Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

Abstract

ABSTRACT:The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

Pemantauan Land Surface Temperature (LST) dan Kaitannya dengan Tutupan Lahan (Studi Kasus: Kota Surabaya Tahun 2014-2022)

“…kondisi suhu di Kota Surabaya secara umum mengalami peningkatan sebesar 1-1,4% sejak 5 tahun terakhir. Dari analisis tren suhu oleh BMKG, Kota Surabaya memiliki tren peningkatan suhu tertinggi sebesar 0,5 0 C setiap 10 tahun [6].…”

Intra-Urban Heat Island Detection and Trend Characterization in Metro Manila Using Surface Temperatures Derived From Multi-Temporal Landsat Data

“…It was adapted for application to Landsat 5 TM data in this study. For Landsat 8, Band 10 TIRS 1 was used since it yields more accurate results (Yu et al, 2014 as cited in Bernales et al, 2016).…”

Analisis Perubahan Temperatur Permukaan Wilayah Surabaya Timur Tahun 2001-2016 Menggunakan Citra LANDSAT

“…Kondisi suhu udara di Kota Surabaya secara umum mengalami peningkatan sebesar 1-1,4% sejak 5 tahun terakhir. Berdasarkan hasil analisis tren suhu yang dilakukan oleh BMKG, wilayah Kota Surabaya memiliki tren peningkatan suhu yang tertinggi, yaitu sebesar 0,5°C/10 tahun [4].…”