Work

Spatial Analysis of Urban Expansion and Energy Consumption Using Nighttime Light Data: A Comparative Study of Google Earth Engine and Traditional Methods for Improved Living Spaces

Year: 2025

Type: article

Abstract: This study employs a dual methodological approach, integrating Google Earth Engine (GEE) and unsupervised classification (UNSUP) to analyze urban expansion patterns in Chiang Mai province using nighttime light imagery. The research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) satellite data from 2014 to 2023 to assess urban growth dynamics. The primary objectives are to (1) evaluate the performance of GEE and UNSUP in nighttime light data processing, (2) validate urban area classification accuracy using multiple assessment metrics, and (3) examine the relationship between nighttime light intensity and electricity consumption through Pearson’s correlation analysis, thereby establishing urban growth patterns. The methodological framework incorporates a dual-threshold classification mechanism in GEE and K-means clustering in traditional geospatial software. Accuracy assessment is conducted using 256 stratified random sampling points, complemented by land use and land cover (LULC) data for ground truth validation. The results indicate that GEE consistently outperforms UNSUP, achieving overall accuracy values between 0.80 and 0.82, compared to 0.73 and 0.76 for UNSUP. The Kappa coefficient for GEE ranges from 0.60 to 0.65, whereas UNSUP demonstrates lower agreement with ground truth data (0.44–0.52). Furthermore, both approaches reveal a significant correlation between electricity consumption and nighttime light intensity, with R2 = 0.9744 for GEE and R2 = 0.9759 for UNSUP, confirming the efficacy of nocturnal illumination data in urban expansion monitoring. The findings indicate that urban areas in Chiang Mai have expanded by approximately 70% over the study period. This research contributes to the field by demonstrating the effectiveness of integrated geospatial methodologies in urban development analysis. The findings offer urban planners and policymakers critical insights for sustainable urban growth management and decision-making.

Source: ISPRS International Journal of Geo-Information

DOI: 10.3390/ijgi14040178

Authors: Thidapath Anucharn, Phongsakorn Hongpradit, Niti Iamchuen, Supattra Puttinaovarat

Institutions: University of Phayao, Prince of Songkla University

Language: English

Topic: Impact of Light on Environment and Health

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,292