Study of the relationship between land use and vegetation changes with the land surface temperature in Namin County
Subject Areas : Applications in earth’s climate changeAzad Kakehmami 1 , Ardavan Ghorbani 2 , Sayyad Asghari Sarasekanrood 3 , Ehsan Ghale 4 , Sahar Ghafari 5
1 - PhD. Student of Rangeland Science, Department of Natural Resources, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
2 - Professor, Department of Natural resources, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
3 - Associate Professor, Department of Natural Geography, Faculty of Humanities, University of Mohaghegh Ardabili, Ardabil, Iran
4 - PhD. Student of Geomorphology, Department of Natural Geography, Faculty of Humanities, University of Mohaghegh Ardabili, Ardabil, Iran
5 - PhD. of Rangeland Science, Department of Natural Resources, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
Keywords: land use, Vegetation, Land surface temperature, Ardabil province analysis,
Abstract :
Background and ObjectiveRapid development of cities due to extensive changes in land use and land cover has had negative effects on global environmental quality. Land cover and land use changes, and the development of urban and agricultural regions and deforestation are changing the regional and local temperature regime. Knowing the land surface temperature degrees contribute significantly to a wide range of issues relating to the Earth science such as urban climate, global environmental changes, and the study of the interaction of human and the environment. The lack of sufficient meteorological stations to be aware of temperature values in regions lacking a station is considered as a major flaw in monitoring the land surface temperature. Due to the information limitations, collecting data especially to a large extent, is associated with many problems and obstacles, and the real-time access is difficult or impossible. Therefore, the need to use remote sensing technology with time conditions along with the feature of continuity and data collection in wide ranges can be very effective. The purpose of this study is to investigate the land surface temperature of Namin county in a period of 28 years and to compare the obtained results with land use and vegetation changes. Materials and MethodsThe data used in this study included Landsat 8 satellite image of the OLI sensor in order to extract land use map and TIRS sensor image to extract land surface temperature for the year 2015. Moreover, Landsat 5 satellite image of the TM sensor were used to extract land use map by using visible and infrared bands, and also to extract land surface temperature by using thermal bands for the year 1987. Images were taken in late spring and early summer due to the lack of high cloudy and snowy covers , as well as the high intensity of sunlight. The eCognition8.9 software was used for object-based classification. Classification in five classes (dry and irrigated farming, rangeland, forest and residential) and six classes (dry and irrigated farming, rangeland, forest, residential and water bodies) were selected for the years 1987 and 2015 respectively. To assess the accuracy and comparison of the obtained maps, the error matrix, overall accuracy, and kappa statistics were used. Split-Window method was used to extract the land surface temperature of the study area. Finally, in order to analyze the relationship between land surface temperature with vegetation index, the correlation coefficients between land surface temperature and vegetation index were calculated based on land use types in the years 1987 and 2015. Results and Discussion The highest land use area in the years 1987 and 2015 belongs to the rangeland use with 43781 and 34114 hectares respectively and the second land use area belongs to dry farming use with 23854 and 33277 hectares respectively. Moreover due to the lack of water use , the lowest land use area in 1987 belongs to residential use with 1301 hectares, while in 2015 with the construction of water structures, water use with an area of 86 hectares has the lowest land use area. The highest land use area increase was in the dry farming with 9423 hectares, which is a significant increase compared to 1987. The highest recorded temperature for Namin county in 1987 and 2015 was related to dry farming use (34°C and 27°C, respectively), indicating the concentration of heat in these regions. This type of land use has the highest temperature due to the factors such as the dryness of the products at this time and the harvest of the products. In 1987, dry farming use had the highest temperature (34°C), but in 2015 it experienced a decrease in temperature (27°C), despite the fact that it had the highest land surface temperature compared to other types of land uses in 2015. The reason can be attributed to the factor of harvesting crops. Due to the fact that the rainfed crops in the study area are mostly wheat, and at this time of the season, most of the wheat is ripe or harvested, so the transpiration of these products is insignificant. The lowest recorded temperatures in Namin county are related to the uses of water bodies (21°C), forest (21°C) and irrigated farming (22°C), respectively. Since water has a high heat capacity, it has the greatest effect on reducing the temperature. In forest and irrigated farming land uses, due to the higher vegetation density, the land surface temperature has the lowest value (23°C and 24°C in 1987 and 21°C and 22°C in 2015 respectively) compared to the other land use types. Agricultural land use in this area has the lowest land surface temperature (24°C in 1987 and 21°C in 2015) after forest areas. Due to the fact that the crops cultivated in this area are plants such as potatoes and these plants have more water needs, therefore these plants have a high greenness value at June to early July, which has led to more transpiration in the area where they are cultivated than other areas, thus it has been very effective in keeping the land surface temperature cool. The rangeland use has had high land surface temperatures (27°C and 25°C, respectively) in the two study years, and there is little difference between the two years. According to the study season which was late June to early July, the high temperature of this land use type is due to the increase in the areas lacking canopy cover or areas having low or scattered vegetation. Due to the fact that in August, most of the leaves and brunches of the existing plants are dry and the transpiration is low, high temperatures are also recorded. The relationship between land surface temperature and vegetation index in rangeland use in the two study years had the highest correlation (0.91 in 1987 and 0.83 in 2015), while the correlation coefficient of the forest use was the lowest (0.46 in 1987 and 0.23 in 2015). Conclusion Land use type and land use and vegetation changes have a significant effect on land surface temperature changes. However, areas without vegetation have a higher land surface temperature than the areas with vegetation. The results showed that there was no significant correlation between vegetation cover and land surface temperature, which is mainly due to sufficient vegetation. In general, the results showed that in most areas with lower temperatures, there is high density vegetation indicating an inverse relationship between vegetation index and land surface temperature.
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Gondwe SV, Muchena R, Boys J. 2018. Detecting Land Use and Land Cover and Land Surface Temperature Change in Lilongwe City, Malawi. Journal of Remote Sensing & GIS, 9(2): 17-26.
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Veysi S, Naseri A, Hamzeh S, Moradi P. 2016. Estimation of sugarcane field temperature using Split Window Algorithm and OLI LandSat 8 satellite images. Journal of RS and GIS Techniques for Natural Resources, 7(1): 27-40. (In Persian)
Wan Z, Dozier J. 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on geoscience and remote sensing, 34(4): 892-905. doi:https://doi.org/10.1109/36.508406.
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Yuan F, Bauer ME. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3): 375-386. doi:https://doi.org/10.1016/j.rse.2006.09.003.
Zhi-qiang L, Zhou Q-g. 2011. Utility of Landsat Image in the Study of Land Cover and Land Surface Temperature Change. Procedia Environmental Sciences, 10: 1287-1292. doi:https://doi.org/10.1016/j.proenv.2011.09.206.
Aboelnour M, Engel BA. 2018. Application of remote sensing techniques and geographic information systems to analyze land surface temperature in response to land use/land cover change in Greater Cairo Region, Egypt. Journal of Geographic Information System, 10(1): 57-88. doi:https://doi.org/10.4236/jgis.2018.101003.
Ahmadi B, Ghorbani A, Safarrad T, Sobhani B. 2015. Evaluation of surface temperature in relation to land use/cover using remote sensing data. Journal of RS and GIS for Natural Resources, 6(1): 61-77. (In Persian)
Ahmadi M, Ashorlo D, Narangifard M. 2013. Temporal–spatial variation and thermal patterns, using ETM+ and TM data for Shiraz city. Iranian Journal of Remote Sencing and GIS, 4(4): 55-67. (In Persian)
Akbari E, Ebrahimi M, Fiezizadeh B, Nezhadsoleimani H. 2016. Evaluating Land Surface Temperature related to the Land use Change Detection by Satellite Image (Case study: Taleghan Basin). Journal of Geography and Environmental Planning, 26(4): 151-170. (In Persian)
Asghari Saraskanroud S, Emami H. 2019. Monitoring the earth surface temperature and relationship land use with surface temperature using of OLI and TIRS Image. Journal of Researches in Geographical Sciences, 19(53): 195-215. doi:https://doi.org/10.29252/jgs.19.53.195. (In Persian)
Du Y, Wu D, Liang F, Li C. 2013. Integration of case-based reasoning and object-based image classification to classify SPOT images: a case study of aquaculture land use mapping in coastal areas of Guangdong province, China. GIScience & Remote Sensing, 50(5): 574-589. doi:https://doi.org/10.1080/15481603.2013.842292.
Feizizadeh B, Didehban K, Gholamnia K. 2016. Extraction of Land Surface Temperature (LST) based on landsat satellite images and split window algorithm Study area: Mahabad Catchment. Journal of Geographical Data, 98: 171-182. (In Persian)
Ganasri BP, Dwarakish GS. 2015. Study of Land use/land Cover Dynamics through Classification Algorithms for Harangi Catchment Area, Karnataka State, INDIA. Aquatic Procedia, 4: 1413-1420. doi:https://doi.org/10.1016/j.aqpro.2015.02.183.
García-Haro F, Camacho-de Coca B, Meliá J, Martínez B. 2005. Operational derivation of vegetation products in the framework of the LSA SAF project. EUMETSAT Meteorological Satellite Conference. Dubrovnik (Croatia). September 19-23 (Eumetsat Publ.: Darmstad). In., pp 1-6.
Ghorbannia Kheybari V, Mirsanjari M, Liaghati H, Armin M. 2017. Estimating land surface temperature of land use and land cover in Dena county using single window algorithm and landsat 8 satellite data. Journal of Environmental Sciences, 15(2): 55-74. (In Persian)
Gondwe SV, Muchena R, Boys J. 2018. Detecting Land Use and Land Cover and Land Surface Temperature Change in Lilongwe City, Malawi. Journal of Remote Sensing & GIS, 9(2): 17-26.
Heidari MA, Tavakoli A. 2017. Analyzing of the Relationship Between Land Surface Temperature Temporal Changes and Spatial Pattern of Land Use changes. The Journal of Spatial Planning, 21(3): 119-144. (In Persian)
Howarth PJ, Wickware GM. 1981. Procedures for change detection using Landsat digital data. International Journal of Remote Sensing, 2(3): 277-291. doi:https://doi.org/10.1080/01431168108948362.
Jensen JR. 2015. Remote sensing and digital image processing. Introductory Digital Image Processing A Remote Sensing Perspective: 1-34.
Johnson B, Tateishi R, Kobayashi T. 2012. Remote sensing of fractional green vegetation cover using spatially-interpolated endmembers. Remote Sensing, 4(9): 2619-2634. doi:https://doi.org/10.3390/rs4092619.
Kakeh Mami A, Ghorbani A, Kayvan Behjoo F, Mirzaei Mosivand A. 2017. Comparison of visual and digital interpretation methods of land use/cover mapping in Ardabil province. Journal of RS and GIS for Natural Resources, 8(3): 121-134. (In Persian)
Lillesand T, Kiefer RW, Chipman J. 2015. Remote sensing and image interpretation. John Wiley & Sons, 736p.
Lu D, Li G, Moran E, Freitas C, Dutra L, Sant’Anna S. 2012. A comparison of maximum likelihood classifier and object-based method based on multiple sensor datasets for land-use/cover classification in the Brazilian Amazon. Proceedings of 4th Geographic Object-Based Image Analysis (GEOBIA), Rio de Janeiro, Brazil: 7-9.
Mir Yaghoubzadeh M, Ghanbarpour M. 2009. The application of remote sensing data in land surface temperature estimation (A case study of the Westin watershed, East Azerbaijan). Journal of Rangeland, 4(2): 723-734. (In Persian)
Nduati EW, Murimi NM, Mundia CN. 2013. Effects of vegetation change and land use/land cover change on land surface temperature in the mara ecosystem. International Journal of Science and Research, 8(2): 22-28.
Rajeshwari A, Mani N. 2014. Estimation of land surface temperature of Dindigul district using Landsat 8 data. International Journal of Research in Engineering and Technology, 3(5): 122-126.
Sobrino JA, Jiménez-Muñoz JC, Paolini L. 2004. Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90(4): 434-440. doi:https://doi.org/10.1016/j.rse.2004.02.003.
Stehman SV. 2004. A critical evaluation of the normalized error matrix in map accuracy assessment. Photogrammetric Engineering & Remote Sensing, 70(6): 743-751. doi:https://doi.org/10.14358/PERS.70.6.743.
USGS L. 2015. 8 (L8) data users handbook. USGS: Reston, VA, USA, URL:http://earthexplorerusgsgov.
Valizadeh Kamran K, Gholamnia K, Eynali G, Moosavi M. 2017. Estimation land surface temperature and extract heat islands using split window algorithm and multivariate regression analysis (Case Study of Zanjan). Journal of Research and Urban Planning, 30(8): 33-50. (In Persian)
Veysi S, Naseri A, Hamzeh S, Moradi P. 2016. Estimation of sugarcane field temperature using Split Window Algorithm and OLI LandSat 8 satellite images. Journal of RS and GIS Techniques for Natural Resources, 7(1): 27-40. (In Persian)
Wan Z, Dozier J. 1996. A generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Transactions on geoscience and remote sensing, 34(4): 892-905. doi:https://doi.org/10.1109/36.508406.
Xiong Y, Huang S, Chen F, Ye H, Wang C, Zhu C. 2012. The impacts of rapid urbanization on the thermal environment: A remote sensing study of Guangzhou, South China. Remote sensing, 4(7): 2033-2056. doi:https://doi.org/10.3390/rs4072033.
Yuan F, Bauer ME. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment, 106(3): 375-386. doi:https://doi.org/10.1016/j.rse.2006.09.003.
Zhi-qiang L, Zhou Q-g. 2011. Utility of Landsat Image in the Study of Land Cover and Land Surface Temperature Change. Procedia Environmental Sciences, 10: 1287-1292. doi:https://doi.org/10.1016/j.proenv.2011.09.206.