Document Type : پژوهشی
Author
Assistant professor, University of Zanjan
Abstract
The purpose of this research is to investigate the relationship between LST and LULC in Heyran region. LULC indices consist of normalized difference of vegetation index (NDVI), normalized difference of built-up index (NDBI) and modified normalized difference of water index (MNDWI). The area of the studied area is 156.95 square kilometers, out of which in 1401, about 122.7 square kilometers are dedicated to forest area and only 33.2 square kilometers are dedicated to agricultural land. The values of the MNDWI index in the richest region in 2017 had an area equal to 12.27 square kilometers and faced a sharp decrease in 1401 and reached 1.68 square kilometers. Built-up areas (NDBI) increased until 2017 and decreased significantly until 1401. The maximum land surface temperature (LST) has reached from 35.42 degrees Celsius in 2013 to 39.04 degrees Celsius in 1401. The area with a temperature of 20 to 25 degrees Celsius has increased from 67.9 square kilometers to 124 square kilometers. Finally, Pearson correlation relationships showed that NDVI and MNDWI index had a negative correlation with LST index and there was a positive correlation between LST index and NDBI index. The highest positive correlation of 0.77 between LST and NDBI belongs to the spring of 2017, and the highest negative correlation of -0.71 belongs to the MNDWI and LST index, which was registered in the fall of 2017.
Keywords
Main Subjects
Abedini, M., Chale, E. (2021). Detection of land use change using object oriented and pixel base tchniques (case study: Mordagh chai basin), Hydrogeomorphology, 8(27): 163- 184.
Abedin, M., Ghale, E., Aghazadeh, N., & mohamadzadeh sheshegaran, M. (2022). Monitoring the surface temperature and studying the land use relationship with surface temperature using OLI and TM image sensors (Case study: Meshginshahr city), Journal of applied researches in Geographical sciences, 22 (67) :375-393
Adeyeri, O.E., Akinsanola, A.A., & Ishola, K.A. (2017). Investigating surface urban heat island characteristics over Abuja, Nigeria: relationship. Remote Sens, Appl. Soc. Environ, 7, 57–68.
Ahmed, B., Kamruzzaman, M., Zhu, X., Rahman, M.S., & Choi, K. (2013). Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh, Rem. Sens, 5, 5969–5998.
Alimoradi, S., khoorani, A., & esmaeilpoor, Y. (2017). Dynamics of vegetation in Karun watershed within Khuzestan province in relation with Temperature factors and precipitation, Journal of applied researches in Geographical sciences, 17 (44) :155-177
Al-Hameedi, W.M.M., Chen, J., Faichia, C., Al-Shaibah, B., Nath, B., Kafy, A.-A., Hu, G., & Al-Aizari, A. (2021). Remote sensing-based urban sprawl modeling using multilayer perceptron neural network Markov chain in Baghdad, Iraq. Rem, Sens, 13, 4034.
Asghari, S., Sadeghi, A., Molanouro, E. (2023). Investigation of changes in snow cover and surface temperature with topographic component of elevation case study (Urmia Lake catchment), Hydrogeomorphology, 10(34): 53- 75.
Damavandi, A., Rahimi, M., & Yazdani, M. (2016). Spatial monitoring of agricultural Drought through time series of NDVI and LST indices of MODIS data (case study: Markazi Province), Scientific- research Quarterly of Geographical Data (SEPEHR), 25(99): 115- 126.
Das, N., Mondal, P., Sutradhar, S., & Ghosh, R. (2021). Assessment of variation of land use/ land cover and its impact on land surface temperature of Asansol subdivision. Egyptian J. Rem. Sens, Space Sci, 24 (1), 131–149.
Dhar, R.B., Chakraborty, S., Chattopadhyay, R., & Sikdar, P.K. (2019). Impact of land-use/ land-cover change on land surface temperature using satellite data: a case study of rajarhat block, north 24-parganas district, West Bengal, J. Indian Soc. Rem. Sens, 47, 331–348.
Fattah, M., & Morshed, S.R. (2022). Assessment of the responses of spatiotemporal vegetation changes to climatic variability in Bangladesh. Theor, Appl. Climatol, 148, 285–301.
Fattah, M., Morshed, S.R., & Morshed, S.Y. (2021). Multi-layer perceptron-Markov chainbased artificial neural network for modelling future land-specific carbon emission pattern and its influences on surface temperature, SN Appl. Sci, 3, 359.
Feizizadeh, B., DIdeban, K., & Gholamnia, K. (2016). Extraction of land surface temperature (LST) based on landsat satellite images and slit window Algorithm study area: Mahabad Catchment, Scientific- Research Quarterly of Geographical data, 25(98): 171- 181.
Gazi, A., & Mondal, I. (2018). Urban heat island and its effect on dweller of Kolkata metropolitan area using geospatial techniques. Int, J Comput Sci Eng, 6 (10), 778–794.
Georgiana, G., & Urițescu, B. (2019). Land use/land cover changes dynamics and their effects on surface urban heat island in Bucharest, Romania. Int, J Appl Earth Obs Geoinf, 80, 115–126.
Heidari, M A., & Tavakoli, A. (2017). Analyzing of the Relationship Between Land Surface Temperature Temporal Changes and Spatial Pattern of Land Use changes, MJSP, 21 (3) :119-144
Hosseini Chamani, F., Farrokhian Firuzi, A., & Amerykhah, H. (2019). Perotransfer function (PTF) for estimation soil moisture using NDVI, land surface temperature (LST) and normalized moisture (NDMI) indices, Journal of water and soil conservation, 26(4): 239-254.
Huang, G.L., & Cadenasso, M.L. (2016). People, landscape, and urban heat island: dynamics among neighborhood social conditions, land cover and surface temperatures, Landsc Ecol, 31, 2507–2515.
Imran, H.M., Hossain, A., & Islam, A.K.M.S. (2021). Impact of land cover changes on land surface temperature and human thermal comfort in Dhaka city of Bangladesh, Earth Syst Environ, 5, 667–693.
Jamali, Z., ownegh, M., & salman mahini A R. (2019). Investigation the relationship between surface temperature and land use and Normalized Difference Vegetation Index in Gorgan plain, MJSP, 23 (3) :175-194
Kafy, A.A. (2021). Impact of Vegetation Cover Loss on Surface Temperature and Carbon Emission in a Fastest-Growing City, Cumilla, Bangladesh, 207. Building and Environment.
Kafy, A.A. (2022). Predicting the Impacts of Land Use/land Cover Changes on Seasonal Urban thermal Characteristics Using Machine Learning Algorithms, 217. Building and Environment.
Kumar, M., Mondal, I., & Pham, Q.B. (2021). Monitoring Forest Landcover Changes in the Eastern Sundarban of Bangladesh from 1989 to 2019. Acta Geophys, 69. Springer, 561577.
Lemonsu, A., Kounkou-Arnaud, R., Desplat, J., Salagnac, J.-L., & Masson, V. (2013). Evolution of the Parisian urban climate under a global changing climate, Clim Chang, 116, 679–692.
Li, X., Zhou, Y., Asrar, G.R., & Imhoff, M., Li, X. (2017). The surface urban heat island response to urban expansion: a panel analysis for the conterminous United States. Sci. Total Environ. 605, 426–435.
Lowe, S.A. (2016). An energy and mortality impact assessment of the urban heat island in the US. Environ, Impact Assess Rev, 56, 139–144.
Maithani, S., Nautiyal, G., & Sharma, A. (2020). Investigating the effect of lockdown during COVID-19 on land surface temperature: study of Dehradun city, India, J Indian Soc Rem Sens, 48, 1297–1311.
Mondal, I., Thakur, S., Ghosh, P.B., & De, T.K. (2021). Assessing the Impacts of Global Sea level rise (SLR) on the Mangrove Forests of Indian Sundarbans using Geospatial Technology, Geographic Information Science for Land Resource Management, 11, Wiley, 209–228.
Mondal, I., Thakur, S., Ghosh, P.B., De, T.K., & Bandyopadhyay, J. (2018). Land use/land cover modeling of sagar island, India using remote sensing and GIS techniques. In: Springer Advances in Intelligent Systems and Computing (AISC), Emerging Technologies in Data Mining and information Security, 755, 771–785.
Morshed, S.R., Fattah, M.A., Haque, M.N., & Morshed, S.Y. (2021). Future ecosystem service value modeling with land cover dynamics by using machine learning based Artificial Neural Network model for Jashore city, Bangladesh, Phys Chem Earth 103021. Parts A/B/C.
Nadizadeh Shorabeh, S., Hamzeh, S., Kiavarz, M., & Afsharipoor, S. (2018). Effects of spatial land use changes and urban development on the increase of land use temperature using landsat multi temporal images (case study: Gorgan city), Geographical Urban Planning Research, (3), 545- 568.
Niliyeh Brojeni, M., & Ahmadi Nadoushan, M. (2020). The relationship between urban vegetation and land surface temperature in Isfahan city using Landsat TM and OLI satellite image and LST index, Environmental Sciences, 17(4), 168- 178.
Obilor, E.I., & Amadi, E.C. (2018). Test for significance of Pearson’s correlation coefficient(r). Int. J. Innov. Math. Stat. Energy Pol. 6 (1), 11–23.
Sedgwick, P. (2014). Spearman’s rank correlation coefficient. BMJ 349.
Steeneveld, G.J., Klompmaker, J.O., Groen, R.J.A., & Holtslag, A.A.M. (2018). An urban climate assessment and management tool for combined heat and air quality judgements at neighborhood scales, Resour Conserv Recycl, 132, 204–217.
Thakur, S., Mondal, I., Bar, S., Nandi, S., Das, P., Ghosh, P.B., & De, T.K. (2020). Shoreline changes and its impact on the mangrove ecosystems of some Islands of Indian Sundarbans, North- East coast of India, J Clean Prod, 284, 124764. Elsevier, (2020).
Thakur, S., Mondal, I., Ghosh, P.B., Das, P., & De, T.K. (2019). A review of the application of multispectral remote sensing in the study of mangrove ecosystems with special emphasis on image processing techniques, J Spat Info Res. Springer Nature.
Zhang, Y. (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 J Geosci, 9, 1–14
Zhang, Y. (2013). NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: a case study in the Koshi River Basin in the middle Himalayas, Global Planet Change 139–148
Zhi, Y., Shan, L., Ke, L., & Yang, R. (2020). Analysis of land surface temperature driving factors and spatial heterogeneity research based on geographically weighted regression model, Complexity, 2020, 2862917.
)