Preview

GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY

Advanced search

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

https://doi.org/10.24057/2071-9388-2025-3413

Abstract

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.

About the Authors

Auzaie Ihza Mahendra
Department of Geographical Information Science, Universitas Gadjah Mada
Indonesia

Yogyakarta 55281 



Prima Widayani
Department of Geographical Information Science, Universitas Gadjah Mada
Indonesia

Yogyakarta 55281 



Sigit Heru Murti
Department of Geographical Information Science, Universitas Gadjah Mada
Indonesia

Yogyakarta 55281 



References

1. Ary M. F. A. A., Rondonuwu D. M., & Warouw, F. (2018). Kampung Susun Di Manado. Social Design. Jurnal Arsitektur DASENG, 7(2).

2. Budiman, A., Sulistyantara, B., & Zain, A. F. (2014). Deteksi Perubahan Ruang Terbuka Hijau Pada 5 Kota Besar Di Pulau Jawa (Studi Kasus : DKI Jakarta, Kota Bandung, Kota Semarang, Kota Jogjakarta, Dan Kota Surabaya). Jurnal Lanskap Indonesia, 6(1).

3. Caio C. de Araujo Barbosa, Atkinson, P. M., & Dearing, J. A. (2015). Remote sensing of ecosystem services: A systematic review. In Ecological Indicators (Vol. 52). https://doi.org/10.1016/j.ecolind.2015.01.007

4. Carter, J. V., Pan, J., Rai, S. N., & Galandiuk, S. (2016). ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves. Surgery (United States), 159(6). https://doi.org/10.1016/j.surg.2015.12.029

5. Cheng, Z., & He, Q. (2019). Remote Sensing Evaluation of the Ecological Environment of Su-Xi-Chang 466 City Group based on Remote Sensing Ecological Index (RSEI). Remote Sensing Technology and Application, 34(3), 531–539.

6. Dai, X., Chen, J., & Xue, C. (2023). Spatiotemporal Patterns and Driving Factors of the Ecological Environmental Quality along the Jakarta– Bandung High-Speed Railway in Indonesia. Sustainability, 15(16), 12426. https://doi.org/10.3390/su151612426

7. Diep, N. T. H., Nguyen, N. T., Hieu, D. C., Huong, N. T. T., & Trang, D. H. (2024). Environmental Quality Monitoring Using Remote Sensing Ecological Index (RSEI) in Can Tho City, Vietnam. IOP Conference Series: Earth and Environmental Science, 1345(1). https://doi.org/10.1088/1755-1315/1345/1/012018

8. Environmental Office Bandung City. (2022). Performance Information Document Environmental Management Bandung City in 2022.

9. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8). https://doi.org/10.1016/j.patrec.2005.10.010

10. Firozjaei, M. K., Fathololoumi, S., Kiavarz, M., Biswas, A., Homaee, M., & Alavipanah, S. K. (2021). Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status. Ecological Indicators, 123. https://doi.org/10.1016/j.ecolind.2021.107375

11. Giofandi, E. A., Syahzaqi, I., Sekarjati, D., Putriana, A. M., Putti, H. M. D. M., & Sekarrini, C. E. (2024). Assessment Of Remote Sensing Approach For Urban Ecological Quality Evaluation In Pekanbaru City, Riau Province Indonesia. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY, 17(1), 28–35. https://doi.org/10.24057/2071-9388-2023-2640

12. Henrys, P. A., & Jarvis, S. G. (2019). Integration of ground survey and remote sensing derived data: Producing robust indicators of habitat extent and condition. Ecology and Evolution, 9(14). https://doi.org/10.1002/ece3.5376

13. Indrawati, L., Sigit Heru Murti, B. S., & Rachmawati, R. (2020). Integrated ecological index (IEI) for urban ecological status based on remote sensing data: A study at Semarang - Indonesia. IOP Conference Series: Earth and Environmental Science, 500(1). https://doi.org/10.1088/1755-1315/500/1/012074

14. Indrawati, L., Sigit Heru Murti, B. S., Rachmawati, R., & Aji, D. S. (2020). Effect of Urban Expansion Intensity on Urban Ecological Status Utilizing Remote Sensing and GIS: A Study of Semarang-Indonesia. IOP Conference Series: Earth and Environmental Science, 451(1), 012018. https://doi.org/10.1088/1755-1315/451/1/012018

15. Kustiwan, I., & Ladimananda, A. (2012). Pemodelan Dinamika Perkembangan Perkotaan dan Daya Dukung Lahan di Kawasan Cekungan Bandung. Tataloka, 14(2).

16. Kwok, R. (2018). Ecology’s remote-sensing revolution. In Nature (Vol. 556, Issue 7699). https://doi.org/10.1038/d41586-018-03924-9

17. Lestiani, D. D., Santoso, M., Kurniawati, S., & Markwitz, A. (2013). CHARACTERISTIC OF AIRBORNE PARTICULATE MATTER SAMPLES COLLECTED FROM TWO SEMI INDUSTRIAL SITES IN BANDUNG, INDONESIA. Indonesian Journal of Chemistry, 13(3), 271–277. https://doi.org/10.22146/ijc.21287

18. Li, Y., Cao, Z., Long, H., Liu, Y., & Li, W. (2017). Dynamic analysis of ecological environment combined with land cover and NDVI changes and implications for sustainable urban–rural development: The case of Mu Us Sandy Land, China. Journal of Cleaner Production, 142, 697– 715. https://doi.org/10.1016/j.jclepro.2016.09.011

19. Mas, J. F., Filho, B. S., Pontius, R. G., Gutiérrez, M. F., & Rodrigues, H. (2013). A suite of tools for ROC analysis of spatial models. ISPRS International Journal of Geo-Information, 2(3). https://doi.org/10.3390/ijgi2030869

20. Ministry of Environment and Forestry of the Republic of Indonesia. (2022). Indeks Kualitas Lingkungan Hidup.

21. Muchtar, H. S., Wijaya, I. N. S., & Setyono, D. A. (2024). Pembangunan Berkelanjutan Perkotaan Dalam Aspek Ekologi Kota Bandung. Planning for Urban Region and Environment Journal (PURE), 13(3), 193–204.

22. Niu, X., & Li, Y. (2020). REMOTE SENSING EVALUATION OF ECOLOGICAL ENVIRONMENT OF ANQING CITY BASED ON REMOTE SENSING ECOLOGICAL INDEX. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020, 733–737. https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-733-2020

23. Obuchowski, N. A., Blackmore, C. C., Karlik, S., & Reinhold, C. (2005). Fundamentals of clinical research for radiologists. American Journal of Roentgenology, 184(2). https://doi.org/10.2214/ajr.184.2.01840364

24. Palapa, T. M., & Maramis, A. A. (2014). Pemantauan Melalui Observasi Lapang, Pencitraan Satelit, dan SIG Tambang Talawaan-Tatelu. Prosiding Seminar Nasional Sains Dan Pendidikan Sains IX 2014, 594–601.

25. Reza, M. I. H., & Abdullah, S. A. (2011). Regional Index of Ecological Integrity: A need for sustainable management of natural resources. In Ecological Indicators (Vol. 11, Issue 2). https://doi.org/10.1016/j.ecolind.2010.08.010

26. Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D., & Willis, K. J. (2016). Sensitivity of global terrestrial ecosystems to climate variability. Nature, 531(7593), 229–232. https://doi.org/10.1038/nature16986

27. Su, S., Zhaoning, G., Wenjing, Z., Yuan, Z., & Yifei, W. (2022). Change of vegetation coverage 560 and assessment of ecological environment quality in Beiyun River Basin. Acta Scientiae Circumstantiae, 42(1), 19–27.

28. Suprayogi, S., Tivianton, A., Nurchayati, W., & Mukarromah, D. (2013). Relevansi Spasial Indeks Kualitas Lingkungan Hidup dengan Pengetahuan Siswa akan Kesiapsiagaan Perubahan Lingkungan dan Iklim (Studi Kasus: SMU, SMP, SD sekitar Kota Yogyakarta, D.I. Yogyakarta). Prosiding Seminar Nasional Geografi UMS.

29. Wang, H., Liu, D., Lin, H., Montenegro, A., & Zhu, X. (2015). NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau. International Journal of Climatology, 35(5). https://doi.org/10.1002/joc.4013

30. Wang, J., Ma, J. L., Xie, F. F., & Xu, X. J. (2020). [Improvement of remote sensing ecological index in arid regions: Taking Ulan Buh Desert as an example]. Ying Yong Sheng Tai Xue Bao = The Journal of Applied Ecology, 31(11), 3795–3804. https://doi.org/10.13287/J.1001-9332.202011.011

31. Wang, Z., Chen, T., Zhu, D., Jia, K., & Plaza, A. (2023). RSEIFE: A new remote sensing ecological index for simulating the land surface ecoenvironment. Journal of Environmental Management, 326, 116851. https://doi.org/10.1016/J.JENVMAN.2022.116851

32. Widiawaty, M. A., Dede, Moh., & Ismail, A. (2019). ANALISIS TIPOLOGI URBAN SPRAWL DI KOTA BANDUNG MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS. Seminar Nasional Geomatika, 3. https://doi.org/10.24895/sng.2018.3-0.1007

33. Xu, H. (2013). A remote sensing urban ecological index and its application. Acta Ecol, 7853–7862.

34. Xu, H., & Deng, W. H. (2022). Rationality analysis of MRSEI and its difference with RSEI. Remote Sensing Technology, 37, 1–7.

35. Xu, H., & Zhang, T. (2013). Assessment of consistency in forest-dominated vegetation observations between ASTER and Landsat ETM+ images in subtropical coastal areas of southeastern China. Agricultural and Forest Meteorology, 168. https://doi.org/10.1016/j.agrformet.2012.08.012

36. Yuan, B., Fu, L., Zou, Y., Zhang, S., Chen, X., Li, F., Deng, Z., & Xie, Y. (2021). Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI. Journal of Cleaner Production, 302, 126995. https://doi.org/10.1016/j.jclepro.2021.126995

37. Yue, H., Liu, Y., Li, Y., & Lu, Y. (2019a). Eco-Environmental Quality Assessment in China’s 35 Major Cities Based On Remote Sensing Ecological Index. IEEE Access, 7, 51295–51311. https://doi.org/10.1109/ACCESS.2019.2911627

38. Yue, H., Liu, Y., Li, Y., & Lu, Y. (2019b). Eco-environmental quality assessment in china’s 35 major cities based on remote sensing ecological index. IEEE Access, 7. https://doi.org/10.1109/ACCESS.2019.2911627

39. Zhang, W., Zhang, W., Ji, J., & Chen, C. (2024). Urban Ecological Quality Assessment Based on Google Earth Engine and Driving Factors Analysis: A Case Study of Wuhan City, China. Sustainability (Switzerland) , 16(9). https://doi.org/10.3390/su16093598

40. Zhu, H., Wang, J. L., Cheng, F., Deng, H., Zhang, E. W., & Li, Y. X. (2020). [Monitoring and evaluation of eco-environmental quality of lake basin regions in Central Yunnan Province, China]. Ying Yong Sheng Tai Xue Bao = The Journal of Applied Ecology, 31(4), 1289–1297. https://doi.org/10.13287/J.1001-9332.202004.011

41. Zou, K. H., O’Malley, A. J., & Mauri, L. (2007). Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. Circulation, 115(5). https://doi.org/10.1161/CIRCULATIONAHA.105.594929


Review

For citations:


Mahendra A., Widayani P., Murti S. Utilization of Remote Sensing Data in Determining the Threshold Value of Urban Ecological Quality Index in Bandung City, West Java, Indonesia. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2025;18(2):20-31. https://doi.org/10.24057/2071-9388-2025-3413

Views: 20


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2071-9388 (Print)
ISSN 2542-1565 (Online)