POLA DISTRIBUSI RUANG TERBUKA HIJAU TERHADAP TEMPERATUR WILAYAH KOTA SAMARINDA - Muhammad Ari Adha Saputra; Achmad Ghozali; Berly Gizela Putri Pramesti; Muhammad Qoirul Purwanto (DAS Institute, 2022)
The physical development of the city of Samarinda that is not in harmony between the existence of Built Space and the distribution of Green Open Space has an impact on changes in the urban microclimate. These changes occur in microclimate elements such as temperature, humidity, intensity of sunlight and wind. If the microclimate element changes, it is feared that a change will occur in a direction that is not in accordance with the comfort of the human body condition. Then it becomes a question in this study, namely how is the relationship between regional temperature and the distribution of green open space in Samarinda City. The targets carried out to achieve the research objectives are to analyze the temperature distribution of the Samarinda City area, analyze the distribution of Samarinda City Green open space, analyze the characteristics of Samarinda City Green open space, and analyze the relationship between regional temperature and the distribution of Samarinda City Green open space. To answer this goal, this research uses interpretation analysis of Landsat 8, Sentinel 2 imagery and statistical analysis in the form of regression analysis. In the sentinel image analysis, the data obtained are the distribution of temperature and the distribution of green open space. Where the minimum temperature is 29.8oC, maximum temperature is 38.8oC, and the distribution of green open space is obtained. The results of the ANN analysis show that there are 4 categories of green open space distribution data, namely clustered, spread out, uniform, and non-green open space. After the data obtained from the two analyzes, a quantitative analysis was carried out using SPSS programming to obtain the relationship between the Y variable and the X variable. In this analysis, the calculated F value was 1.930 with a significance level of 0.009 where the regression model can be used to predict the independent variables in this data. RTH with temperature showed a significant closeness. In the research area, it is stated that green open space has a significant relationship to the regional temperature distribution. In addition, the research findings also show that the ratio of green open space, the distribution of green open space, and the percentage of dense vegetation have an inverse relationship, which means that if green open space increases then the temperature decreases and vice versa.