An analysis of the land use/land cover changes of Shadegan International Wetland in the last two decades
Subject Areas : Geospatial systems developmentAsma Rafei 1 , Afshin Danehkar 2 , Mehdi Zandebasiri 3 , Masoud Bagherzadekarimi 4
1 - Master of Environment, Faculty of Natural Resources, Department of Environment, University of Tehran, Karaj, Iran
2 - Professor, Department of Environment, Faculty of Natural Resources, University of Tehran, Karaj, Iran
3 - Assistant Prof., Research Division of Natural Resources, Chaharmahal and Bakhtiari Agricultural and Natural Resources Research and Education Center, AREEO, Shahrekord, I. R. Iran
4 - Head of Urmia Lake Basin, Iran Resource Management Company, Ministry of Energy, Tehran, Iran
Keywords: Land use/ Land cover changes, Change detection, Shadegan Wetland, Wildlife Refuge,
Abstract :
Background and Objective Wetlands, as one of the most sensitive ecosystems on Earth, are always facing various changes in their range, and changes in cover and use are among the most effective of these changes. The land has always been affected by human activities and uses. Those human activities that are limited to certain places and find a relatively stable position, create human uses. Therefore, analysis of wetland change has become a management priority. land use/land cover (LULC) plays a key role in the study of environmental developments at the local, regional and global levels. Human activity and change in the Earth's surface lead to changes in the structure and ecological processes of the Earth's natural systems. These changes mainly affect the main aspects of land functions (including energy balance, water, soil, and food network). In addition, pressure on natural resources, which is due to the human need for environmental resources and is often influenced by population growth drivers, leads to changes in the Earth's surface. Landscape changes due to human interventions lead to different developments and trends in land use/land cover. Therefore, time/coverage analysis is very important for understanding and routing spatial changes from the past to the present and planning for the future. Today, high-resolution multispectral and multi-temporal satellite data are used as an essential tool for estimating aspects such as vegetation, deforestation, and urban sprawl. Remote sensing and GIS technology provide a platform for studying landscape deformation across the earth's surface. Remote sensing data provide valuable information in a relatively short time and cost-effectively. High-resolution satellite imagery or aerial photographs can be used to study land use/land cover changes in different ecosystems and areas. The fact that Shadegan Wetland is one of the international wetlands in the country, which is currently on the Montreux list due to human interventions, can assess the developments around the wetland, especially in the process and type of land use/land cover changes, in identifying the drivers The main impact on this wetland is associated with its practical importance and helping to remove this wetland from the Montreux list. And waterfront can be used to adjust the exit bill of this wetland from the Montreux list. In this study, integrated remote sensing and GIS methods have been used to detect land use/land cover changes in the enclosed area and affect Shadegan wetland.Materials and Methods The study area is located in Shabangan Wetland, surrounded by the Ozon Plain. Due to the immediate man-made effects on Shadegan Wetland, especially the role of the surrounding roads and waterways, this area was closed on the latest Google Earth satellite images and then transferred to the layers used. In this area, the international distance is 48 degrees and 19 minutes and 16 seconds to 49 degrees and 3 minutes and 44 seconds and the northern latitude is 29 degrees and 55 minutes, 44 seconds to 38 degrees, 28 minutes and 42 seconds at a distance of about 60 kilometres. It is located south of Ahvaz, the capital of the province, and 5 km south of Shadegan. In this research, images of the 20 years of the Landsat satellite from the years 1999-to 2019 have been used. ENVI software is also used to classify images. After preprocessing and making the relevant corrections using the supervised classification method and the algorithm, the maximum likelihood of processing and highlighting the images was done, and also the kappa accuracy and coefficient of each layer were estimated for accuracy. Then, the preparation of cover and land use maps included different classes of natural land cover and human land uses. In the detection, the most important changes were made around the Shadegan wetland, so in this process, major changes in the existing classes were considered. To detect changes, the Change Detection method was used in ENVI software, which can provide complete information on changes in land use/land cover types. Land use changes were selected in 5 periods with a time interval of 20 (2019-1999).Results and Discussion Five-time periods of satellite data on the use and coverage of Shadegan Wetland in the years 2017, 2014, 2001, 1999, and 2019 were prepared after pre-processing and making relevant corrections using the supervised classification method and the maximum probability of processing and highlighting algorithm. Pictures were taken. The Kappa coefficient and the overall accuracy coefficient were used to evaluate the accuracy of the generated maps and according to the results, the 2019 data had the highest kappa coefficient and the highest overall accuracy. According to land cover and land use classes, the land use/land cover map of the study area was prepared for the mentioned five time periods. The findings of this study show that the land area of Shadegan wetland has changed from about 90,000 hectares in 2001 to about 150,000 hectares in 1999 during the 20 years ending 2019 the area of the wetland has decreased by about 40% in two years. After that, the wetland lands have increased and this increase continues gradually until today. However, despite this increase, the area of the wetland has not been provided in 1999, the area has decreased by about 16% compared to this year.Conclusion Considering the trend of bare lands without cover and saline lands, it can be concluded that these two diagrams have an inverse trend towards each other, which can be seen at this point or the intersection of the two desired covers. For this purpose, the desired cover must be obtained, which is created by runoff, so that in a period, the lands began to lose their coverage and became saline lands and salt ponds. Also, considering the increase in uncovering land in 2001 and the water trend, it can be concluded that this increase was due to the decrease in surface water. Due to the trend of saline lands in the relevant period and being in line with the water trend, if the water supply of the wetland is provided, thousands of saline’s will become natural lands. Also, the relative increase in water in recent years and the decrease in bare uncovered land, and the increase in saline land, indicate that the water that replaces bare uncovered land is saline. The two groups of land use and agricultural activity did not cause drastic changes in the study period and according to Table 4, the average percentage of changes in these two land uses was 4.5% and more than 1%, respectively, which is expected to have a significant impact on There is no process of destruction and destruction of lands around the wetland and therefore cannot be considered as a critical factor.
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Ansari A, Golabi MH. 2019. Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands – A case study: Meighan Wetland, Iran. International Soil and Water Conservation Research, 7(1): 64-70. doi:https://doi.org/10.1016/j.iswcr.2018.10.001.
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Hansen MC, Egorov A, Potapov PV, Stehman SV, Tyukavina A, Turubanova SA, Roy DP, Goetz SJ, Loveland TR, Ju J, Kommareddy A, Kovalskyy V, Forsyth C, Bents T. 2014. Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD). Remote Sensing of Environment, 140: 466-484. doi:https://doi.org/10.1016/j.rse.2013.08.014.
Hossain F, Moniruzzaman DM. 2021. Environmental change detection through remote sensing technique: A study of Rohingya refugee camp area (Ukhia and Teknaf sub-district), Cox's Bazar, Bangladesh. Environmental Challenges, 2: 100024. doi:https://doi.org/10.1016/j.envc.2021.100024.
Islam K, Jashimuddin M, Nath B, Nath TK. 2018. Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 21(1): 37-47. doi:https://doi.org/10.1016/j.ejrs.2016.12.005.
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Kemarau RA, Eboy OV. 2021. Land Cover Change Detection in Kuching, Malaysia Using Satellite Imagery. Borneo Journal of Sciences & Technology, 3(1): 61-65. doi:http://doi.org/10.3570/bjost.2021.3.1-09.
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Nasser Mohamed Eid A, Olatubara CO, Ewemoje TA, Farouk H, El-Hennawy MT. 2020. Coastal wetland vegetation features and digital Change Detection Mapping based on remotely sensed imagery: El-Burullus Lake, Egypt. International Soil and Water Conservation Research, 8(1): 66-79. doi:https://doi.org/10.1016/j.iswcr.2020.01.004.
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Prakasam C. 2010. Land use and land cover change detection through remote sensing approach: A case study of Kodaikanal taluk, Tamil nadu. International Journal of Geomatics and Geosciences, 1(2): 150-158.
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Rafei A, Danehkar A, Zandebasiri M, Bagherzadeh Karimi M. 2020. Application of Linear Planning in Measuring the Feasibility of Shadegan Wetland Indicative According to Ramsar Convention Criteria. Journal of Environmental Studies, 46(3): 421-436. (In Persian).
Rafei A, Danehkar A, Zandebasiri M, Bagherzadekarimi M. 2022. An analysis of the land use/land cover changes of Shadegan International Wetland in the last two decades. Journal of RS and GIS for Natural Resources, 13(2): 1-5. doi:http://dorl.net/dor/20.1001.1.26767082.1401.13.2.1.1. (In Persian).
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Shawul AA, Chakma S. 2019. Spatiotemporal detection of land use/land cover change in the large basin using integrated approaches of remote sensing and GIS in the Upper Awash basin, Ethiopia. Environmental Earth Sciences, 78(5): 1-13. doi:https://doi.org/10.1007/s12665-019-8154-y.
Sibanda S, Ahmed F. 2021. Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub-catchment, Zimbabwe. Modeling Earth Systems and Environment, 7(1): 57-70. doi:https://doi.org/10.1007/s40808-020-00963-y.
Soffianian A, Madanian M. 2011. Comparison of maximum likelihood and minimum distance to mean classifiers in preparing land cover map (a case study: Isfahan area). Journal of Science and Technology of Agriculture and Natural Resources, 15(57 (B)): 253-264. (In Persian).
Vivekananda G, Swathi R, Sujith A. 2021. Multi-temporal image analysis for LULC classification and change detection. European Journal of Remote Sensing, 54(sup2): 189-199. doi:https://doi.org/10.1080/22797254.2020.1771215.
Wang Y, Mitchell BR, Nugranad-Marzilli J, Bonynge G, Zhou Y, Shriver G. 2009. Remote sensing of land-cover change and landscape context of the National Parks: A case study of the Northeast Temperate Network. Remote Sensing of Environment, 113(7): 1453-1461. doi:https://doi.org/10.1016/j.rse.2008.09.017.
Yuan T, Yiping X, Lei Z, Danqing L. 2015. Land use and cover change simulation and prediction in Hangzhou city based on CA-Markov model. International Proceedings of Chemical, Biological and Environmental Engineering, 90: 108-113. doi:https://doi.org/10.7763/IPCBEE.2015.V90.17.
_||_Al-Doski J, Mansor SB, Shafri HZM. 2013. Monitoring Land Cover Changes in Halabja City, Iraq. International Journal of Sensor and Related Networks (IJSRN) Volume, 1: 20-30. http://ijsrn.info/article/IJSRNV21I103.pdf.
Ansari A, Golabi MH. 2019. Prediction of spatial land use changes based on LCM in a GIS environment for Desert Wetlands – A case study: Meighan Wetland, Iran. International Soil and Water Conservation Research, 7(1): 64-70. doi:https://doi.org/10.1016/j.iswcr.2018.10.001.
Chavez PS. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3): 459-479. doi:https://doi.org/10.1016/0034-4257(88)90019-3.
Congalton RG, Green K. 2019. Assessing the accuracy of remotely sensed data: principles and practices. CRC press. doi:https://doi.org/10.1201/9780429052729.
Hansen MC, Egorov A, Potapov PV, Stehman SV, Tyukavina A, Turubanova SA, Roy DP, Goetz SJ, Loveland TR, Ju J, Kommareddy A, Kovalskyy V, Forsyth C, Bents T. 2014. Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD). Remote Sensing of Environment, 140: 466-484. doi:https://doi.org/10.1016/j.rse.2013.08.014.
Hossain F, Moniruzzaman DM. 2021. Environmental change detection through remote sensing technique: A study of Rohingya refugee camp area (Ukhia and Teknaf sub-district), Cox's Bazar, Bangladesh. Environmental Challenges, 2: 100024. doi:https://doi.org/10.1016/j.envc.2021.100024.
Islam K, Jashimuddin M, Nath B, Nath TK. 2018. Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 21(1): 37-47. doi:https://doi.org/10.1016/j.ejrs.2016.12.005.
Jamaat A, Safaie A. 2021. Detection of land use-land cover changes in Anzali Wetland using a remote sensing-based approach. In: EGU General Assembly Conference Abstracts. pp EGU21-12119. doi:https://doi.org/12110.15194/egusphere-egu12121-12119.
Kemarau RA, Eboy OV. 2021. Land Cover Change Detection in Kuching, Malaysia Using Satellite Imagery. Borneo Journal of Sciences & Technology, 3(1): 61-65. doi:http://doi.org/10.3570/bjost.2021.3.1-09.
Kumar A, Jayappa K, Deepika B. 2010. Application of remote sensing and geographic information system in change detection of the Netravati and Gurpur river channels, Karnataka, India. Geocarto International, 25(5): 397-425. doi:https://doi.org/10.1080/10106049.2010.496004.
Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW, Coomes OT, Dirzo R, Fischer G, Folke C, George PS, Homewood K, Imbernon J, Leemans R, Li X, Moran EF, Mortimore M, Ramakrishnan PS, Richards JF, Skånes H, Steffen W, Stone GD, Svedin U, Veldkamp TA, Vogel C, Xu J. 2001. The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change, 11(4): 261-269. doi:https://doi.org/10.1016/S0959-3780(01)00007-3.
Lillesand T, Kiefer RW, Chipman J. 2015. Remote sensing and image interpretation. John Wiley & Sons. 736 p.
Mas J-F. 1999. Monitoring land-cover changes: a comparison of change detection techniques. International Journal of Remote Sensing, 20(1): 139-152. doi:https://doi.org/10.1080/014311699213659.
Matlhodi B, Kenabatho PK, Parida BP, Maphanyane JG. 2019. Evaluating land use and land cover change in the Gaborone dam catchment, Botswana, from 1984–2015 using GIS and remote sensing. Sustainability, 11(19): 5174. doi:https://doi.org/10.3390/su11195174.
Nasser Mohamed Eid A, Olatubara CO, Ewemoje TA, Farouk H, El-Hennawy MT. 2020. Coastal wetland vegetation features and digital Change Detection Mapping based on remotely sensed imagery: El-Burullus Lake, Egypt. International Soil and Water Conservation Research, 8(1): 66-79. doi:https://doi.org/10.1016/j.iswcr.2020.01.004.
Odindi J, Mhangara P, Kakembo V. 2012. Remote sensing land-cover change in Port Elizabeth during South Africa's democratic transition. South African Journal of Science, 108(5): 1-7. doi:https://hdl.handle.net/10520/EJC120714.
Pourkhabbaz HR, Yusefi KS, Salehipour F. 2015. Study of land use changes trend in Shadegan wetland using remote sensing and GIS and offering management solutions. Journal of Wetland Ecobiology, 7(3): 55-66. https://www.sid.ir/en/Journal/ViewPaper.aspx?ID=529809. (In Persian).
Prakasam C. 2010. Land use and land cover change detection through remote sensing approach: A case study of Kodaikanal taluk, Tamil nadu. International Journal of Geomatics and Geosciences, 1(2): 150-158.
Rafei A, Danehkar A. 2021. An Analysis of Montreux List Wetlands. Zist Sepehr Student Magazine, 14(1): 26-36. (In Persian).
Rafei A, Danehkar A. 2021. The natural landscape and environmental features of Shadegan wetland. Iran Nature, 6(4): 135-146. doi:http://doi.org/10.22092/IRN.2021.353048.1322. (In Persian).
Rafei A, Danehkar A, Zandebasiri M, Bagherzadeh Karimi M. 2020. Application of Linear Planning in Measuring the Feasibility of Shadegan Wetland Indicative According to Ramsar Convention Criteria. Journal of Environmental Studies, 46(3): 421-436. (In Persian).
Rafei A, Danehkar A, Zandebasiri M, Bagherzadekarimi M. 2022. An analysis of the land use/land cover changes of Shadegan International Wetland in the last two decades. Journal of RS and GIS for Natural Resources, 13(2): 1-5. doi:http://dorl.net/dor/20.1001.1.26767082.1401.13.2.1.1. (In Persian).
Richards JA. 2022. Correcting and Registering Images. In: Richards JA (ed) Remote Sensing Digital Image Analysis. Springer International Publishing, Cham, pp 31-85. doi:https://doi.org/10.1007/1978-1003-1030-82327-82326_82322.
Shawul AA, Chakma S. 2019. Spatiotemporal detection of land use/land cover change in the large basin using integrated approaches of remote sensing and GIS in the Upper Awash basin, Ethiopia. Environmental Earth Sciences, 78(5): 1-13. doi:https://doi.org/10.1007/s12665-019-8154-y.
Sibanda S, Ahmed F. 2021. Modelling historic and future land use/land cover changes and their impact on wetland area in Shashe sub-catchment, Zimbabwe. Modeling Earth Systems and Environment, 7(1): 57-70. doi:https://doi.org/10.1007/s40808-020-00963-y.
Soffianian A, Madanian M. 2011. Comparison of maximum likelihood and minimum distance to mean classifiers in preparing land cover map (a case study: Isfahan area). Journal of Science and Technology of Agriculture and Natural Resources, 15(57 (B)): 253-264. (In Persian).
Vivekananda G, Swathi R, Sujith A. 2021. Multi-temporal image analysis for LULC classification and change detection. European Journal of Remote Sensing, 54(sup2): 189-199. doi:https://doi.org/10.1080/22797254.2020.1771215.
Wang Y, Mitchell BR, Nugranad-Marzilli J, Bonynge G, Zhou Y, Shriver G. 2009. Remote sensing of land-cover change and landscape context of the National Parks: A case study of the Northeast Temperate Network. Remote Sensing of Environment, 113(7): 1453-1461. doi:https://doi.org/10.1016/j.rse.2008.09.017.
Yuan T, Yiping X, Lei Z, Danqing L. 2015. Land use and cover change simulation and prediction in Hangzhou city based on CA-Markov model. International Proceedings of Chemical, Biological and Environmental Engineering, 90: 108-113. doi:https://doi.org/10.7763/IPCBEE.2015.V90.17.