Spatiotemporal analysis of land surface temperature owing to NDVI: A case study of Vadodara District, Gujarat
DOI:
https://doi.org/10.58825/jog.2023.17.1.83Keywords:
Land Use Landcover, Land Surface Temperature (LST), NDVI (Normalized Difference Vegetation Index), SMI (Soil Moisture Index)Abstract
The expeditious extension of LULC in the name of development is the root cause of global warming. Replacement of natural resources due to the expansion of manmade erections is accountable for the increase in LST of Earth’s topography. The impression of change in LULC is reflected in LST. To seize the rising temperature, the lamentation of a new plan of action for urbanization is of utmost requisite. This paper examines the change in LULC and its spatiotemporal impact on LST in Vadodara, which is situated on the bank of river Vishmamitri river. Vadodara an arid region has three main seasons and these are summer, monsoon, and winter. The climate is characterized by hot summer and dryness in the non-rainy seasons. May is the hottest month while January is the coldest month. The annual rainfall of the district is 475.2 mm. Hence, to analyze we used multi-spectral and multi thermal Landsat TM and ETM+ satellite images to monitor the evaluation of LULC and its impact on LST from 2001(pre-monsoon) to 2021(pre- monsoon). The study explores to what extent observed LST can be examined by vegetation cover measured through NDVI from2001 to 2021. To achieve this an analysis of co-relation is performed between LULC and LST and by using spectral indices comprising NDVI with the help of software like ArcGIS 10.2 and Erdas Imagine 2014. It had been observed that a considerable increase in LST in Vadodara was 58.338°C (Max) and 21.9014°C (Min) in 2001(pre-monsoon) to 60.844 °C (Max) and 24.6784 °C (Min) in 2021(pre-monsoon) which is just about 2°C increase in Max and 3 degrees increase in Min for LST in past 20 years. It was also observed that there is an inverse relationship between LST and NDVI. The value of NDVI is observed that change from 0.380711(H) and -0.59322(L) in 2001 to 0.551(H) to -0.351193(L)in 2021. Moreover, SMI places a vital role to investigate and verify the relation between LST and NDVI. Henceforth, to verify SMI was also calculated and it was noticed that places with high LST value and low NDVI value contained less soil moisture and places with less LST value and high NDVI values contained more soil moisture. Thus, it can be concluded that, if urban planners and decision-makers implement suitable land-use strategies then Earth’s topography can be protected from adverse effects of urban heat by planting adequate and appropriate trees in bare soil and beside the impervious areas, thus the expansion of UHI can also be controlled. Moreover, with the help of SMI values, it will also be beneficial for the agricultural sector.
References
Arnold J.E. (1999). SOIL MOISTURE. https://weather.msfc.nasa.gov/landprocess/
Avdan U. and G. Jovanovska (2016). Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors, 2016. https://doi.org/10.1155/2016/1480307
Ballinas, M. and V.L. Barradas (2016). The Urban Tree as a Tool to Mitigate the Urban Heat Island in Mexico City: A Simple Phenomenological Model. Journal of Environmental Quality, 45(1). https://doi.org/10.2134/jeq2015.01.0056
Barnes K., J. Morgan, M. Roberge and S. Lowe (2001). Sprawl Development: Its Patterns, Consequences, and Measurement. Annals of Physics, 54.
Barsi J. A., J.R. Schott, S.J. Hook, N.G. Raqueno, B.L. Markham and R.G. Radocinski (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11). https://doi.org/10.3390/rs61111607
Bateni S. M. and D. Entekhabi (2012). Relative efficiency of land surface energy balance components. Water Resources Research, 48(4). https://doi.org/10.1029/2011WR011357
Bendib A., H. Dridi and M.I. Kalla (2017). Contribution of Landsat 8 data for the estimation of land surface temperature in Batna city, Eastern Algeria. Geocarto International, 32(5). https://doi.org/10.1080/10106049.2016.1156167
Bhattacharjee P.R and P. Nayak (2003). Socio-economic rationale of a regional development council for the Barak Valley of Assam. Journal of NEICSSR, 27(1), 13–26. https://www.researchgate.net/publication/267765309_A_remote_sensing_study_for_land_cover_change_in_south_Assam_India
Carlson T. N. and D.A. Ripley (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment, 62(3). https://doi.org/10.1016/S0034-4257(97)00104-1
Chapman L. and J.E. Thornes (2003). The use of geographical information systems in climatology and meteorology. Progress in Physical Geography, 27(3). https://doi.org/10.1191/030913303767888464
Das P., & S. Joshi. (2013). A remote sensing study for land cover change in south Assam, India. Earth Science India, 6 (lll), 136–146. https://www.researchgate.net/publication/267765309_A_remote_sensing_study_for_land_cover_change_in_south_Assam_India
Deng, Y., S. Wang, X. Bai, Y. Tian, L. Wu, J. Xiao, F. Chen and Q. Qian. (2018). Relationship among land surface temperature and LUCC, NDVI in typical karst area. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-017-19088-x
Dupigny-Giroux, L. and J. E. Lewis. (1999). A Moisture Index for Surface Characterization over Semi-Arid Area. Photogrametric Engineering Remote Sensing, 65(8), 937–945. https://www.asprs.org/wpcontent/uploads/pers/1999journal/aug/1999_aug_937-945.pdf
Dutta R. (2016). Review of Normalized Difference Vegetation Index (NDVI) as an Indicator of Drought.
Dyras I., H. Dobesch, E. Grueter, A. Perdigao, O.E. Tveito, J.E. Thornes, F. van der Wel and I. Bottai (2005). The use of Geographic Information Systems in climatology and meteorology: COST 719. Meteorological Applications, 12(1). https://doi.org/10.1017/S1350482705001544
Enkhjargal Natsagdorj. (2021). Soil moisture analysis using remotely sensed data in the agricultural region of Mongolia [GHENT UNIVERSITY]. https://www.researchgate.net/publication/351225146_Soil_moisture_analysis_using_remotely_sensed_data_in_the_agricultural_region_of_Mongolia
Estes J. and Jensen. (1998). Development of remote sensing digital image processing systems and raster GIS. The History of Geographic Information Systems. Longman, New York, 163–180.
Feizizadeh B. and T. Blaschke (2012). Thermal remote sensing for land surface temperature monitoring: Maraqeh County, Iran. International Geoscience and Remote Sensing Symposium (IGARSS). ttps://doi.org/10.1109/IGARSS.2012.6350808
GIS Geography. (2018). What is NDVI (Normalized Difference Vegetation Index)? Web Page GIS Geography.
GIS Geography. (2022). What is NDVI (Normalized Difference Vegetation Index)? https://gisgeography.com/ndvi-normalized-difference-vegetation-index/
Gonzalez R.R. (2021). Landsat 8 satellite data-based estimation of soil moisture in McMurdo Dry Valleys, Antarctica. https://run.unl.pt/bitstream/10362/113892/1/TGEO0270.pdf
Guha, S., H. Govil, A. Dey and N. 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). https://doi.org/10.1080/22797254.2018.1474494
Guillevic P. C., J.L. Privette, B. Coudert, M.A. Palecki, J. Demarty, C. Ottlé and J.A. Augustine (2012). Land surface temperature product validation using NOAA’s surface climate observation networks-scaling methodology for the visible infrared imager radiometer suite (VIIRS). Remote Sensing of Environment, 124. https://doi.org/10.1016/j.rse.2012.05.004
Igbokwe J.I, I.C. Ezeomedo and J. Ejikeme (2013). Identification of Urban Sprawl Using Remote Sensing and GIS Technique: A Case Study of Onitsha and Its Environs in South East Nigeria. Environmental Science, Mathematics, 2, 41–49.
Jin, M, R. E. Dickinson and A.M. Vogelmann (1997). A comparison of CCM2-BATS skin temperature and surface-air temperature with satellite and surface observations. In Journal of Climate (Vol. 10, Issue 7).
Li, Z. L., B.H. Tang, H. Wu, H. Ren, G. Yan, Z. Wan, I.F. Trigo and J.A. Sobrino (2013). Satellite-derived land surface temperature: Current status and perspectives. In Remote Sensing of Environment (Vol. 131). https://doi.org/10.1016/j.rse.2012.12.008
Lillesand, T.M. and R.W. Kiefer (1979). Remote sensing and image interpretation. Remote Sensing and Image Interpretation. https://doi.org/10.2307/634969
Moawad B.M. (2012). Geoscience general tool package. https://www.sciencedirect.com/science/article/pii/S1110982318304551#b9045
Mohamed E. S., A. Ali, M. El-Shirbeny, K. Abutaleb and S. M. Shaddad (2020). Mapping soil moisture and their correlation with crop pattern using remotely sensed data in arid region. Egyptian Journal of Remote Sensing and Space Science, 23(3). https://doi.org/10.1016/j.ejrs.2019.04.003
MuñozJiménez- Juan C., S. José A., G. Alan, S. Donald and W.T. Gustafson. (2006). Improved land surface emissivities over agricultural areas using ASTER NDVI. Remote Sensing of Environment, 474–487.
Nasar U and M. Minallah (2020). Exploring the Relationship between Land Surface Temperature and Land Use Change in Lahore Using Landsat Data. Pakistan Journal of Scientific and Industrial Research Series A: Physical Sciences, 63(3). https://doi.org/10.52763/pjsir.phys.sci.63.3.2020.188.200
Principal Chief Conservator of Forest & Head of the Forest Force (HoFF), G. of G. (2020). Schemes. https://forests.gujarat.gov.in/schemes-details.htm
Quattrochi, D. A. and J. C. Luvall (1999). Thermal infrared remote sensing for analysis of landscape ecological processes: Methods and applications. Landscape Ecology, 14(6). https://doi.org/10.1023/A:1008168910634
Rouse J.W., R.W. Haas, J.A. Schell, D.W. Deering and J.C. Harlan (1974). Monitoring the Vernal Advancements (Greenwave Effect) and Retrogradation of Natural Vegetation. https://ntrs.nasa.gov/citations/19740022555
Rushayati, S. B., A.D. Shamila and L.B. Prasetyo (2018). The Role of Vegetation in Controlling Air Temperature Resulting from Urban Heat Island. Forum Geografi, 32(1). https://doi.org/10.23917/forgeo.v32i1.5289
Saha A., M. Patil, V.C. Goyal and D.S. Rathore (2018). Assessment and Impact of Soil Moisture Index in Agricultural Drought Estimation Using Remote Sensing and GIS Techniques. https://doi.org/10.3390/ecws-3-05802
Shah D. B., M.R. Pandya, H.J. Trivedi and A.R. Jani (2012). Estimation of minimum and maximum air temperature using MODIS data over Gujarat. In Journal of Agrometeorology (Vol. 14, Issue 2).
Singh R.P. and N. Singh. (2016). Normalised Difference Vegetation Index (NDVI) Based Classification to Access the Change in Land Use/Landcover (LULC) in Lower Assam, India. School of Environmental Science. https://www.researchgate.net/publication/315943042_Normalized_Difference_Vegetation_Index_NDVI_Based_Classification_to_Assess_the_Change_in_Land_UseLand_Cover_LULC_in_Lower_Assam_India
Sobrino J. A., J.C. Jiménez-Muñoz, P.J. Zarco-Tejada, G. Sepulcre-Cantó and E. de Miguel(2006). Land surface temperature derived from airborne hyperspectral scanner thermal infrared data. Remote Sensing of Environment, 102(1–2). https://doi.org/10.1016/j.rse.2006.02.001
Thakkar M. (2013, March 1). Narmada water changing crop pattern in the region. Business Standard.
Tomlinson C. J., L. Chapman, J.E. Thornes and C. Baker (2011). Remote sensing land surface temperature for meteorology and climatology: A review. In Meteorological Applications (Vol. 18, Issue 3). https://doi.org/10.1002/met.287
Twumasi Y. A., E.C. Merem, J.B. Namwamba, O.S. Mwakimi, T. Ayala-Silva, D.B. Frimpong, Z.H. Ning, A.B. Asare-Ansah, J.B. Annan, J. Oppong, P.M. Loh, F. Owusu, V. Jeruto, B.M. Petja, R. Okwemba, J. McClendon-Peralta, C.O. Akinrinwoye and H.J. Mosby (2021). Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana. Advances in Remote Sensing, 10(04). https://doi.org/10.4236/ars.2021.104009
Voogt, J. A. and T.R. Oke (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3). https://doi.org/10.1016/S0034-4257(03)00079-8
Weng, Q., D. Lu and J. Schubring (2004). Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4). https://doi.org/10.1016/j.rse.2003.11.005
Xu H. Q. and B.Q. Chen (2004). Remote sensing of the urban heat island and its changes in Xiamen City of SE China. Journal of Environmental Sciences, 16(2).
Yadav, K. K., N. Gupta and V. Kumar (2016). Remote Sensing and Geographical Information System (GIS) and Its Applicationn in Various Fields. American String Teacher, 66(1).
Yuvaraj R. M. (2020). Extents of Predictors for Land Surface Temperature Using Multiple Regression Model. Scientific World Journal, 2020. https://doi.org/10.1155/2020/3958589
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