Mapping GHG Emission Vulnerability Using Convolutional Autoencoder And Multi-Sensor Satellite In Bali, Indonesia
https://doi.org/10.24057/2071-9388-2025-4005
Abstract
Global warming, driven by the rising concentration of greenhouse gases (GHGs), demands innovative, data- driven approaches to assess emission vulnerability at regional scales. This study developed a novel framework utilizing an unsupervised Convolutional Autoencoder (CAE) deep learning model combined with multi-sensor satellite data to map GHG emission vulnerability. The framework integrated nine environmental indicators, including tropospheric gases, land surface temperature, vegetation cover, anthropogenic proxies, and elevation, all sourced from freely accessible remote sensing platforms. The CAE model effectively captured complex spatial patterns and reduced high-dimensional inputs into 128 latent features, enabling vulnerability assessment without requiring labeled training data. Results indicated that southern coastal regions, particularly Denpasar and Badung, exhibited the highest vulnerability due to dense urbanization and tourism-related activities. Based on zonal statistics, 11.31% of local administrative zones were identified as having high to very high vulnerability, while 18.72% were classified as moderate, and 69.97% as low to very low. The most vulnerable areas were concentrated along the southern coastline, known as a hub for tourism and economic activity, with additional pockets of vulnerability found in several northern coastal zones. These findings demonstrate the capacity of unsupervised deep learning to detect emission hotspots and spatial variability, particularly in data-limited environments. The integration of scalable algorithms with open- access satellite data allows for rapid, cost-efficient assessments to inform evidence-based climate planning and mitigation strategies. This study introduces a practical and transferable approach for spatial quantification of GHG vulnerability, offering actionable insights for advancing global climate policy and supporting the Sustainable Development Goals.
Keywords
greenhouse gas,
vulnerability mapping,
convolutional autoencoder,
remote sensing,
deep learning,
spatial modeling,
climate change
Supporting agencies: This research was conducted without financial support from any funding agency or institution. The authors gratefully acknowledge the developers of the Google Earth Engine (GEE) platform for providing open-access satellite data and tools that enabled advanced geospatial analysis. Appreciation is also extended to the open-source Python community for the development of powerful libraries used in this study. We sincerely thank the anonymous reviewers for their valuable insights and constructive feedback, which significantly improved the quality of this manuscript.
About the Authors
Moh Saifulloh
Faculty of Marine Sciences and Fisheries, Udayana University; Spatial Data Infrastructure Development Center (PPIDS)
Indonesia
Indonesia
Badung, 80361
Denpasar City, 80225
I Gusti Ngurah Santosa
Faculty of Agriculture, Udayana University
Indonesia
Indonesia
Denpasar, 80225
I Nyoman Sunarta
Doctoral Program of Tourism Sciences, Udayana University
Indonesia
Indonesia
80225, Denpasar
I Gusti Agung Ayu Ambarawati
Faculty of Agriculture, Udayana University
Indonesia
Indonesia
Denpasar, 80225
I Made Sudarma
Research Center for Environmental (PPLH) of Udayana University
Indonesia
Indonesia
Denpasar, 80225
Abd. Rahman As-syakur
Research Center for Environmental (PPLH) of Udayana University
Indonesia
Indonesia
Denpasar, 80225
References
1. Andyana I.W.S., As-Syakur A.R., Sunarta I.N., Suyarto R., Diara I.W., Susila K.D., Saifulloh M., Kusmiyarti T.B. and Wiyanti W. (2023). Urban tourism expansion monitoring by remote sensing and random forest. IOP Conference Series: Earth and Environmental Science, 1180(1). DOI: 10.1088/1755-1315/1180/1/012046
2. Ayyamperumal R., Banerjee A., Zhang Z., Nazir N., Li F., Zhang C. and Huang X. (2024). Quantifying climate variation and associated regional air pollution in southern India using Google Earth Engine. Science of the Total Environment, 909. DOI: 10.1016/j.scitotenv.2023.168470
3. Azarang A., Manoochehri H.E. and Kehtarnavaz N. (2019). Convolutional Autoencoder-Based Multispectral Image Fusion. IEEE Access, 7. DOI: 10.1109/ACCESS.2019.2905511
4. Bhatti U.A., Bhatti M.A., Tang H., Syam M.S., Awwad E.M., Sharaf M. and Ghadi Y.Y. (2024). Global production patterns: Understanding the relationship between greenhouse gas emissions, agriculture greening and climate variability. Environmental Research, 245. DOI: 10.1016/j.envres.2023.118049
5. Costa N., Pérez L. and Sánchez L. (2024). Rapidae: A python library for creation, experimentation, and benchmarking of autoencoder models. 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–8.
6. Cui W., Zhou Q. and Zheng Z. (2018). Application of a Hybrid model based on a convolutional auto-encoder and convolutional neural network in object-oriented remote sensing classification. Algorithms, 11(1). DOI: 10.3390/a11010009
7. Dar J.A., Subashree K., Bhat N.A., Sundarapandian S., Xu M., Saikia P., Kumar A., Kumar A., Khare P.K. and Khan M.L. (2020). Role of major forest biomes in climate change mitigation: An eco-biological perspective. In: Environmental Science and Engineering. DOI: 10.1007/978-3-030-32463-6_24
8. Diara I.W., Susila K.D., Wiyanti W., Sunarta I.N., Kusmiyarti T.B. and Saifulloh M. (2024). Exploring the Influence of Various Land Use Land Cover on Land Surface Temperature of Coastal Tourism Areas in Bali Using Landsat 9. Environmental Research, Engineering and Management, 80(2), 118–132.
9. Edo G.I., Itoje-akpokiniovo L.O., Obasohan P., Ikpekoro V.O., Samuel P.O., Jikah A.N., Nosu L.C., Ekokotu H.A., Ugbune U. and Oghroro E.E.A. (2024). Impact of environmental pollution from human activities on water, air quality and climate change. Ecological Frontiers.
10. Feng Q., Niu B., Chen B., Ren Y., Zhu D., Yang J., Liu J., Ou C. and Li B. (2021). Mapping of plastic greenhouses and mulching films from very high resolution remote sensing imagery based on a dilated and non-local convolutional neural network. International Journal of Applied Earth Observation and Geoinformation, 102. DOI: 10.1016/j.jag.2021.102441
11. Feng Z., Wang X., Yuan J., Zhang Y. and Yu M. (2023). Changes in air pollution, land surface temperature, and urban heat islands during the COVID-19 lockdown in three Chinese urban agglomerations. Science of the Total Environment, 892. DOI: 10.1016/j.scitotenv.2023.164496
12. Fioletov V., McLinden C.A., Griffin D., Zhao X. and Eskes H. (2025). Global seasonal urban, industrial, and background NO2 estimated from TROPOMI satellite observations. Atmospheric Chemistry and Physics, 25(1), 575–596.
13. Garajeh K.M., Laneve G., Rezaei H., Sadeghnejad M., Mohamadzadeh N. and Salmani B. (2023). Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine. Pollutants, 3(2). DOI: 10.3390/pollutants3020019
14. Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D. and Moore R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202. DOI: 10.1016/j.rse.2017.06.031
15. Grekousis G., Sunarta I.N. and Stratoulias D. (2024). Tracing vulnerable communities to ambient air pollution exposure: A geodemographic and remote sensing approach. Environmental Research, 258, 119491.
16. Grimm N.B., Chapin F.S., Bierwagen B., Gonzalez P., Groffman P.M., Luo Y., Melton F., Nadelhoffer K., Pairis A., Raymond P.A., Schimel J. and Williamson C.E. (2013). The impacts of climate change on ecosystem structure and function. In: Frontiers in Ecology and the Environment , Vol. 11, Issue 9. DOI: 10.1890/120282
17. Han X., Zhong Y. and Zhang L. (2017). Spatial-spectral unsupervised convolutional sparse auto-encoder classifier for hyperspectral imagery. Photogrammetric Engineering and Remote Sensing, 83(3). DOI: 10.14358/PERS.83.3.195
18. Han Y., Chi H., Huang J., Gao X., Zhang Z. and Ling F. (2024). TemPanSharpening: A multi-temporal Pansharpening solution based on deep learning and edge extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 211, 406–424.
19. Hassaan M.A., Abdallah S.M., Shalaby E.S.A. and Ibrahim A.A. (2023). Assessing vulnerability of densely populated areas to air pollution using Sentinel-5P imageries: a case study of the Nile Delta, Egypt. Scientific Reports, 13(1). DOI: 10.1038/s41598-023-44186-4
20. Hou C., Xie Y. and Zhang Z. (2022). An improved convolutional neural network based indoor localization by using Jenks natural breaks algorithm. China Communications, 19(4). DOI: 10.23919/JCC.2022.04.021
21. Jiang W. (2018). Object-based deep convolutional autoencoders for high-resolution remote sensing image classification. Journal of Applied Remote Sensing, 12(03). DOI: 10.1117/1.jrs.12.035002
22. Ke C., He S. and Qin Y. (2023). Comparison of natural breaks method and frequency ratio dividing attribute intervals for landslide susceptibility mapping. Bulletin of Engineering Geology and the Environment, 82(10). DOI: 10.1007/s10064-023-03392-0
23. Li Z., Chen B., Wu S., Su M., Chen J.M. and Xu B. (2024). Deep learning for urban land use category classification: A review and experimental assessment. Remote Sensing of Environment, 311, 114290.
24. Liu Y., Wang Y., Peng J., Du Y., Liu X., Li S. and Zhang D. (2015). Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data. Remote Sensing, 7(2), 2067–2088.
25. Maurya N.K., Pandey P.C., Sarkar S., Kumar R. and Srivastava P.K. (2022). Spatio-Temporal Monitoring of Atmospheric Pollutants Using Earth Observation Sentinel 5P TROPOMI Data: Impact of Stubble Burning a Case Study. ISPRS International Journal of Geo-Information, 11(5). DOI: 10.3390/ijgi11050301
26. McRoberts R.E., Næsset E., Sannier C., Stehman S. V. and Tomppo E.O. (2020). Remote sensing support for the gain-loss approach for greenhouse gas inventories. In: Remote Sensing , Vol. 12, Issue 11. DOI: 10.3390/rs12111891
27. Pan Y., Wu J., Liu G., Liu W. and Ma L. (2024). Differential responses of temperature sensitivity of greenhouse gases emission to seasonal variations in plateau riparian zones. Environmental Pollution, 353, 124190.
28. Pecl G.T., Araújo M.B., Bell J.D., Blanchard J., Bonebrake T.C., Chen I.C., Clark T.D., Colwell R.K., Danielsen F., Evengård B., Falconi L., Ferrier S., Frusher S., Garcia R.A., Griffis R.B., Hobday A.J., Janion-Scheepers C., Jarzyna M.A., Jennings S., … Williams S.E. (2017). Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. In: Science , Vol. 355, Issue 6332. DOI: 10.1126/science.aai9214
29. Pintelas E., Livieris I.E. and Pintelas P.E. (2021). A convolutional autoencoder topology for classification in high-dimensional noisy image datasets. Sensors, 21(22). DOI: 10.3390/s21227731
30. Priyadarshini S.L.A., Kaushik V. and Kataria R. (2025). The Role of Anthropogenic Activities, Energy Demand, and Global Climate Change. In: The Intersection of Global Energy Politics and Climate Change: A Comprehensive Analysis of Energy Markets and Economics , 35–61. Springer.
31. Puspitasari S.A. and Wu C.-F. (2025). Assessing future climate change with a weather generator: A case study in Bali, Indonesia. BIO Web of Conferences, 155, 3003.
32. Rahaman Z.A., Kafy A. Al, Saha M., Rahim A.A., Almulhim A.I., Rahaman S.N., Fattah M.A., Rahman M.T., S K., Faisal A. Al and Al Rakib A. (2022). Assessing the impacts of vegetation cover loss on surface temperature, urban heat island and carbon emission in Penang city, Malaysia. Building and Environment, 222. DOI: 10.1016/j.buildenv.2022.109335
33. Rzasa K. and Ciski M. (2021). Determination of the level of sustainable development of the cities - A proposal for a method of classifying Objects based on natural breaks. Acta Scientiarum Polonorum, Administratio Locorum, 20(3). DOI: 10.31648/aspal.6701
34. Sakti A.D., Anggraini T.S., Ihsan K.T.N., Misra P., Trang N.T.Q., Pradhan B., Wenten I.G., Hadi P.O. and Wikantika K. (2023). Multi-air pollution risk assessment in Southeast Asia region using integrated remote sensing and socio-economic data products. Science of the Total Environment, 854. DOI: 10.1016/j.scitotenv.2022.158825
35. Saifulloh, M., Sunarta, I. N., Baiquni, M., Trigunasih, N. M., Adikampana, I. M. (2025). Linking annual land subsidence to built-up density across coastal tourism zones via Sentinel-1 differential interferometric synthetic aperture radar. Ecological Engineering & Environmental Technology, 26(9), 325-335.
36. Scafetta N. (2024). Impacts and risks of “realistic” global warming projections for the 21st century. Geoscience Frontiers, 15(2). DOI: 10.1016/j.gsf.2023.101774
37. Subramanian V. (2018). Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch. In: O’Reilly Media.
38. Sudarma I., Saifulloh M., Diara I.W. and As-Syakur A. (2024). Carbon Stocks Dynamics of Urban Green Space Ecosystems Using Time-Series Vegetation Indices. Ecological Engineering & Environmental Technology (EEET), 25(9).
39. Sunarta I.N. and Saifulloh M. (2022a). Coastal tourism: impact for built-up area growth and correlation to vegetation and water indices derived from Sentinel-2 remote sensing imagery. Geojournal of Tourism and Geosites , 41(2). DOI: 10.30892/gtg.41223-857
40. Sunarta I.N. and Saifulloh M. (2022b). Spatial variation of NO2 levels during the Covid-19 pandemic in the Bali tourism area. Geographia technica, 17(1). DOI: 10.21163/GT_2022.171.11
41. Sunarta I.N., Suyarto R., Saifulloh M., Wiyanti W., Susila K.D. and Kusumadewi L.G.L. (2022). Surface urban heat island (SUHI) phenomenon in Bali and Lombok tourism areas based on remote sensing. Journal of Southwest Jiaotong University, 57(4). DOI: 10.35741/issn.0258-2724.57.4.44
42. Susila K.D., Trigunasih N.M. and Saifulloh M. (2024). Monitoring Agricultural Drought in Savanna Ecosystems Using the Vegetation Health Index--Implications of Climate Change. Ecological Engineering & Environmental Technology (EEET), 25(9).
43. Toersilowati L., Siswanto B., Maryadi E., Susanti I., Suhermat M., Witono A., Sipayung S.B., Rahayu S.A., Aminuddin J. and Lubis R.F. (2022). The Projections of Climate Change using Conformal Cubic Atmospheric Model (CCAM) in Bali-Indonesia. IOP Conference Series: Earth and Environmental Science, 1047(1), 12033.
44. Trigunasih N.M. and Saifulloh M. (2022). The Investigating Water Infiltration Conditions Caused by Annual Urban Flooding Using Integrated Remote Sensing and Geographic Information Systems. Journal of Environmental Management & Tourism, 13(5), 1467–1480.
45. Valjarević A., Milanović M., Gultepe I., Filipović D. and Lukić T. (2022). Updated Trewartha climate classification with four climate change scenarios. Geographical Journal, 188(4). DOI: 10.1111/geoj.12458
46. Wang S., Cohen J.B., Guan L., Lu L., Tiwari P. and Qin K. (2025). Observationally constrained global NOx and CO emissions variability reveals sources which contribute significantly to CO2 emissions. Npj Climate and Atmospheric Science, 8(1), 87.
47. Xiong X., Liu J., Chen L., Ju W. and Moshary F. (2021). Special issue “remote sensing of greenhouse gases and air pollution.” In: Remote Sensing , Vol. 13, Issue 11. DOI: 10.3390/rs13112057
48. Yang L., Muhammad I., Chi Y.X., Liu Y.X., Wang G.Y., Wang Y. and Zhou X.B. (2022). Straw return and nitrogen fertilization regulate soil greenhouse gas emissions and global warming potential in dual maize cropping system. Science of the Total Environment, 853. DOI: 10.1016/j.scitotenv.2022.158370
49. Yu Q., Li S. and Chen N. (2024). Urbanization and greenhouse gas emissions from municipal wastewater in coastal provinces of China: Spatiotemporal patterns, driving factors, and mitigation strategies. Environmental Research, 259, 119398.
Review
For citations:
Saifulloh M., Santosa I.N., Sunarta I., Ambarawati I.A., Sudarma I.M., As-syakur A.R. Mapping GHG Emission Vulnerability Using Convolutional Autoencoder And Multi-Sensor Satellite In Bali, Indonesia. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2025;18(3):135-144. https://doi.org/10.24057/2071-9388-2025-4005
Views:
184
Email this article 