Utilization of Remote Sensing Data in Determining the Threshold Value of Urban Ecological Quality Index in Bandung City, West Java, Indonesia

Bandung City has the highest land conversion rate in Indonesia and was named a city with a moderate environmental quality index status in 2022. This status has been exacerbated by the diminishing green spaces in the city due to rapid urbanization. Conducting ecological assessments has become increasingly important, one approach being the utilization of remote sensing data. Remote sensing data, specifically Landsat 8 OLI/TIRS, processed to derive the RSEI (Remote Sensing Ecological Index) based on the PCA value of PC1, requires further development. Several limitations of the RSEI in assessing ecological quality, such as the subjectivity of remote sensing data, the use of equal interval methods for index classification, and the inability to validate the results, are the focus of development in this study. Based on these weaknesses, the RSEIT offers advancements in integrating actual data to support RSEI, determining index thresholds, and enabling model validation. The findings of this study demonstrate that: (1) ecological issues such as floods, waste accumulation, and landslides are the most prevalent problems in the study area; (2) compared to RSEI, which relies solely on remote sensing data, RSEIT is a model that can be validated with actual data. During the dry and rainy seasons, it achieves threshold values of 0.474 and 0.566, respectively, demonstrating a model performance accuracy exceeding 70%. The average validation results show an overall accuracy of 83.34%, a sensitivity of 78.55%, and a specificity of 87.50% across both seasons; and (3) urban centers, characterized by extensive surface hardening, minimal vegetation, and numerous ecological issues, predominantly fall under the poor RSEIT category, especially during the dry season. In contrast, suburban areas with higher proportions of green space and fewer ecological problems are largely classified under the good RSEIT category, particularly during the rainy season. This study can be further enhanced by refining the threshold aspects and strengthening actual data collection through the involvement of various stakeholders with expertise in ecology.