Land Use Change Monitoring Using Landsat Satellite Image Data (Case study: Khan Mirza Plain)

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KARIMIAN kiyan; AMINI ABBAS; BAGHERI MOHSEN; GHAIUMI MOHAMMADI HAMID

Title

Land Use Change Monitoring Using Landsat Satellite Image Data (Case study: Khan Mirza Plain)

Author(s)

KARIMIAN kiyan | AMINI ABBAS | BAGHERI MOHSEN | GHAIUMI MOHAMMADI HAMID | Issue Writer Certificate

Keywords

Khan Mirza PlainQ1

Land Use ChangeQ1

LandsatQ1

Satellite imagesQ1

monitoringQ1

Abstract

Accurate and real time information on land use and land cover and their changes is very important in urban management decisions, ecosystem monitoring and urban planning. In recent decades, widespread changes in land use of the Khan Mirza Plain as one of the northern Karun watersheds have occurred, that need to monitoring these changes. In this study, Landsat 5, 7 and 8 Satellite images and TM, ETM, and OLI sensors for the period of 1996, 2006, and 2016 were used to produce of land use and land cover map of Khan Mirza Plain by four methods: maximum likelihood, artificial neural network, minimum distance and Mahalanobis distance and theirs Kappa coefficient were evaluated. The results of the evaluation of the accuracy of these two methods by using Kappa coefficients have shown that the artificial neural network algorithm is more accurate than the maximum likelihood algorithm. Also, by results of two algorithms of artificial neural network and maximum likelihood with an overall accuracy of 90. 29 and 86. 79, all of land cover maps were classified in six classes (agriculture, rangeland, residential area, rocky and bare lands, gardens and flatlands). The analysis of the classifications showed that agricultural and residential classes had a rising trend, 62. 5% and 3. 5%, respectively, and rangeland, rocky and bare lands and flatlands were decreased. The largest change is related to the conversion of rocky and bare lands class to the agricultural class, which 1673 hectares of rocky and bare lands in 2006 changes into agricultural lands in 2016. Another obvious Land Use Change in this area, are change of rangelands into residential areas, which 40. 8 ha of rangelands changed into residential area. In overall, this research showed that the best way to produce of land use map in the study area is to use artificial neural network algorithm. According to the results, it is suggested using this method to produce of Land Use Change map for this region. Accurate and real time information on land use and land cover and their changes is very important in urban management decisions, ecosystem monitoring and urban planning. In recent decades, widespread changes in land use of the Khan Mirza Plain as one of the northern Karun watersheds have occurred, that need to monitoring these changes. In this study, Landsat 5, 7 and 8 Satellite images and TM, ETM, and OLI sensors for the period of 1996, 2006, and 2016 were used to produce of land use and land cover map of Khan Mirza Plain by four methods: maximum likelihood, artificial neural network, minimum distance and Mahalanobis distance and theirs Kappa coefficient were evaluated. The results of the evaluation of the accuracy of these two methods by using Kappa coefficients have shown that the artificial neural network algorithm is more accurate than the maximum likelihood algorithm. Also, by results of two algorithms of artificial neural network and maximum likelihood with an overall accuracy of 90. 29 and 86. 79, all of land cover maps were classified in six classes (agriculture, rangeland, residential area, rocky and bare lands, gardens and flatlands). The analysis of the classifications showed that agricultural and residential classes had a rising trend, 62. 5% and 3. 5%, respectively, and rangeland, rocky and bare lands and flatlands were decreased. The largest change is related to the conversion of rocky and bare lands class to the agricultural class, which 1673 hectares of rocky and bare lands in 2006 changes into agricultural lands in 2016. Another obvious Land Use Change in this area, are change of rangelands into residential areas, which 40. 8 ha of rangelands changed into residential area. In overall, this research showed that the best way to produce of land use map in the study area is to use artificial neural network algorithm. According to the results, it is suggested using this method to produce of Land Use Change map for this region. Accurate and real time information on land use and land cover and their changes is very important in urban management decisions, ecosystem monitoring and urban planning. In recent decades, widespread changes in land use of the Khan Mirza Plain as one of the northern Karun watersheds have occurred, that need to monitoring these changes. In this study, Landsat 5, 7 and 8 Satellite images and TM, ETM, and OLI sensors for the period of 1996, 2006, and 2016 were used to produce of land use and land cover map of Khan Mirza Plain by four methods: maximum likelihood, artificial neural network, minimum distance and Mahalanobis distance and theirs Kappa coefficient were evaluated. The results of the evaluation of the accuracy of these two methods by using Kappa coefficients have shown that the artificial neural network algorithm is more accurate than the maximum likelihood algorithm. Also, by results of two algorithms of artificial neural network and maximum likelihood with an overall accuracy of 90. 29 and 86. 79, all of land cover maps were classified in six classes (agriculture, rangeland, residential area, rocky and bare lands, gardens and flatlands). The analysis of the classifications showed that agricultural and residential classes had a rising trend, 62. 5% and 3. 5%, respectively, and rangeland, rocky and bare lands and flatlands were decreased. The largest change is related to the conversion of rocky and bare lands class to the agricultural class, which 1673 hectares of rocky and bare lands in 2006 changes into agricultural lands in 2016. Another obvious Land Use Change in this area, are change of rangelands into residential areas, which 40. 8 ha of rangelands changed into residential area. In overall, this research showed that the best way to produce of land use map in the study area is to use artificial neural network algorithm. According to the results, it is suggested using this method to produce of Land Use Change map for this region.

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APA: Copy

KARIMIAN, kiyan, AMINI, ABBAS, BAGHERI, MOHSEN, & GHAIUMI MOHAMMADI, HAMID. (2020). Land Use Change Monitoring Using Landsat Satellite Image Data (Case study: Khan Mirza Plain). HUMAN GEOGRAPHY RESEARCH QUARTERLY, 52(2 ), 419-436. SID. https://sid.ir/paper/375097/en

Vancouver: Copy

KARIMIAN kiyan, AMINI ABBAS, BAGHERI MOHSEN, GHAIUMI MOHAMMADI HAMID. Land Use Change Monitoring Using Landsat Satellite Image Data (Case study: Khan Mirza Plain). HUMAN GEOGRAPHY RESEARCH QUARTERLY[Internet]. 2020;52(2 ):419-436. Available from: https://sid.ir/paper/375097/en

IEEE: Copy

kiyan KARIMIAN, ABBAS AMINI, MOHSEN BAGHERI, and HAMID GHAIUMI MOHAMMADI, “Land Use Change Monitoring Using Landsat Satellite Image Data (Case study: Khan Mirza Plain),” HUMAN GEOGRAPHY RESEARCH QUARTERLY, vol. 52, no. 2 , pp. 419–436, 2020, [Online]. Available: https://sid.ir/paper/375097/en

Scientific Information Database

ISSN: 2588-4824

Journal Paper

Land Use Change Monitoring Using Landsat Satellite Image Data (Case study: Khan Mirza Plain)