Monitoring Trends in Light Pollution in Nakhon Ratchasima Province Based on Nighttime Satellite Imagery

Authors

  • Waranya Silayot ภาควิชาภูมิศาสตร์ คณะมนุษยศาสตร์และสังคมศาสตร์ มหาวิทยาลัยมหาสารคาม
  • Thanyarat Chaiyakarm

Abstract

This article aimed to analyze and monitor expansion of light pollution from nighttime light (NLT) images during 2013 – 2021 in Nakhon Ratchasima province using nighttime satellite imagery from Suomi National Polar-orbiting Partnership (Suomi-NPP), Visible Infrared Imager Radiometer Suite (VIIRS) based on supervised classification and compared to unsupervised classification. Classification and monitoring results of both methods are consistent. Light pollution could be classified into 5 levels, namely, the highest intensity of light pollution ranged from 18.17 - 86.01, the high intensity ranged from 8.38 - 18.16, the medium intensity ranged from 3.14 - 8.37, the low intensity ranged from 0.91 – 3.40, and the lowest intensity ranged from 0.01 – 0.90. It was found that urban areas were more likely to have light pollution at the highest, high, medium, low and the lowest intensity. Light pollution has intensified every year in the areas of 39.93, 116.63, 353.05, 1,741.79, and 81.55 square kilometers, respectively. The light pollution expansion followed transportation routes, especially Mittraphap Road, the main road considered as the gateway to the northeast region that passes through Nakhon Ratchasima, and was concentrated in the inner city of the province. The analysis of the relationship between nighttime light pollution and population density was conducted using simple linear regression analysis and Pearson’s correlation coefficient. The statistical significance level was set at 0.05. According to the study, the coefficient of determination (R2) light pollution and population density was 0.4956, showing a moderate level of relationship Keywords:  Light Pollution; Nighttime Light Satellite Imagery; Urban Land Use; Population Density

Author Biography

Thanyarat Chaiyakarm

  

References

Chalkias, C., Petrakis, M., Psiloglou, B., & Lianou, M. (2006). Modelling of light pollution in suburban
areas using remotely sensed imagery and GIS. J. Environ. Manag, 79, P 57 - 63.
H A Prastyo, D Herdiwijaya. (2019). Spatial Analysis of Light Pollution Dynamics Around Bosscha
Observatory and Timau National Observatory Based on VIIRS-DNB Satellite Images.
10th Southeast Asia Astronomy Network (SEAAN). IOP Conf. Series: Journal of Physics: Conf.
Series 1231 (2019) 012002. doi:10.1088/1742-6596/1231/1/012002.
Hölker; Franz; Wolter; Christian; Perkin; Elizabeth K; Tockner and Klement. (2010). Light pollution
as a biodiversity threat. Trends in ecology & evolution, 25(12), P 681-682.
Marisa Mhokprakhon and Thanyarat Chaiyakarm. (2023). Estimation of Land Surface Temperature in
Northeast, Thailand Using Multi – Temporal Satellite Imageries. Burapha Science Journal Volume 28 (No.1) January – April 2023. P 136 – 154. (in Thai)
Metta Kengchuwong. (2018). Study on Particulate Matter in Ambient Air and Impacts to People in Urban
Area of Maha Sarakham Municipality. Environmental Science Programme/ Faculty of Science and Technology, Rajabhat Maha Sarakham University. (in Thai)
Pavan Kumara; Sufia Rehmana; Haroon Sajjada; Bismay Ranjan Tripathy; Meenu Ranic and
Sourabh Singhc. (2019). Analyzing trend in artificial light pollution pattern in India using NTL sensor's data. Urban Climate, 27, 272-283. doi: 10.1016/j.uclim.2018.12.005
Pengpeng Han; Jinliang Huang; Rendong Li; Lihui Wang; Yanxia Hu; Jiuling Wang and Wei Huang.
(2014). Monitoring Trends in Light Pollution in China Based on Nighttime Satellite Imagery.
Remote Sensing, 6(6), 5541-5558. doi:10.3390/rs6065541.
Wenli Xiang and Minghong Tan (2017). Changes in Light Pollution and the Causing Factors in
China’s Protected Areas, 1992–2012. Remote Sensing, 9(10), 1026. doi:10.3390/rs9101026.

Downloads

Published

2023-05-11