Urbanization and climate change have led to rising land surface temperatures (LST) in Louisiana’s cities, particularly New Orleans and Baton Rouge, where impervious surfaces and reduced vegetation intensify the urban heat island effect. This study examines seasonal and spatial LST dynamics in these two urban areas to provide insights for targeted climate adaptation. Landsat 9 Operational Land Imager (OLI) thermal infrared data were analyzed, with LST derived from digital values converted to top-of-atmosphere spectral radiance, brightness temperature, and adjusted using land surface emissivity calculated from NDVI. The study fills research gap by comparing both cities using consistent methods to reveal shared and city-specific temperature pattern. Results revealed distinct seasonal variations in land surface temperature (LST), with Baton Rouge exhibiting a mean of 32.53？C in the dry season and 19.26？C in the wet season. In contrast, New Orleans recorded 30.90？C and 18.85？C, respectively. Spatial study indicated increased temperature fluctuation in New Orleans, with a greater proportion of land surpassing 35？C during the dry season. These variations are attributed to fluctuations in surface moisture levels, vegetation density, and urban land use. The research illustrates the effectiveness of thermal infrared satellite data in evaluating urban thermal dynamics. It offers significant insights for climate monitoring, environmental planning, and the formulation of localized heat mitigation methods in humid Subtropical areas. Louisiana’s urban planning bodies should adopt green infrastructure initiatives, such as expanding tree coverage, installing green roofs, and utilizing permeable materials to help curb rising land surface temperatures and lessen urban heat island impacts. In addition, using localized heat vulnerability maps can support more effective resource distribution and emergency planning with a focus on protecting neighborhoods most vulnerable to seasonal heat.
References
[1]
Ahmed, M., Aloshan, M.A., Mohammed, W., Mesbah, E., Alsaleh, N.A. and Elghonaimy, I. (2024) Characterizing Land Surface Temperature (LST) through Remote Sensing Data for Small-Scale Urban Development Projects in the Gulf Cooperation Council (GCC). Sustainability, 16, Article 3873. https://doi.org/10.3390/su16093873
[2]
Hondula, D.M., Balling, R.C., Vanos, J.K. and Georgescu, M. (2015) Rising Temperatures, Human Health, and the Role of Adaptation. CurrentClimateChangeReports, 1, 144-154. https://doi.org/10.1007/s40641-015-0016-4
[3]
Uejio, C.K., Wilhelmi, O.V., Golden, J.S., Mills, D.M., Gulino, S.P. and Samenow, J.P. (2011) Intra-Urban Societal Vulnerability to Extreme Heat: The Role of Heat Exposure and the Built Environment, Socioeconomics, and Neighborhood Stability. Health & Place, 17, 498-507. https://doi.org/10.1016/j.healthplace.2010.12.005
[4]
Voogt, J.A. and Oke, T.R. (2003) Thermal Remote Sensing of Urban Climates. Remote Sensing of Environment, 86, 370-384. https://doi.org/10.1016/s0034-4257(03)00079-8
[5]
Oke, T.R. (1982) The Energetic Basis of the Urban Heat Island. Quarterly Journal of the Royal Meteorological Society, 108, 1-24. https://doi.org/10.1002/qj.49710845502
[6]
Weng, Q. (2009) Thermal Infrared Remote Sensing for Urban Climate and Environmental Studies: Methods, Applications, and Trends. ISPRSJournalofPhotogrammetryandRemoteSensing, 64, 335-344. https://doi.org/10.1016/j.isprsjprs.2009.03.007
[7]
Summers, J.K., Sanderson, R., Trahan, R., Hendricks, K., Ruffin, M., Williams, A., et al. (2024) Development of Community-Level Capacity of Resilience to Natural Hazards for Environmental-and Social-Justice-Challenged Communities: 1. Approach, Concepts, and Assessment of Existing Information. Sustainability, 16, Article 963. https://doi.org/10.3390/su16030963
[8]
Loh, P.M., Twumasi, Y.A., Ning, Z.H., Anokye, M., Frimpong, D.B., Oppong, J., et al. (2023) Spatiotemporal Analysis of COVID-19 Lockdown Impact on the Land Surface Temperatures of Different Land Cover Types in Louisiana. JournalofGeographicInformationSystem, 15, 458-481. https://doi.org/10.4236/jgis.2023.155023
[9]
Stone Jr., B. (2012) The City and the Coming Climate: Climate Change in the Places We Live. Cambridge University Press. https://doi.org/10.1017/cbo9781139061353
[10]
Weng, Q., Lu, D. and Schubring, J. (2004) Estimation of Land Surface Temperature-Vegetation Abundance Relationship for Urban Heat Island Studies. RemoteSensingofEnvironment, 89, 467-483. https://doi.org/10.1016/j.rse.2003.11.005
[11]
WeatherSpark (2025) Average Weather in Louisiana, Missouri, United States Year-Round. https://weatherspark.com/y/11482/Average-Weather-in-Louisiana-Missouri-United-States-Year-Round
[12]
Ermida, S.L., Soares, P., Mantas, V., Göttsche, F. and Trigo, I.F. (2020) Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series. RemoteSensing, 12, Article 1471. https://doi.org/10.3390/rs12091471
[13]
Twumasi, Y.A., Merem, E.C., Namwamba, J.B., Mwakimi, O.S., Ayala-Silva, T., Frimpong, D.B., et al. (2021) Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. a Comparative Analysis of Two Cities in Ghana. AdvancesinRemoteSensing, 10, 131-149. https://doi.org/10.4236/ars.2021.104009
[14]
Simon, O., Yamungu, N. and Lyimo, J. (2022) Simulating Land Surface Temperature Using Biophysical Variables Related to Building Density and Height in Dar Es Salaam, Tanzania. GeocartoInternational, 38, Article ID: 2142971. https://doi.org/10.1080/10106049.2022.2142971
[15]
Chander, G., Markham, B.L. and Helder, D.L. (2009) Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors. Remote Sensing of Environment, 113, 893-903. https://doi.org/10.1016/j.rse.2009.01.007
[16]
Simon, O., Yamungu, N. and Lyimo, J. (2023) Influence of Urban Land Use Land Cover Changes on Land Surface Temperature in Dar Es Salaam Metropolitan City, Tanzania: The Use of Geospatial Approach. Tanzania Journal of Science, 49, 356-368. https://doi.org/10.4314/tjs.v49i2.7
[17]
Li, L., Mu, X., Jiang, H., Chianucci, F., Hu, R., Song, W., et al. (2023) Review of Ground and Aerial Methods for Vegetation Cover Fraction (fCover) and Related Quantities Estimation: Definitions, Advances, Challenges, and Future Perspectives. ISPRSJournalofPhotogrammetryandRemoteSensing, 199, 133-156. https://doi.org/10.1016/j.isprsjprs.2023.03.020
[18]
Kalyan, S. and Pathak, B. (2024) Urban Sprawl Impact Assessment on the Land Surface Temperature over the Green Capital of Gujarat Using a Geospatial Approach. EnvironmentalMonitoringandAssessment, 196, Article No. 866. https://doi.org/10.1007/s10661-024-13038-7
[19]
Zheng, X., Guo, Y., Zhou, Z. and Wang, T. (2024) Improvements in Land Surface Temperature and Emissivity Retrieval from Landsat-9 Thermal Infrared Data. RemoteSensingofEnvironment, 315, Article ID: 114471. https://doi.org/10.1016/j.rse.2024.114471
[20]
Hu, L., Sun, Y., Collins, G. and Fu, P. (2020) Improved Estimates of Monthly Land Surface Temperature from MODIS Using a Diurnal Temperature Cycle (DTC) Model. ISPRSJournalofPhotogrammetryandRemoteSensing, 168, 131-140. https://doi.org/10.1016/j.isprsjprs.2020.08.007
[21]
Stathopoulou, M. and Cartalis, C. (2007) Daytime Urban Heat Islands from Landsat ETM+ and Corine Land Cover Data: An Application to Major Cities in Greece. SolarEnergy, 81, 358-368. https://doi.org/10.1016/j.solener.2006.06.014
