Assessing the relationship between land surface temperature with vegetation and water area change in Arsanjan county, Iran

Subject Areas : Agriculture, rangeland, watershed and forestry

Ali Ebrahimi ¹ (PhD Student of Watershed Management Science and Engineering, Department of Forest, Rangeland and Watershed Management, Faculty of Natural Resources and Environment, Islamic Azad University, Science and Research Branch, Tehran, Iran)
Baharak Motamedvaziri ² (Assistant Professor, Department of Forest, Rangeland and Watershed Management, Faculty of Natural Resources and Environment, Islamic Azad University, Science and Research Branch, Tehran, Iran)
Seyed Mohammad Jafar Nazemosadat ³ (Professor, Atmospheric & Oceanic Research Center, Department of Water Engineering, University of Shiraz, Iran)
Hassan Ahmadi ⁴ (Professor, Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran)

Received: 2020-06-12 Accepted : 2020-10-08 Published : 2020-12-21

Keywords: Normalized difference vegetation index (NDVI), Land surface temperature (LST), Bakhtegan, Landsat, Arsanjan,

Abstract :

Background and ObjectiveLand cover and soil moisture changes have a significant impact on land surface temperature (LST). Therefore, LST can be used to study land cover and desertification changes. Arsanjan County, which is located in the northeast of Fars province, has a relatively good forest and rangeland. Unfortunately, excessive harvesting of the groundwater resources and also reduced precipitation in this area caused to decrease water levels and dried up many wells in this area during recent years. So the area of the farmland and Bakhtegan Lake has decreased in this region during the last decades. However, so far, the condition of the LST and its relationship with land cover changes has not been assessed in Arsanjan County. In this study, spatial-temporal changes of LST and its relationship with vegetation and the water area of Bakhtegan Lake have been studied. Materials and Methods The eleven images related to Level-1 data of Landsat satellite was taken from 2003 to 2018. Since the vegetation situation in the study area is in the best vegetation and water area condition in April and May, so the images related to these months were selected to check the fluctuation of vegetation cover and water level of Bakhtegan Lake. The data pre-processing was performed in three sections: geometric, radiometric and atmospheric correction by ENVI software. The FLAASH algorithm, which is one of the best methods of atmospheric correction, was applied for atmospheric correction. In this study, NDVI was used to estimate the amount of vegetation. The Planck algorithm method was applied to calculate the LST. The change detection process was done using the index differencing method. To classify the LST map and the temporal-spatial changes, the LST difference map was normalized. Then, the normalized image was categorized using the standard deviation parameter in five temperature classes. Results and Discussion In the present study, 11 Landsat images were examined to investigate the spatial-temporal changes in land coverage and LST and the relationship between these two parameters from 2003 to 2018. The NDVI mean value was 0.25 in 2003, which decreased to 0.18 in 2018. On the other hand, the LST mean value had an upward trend as it increased from 29℃ in 2003 to 41.7℃ in 2018. The NDVI mean value was 0.66 in the farmland in 2003, however, its value reached to 0.33 in 2018. In contrast, LST mean value increased in the farmland from 20.9℃ in 2003 to 39.5.5℃ in 2018. Also, the LST mean value in the lake area increased from 20.1℃ in 2003 to 36.5 in 2018. Based on the results, the NDVI mean value in the rangeland and farmland decreased by 0.07 and 0.33, respectively, in 2018. However, due to the positive relationship between NDVI and LST in water-covered areas, the NDVI mean value increased by 0.39 in Bakhtegan Lake area in 2018. In contrast, the LST mean value in the rangeland, farmland and Bakhtegan Lake increased by 12.7℃, 18.6℃ and 16.4℃, respectively, in 2018 compared to 2003. The results indicated a negative relationship between NDVI and LST (R2= 0.862). The LST value decreases by increasing NDVI value in the vegetated area. In contrast, there was a positive correlation between NDVI and LST in salt-marshes and barren areas. According to the results, the highest negative correlation was obtained for the farmland, which was  -0.94. The reason for this high correlation can be related to the high density of vegetation cover in agricultural areas. The low negative correlation between NDVI and LST in the rangeland indicates the low vegetation density in rangeland and forest area. In order to study the area of decrease or increase of LST in the farmland, rangeland and water classes, the LST difference map was classified to five categories including very low temperature, low temperature, medium temperature, high temperature and very high temperature. According to the result of LST classification, the highest area was related to the moderate temperature class in all land covers, so that the highest area of this temperature class was associated with the rangeland by 86733 hectares. Since the vegetation density, especially in the farmland, had a significant decrease in 2018 compared to 2003, the area of high and very high-temperature classes increased in 2018, so that their area reached to 4625 ha and 7192 ha, respectively, in the farmland. Also, since the water area of the lake decreased in 2018 compared to 2003, the area of high and very high-temperature classes in these classes reached to 1824 ha and 3919 ha, respectively. Conclusion According to the results, the NDVI mean value in 2018 decreased in the farmland and rangeland and increased in the Bakhtegan Lake area. In contrast, the LST increased in the mentioned areas. The results of the LST classification showed that the highest amount of LST change is related to the moderate temperature class. Since the vegetation density, especially in the agricultural area, had a significant decrease in 2018 compared to 2003, the results showed that the area of high and very high temperatures had a higher increase than low and very low temperatures. Also, since the lake's water level decreased in 2018 compared to 2003, the area of high and very high temperatures in these classes increased. The findings show that there is a negative correlation between vegetation and land surface temperatures.

References:

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 (Journal of Applied RS & GIS Techniques in Natural Resource Science), 6(1): 66-77. (In Persian).

Balew A, Korme T. 2020. Monitoring land surface temperature in Bahir Dar city and its surrounding using Landsat images. The Egyptian Journal of Remote Sensing and Space Science. doi:https://doi.org/10.1016/j.ejrs.2020.02.001.

Chu H, Venevsky S, Wu C, Wang M. 2019. NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Science of The Total Environment, 650: 2051-2062. doi:https://doi.org/10.1016/j.scitotenv.2018.09.115.

Duan S-B, Li Z-L, Li H, Göttsche F-M, Wu H, Zhao W, Leng P, Zhang X, Coll C. 2019. Validation of Collection 6 MODIS land surface temperature product using in situ measurements. Remote Sensing of Environment, 225: 16-29. doi:https://doi.org/10.1016/j.rse.2019.02.020.

Eckert S, Hüsler F, Liniger H, Hodel E. 2015. Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia. Journal of Arid Environments, 113: 16-28. doi:https://doi.org/10.1016/j.jaridenv.2014.09.001.

Fathizad H, Tazeh M, Kalantari S, Shojaei S. 2017. The investigation of spatiotemporal variations of land surface temperature based on land use changes using NDVI in southwest of Iran. Journal of African Earth Sciences, 134: 249-256. doi:https://doi.org/10.1016/j.jafrearsci.2017.06.007.

Govil H, Guha S, Diwan P, Gill N, Dey A. 2020. Analyzing linear relationships of LST with NDVI and MNDISI using various resolution levels of Landsat 8 OLI and TIRS data. In:  Data Management, Analytics and Innovation. Springer, pp 171-184. https://doi.org/110.1007/1978-1981-1032-9949-1008_1013.

Guha S, Govil H. 2020. An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability. doi:10.1007/s10668-020-00657-6.

Khan N, Shahid S, Chung E-S, Kim S, Ali R. 2019. Influence of surface water bodies on the land surface temperature of Bangladesh. Sustainability, 11(23): 6754. doi:https://doi.org/10.3390/su11236754.

Kianisalmi E, Ebrahimi A. 2019. Assessing the impact of urban expansion and land cover changes on land surface temperature in Shahrekord city. Journal of RS and GIS for Natural Resources (Journal of Applied RS & GIS Techniques in Natural Resource Science), 9(4): 102-118. (In Persian).

Lamchin M, Lee W-K, Jeon SW, Wang SW, Lim CH, Song C, Sung M. 2018. Long-term trend and correlation between vegetation greenness and climate variables in Asia based on satellite data. Science of The Total Environment, 618: 1089-1095. doi:https://doi.org/10.1016/j.scitotenv.2017.09.145.

Lee PS-H, Park J. 2020. An Effect of Urban Forest on Urban Thermal Environment in Seoul, South Korea, Based on Landsat Imagery Analysis. Forests, 11(6): 630. doi:https://doi.org/10.3390/f11060630.

Liaqut A, Younes I, Sadaf R, Zafar H. 2019. Impact of urbanization growth on land surface temperature using remote sensing and GIS: a case study of Gujranwala City, Punjab, Pakistan. International Journal of Economic and Environmental Geology: 44-49. doi:https://doi.org/10.46660/ijeeg.Vol0.Iss0.0.138.

Marzban F, Sodoudi S, Preusker R. 2018. The influence of land-cover type on the relationship between NDVI–LST and LST-Tair. International Journal of Remote Sensing, 39(5): 1377-1398. doi:https://doi.org/10.1080/01431161.2017.1402386.

Pu R, Gong P, Tian Y, Miao X, Carruthers RI, Anderson GL. 2008. Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada, USA. International Journal of Remote Sensing, 29(14): 3987-4011. doi:https://doi.org/10.1080/01431160801908095.

Rahmad R, Nurman A, Pinem K. 2019. Impact Of NDVI Change To Spatial Distribution Of Land Surface Temperature (A Study in Medan City, Indonesia). In: 1st International Conference on Social Sciences and Interdisciplinary Studies (ICSSIS 2018). Atlantis Press,  https://doi.org/10.2991/icssis-18.2019.33.

Solangi GS, Siyal AA, Siyal P. 2019. Spatiotemporal dynamics of land surface temperature and its impact on the vegetation. Civil Engineering Journal, 5(8): 1753-1763. doi:http://dx.doi.org/10.28991/cej-2019-03091368.

Sruthi S, Aslam MAM. 2015. Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data; a Case Study of Raichur District. Aquatic Procedia, 4: 1258-1264. doi:https://doi.org/10.1016/j.aqpro.2015.02.164.

Sun D, Kafatos M. 2007. Note on the NDVI‐LST relationship and the use of temperature‐related drought indices over North America. Geophysical Research Letters, 34(24). doi: https://doi.org/10.1029/2007GL031485.

Tan J, Yu D, Li Q, Tan X, Zhou W. 2020. Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China. Scientific Reports, 10(1): 9245. doi:10.1038/s41598-020-66168-6.

Walawender JP, Szymanowski M, Hajto MJ, Bokwa A. 2014. Land Surface Temperature Patterns in the Urban Agglomeration of Krakow (Poland) Derived from Landsat-7/ETM⁺ Data. Pure and Applied Geophysics, 171(6): 913-940. doi:10.1007/s00024-013-0685-7.

Wan Mohd Jaafar WS, Abdul Maulud KN, Muhmad Kamarulzaman AM, Raihan A, Md Sah S, Ahmad A, Saad SNM, Mohd Azmi AT, Jusoh Syukri NKA, Razzaq Khan W. 2020. The Influence of Deforestation on Land Surface Temperature-A Case Study of Perak and Kedah, Malaysia. Forests, 11(6): 670. doi:https://doi.org/10.3390/f11060670.

Wang S, Ma Q, Ding H, Liang H. 2018. Detection of urban expansion and land surface temperature change using multi-temporal landsat images. Resources, Conservation and Recycling, 128: 526-534. doi:https://doi.org/10.1016/j.resconrec.2016.05.011.

Wu C, Li J, Wang C, Song C, Chen Y, Finka M, La Rosa D. 2019. Understanding the relationship between urban blue infrastructure and land surface temperature. Science of The Total Environment, 694: 133742. doi:https://doi.org/10.1016/j.scitotenv.2019.133742.

Xunqiang M, Chen C, Fuqun Z, Hongyuan L. 2011. Study on temporal and spatial variation of the urban heat island based on Landsat TM/ETM⁺ in central city and Binhai New Area of Tianjin. In: 2011 International Conference on Multimedia Technology. IEEE, pp 4616-4622. https://doi.org/4610.1109/ICMT.2011.6003213.

Zareie S, Khosravi H, Nasiri A, Dastorani M. 2016. Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran. Solid Earth, 7(6): 1551. doi:https://doi.org/10.5194/se-7-1551-2016.

Zhang F, Tiyip T, Kung H, Johnson VC, Maimaitiyiming M, Zhou M, Wang J. 2016. Dynamics of land surface temperature (LST) in response to land use and land cover (LULC) changes in the Weigan and Kuqa river oasis, Xinjiang, China. Arabian Journal of Geosciences, 9(7): 499. doi:https://doi.org/10.1007/s12517-016-2521-8.