Remote Sensing Based Land Surface Temperature Analysis in Diverse Environment of Lalgudi Block

Main Article Content

J. Ramachandran
R. Lalitha
K. Sivasubramanian

Abstract

Introduction: Land Surface Temperature (LST) is a significant climatic variable and defined as how hot the "surface" of the Earth would feel to the physical touch in a particular location. A spatial analysis of the land surface temperature with respect to different land use/cover changes is vital to evaluate the hydrological processes.


Methods: The objective of this paper is to assess the spatial variation of land surface temperature derived from thermal bands of the Landsat 8 Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) by using split window algorithm.


Place and Data: The study was conducted in Lalgudi block of Trichy District, Tamil Nadu, India. The block has diverse environment like forest area, barren land, river sand bed, water bodies, dry vegetation, cultivated areas (paddy, sugarcane, banana etc.) and settlements. Landsat 8 satellite images for four selected scenes (December 2014 & January 2015 and December 2017 & January 2018) were used to estimate the LST.


Results: The spatial and temporal variation of Normalized Difference Vegetation Index (NDVI) and LST were estimated. The average NDVI values of cropped fields varied from 0.3 to 0.5 in all the scenes. The maximum value of LST ranging from 35 to 40°C was recorded in river sand bed. Subsequently, semi-urban settlements in the central part of Lalgudi block exhibited higher temperature ranging from 28 – 30°C. The LST of paddy crop and sugarcane was in the range of 23 to 25°C. The water bodies exhibited LST around 20°C. The coconut plantations, forest area and Prosopis juliflora showed LST value ranging from 24 – 29°C. This kind of block level monitoring studies helps in adopting suitable policies to overcome or minimize the problems triggered by increase in land surface temperature.

Keywords:
Land surface temperature, normalized difference vegetation index, land use\cover

Article Details

How to Cite
Ramachandran, J., Lalitha, R., & Sivasubramanian, K. (2019). Remote Sensing Based Land Surface Temperature Analysis in Diverse Environment of Lalgudi Block. International Journal of Environment and Climate Change, 9(3), 142-149. https://doi.org/10.9734/ijecc/2019/v9i330103
Section
Original Research Article

References

Song QB, Zhao Y, Liu YQ, Zhang J, Xin SJ, Dong GH. Sex difference in the prevalence of metabolic syndrome and cardiovascular-related risk factors in urban adults from 33 communities of China: The CHPSNE study. Diab Vasc Dis Res. 2015; 12(3):189-98.

Ajayi EA, Ajayi OA, Adeoti OA. Metabolic syndrome: Prevalence and association with electrocardiographic abnormalities in Nigerian hypertensive patients. Metab Syndr Relat Disord. 2014;12(8):437-42.

Turi BC, Codogno JS, Fernandes RA, Monteiro HL, Turi BC, Codogno JS, et al. Low levels of physical activity and metabolic syndrome: Cross-sectional study in the Brazilian public health system. Cienc saude coletiva. 2016;21(4):1043-50.

Granfeldt G, Ibarra J, Mosso C, Munoz S, Carrillo KS, Zapata D. Capacidad predictiva de los indices antropometricos en la deteccion de Sindrome Metabolico en adultos chilenos. Arch Latinoam Nutr. 2015;65(3):152-7.

Mora G, Salguedo G, Ruiz M, Ramos E, Alario A, Fortich A, et al. Concordancia entre cinco definiciones de sindrome metabolico. Cartagena, Colombia. Rev Esp Salud Publica. 2012;86(3):301-11.

Bahrani R, Chan YM, Khor GL, Rahman HA, Esmailzadeh A, Wong TW. The relationship between metabolic syndrome and its components with socio-economic status among adolescents in Shiraz, southern Iran. Southeast Asian J Trop Med Public Health. 2016;47(2):263-76.

Rerksuppaphol S, Rerksuppaphol L. Metabolic syndrome in obese thai children: Defined using modified [the national cholesterol education program/adult treatment panel III] criteria. J Med Assoc Thai. 2015;98(Suppl 10):S88-95.

Philco P, Seron P, Munoz S, Navia P, Lanas F. Factores asociados a sindrome metabolico en la comuna de Temuco, Chile. Rev Med Chil. 2012;140(3):334- 9.

Kuk JL, Ardern CI. Age and sex differences in the clustering of metabolic syndrome factors: Association with mortality risk. Diabetes Care. 2010;33(11): 2457-61.

Sheu WHH, Chuang SY, Lee WJ, Tsai ST, Chou P, Chen CH. Predictors of incident diabetes, metabolic syndrome in middleaged adults: A 10-year follow-up study from Kinmen, Taiwan. Diabetes Res Clin Pract. 2006;74(2):162-8.

Alnory A, Gad H, Hegazy G, Shaker O. The association of vaspin rs2236242 and leptin rs7799039 polymorphism with metabolic syndrome in Egyptian women. Turk J Med Sci. 2016;46(5):1335-40.

Hotta K, Kitamoto T, Kitamoto A, Mizusawa S, Matsuo T, Nakata Y, et al. Association of variations in the FTO, SCG3 and MTMR9 genes with metabolic syndrome in a Japanese population. J Hum Genet. 2011;56(9):647-51.

Zhao X, Xi B, Shen Y, Wu L, Hou D, Cheng H, et al. An obesity genetic risk score is associated with metabolic syndrome in Chinese children. Gene. 2014;535(2):299-302.

Mazidi M, Rezaie P, Kengne AP, Mobarhan MG, Ferns GA. Gut microbiome and metabolic syndrome. Diabetes Metab Syndr. 2016;10(2 Suppl 1):S150-7.

Hunter I, Soler A, Joseph G, Hutcheson B, Bradford C, Zhang FF, et al. Cardiovascular function in male and female JCR: LA-cp rats: Effect of high-fat/high-sucrose diet. Am J Physiol Heart Circ Physiol. 2017;312(4):H742-51.

Nettleton JA, Lutsey PL, Wang Y, Lima JA, Michos ED, Jacobs DR. Diet soda intake and risk of incident metabolic syndrome and type 2 diabetes in the Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care. 2009;32(4):688-94.

Haby MM, et al. A new approach to assessing the health benefit from obesity interventions in children and adolescents: The assessing cost-effectiveness in obesity project. International Journal of Obesity (London). 2006;30(10):1463–75.

Carter R, et al. Assessing cost-effectiveness in obesity (ACE-Obesity): An overview of the ACE approach, economic methods and cost results. BMC Public Health. 2009;9:419.

Tackling Obesities: Future Choices – Project report. Foresight, London, Government Office for Science; 2007.

Available:http://www.bis.gov.uk/foresight/our-work/projects/published-projects/tackling-obesities

[Accessed 30 November 2011]

Robertson A, et al. eds. Food and health in Europe: A new basis for action (WHO regional publications. European series, No. 96). Copenhagen, World Health Organization, 2004.65 Best options for promoting healthy weight and preventing weight gain in NSW. Sydney, New South Wales Department of Health; 2005.

Griffiths J, Maggs H, George E. ‘Stakeholder involvement’: Background paper prepared for the WHO/WEF joint event on Preventing Noncommunicable Diseases in the Workplace (Dalian/China, September 2007). Geneva, World Health Organization; 2008.

Milio N. Nutrition and health: Patterns and policy perspectives in food-rich countries. Social Science & Medicine. 1989;29(3): 413–23.

Swinburn BA. Obesity prevention: The role of policies, laws and regulations. (Commentary). Australia & New Zealand Health Policy. 2008;5:12.

Snowdon W, et al. Prioritizing policy interventions to improve diets? Will it work, can it happen, will it do harm? Health Promotion International. 2010;25(1):123–33.

Keating CL, et al. Cost-effectiveness of surgically induced weight loss for the management of type 2 diabetes: Modeled lifetime analysis. Diabetes Care. 2009; 32(4):567–74.

Picot J, et al. The clinical effectiveness and cost-effectiveness of bariatric (weight loss) surgery for obesity: A systematic review and economic evaluation. Health Techno-logy Assessment. 2009;13(41):1–190, 215–357,iii-iv.

Kruszynska YT, Yu JG, Olefsky JM, Sobel BE. Effects of troglitazone on blood concentrations of plasminogen activator inhibitor 1 in patients with type 2 diabetes and in lean and obese normal subjects. Diabetes. 2000;49:633-9.

Billiet L, Doaty S, Katz JD, Velasquez MT. Review of hyperuricemia as new marker for metabolic syndrome. ISRN Rheumato-logy. 2014;852954.