Spatiotemporal Analysis of Surface Urban Heat Islands using Otsu Threshold and Gaussian Approach for Local Climate Change Detection Atika Izzaty (a)*, Hone-Jay Chu (b) and Mohammad Adil Aman (b)
a) Geodetic Engineering, Universitas Hasanuddin, Makassar 90245, Indonesia
*atikaizzaty[at]unhas.ac.id
b) Department of Geomatics, National Cheng Kung University, Tainan 701, Taiwan
Abstract
Both visible and subtle forms of natural events are common on Earth. In light of these continuous changes, it is essential to monitor and comprehend environmental conditions. Because urbanized areas are centers of industrial development and population mobility, climate change is especially noticeable there. This study uses the Otsu Threshold and Gaussian Mixture Model (GMM) techniques to examine environmental changes, with a focus on fluctuations in air and land surface temperatures. The ability to identify different environmental changes has been greatly improved by remote sensing technology, especially when using multispectral sensors on Landsat satellite imagery. Trends for air temperature have increased noticeably in recent years, indicating notable environmental changes in the Taipei Area as compared to Makassar City between 2018 and 2023. The climate patterns in Taipei have become more unpredictable throughout the course of these five years. Likewise, Makassar has experienced erratic and sometimes sudden wet seasons. The outcomes of the Otsu and GMM models, which successfully apply thresholding techniques to precisely identify urban heat zones, both exhibit these patterns. Interestingly, there was an increase in surface temperatures in 2022. Pixels with temperatures below 19.07 degree Celsius were categorized as low-temperature areas in the GMM model, while pixels with temperatures exceeding 25.70 degree Celsius were categorized as high-temperature zones. In contrast, the Otsu model divided temperatures into four thresholds: 19.07, 21.69, 23.32 then 25.70 in degree Celsius. By using thresholding and cluster analysis approaches, both models showed excellent skills in efficiently processing the data. The GMM model clustered the data according to the thermal conditions identified in each satellite image, whereas the thresholding strategy provided a unique temperature threshold to each image.
