EVALUATION OF THE EFFECTS OF LAND COVER CHANGES AND URBANIZATION ON LAND SURFACE TEMPERATURE: A REMOTE SENSING STUDY OF SUB-WATERSHED OF OUED FEKAN, NORTHWEST ALGERIA
Year 2020,
Volume: 38 Issue: 2, 907 - 926, 01.06.2021
Mohammed Chraır
Abdelkader Khaldı
Mohamed Amine Hamadouche
Abderrahmane Hamımed
Flavie Cernesson
Mehmet Alkan
Abstract
Urban growth is a worldwide phenomenon. The rate of urbanisation in developing countries such as Algeria is speedy. Sub-watershed of Oued Fekan is included in the large watershed of Macta which is located in north-western Algeria and is one of the most important sites of this country characterized by an abundant amount of biodiversity as well as a highly productive ecosystem. The valuable landscape undergoes a radical change in the form of a sub-watershed recently due to anthropogenic change on land use and land cover. The exponential increase in population and human activities are increasing the demand for land and soil resources for agriculture, urban and industrial uses. Anthropogenic factors, especially urban sprawl, have a significant role in controlling the temperature change.
In this paper, four Landsat-8 OLI/TIRS images of 2018 have been used from different seasons to estimate land surface temperature (LST), Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) in order to study the phenomenon of difference distribution temperature in urban with the surrounding rural areas. Analysis based on linear regression was used to generate relationships between LST with NDVI and NDBI. Our analysis indicates that for the four seasons, a strong linear relationship between NDBI and LST was marked compared with the relationship between NDVI and LST, which was less intense and varied by seasons. We suggest that NDBI is a visible indicator for studying surface Urban Heat Island phenomenon (UHI). Useful information that occurs as a consequence of land-use changes and urbanization are then provided for understanding the local climate and environmental changes of our study area.
References
- [1] Jing, J. & Tian, G. (2010). Analysis of the impact of Land use/Land cover change on Land Surface Temperature with Remote Sensing. Procedia Environ. Sci.2,571-575. https://doi.org/10.1016/j.proenv.2010.10.062
- [2] Zhao-LiangLi& Bo-Hui Tang & Hua Wu & Hua zhong Ren &Guangjian Yan & Zheng ming Wan & Isabel F.Trigo José &A.Sobrino. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, Vol. 131, no.15, pp. 14-37.DOI:10.1016/j.rse.2012.12.008
- [3] Oke, T.R. (1973). City size and the urban heat island.Atmos. Environ. 7:769–779, http://dx. DOI.org/10.1016/0004-6981(73)90140-6.
- [4] Dousset, B.&Gourmelon, F. (2003).Satellitemulti-sensor data analysis of urban surface temperatures and land cover. ISPRS J. Photogramm. Remote Sens. 58:43–54. http://dx.DOI.org/10.1016/S0924-2716(03)00016-9.
- [5] Sun, Q.& Tan, J.& Xu, Y. (2010). An ERDAS image processing method for retrieving LST and describing urban heat evolution: a case study in the Pearl River Delta Region in South China. Environ. Earth Sci. 59 (5), 1047–1055.https://DOI.org/10.1007/s12665-009-0096-3
- [6] Liu, L.& Zhang, Y.(2011). Urban Heat Island analysis using the Landsat TM data and ASTER data: a case study in Hong Kong. Remote Sens. 3:1535–1552. DOI: 10.3390/rs3071535
- [7] Lu, Y.& Feng, P.& Shen, C.& Sun, J. (2009).Urban heat island in summer of Nanjing based on TM data. Urban Remote Sensing Joint Event, Shanghai, China: pp. 1–5.DOI.org/10.1109/URS.2009.5137628.
- [8] Buyantuyev, A.& Wu, J. (2010).Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landsc. Ecol. 25:17–33. DOI.org/10.1007/s10980-009-9402-4.
- [9] Weng, Q. (2003). Fractal analysis of satellite-detected urban heat island effect.Photogramm.Eng. Remote. Sens. 69, 555–566.https://doi.org/10.14358/PERS.69.5.555.
- [10] Weng, Q.& Lu, D.&Schubring, J.(2004).Estimation of land surface temperature e vegetation abundance relationship for urban heat island studies.Remote Sens. Environ. 89: 467–483.DOI.org/10.1016/j.rse.2003.11.005.
- [11] Balling, R.C.&Brazel, S.W. (1988). High resolution surface temperature pattern in a complex urban terrain. Photogramm. Eng. Remote. Sens. 54 (9), 1289–1293.
- [12] Sobrino, J.A.&Coll, C.&Caselles, V. (1991). Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sens. Environ. 38 (1):19–34.DOI.org/10.1016/0034-4257(91)90069-I.
- [13] Streutker, D.R. (2002). A remote sensing study of the urban heat island ofHouston, Texas.Int. J. Remote Sens. 23 (13):2595–2608. DOI.org/10.1080/01431160110115023.
- [14] Streutker, D.R. (2003). Satellite-measured growth of the urban heat island of Houston, Texas.Remote Sens. Environ. 85:282–289.DOI.org/10.1016/S0034-4257(03)00007-5.
- [15] Weng, Q.& Lu, D.&Schubring, J. (2004). Estimation of land surface temperature e vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 89: 467–483. DOI.org/10.1016/j.rse.2003.11.005.
- [16] Fu, P.&Weng, Q. (2016).Consistent land surface temperature data generation from irregularly spaced Landsat imagery.Remote Sens. Environ. 184:175–187, 2016.DOI.org/10.1016/j.rse.06.019.
- [17] Hu, L.&Brunsell, N.A. (2013).The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring.Remote Sens. Environ. 134:162–174.DOI.org/10.1016/j.rse.2013.02.022.
- [18] Nehal, L.&Hamimed, A.&Khaldi, A.&Souidi, Z.&Zaagane, M.(2017).Evapotranspiration and Surface Energy Fluxes Estimation Using the Landsat-7 Enhanced Thematic Mapper Plus Image over a Semiarid Agrosystem in the North-West of
Algeria. RevistaBrasileira de Meteorologia, v. 32, n. 4, 691-702. DOI:http://dx.DOI.org/10.1590/0102-7786324016
- [19] Hua Li, Qinhuo Liu, “Comparison of NDBI and NDVI as indicators of surface urban heat island effect in MODIS imagery,” Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 728503 (29 December 2008); https://doi.org/10.1117/12.815679
- [20] Mushore, T.D., Mutanga, O., Odindi, J., & Dube, T. (2016). Assessing the potential of integrated Landsat 8 thermal bands, with the traditional reflective bands and derived vegetation indices in classifying urban landscapes. Geocarto
International, 1–14. doi:10.1080/ 10106049.2016.1188168
- [21] Ma, Q., Wu, J., & He, C. (2016). A hierarchical analysis of the relationship between urban impervious surfaces and land surface temperatures: Spatial scale dependence, temporal variations, and bioclimatic modulation. Landscape Ecology, 31, 1139–1153.
- [22] Tran., D.X., Pla, F., Carmona, P.L., Myint, S.W., Caetano, M., & Kieua, P.V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119–
132.
- [23] Deilami, K., & Kamruzzaman, M. (2017). Modeling the urban heat island effect of smart growth policy scenarios in Brisbane. Land Use Policy, 64, 38–55.
- [24] Estoque, R.C., Murayama, Y., & Myint, S.W. (2017). Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of southeast Asia. Science of the Total Environment, 577, 349–359.
- [25] Subhanil Guha, Himanshu Govil, Anindita Dey & Neetu Gill (2018) Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy, European Journal of Remote Sensing, 51:1,
667-678, DOI:10.1080/22797254.2018.1474494
- [26] Bekkoussa, S.&Bekkoussa, B.&Taupin, J. D.&Patris, N.&Meddi, M. (2018).Groundwater hydro chemical characterization and quality assessment in the Ghriss Plain basin, northwest Algeria. Journal of Water Supply: Research and Technology-Aqua, jws2018013.DOI: 10.2166/aqua.2018.013.
- [27] Berk, A.; Confortı, P.; Hawes, F.; Perkıns, T.; Guıang, C.; Acharya, P. (2016). Next Generation MODTRAN for Improved Atmospheric Correction of Spectral Imagery. Spectral Sciences, Inc. Burlington United States.
- [28] Hamadouche, M. A.&Mederbal, K.&Khaldi, A.&Kouri, L.&Fikir, Y&Anteur, D. (2017). GIS Based Remote Sensing Data to
Monitor Biodiversity in the Cultural Parks of Ahaggar and TassilNajjer (Southeast of Algeria). Journal of Applied Environmental and Biological Sciences. ISSN: 2090-4274.
- [29] Zha, Y. & Gao, J. & NiI, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24 (3):583–594. DOI.org/10.1080/01431160304987.
- [30] Barsi, J.A.& Barker, J.L.& Schott, J.R. (2003).An Atmospheric Correction Parameter Calculator for a Single Thermal Band Earth-Sensing Instrument.J. Proc. IEEE. Int. v. 5,p. 3014-3016.
- [31] Van de griend, A.A. & OWE, M. (1993). On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Int. J. Remote. Sens, v. 14,p. 1119-1131. DOI: 10.1080/01431169308904400.
- [32] 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 Sens. 7:4268–4289. https://DOI.org/10.3390/rs70404268
- [33] Amiri, R., Weng, Q., Ali mohammadi, A., &Alavipanah, S. K. (2009).Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran.Remote Sensing of Environment, 113, 2606–2617.