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
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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
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