, E., BAHRAMI, SH., & AKBARI, E.. (2016). COMPARE THE MAXIMUM LIKELIHOOD AND ARTIFICIAL NEURAL NETWORKS EVALUATE THE CHANGES USING LANDSAT SATELLITE IMAGES IN MANGROVE FORESTS IN THE GANDO PROTECTED AREA, SISTAN-BALUCHISTAN PROVINCE. JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY, 23(SUPPLEMENT 1), 23-48. SID. https://sid.ir/paper/156834/en

Vancouver: Copy

TAGHAVI MOGHADAM E., BAHRAMI SH., AKBARI E.. COMPARE THE MAXIMUM LIKELIHOOD AND ARTIFICIAL NEURAL NETWORKS EVALUATE THE CHANGES USING LANDSAT SATELLITE IMAGES IN MANGROVE FORESTS IN THE GANDO PROTECTED AREA, SISTAN-BALUCHISTAN PROVINCE. JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY[Internet]. 2016;23(SUPPLEMENT 1):23-48. Available from: https://sid.ir/paper/156834/en

IEEE: Copy

E. TAGHAVI MOGHADAM, SH. BAHRAMI, and E. AKBARI, “COMPARE THE MAXIMUM LIKELIHOOD AND ARTIFICIAL NEURAL NETWORKS EVALUATE THE CHANGES USING LANDSAT SATELLITE IMAGES IN MANGROVE FORESTS IN THE GANDO PROTECTED AREA, SISTAN-BALUCHISTAN PROVINCE,” JOURNAL OF WOOD AND FOREST SCIENCE AND TECHNOLOGY, vol. 23, no. SUPPLEMENT 1, pp. 23–48, 2016, [Online]. Available: https://sid.ir/paper/156834/en

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