Paper
29 January 2024 Dynamics of multi-temporal analysis of coastal wetland area change using cloud computing
Author Affiliations +
Proceedings Volume 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet; 129770W (2024) https://doi.org/10.1117/12.3009761
Event: 8th Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 2023, Yogyakarta, Indonesia
Abstract
Wetland areas play a crucial role in ecological development and land utilization. The availability of wetlands is urgently needed, as they are vital for maintaining ecosystem integrity. However, land conversion poses a significant threat to wetland management in Indonesia. This study focuses on the rapid changes in land use occurring in Bekasi Regency, which is undergoing extensive development as an industrial center. The dynamic nature of human needs, coupled with advancements in technology, has led to the conversion of wetland areas into new land uses, thereby posing a threat to the natural environment. This research aims to examine the changes in land use dynamics within the Bekasi wetlands area from 1988 to 2022. The detection of changes was performed by integrating Landsat data and using the transformation index indicator methods on a cloud computing platform, specifically the Google Earth Engine (GEE). The results indicate that wetland degradation in northern Bekasi Regency has resulted in a reduction of over 11.5% of wetland area. Concurrently, the non-wetland area has experienced an increase, spanning approximately 3,224.40 hectares, while the wetland area has shown a decreasing trend, covering 3,828.80 hectares.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Shandi Brian Agya Novenda, Nur Mohammad Farda, and Farhan Iqbal Yudhanto "Dynamics of multi-temporal analysis of coastal wetland area change using cloud computing", Proc. SPIE 12977, Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet, 129770W (29 January 2024); https://doi.org/10.1117/12.3009761
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KEYWORDS
Landsat

Cloud computing

Data processing

Ecosystems

Water

Data modeling

Remote sensing

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