Risk Screening Environmental Indicators Model Change Based on Spectral Transformation Around New Yogyakarta International Airport

The development of New Yogyakarta International Airport (NYIA) in Temon sub-district is aimed at improving the progress of the surrounding region, where the construction has an impact on the increase in built-up land of 572.38 hectare (2013-2017) and 268.67 hectare (2017-2023) which is potentially a decrease in the environmental quality index. The purpose of the research is to analyze changes in the environmental quality index (Risk Screening Environmental Indicators of) 2013, 2017 and 2024 around NYIA. The research designs use quantitative approaches with scoring approaches, while research methods use spectral transformation and Principal Component Analysis transformation. The research has limited the use of Landsat 8 image data used as a primary data source with a spatial resolution of 30 meters, where the image has not yet been able to deliver the results of the research with a high degree of exhaustion. The originality of the research is the identification of changes in the environmental quality index that are correlated with changes in built-up land and vegetation coverage. The results of the study showed a decrease in RSEI values, where high-level RSEIs decreased by about 295.17 hectare (2013-2017) and 1720.91 hectare (2017-2024), in addition there was an increase in the area of low-levelRSEI by about 122.33 hectare (2013- 2017) and 1898.79 hectare (2017-2024). The decline in RSEI in the area study has been correlated with increased built-up land and decreased vegetation area, with built-up land increasing by 572.38 hectare (2013-2017) and 269.97 hectare (2017-2024), besides decreasing vegetation areas by 137.82 hectare (2013-2017), and 97.34 hectare (2017-2024). The study concluded that there was a decrease in the environmental quality index, where increased built-up land and decreased vegetation area were influential factors. This research opens up further research opportunities to predict the environmental quality index with the cellular automata model.