Assessment Of Urban Heat Island Intensity Using Land Surface Temperature Data From Landsat 8 Processing

• Main Author: Prya M Sembiring M, Yans
• Format: Theses
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/40205
• Institution: Institut Teknologi Bandung

Urban heat island is a closed isotherm which shows a relatively warm surface area, which is as warmer temperatures in urban areas compared to the surrounding rural environment. With the development of society and the acceleration of the process of urbanization as a result of development, urban heat islands have become more significant and have had a negative impact on the conditions of air quality, the human environment, and affect energy use, to climate change in the future. The study of heat islands is very important, because heat islands greatly affect the condition of air quality, affect human health, and affect energy use. Increasing the heat island is also one of the factors of global climate change. In this study UHI was identified using Land Surface Temperature (LST) data obtained using satellite image data. Land Surface Temperature (LST) is the amount of temperature between the surface of the earth and the atmosphere. Before the existence of Earth Observation Satellites (EOS), it was difficult to estimate LST from an area. Usually the estimation of ESG is done by calculating several samples of the field and being interpolated into isotherms to change the data point to area. Now with the existence of high-resolution satellite imagery it is very easy to calculate the LST spatially. LSTs in a region can be calculated using thermal infrared bands (bands 10 and 11) in Landsat 8 images. In recent years the city of Bandung has seen an increase in population and a very significant change in land use. Therefore it is necessary to do research on the UHI phenomenon in Bandung using Landsat 8 satellite data. The algorithm used in this study is the Improve Mono Window algorithm in band 10 and band 11 which in the previous research was carried out only in band 10, and the algorithm Split Window. From the two methods, the best algorithm is determined by using MODIS LST data as validation data. While the assessment of the intensity of the UHI is done using the Weighted Average Heat Island Index. From the results of processing and comparison of LST values with the IMW and SW algorithms with MODIS LST as validation data, it shows that the IMW algorithm using band 10 has a smaller error rate. The next LST calculation is carried out on 3 images with different dates in 2013 and 3 images with different dates in 2018. The average LST value in 2018 shows an increase compared to the average LST value in 2013. The intensity of UHI in 2013 strong, and the intensity of UHI in 2018 is very strong. It is expected that with research on UHI in the city of Bandung, it can provide input by the parties concerned to develop a mature and efficient mitigation strategy.