Determining the influence of population density on the land surface temperature based on remote sensing data and GIS techniques: application to Prizren, Kosovo

• Autorzy: Dushi Mimoza , Berila Albert

Identyfikatory

DOI

10.22630/srees.2324

• Języki publikacji: EN

Abstrakty

EN

The whole study was conducted for the Municipality of Prizren and aims to to determine the effect that the population density has on land surface temperature (LST). All this was achieved through the connection of land surface temperature (LST) and population density. The free Landsat 8 satellite image downloaded from the United States Geological Survey website was used and then processed using GIS and remote sensing techniques. To understand the relationship between population density and LST, we performed a regression analysis. This analysis showed a strong positive relationship with a value of r = 0.8206, emphasizing the important role that the population has in creating empowering areas that generate surface urban heat island (SUHI) effect. The results of the study clearly showed that in the northern, central, and western parts there are pixels with high LST values. This presentation corresponds with the population density, which means that it is precisely the actions of the population that help generate, display, and strengthen the harmful effect of the SUHI. The map with areas of high LST pixels are of great importance to the policymakers and urban planners of Prizren so that they can orient themselves in these areas and take all actions necessary to minimize this harmful effect which is worrying citizens. If it continues with unplanned development, the peripheral parts of Prizren are seriously endangered by the damage of the spaces which offer protection (green spaces) from the SUHI phenomenon.

Słowa kluczowe

EN

population density; LST; SUHI; GIS; remote sensing; Prizren

• Wydawca: Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
• Czasopismo: Przegląd Naukowy Inżynieria i Kształtowanie Środowiska
• Rocznik: 2022
• Tom: Vol. 31, No. 1
• Strony: 47--62
• Opis fizyczny: Bibliogr. 47 poz., mapy, rys., tab., wykr.

Twórcy

autor

Dushi Mimoza

• University of Prishtina, Faculty of Mathematics and Natural Sciences, Kosovo

• https://orcid.org/0000-0002-1853-6829+

autor

Berila Albert

• University of Prishtina, Faculty of Mathematics and Natural Sciences, Kosovo

• https://orcid.org/0000-0003-0754-269X+

Bibliografia

• Adeyeri, O. E., Akinsanola, A. A. & Ishola, K. A. (2017). Investigating surface urban heat island characteristics over Abuja, Nigeria: Relationship between land surface temperature and multiple vegetation indices. Remote Sensing Applications: Society and Environment, 7, 57–68.
• Alemu, M. M. (2019). Analysis of Spatio-Temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia. Journal of Resources and Ecology, 10 (1), 77–85.
• Berila, A. & Dushi, M. (2021). Measuring Surface Urban Heat Island in response to population density based on Remote Sensing data and GIS techniques: application to Prishtina, Kosovo. Folia Geographica, 63 (2), 38–57.
• Berila, A. & Isufi, F. (2021a). Two decades (2000–2020) measuring urban sprawl using GIS, RS and landscape metrics: a case study of Municipality of Prishtina (Kosovo). Journal of Ecological Engineering, 22 (6), 114–125.
• Berila, A. & Isufi, F. (2021b). Mapping summer SUHI and its impact on the environment using GIS and Remote Sensing techniques: a case study on Municipality of Prishtina (Kosovo). European Journal of Geography, 12 (3), 113–129.
• Buyantuyev, A. & Wu, J. (2009). Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecology, 25 (1), 17–33.
• Carrasco, R. A., Pinheiro, M. M. F., Junior, J. M., Cicerelli, R. E., Silva, P. A., Osco, L. P. & Ramos, A. P. M. (2020). Land use/land cover change dynamics and their effects on land surface temperature in the western region of the state of Săo Paulo, Brazil. Regional Environmental Change, 20 (3), 1–12.
• Chavez, P. S. (1996). Image-based atmospheric corrections – revisited and improved. Photogrammetric Engineering and Remote Sensing, 62 (9), 1025–1036.
• Clay, R., Guan, H., Wild, N., Bennett, J., Vinodkumar & Ewenz, C. (2016). Urban Heat Island traverses in the City of Adelaide, South Australia. Urban Climate, 17, 89–101.
• Despini, F., Ferrari, C., Bigi, A., Libbra, A., Teggi, S., Muscio, A. & Ghermandi, G. (2016). Correlation between remote sensing data and ground based measurements for solar reflectance retrieving. Energy and Buildings, 114, 227–233.
• Fabrizi, R., Bonafoni, S. & Biondi, R. (2010). Satellite and ground-based sensors for the urban heat island analysis in the city of Rome. Remote Sensing, 2 (5), 1400–1415.
• Gang, W., QiuPing, Z., RongBo, X. & DongSheng, G. (2019). On impacts of land use, population density and altitude on the urban heat island. Journal of Yunnan University – Natural Sciences Edition, 41 (1), 82–90.
• Guha, S. & Govil, H. (2020). Land surface temperature and normalized difference vegetation index relationship: a seasonal study on a tropical city. SN Applied Sciences, 2 (10), 1–14.
• Harlan, S. L. & Ruddell, D. M. (2011). Climate change and health in cities: impacts of heat and air pollution and potential co-benefits from mitigation and adaptation. Current Opinion in Environmental Sustainability, 3 (3), 126–134.
• Igun, E. & Williams, M. (2018). Impact of urban land cover change on land surface temperature. Global Journal of Environmental Science and Management, 4 (1), 47–58.
• Isaya Ndossi, M. & Avdan, U. (2016). Application of open source coding technologies in the production of Land Surface Temperature (LST) maps from Landsat: a PyQGIS plugin. Remote Sensing, 8 (5), 413. https://doi.org/10.3390/rs8050413
• Isufi, F., Berila, A. & Bulliqi, S. (2021). Measuring UHI using Landsat 8 OLI and TIRS data with NDVI and NDBI in Municipality of Prishtina. Disaster Advances, 14 (11), 25–36.
• Jiménez-Muńoz, J. C., Sobrino, J. A., Gillespie, A., Sabol, D. & Gustafson, W. T. (2006). Improved land surface emissivities over agricultural areas using ASTER NDVI. Remote Sensing of Environment, 103 (4), 474–487.
• Käfer, P. S., Rolim, S. B. A., Diaz, L. R., Rocha, N. S. da, Iglesias, M. L. & Rex, F. E. (2020). Comparative analysis of split-window and single-channel algorithms for land surface temperature retrieval of a pseudo-invariant target. Boletim de Cięncias Geodésicas, 26 (2), 1–17.
• Kamran, K. V., Pirnazar, M. & Bansouleh, V. F. (2015). Land surface temperature retrieval from Landsat 8 TIRS: comparison between split window algorithm and SEBAL method. Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015), 9535, 11–22.
• Landsberg, H. E. (1981). The urban climate. Cambridge, MA: Academic Press.
• Li, L., Tan, Y., Ying, S., Yu, Z., Li, Z. & Lan, H. (2014). Impact of land cover and population density on land surface temperature: case study in Wuhan, China. Journal of Applied Remote Sensing, 8 (1), 084993. https://doi.org/10.1117/1.JRS.8.084993
• Lo, C. & Faber, J. B. (1998). Integration of landsat thematic mapper and census data for quality of life assessment. Remote Sensing of Environment, 62 (2), 143–157.
• Mallick, J. (2021). Evaluation of Seasonal Characteristics of Land Surface Temperature with NDVI and Population Density. Polish Journal of Environmental Studies, 30 (4), 3163–3180.
• Mohajerani, A., Bakaric, J. & Jeffrey-Bailey, T. (2017). The urban heat island effect, its causes, and mitigation, with reference to the thermal properties of asphalt concrete. Journal of Environmental Management, 197, 522–538.
• Montanaro, M., Gerace, A., Lunsford, A. & Reuter, D. (2014). Stray light artifacts in imagery from the Landsat 8 thermal infrared sensor. Remote Sensing, 6 (11), 10435–10456.
• Morefield, P., Fann, N., Grambsch, A., Raich, W., & Weaver, C. (2018). Heat-related health impacts under scenarios of climate and population change. International Journal of Environmental Research and Public Health, 15 (11), 2438. https://doi.org/10.3390/ijerph15112438
• Nichol, J. E., Fung, W. Y., Lam, K. & Wong, M. S. (2009). Urban heat island diagnosis using ASTER satellite images and “in situ” air temperature. Atmospheric Research, 94 (2), 276–284.
• Peres, L. F., Lucena, A. J., Rotunno Filho, O. C. & Almeida França, J. R. de (2018). The urban heat island in Rio de Janeiro, Brazil, in the last 30 years using remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 64, 104–116.
• Pour, T. & Voženílek, V. (2020). Thermal data analysis for urban climate research: A case study of Olomouc, Czechia. Geographia Cassoviensis, 14 (1), 77–91.
• Roth, M., Oke, T. R., & Emery, W. J. (1989). Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology. International Journal of Remote Sensing, 10 (11), 1699–1720.
• Salih, M. M., Jasim, O. Z., Hassoon, K. I. & Abdalkadhum, A. J. (2018). Land surface temperature retrieval from LANDSAT-8 thermal infrared sensor data and validation with infrared thermometer camera. International Journal of Engineering & Technology, 7 (4.20), 608–612.
• Santamouris, M., Synnefa, A. & Karlessi, T. (2011). Using advanced cool materials in the urban built environment to mitigate heat islands and improve thermal comfort conditions. Solar Energy, 85 (12), 3085–3102.
• Sherafati, S., Saradjian, M. R. & Rabbani, A. (2018). Assessment of surface urban heat island in three cities surrounded by different types of land-cover using satellite images. Journal of the Indian Society of Remote Sensing, 46 (7), 1013–1022.
• Sobrino, J. A., Jiménez-Muńoz, J. C. & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90 (4), 434–440.
• Song, J., Chen, W., Zhang, J., Huang, K., Hou, B. & Prishchepov, A. V. (2020). Effects of building density on land surface temperature in China: Spatial patterns and determinants. Landscape and Urban Planning, 198, 103794. https://doi.org/10.1016/j.landurbpla n.2020.103794
• UN-HABITAT (2012). Prizren Municipal Development Plan 2025 Strategic Environmental Assessment (SEA) Report (draft). Kosovo. Retrieved from: http://unhabitat-kosovo.org/old/repository/docs/SEA_Prizren_draft_786745.pdf
• United States Geological Survey [USGS] (2019). Landsat 8 (L8). Data Users Handbook. LSDS-1574. Ver. 5.0. USGC EROS Center, Sioux Falls, SD. Retrieved from: https://prdwret.s3.us-west-2.amazonaws.com/assets/ palladium/production/atoms/files/LSDS1574_L8_Data_Users_Handbook-v5.0.pdf
• Universiteti Teknik i Stambollit, Urban Design Studio, Plan&Art [UTS, UDS & Plan &Art] (2012). Plani Zhvillimor i Komunës së Prizrenit 2013–2025. Retrieved from: https://www.online-transparency.org/repository/docs/Plani_Zhvillimor_i_Komunes_se_Prizrenit_2013-20251.pdf [access 15.02. 2021].
• Ursu, C. D. (2019). The Land Surface Temperature evolution (LST) using landsat scenes. Case study: the industrial platform Săvineşti. Geographia Technica, 14 (2), 131–142.
• Voogt, J. A. & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86 (3), 370–384.
• Wang, F., Qin, Z., Song, C., Tu, L., Karnieli, A. & Zhao, S. (2015). An improved mono-window algorithm for Land Surface Temperature retrieval from Landsat 8 thermal infrared sensor data. Remote Sensing, 7 (4), 4268–4289.
• Wang, H., Zhang, Y., Tsou, J. & Li, Y. (2017). Surface urban heat island analysis of Shanghai (China) based on the change of land use and land cover. Sustainability, 9 (9), 1538. https://doi.org/10.3390/su9091538
• Weng, Q., Lu, D. & Schubring, J. (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89 (4), 467–483.
• Yakar, İ. & Bilgi, S. (2019). Land Surface Temperature mapping by the use of Remote Sensing and GIS: case study of Istanbul metropolitan area. In H. M. Yilmaz et al. (eds.), X. Teknik Sempozyumu. Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği (pp. 77–81). Aksaray: Basım.
• Yuan, F. & Bauer, M. E. (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.
• Yuvaraj, R. M. (2020). Extents of Predictors for Land Surface Temperature Using Multiple Regression Model. The Scientific World Journal, 2020, 3958589. https://doi.org/10.1155/2020/3958589

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)